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Wei Wang

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hep-ph hep-ex Computer Vision Machine Learning astro-ph.HE Computation and Language physics.soc-ph Artificial Intelligence math.AP Social and Information Networks astro-ph.SR cond-mat.mtrl-sci eess.SP math.CO Robotics cond-mat.str-el eess.IV Information Retrieval Distributed, Parallel, and Cluster Computing Cryptography and Security hep-lat Information Theory math.IT Networking and Internet Architecture math.DG Biological Physics math.NA quant-ph cond-mat.mes-hall nucl-ex math.CV math.PR nucl-th physics.app-ph physics.optics Systems and Control astro-ph.GA cond-mat.supr-con eess.SY math-ph math.DS math.MP Numerical Analysis physics.chem-ph math.LO math.SG astro-ph.EP astro-ph.IM Databases Software Engineering Biomolecules cond-mat.stat-mech math.OC math.QA Quantitative Methods Applications astro-ph cond-mat.quant-gas cond-mat.soft hep-th Human-Computer Interaction math.AT math.CT math.GR math.GT math.RA math.SP cond-mat cond-mat.dis-nn Discrete Mathematics econ.EM math.CA math.RT Neural and Evolutionary Computing Neurons and Cognition physics.acc-ph physics.flu-dyn q-fin.RM

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preprint2026arXiv

Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents

Current embodied agents are often limited to passive instruction-following or reactive need-satisfaction, lacking a stable, high-order value framework essential for long-term, self-directed behavior and resolving motivational conflicts. We introduce \textit{ValuePlanner}, a hierarchical cognitive architecture that decouples high-level value scheduling from low-level action execution. \textit{ValuePlanner} employs an LLM-based cognitive module to generate symbolic subgoals by reasoning through abstract value trade-offs, which are then translated into executable action plans by a classical PDDL planner. This process is refined via a closed-loop feedback mechanism. Evaluating such autonomy requires methods beyond task-success rates, and we therefore propose a value-centric evaluation suite measuring cumulative value gain, preference alignment, and behavioral diversity. Experiments in the TongSim household environment demonstrate that \textit{ValuePlanner} arbitrates competing values to generate coherent, long-horizon, self-directed behavior absent from instruction-following and needs-driven baselines. Our work offers a structured approach to bridging intrinsic values and grounded behavior for autonomous agents.

preprint2026arXiv

DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization

Although Large Language Models (LLMs) have made remarkable progress, current preference optimization methods still struggle to align directional consistency while preserving reasoning diversity. To address this limitation, we propose Directional-Groupwise Preference Optimization (DGPO), a lightweight framework that aggregates supervision signals at the group level and explicitly models direction-aware alignment through multi-candidate comparisons. DGPO organizes forward and reverse question-answer instances into structured sets and optimizes a margin-based likelihood objective that separates coherent reasoning paths from inconsistent alternatives. This group-wise formulation captures richer relative information than pairwise objectives and reinforces consistency across diverse reasoning pathways. Empirical results show that our constructed reverse data yields a 3.2% average improvement across five benchmarks, while DGPO further delivers consistent gains across multiple datasets and model families, achieving average accuracy improvements of up to 3.6%.

preprint2026arXiv

METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution

Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficiently generate candidates, but they typically require explicit numerical property targets and are less suitable for early-stage exploration, where researchers often begin with incomplete constraints and qualitative intents expressed in natural language. Large language models can interpret such intents, but they lack geometric awareness and physical property validity. To address this gap, we propose MetaSymbO, a multi-agent framework for language-guided Metamaterial discovery via Symbolic-driven latent evOlution. Specifically, MetaSymbO contains three agents: a Designer that interprets free-form design intents and retrieves a semantically consistent scaffold, a Generator that synthesizes candidate microstructures in a disentangled latent space, and a Supervisor that provides fast property-aware feedback for iterative refinement. To move beyond the limitations of reproducing known samples from literature and training data, we further introduce symbolic-driven latent evolution, which applies programmable operators over disentangled latent factors to compose, modify, and refine structures at inference time. Extensive experiments demonstrate that (i) MetaSymbO improves structural validity by up to 34% in symmetry and nearly 98% in periodicity compared to state-of-the-art baselines; (ii) MetaSymbO achieves about 6-7% higher language-guidance scores while maintaining superior structure novelty compared to advanced reasoning LLMs; (iii) qualitative analyses confirm the effectiveness of symbolic logic operators in enabling programmable semantic alignment; and (iv) realworld case studies on auxetic, high-stiffness metamaterial design further validate its practical capability.

preprint2026arXiv

Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend

Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both prefill and decode. Beyond network transfer, routing-driven layout transformation, temporary relay, and output restoration can add substantial overhead. Existing MoE communication paths are often buffer-centric, using explicit inter-process relay and reordering buffers around collective transfer. This report presents a relay-buffer-free communication design for MoE inference acceleration on Ascend systems. The design reorganizes dispatch and combine around direct placement into destination expert windows and direct reading from remote expert windows. Built on globally pooled high-bandwidth memory and symmetric-memory allocation, it removes most intermediate relay and reordering buffers while retaining only lightweight control state, including counts, offsets, and synchronization metadata. We instantiate the design as two schedules for the main phases of MoE inference: a prefill schedule with richer planning state for throughput-oriented execution, and a compact decode schedule for latency-sensitive execution. Experiments on Ascend-based MoE workloads show reduced dispatch and combine latency in both settings. At the serving level, the implementation improves time to first token (TTFT), preserves competitive time per output token (TPOT), and enlarges the feasible scheduling space under practical latency constraints. These results indicate that, on platforms with globally addressable device memory, reducing intermediate buffering and output restoration around expert execution is an effective direction for accelerating MoE inference.

preprint2025arXiv

Parametric amplification of continuous variable entangled state for loss-tolerant multi-phase estimation

Quantum parameter estimation exploits quantum states to achieve estimation sensitivity beyond classical limit. In continuous variable (CV) regime, squeezed state has been exploited to implement deterministic phase estimation. It is however, often restricted by fragility of quantum states. The quantum phase estimation sensitivity of squeezed state is significantly affected by loss or detection inefficiency, which restrict its applications. This issue can be solved by using a method of parametric amplification of squeezed state \cite{OPA}. In this work, we implement multi-phase estimation with optical parametric amplification of entanglement generated from squeezed states. We find multi-phase estimation sensitivity is robust against loss or detection inefficiency, where we use two-mode Einstein-Podolsky-Rosen entangled state and four-mode cluster state for analysis. Our work provides a method for realizing large-scale quantum metrology in real-world applications against loss or detection inefficiency.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning

Recent Newton-type federated learning algorithms have demonstrated linear convergence with respect to the communication rounds. However, communicating Hessian matrices is often unfeasible due to their quadratic communication complexity. In this paper, we introduce a novel approach to tackle this issue while still achieving fast convergence rates. Our proposed method, named as Federated Newton Sketch methods (FedNS), approximates the centralized Newton's method by communicating the sketched square-root Hessian instead of the exact Hessian. To enhance communication efficiency, we reduce the sketch size to match the effective dimension of the Hessian matrix. We provide convergence analysis based on statistical learning for the federated Newton sketch approaches. Specifically, our approaches reach super-linear convergence rates w.r.t. the communication rounds for the first time. We validate the effectiveness of our algorithms through various experiments, which coincide with our theoretical findings.

preprint2024arXiv

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.

preprint2024arXiv

Quark masses and low energy constants in the continuum from the tadpole improved clover ensembles

We present the light-flavor quark masses and low energy constants using the 2+1 flavor full-QCD ensembles with stout smeared clover fermion action and Symanzik gauge actions. Both the fermion and gauge actions are tadpole improved self-consistently. The simulations are performed on 11 ensembles at 3 lattice spacings $a\in[0.05,0.11]$ fm, 4 spatial sizes $L\in[2.5, 5.1]$ fm, 7 pion masses $m_π\in[135,350]$ MeV, and several values of the strange quark mass. The quark mass is defined through the partially conserved axial current (PCAC) relation and renormalized to $\overline{\mathrm{MS}}$ 2 GeV through the intermediate regularization independent momentum subtraction (RI/MOM) scheme. The systematic uncertainty of using the symmetric momentum subtraction (SMOM) scheme is also included. Eventually, we predict $m_u=2.45(22)(20)$ MeV, $m_d=4.74(11)(09)$ MeV, and $m_s=98.8(2.9)(4.7)$ MeV with the systematic uncertainties from lattice spacing determination, continuum extrapolation and renormalization constant included. We also obtain the chiral condensate $Σ^{1/3}=268.6(3.6)(0.7)$ MeV and the pion decay constant $F=86.6(7)(1.4) $ MeV in the $N_f=2$ chiral limit, and the next-to-leading order low energy constants $\ell_3=2.43(54)(05)$ and $\ell_4=4.322(75)(96)$.

preprint2024arXiv

Rigorous uniaxial limit of the Qian--Sheng inertial Q-tensor hydrodynamics for liquid crystals

This article is concerned with the rigorous connections between the inertial Qian--Sheng model and the Ericksen--Leslie model for the liquid crystal flow, under a more general condition of coefficients. More specifically, in the framework of Hilbert expansions, we show that: (i) when the elastic coefficients tend to zero (also called the uniaxial limit), the smooth solution to the inertial Qian--Sheng model converges to that to the full inertial Ericksen--Leslie model; (ii) when the elastic coefficients and the inertial coefficient tend to zero simultaneously, the smooth solution to the inertial Qian--Sheng model converges to that to the noninertial Ericksen--Leslie model.

preprint2024arXiv

When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of neural growth is determining the optimal growth timing. However, few studies investigate this systematically. Our study reveals that neural growth inherently exhibits a regularization effect, whose intensity is influenced by the chosen policy for growth timing. While this regularization effect may mitigate the overfitting risk of the model, it may lead to a notable accuracy drop when the model underfits. Yet, current approaches have not addressed this issue due to their lack of consideration of the regularization effect from neural growth. Motivated by these findings, we propose an under/over fitting risk-aware growth timing policy, which automatically adjusts the growth timing informed by the level of potential under/overfitting risks to address both risks. Comprehensive experiments conducted using CIFAR-10/100 and ImageNet datasets show that the proposed policy achieves accuracy improvements of up to 1.3% in models prone to underfitting while achieving similar accuracies in models suffering from overfitting compared to the existing methods.

preprint2023arXiv

A First Search for Solar $^8$B Neutrino in the PandaX-4T Experiment using Neutrino-Nucleus Coherent Scattering

A search for interactions from solar $^8$B neutrinos elastically scattering off xenon nuclei using PandaX-4T commissioning data is reported. The energy threshold of this search is further lowered compared with the previous search for dark matter, with various techniques utilized to suppress the background that emerges from data with the lowered threshold. A blind analysis is performed on the data with an effective exposure of 0.48 tonne$\cdot$year, and no significant excess of events is observed. Among results obtained using the neutrino-nucleus coherent scattering, our results give the best constraint on the solar $^8$B neutrino flux. We further provide a more stringent limit on the cross section between dark matter and nucleon in the mass range from 3 to 9 GeV/c$^2$.

preprint2023arXiv

Computing Shor's algorithmic steps with classical light beams

When considered as orthogonal bases in distinct vector spaces, the unit vectors of polarization directions and the Laguerre-Gaussian modes of polarization amplitude are inseparable, constituting a so-called classical entangled light beam. Equating this classical entanglement to quantum entanglement necessary for computing purpose, we show that the parallelism featured in Shor's factoring algorithm is equivalent to the concurrent light-path propagation of an entangled beam or pulse train. A gedanken experiment is proposed for executing the key algorithmic steps of modular exponentiation and Fourier transform on a target integer $N$ using only classical manipulations on the amplitudes and polarization directions. The multiplicative order associated with the sought-after integer factors is identified through a four-hole diffraction interference from sources obtained from the entangled beam profile. The unique mapping from the fringe patterns to the computed order is demonstrated through simulations for the case $N=15$.

preprint2023arXiv

Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization

Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target images makes the standard image-to-image translation architectures unsuitable for this task. Most state-of-the-art methods focus on alignment for global pose-transfer tasks. However, they fail to deal with region-specific texture-transfer tasks, especially for person images with complex textures. To solve this problem, we propose a novel Spatially-Adaptive Warped Normalization (SAWN) which integrates a learned flow-field to warp modulation parameters. It allows us to efficiently align person spatially-adaptive styles with pose features. Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions. Our experimental results on the widely used DeepFashion dataset demonstrate a significant improvement of the proposed method over the state-of-the-art methods on pose-transfer and texture-transfer tasks. The code is available at https://github.com/zhangqianhui/Sawn.

preprint2023arXiv

From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning

Maximizing quality of experience (QoE) for interactive video streaming has been a long-standing challenge, as its delay-sensitive nature makes it more vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has demonstrated great potential, existing works are either limited by fixed models or require enormous data/time for online adaptation, which struggle to fit time-varying and diverse network states. Driven by these practical concerns, we perform large-scale measurements on WeChat for Business's interactive video service to study real-world network fluctuations. Surprisingly, our analysis shows that, compared to time-varying network metrics, network sequences exhibit noticeable short-term continuity, sufficient for few-shot learning requirements. We thus propose Fiammetta, the first meta-RL-based bitrate adaptation algorithm for interactive video streaming. Building on the short-term continuity, Fiammetta accumulates learning experiences through offline meta-training and enables fast online adaptation to changing network states through a few gradient updates. Moreover, Fiammetta innovatively incorporates a probing mechanism for real-time monitoring of network states, and proposes an adaptive meta-testing mechanism for seamless adaptation. We implement Fiammetta on a testbed whose end-to-end network follows the real-world WeChat for Business traces. The results show that Fiammetta outperforms prior algorithms significantly, improving video bitrate by 3.6%-16.2% without increasing stalling rate.

preprint2023arXiv

Integer-Based Pattern Synthesis for Asymmetric Multi-Reflection RIS

This study delves into the radiation pattern synthesis of reconfigurable intelligent surfaces (RIS) / reflection metasurfaces. Through superimposing multiple single-reflection profiles, which comprise the amplitude and/or phase settings of all constituent elements, a single incident wave can be effectively reflected in multiple asymmetric directions. However, some mismatch and interference between adjacent reflection beams may be caused by this superposition as well. Additionally, it is constrained by the inherent limitation that achieving linear and continuous amplitude adjustments and phase shifts in real-world designs is challenging. Consequently, the reconfigurable amplitude and phase must be approximated to discrete values, necessitating the arrangement of reflection profile before and after optimization based on integer. Therefore, in this paper, we adapt the traditional particle swarm optimization (PSO) algorithm to discretized integer-based PSO by proposing the concepts of 'discard rate' and 'knowledge.' With the enhancement of the integer-based programming, the multiple asymmetric reflection pattern can be synthesized with suppressed sidelobe levels within limited iterations and time cost.

preprint2023arXiv

Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?

Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their security is significantly threatened by the injection of triggers during model training (i.e., backdoor attacks). Although existing backdoor defenses and manual data selection can mitigate those using human-eye-sensitive triggers, such as patches or adversarial noises, the more challenging natural backdoor triggers remain insufficiently researched. To further investigate natural triggers, we propose a novel analysis-by-synthesis backdoor attack against face forgery detection models, which embeds natural triggers in the latent space. We thoroughly study such backdoor vulnerability from two perspectives: (1) Model Discrimination (Optimization-Based Trigger): we adopt a substitute detection model and find the trigger by minimizing the cross-entropy loss; (2) Data Distribution (Custom Trigger): we manipulate the uncommon facial attributes in the long-tailed distribution to generate poisoned samples without the supervision from detection models. Furthermore, to completely evaluate the detection models towards the latest AIGC, we utilize both state-of-the-art StyleGAN and Stable Diffusion for trigger generation. Finally, these backdoor triggers introduce specific semantic features to the generated poisoned samples (e.g., skin textures and smile), which are more natural and robust. Extensive experiments show that our method is superior from three levels: (1) Attack Success Rate: ours achieves a high attack success rate (over 99%) and incurs a small model accuracy drop (below 0.2%) with a low poisoning rate (less than 3%); (2) Backdoor Defense: ours shows better robust performance when faced with existing backdoor defense methods; (3) Human Inspection: ours is less human-eye-sensitive from a comprehensive user study.

preprint2023arXiv

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods' superiority.

preprint2023arXiv

Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction

We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can generate more diverse data while keeping the original event structure unchanged as much as possible. Specifically, it first randomly masks out an adjunct sentence fragment and then infills a variable-length text span with a fine-tuned infilling model. The main advantage lies in that it can replace a fragment of arbitrary length in the text with another fragment of variable length, compared to the existing methods which can only replace a single word or a fixed-length fragment. On trigger and argument extraction tasks, the proposed framework is more effective than baseline methods and it demonstrates particularly strong results in the low-resource setting. Our further analysis shows that it achieves a good balance between diversity and distributional similarity.

preprint2023arXiv

Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18dB.

preprint2023arXiv

Sparsity Exploitation via Joint Receive Processing and Transmit Beamforming Design for MIMO-OFDM ISAC Systems

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.

preprint2023arXiv

Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially on aging but still functional fleets, is described as being in a very early and emerging phase. This introduces very large challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This paper surveys the existing work regarding the implementation of DL methods in ASV-related fields. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations, are presented. Finally, this survey is completed by highlighting the current challenges and future research directions.

preprint2023arXiv

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

preprint2022arXiv

$μ$AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware

Fuzzing is one of the most effective approaches to finding software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus cannot be used to fuzz the corresponding driver code. In this work, we present $μ$AFL, a hardware-in-the-loop approach to fuzzing microcontroller firmware. It leverages debugging tools in existing embedded system development to construct an AFL-compatible fuzzing framework. Specifically, we use the debug dongle to bridge the fuzzing environment on the PC and the target firmware on the microcontroller device. To collect code coverage information without costly code instrumentation, $μ$AFL relies on the ARM ETM hardware debugging feature, which transparently collects the instruction trace and streams the results to the PC. However, the raw ETM data is obscure and needs enormous computing resources to recover the actual instruction flow. We therefore propose an alternative representation of code coverage, which retains the same path sensitivity as the original AFL algorithm, but can directly work on the raw ETM data without matching them with disassembled instructions. To further reduce the workload, we use the DWT hardware feature to selectively collect runtime information of interest. We evaluated $μ$AFL on two real evaluation boards from two major vendors: NXP and STMicroelectronics. With our prototype, we discovered ten zero-day bugs in the driver code shipped with the SDK of STMicroelectronics and three zero-day bugs in the SDK of NXP. Eight CVEs have been allocated for them. Considering the wide adoption of vendor SDKs in real products, our results are alarming.

preprint2022arXiv

3D-Aware Semantic-Guided Generative Model for Human Synthesis

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid/semi-rigid objects, such as human faces or cars. However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications. This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which combines a GNeRF with a texture generator. The former learns an implicit 3D representation of the human body and outputs a set of 2D semantic segmentation masks. The latter transforms these semantic masks into a real image, adding a realistic texture to the human appearance. Without requiring additional 3D information, our model can learn 3D human representations with a photo-realistic, controllable generation. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines. The code is available at https://github.com/zhangqianhui/3DSGAN

preprint2022arXiv

A Novel Mobility Model to Support the Routing of Mobile Energy Resources

Mobile energy resources (MERs) have received increasing attention due to their effectiveness in boosting the power system resilience in a flexible way. In this paper, a novel mobility model for MERs is proposed, which can support the routing of MERs to provide various services for the power system. Two key points, the state transitions and travel time of MERs, are formulated by linear constraints. The feasibility of the proposed model, especially its advantages in model size and computational efficiency for the routing of MERs among many nodes with a small time span, is demonstrated by a series of tests.

preprint2022arXiv

A review on vision-based analysis for automatic dietary assessment

Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but time-consuming, and contain substantial biases and errors. Recent advances in Artificial Intelligence (AI), especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scope and approach: This review presents Vision-Based Dietary Assessment (VBDA) architectures, including multi-stage architecture and end-to-end one. The multi-stage dietary assessment generally consists of three stages: food image analysis, volume estimation and nutrient derivation. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight critical challenges and future trends for VBDA. Key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This review aims to encourage researchers to propose more practical solutions for VBDA.

preprint2022arXiv

A Search for the Cosmic Ray Boosted Sub-GeV Dark Matter at the PandaX-II Experiment

We report a novel search for the cosmic ray boosted dark matter using the 100~tonne$\cdot$day full data set of the PandaX-II detector located at the China Jinping Underground Laboratory. With the extra energy gained from the cosmic rays, sub-GeV dark matter particles can produce visible recoil signals in the detector. The diurnal modulations in rate and energy spectrum are utilized to further enhance the signal sensitivity. Our result excludes the dark matter-nucleon elastic scattering cross section between 10$^{-31}$cm$^{2}$ and 10$^{-28}$cm$^{2}$ for a dark matter masses from 0.1 MeV/$c^2$ to 0.1 GeV/$c^2$, with a large parameter space previously unexplored by experimental collaborations.

preprint2022arXiv

A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment

In the two-component Majorana dark matter model, one dark matter particle can scatter off the target nuclei, and turn into a slightly heavier component. In the framework of a simplified model with a vector boson mediator, both the tree-level and loop-level processes contribute to the signal in direct detection experiment. In this paper, we report the search results for such dark matter from PandaX-II experiment, using total data of the full 100.7 tonne$\cdot$day exposure. No significant excess is observed, so strong constraints on the combined parameter space of mediator mass and dark matter mass are derived. With the complementary search results from collider experiments, a large range of parameter space can be excluded.

preprint2022arXiv

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing DRL to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in DRL. Next, we discuss DRL deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of DRL in data processing and analytics, ranging from data preparation, natural language processing to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using DRL in data processing and analytics.

preprint2022arXiv

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

In this paper, we propose a new GPU implementation of the Katsevich algorithm for helical CT reconstruction. Our implementation divides the sinograms and reconstructs the CT images pitch by pitch. By utilizing the periodic properties of the parameters of the Katsevich algorithm, our method only needs to calculate these parameters once for all the pitches and so has lower GPU-memory burdens and is very suitable for deep learning. By embedding our implementation into the network, we propose an end-to-end deep network for the high pitch helical CT reconstruction with sparse detectors. Since our network utilizes the features extracted from both sinograms and CT images, it can simultaneously reduce the streak artifacts caused by the sparsity of sinograms and preserve fine details in the CT images. Experiments show that our network outperforms the related methods both in subjective and objective evaluations.

preprint2022arXiv

A van der Waals Interface Hosting Two Groups of Magnetic Skyrmions

Multiple magnetic skyrmion phases add an additional degree of freedom for skyrmion based ultrahigh-density spin memory devices. Extending the field to two-dimensional van der Waals magnets is a rewarding challenge, where the realizable degree of freedoms (e.g. thickness, twisting angle and electrical gating) and high skyrmion density result in intriguing new properties and enhanced functionality. We report a van der Waals interface, formed by two 2D ferromagnets Cr2Ge2Te6 and Fe3GeTe2 with a Curie temperature of ~65 K and ~205 K, respectively, hosting two groups of magnetic skyrmions. Two sets of topological Hall effect are observed below 60 K when Cr2Ge2Te6 is magnetically ordered. These two groups of skyrmions are directly imaged using magnetic force microscopy. Interestingly, the magnetic skyrmions persist in the heterostructure in the remanent state with zero applied magnetic field. Our results are promising for the realization of skyrmionic devices based on van der Waals heterostructures hosting multiple skyrmion phases.

preprint2022arXiv

Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach

With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In particular, the abnormal signals may emulate the normal signals, which makes it very challenging for abnormal signal recognition. In this paper, we propose a new abnormal signal recognition scheme, which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals. Firstly, we emulate synthetic abnormal communication signals including seven jamming patterns. Then, we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver. To improve the performance, we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm. Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations, even under low signal-to-noise ratio (SNR) and low jamming-to-signal ratio (JSR) conditions.

preprint2022arXiv

Afterpulse measurement of JUNO 20-inch PMTs

In this article we present the large photo-multiplier tube (PMT) afterpulse measurement results of Jiangmen Underground Neutrino Observatory (JUNO) experiment. Totally 11 dynode-PMTs (R12860) from Hamamatsu company and 150 micro-channel plate PMTs (MCP-PMTs, GDB-6201) from NNVT company were tested, an afterpulse model is built according to the afterpulse time distribution and probability of occurrence for these two types of PMTs. The average ratio between the total afterpulse charge with the delay between 0.5 $μ$ s and 20 $μ$ s to the primary pulse charge is 5.6%(13.2%) for the tested MCP-PMTs (dynode-PMTs). JUNO experiment will deploy 20,012 20-inch PMTs, and this study will benefit the detector simulation, event reconstruction and data analysis of JUNO experiment.

preprint2022arXiv

An Asymmetrical Model for High Energy Radiation of Cassiopeia A

Cassiopeia A (Cas A) supernova remnant shows strong radiation from radio to gamma-ray bands. The mechanism of gamma-ray radiation in Cas A and its possible contribution to PeV cosmic rays are still under debate. The X-ray imaging reveals an asymmetric profile of Cas A, suggesting the existence of a jet-like structure. In this work, we propose an asymmetrical model for Cas A, consisting of a fast moving jet-like structure and a slowly expanding isotropic shell. This model can account for the multi-wavelength spectra of Cas A, especially for the power-law hard X-ray spectrum from $\sim$ 60 to 220 keV. The GeV to TeV emission from Cas A should be contributed by both hadronic and leptonic processes. Moreover, the jet-like structure may produce a gamma-ray flux of $\sim 10^{-13}\rm erg\ cm^{-2}\ s^{-1}$ at $\sim 100$ TeV, to be examined by LHAASO and CTA.

preprint2022arXiv

Approximating the ground state eigenvalue via the effective potential

In this paper, we study 1-d random Schrödinger operators on a finite interval with Dirichlet boundary conditions. We are interested in the approximation of the ground state energy using the minimum of the effective potential. For the 1-d continuous Anderson Bernoulli model, we show that the ratio of the ground state energy and the minimum of the effective potential approaches $\frac{π^2}{8}$ as the domain size approaches infinity. Besides, we will discuss various approximations to the ratio in different situations. There will be numerical experiments supporting our main results for the ground state energy and also supporting approximations for the excited states energies.

preprint2022arXiv

Batch-efficient EigenDecomposition for Small and Medium Matrices

EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in the deep neural networks. In this paper, we propose a QR-based ED method dedicated to the application scenarios of computer vision. Our proposed method performs the ED entirely by batched matrix/vector multiplication, which processes all the matrices simultaneously and thus fully utilizes the power of GPUs. Our technique is based on the explicit QR iterations by Givens rotation with double Wilkinson shifts. With several acceleration techniques, the time complexity of QR iterations is reduced from $O{(}n^5{)}$ to $O{(}n^3{)}$. The numerical test shows that for small and medium batched matrices (\emph{e.g.,} $dim{<}32$) our method can be much faster than the Pytorch SVD function. Experimental results on visual recognition and image generation demonstrate that our methods also achieve competitive performances.

preprint2022arXiv

Computer Vision Based Parking Optimization System

An improvement in technology is linearly related to time and time-relevant problems. It has been seen that as time progresses, the number of problems humans face also increases. However, technology to resolve these problems tends to improve as well. One of the earliest existing problems which started with the invention of vehicles was parking. The ease of resolving this problem using technology has evolved over the years but the problem of parking still remains unsolved. The main reason behind this is that parking does not only involve one problem but it consists of a set of problems within itself. One of these problems is the occupancy detection of the parking slots in a distributed parking ecosystem. In a distributed system, users would find preferable parking spaces as opposed to random parking spaces. In this paper, we propose a web-based application as a solution for parking space detection in different parking spaces. The solution is based on Computer Vision (CV) and is built using the Django framework written in Python 3.0. The solution works to resolve the occupancy detection problem along with providing the user the option to determine the block based on availability and his preference. The evaluation results for our proposed system are promising and efficient. The proposed system can also be integrated with different systems and be used for solving other relevant parking problems.

preprint2022arXiv

Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations

Although single-image super-resolution (SISR) methods have achieved great success on single degradation, they still suffer performance drop with multiple degrading effects in real scenarios. Recently, some blind and non-blind models for multiple degradations have been explored. However, those methods usually degrade significantly for distribution shifts between the training and test data. Towards this end, we propose a conditional meta-network framework (named CMDSR) for the first time, which helps SR framework learn how to adapt to changes in input distribution. We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet). Specifically, the ConditionNet of our framework first learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. Moreover, in order to better extract degradation prior, we propose a task contrastive loss to decrease the inner-task distance and increase the cross-task distance between task-level features. Without predefining degradation maps, our blind framework can conduct one single parameter update to yield considerable SR results. Extensive experiments demonstrate the effectiveness of CMDSR over various blind, even non-blind methods. The flexible BaseNet structure also reveals that CMDSR can be a general framework for large series of SISR models. Our code is available at \url{https://github.com/guanghaoyin/CMDSR}.

preprint2022arXiv

Constructing graphs having Laplacian pair state transfer by an edge perturbation

In this paper, we give some sufficient conditions for graphs with an edge perturbation between twin vertices to have Laplacian perfect pair state transfer as well as Laplacian pretty good pair state transfer. By those sufficient conditions, we also construct many new graphs having Laplacian perfect pair state transfer as well as Laplacian pretty good pair state transfer.

preprint2022arXiv

Content-Variant Reference Image Quality Assessment via Knowledge Distillation

Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situation also applies to image quality assessment (IQA). Although recent no-reference (NR-IQA) methods have made great progress to predict image quality free from the reference image, they still have the potential to achieve better performance since HQ image information is not fully exploited. In contrast, full-reference (FR-IQA) methods tend to provide more reliable quality evaluation, but its practicability is affected by the requirement for pixel-level aligned reference images. To address this, we firstly propose the content-variant reference method via knowledge distillation (CVRKD-IQA). Specifically, we use non-aligned reference (NAR) images to introduce various prior distributions of high-quality images. The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality. Further, the knowledge distillation transfers more HQ-LQ distribution difference information from the FR-teacher to the NAR-student and stabilizing CVRKD-IQA performance. Moreover, to fully mine the local-global combined information, while achieving faster inference speed, our model directly processes multiple image patches from the input with the MLP-mixer. Cross-dataset experiments verify that our model can outperform all NAR/NR-IQA SOTAs, even reach comparable performance with FR-IQA methods on some occasions. Since the content-variant and non-aligned reference HQ images are easy to obtain, our model can support more IQA applications with its relative robustness to content variations. Our code and more detailed elaborations of supplements are available: https://github.com/guanghaoyin/CVRKD-IQA.

preprint2022arXiv

COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas

Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations.

preprint2022arXiv

Could TDE outflows produce the PeV neutrino events?

A tidal disruption event (TDE), AT2019dsg, was observed to be associated with a PeV neutrino event, IceCube-191001A, lagging the optical outburst by a half year. It is known that TDEs may generate ultrafast outflows. If the TDE occurs in a cloudy environment, the outflow-cloud interactions may form shock waves which generate accelerated protons and hence delayed neutrinos from hadronic interactions in clouds. Here we investigate the neutrino production in AT2019dsg by examining the TDE outflow-cloud interaction model. We find that, for an outflow with a velocity of 0.07c and a kinetic luminosity of $10^{45}\rm erg\ s^{-1}$, protons may be accelerated up to $\sim$ 60 PeV by the bow shocks, and generate PeV neutrinos by interactions with clouds. The predicted neutrino number in this model depends on the uncertainties of model parameters and in order to match the observations, some challenging values of parameters have been involved. The PeV neutrino event number can be $\sim 4\times10^{-3}$ for a hard proton index $Γ=1.5$.

preprint2022arXiv

Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class. To this end, we propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations, so that the domain shift and few-shot learning can be addressed simultaneously. We evaluate our approach on two new CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets respectively. Remarkably, our approach outperforms multiple elaborated baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on average on DomainNet. Code is available at https://github.com/WentaoChen0813/CDCS-FSL

preprint2022arXiv

Cybertwin-enabled 6G Space-air-ground Integrated Networks: Architecture, Open Issue, and Challenges

Space-air-ground integrated network (SAGIN) is considered as a core requirement in emerging 6G networks, which integrates the terrestrial and non-terrestrial networks to reach the full network coverage and ubiquitous services. To envision the ubiquitous intelligence and the deep integration in 6G SAGIN, a paradigm of cybertwin-enabled 6G SAGIN is presented in this paper. Specifically, a cybertwin-enabled SAGIN architecture is first presented, where a novel five-dimension digital twin (DT) model is presented. Particularly, three categories of critical technologies are presented based on the cybertwin of SAGIN, i.e., cybertwin-based multi-source heterogeneous network integration, cybertwin-based integrated cloud-edge-end, and cybertwin-based integrated sensing-communication-computing. Besides, two open issues in the cybertwin-enabled SAGIN are studied, i.e., the networking decision and optimization and the cybertwin-enabled cross-layer privacy and security, where the challenges are discussed and the potential solutions are directed. In addition, a case study with federal learning is developed and open research issues are discussed.

preprint2022arXiv

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This unique capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition using in-situ environmental TEM data, informing models of morphological evolution and catalytic properties. The model performance and achieved accuracy of predictions are desirable based on, for scientific data characteristic, based on limited size of training data sets. The model convergence and values for the loss function mean square error show dependence on the training strategy, and structural similarity measure between predicted structure images and ground truth reaches the value of about 0.7. This computed structural similarity is smaller than values obtained when the deep learning architecture is trained using much larger benchmark data sets, it is sufficient to show the structural transition of Au nanoparticles. While performance parameters of our model applied to scientific data fall short of those achieved for the non-scientific big data sets, we demonstrate model ability to predict the evolution, even including the particle structural phase transformation, of Au nano particles as catalyst for CO oxidation under the chemical reaction conditions. Using this approach, it may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.

preprint2022arXiv

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Transit Timing Variation (TTV) of hot Jupiters provides direct observational evidence of planet tidal dissipation. Detecting tidal dissipation through TTV needs high precision transit timings and long timing baselines. In this work, we predict and discuss the potential scientific contribution of SiTian Survey in detecting and analyzing exoplanet TTV. We develop a tidal dissipation detection pipeline for SiTian Survey that aims at time-domain astronomy with 72 1-meter optical telescopes. The pipeline includes the modules of light curve deblending, transit timing obtaining, and TTV modeling. SiTian is capable to detect more than 25,000 exoplanets among which we expect $\sim$50 sources showing evidence of tidal dissipation. We present detection and analysis of tidal dissipating targets, based on simulated SiTian light curves of XO-3b and WASP-161b. The transit light curve modeling gives consistent results within 1$σ$ to input values of simulated light curves. Also, the parameter uncertainties predicted by Monte-Carlo Markov Chain are consistent with the distribution obtained from simulating and modeling the light curve 1000 times. The timing precision of SiTian observations is $\sim$ 0.5 minutes with one transit visit. We show that differences between TTV origins, e.g., tidal dissipation, apsidal precession, multiple planets, would be significant, considering the timing precision and baseline. The detection rate of tidal dissipating hot Jupiters would answer a crucial question of whether the planet migrates at an early formation stage or random stages due to perturbations, e.g., planet scattering, secular interaction. SiTian identified targets would be constructive given that the sample would extend tenfold.

preprint2022arXiv

Did bio-homochirality arise from spin-polarized electron?

The origin of bio-homochirality is a subject of much debate. The emergence of chirality and life on earth is a break of symmetry to be compared with the breaks of symmetry in the evolution of the universe. Based on a perspective of asymmetry transfer, the chirality at molecular level might stem from electron spin at subatomic level. Accordingly, in this paper a spin-induced chiral selectivity (SICS) mechanism and its outreach are introduced and discussed. The stress force or spin torque derived from quantum electrodynamics (QED) might be the driving force for the transfer of asymmetry and the formation of molecular chirality. Some recent experimental results seem to support the SICS conjecture. If spin-polarized electrons (SPEs) did cause life to become chirally selective, a magnetic half-metal material such as greigite (Fe3S4), a mineral present in a primordial site where life could have emerged, might act as a spin filter to produce SPEs, which then induced the asymmetric synthesis of chiral molecules via the SICS mechanism. All these tentative thoughts may help explain how homochirality and life could have arisen on the early Earth.

preprint2022arXiv

Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling

Salient object detection has been long studied to identify the most visually attractive objects in images/videos. Recently, a growing amount of approaches have been proposed all of which rely on the contour/edge information to improve detection performance. The edge labels are either put into the loss directly or used as extra supervision. The edge and body can also be learned separately and then fused afterward. Both methods either lead to high prediction errors near the edge or cannot be trained in an end-to-end manner. Another problem is that existing methods may fail to detect objects of various sizes due to the lack of efficient and effective feature fusion mechanisms. In this work, we propose to decompose the saliency detection task into two cascaded sub-tasks, \emph{i.e.}, detail modeling and body filling. Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge. Then the body filling learns the body part which will be filled into the detail map to generate more accurate saliency map. To effectively fuse the features and handle objects at different scales, we have also proposed two novel multi-scale detail attention and body attention blocks for precise detail and body modeling. Experimental results show that our method achieves state-of-the-art performances on six public datasets.

preprint2022arXiv

Disorder-induced broadening of the spin waves in a triangular-lattice quantum-spin-liquid candidate YbZnGaO$_4$

Disorder is important in the study of quantum spin liquids, but its role on the spin dynamics remains elusive. Here, we explore the disorder effect by investigating the magnetic-field dependence of the low-energy magnetic excitations in a triangular-lattice frustrated magnet YbZnGaO$_4$ with inelastic neutron scattering. With an intermediate field of 2.5 T applied along the $c$-axis, the broad continuum at zero field becomes more smeared both in energy and momentum. With a field up to 10 T, which fully polarizes the magnetic moments, we observe clear spin-wave excitations with a gap of $\sim$1.4 meV comparable to the bandwidth. However, the spectra are significantly broadened. The excitation spectra both at zero and high fields can be reproduced by performing classical Monte Carlo simulations which take into account the disorder effect arising from the random site mixing of nonmagnetic Zn$^{2+}$ and Ga$^{3+}$ ions. These results elucidate the critical role of disorder in broadening the magnetic excitation spectra and mimicking the spin-liquid features in frustrated quantum magnets.

preprint2022arXiv

Disproof of a conjecture on the main spectrum of generalized Bethe trees

An eigenvalue of the adjacency matrix of a graph is said to be main if the all-ones vector is not orthogonal to its associated eigenspace. A generalized Bethe tree with $k$ levels is a rooted tree in which vertices at the same level have the same degree. França and Brondani [On the main spectrum of generalized Bethe trees, Linear Algebra Appl., 628 (2021) 56-71] recently conjectured that any generalized Bethe tree with $k$ levels has exactly $k$ main eigenvalues whenever $k$ is even. We disprove the conjecture by constructing a family of counterexamples for even integers $k\ge 6$.

preprint2022arXiv

Disproof of a conjecture on the minimum Wiener index of signed trees

The Wiener index of a connected graph is the sum of distances between all unordered pairs of vertices. Sam Spiro [The Wiener index of signed graphs, Appl. Math. Comput., 416(2022)126755] recently introduced the Wiener index for a signed graph and conjectured that the path $P_n$ with alternating signs has the minimum Wiener index among all signed trees with $n$ vertices. By constructing an infinite family of counterexamples, we prove that the conjecture is false whenever $n$ is at least 30.

preprint2022arXiv

Distributed Eco-Driving Algorithm of Vehicle Platoon Using Traffic Light and Road Slope Information

This paper investigates the problem of ecological driving (eco-driving) of vehicle platoons. To reduce the probability of the platoon avoiding red lights and increase fuel efficiency, a two-layer control architecture is proposed. The first layer is in charge of optimizing the leader's long-term motion profile using the traffic light and road slope information. The long-term planning model is defined based on the reachability analysis of the platoon to the green light windows. An event-triggered mechanism is proposed to operate the long-term planning model. The second layer is the short-term adaptation, in which the leader attempts to follow the planning motion profile in real time, while the follower keeps track of the nearest preceding vehicle and the leader, to preserve the desired inter-vehicular distances. A Newton's method-based algorithm is implemented to effectively solve both the long-term planning and short-term adaptation problems. The effectiveness of the proposed formulation and algorithm is illustrated by the simulations.

preprint2022arXiv

Distribution Amplitudes of $K^*$ and $ϕ$ at Physical Pion Mass from Lattice QCD

We present the first lattice QCD calculation of the distribution amplitudes of longitudinally and transversely polarized vector mesons $K^*$ and $ϕ$ using large momentum effective theory. We use the clover fermion action on three ensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ) action, generated by MILC collaboration, at physical pion mass and \{0.06, 0.09, 0.12\} fm lattice spacings, and choose three different hadron momenta $P_z=\{1.29, 1.72, 2.15\}$ GeV. The resulting lattice matrix elements are nonperturbatively renormalized in a hybrid scheme proposed recently. An extrapolation to the continuum and infinite momentum limit is carried out. We find that while the longitudinal distribution amplitudes tend to be close to the asymptotic form, the transverse ones deviate rather significantly from the asymptotic form. Our final results provide crucial {\it ab initio} theory inputs for analyzing pertinent exclusive processes.

preprint2022arXiv

DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation

Deep learning has shown impressive performance in semantic segmentation, but it is still unaffordable for resource-constrained mobile devices. While offloading computation tasks is promising, the high traffic demands overwhelm the limited bandwidth. Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy. This paper introduces STAC, a DNN-driven compression scheme tailored for edge-assisted semantic video segmentation. STAC is the first to exploit DNN's gradients as spatial sensitivity metrics for spatial adaptive compression and achieves superior compression ratio and accuracy. Yet, it is challenging to adapt this content-customized compression to videos. Practical issues include varying spatial sensitivity and huge bandwidth consumption for compression strategy feedback and offloading. We tackle these issues through a spatiotemporal adaptive scheme, which (1) takes partial strategy generation operations offline to reduce communication load, and (2) propagates compression strategies and segmentation results across frames through dense optical flow, and adaptively offloads keyframes to accommodate video content. We implement STAC on a commodity mobile device. Experiments show that STAC can save up to 20.95% of bandwidth without losing accuracy, compared to the state-of-the-art algorithm.

preprint2022arXiv

Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification

Identifying the causal structures between two statistically correlated events has been widely investigated in many fields of science. While some of the well-studied classical methods are carefully generalized to quantum version of causal inference for certain cases, an effective and efficient way to detect the more general quantum causal structures is still lacking. Here, we introduce a quantity named `Causal Determinant' to efficiently identify the quantum causal structures between two quantum systems and experimentally verify the validity of the method. According to the causal determinant, the quantum direct cause imposed by an arbitrary unitary operator can be perfectly discriminated with the quantum common cause, in which the two quantum systems share a joint quantum state. In addition, the causal determinant has the capability to discriminate between more general causal structures and predict the range of their parameters. The ability to detect more general quantum causal structures of our method can shed new light on the field of quantum causal inference.

preprint2022arXiv

Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices

The tunability of conductance states of various emerging non-volatile memristive devices emulates the plasticity of biological synapses, making it promising in the hardware realization of large-scale neuromorphic systems. The inference of the neural network can be greatly accelerated by the vector-matrix multiplication (VMM) performed within a crossbar array of memristive devices in one step. Nevertheless, the implementation of the VMM needs complex peripheral circuits and the complexity further increases since non-idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs). Here, we present an efficient online training method of the memristive deep belief net (DBN). The proposed memristive DBN uses stochastically binarized activations, reducing the complexity of peripheral circuits, and uses the contrastive divergence (CD) based gradient descent learning algorithm. The analog VMM and digital CD are performed separately in a mixed-signal hardware arrangement, making the memristive DBN high immune to non-idealities of synaptic devices. The number of write operations on memristive devices is reduced by two orders of magnitude. The recognition accuracy of 95%~97% can be achieved for the MNIST dataset using pulsed synaptic behaviors of various memristive synaptic devices.

preprint2022arXiv

Efficient, High-purity, Robust Sound Frequency Conversion with a Linear Metasurface

The intrinsic limitation of the material nonlinearity inevitably results in the poor mode purity, conversion efficiency and real-time reconfigurability of the generated harmonic waves, both in optics and acoustics. Rotational Doppler effect provides us an intuitive paradigm to shifting the frequency in a linear system, which needs to be facilitated by a spiraling phase change upon the wave propagation. Here we numerically and experimentally present a rotating linear vortex metasurface and achieve close-to-unity mode purity (above 95%) and conversion efficiency (above 65%) in audible sound frequency as low as 3000 Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable us to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. Our strategy takes a step further towards the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection.

preprint2022arXiv

Emotion Recognition From Gait Analyses: Current Research and Future Directions

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.

preprint2022arXiv

Enhancing Security of Memristor Computing System Through Secure Weight Mapping

Emerging memristor computing systems have demonstrated great promise in improving the energy efficiency of neural network (NN) algorithms. The NN weights stored in memristor crossbars, however, may face potential theft attacks due to the nonvolatility of the memristor devices. In this paper, we propose to protect the NN weights by mapping selected columns of them in the form of 1's complements and leaving the other columns in their original form, preventing the adversary from knowing the exact representation of each weight. The results show that compared with prior work, our method achieves effectiveness comparable to the best of them and reduces the hardware overhead by more than 18X.

preprint2022arXiv

Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition

With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks (CNN) for GW signal recognition. The whole ensemble model consists of two sub-ensemble models. Each sub-ensemble model is also an ensemble model of deep learning. The two sub-ensemble models treat data of Hanford and Livinston detectors respectively. Proper voting scheme is adopted to combine the two sub-ensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. There is no false trigger happens although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.

preprint2022arXiv

Evidence for magnetic fractional excitations in a Kitaev quantum-spin-liquid candidate $α$-RuCl$_3$

$α$-RuCl$_3$ has been studied extensively because of its proximity to the Kitaev quantum-spin-liquid (QSL) phase and the possibility of approaching it by tuning the competing interactions. Here we present the first polarized inelastic neutron scattering study on $α$-RuCl$_3$ single crystals to explore the scattering continuum around the $Γ$ point at the Brillouin zone center, which was hypothesized to be resulting from the Kitaev QSL state but without concrete evidence. With polarization analyses, we find that while the spin-wave excitations around the M point vanish above the transition temperature $T_{\rm N}$, the pure magnetic continuous excitations around the $Γ$ point are robust against temperature. Furthermore, by calculating the dynamical spin-spin correlation function using the cluster perturbation theory, we derive magnetic dispersion spectra based on the $K$-$Γ$ model, which involves with a ferromagnetic Kitaev interaction of -7.2 meV and an off-diagonal interaction of 5.6 meV. We find this model can reproduce not only the spin-wave excitation spectra around the M point, but also the non-spin-wave continuous magnetic excitations around the $Γ$ point. These results provide evidence for the existence of fractional excitations around the $Γ$ point originating from the Kitaev QSL state, and further support the validity of the $K$-$Γ$ model as the effective minimal spin model to describe $α$-RuCl$_3$.

preprint2022arXiv

Evidence for strong correlations at finite temperatures in the dimerized magnet Na$_2$Cu$_2$TeO$_6$

Dimerized magnets forming alternating Heisenberg chains exhibit quantum coherence and entanglement and thus can find potential applications in quantum information and computation. However, magnetic systems typically undergo thermal decoherence at finite temperatures. Here, we show inelastic neutron scattering results on an alternating antiferromagnetic-ferromagnetic chain compound Na$_2$Cu$_2$TeO$_6$ that the excited quasiparticles can counter thermal decoherence and maintain strong correlations at elevated temperatures. At low temperatures, we observe clear dispersive singlet-triplet excitations arising from the dimers formed along the crystalline $b$-axis. The excitation gap is of $\sim$18 meV and the bandwidth is about half of the gap. The band top energy has a weak modulation along the [100] direction, indicative of a small interchain coupling. The gap increases while the bandwidth decreases with increasing temperature, leading to a strong reduction in the available phase space for the triplons. As a result, the Lorentzian-type energy broadening becomes highly asymmetric as the temperature is raised. These results are associated with a strongly correlated state resulting from hard-core constraint and quasiparticle interactions. We consider these results to be not only evidence for strong correlations at finite temperatures in Na$_2$Cu$_2$TeO$_6$, but also for the universality of the strongly correlated state in a broad range of quantum magnetic systems.

preprint2022arXiv

Exact ground state and elementary excitations of a competing spin chain with twisted boundary condition

A novel Bethe ansatz scheme is proposed to investigate the exact physical properties of an integrable anisotropic quantum spin chain with competing interactions among the nearest, next nearest neighbor and chiral three spin couplings, where the boundary condition is the twisted one. The eigenvalue of the transfer matrix is characterized by its zero roots instead of the traditional Bethe roots. Based on the exact solution, the conserved momentum and charge operators of this U(1)-symmetry broken system are obtained. The ground state energy and density of rapidities are calculated. It is found that there exist three kinds of elementary excitations and the corresponding dispersion relations are obtained, which gives a different picture from that with periodic boundary condition.

preprint2022arXiv

Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences

Real-life events, behaviors and interactions produce sequential data. An important but rarely explored problem is to analyze those nonoccurring (also called negative) yet important sequences, forming negative sequence analysis (NSA). A typical NSA area is to discover negative sequential patterns (NSPs) consisting of important non-occurring and occurring elements and patterns. The limited existing work on NSP mining relies on frequentist and downward closure property-based pattern selection, producing large and highly redundant NSPs, nonactionable for business decision-making. This work makes the first attempt for actionable NSP discovery. It builds an NSP graph representation, quantify both explicit occurrence and implicit non-occurrence-based element and pattern relations, and then discover significant, diverse and informative NSPs in the NSP graph to represent the entire NSP set for discovering actionable NSPs. A DPP-based NSP representation and actionable NSP discovery method EINSP introduces novel and significant contributions for NSA and sequence analysis: (1) it represents NSPs by a determinantal point process (DPP) based graph; (2) it quantifies actionable NSPs in terms of their statistical significance, diversity, and strength of explicit/implicit element/pattern relations; and (3) it models and measures both explicit and implicit element/pattern relations in the DPP-based NSP graph to represent direct and indirect couplings between NSP items, elements and patterns. We substantially analyze the effectiveness of EINSP in terms of various theoretical and empirical aspects including complexity, item/pattern coverage, pattern size and diversity, implicit pattern relation strength, and data factors.

preprint2022arXiv

Exploring neutrinos from proton decays catalyzed by GUT monopoles in the Sun

We explore the neutrino signals from proton decays catalyzed by GUT monopoles in the Sun. Three typical proton decay modes, $p \rightarrow e^+ + (ρ^0, η, ω...) \rightarrow π^+$, $p \rightarrow μ^+ K^0$ and $p \rightarrow \barν_e π^+$, have been analyzed for the Super-Kamiokande experiment. The monopole-induced neutrinos arise from interactions and subsequent decays of the proton decay products. To obtain the neutrino energy spectra, we use the Geant4 software to simulate propagations of daughter particles in the highly-dense solar center. It is found that $K^0$ can produce a large amount of 236 MeV monoenergetic $ν_μ$ neutrinos through the charge exchange process $K^0 + p \rightarrow K^+ + n$ and the subsequent decay $K^+ \rightarrow μ^+ ν_μ$ at rest. Based on this interesting feature, $p \rightarrow μ^+ K^0$ can give the best discovery potential among three decay modes for most of the parameter space. In addition, we present the Super-Kamiokande sensitivities to the monopole flux for three proton decay modes.

preprint2022arXiv

FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate

Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem. We propose FAIVConf, a specially designed video compression framework for video conferencing, based on the effective neural human face generation techniques. FAIVConf brings together several designs to improve the system robustness in real video conference scenarios: face-swapping to avoid artifacts in background animation; facial blurring to decrease transmission bit-rate and maintain the quality of extracted facial landmarks; and dynamic source update for face view interpolation to accommodate a large range of head poses. Our method achieves a significant bit-rate reduction in the video conference and gives much better visual quality under the same bit-rate compared with H.264 and H.265 coding schemes.

preprint2022arXiv

Fast Differentiable Matrix Square Root

Computing the matrix square root or its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationally efficient enough in either the forward pass or in the backward pass. In this paper, we propose two more efficient variants to compute the differentiable matrix square root. For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP), and the other method is to use Matrix Padé Approximants (MPA). The backward gradient is computed by iteratively solving the continuous-time Lyapunov equation using the matrix sign function. Both methods yield considerable speed-up compared with the SVD or the Newton-Schulz iteration. Experimental results on the de-correlated batch normalization and second-order vision transformer demonstrate that our methods can also achieve competitive and even slightly better performances. The code is available at \href{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}.

preprint2022arXiv

FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space

Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context. While distant supervision is the most common way to acquire supervised training data, it brings in label noise, as it assigns type labels to the entity mentions irrespective of mentions context. In attempts to deal with the label noise, leading research on the FG-NET assumes that the fine-grained entity typing data possesses a euclidean nature, which restraints the ability of the existing models in combating the label noise. Given the fact that the fine-grained type hierarchy exhibits a hierarchical structure, it makes hyperbolic space a natural choice to model the FG-NET data. In this research, we propose FGNET-RH, a novel framework that benefits from the hyperbolic geometry in combination with the graph structures to perform entity typing in a performance-enhanced fashion. FGNET-RH initially uses LSTM networks to encode the mention in relation with its context, later it forms a graph to distill/refine the mention encodings in the hyperbolic space. Finally, the refined mention encoding is used for entity typing. Experimentation using different benchmark datasets shows that FGNET-RH improves the performance on FG-NET by up to 3.5-% in terms of strict accuracy.

preprint2022arXiv

Field-Dependent Magnetic Domain Behavior in van der Waals Fe$_3$GeTe$_2$

Two-dimensional magnetic van der Waals (vdW) materials can show a variety of topological nontrivial spin textures, such as Bloch- or Néel-type stripe, skyrmion or bubble domains under certain external stimuli. It is critical to understand the magnetic domain behavior in vdW materials in order to control their size, and density in response to external stimuli such as electric and magnetic fields. Here we examine the magnetic field dependence of topologically non-trivial magnetization spin textures in vdW Fe$_3$GeTe$_2$. Néel-type stripe domains and skyrmions are formed depending on the magnetic field-cooling protocol used during in-situ Lorentz transmission electron microscopy (LTEM) experiments. Use of quantitative reconstruction of magnetic induction maps, and micromagnetic simulations, allow for understanding the LTEM results of Néel-type stripe domains as well as skyrmions. In addition, the deformation of skyrmion contrast is observed as a result of the introduction of an in-plane magnetic field. We demonstrate the stability of the stripe domains and skyrmions in response to externally applied magnetic field due to energy barrier for domain wall annihilation. Our results establish an understanding of the energy landscape that governs the behavior of the topologically non-trivial spin textures in vdW materials which can be harnessed for spintronic applications.

preprint2022arXiv

First Lattice QCD determination of semileptonic decays of charmed-strange baryons $Ξ_c$

While the standard model is the most successfully theory to describe all interactions and constituents in elementary particle physics, it has been constantly examined for over four decades. Weak decays of charm quarks can measure the coupling strength of quarks in different families and serve as an ideal probe for CP violation. As the lowest charm-strange baryons with three different flavors, $Ξ_c$ baryons (made of $csu$ or $csd$) have been extensively studied in experiments at the large hadron collider and in electron-positron collision. However the lack of reliable knowledge in theory becomes the unavoidable obstacle in the way. In this work, we use the state-of-the-art Lattice QCD techniques, and generate 2+1 clover fermion ensembles with two lattice spacings, $a=(0.108{\rm fm},0.080{\rm fm})$. We then present the first {\it ab-initio} lattice QCD determination of form factors governing $Ξ_{c}\to Ξ\ell^+ν_{\ell}$, analogous with the notable $β$-decay of nuclei. Our theoretical results for decay widths are consistent with and about two times more precise than the latest measurements by ALICE and Belle collaborations. Together with experimental measurements, we independently determine the quark-mixing matrix element $|V_{cs}|$, which is found in good agreement with other determinations.

preprint2022arXiv

Fluorination Increases Hydrophobicity at the Macroscopic Level but not at the Microscopic Level

Hydrophobic interactions have been studied in detail in the past based on hydrophobic polymers, such as polystyrene (PS). Because fluorinated materials have relatively low surface energy, they often show both oleophobicity and hydrophobicity at the macroscopic level. However, it remains unknown how fluorination of hydrophobic polymer influences hydrophobicity at the microscopic level. In this work, we synthesized PS and fluorine-substituted PS (FPS) by reversible addition-fragmentation chain transfer polymerization method. Contact angle measurements confirmed that FPS is more hydrophobic than PS at the macroscopic level due to the introduction of fluorine. However, single molecule force spectroscopy experiments showed that the forces required to unfold the PS and FPS nanoparticles in water are indistinguishable, indicating that the strength of the hydrophobic ffect that drives the self-assembly of PS and FPS nanoparticles is the same at the microscopic level. The divergence of hydrophobic effect at the macroscopic and microscopic level may hint different underlying mechanisms: the hydrophobicity is dominated by the solvent hydration at the microscopic level and the surface-associated interaction at the macroscopic level.

preprint2022arXiv

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

preprint2022arXiv

Generalized Dynamic Junction Theory to Resolve the Mechanism of Direct Current Generation in Liquid-Solid Interfaces

Despite the unsettled mechanism of electricity generation from the continuous flow of liquids on a surface, the charge-discharge theory has been widely accepted for alternating current (AC) generation from a moving droplet. It has been recently extended to rationalize direct current (DC) generation across a droplet moving between two different materials. By designing a reconfigurable contact between a metal wire and a water droplet moving on graphene, we show that the charge-discharge theory cannot explain the reversal of current when water-metal interfaces switch from dynamic to static. All experiments can be described after we distinguish a dynamic from a static interface and generalize the photovoltaic-like effect to all dynamic junctions: excited electrons and holes in a moving interface will be separated and swept under the built-in electrical field, leading to a DC response. This generalized theory will lead to an understanding and the design of efficient electricity generation based on interfacial charge transfer.

preprint2022arXiv

Green Interference Based Symbiotic Security in Integrated Satellite-terrestrial Communications

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

preprint2022arXiv

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks. Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.

preprint2022arXiv

Implementation of residual nucleus de-excitations associated with proton decays in $^{12} {\rm C}$ based on the GENIE generator and TALYS code

We implement the de-excitation processes of residual nuclei associated with proton decays in $^{12} {\rm C}$ based on the GENIE generator and TALYS code. To derive the reasonable excitation energy spectra of residual nuclei $^{11} {\rm B}$, $^{10} {\rm B}$ and $^{10} {\rm Be}$, the default GENIE nucleon decay generator is modified in terms of the Spectral Function nuclear model. Then we use the TALYS code to estimate the de-excitation processes of residual nuclei. The TALYS calculation can partly account for the experimental data.

preprint2022arXiv

Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives

Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings. However, the unsupervised contrastive learning methods still lag far behind the supervised counterparts. We attribute this to the quality of positive and negative samples, and aim to improve both. Specifically, for positive samples, we propose switch-case augmentation to flip the case of the first letter of randomly selected words in a sentence. This is to counteract the intrinsic bias of pre-trained token embeddings to frequency, word cases and subwords. For negative samples, we sample hard negatives from the whole dataset based on a pre-trained language model. Combining the above two methods with SimCSE, our proposed Contrastive learning with Augmented and Retrieved Data for Sentence embedding (CARDS) method significantly surpasses the current SOTA on STS benchmarks in the unsupervised setting.

preprint2022arXiv

Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality

Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities. In this paper, we systematically study how to improve the covariance conditioning by enforcing orthogonality to the Pre-SVD layer. Existing orthogonal treatments on the weights are first investigated. However, these techniques can improve the conditioning but would hurt the performance. To avoid such a side effect, we propose the Nearest Orthogonal Gradient (NOG) and Optimal Learning Rate (OLR). The effectiveness of our methods is validated in two applications: decorrelated Batch Normalization (BN) and Global Covariance Pooling (GCP). Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve the covariance conditioning and generalization. Moreover, the combinations with orthogonal weight can further boost the performances.

preprint2022arXiv

Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering

Representations of events described in text are important for various tasks. In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering framework for event representation learning. SWCC learns event representations by making better use of co-occurrence information of events. Specifically, we introduce a weakly supervised contrastive learning method that allows us to consider multiple positives and multiple negatives, and a prototype-based clustering method that avoids semantically related events being pulled apart. For model training, SWCC learns representations by simultaneously performing weakly supervised contrastive learning and prototype-based clustering. Experimental results show that SWCC outperforms other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In addition, a thorough analysis of the prototype-based clustering method demonstrates that the learned prototype vectors are able to implicitly capture various relations between events.

preprint2022arXiv

Inclusive approach to hunt for the beauty-charmed baryons $Ξ_{bc}$

With a distinctive internal structure from all established hadrons, the beauty-charmed baryons $Ξ_{bc}$ can provide us with new points of view to decipher the strong interaction. In this work, we point out that the inclusive $Ξ_{bc} \to Ξ_{cc}^{++}+X$ decay is a golden channel for the experimental discovery of $Ξ_{bc}$ at the LHC. A unique feature of this process is that the $Ξ_{cc}^{++}$ is displaced, which greatly reduces the combinatorial background. A feasibility analysis is performed on the $Ξ_{bc}^+$ search, which is expected to have a longer lifetime than $Ξ_{bc}^0$ and thus a better displacement resolution. The $Ξ_{bc}^+ \to Ξ_{cc}^{++}+X$ branching ratio is calculated within the heavy diquark effective theory. Combining the $Ξ_{bc}$ production rate and the $Ξ_{cc}^{++}$ detection efficiency, we anticipate that hundreds of signal events will be collected during LHCb Run 3.

preprint2022arXiv

Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning

Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from "adapting to wireless channels" to "changing wireless channels". However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create the smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.

preprint2022arXiv

Language-agnostic BERT Sentence Embedding

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM) (Conneau and Lample, 2019), dual encoder translation ranking (Guo et al., 2018), and additive margin softmax (Yang et al., 2019a). We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%. Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by Artetxe and Schwenk (2019b), while still performing competitively on monolingual transfer learning benchmarks (Conneau and Kiela, 2018). Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.

preprint2022arXiv

Laplacian pretty good edge state transfer in paths

In this paper, we first give a necessary and sufficient condition for a graph to have Laplacian pretty good pair state transfer. As an application of such result, we give a complete characterization of Laplacian pretty good edge state transfer in paths.

preprint2022arXiv

Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation

Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supervised approaches have led to great success with the advance of deep learning, but they rely on large quantities of ground-truth depth annotations that are expensive to acquire. Unsupervised domain adaptation (UDA) transfers knowledge from labeled source data to unlabeled target data, so as to relax the constraint of supervised learning. However, existing UDA approaches may not completely align the domain gap across different datasets because of the domain shift problem. We believe better domain alignment can be achieved via well-designed feature decomposition. In this paper, we propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components. LFDA only attempts to align the content component since it has a smaller domain gap. Meanwhile, it excludes the style component which is specific to the source domain from training the primary task. Furthermore, LFDA uses separate feature distribution estimations to further bridge the domain gap. Extensive experiments on three domain adaptative MDE scenarios show that the proposed method achieves superior accuracy and lower computational cost compared to the state-of-the-art approaches.

preprint2022arXiv

Long Term Motion Prediction Using Keyposes

Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. Instead, it is more effective to predict a few keyposes and approximate intermediate ones by interpolating the keyposes. We demonstrate that our approach enables us to predict realistic motions for up to 5 seconds in the future, which is far longer than the typical 1 second encountered in the literature. Furthermore, because we model future keyposes probabilistically, we can generate multiple plausible future motions by sampling at inference time. Over this extended time period, our predictions are more realistic, more diverse and better preserve the motion dynamics than those state-of-the-art methods yield.

preprint2022arXiv

Long-Range Optical Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is a remarkable technology to support both the data and the energy transfer in the era of Internet of Things (IoT). In this paper, we proposed a long-range optical wireless information and power transfer system utilizing retro-reflectors, a gain medium, a telescope internal modulator to form the resonant beam, achieving high-power and high-rate SWIPT. We adopt the transfer matrix, which can depict the beam modulated, resonator stability, transmission loss, and beam distribution. Then, we provide a model for energy harvesting and data receiving, which can evaluate the SWIPT performance. Numerical results illustrate that the proposed system can simultaneously supply 0$\sim$9 W electrical power and 18 bit/s/Hz spectral efficiency over 20 m distance.

preprint2022arXiv

Low Gain Avalanche Detectors with Good Time Resolution Developed by IHEP and IME for ATLAS HGTD project

This paper shows the simulation and test results of 50um thick Low Gain Avalanche Detectors (LGAD) sensors designed by the Institute of High Energy Physics (IHEP) and fabricated by the Institute of Microelectronics of the Chinese Academy of Sciences (IME). Three wafers have been produced with four different gain layer implant doses each. Different production processes, including variation in the n++ layer implant energy and carbon co-implantation were used. Test results show that the IHEP-IME sensors with the higher dose of gain layer have lower breakdown voltages and higher gain layer voltages from capacitance-voltage properties, which are consistent with the TCAD simulation. Beta test results show that the time resolution of IHEP-IME sensors is better than 35ps when operated at high voltage and the collected charges of IHEP-IME sensors are larger than 15fC before irradiation, which fulfill the required specifications of sensors before irradiations for the ATLAS HGTD project.

preprint2022arXiv

Low Radioactive Material Screening and Background Control for the PandaX-4T Experiment

PandaX-4T is a ton-scale dark matter direct detection experiment using a dual-phase TPC technique at the China Jinping Underground Laboratory. Various ultra-low background technologies have been developed and applied to material screening for PandaX-4T, including HPGe gamma spectroscopy, ICP-MS, NAA, radon emanation measurement system, krypton assay station, and alpha detection system. Low background materials were selected to assemble the detector. Surface treatment procedures were investigated to further suppress radioactive background. Combining measured results and Monte Carlo simulation, the total material background rates of PandaX-4T in the energy region of 1-25 keV$\rm{}_{ee}$ are estimated to be (9.9 $\pm$ 1.9) $\times \ 10^{-3}$ mDRU for electron recoil and (2.8 $\pm$ 0.6) $\times \ 10^{-4}$ mDRU for nuclear recoil. In addition, $^{nat}$Kr in the detector is estimated to be <8 ppt.

preprint2022arXiv

Making Parameterization and Constrains of Object Landmark Globally Consistent via SPD(3) Manifold and Improved Cost Functions

Object-level SLAM introduces semantic meaningful and compact object landmarks that help both indoor robot applications and outdoor autonomous driving tasks. However, the back end of object-level SLAM suffers from singularity problems because existing methods parameterize object landmark separately by their scales and poses. Under that parameterization method, the same abstract object can be represented by rotating the object coordinate frame by 90 deg and swapping its length with width value, making the pose of the same object landmark not globally consistent. To avoid the singularity problem, we first introduce the symmetric positive-definite (SPD) matrix manifold as an improved object-level landmark representation and further improve the cost functions in the back end to make them compatible with the representation. Our method demonstrates a faster convergence rate and more robustness in simulation experiments. Experiments on real datasets also reveal that using the same front-end data, our strategy improves the mapping accuracy by 22% on average.

preprint2022arXiv

Mass Testing and Characterization of 20-inch PMTs for JUNO

Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5,000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

preprint2022arXiv

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AG

preprint2022arXiv

Network structural perturbation against interlayer link prediction

Interlayer link prediction aims at matching the same entities across different layers of the multiplex network. Existing studies attempt to predict more accurately, efficiently, or generically from the aspects of network structure, attribute characteristics, and their combination. Few of them analyze the effects of intralayer links. Namely, few works study the backbone structures which can effectively preserve the predictive accuracy while dealing with a smaller number of intralayer links. It can be used to investigate what types of intralayer links are most important for correct prediction. Are there any intralayer links whose presence leads to worse predictive performance than their absence, and how to attack the prediction algorithms at the minimum cost? To this end, two kinds of network structural perturbation methods are proposed. For the scenario where the structural information of the whole network is completely known, we offer a global perturbation strategy that gives different perturbation weights to different types of intralayer links and then selects a predetermined proportion of intralayer links to remove according to the weights. In contrast, if these information cannot be obtained at one time, we design a biased random walk procedure, local perturbation strategy, to execute perturbation. Four kinds of interlayer link prediction algorithms are carried out on different real-world and artificial perturbed multiplex networks. We find out that the intralayer links connected with small degree nodes have the most significant impact on the prediction accuracy. The intralayer links connected with large degree nodes may have side effects on the interlayer link prediction.

preprint2022arXiv

Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression

Developing models with high interpretability and even deriving formulas to quantify relationships between biological data is an emerging need. We propose here a framework for ab initio derivation of sequence motifs and linear formula using a new approach based on the interpretable neural network model called contextual regression model. We showed that this linear model could predict gene expression levels using promoter sequences with a performance comparable to deep neural network models. We uncovered a list of 300 motifs with important regulatory roles on gene expression and showed that they also had significant contributions to cell-type specific gene expression in 154 diverse cell types. This work illustrates the possibility of deriving formulas to represent biology laws that may not be easily elucidated. (https://github.com/Wang-lab-UCSD/Motif_Finding_Contextual_Regression)

preprint2022arXiv

Neutron spectroscopy evidence for a possible magnetic-field-induced gapless quantum-spin-liquid phase in a Kitaev material $α$-RuCl$_3$

As one of the most promising Kitaev quantum-spin-liquid (QSL) candidates, $α$-RuCl$_3$ has received a great amount of attention. However, its ground state exhibits a long-range zigzag magnetic order, which defies the QSL phase. Nevertheless, the magnetic order is fragile and can be completely suppressed by applying an external magnetic field. Here, we explore the evolution of magnetic excitations of $α$-RuCl$_3$ under an in-plane magnetic field, by carrying out inelastic neutron scattering measurements on high-quality single crystals. Under zero field, there exist spin-wave excitations near the $M$ point and a continuum near the $\mitΓ$ point, which are believed to be associated with the zigzag magnetic order and fractional excitations of the Kitaev QSL state, respectively. By increasing the magnetic field, the spin-wave excitations gradually give way to the continuous excitations. On the verge of the critical field $μ_0H_{\rm c}=7.5$ T, the former vanish and only the latter is left, indicating the emergence of a pure QSL state. By further increasing the field strength, the excitations near the $\mitΓ$ point become more intense. By following the gap evolution of the excitations near the $\mitΓ$ point, we are able to establish a phase diagram composed of three interesting phases, including a gapped zigzag order phase at low fields, possibly-gapless QSL phase near $μ_0H_{\rm c}$, and gapped partially polarized phase at high fields. These results demonstrate that an in-plane magnetic field can drive $α$-RuCl$_3$ into a long-sought QSL state near the critical field.

preprint2022arXiv

Neutron spectroscopy evidence on the dual nature of magnetic excitations in a van der Waals metallic ferromagnet Fe$_{2.72}$GeTe$_{2}$

In the local or itinerant extreme, magnetic excitations can be described by the Heisenberg model which treats electron spins as localized moments, or by the itinerant-electron model where the exchange interaction between electrons leads to unequal numbers of electrons with up and down spins. However, it has been elusive when both local moments and itinerant electrons are present in the intermediate range. Using inelastic neutron scattering, we provide direct spectroscopic evidence on the coexistence of and interplay between local moments and itinerant electrons in a van der Waals metallic ferromagnet Fe$_{2.72}$GeTe$_{2}$, which can sustain tunable room-temperature ferromagnetism down to the monolayer limit. We find that there exist ferromagnetic spin-wave excitations dispersing from the zone center at low energies resulting from local moments, and a column-like broad continuum at the zone boundary at high energies up to over 100 meV resulting from itinerant electrons. Unlike the two-dimensional crystal structure, the low-energy mode exhibits a three-dimensional nature, and the high-energy mode also has an out-of-plane dependence. Both modes persist well above the Curie temperature of 160 K. Our neutron spectroscopic data reveal that the low-energy spin waves at 100 K are more coherent than those at 4 K, which is evidence of the weakening of the Kondo screening at high temperatures. These results unambiguously demonstrate the coexistence of local moments and itinerant electrons, and the Kondo effect between these two components in Fe$_{2.72}$GeTe$_{2}$. Such behaviors are generally expected in heavy-fermion systems with heavy $f$ electrons but rarely clearly observed in materials with light $d$ electrons. These findings shed light on the understanding of magnetism in transition-metal compounds.

preprint2022arXiv

Neutron-induced nuclear recoil background in the PandaX-4T experiment

Neutron-induced nuclear recoil background is critical to the dark matter searches in the PandaX-4T liquid xenon experiment. This paper studies the feature of neutron background in liquid xenon and evaluates their contribution in the single scattering nuclear recoil events through three methods. The first method is fully Monte Carlo simulation based. The last two are data-driven methods that also use the multiple scattering signals and high energy signals in the data, respectively. In the PandaX-4T commissioning data with an exposure of 0.63 tonne-year, all these methods give a consistent result that there are $1.15\pm0.57$ neutron-induced background in dark matter signal region within an approximated nuclear recoil energy window between 5 and 100 keV.

preprint2022arXiv

Non-Hermitian Morphing of Topological Modes

Topological modes (TMs) are usually localized at defects or boundaries of a much larger topological lattice. Recent studies of non-Hermitian band theories unveiled the non-Hermitian skin effect (NHSE), by which the bulk states collapse to the boundary as skin modes. Here, we experimentally demonstrate that the NHSE can conversely affect wavefunctions of TMs by delocalizing them from the boundary. At a critical non-Hermitian parameter, the in-gap TMs even become completely extended in the entire bulk lattice, forming an extended mode outside of a continuum. These extended modes are still protected by bulk-band topology, making them robust against local disorders. The delocalization effect is experimentally realized in active mechanical lattices in both one-dimensional (1D) and two-dimensional (2D) topological lattices, as well as in a higher-order topological lattice. Furthermore, by the judicious engineering of the non-Hermiticity distribution, the TMs can deform into a diversity of shapes. Our findings not only broaden and deepen the current understanding of the TMs and the NHSE but also open new grounds for topological applications.

preprint2022arXiv

Nonperturbative Determination of Collins-Soper Kernel from Quasi Transverse-Momentum Dependent Wave Functions

In the framework of large-momentum effective theory at one-loop matching accuracy, we perform a lattice calculation of the Collins-Soper kernel which governs the rapidity evolution of transverse-momentum-dependent (TMD) distributions. We first obtain the quasi TMD wave functions at three different meson momenta on a lattice with valence clover quarks on a dynamical HISQ sea and lattice spacing $a=0.12$~fm from MILC, and renormalize the pertinent linear divergences using Wilson loops. Through one-loop matching to the light-cone wave functions, we determine the Collins-Soper kernel with transverse separation up to 0.6~fm. We study the systematic uncertainties from operator mixing and scale dependence, as well as the impact from higher power corrections. Our results potentially allow for a determination of the soft function and other transverse-momentum dependent quantities at one-loop accuracy.

preprint2022arXiv

On quaternionic pluripotential theory associated to quaternionic $m$-subharmonic functions

Many aspects of pluripotential theory are generalized to quaternionic $m$-subharmonic functions. We introduce quaternionic version of notions of the $m$-Hessian operator, $m$-subharmonic functions, $m$-Hessian measure, $m$-capapcity, the relative $m$-extremal function and the $m$-Lelong number, and show various propositions for them, based on $d_0$ and $ d_1$ operators, the quaternionic counterpart of $\partial$ and $\overline{\partial}$, and quaternionic closed positve currents. The definition of quaternionic $m$-Hessian operator can be extended to locally bounded quaternionic $m$-subharmonic functions and the corresponding convergence theorem is proved. The comparison principle and the quasicontinuity of bounded quaternionic $m$-subharmonic functions are established. We also find the fundamental solution of the quaternionic $m$-Hessian operator.

preprint2022arXiv

On Supervised Feature Selection from High Dimensional Feature Spaces

The application of machine learning to image and video data often yields a high dimensional feature space. Effective feature selection techniques identify a discriminant feature subspace that lowers computational and modeling costs with little performance degradation. A novel supervised feature selection methodology is proposed for machine learning decisions in this work. The resulting tests are called the discriminant feature test (DFT) and the relevant feature test (RFT) for the classification and regression problems, respectively. The DFT and RFT procedures are described in detail. Furthermore, we compare the effectiveness of DFT and RFT with several classic feature selection methods. To this end, we use deep features obtained by LeNet-5 for MNIST and Fashion-MNIST datasets as illustrative examples. Other datasets with handcrafted and gene expressions features are also included for performance evaluation. It is shown by experimental results that DFT and RFT can select a lower dimensional feature subspace distinctly and robustly while maintaining high decision performance.

preprint2022arXiv

On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition

The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covariance Pooling (GCP) layer to learn powerful representations with second-order statistics, which can effectively model inter-class differences. In our previous conference paper, we show that truncating small eigenvalues of the GCP covariance can attain smoother gradient and improve the performance on large-scale benchmarks. However, on fine-grained datasets, truncating the small eigenvalues would make the model fail to converge. This observation contradicts the common assumption that the small eigenvalues merely correspond to the noisy and unimportant information. Consequently, ignoring them should have little influence on the performance. To diagnose this peculiar behavior, we propose two attribution methods whose visualizations demonstrate that the seemingly unimportant small eigenvalues are crucial as they are in charge of extracting the discriminative class-specific features. Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues. Without introducing any additional parameters, this branch simply amplifies the small eigenvalues and achieves state-of-the-art performances of GCP methods on three fine-grained benchmarks. Furthermore, the performance is also competitive against other FGVC approaches on larger datasets. Code is available at \href{https://github.com/KingJamesSong/DifferentiableSVD}{https://github.com/KingJamesSong/DifferentiableSVD}.

preprint2022arXiv

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new property termed equity, which indicates the balance degree of predicted classes, to demystify the efficacy of nuclear norm maximization for UDA theoretically. With this in mind, we offer a new discriminability-and-equity maximization paradigm built on squares loss, such that predictions are equalized explicitly. To verify its feasibility and flexibility, two new losses termed Class Weighted Squares Maximization (CWSM) and Normalized Squares Maximization (NSM), are proposed to maximize both predictive discriminability and equity, from the class level and the sample level, respectively. Importantly, we theoretically relate these two novel losses (i.e., CWSM and NSM) to the equity maximization under mild conditions, and empirically suggest the importance of the predictive equity in UDA. Moreover, it is very efficient to realize the equity constraints in both losses. Experiments of cross-domain image classification on three popular benchmark datasets show that both CWSM and NSM contribute to outperforming the corresponding counterparts.

preprint2022arXiv

On the walk matrix of the Dynkin graph $D_n$

Let $W(D_n)$ denote the walk matrix of the Dynkin graph $D_n$, a tree obtained from the path of order $n-1$ by adding a pendant edge at the second vertex. We prove that $\text{rank}\,W(D_n)=n-2$ if $4\mid n$ and $\text{rank}\,W(D_n)=n-1$ otherwise. Furthermore, we prove that the Smith normal form of $W(D_n)$ is $$\text{diag}[\underbrace{1,1,\ldots,1}_{\lceil\frac{n}{2}\rceil},\underbrace{2,2,\ldots,2}_{\lfloor\frac{n}{2}\rfloor-1},0]$$ when $4\nmid n$. This confirms a recent conjecture in [W.Wang, F.Liu, W.Wang, Generalized spectral characterizations of almost controllable graphs, European J. Combin., 96(2021):103348].

preprint2022arXiv

Optical characterization of two cataclysmic variables: RBS 0490 and SDSS J075939.79+191417.3

We present optical photometric and spectroscopic observations of two Cataclysmic Variables (CVs), namely RBS 0490 and SDSS J075939.79+191417.3. The optical variations of RBS 0490 have been found to occur at the period of 1.689$\pm$0.001 hr which appears to be a probable orbital period of the system. Present photometric observations of SDSS J075939.79+191417.3 confirm and refine the previously determined orbital period as 3.14240928$\pm$0.00000096 hr. The presence of long-duration eclipse features in the light curves of SDSS J075939.79+191417.3 indicates eclipses might be due to an accretion disc and bright spot. The orbital inclination of SDSS J075939.79+191417.3 is estimated to be $\sim$ 78 $^\circ$ using the eclipse morphology. The phased-light curve variations during the orbital cycle of RBS 0490 provide evidence of the emission from an independent second accretion region or a second fainter pole. Optical spectra of RBS 0490 and SDSS J075939.79+191417.3 show the presence of strong Balmer, weak He II ($λ$4686) emission lines, along with the detection of strong $Hβ$ emission lines with a large value of equivalent width. The characteristic features of RBS 0490 seem to favour low-field polars, while SDSS J075939.79+191417.3 appears to be similar to the non-magnetic systems.

preprint2022arXiv

Optimal Transport for Unsupervised Denoising Learning

Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an optimal criterion for unsupervised denoising learning without any prior knowledge on the degradation model is still an open question. Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory. This criterion has favorable properties, e.g., approximately maximal preservation of the information of the signal, whilst achieving perceptual reconstruction. Furthermore, though a relaxed unconstrained formulation is used in practical implementation, we prove that the relaxed formulation in theory has the same solution as the original constrained formulation. Experiments on synthetic and real-world data, including realistic photographic, microscopy, depth, and raw depth images, demonstrate that the proposed method even compares favorably with supervised methods, e.g., approaching the PSNR of supervised methods while having better perceptual quality. Particularly, for spatially correlated noise and realistic microscopy images, the proposed method not only achieves better perceptual quality but also has higher PSNR than supervised methods. Besides, it shows remarkable superiority in harsh practical conditions with complex noise, e.g., raw depth images. Code is available at https://github.com/wangweiSJTU/OTUR.

preprint2022arXiv

Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

In this paper, we propose a secure computation offloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

preprint2022arXiv

Pisces: Efficient Federated Learning via Guided Asynchronous Training

Federated learning (FL) is typically performed in a synchronous parallel manner, where the involvement of a slow client delays a training iteration. Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration. However, this is not always possible in practice, and the selection strategy often has to navigate an unpleasant trade-off between the speed and the data quality of clients. In this paper, we present Pisces, an asynchronous FL system with intelligent participant selection and model aggregation for accelerated training. To avoid incurring excessive resource cost and stale training computation, Pisces uses a novel scoring mechanism to identify suitable clients to participate in a training iteration. It also adapts the pace of model aggregation to dynamically bound the progress gap between the selected clients and the server, with a provable convergence guarantee in a smooth non-convex setting. We have implemented Pisces in an open-source FL platform called Plato, and evaluated its performance in large-scale experiments with popular vision and language models. Pisces outperforms the state-of-the-art synchronous and asynchronous schemes, accelerating the time-to-accuracy by up to 2.0x and 1.9x, respectively.

preprint2022arXiv

Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation

The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results. Thus, it is necessary to propose an effective automatic cartilage segmentation model for longitudinal research on osteoarthritis. In this research, to relieve the problem of inaccurate discontinuous segmentation caused by the limited receptive field in convolutional neural networks, we proposed a novel position-prior clustering-based self-attention module (PCAM). In PCAM, long-range dependency between each class center and feature point is captured by self-attention allowing contextual information re-allocated to strengthen the relative features and ensure the continuity of segmentation result. The clutsering-based method is used to estimate class centers, which fosters intra-class consistency and further improves the accuracy of segmentation results. The position-prior excludes the false positives from side-output and makes center estimation more precise. Sufficient experiments are conducted on OAI-ZIB dataset. The experimental results show that the segmentation performance of combination of segmentation network and PCAM obtains an evident improvement compared to original model, which proves the potential application of PCAM in medical segmentation tasks. The source code is publicly available from link: https://github.com/LeongDong/PCAMNet

preprint2022arXiv

Queue-Aware STAR-RIS Assisted NOMA Communication Systems

In this paper, the queue-aware simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the system stability, where the long-term stability-oriented problem is reformulated to maximize the per-slot queue-weighted sum rate (QWSR) of users based on the Lyapunov drift theory. By jointly optimizing the NOMA decoding order, the active beamforming coefficients at the BS, and the passive transmission and reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). For ES, the blocked coordinate descent and the successive convex approximation methods are invoked to handle the highly-coupled and non-convex problem. For MS, the proposed algorithm is further extended to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem. For TS, the formulated problem is decomposed into two subproblems, each of which can be solved in a similar manner to ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance than the conventional schemes; ii) the reformulated QWSR maximization problem confirms the system stability; and iii) TS achieves superior performance with respect to both the QWSR and the average queue length.

preprint2022arXiv

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

preprint2022arXiv

Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning

The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters. However, existing D-KFAC algorithms require to compute and communicate a large volume of second-order information, i.e., Kronecker factors (KFs), before preconditioning gradients, resulting in large computation and communication overheads as well as a high memory footprint. In this paper, we propose DP-KFAC, a novel distributed preconditioning scheme that distributes the KF constructing tasks at different DNN layers to different workers. DP-KFAC not only retains the convergence property of the existing D-KFAC algorithms but also enables three benefits: reduced computation overhead in constructing KFs, no communication of KFs, and low memory footprint. Extensive experiments on a 64-GPU cluster show that DP-KFAC reduces the computation overhead by 1.55x-1.65x, the communication cost by 2.79x-3.15x, and the memory footprint by 1.14x-1.47x in each second-order update compared to the state-of-the-art D-KFAC methods.

preprint2022arXiv

Scalable semi-classical implementation of Shor factoring using time-multiplexed degenerate optical parametric oscillators

A scheme to encode arbitrarily long integer pairs on degenerate optical parametric oscillations multiplexed in time is proposed. The classical entanglement between the polarization directions and the phases of the oscillating pulses, regarded as two computational registers, furnishes the integer correlations within each pair. We show the major algorithmic steps, modular exponentiation and discrete Fourier transform, of Shor's quantum factoring algorithm can be executed in the registers as pulse interferences under the assistance of external logics. The factoring algorithm is thus rendered equivalent to a semi-classical optical-path implementation that is scalable and decoherence-free. The sought-after multiplicative order, from which the prime factors are deduced, is identified from a two-dimensional fringe image generated by four-hole interference measured at the end of the path.

preprint2022arXiv

Secrecy Outage Probability Fairness for Intelligent Reflecting Surface-Assisted Uplink Channel

This paper investigates physical layer security (PLS) in the intelligent reflecting surface (IRS)-assisted multiple-user uplink channel. Since the instantaneous eavesdropper's channel state information (CSI) is unavailable, the secrecy rate can not be measured. In this case, existing investigations usually focus on the maximization of the minimum (max-min) of signal to interference plus noise power ratio (SINRs) among multiple users, and do not consider secrecy outage probability caused by eavesdroppers. In this paper, we first formulate the minimization of the maximum (min-max) secrecy outage probability among multiple users. The formulated problem is solved by alternately optimizing receiving matrix and phase shift matrix. Simulations demonstrate that the maximum secrecy outage probability is significantly reduced with the proposed algorithm compared to max-min SINR strategies, meaning our scheme has a higher security performance.

preprint2022arXiv

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24\times and 1.28\times compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively

preprint2022arXiv

Spin conservation of cosmic filaments

Cosmic filaments are the largest collapsing structure in the Universe. Recently both observations and simulations inferred that cosmic filaments have coherent angular momenta (spins). Here we use filament finders to identify the filamentary structures in cosmological simulations and study their physical origins, which are well described by the primordial tidal torque of their Lagrangian counterpart regions -- protofilaments. This initial angular momenta statistically preserve their directions to low redshifts. We further show that a spin reconstruction method can predict the spins of filaments and potentially relate their spins to the initial conditions of the Universe. This correlation provides a new way of constraining and obtaining additional information of the initial perturbations of the Universe.

preprint2022arXiv

Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto-optical investigations and applications. Here, we uncover strong light-magnetic ordering interactions in 2D antiferromagnetic MnPS3 utilizing a newly-emerged near-infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the DFT, STEM and XPS, we illustrate the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion-mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet-ozone treatment. Our studies offer an effective approach to investigate light-magnetic ordering interactions in 2D antiferromagnetic semiconductors.

preprint2022arXiv

STT: Soft Template Tuning for Few-Shot Adaptation

Prompt tuning has been an extremely effective tool to adapt a pre-trained model to downstream tasks. However, standard prompt-based methods mainly consider the case of sufficient data of downstream tasks. It is still unclear whether the advantage can be transferred to the few-shot regime, where only limited data are available for each downstream task. Although some works have demonstrated the potential of prompt-tuning under the few-shot setting, the main stream methods via searching discrete prompts or tuning soft prompts with limited data are still very challenging. Through extensive empirical studies, we find that there is still a gap between prompt tuning and fully fine-tuning for few-shot learning. To bridge the gap, we propose a new prompt-tuning framework, called Soft Template Tuning (STT). STT combines manual and auto prompts, and treats downstream classification tasks as a masked language modeling task. Comprehensive evaluation on different settings suggests STT can close the gap between fine-tuning and prompt-based methods without introducing additional parameters. Significantly, it can even outperform the time- and resource-consuming fine-tuning method on sentiment classification tasks.

preprint2022arXiv

Study of background from accidental coincidence signals in the PandaX-II experiment

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved.

preprint2022arXiv

The Chinese Hα Solar Explorer (CHASE) mission: An overview

The Chinese Hα Solar Explorer (CHASE), dubbed "Xihe" - Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration (CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the Hα waveband. The Hα Imaging Spectrograph (HIS) is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 Å and from 6567.8 to 6570.6 Å with 0.024 Å pixel spectral resolution and 1 minute temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 Å with the full width at half maximum of 13.4 Å. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations.

preprint2022arXiv

The list-coloring function of signed graphs

It is known that, for any $k$-list assignment $L$ of a graph $G$, the number of $L$-list colorings of $G$ is at least the number of the proper $k$-colorings of $G$ when $k>(m-1)/\ln(1+\sqrt{2})$. In this paper, we extend the Whitney's broken cycle theorem to $L$-colorings of signed graphs, by which we show that if $k> \binom{m}{3}+\binom{m}{4}+m-1$ then, for any $k$-assignment $L$, the number of $L$-colorings of a signed graph $Σ$ with $m$ edges is at least the number of the proper $k$-colorings of $Σ$. Further, if $L$ is $0$-free (resp., $0$-included) and $k$ is even (resp., odd), then the lower bound $\binom{m}{3}+\binom{m}{4}+m-1$ for $k$ can be improved to $(m-1)/\ln(1+\sqrt{2})$.

preprint2022arXiv

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data. Given an acceptable level of privacy guarantee, the goal of FL is to minimize the time-to-accuracy of model training. Compared with distributed ML in data centers, there are four distinct challenges to achieving short time-to-accuracy in FL training, namely the lack of information for optimization, the tradeoff between statistical and system utility, client heterogeneity, and large configuration space. In this paper, we survey recent works in addressing these challenges and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review system works including measurement studies and benchmarking tools that aim to support FL developers.

preprint2022arXiv

Towards Fine-Grained Reasoning for Fake News Detection

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. In particular, we propose a fine-grained reasoning framework by following the human information-processing model, introduce a mutual-reinforcement-based method for incorporating human knowledge about which evidence is more important, and design a prior-aware bi-channel kernel graph network to model subtle differences between pieces of evidence. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our approach.

preprint2022arXiv

TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation

The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ideal representation should be compact, complete, efficient, and reusable for subsequent recognition in our opinion. However, previous representations have flaws in one or more aspects. Thin-Plate-Spline (TPS) transformation has achieved great success in scene text recognition. Inspired by this, we reversely think of its usage and sophisticatedly take TPS as an exquisite representation for arbitrary shape text representation. The TPS representation is compact, complete, and efficient. With the predicted TPS parameters, the detected text region can be directly rectified to a near-horizontal one to assist the subsequent recognition. To further exploit the potential of the TPS representation, the Border Alignment Loss is proposed. Based on these designs, we implement the text detector TPSNet, which can be extended to a text spotter conveniently. Extensive evaluation and ablation of several public benchmarks demonstrate the effectiveness and superiority of the proposed method for text representation and spotting. Particularly, TPSNet achieves the detection F-Measure improvement of 4.4\% (78.4\% vs. 74.0\%) on Art dataset and the end-to-end spotting F-Measure improvement of 5.0\% (78.5\% vs. 73.5\%) on Total-Text, which are large margins with no bells and whistles.

preprint2022arXiv

Two-Stage Robust Optimization with Decision Dependent Uncertainty

The type of decision dependent uncertainties (DDUs) imposes a great challenge in decision making, while existing methodologies are not sufficient to support many real practices. In this paper, we present a systematic study to handle this challenge in two-stage robust optimization~(RO). Our main contributions include three sophisticated variants of column-and-constraint generation method to exactly compute DDU-based two-stage RO. By a novel application of core concepts of linear programming, we provide rigorous analyses on their computational behaviors. Interestingly, in terms of the iteration complexity of those algorithms, DDU-based two-stage RO is not more demanding than its decision independent uncertainty (DIU) based counterpart. It is worth highlighting a counterintuitive discovery that converting a DIU set into a DDU set by making use of "deep knowledge" and then computing the resulting DDU-based formulation may lead to a significant improvement. Indeed, as shown in this paper, in addition to capturing the actual dependence existing in the real world, DDU is a powerful and flexible tool to represent and leverage analytical properties or simply domain expertise to achieve a strong solution capacity. So, we believe it will open a new direction to solve large-scale DIU- or DDU-based RO. Other important results include basic structural properties for two-stage RO, an approximation scheme to deal with mixed integer recourse, and a couple of enhancement techniques for the developed algorithms, as well as an organized numerical study to help us appreciate all algorithms and enhancement techniques' computational performances.

preprint2022arXiv

Two-Stage Robust Unit Commitment Problem with Complex Temperature and Demand Uncertainties

In this paper, we present and study a robust unit commitment model and some variants that consider complex temperature and demand uncertainties. Since there is a strong relationship among the efficiency of gas generators, demand, and temperature in practical systems, our robust models have both left- and right-hand-side (LHS and RHS, respectively) uncertainties. Unlike many existing robust models with RHS uncertainty only, the introduction of LHS uncertainty imposes a huge challenge in computing robust solutions. For those complex formulations, we analyze their structures, derive important properties, and design exact and fast approximation solution strategies under the column-and-constraint generation framework. Numerical experiments are conducted on typical IEEE test systems, which showcase the great performance of our solution methods and demonstrate a clear impact of complex and correlated uncertainties in system operations.

preprint2022arXiv

Unsupervised High-Resolution Portrait Gaze Correction and Animation

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels. To address this issue, we first create two new portrait datasets: CelebGaze and high-resolution CelebHQGaze. Second, we formulate the gaze correction task as an image inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze Animation Module (GAM). Moreover, we propose an unsupervised training strategy, i.e., Synthesis-As-Training, to learn the correlation between the eye region features and the gaze angle. As a result, we can use the learned latent space for gaze animation with semantic interpolation in this space. Moreover, to alleviate both the memory and the computational costs in the training and the inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM and GAM. Extensive experiments validate the effectiveness of our method for both the gaze correction and the gaze animation tasks in both low and high-resolution face datasets in the wild and demonstrate the superiority of our method with respect to the state of the arts. Code is available at https://github.com/zhangqianhui/GazeAnimationV2

preprint2022arXiv

UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene

3D scenes photorealistic stylization aims to generate photorealistic images from arbitrary novel views according to a given style image while ensuring consistency when rendering from different viewpoints. Some existing stylization methods with neural radiance fields can effectively predict stylized scenes by combining the features of the style image with multi-view images to train 3D scenes. However, these methods generate novel view images that contain objectionable artifacts. Besides, they cannot achieve universal photorealistic stylization for a 3D scene. Therefore, a styling image must retrain a 3D scene representation network based on a neural radiation field. We propose a novel 3D scene photorealistic style transfer framework to address these issues. It can realize photorealistic 3D scene style transfer with a 2D style image. We first pre-trained a 2D photorealistic style transfer network, which can meet the photorealistic style transfer between any given content image and style image. Then, we use voxel features to optimize a 3D scene and get the geometric representation of the scene. Finally, we jointly optimize a hyper network to realize the scene photorealistic style transfer of arbitrary style images. In the transfer stage, we use a pre-trained 2D photorealistic network to constrain the photorealistic style of different views and different style images in the 3D scene. The experimental results show that our method not only realizes the 3D photorealistic style transfer of arbitrary style images but also outperforms the existing methods in terms of visual quality and consistency. Project page:https://semchan.github.io/UPST_NeRF.

preprint2022arXiv

Wasserstein Adversarial Transformer for Cloud Workload Prediction

Predictive Virtual Machine (VM) auto-scaling is a promising technique to optimize cloud applications operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications. However, developing a model that accurately predicts cloud workload changes is extremely challenging due to the dynamic nature of cloud workloads. Long-Short-Term-Memory (LSTM) models have been developed for cloud workload prediction. Unfortunately, the state-of-the-art LSTM model leverages recurrences to predict, which naturally adds complexity and increases the inference overhead as input sequences grow longer. To develop a cloud workload prediction model with high accuracy and low inference overhead, this work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. The extensive evaluations with real-world workload traces show WGAN-gp Transformer achieves 5 times faster inference time with up to 5.1 percent higher prediction accuracy against the state-of-the-art approach. We also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms the LSTM-based mechanism by significantly reducing VM over-provisioning and under-provisioning rates.

preprint2022arXiv

Weak decays of triply heavy baryons in light front approach

We analyze weak decays of triply heavy baryon $Ω_{ccc}^{++}$ and calculate the form factors for $Ω_{ccc}^{++}\to Ξ_{cc}^{++}/Ω_{cc}^+$ transitions in a light front quark model. The momentum distributions in form factors are accessed via the pole-model parametrization. Using the results for form factors, we predict decay branching fractions for semi-leptonic decays, and obtain $\mathcal{B}(Ω_{ccc}^{++}\toΞ_{cc}^+e^+ν_e)=0.08\%$, $\mathcal{B}(Ω_{ccc}^{++}\toΩ_{cc}^+e^+ν_e)=1.23\%$. Forward-backward asymmetry and angular distributions are then investigated in this work. We also estimate branching fractions for a few color-allowed processes and find that the $Ω_{ccc}^{++}\toΩ_{cc}^+π^+$ has a sizable branching fraction: $\mathcal{B}(Ω_{ccc}^{++}\toΩ_{cc}^+π^+)=1.82\%$. We point out that the $Ω_{ccc}^{++}\toΩ_{cc}^+π^+$ is a golden channel for the discovery of the $Ω_{ccc}^{++}$. This analysis can provide a useful reference for future experimental search in future.

preprint2022arXiv

X-ray fine structure of a limb solar flare revealed by Insight-HXMT, RHESSI and Fermi

We conduct a detailed analysis of an M1.3 limb flare occurring on 2017 July 3, which have the X-ray observations recorded by multiple hard X-ray telescopes, including Hard X-ray Modulation Telescope (Insight-HXMT), Ramaty High Energy Solar Spectroscopic Imager (RHESSI), and The Fermi Gamma-ray Space Telescope (FERMI). Joint analysis has also used the EUV imaging data from the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamic Observatory. The hard X-ray spectral and imaging evolution suggest a lower corona source, and the non-thermal broken power law distribution has a rather low break energy $\sim$ 15 keV. The EUV imaging shows a rather stable plasma configuration before the hard X-ray peak phase, and accompanied by a filament eruption during the hard X-ray flare peak phase. Hard X-ray image reconstruction from RHESSI data only shows one foot point source. We also determined the DEM for the peak phase by SDO/AIA data. The integrated EM beyond 10 MK at foot point onset after the peak phase, while the $>$ 10 MK source around reconnection site began to fade. The evolution of EM and hard X-ray source supports lower corona plasma heating after non-thermal energy dissipation. The combination of hard X-ray spectra and images during the limb flare provides the understanding on the interchange of non-thermal and thermal energies, and relation between lower corona heating and the upper corona instability.

preprint2021arXiv

A Computation Offloading Model over Collaborative Cloud-Edge Networks with Optimal Transport Theory

As novel applications spring up in future network scenarios, the requirements on network service capabilities for differentiated services or burst services are diverse. Aiming at the research of collaborative computing and resource allocation in edge scenarios, migrating computing tasks to the edge and cloud for computing requires a comprehensive consideration of energy consumption, bandwidth, and delay. Our paper proposes a collaboration mechanism based on computation offloading, which is flexible and customizable to meet the diversified requirements of differentiated networks. This mechanism handles the terminal's differentiated computing tasks by establishing a collaborative computation offloading model between the cloud server and edge server. Experiments show that our method has more significant improvements over regular optimization algorithms, including reducing the execution time of computing tasks, improving the utilization of server resources, and decreasing the terminal's energy consumption.

preprint2021arXiv

A Hybrid Renormalization Scheme for Quasi Light-Front Correlations in Large-Momentum Effective Theory

In large-momentum effective theory (LaMET), calculating parton physics starts from calculating coordinate-space-$z$ correlation functions $\tilde h(z, a,P^z)$ in a hadron of momentum $P^z$ in lattice QCD. Such correlation functions involve both linear and logarithmic divergences in lattice spacing $a$, and thus need to be properly renormalized. We introduce a hybrid renormalization procedure to match these lattice correlations to those in the continuum $\overline{\rm MS}$ scheme, without introducing extra non-perturbative effects at large $z$. We analyze the effect of ${\cal O}(Λ_{\rm QCD})$ ambiguity in the Wilson line self-energy subtraction involved in this hybrid scheme. To obtain the momentum-space distributions, we recommend to extrapolate the lattice data to the asymptotic $z$-region using the generic properties of the coordinate space correlations at moderate and large $P^z$, respectively.

preprint2021arXiv

A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions

In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich's helical CT formula to 2D fan-beam CT reconstruction. Then, we propose a new weighting function to deal with the redundant projection data. By extending the weighted arc based fan-beam algorithm to circle cone-beam geometry, we also obtain a new FDK-similar algorithm for circle cone-beam CT reconstruction. Experiments show that our methods can obtain higher PSNR and SSIM compared to the Parker-weighted conventional fan-beam algorithm and the FDK algorithm for super-short-scan trajectories.

preprint2021arXiv

A Spike Learning System for Event-driven Object Recognition

Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found increased attention in high-resolution and high-speed applications. A lot of work has been conducted to enhance recognition accuracy. However, the essential topic of recognition delay or time efficiency is largely under-explored. In this paper, we present a spiking learning system that uses the spiking neural network (SNN) with a novel temporal coding for accurate and fast object recognition. The proposed temporal coding scheme maps each event's arrival time and data into SNN spike time so that asynchronously-arrived events are processed immediately without delay. The scheme is integrated nicely with the SNN's asynchronous processing capability to enhance time efficiency. A key advantage over existing systems is that the event accumulation time for each recognition task is determined automatically by the system rather than pre-set by the user. The system can finish recognition early without waiting for all the input events. Extensive experiments were conducted over a list of 7 LiDAR and DVS datasets. The results demonstrated that the proposed system had state-of-the-art recognition accuracy while achieving remarkable time efficiency. Recognition delay was shown to reduce by 56.3% to 91.7% in various experiment settings over the popular KITTI dataset.

preprint2021arXiv

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerning the joint probability distribution (P(X, Y)) distance for this problem, since its empirical estimation derived from the maximum mean discrepancy (joint maximum mean discrepancy, JMMD) will involve complex tensor-product operator that is hard to manipulate. To solve this issue, this paper theoretically derives a unified form of JMMD that is easy to optimize, and proves that the marginal, class conditional and weighted class conditional probability distribution distances are our special cases with different label kernels, among which the weighted class conditional one not only can realize feature alignment across domains in the category level, but also deal with imbalance dataset using the class prior probabilities. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one. Therefore, we leverage Hilbert Schmidt independence criterion and propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift. Finally, we conduct extensive experiments on several cross-domain datasets to demonstrate the validity and effectiveness of the revealed theoretical results.

preprint2021arXiv

An Empirical Assessment of Global COVID-19 Contact Tracing Applications

The rapid spread of COVID-19 has made manual contact tracing difficult. Thus, various public health authorities have experimented with automatic contact tracing using mobile applications (or "apps"). These apps, however, have raised security and privacy concerns. In this paper, we propose an automated security and privacy assessment tool, COVIDGUARDIAN, which combines identification and analysis of Personal Identification Information (PII), static program analysis and data flow analysis, to determine security and privacy weaknesses. Furthermore, in light of our findings, we undertake a user study to investigate concerns regarding contact tracing apps. We hope that COVIDGUARDIAN, and the issues raised through responsible disclosure to vendors, can contribute to the safe deployment of mobile contact tracing. As part of this, we offer concrete guidelines, and highlight gaps between user requirements and app performance.

preprint2021arXiv

Anti-self-dual connections over the $5$D Heisenberg group and the twistor method

In this paper, we introduce notions of $α$-planes in $5$D complex Heisenberg group and the twistor space as the moduli space of all $α$-planes. So we can define an anti-self-dual (ASD) connection as a connection flat over all $α$-planes. This geometric approach allows us to establish Penrose-Ward correspondence between ASD connections over $5$D complex Heisenberg group and a class of holomorphic vector bundles on the twistor space. By Atiyah-Ward ansätz, we also construct a family of ASD connections on $5$D complex Heisenberg group. When restricted to $5$D real Heisenberg group, the flat model of $5$D contact manifolds, an ASD connection satisfies the horizontal part of the contact instanton equation introduced by physicists.

preprint2021arXiv

Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.

preprint2021arXiv

Chatbot for fitness management using IBM Watson

Chatbots have revolutionized the way humans interact with computer systems and they have substituted the use of service agents, call-center representatives etc. Fitness industry has always been a growing industry although it has not adapted to the latest technologies like AI, ML and cloud computing. In this paper, we propose an idea to develop a chatbot for fitness management using IBM Watson and integrate it with a web application. We proposed using Natural Language Processing (NLP) and Natural Language Understanding (NLU) along with frameworks of IBM Cloud Watson provided for the Chatbot Assistant. This software uses a serverless architecture to combine the services of a professional by offering diet plans, home exercises, interactive counseling sessions, fitness recommendations.

preprint2021arXiv

Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM

Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz. Quantitative site testing is still needed to confirm the site's properties. In this paper, we present a statistical analysis of cloud cover and aurora contamination from the Kunlun Cloud and Aurora Monitor (KLCAM). KLCAM is an automatic, unattended all-sky camera aiming for long-term monitoring of the usable observing time and optical sky background at Dome~A. It was installed at Dome~A in January 2017, worked through the austral winter, and collected over 47,000 images over 490 days. A semi-quantitative visual data analysis of cloud cover and auroral contamination was carried out by five individuals. The analysis shows that the night sky was free of cloud for 83 per cent of the time, which ranks Dome~A highly in a comparison with other observatory sites. Although aurorae were detected somewhere on an image for nearly 45 per cent of the time, the strongest auroral emission lines can be filtered out with customized filters.

preprint2021arXiv

CREATe: Clinical Report Extraction and Annotation Technology

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies. However, to our knowledge, there has been no attempt to develop an end-to-end system to annotate, index, or otherwise curate these reports. In this paper, we propose a novel computational resource platform, CREATe, for extracting, indexing, and querying the contents of clinical case reports. CREATe fosters an environment of sustainable resource support and discovery, enabling researchers to overcome the challenges of information science. An online video of the demonstration can be viewed at https://youtu.be/Q8owBQYTjDc.

preprint2021arXiv

Dark Matter Search Results from the PandaX-4T Commissioning Run

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$.

preprint2021arXiv

Direct evidence for intermediate multiferroic phase in LiCuFe2(VO4)3

Magnetic susceptibility, specific heat, dielectric, and electric polarization of LiCuFe2(VO4)3 have been investigated. Two sequential antiferromagnetic transitions at TN1 ~ 9.95 K and TN2 ~ 8.17 K are observed under zero magnetic field. While a dielectric peak at TN1 is clearly identified, the measured pyroelectric current also exhibits a sharp peak at TN1, implying the magnetically relevant ferroelectricity. Interestingly, another pyroelectric peak around TN2 with opposite signal is observed, resulting in the disappearance of electric polarization below TN2. Besides, the electric polarization is significantly suppressed in response to external magnetic field, evidencing remarkable magnetoelectric effect. These results suggest the essential relevance of the magnetic structure with the ferroelectricity in LiCuFe2(VO4)3, deserving for further investigation of the underlying mechanism.

preprint2021arXiv

Exploiting Deep Learning for Secure Transmission in an Underlay Cognitive Radio Network

This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)-based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications.

preprint2021arXiv

Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection

Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clues for diversity. However, most of the comment generation approaches only focus on saliency information extraction, while the reader-aware factors implied by comments are neglected. To address this issue, we propose a unified reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments. For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information. The obtained topic representations as well as the selected saliency information are incorporated into the decoder to generate diversified and informative comments. Experimental results on three datasets show that our framework outperforms existing baseline methods in terms of both automatic metrics and human evaluation. The potential ethical issues are also discussed in detail.

preprint2021arXiv

Internal Calibration of the PandaX-II Detector with Radon Gaseous Sources

We have developed a low-energy electron recoil (ER) calibration method with $^{220}$Rn for the PandaX-II detector. $^{220}$Rn, emanated from natural thorium compounds, was fed into the detector through the xenon purification system. From 2017 to 2019, we performed three dedicated calibration campaigns with different radon sources. We studied the detector response to $α$, $β$, and $γ$ particles with focus on low energy ER events. During the runs in 2017 and 2018, the amount of radioactivity of $^{222}$Rn were on the order of 1\% of that of $^{220}$Rn and thorium particulate contamination was negligible, especially in 2018. We also measured the background contribution from $^{214}$Pb for the first time in PandaX-II with the help from a $^{222}$Rn injection. Calibration strategy with $^{220}$Rn and $^{222}$Rn will be implemented in the upcoming PandaX-4T experiment and can be useful for other xenon-based detectors as well.

preprint2021arXiv

Investigations of Smart Health Reliability

A balanced investigation into the reliability of wireless smart health devices when it comes to the collection of biometric data under varying network/environmental conditions. Followed by a program implementation to begin introductory analysis on measurement accuracy and data collection to gauge the reliability of smart health devices.

preprint2021arXiv

Joint Beam Training and Positioning For Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications

Intelligent reflecting surface (IRS) offers a cost effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training design for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to estimate angle of arrival (AoA) and angle of departure (AoD) of the line of sight (LoS) path between BS/AP (or IRSs) and MT. Then, with the estimate of AoAs and AoDs, we propose an iterative positioning algorithm that achieves centimeter-level positioning accuracy. The obtained location information is not only a fringe benefit but also enables us to cross verify and enhance the estimation of AoA and AoD, and facilitates the prediction of blockage indicator. Numerical results show the superiority of our proposed beam training scheme and verify the performance gain brought by location information.

preprint2021arXiv

JUNO Physics and Detector

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. With 6 years of data, the neutrino mass ordering can be determined at 3-4 sigma and three oscillation parameters can be measured to a precision of 0.6% or better by detecting reactor antineutrinos. With 10 years of data, DSNB could be observed at 3-sigma; a lower limit of the proton lifetime of 8.34e33 years (90% C.L.) can be set by searching for p->nu_bar K^+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A core-collapse supernova at 10 kpc would lead to ~5000 IBD and ~2000 (300) all-flavor neutrino-proton (electron) scattering events. Geo-neutrinos can be detected with a rate of ~400 events/year. We also summarize the final design of the JUNO detector and the key R&D achievements. All 20-inch PMTs have been tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the >20 m attenuation length of LS, we expect a yield of 1345 p.e. per MeV and an effective energy resolution of 3.02%/\sqrt{E (MeV)}$ in simulations. The underwater electronics is designed to have a loss rate <0.5% in 6 years. With degassing membranes and a micro-bubble system, the radon concentration in the 35-kton water pool could be lowered to <10 mBq/m^3. Acrylic panels of radiopurity <0.5 ppt U/Th are produced. The 20-kton LS will be purified onsite. Singles in the fiducial volume can be controlled to ~10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a LS testing facility OSIRIS, and a near detector TAO.

preprint2021arXiv

Light yield and field dependence measurement in PandaX-II dual-phase xenon detector

The dual-phase xenon time projection chamber (TPC) is one of the most sensitive detector technology for dark matter direct search, where the energy deposition of incoming particle can be converted into photons and electrons through xenon excitation and ionization. The detector response to signal energy deposition varies significantly with the electric field in liquid xenon. We study the detector's light yield and its dependence on the electric field in the PandaX-II dual-phase detector containing 580~kg liquid xenon in the sensitive volume. From our measurements, the light yield at electric fields from 0~V/cm to 317~V/cm is obtained for energy depositions up to 236~keV.

preprint2021arXiv

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99 degrees for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.

preprint2021arXiv

Machine Learning and Artificial Intelligence in Next-Generation Wireless Network

Due to the advancement in technologies, the next-generation wireless network will be very diverse, complicated, and according to the changed demands of the consumers. The current network operator methodologies and approaches are traditional and cannot help the next generation networks to utilize their resources most appropriately. The limited capability of the traditional tools will not allow the network providers to fulfill the demands of the network's subscribers in the future. Therefore, this paper will focus on machine learning, automation, artificial intelligence, and big data analytics for improving the capacity and effectiveness of next-generation wireless networks. The paper will discuss the role of these new technologies in improving the service and performance of the network providers in the future. The paper will find out that machine learning, big data analytics, and artificial intelligence will help in making the next-generation wireless network self-adaptive, self-aware, prescriptive, and proactive. At the end of the paper, it will be provided that future wireless network operators cannot work without shifting their operational framework to AI and machine learning technologies.

preprint2021arXiv

Next-to-next-to-leading order corrections to quark Quasi parton distribution functions

We present the next-to-next-to-leading order (NNLO) calculation of quark quasi parton distribution functions (PDFs) in the large momentum effective theory. The nontrivial factorization at this order is established explicitly and the full analytic matching coefficients between the quasi distribution and the lightcone distribution are derived. We demonstrate that the NNLO numerical contributions can improve the behavior of the extracted PDFs sizably. With the unprecedented precision study of nucleon tomography at the planned electron-ion collider, high precision Lattice QCD simulations with our NNLO results implemented will enable to test the QCD theory and more precise results on the PDFs of nucleons will be obtained.

preprint2021arXiv

NumaPerf: Predictive and Full NUMA Profiling

Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing NUMA-profiling tools share some similar shortcomings, such as portability, effectiveness, and helpfulness issues. This paper proposes a novel profiling tool - NumaPerf - that overcomes these issues. NumaPerf aims to identify potential performance issues for any NUMA architecture, instead of only on the current hardware. To achieve this, NumaPerf focuses on memory sharing patterns between threads, instead of real remote accesses. NumaPerf further detects potential thread migrations and load imbalance issues that could significantly affect the performance but are omitted by existing profilers. NumaPerf also separates cache coherence issues that may require different fix strategies. Based on our extensive evaluation, NumaPerf is able to identify more performance issues than any existing tool, while fixing these bugs leads to up to 5.94x performance speedup.

preprint2021arXiv

On the asymptotical regularization with convex constraints for inverse problems

In this paper, we consider the asymptotical regularization with convex constraints for nonlinear ill-posed problems. The method allows to use non-smooth penalty terms, including the L1-like and the total variation-like penalty functionals, which are significant in reconstructing special features of solutions such as sparsity and piecewise constancy. Under certain conditions we give convergence properties of the methods. Moreover, we propose Runge-Kutta type methods to discrete the initial value problems to construct new type iterative regularization methods.

preprint2021arXiv

Optimal networks for dynamical spreading

The inverse problem of finding the optimal network structure for a specific type of dynamical process stands out as one of the most challenging problems in network science. Focusing on the susceptible-infected-susceptible type of dynamics on annealed networks whose structures are fully characterized by the degree distribution, we develop an analytic framework to solve the inverse problem. We find that, for relatively low or high infection rates, the optimal degree distribution is unique, which consists of no more than two distinct nodal degrees. For intermediate infection rates, the optimal degree distribution is multitudinous and can have a broader support. We also find that, in general, the heterogeneity of the optimal networks decreases with the infection rate. A surprising phenomenon is the existence of a specific value of the infection rate for which any degree distribution would be optimal in generating maximum spreading prevalence. The analytic framework and the findings provide insights into the interplay between network structure and dynamical processes with practical implications.

preprint2021arXiv

Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goal of this study is to develop a general model capable of predicting the solubility of a broad range of organic molecules. Using the largest currently available solubility dataset, we implement deep learning-based models to predict solubility from molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system (SMILES) strings, molecular graphs, and three-dimensional (3D) atomic coordinates using four different neural network architectures - fully connected neural networks (FCNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

preprint2021arXiv

Radio Emission from Outflow-Cloud Interaction and Its Constraint on TDE Outflow

Tidal disruption event (TDE) can launch an ultrafast outflow. If the black hole is surrounded by large amounts of clouds, outflow-cloud interaction will generate bow shocks, accelerate electrons and produce radio emission. Here we investigate the interaction between a non-relativistic outflow and clouds in active galaxies, which is manifested as outflow-BLR (broad line region) interaction, and can be extended to outflow-torus interaction. This process can generate considerable radio emission, which may account for the radio flares appearing a few months later after TDE outbursts. Benefitting from efficient energy conversion from outflow to shocks and the strong magnetic field, outflow-cloud interaction may play a non-negligible, or even dominating role in generating radio flares in a cloudy circumnuclear environment if the CNM density is no more than 100 times the Sgr A*-like one. In this case, the evolution of radio spectra can be used to directly constrain the properties of outflows.

preprint2021arXiv

Results of Dark Matter Search using the Full PandaX-II Exposure

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

preprint2021arXiv

Stacking effect and Coulomb correlation in layered charge density wave phase of 1T-NbS2

Based on first-principles calculations, we explored the interplay between stacking effect and electron-electron correlation in the layered vdW material of bulk 1T-NbS2 with a 2D charge density wave (CDW) order. Without considering the Coulomb correlation, two energetically favorable out-of-plane stacking configurations are identified: one is a metallic phase with a single-layer stacking pattern, another is a band insulator with a paired-bilayer stacking configuration. Even though the Coulomb correlation is taken into account, the two energetic favorable stacking orders are still far more stable than other stacking orders. Furthermore, increasing the Coulomb interaction, the paired-bilayer stacking configuration transforms from nonmagnetic band insulator to antiferromagnetic insulator, while the single-layer stacking undergoes a Slater-Mott metal-insulator transition, which indicates the non-negligible role of electron-electron correlation interactions. In addition, the electronic structure and magnetic ground state change drastically among different stacking configurations, providing a platform to tune the electronic structures and interlayer magnetic interactions by altering the stacking order. In contrast to the widely accepted scenario of Mott localization as the driving force behind the gap formation in the CDW phase of layered transition metal dichalcogenides, our results not only highlight the crucial role of stacking order in the electronic structures of 1T-NbS2, but also shed fresh light on the distinct effects of Coulomb interaction in different stacking arrangements.

preprint2021arXiv

The landscape law for tight binding Hamiltonians

The present paper extends the landscape theory pioneered in [FM, ADFJM2, DFM] to the tight-binding Schrödinger operator on $\Z^d$. In particular, we establish upper and lower bounds for the integrated density of states in terms of the counting function based upon the localization landscape.

preprint2021arXiv

The Systematic Design of Non-commensurate Impedance Matching Tapers for Ultra Wideband Gradient-Index (GRIN) Lens Antennas

We propose a general method for designing wideband matching tapers in inhomogeneous media where phase velocity is coupled to the taper impedance profile. Such tapers are used to match wideband gradient index (GRIN) lens antennas. To simplify fabrication tapers are often constrained to physically uniform layers wherein commensurate line theory cannot predict the frequency response. Therefore, we present a new design algorithm which derives an effective permittivity $ε_{eff}$ to equalize the electrical length of commensurate and non-commensurate line tapers. The algorithm provides a systematic design method with predictable frequency response for non-commensurate line tapers. Nevertheless, there are several unavoidable nonidealities present in such discretized tapers which we discuss and provide recommendations for mitigation. The algorithm is used to design a Klopfenstein taper with return loss better than 15 dB from 8 to 78 GHz. The design is fabricated and measurements agree with simulation across the WR90, WR28, and WR12 bands. An approximate efficiency formula is proposed which predicts aperture efficiency of taper-matched lenses without the need for time-consuming full-wave simulations. Various lenses are designed and compared to highlight the advantages of Klopfenstein tapers in GRIN lens design. The results demonstrate the usefulness of the proposed design method.

preprint2021arXiv

The tangential $k$-Cauchy-Fueter complexes and Hartogs' phenomenon over the right quaternionic Heisenberg group

We construct the tangential $k$-Cauchy-Fueter complexes on the right quaternionic Heisenberg group, as the quaternionic counterpart of $\overline{\partial}_b$-complex on the Heisenberg group in the theory of several complex variables. We can use the $L^2$ estimate to solve the nonhomogeneous tangential $k$-Cauchy-Fueter equation under the compatibility condition over this group modulo a lattice. This solution has an important vanishing property when the group is higher dimensional. It allows us to prove the Hartogs' extension phenomenon for $k$-CF functions, which are the quaternionic counterpart of CR functions.

preprint2021arXiv

The Yamabe operator and invariants on octonionic contact manifolds and convex cocompact subgroups of ${\rm F}_{4(-20)}$

An octonionic contact (OC) manifold is always spherical. We construct the OC Yamabe operator on an OC manifold and prove its transformation formula under conformal OC transformations. An OC manifold is scalar positive, negative or vanishing if and only if its OC Yamabe invariant is positive, negative or zero, respectively. On a scalar positive OC manifold, we can construct the Green function of the OC Yamabe operator, and apply it to construct a conformally invariant tensor. It becomes an OC metric if the OC positive mass conjecture is true. We also show the connected sum of two scalar positive OC manifolds to be scalar positive if the neck is sufficiently long. On the OC manifold constructed from a convex cocompact subgroup of ${\rm F}_{4(-20)}$, we construct a Nayatani type Carnot-Carathéodory metric. As a corollary, such an OC manifold is scalar positive, negative or vanishing if and only if the Poincaré critical exponent of the subgroup is less than, greater than or equal to $10,$ respectively.

preprint2021arXiv

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

preprint2020arXiv

"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition

Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.

preprint2020arXiv

$B$-meson light-cone distribution amplitude from the Euclidean quantity

A new method for the model-independent determination of the light-cone distribution amplitude (LCDA) of the $B$-meson in heavy quark effective theory (HQET) is proposed by combining the large momentum effective theory (LaMET) and the numerical simulation technique on the Euclidean lattice. We demonstrate the autonomous scale dependence of the non-local quasi-HQET operator with the aid of the auxiliary field approach, and further determine the perturbative matching coefficient entering the hard-collinear factorization formula for the $B$-meson quasi-distribution amplitude at the one-loop accuracy. These results will be crucial to explore the partonic structure of heavy-quark hadrons in the static limit and to improve the theory description of exclusive $B$-meson decay amplitudes based upon perturbative QCD factorization theorems.

preprint2020arXiv

A Benchmarking Framework for Interactive 3D Applications in the Cloud

With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D applications have become a major type of workloads for the cloud. However, despite their growing importance, there is limited public research on how to design cloud systems to efficiently support these applications, due to the lack of an open and reliable research infrastructure, including benchmarks and performance analysis tools. The challenges of generating human-like inputs under various system/application randomness and dissecting the performance of complex graphics systems make it very difficult to design such an infrastructure. In this paper, we present the design of a novel cloud graphics rendering research infrastructure, Pictor. Pictor employs AI to mimic human interactions with complex 3D applications. It can also provide in-depth performance measurements for the complex software and hardware stack used for cloud 3D graphics rendering. With Pictor, we designed a benchmark suite with six interactive 3D applications. Performance analyses were conducted with these benchmarks to characterize 3D applications in the cloud and reveal new performance bottlenecks. To demonstrate the effectiveness of Pictor, we also implemented two optimizations to address two performance bottlenecks discovered in a state-of-the-art cloud 3D-graphics rendering system, which improved the frame rate by 57.7% on average.

preprint2020arXiv

A deep network for sinogram and CT image reconstruction

A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free. However, in practice, the sinogram is usually contaminated by noise, which degrades the quality of a reconstructed CT image. In this paper, we design a deep network for sinogram and CT image reconstruction. The network consists of two cascaded blocks that are linked by a filter backprojection (FBP) layer, where the former block is responsible for denoising and completing the sinograms while the latter is used to removing the noise and artifacts of the CT images. Experimental results show that the reconstructed CT images by our methods have the highest PSNR and SSIM in average compared to state of the art methods.

preprint2020arXiv

A model-guided deep network for limited-angle computed tomography

In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network.We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approxiamte the solutions of the first two subproblems and, thus, obtain an end-to-end deep network for the limited-angle CT image reconstruction. Our network tackles both the sinograms and the CT images, and can simultaneously suppress the artifacts caused by the incomplete data and recover fine structural information in the CT images. Experimental results show that our method outperforms the existing algorithms for the limited-angle CT image reconstruction.

preprint2020arXiv

A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways

We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.

preprint2020arXiv

A robust determination of halo environment in the cosmic field

A number of methods for studying the large-scale cosmic matter distribution exist in the literature. One particularly common method employed to define the cosmic web is to examine the density, velocity or potential field. Such methods are advantageous since a Hessian matrix can be constructed whose eigenvectors (and eigenvalues) indicate the principal directions (and strength) of local collapse or expansion. Technically this is achieved by diagonalizing the Hessian matrix using a fixed finite grid. The resultant large-scale structure quantification is thus inherently limited by the grid's finite resolution. Here, we overcome the obstacle of finite grid resolution by introducing a new method to determine halo environment using an adaptive interpolation which is more robust to resolution than the typical "Nearest Grid Point" (NGP) method. Essentially instead of computing and diagonalizing the Hessian matrix once for the entire grid, we suggest doing so once for each halo or galaxy in question. We examine how the eigenvalues and eigenvector direction's computed using our algorithm and the NGP method converge for different grid resolutions, finding that our new method is convergent faster. Namely changes of resolution have a much smaller effect than in the NGP method. We therefore suggest this method for future use by the community.

preprint2020arXiv

A testable hypothesis on the possible role of electron spin in the origin of bio-homochirality

The emergence of biomolecular homochirality is a critical open question in the field of origins of life. In order to seek out an answer to this unsettled issue, a number of mechanisms have been offered over time, but it still remains a great challenge to date. In this paper, based on the hydrothermal vent theory for the origins of life, I tentatively put forward a new hypothesis that the prebiotic emergence of the uniform chirality of biomolecules might have been specifically determined by the spin state of electrons during their prebiotic syntheses on the surfaces of greigite, a mineral which has been frequently argued to have played an important role in the evolutionary context of life. An experimental model to test the hypothesis has also been proposed. Taken into consideration the possible widespread existence of greigite in submarine hydrothermal vent systems which have been frequently argued as a potential cradle for the origins of life, the suggested model, if could be experimentally demonstrated, may be suggestive of where and how life originated on early Earth.

preprint2020arXiv

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

The multi-UAV network is promising to extend conventional networks by providing broader coverage and better reliability. Nevertheless, the broadcast nature of wireless signals and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs) and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm and an OCF algorithm are proposed to solve these two sequential games whose convergences and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms and the effectiveness of our two-stage game framework in the terms of secrecy performance.

preprint2020arXiv

A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps

Authorship identification is the process of identifying and classifying authors through given codes. Authorship identification can be used in a wide range of software domains, e.g., code authorship disputes, plagiarism detection, exposure of attackers' identity. Besides the inherent challenges from legacy software development, framework programming and crowdsourcing mode in Android raise the difficulties of authorship identification significantly. More specifically, widespread third party libraries and inherited components (e.g., classes, methods, and variables) dilute the primary code within the entire Android app and blur the boundaries of code written by different authors. However, prior research has not well addressed these challenges. To this end, we design a two-phased approach to attribute the primary code of an Android app to the specific developer. In the first phase, we put forward three types of strategies to identify the relationships between Java packages in an app, which consist of context, semantic and structural relationships. A package aggregation algorithm is developed to cluster all packages that are of high probability written by the same authors. In the second phase, we develop three types of features to capture authors' coding habits and code stylometry. Based on that, we generate fingerprints for an author from its developed Android apps and employ several machine learning algorithms for authorship classification. We evaluate our approach in three datasets that contain 15,666 apps from 257 distinct developers and achieve a 92.5% accuracy rate on average. Additionally, we test it on 2,900 obfuscated apps and our approach can classify apps with an accuracy rate of 80.4%.

preprint2020arXiv

Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in neural networks. By constraining all quantization levels as the sum of Powers-of-Two terms, APoT quantization enjoys high computational efficiency and a good match with the distribution of weights. A simple reparameterization of the clipping function is applied to generate a better-defined gradient for learning the clipping threshold. Moreover, weight normalization is presented to refine the distribution of weights to make the training more stable and consistent. Experimental results show that our proposed method outperforms state-of-the-art methods, and is even competitive with the full-precision models, demonstrating the effectiveness of our proposed APoT quantization. For example, our 4-bit quantized ResNet-50 on ImageNet achieves 76.6% top-1 accuracy without bells and whistles; meanwhile, our model reduces 22% computational cost compared with the uniformly quantized counterpart. The code is available at https://github.com/yhhhli/APoT_Quantization.

preprint2020arXiv

Adversarial Attack on Hierarchical Graph Pooling Neural Networks

Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many tasks, such as node classification and graph classification. The research on the robustness of these models has also started to attract attentions in the machine learning field. However, most of the existing work in this area focus on the GNNs for node-level tasks, while little work has been done to study the robustness of the GNNs for the graph classification task. In this paper, we aim to explore the vulnerability of the Hierarchical Graph Pooling (HGP) Neural Networks, which are advanced GNNs that perform very well in the graph classification in terms of prediction accuracy. We propose an adversarial attack framework for this task. Specifically, we design a surrogate model that consists of convolutional and pooling operators to generate adversarial samples to fool the hierarchical GNN-based graph classification models. We set the preserved nodes by the pooling operator as our attack targets, and then we perturb the attack targets slightly to fool the pooling operator in hierarchical GNNs so that they will select the wrong nodes to preserve. We show the adversarial samples generated from multiple datasets by our surrogate model have enough transferability to attack current state-of-art graph classification models. Furthermore, we conduct the robust train on the target models and demonstrate that the retrained graph classification models are able to better defend against the attack from the adversarial samples. To the best of our knowledge, this is the first work on the adversarial attack against hierarchical GNN-based graph classification models.

preprint2020arXiv

Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition

We consider the problem of semi-supervised 3D action recognition which has been rarely explored before. Its major challenge lies in how to effectively learn motion representations from unlabeled data. Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain. However, few effective self-supervised approaches exist for 3D action recognition, and directly applying SSL for semi-supervised learning suffers from misalignment of representations learned from SSL and supervised learning tasks. To address these issues, we present Adversarial Self-Supervised Learning (ASSL), a novel framework that tightly couples SSL and the semi-supervised scheme via neighbor relation exploration and adversarial learning. Specifically, we design an effective SSL scheme to improve the discrimination capability of learned representations for 3D action recognition, through exploring the data relations within a neighborhood. We further propose an adversarial regularization to align the feature distributions of labeled and unlabeled samples. To demonstrate effectiveness of the proposed ASSL in semi-supervised 3D action recognition, we conduct extensive experiments on NTU and N-UCLA datasets. The results confirm its advantageous performance over state-of-the-art semi-supervised methods in the few label regime for 3D action recognition.

preprint2020arXiv

An Electrocommunication System Using FSK Modulation and Deep Learning Based Demodulation for Underwater Robots

Underwater communication is extremely challenging for small underwater robots which typically have stringent power and size constraints. In our previous work, we developed an artificial electrocommunication system which could be an alternative for the communication of small underwater robots. This paper further presents a new electrocommunication system that utilizes Binary Frequency Shift Keying (2FSK) modulation and deep-learning-based demodulation for underwater robots. We first derive an underwater electrocommunication model that covers both the near-field area and a large transition area outside of the near-field area. 2FSK modulation is adopted to improve the anti-interference ability of the electric signal. A deep learning algorithm is used to demodulate the electric signal by the receiver. Simulations and experiments show that with the same testing condition, the new communication system outperforms the previous system in both the communication distance and the data transmitting rate. In specific, the newly developed communication system achieves stable communication within the distance of 10 m at a data transfer rate of 5 Kbps with a power consumption of less than 0.1 W. The substantial increase in communication distance further improves the possibility of electrocommunication in underwater robotics.

preprint2020arXiv

Analysis of $B_c \to τν_τ$ at CEPC

The precise determination of the $B_c \to τν_τ$ branching ratio provides an advantageous opportunity for understanding the electroweak structure of the Standard Model, measuring the CKM matrix element $|V_{cb}|$ and probing new physics models. In this paper, we discuss the potential of measuring the processes of $B_c \to τν_τ$ with $τ$ decaying leptonically at the proposed Circular Electron Positron Collider (CEPC). We conclude that during the $Z$ pole operation, the channel signal can achieve five $σ$ significance with $\sim 10^9$ $Z$ decays, and the signal strength accuracies for $B_c \to τν_τ$ can reach around 1% level at the nominal CEPC $Z$ pole statistics of one trillion $Z$ decays assuming the total $B_c \to τν_τ$ yield is $3.6 \times 10^6$. Our theoretical analysis indicates the accuracy could provide a strong constraint on the general effective Hamiltonian for the $b \to cτν$ transition. If the total $B_c$ yield can be determined to $\mathcal{O}(1\%)$ level of accuracy in the future, these results also imply $|V_{cb}|$ could be measured up to $\mathcal{O}(1\%)$ level of accuracy.

preprint2020arXiv

Anyons and Fractional Quantum Hall Effect in Fractal Dimensions

The fractional quantum Hall effect is a paradigm of topological order and has been studied thoroughly in two dimensions. Here, we construct a new type of fractional quantum Hall system, which has the special property that it lives in fractal dimensions. We provide analytical wave functions and exact few-body parent Hamiltonians, and we show numerically for several different Hausdorff dimensions between 1 and 2 that the systems host anyons. We also find examples of fractional quantum Hall physics in fractals with Hausdorff dimension 1 and ln(4)/ln(5). Our results suggest that the local structure of the investigated fractals is more important than the Hausdorff dimension to determine whether the systems are in the desired topological phase. The study paves the way for further investigations of strongly-correlated topological systems in fractal dimensions.

preprint2020arXiv

Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention

The major challenge in audio-visual event localization task lies in how to fuse information from multiple modalities effectively. Recent works have shown that attention mechanism is beneficial to the fusion process. In this paper, we propose a novel joint attention mechanism with multimodal fusion methods for audio-visual event localization. Particularly, we present a concise yet valid architecture that effectively learns representations from multiple modalities in a joint manner. Initially, visual features are combined with auditory features and then turned into joint representations. Next, we make use of the joint representations to attend to visual features and auditory features, respectively. With the help of this joint co-attention, new visual and auditory features are produced, and thus both features can enjoy the mutually improved benefits from each other. It is worth noting that the joint co-attention unit is recursive meaning that it can be performed multiple times for obtaining better joint representations progressively. Extensive experiments on the public AVE dataset have shown that the proposed method achieves significantly better results than the state-of-the-art methods.

preprint2020arXiv

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upper-layer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challenge-response protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.

preprint2020arXiv

Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning

The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge. Therefore, an automated diagnosis system able to save the effort of doctors is of great value. At present, the applications of artificial intelligence in CXR diagnosis usually use pattern recognition to classify the scans. However, such methods rely on labeled databases, which are costly and usually have large error rates. In this work, we built a database containing more than 12,000 CXR scans and radiological reports, and developed a model based on deep convolutional neural network and recurrent network with attention mechanism. The model learns features from the CXR scans and the associated raw radiological reports directly; no additional labeling of the scans are needed. The model provides automated recognition of given scans and generation of reports. The quality of the generated reports was evaluated with both the CIDEr scores and by radiologists as well. The CIDEr scores are found to be around 5.8 on average for the testing dataset. Further blind evaluation suggested a comparable performance against human radiologist.

preprint2020arXiv

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time.

preprint2020arXiv

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking drug-drug interaction as an example, existing methods using machine learning either only utilize the link structure between drugs without using the graph representation of each drug molecule, or only leverage the individual drug compound structures without using graph structure for the higher-level DDI graph. The key idea of our method is to fundamentally view the data as a bi-level graph, where the highest level graph represents the interaction between biological entities (interaction graph), and each biological entity itself is further expanded to its intrinsic graph representation (representation graphs), where the graph is either flat like a drug compound or hierarchical like a protein with amino acid level graph, secondary structure, tertiary structure, etc. Our model not only allows the usage of information from both the high-level interaction graph and the low-level representation graphs, but also offers a baseline for future research opportunities to address the bi-level nature of the data.

preprint2020arXiv

Bidirectional Self-Folding with Atomic Layer Deposition Nanofilms for Microscale Origami

Origami design principles are scale invariant and enable direct miniaturization of origami structures provided the sheets used for folding have equal thickness to length ratios. Recently, seminal steps have been taken to fabricate microscale origami using unidirectionally actuated sheets with nanoscale thickness. Here, we extend the full power of origami-inspired fabrication to nanoscale sheets by engineering bidirectional folding with 4 nm thick atomic layer deposition (ALD) SiNx-SiO2 bilayer films. Strain differentials within these bilayers result in bending, producing microscopic radii of curvature. We lithographically pattern these bilayers and localize the bending using rigid panels to fabricate a variety of complex micro-origami devices. Upon release, these devices self-fold according to prescribed patterns. Our approach combines planar semiconductor microfabrication methods with computerized origami design, making it easy to fabricate and deploy such microstructures en masse. These devices represent an important step forward in the fabrication and assembly of deployable micromechanical systems that can interact with and manipulate micro- and nanoscale environments.

preprint2020arXiv

Clinical Trial Drug Safety Assessment for Studies and Submissions Impacted by COVID-19

In this paper, we provide guidance on how standard safety analyses and reporting of clinical trial safety data may need to be modified, given the potential impact of the COVID-19 pandemic. The impact could include missed visits, alternative methods for assessments (such as virtual visits), alternative locations for assessments (such as local labs), and study drug interruptions. We focus on safety planning for Phase 2-4 clinical trials and integrated summaries for submissions. Starting from the recommended safety analyses proposed in white papers and a workshop, created as part of an FDA/PHUSE collaboration (PHUSE 2013, 2015, 2017, 2019), we assess what modifications might be needed. Impact from COVID-19 will likely affect treatment arms equally, so analyses of adverse events from controlled data can, to a large extent, remain unchanged. However, interpretation of summaries from uncontrolled data (summaries that include open-label extension data) will require even more caution than usual. Special consideration will be needed for safety topics of interest, especially events expected to have a higher incidence due to a COVID-19 infection or due to quarantine or travel restrictions (e.g., depression). Analyses of laboratory measurements may need to be modified to account for the combination of measurements from local and central laboratories.

preprint2020arXiv

Comprehensive study of the global phase diagram in the triangular $J$-$K$-$Γ$ model

The celebrated Kitaev honeycomb model provides an analytically tractable example with an exact quantum spin liquid ground state. While in real materials, other types of interactions besides the Kitaev coupling ($K$) are present, such as the Heisenberg ($J$) and symmetric off-diagonal ($Γ$) terms, and these interactions can also be generalized to a triangular lattice. Here, we carry out a comprehensive study of the $J$-$K$-$Γ$ model on the triangular lattice covering the full parameters region, using the combination of the exact diagonalization, classical Monte Carlo and analytic methods. In the HK limit ($Γ=0$), we find five quantum phases which are quite similar to their classical counterparts. Among them, the stripe-A and dual Néel phase are robust against the $Γ$ term, in particular the stripe-A extends to the region connecting the $K=-1$ and $K=1$ for $Γ<0$. Though the 120$^\circ$ Néel phase also extends to a finite $Γ$, its region has been largely reduced compared to the previous classical result. Interestingly, the ferromagnetic (dubbed as FM-A) phase and the stripe-B phase are unstable in response to an infinitesimal $Γ$ interaction. Moreover, we find five new phases for $Γ\ne 0$ which are elaborated by both the quantum and classical numerical methods. Part of the space previously identified as 120$^\circ$ Néel phase in the classical study is found to give way to the modulated stripe phase. Depending on the sign of the $Γ$, the FM-A phase transits into the FM-B ($Γ>0$) and FM-C ($Γ<0$) phase with different spin orientations, and the stripe-B phase transits into the stripe-C ($Γ>0$) and stripe-A ($Γ<0$). Around the positive $Γ$ point, due to the interplay of the Heisenberg, Kiatev and $Γ$ interactions, we find a possible quantum spin liquid with a continuum in spin excitations.

preprint2020arXiv

Confinement transition in the QED$_3$-Gross-Neveu-XY universality class

The coupling between fermionic matter and gauge fields plays a fundamental role in our understanding of nature, while at the same time posing a challenging problem for theoretical modeling. In this situation, controlled information can be gained by combining different complementary approaches. Here, we study a confinement transition in a system of $N_f$ flavors of interacting Dirac fermions charged under a U(1) gauge field in 2+1 dimensions. Using Quantum Monte Carlo simulations, we investigate a lattice model that exhibits a continuous transition at zero temperature between a gapless deconfined phase, described by three-dimensional quantum electrodynamics, and a gapped confined phase, in which the system develops valence-bond-solid order. We argue that the quantum critical point is in the universality class of the QED$_3$-Gross-Neveu-XY model. We study this field theory within a $1/N_f$ expansion in fixed dimension as well as a renormalization group analysis in $4-ε$ space-time dimensions. The consistency between numerical and analytical results is revealed from large to intermediate flavor number.

preprint2020arXiv

Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks

Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile. To remove dependency on inaccurate and incomplete network models and enhance resiliency against communication or controller failure, we propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The VVC problem is formulated as a networked multi-agent Markov decision process, which is solved using the maximum entropy reinforcement learning framework and a novel communication-efficient consensus strategy. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark. In addition, the proposed algorithm is shown to be communication efficient and resilient.

preprint2020arXiv

Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation. The proposed C$^2$GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an interactive manner. C$^2$GAN contains two different types of generators, i.e., keypoint-oriented generator and image-oriented generator. Both of them are mutually connected in an end-to-end learnable fashion and explicitly form three cycled sub-networks, i.e., one image generation cycle and two keypoint generation cycles. Each cycle not only aims at reconstructing the input domain, and also produces useful output involving in the generation of another cycle. By so doing, the cycles constrain each other implicitly, which provides complementary information from the two different modalities and brings extra supervision across cycles, thus facilitating more robust optimization of the whole network. Extensive experimental results on two publicly available datasets, i.e., Radboud Faces and Market-1501, demonstrate that our approach is effective to generate more photo-realistic images compared with state-of-the-art models.

preprint2020arXiv

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.

preprint2020arXiv

Database Meets Deep Learning: Challenges and Opportunities

Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.

preprint2020arXiv

Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference

Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.

preprint2020arXiv

Deep Multi-Task Learning via Generalized Tensor Trace Norm

The trace norm is widely used in multi-task learning as it can discover low-rank structures among tasks in terms of model parameters. Nowadays, with the emerging of big datasets and the popularity of deep learning techniques, tensor trace norms have been used for deep multi-task models. However, existing tensor trace norms cannot discover all the low-rank structures and they require users to manually determine the importance of their components. To solve those two issues together, in this paper, we propose a Generalized Tensor Trace Norm (GTTN). The GTTN is defined as a convex combination of matrix trace norms of all possible tensor flattenings and hence it can discover all the possible low-rank structures. In the induced objective function, we will learn combination coefficients in the GTTN to automatically determine the importance. Experiments on real-world datasets demonstrate the effectiveness of the proposed GTTN.

preprint2020arXiv

DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images. These learning-based approaches have led to more accurate and robust VO systems. However, they have not been well applied to point cloud data yet. In this work, we investigate how to exploit deep learning to estimate point cloud odometry (PCO), which may serve as a critical component in point cloud-based downstream tasks or learning-based systems. Specifically, we propose a novel end-to-end deep parallel neural network called DeepPCO, which can estimate the 6-DOF poses using consecutive point clouds. It consists of two parallel sub-networks to estimate 3-D translation and orientation respectively rather than a single neural network. We validate our approach on KITTI Visual Odometry/SLAM benchmark dataset with different baselines. Experiments demonstrate that the proposed approach achieves good performance in terms of pose accuracy.

preprint2020arXiv

DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination

Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model.

preprint2020arXiv

Degree-Aware Alignment for Entities in Tail

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA. To mitigate the issue, we revisit and investigate into the conventional EA pipeline in pursuit of elegant performance. For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings. For alignment, under a novel complementary framework of consolidating structural and name signals, we identify entity's degree as important guidance to effectively fuse two different sources of information. To this end, a degree-aware co-attention network is conceived, which dynamically adjusts the significance of features in a degree-aware manner. For post-alignment, we propose to complement original KGs with facts from their counterparts by using confident EA results as anchors via iterative training. Comprehensive experimental evaluations validate the superiority of our proposed techniques.

preprint2020arXiv

Designer Monte Carlo Simulation for Gross-Neveu Transition

In this manuscript, we study quantum criticality of Dirac fermions via large-scale numerical simulations, focusing on the Gross-Neveu-Yukawa(GNY) chiral-Ising quantum critical point with critical bosonic modes coupled with Dirac fermions. We show that finite-size effects at this quantum critical point can be efficiently minimized via model design, which maximizes the ultraviolet cutoff and at the same time places the bare control parameters closer to the nontrivial fixed point to better expose the critical region. Combined with the efficient self-learning quantum Monte Carlo algorithm, which enables non-local update of the bosonic field, we find that moderately-large system size (up to $16\times 16$) is already sufficient to produce robust scaling behavior and critical exponents.The conductance of the Dirac fermions is also calculated and its frequency dependence is found to be consistent with the scaling behavior predicted by the conformal field theory. The methods and model-design principles developed for this study can be generalized to other fermionic QCPs, and thus provide a promising direction for controlled studies of strongly-correlated itinerant systems.

preprint2020arXiv

Distributed Localization without Direct Communication Inspired by Statistical Mechanics

Distributed localization is essential in many robotic collective tasks such as shape formation and self-assembly.Inspired by the statistical mechanics of energy transition, this paper presents a fully distributed localization algorithm named as virtual particle exchange (VPE) localization algorithm, where each robot repetitively exchanges virtual particles (VPs) with neighbors and eventually obtains its relative position from the virtual particle (VP) amount it owns. Using custom-designed hardware and protocol, VPE localization algorithm allows robots to achieve localization using sensor readings only, avoiding direct communication with neighbors and keeping anonymity. Moreover, VPE localization algorithm determines the swarm center automatically, thereby eliminating the requirement of fixed beacons to embody the origin of coordinates. Theoretical analysis proves that the VPE localization algorithm can always converge to the same result regardless of initial state and has low asymptotic time and memory complexity. Extensive localization simulations with up to 10000 robots and experiments with 52 lowcost robots are carried out, which verify that VPE localization algorithm is scalable, accurate and robust to sensor noises. Based on the VPE localization algorithm, shape formations are further achieved in both simulations and experiments with 52 robots, illustrating that the algorithm can be directly applied to support swarm collaborative tasks.

preprint2020arXiv

Distributed Motion Control for Multiple Connected Surface Vessels

We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach leverages the implicit information of the structure's motion for force and torque allocation without explicit communication among the robots. In our system, a leader robot steers the entire group by adjusting its force and torque according to the structure's deviation from the desired trajectory, while follower robots run distributed consensus-based controllers to match their inputs to amplify the leader's intent using only onboard sensors as feedback. To cope with the complex and highly coupled system dynamics in the water, the leader robot employs a nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamics model of the floating modular structure in order to achieve superior performance for leader-following control. Our method has a wide range of potential applications in transporting humans and goods in many of today's existing waterways. We conducted trajectory and orientation tracking experiments in hardware with three custom-built autonomous modular robotic boats, called Roboat, which are capable of holonomic motions and onboard state estimation. Simulation results with up to 65 robots also prove the scalability of our proposed approach.

preprint2020arXiv

Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild

In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose. We have created a new dataset called CelebAGaze, which consists of two domains X, Y, where the eyes are either staring at the camera or somewhere else. Our method consists of three novel modules: the Gaze Correction module (GCM), the Gaze Animation module (GAM), and the Pretrained Autoencoder module (PAM). Specifically, GCM and GAM separately train a dual in-painting network using data from the domain $X$ for gaze correction and data from the domain $Y$ for gaze animation. Additionally, a Synthesis-As-Training method is proposed when training GAM to encourage the features encoded from the eye region to be correlated with the angle information, resulting in a gaze animation which can be achieved by interpolation in the latent space. To further preserve the identity information~(e.g., eye shape, iris color), we propose the PAM with an Autoencoder, which is based on Self-Supervised mirror learning where the bottleneck features are angle-invariant and which works as an extra input to the dual in-painting models. Extensive experiments validate the effectiveness of the proposed method for gaze correction and gaze animation in the wild and demonstrate the superiority of our approach in producing more compelling results than state-of-the-art baselines. Our code, the pretrained models and the supplementary material are available at: https://github.com/zhangqianhui/GazeAnimation.

preprint2020arXiv

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range.

preprint2020arXiv

Dynamic Malware Analysis with Feature Engineering and Feature Learning

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released ("zero-day") malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

preprint2020arXiv

Effective and Efficient Dropout for Deep Convolutional Neural Networks

Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitting, dropout has been widely used as an efficient regularization technique in practice. However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs. In this paper, we revisit this issue and examine various dropout variants in an attempt to improve existing dropout-based regularization techniques for CNNs. We attribute the failure of standard dropout to the conflict between the stochasticity of dropout and its following Batch Normalization (BN), and propose to reduce the conflict by placing dropout operations right before the convolutional operation instead of BN, or totally address this issue by replacing BN with Group Normalization (GN). We further introduce a structurally more suited dropout variant Drop-Conv2d, which provides more efficient and effective regularization for deep CNNs. These dropout variants can be readily integrated into the building blocks of CNNs and implemented in existing deep learning platforms. Extensive experiments on benchmark datasets including CIFAR, SVHN and ImageNet are conducted to compare the existing building blocks and the proposed ones with dropout training. Results show that our building blocks improve over state-of-the-art CNNs significantly, which is mainly due to the better regularization and implicit model ensemble effect.

preprint2020arXiv

Effective edge-based approach for promoting the spreading of SIR model

Promoting some typical spreading dynamics, for instance, the spreading of information, commercial message, vaccination guidance, innovation, and political movement, can bring benefits to all aspects of the socio-economic systems. In this study, we propose a strategy for promoting the spreading of the susceptible-infected-recovered model, which is widely applied to describe these common spreading dynamics in real life. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way of promoting the spreading of the susceptible-infected-recovered model by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading models.

preprint2020arXiv

Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics

Recent studies have demonstrated that the allocation of individual resources has a significant influence on the dynamics of epidemic spreading. In the real scenario, individuals have a different level of awareness for self-protection when facing the outbreak of an epidemic. To investigate the effects of the heterogeneous self-awareness distribution on the epidemic dynamics, we propose a resource-epidemic coevolution model in this paper. We first study the effects of the heterogeneous distributions of node degree and self-awareness on the epidemic dynamics on artificial networks. Through extensive simulations, we find that the heterogeneity of self-awareness distribution suppresses the outbreak of an epidemic, and the heterogeneity of degree distribution enhances the epidemic spreading. Next, we study how the correlation between node degree and self-awareness affects the epidemic dynamics. The results reveal that when the correlation is positive, the heterogeneity of self-awareness restrains the epidemic spreading. While, when there is a significant negative correlation, strong heterogeneous or strong homogeneous distribution of the self-awareness is not conducive for disease suppression. We find an optimal heterogeneity of self-awareness, at which the disease can be suppressed to the most extent. Further research shows that the epidemic threshold increases monotonously when the correlation changes from most negative to most positive, and a critical value of the correlation coefficient is found. When the coefficient is below the critical value, an optimal heterogeneity of self-awareness exists; otherwise, the epidemic threshold decreases monotonously with the decline of the self-awareness heterogeneity. At last, we verify the results on four typical real-world networks and find that the results on the real-world networks are consistent with those on the artificial network.

preprint2020arXiv

Efficient Bitwidth Search for Practical Mixed Precision Neural Network

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activations from different layers with different precision to improve the overall performance. However, it is challenging to find the optimal bitwidth (i.e., precision) for weights and activations of each layer efficiently. Meanwhile, it is yet unclear how to perform convolution for weights and activations of different precision efficiently on generic hardware platforms. To resolve these two issues, in this paper, we first propose an Efficient Bitwidth Search (EBS) algorithm, which reuses the meta weights for different quantization bitwidth and thus the strength for each candidate precision can be optimized directly w.r.t the objective without superfluous copies, reducing both the memory and computational cost significantly. Second, we propose a binary decomposition algorithm that converts weights and activations of different precision into binary matrices to make the mixed precision convolution efficient and practical. Experiment results on CIFAR10 and ImageNet datasets demonstrate our mixed precision QNN outperforms the handcrafted uniform bitwidth counterparts and other mixed precision techniques.

preprint2020arXiv

Enabling Low-Power OFDM for IoT by Exploiting Asymmetric Clock Rates

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT's TX power when the AP sets $8\times$ clock rate.

preprint2020arXiv

Evidence for magnon-phonon coupling in the topological magnet Cu$_3$TeO$_6$

We perform thermodynamic and inelastic neutron scattering (INS) measurements to study the lattice dynamics (phonons) of a cubic collinear antiferromagnet Cu$_3$TeO$_6$ which hosts topological spin excitations (magnons). While the specific heat and thermal conductivity results show that the thermal transport is dominated by phonons, the deviation of the thermal conductivity from a pure phononic model indicates that there is a strong coupling between magnons and phonons. In the INS measurements, we find a mode in the excitation spectra at 4.5 K, which exhibits a slight downward dispersion around the Brillouin zone center. This mode disappears above the Néel temperature, and thus cannot be a phonon. Furthermore, the dispersion is distinct from that of a magnon. Instead, it can be explained by the magnon-polaron mode, which is new collective excitations resulting from the hybridization between magnons and phonons. We consider the suppression of the thermal conductivity and emergence of the magnon-polaron mode to be evidence for magnon-phonon coupling in Cu$_3$TeO$_6$.

preprint2020arXiv

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect

Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over 3 decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand.

preprint2020arXiv

Exploring the Mass Segregation Effect of X-ray Sources in Globular Clusters. III. Signs of Binary Disruption in M28

Using archival {\it Chandra} observations with a total effective exposure of 323 ks, we derive an updated catalog of point sources in the bulge globular cluster M28. The catalog contains 502 X-ray sources within an area of $\sim475\, \rm arcmin^{2}$, and more than $90\%$ of these sources are first detected in this cluster. We find significant dips in the radial distribution profiles of X-ray sources in M28, with the projected distance and width of the distribution dip for bright ($L_{X} \gtrsim 4.5\times 10^{30} {\rm\ erg\ \,s^{-1}}$) X-ray sources are larger than the faint ($L_{X} \lesssim 4.5\times 10^{30} {\rm\ erg\ \,s^{-1}}$) sources. The "generalized King model" fitting give a slightly larger average mass for the bright sources ($1.30\pm0.15\,M_{\odot}$) than the faint sources ($1.09\pm0.14\,M_{\odot}$), which support a universal mass segregation delay between heavy objects in GCs. Compared with 47 Tuc and Terzan 5, we show that the dynamical age of M28 is comparable to Terzan 5 and much smaller than 47 Tuc, but it is evolving more fast (i.e., with smaller two-body relaxation timescale) than 47 Tuc. These features may suggest an acceleration effect of cluster dynamical evolution by tidal shock in M28. Besides, we find an abnormal deficiency of X-ray sources in the central region ($R \lesssim 1.5 \rm~arcmin$) of M28 than its outskirts, which indicate that M28 may have suffered an early phase of primordial binary disruption within its central region, and mass segregation effect will erase such a phenomenon as cluster evolve to older dynamical age.

preprint2020arXiv

Exploring the parameter reusability of CNN

In recent times, using small data to train networks has become a hot topic in the field of deep learning. Reusing pre-trained parameters is one of the most important strategies to address the issue of semi-supervised and transfer learning. However, the fundamental reason for the success of these methods is still unclear. In this paper, we propose a solution that can not only judge whether a given network is reusable or not based on the performance of reusing convolution kernels but also judge which layers' parameters of the given network can be reused, based on the performance of reusing corresponding parameters and, ultimately, judge whether those parameters are reusable or not in a target task based on the root mean square error (RMSE) of the corresponding convolution kernels. Specifically, we define that the success of a CNN's parameter reuse depends upon two conditions: first, the network is a reusable network; and second, the RMSE between the convolution kernels from the source domain and target domain is small enough. The experimental results demonstrate that the performance of reused parameters applied to target tasks, when these conditions are met, is significantly improved.

preprint2020arXiv

Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid scintillator detectors. In this paper we present a comprehensive assessment of JUNO's potential for detecting $^8$B solar neutrinos via the neutrino-electron elastic scattering process. A reduced 2~MeV threshold on the recoil electron energy is found to be achievable assuming the intrinsic radioactive background $^{238}$U and $^{232}$Th in the liquid scintillator can be controlled to 10$^{-17}$~g/g. With ten years of data taking, about 60,000 signal and 30,000 background events are expected. This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter, which will shed new light on the tension between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework. If $Δm^{2}_{21}=4.8\times10^{-5}~(7.5\times10^{-5})$~eV$^{2}$, JUNO can provide evidence of neutrino oscillation in the Earth at the about 3$σ$~(2$σ$) level by measuring the non-zero signal rate variation with respect to the solar zenith angle. Moveover, JUNO can simultaneously measure $Δm^2_{21}$ using $^8$B solar neutrinos to a precision of 20\% or better depending on the central value and to sub-percent precision using reactor antineutrinos. A comparison of these two measurements from the same detector will help elucidate the current tension between the value of $Δm^2_{21}$ reported by solar neutrino experiments and the KamLAND experiment.

preprint2020arXiv

Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FGNET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention sentence-specific context. This is inadequate for highly overlapping and noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively.

preprint2020arXiv

Fractionalized Spin Excitations in the Edge Ferromagnetic State of Graphene: Signature of the Ferromagnetic Luttinger Liquid

The elementary excitations from the conventional magnetic ordered states, such as ferromagnets and antiferromagnets, are magnons. Here, we elaborate a case where the well-defined magnons are absent completely and the spin excitation spectra exhibit an entire continuum in the itinerant edge ferromagnetic state of graphene arising from the flatband edge electronic states. Based on the further studies of the entanglement entropy and finite-size analysis, we show that the continuum other than the Stoner part results from the spin-1/2 spinons deconfined from magnons. The spinon continuum in a magnetically ordered state is ascribed to a ferromagnetic Luttinger liquid in this edge ferromagnet. The investigation is carried out by using the numerical exact diagonalization method with a projection of the interacting Hamiltonian onto the flat band.

preprint2020arXiv

Global Well-posedness for the Three Dimensional Simplified Inertial Ericksen-Leslie Systems Near Equilibrium

We study a simplified inertial Ericksen-Leslie system for the nematic liquid crystal flow, which can be viewed as a system coupling Navier-Stokes equations and wave map equations. We prove the global existence of classical solution with initial data near equilibrium.

preprint2020arXiv

GraphCrop: Subgraph Cropping for Graph Classification

We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification. Observing that the omission of sub-structures does not necessarily change the class label of the whole graph, we develop the \textbf{GraphCrop} (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub-structure omission. In principle, GraphCrop utilizes a node-centric strategy to crop a contiguous subgraph from the original graph while maintaining its connectivity. By preserving the valid structure contexts for graph classification, we encourage GNNs to understand the content of graph structures in a global sense, rather than rely on a few key nodes or edges, which may not always be present. GraphCrop is parameter learning free and easy to implement within existing GNN-based graph classifiers. Qualitatively, GraphCrop expands the existing training set by generating novel and informative augmented graphs, which retain the original graph labels in most cases. Quantitatively, GraphCrop yields significant and consistent gains on multiple standard datasets, and thus enhances the popular GNNs to outperform the baseline methods.

preprint2020arXiv

Importance Filtered Cross-Domain Adaptation

In Domain Adaptation (DA), the category-relevant losses usually occupy a dominant position, while they are usually built with hard or soft labels in existing models. We observed that hard labels are overconfident due to hard samples existed, and soft labels are ambiguous as too many small noisy probabilities involved, and both of them are easily to cause negative transfer. Besides, the category-irrelevant losses in Closed-Set DA (CSDA) paradigm fail to work in Open-Set DA (OSDA), and they also have to be in a category-relevant form, since target data samples are split into shared and private classes. To this end, we propose a newly-unified DA framework (i.e., Importance Filtered Cross-Domain Adaptation, IFCDA). Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably. Specifically, the soft labels are divided into confident and ambiguous ones. Then, only the maximum probability in each confident label is retained, and a threshold value is set to truncate each ambiguous label so that only prominent probabilities are reserved. Moreover, a general graph-based label propagation is contrived to attain soft labels in both CSDA and OSDA, where an extra component is embedded into label vector, so that it could detect target novel classes. Finally, the category-relevant losses in both scenarios are reformulated using filtered soft labels, while the category-irrelevant MMD loss in CSDA is reformulated as a form like class-wise MMD using newly-designed importance filtered soft labels. Notably, CSDA paradigm is a special case when all extra components are set to 0, thus the proposed approach is geared to both CSDA and OSDA. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that the proposed approach outperforms several state-of-the-art methods in both scenarios.

preprint2020arXiv

Improving Smart Conference Participation through Socially-Aware Recommendation

This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.

preprint2020arXiv

Improving the Energy Resolution of the Reactor Antineutrino Energy Reconstruction with Positron Direction

The energy resolution is crucial for the reactor neutrino experiments which aims to determine neutrino mass ordering by precise measurement of the reactor antineutrino energy spectrum. A non-negligible effect in the antineutrino energy resolution is the spread of the kinetic energy of the recoiled neutron and the corresponding positron when detecting the antineutrinos via Inverse Beta-Decay (IBD) reaction. The emission direction of the produced positron in IBD reaction can be used to estimate the kinetic energy of neutron and thus the reconstructed antineutrino energy resolution can be improved. To demonstrate the feasibility, a simple positron direction reconstruction method is implemented in a toy liquid scintillator detector like the Taishan Antineutrino Observatory (TAO) with 4500 photoelectron yield per MeV. A 4% to 26% improvement of energy resolution can be achieved for 5 MeV reactor antineutrinos at TAO.

preprint2020arXiv

Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy

Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignores this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific features with a transferable decorrelated normalization module, which preserves specific domain information whilst easing the side effect of feature redundancy on the sequel domain-invariance. In the second level, domain-invariant feature redundancy caused by domain-shared representation is further mitigated via an alternative brand orthogonality for better generalization. These two novel aspects can be easily plugged into any BN-based backbone neural networks. Specifically, simply applying them to ResNet50 has achieved competitive performance to the state-of-the-arts on five popular benchmarks. Our code will be available at https://github.com/dreamkily/gUDA.

preprint2020arXiv

Interlayer link prediction in multiplex social networks: an iterative degree penalty algorithm

Online social network (OSN) applications provide different experiences; for example, posting a short text on Twitter and sharing photographs on Instagram. Multiple OSNs constitute a multiplex network. For privacy protection and usage purposes, accounts belonging to the same user in different OSNs may have different usernames, photographs, and introductions. Interlayer link prediction in multiplex network aims at identifying whether the accounts in different OSNs belong to the same person, which can aid in tasks including cybercriminal behavior modeling and customer interest analysis. Many real-world OSNs exhibit a scale-free degree distribution; thus, neighbors with different degrees may exert different influences on the node matching degrees across different OSNs. We developed an iterative degree penalty (IDP) algorithm for interlayer link prediction in the multiplex network. First, we proposed a degree penalty principle that assigns a greater weight to a common matched neighbor with fewer connections. Second, we applied node adjacency matrix multiplication for efficiently obtaining the matching degree of all unmatched node pairs. Thereafter, we used the approved maximum value method to obtain the interlayer link prediction results from the matching degree matrix. Finally, the prediction results were inserted into the priori interlayer node pair set and the above processes were performed iteratively until all unmatched nodes in one layer were matched or all matching degrees of the unmatched node pairs were equal to 0. Experiments demonstrated that our advanced IDP algorithm significantly outperforms current network structure-based methods when the multiplex network average degree and node overlapping rate are low.

preprint2020arXiv

Interpretation of $Y_b (10750)$ as a tetraquark and its production mechanism

Recently, the Belle Collaboration has updated the analysis of the cross sections for the processes $e^+ e^- \to Υ(nS)\, π^+ π^-$ ($n = 1,\, 2,\, 3$) in the $e^+ e^-$ center-of-mass energy range from 10.52 to 11.02 GeV. A new structure, called here $Y_b (10750)$, with the mass $M (Y_b) = (10752.7 \pm 5.9^{+0.7}_{-1.1})$ MeV and the Breit-Wigner width $Γ(Y_b) = (35.5^{+17.6 +3.9}_{-11.3 -3.3})$ MeV was observed \cite{Abdesselam:2019gth}. We interpret $Y_b (10750)$ as a compact $J^{PC} = 1^{--}$ state with a dominant tetraquark component. The mass eigenstate $Y_b (10750)$ is treated as a linear combination of the diquark-antidiquark and $b \bar b$ components due to the mixing via gluonic exchanges shown recently to arise in the limit of large number of quark colors. The mixing angle between $Y_b$ and $Υ(5S)$ can be estimated from the electronic width, recently determined to be $Γ_{ee} (Y_b) = (13.7 \pm 1.8)$ eV. The mixing provides a plausible mechanism for $Y_b (10750)$ production in high energy collisions from its $b \bar b$ component and we work out the Drell-Yan and prompt production cross sections for $p p \to Y_b (10750) \to Υ(nS)\, π^+ π^-$ at the LHC. The resonant part of the dipion invariant mass spectrum in $Y_b (10750) \to Υ(1S)\, π^+ π^-$ and the corresponding angular distribution of $π^+$-meson in the dipion rest frame are presented as an example.

preprint2020arXiv

Kernel Truncated Regression Representation for Robust Subspace Clustering

Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.

preprint2020arXiv

Kinship Identification through Joint Learning Using Kinship Verification Ensembles

Kinship verification is a well-explored task: identifying whether or not two persons are kin. In contrast, kinship identification has been largely ignored so far. Kinship identification aims to further identify the particular type of kinship. An extension to kinship verification run short to properly obtain identification, because existing verification networks are individually trained on specific kinships and do not consider the context between different kinship types. Also, existing kinship verification datasets have biased positive-negative distributions which are different than real-world distributions. To this end, we propose a novel kinship identification approach based on joint training of kinship verification ensembles and classification modules. We propose to rebalance the training dataset to become more realistic. Large scale experiments demonstrate the appealing performance on kinship identification. The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.

preprint2020arXiv

Learning a Multi-Domain Curriculum for Neural Machine Translation

Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.

preprint2020arXiv

Learning Effective Representations for Person-Job Fit by Feature Fusion

Person-job fit is to match candidates and job posts on online recruitment platforms using machine learning algorithms. The effectiveness of matching algorithms heavily depends on the learned representations for the candidates and job posts. In this paper, we propose to learn comprehensive and effective representations of the candidates and job posts via feature fusion. First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and job post) and then learn features for them. By fusing the features from the free text and the entities, we get a comprehensive representation for the information explicitly stated in the resume and job post. Second, however, some information of a candidate or a job may not be explicitly captured in the resume or job post. Nonetheless, the historical applications including accepted and rejected cases can reveal some implicit intentions of the candidates or recruiters. Therefore, we propose to learn the representations of implicit intentions by processing the historical applications using LSTM. Last, by fusing the representations for the explicit and implicit intentions, we get a more comprehensive and effective representation for person-job fit. Experiments over 10 months real data show that our solution outperforms existing methods with a large margin. Ablation studies confirm the contribution of each component of the fused representation. The extracted semantic entities help interpret the matching results during the case study.

preprint2020arXiv

Learning to Detect Malicious Clients for Robust Federated Learning

Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.

preprint2020arXiv

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior ``sub-keyframes.'' The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.

preprint2020arXiv

Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density

Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care. Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD). However, the discovery of the determinant factors of BMD in a medical way is expensive and time-consuming. In this paper, we propose a novel algorithm, Prior-Knowledge-driven local Causal structure Learning (PKCL), to discover the underlying causal mechanism between BMD and its factors from the clinical data. Since there exist limited data but redundant prior knowledge for medicine, PKCL adequately utilize the prior knowledge to mine the local causal structure for the target relationship. Combining the medical prior knowledge with the discovered causal relationships, PKCL can achieve more reliable results without long-standing medical statistical experiments. Extensive experiments are conducted on a newly provided clinical data set. The experimental study of PKCL on the data is proved to highly corresponding with existing medical knowledge, which demonstrates the superiority and effectiveness of PKCL. To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.

preprint2020arXiv

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

preprint2020arXiv

Néel-type skyrmion in WTe2/Fe3GeTe2 van der Waals heterostructure

The promise of high-density and low-energy-consumption devices motivates the search for layered structures that stabilize chiral spin textures such as topologically protected skyrmions. At the same time, layered structures provide a new platform for the discovery of new physics and effects. Recently discovered long-range intrinsic magnetic orders in the two-dimensional van der Waals materials offer new opportunities. Here we demonstrate the Dzyaloshinskii-Moriya interaction and Néel-type skyrmions are induced at the WTe2/Fe3GeTe2 interface. Fe3GeTe2 is a ferromagnetic material with strong perpendicular magnetic anisotropy. We demonstrate that the strong spin orbit interaction in 1T'-WTe2 does induce a large interfacial Dzyaloshinskii-Moriya interaction at the interface with Fe3GeTe2 due to the inversion symmetry breaking to stabilize skyrmions. Transport measurements show the topological Hall effect in this heterostructure for temperatures below 100 K. Furthermore, Lorentz transmission electron microscopy is used to directly image Néel-type skyrmions along with aligned and stripe-like domain structure. This interfacial coupling induced Dzyaloshinskii-Moriya interaction is estimated to have a large energy of 1.0 mJ/m^2, which can stabilize the Néel-type skyrmions in this heterostructure. This work paves a path towards the skyrmionic devices based on van der Waals heterostructures.

preprint2020arXiv

Novel method to reliably determine the photon helicity in $b\to sγ$

A sizable right-handed photon polarization in $b\to sγ$ is a clear signal for new physics. In this work we point out that the photon helicity in $b\to sγ$ can be unambiguously extracted by combining the measurements in $B\to K_1γ$ and the Cabibbo-favored $D\to K_1 e^+ν$ decay. We propose a ratio of up-down asymmetries in $D\to K_1 e^+ν$ to quantify the hadronic effects. { A method for measuring in experiment the involved partial decay widths in the ratio is discussed, and experimental facilities like BESIII, Belle-II and LHCb are likely to measure this ratio. We also give the angular distribution that is useful to extract the photon polarization in the presence of different kaon resonances. }

preprint2020arXiv

Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments

Aiming at the application environment of indoor mobile robots, this paper proposes a sparse object-level SLAM algorithm based on an RGB-D camera. A quadric representation is used as a landmark to compactly model objects, including their position, orientation, and occupied space. The state-of-art quadric-based SLAM algorithm faces the observability problem caused by the limited perspective under the plane trajectory of the mobile robot. To solve the problem, the proposed algorithm fuses both object detection and point cloud data to estimate the quadric parameters. It finishes the quadric initialization based on a single frame of RGB-D data, which significantly reduces the requirements for perspective changes. As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters. Experiments have shown that compared with the state-of-art algorithm, especially on the forward trajectory of mobile robots, the proposed algorithm significantly improves the accuracy and convergence speed of quadric reconstruction. Finally, we made available an opensource implementation to replicate the experiments.

preprint2020arXiv

On Non-standard Models of Arithmetic with Uncountable Standard Systems

In 1960s, Dana Scott gave a recursion theoretic characterization of standard systems of countable non-standard models of arithmetic, i.e., collections of sets of standard natural numbers coded in non-standard models. Later, Knight and Nadel proved that Scott's characterization also applies to non-standard models of arithmetic with cardinality $\aleph_1$. But the question, whether the limit on cardinality can be removed from the above characterization, remains a long standing question, known as the Scott Set Problem. This article presents two constructions of non-standard models of arithmetic with non-trivial uncountable standard systems. The first one leads to a new proof of the above theorem of Knight and Nadel, and the second proves the existence of models with non-trivial standard systems of cardinality the continuum. A partial answer to the Scott Set Problem under certain set theoretic hypothesis also follows from the second construction.

preprint2020arXiv

One-Shot Image Classification by Learning to Restore Prototypes

One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.

preprint2020arXiv

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text with some masked tokens. The training goals of existing techniques are often inconsistent with the goals of many language generation tasks, such as generative question answering and conversational response generation, for producing new text given context. This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context. The new scheme alleviates the mismatch introduced by the existing denoising scheme between pre-training and fine-tuning where generation is more than reconstructing original text. An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

preprint2020arXiv

Permutation cycles of hardcore Bose-Hubbard models on square and Kagome lattices

In this paper, we study the statistics of permutation cycles of ground-state hardcore lattice bosons described by various two-dimensional Bose-Hubbard-type models on both square and Kagome lattices. We find that it is possible to differentiate quantum phases by the statistics of permutations cycles. Indeed, features in the permutation cycles statistics can be used to uniquely identify certain insulating phases, and are consistent with local resonances of occupation numbers in the ground-state expansion of the phase. We also confirm that suitable quantities derived from the probability distribution of the length of permutation cycles can be used to detect superfluid to insulator phase transitions.

preprint2020arXiv

Progressive Cluster Purification for Unsupervised Feature Learning

In unsupervised feature learning, sample specificity based methods ignore the inter-class information, which deteriorates the discriminative capability of representation models. Clustering based methods are error-prone to explore the complete class boundary information due to the inevitable class inconsistent samples in each cluster. In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner. Our approach, referred to as Progressive Cluster Purification (PCP), implements progressive clustering by gradually reducing the number of clusters during training, while the sizes of clusters continuously expand consistently with the growth of model representation capability. With a well-designed cluster purification mechanism, it further purifies clusters by filtering noise samples which facilitate the subsequent feature learning by utilizing the refined clusters as pseudo-labels. Experiments on commonly used benchmarks demonstrate that the proposed PCP improves baseline method with significant margins. Our code will be available at https://github.com/zhangyifei0115/PCP.

preprint2020arXiv

QCD Sum Rules Analysis of Weak Decays of Doubly-Heavy Baryons

We calculate the weak decay form factors of doubly-heavy baryons using three-point QCD sum rules. The Cutkosky rules are used to derive the double dispersion relations. We include perturbative contributions and condensation contributions up to dimension five, and point out that the perturbative contributions and condensates with lowest dimensions dominate. An estimate of part of gluon-gluon condensates show that it plays a less important role. With these form factors at hand, we present a phenomenological study of semileptonic decays. The future experimental facilities can test these predictions, and deepen our understanding of the dynamics in decays of doubly-heavy baryons.

preprint2020arXiv

Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures

When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise. Kratscher et al. [1] propose a new framework to examine the qualitative robustness of estimators for tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from earlier work for studying the robustness of risk measurement procedures by Cont et al. [2]. In this paper, we follow the stream of research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet-Mourier metric to quantify the variation of the true underlying probability measure in the analysis of the discrepancy between the laws of the plug-in estimators of law invariant risk measure based on the true data and perturbed data, which enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous with respect to a class of admissible laws. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for tail-dependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our theory.

preprint2020arXiv

Quasi parton distribution functions at NNLO: flavor non-diagonal quark contributions

We present a next-to-next-to-leading order (NNLO) calculation of the quasi parton distribution functions (Quasi-PDFs) in the large momentum effective theory (LaMET). We focus on the flavor non-diagonal quark-quark channel and demonstrate the LaMET factorization at the NNLO accuracy in the modified minimal subtraction scheme. The matching coefficient between the quasi-PDF and the light-cone PDF is derived. This provides a first step towards a complete NNLO analysis of quasi-PDFs and to better understand the nucleon structures from the first principle of QCD.

preprint2020arXiv

Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related product categories. Given a brand, to come up with themes for a new ad is a painstaking and time consuming process for creative strategists. Strategists typically draw inspiration from the images and text used for past ad campaigns, as well as world knowledge on the brands. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. The theme recommender is based on aggregating results from a visual question answering (VQA) task, which ingests the following: (i) ad images, (ii) text associated with the ads as well as Wikipedia pages on the brands in the ads, and (iii) questions around the ad. We leverage transformer based cross-modality encoders to train visual-linguistic representations for our VQA task. We study two formulations for the VQA task along the lines of classification and ranking; via experiments on a public dataset, we show that cross-modal representations lead to significantly better classification accuracy and ranking precision-recall metrics. Cross-modal representations show better performance compared to separate image and text representations. In addition, the use of multimodal information shows a significant lift over using only textual or visual information.

preprint2020arXiv

Resource Allocation Technique for Hybrid TDMA-NOMA System with Opportunistic Time Assignment

In this paper, we develop a resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system with opportunistic time assignment. In particular, the available transmission time is divided into several time-slots, through which multiple users are served by exploiting power-domain NOMA. To fully exploit underlying benefits of this hybrid TDMA-NOMA system, we utilize the available resources efficiently by jointly allocating transmit power and time-slots to several groups of users in the system. Furthermore, these resources are allocated to maximize minimum rate of the users in the system. However, this max-min resource allocation problem is non-convex due to coupled design parameters of time and power allocations. Hence, we exploit a novel second-order cone formulation to overcome this non-convexity issue and develop an iterative algorithm to realize a solution to the original max-min problem. Simulation results show that this joint resource allocation technique has a considerable performance enhancement in terms of both minimum achieved rate and overall system throughput compared to that of the conventional resource allocation technique where equal time-slots are assigned to the groups of users.

preprint2020arXiv

Rethink Maximum Mean Discrepancy for Domain Adaptation

Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by establishing the Maximum Mean Discrepancy (MMD) and the discriminative distances. However, they usually accumulate to consider those statistics and deal with their relationships by estimating parameters blindly. This paper theoretically proves two essential facts: 1) minimizing the MMD equals to maximize the source and target intra-class distances respectively but jointly minimize their variance with some implicit weights, so that the feature discriminability degrades; 2) the relationship between the intra-class and inter-class distances is as one falls, another rises. Based on this, we propose a novel discriminative MMD. On one hand, we consider the intra-class and inter-class distances alone to remove a redundant parameter, and the revealed weights provide their approximate optimal ranges. On the other hand, we design two different strategies to boost the feature discriminability: 1) we directly impose a trade-off parameter on the implicit intra-class distance in MMD to regulate its change; 2) we impose the similar weights revealed in MMD on inter-class distance and maximize it, then a balanced factor could be introduced to quantitatively leverage the relative importance between the feature transferability and its discriminability. The experiments on several benchmark datasets not only prove the validity of theoretical results but also demonstrate that our approach could perform better than the comparative state-of-art methods substantially.

preprint2020arXiv

Roboat II: A Novel Autonomous Surface Vessel for Urban Environments

This paper presents a novel autonomous surface vessel (ASV), called Roboat II for urban transportation. Roboat II is capable of accurate simultaneous localization and mapping (SLAM), receding horizon tracking control and estimation, and path planning. Roboat II is designed to maximize the internal space for transport and can carry payloads several times of its own weight. Moreover, it is capable of holonomic motions to facilitate transporting, docking, and inter-connectivity between boats. The proposed SLAM system receives sensor data from a 3D LiDAR, an IMU, and a GPS, and utilizes a factor graph to tackle the multi-sensor fusion problem. To cope with the complex dynamics in the water, Roboat II employs an online nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamical model of the vessel in order to achieve superior performance for tracking control. The states of Roboat II are simultaneously estimated using a nonlinear moving horizon estimation (NMHE) algorithm. Experiments demonstrate that Roboat II is able to successfully perform online mapping and localization, plan its path and robustly track the planned trajectory in the confined river, implying that this autonomous vessel holds the promise on potential applications in transporting humans and goods in many of the waterways nowadays.

preprint2020arXiv

Robot-assisted Backscatter Localization for IoT Applications

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

preprint2020arXiv

Secondary-electron radiation accompanying hadronic GeV-TeV gamma-rays from supernova remnants

The synchrotron radiation from secondary electrons and positrons (SEPs) generated by hadronic interactions in the shock of supernova remnant (SNR) could be a distinct evidence of cosmic ray (CR) production in SNR shocks. Here we provide a method where the observed gamma-ray flux from SNRs, created by pion decays, is directly used to derive the SEP distribution and hence the synchrotron spectrum. We apply the method to three gamma-ray bright SNRs. In the young SNR RX J1713.7-3946, if the observed GeV-TeV gamma-rays are of hadronic origin and the magnetic field in the SNR shock is $B\gtrsim 0.5$mG, the SEPs may produce a spectral bump at $10^{-5}-10^{-2}$eV, exceeding the predicted synchrotron component of the leptonic model, and a soft spectral tail at $\gtrsim 100$keV, distinct from the hard spectral slope in the leptonic model. In the middle-aged SNRs IC443 and W44, if the observed gamma-rays are of hadronic origin, the SEP synchrotron radiation with $B\sim 400 - 500 μ$G can well account for the observed radio flux and spectral slopes, supporting the hadronic origin of gamma-rays. Future microwave to far-infrared and hard X-ray (>100keV) observations are encouraged to constraining the SEP radiation and the gamma-ray origin in SNRs.

preprint2020arXiv

Self-awareness based resource allocation strategy for containment of epidemic spreading

Resource support between individuals is of particular importance in controlling or mitigating epidemic spreading, especially during pandemics. Whereas there remains the question of how we can protect ourselves from being infected while helping others by donating resources in fighting against the epidemic. To answer the question, we propose a novel resource allocation model by considering the awareness of self-protection of individuals. In the model, a tuning parameter is introduced to quantify the reaction strength of individuals when they are aware of the disease. And then, a coupled model of resource allocation and disease spreading is proposed to study the impact of self-awareness on resource allocation and, its impact on the dynamics of epidemic spreading. Through theoretical analysis and extensive Monte Carlo simulations, we find that in the stationary state, the system converges to two states: the whole healthy or the completely infected, which indicates an abrupt increase in the prevalence when there is a shortage of resources. More importantly, we find that too cautious and too selfless for the people during the outbreak of an epidemic are both not suitable for disease control. Through extensive simulations, we find the optimal point, at which there is a maximum value of the epidemic threshold, and an outbreak can be delayed to the greatest extent. At last, we study further the effects of network structure on the coupled dynamics. We find that the degree heterogeneity promotes the outbreak of disease, and the network structure does not alter the optimal phenomenon in behavior response.

preprint2020arXiv

Self-Training for Domain Adaptive Scene Text Detection

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

preprint2020arXiv

SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic

Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.

preprint2020arXiv

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance. The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into a vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. To the best of our knowledge, we are among the first to adopt neural networks to explicitly model the similarity between two graphs, and provide a new direction for future research on graph similarity computation and graph similarity search.

preprint2020arXiv

Single-Stage 6D Object Pose Estimation

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, however, is suboptimal: First, it is not end-to-end trainable. Second, training the deep network relies on a surrogate loss that does not directly reflect the final 6D pose estimation task. In this work, we introduce a deep architecture that directly regresses 6D poses from correspondences. It takes as input a group of candidate correspondences for each 3D keypoint and accounts for the fact that the order of the correspondences within each group is irrelevant, while the order of the groups, that is, of the 3D keypoints, is fixed. Our architecture is generic and can thus be exploited in conjunction with existing correspondence-extraction networks so as to yield single-stage 6D pose estimation frameworks. Our experiments demonstrate that these single-stage frameworks consistently outperform their two-stage counterparts in terms of both accuracy and speed.

preprint2020arXiv

Socially-Aware Conference Participant Recommendation with Personality Traits

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and haven't gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the above recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant dataset. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.

preprint2020arXiv

Sparsely-Labeled Source Assisted Domain Adaptation

Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is conducted on both the source and target domains, so that the discriminative structures of data could be leveraged elegantly. Then the label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatch problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate those three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not promote to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated as different expressions, thus the implicit variables are revealed in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and facilitate to each other, to improve the recognition performance obviously.

preprint2020arXiv

Spin dynamics of a magnetic Weyl semimetal Sr$_{1-x}$Mn$_{1-y}$Sb$_2$

Dirac matters provide a platform for exploring the interplay of their carriers with other quantum phenomena. Sr$_{1-x}$Mn$_{1-y}$Sb$_2$ has been proposed to be a magnetic Weyl semimetal and provides an excellent platform to study the coupling between Weyl fermions and magnons. Here, we report comprehensive inelastic neutron scattering (INS) measurements on single crystals of Sr$_{1-x}$Mn$_{1-y}$Sb$_2$, which have been well characterized by magnetization and magnetotransport measurements, both of which demonstrate that the material is a topologically nontrivial semimetal. The INS spectra clearly show a spin gap of $\sim6$ meV. The dispersion in the magnetic Mn layer extends up to about 76 meV, while that between the layers has a narrow band width of 6 meV. We find that the linear spin-wave theory using a Heisenberg spin Hamiltonian can reproduce the experimental spectra with the following parameters: a nearest-neighbor ($SJ_1\sim28.0$ meV) and next-nearest-neighbor in-plane exchange interaction ($SJ_2\sim9.3$ meV) , interlayer exchange coupling ($SJ_c\sim-0.1$ meV), and spin anisotropy constant ($SD\sim-0.07$ meV). Despite the coexistence of Weyl fermions and magnons, we find no clear evidence that the magnetic dynamics are influenced by the Weyl fermions in Sr$_{1-x}$Mn$_{1-y}$Sb$_2$, possibly because that the Weyl fermions and magnons reside in the Sb and Mn layers separately, and the interlayer coupling is weak due to the quasi-two-dimensional nature of the material, as also evident from the small $SJ_c$ of -0.1 meV.

preprint2020arXiv

Structural transition, metallization and superconductivity in quasi 2D layered PdS$_2$ under compression

Based on first-principles simulations and calculations, we explore the evolution of crystal structure, electronic structure and transport properties of quasi 2D layered PdS2 under uniaxial stress and hydrostatic pressure. The coordination of the Pd ions plays crucial roles in the structural transition, electronic structure and transport properties of PdS2. An interesting ferroelastic phase transition with lattice reorientation is revealed under uniaxial compressive stress, which originates from the bond reconstructions of the unusual PdS4 square-planar coordination. By contrast, the layered structure transforms to 3D cubic pyrite-type structure under hydrostatic pressure. In contrast to the experimental proposed coexistence of layered PdS2-type structure with cubic pyrite-type structure at intermediate pressure range, we predict that the compression-induced intermediate phase showing the same structural symmetry with the ambient phase, except of sharply contracted interlayer-distances. The coordination environments of the Pd ions have changed from square-planar to distorted octahedra in the intermediate phase, which results in the bandwidth broaden and orbital-selective metallization. In addition, the superconductivity comes from the cubic pyrite-type structure protected topological nodal-line states. The strong correlations between structural transition, electronic structure and transport properties in PdS2 provide a platform to study the fundamental physics of the interplay between crystal structure and transport behavior, and the competition between diverse phases.

preprint2020arXiv

Studying the neutrino wave-packet effects at medium-baseline reactor neutrino oscillation experiments and the potential benefits of an extra detector

We examine the potential of the future medium-baseline reactor neutrino oscillation (MBRO) experiments in studying neutrino wave-packet impact. In our study, we treat neutrinos as wave packets and use the corresponding neutrino flavor transition probabilities. The delocalization, separation and spreading of the wave packets lead to decoherence and dispersion effects, which modify the plane-wave neutrino oscillation pattern, by amounts that depend on the energy uncertainties in the initial neutrino wave packets. We find that MBRO experiments could be sensitive to the wave-packet impact, since the baseline is long enough and also the capability of observing small corrections to the neutrino oscillations due to excellent detector energy resolution. Besides studying the constraints on the decoherence parameter, we also examine the potential wave-packet impacts on the precision of measuring $θ_{12}$ and other oscillation parameters in the future medium-baseline reactor neutrino oscillation experiments. Moreover, we also probe the potential benefits of an additional detector for studying such exotic neutrino physics.

preprint2020arXiv

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022.

preprint2020arXiv

Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving

Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been successfully applied in this area, most existing methods still heavily rely on the pre-processing of the pulse signals derived from LiDAR sensors, and therefore introduce additional computational overhead and considerable latency. In this paper, we propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN). Being evaluated on various datasets (including Sim LiDAR, KITTI and DVS-barrel) derived from LiDAR and dynamic vision sensor (DVS), our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency. It highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to directly perform object recognition on raw temporal pulses.

preprint2020arXiv

The optimal edge for containing the spreading of SIS model

Numerous real-world systems, for instance, the communication platforms and transportation systems, can be abstracted into complex networks. Containing spreading dynamics (e.g., epidemic transmission and misinformation propagation) in networked systems is a hot topic in multiple fronts. Most of the previous strategies are based on the immunization of nodes. However, sometimes, these node--based strategies can be impractical. For instance, in the train transportation networks, it is dramatic to isolating train stations for flu prevention. On the contrary, temporarily suspending some connections between stations is more acceptable. Thus, we pay attention to the edge-based containing strategy. In this study, we develop a theoretical framework to find the optimal edge for containing the spreading of the susceptible-infected-susceptible model on complex networks. In specific, by performing a perturbation method to the discrete-Markovian-chain equations of the SIS model, we derive a formula that approximately provides the decremental outbreak size after the deactivation of a certain edge in the network. Then, we determine the optimal edge by simply choosing the one with the largest decremental outbreak size. Note that our proposed theoretical framework incorporates the information of both network structure and spreading dynamics. Finally, we test the performance of our method by extensive numerical simulations. Results demonstrate that our strategy always outperforms other strategies based only on structural properties (degree or edge betweenness centrality). The theoretical framework in this study can be extended to other spreading models and offers inspirations for further investigations on edge-based immunization strategies.

preprint2020arXiv

The role of traps in the photocurrent generation mechanism in thin In-Se photodetectors

Due to the excellent electrical transport properties and optoelectronic performance, thin indium selenide (InSe) has recently attracted attention in the field of 2D semiconducting materials. However, the mechanism behind the photocurrent generation in thin InSe photodetectors remains elusive. Here, we present a set of experiments aimed at explaining the strong scattering in the photoresponsivity values reported in the literature for thin InSe photodetectors. By performing optoelectronic measurements on thin InSe-based photodetectors operated under different environmental conditions we find that the photoresponsivity, the response time and the photocurrent power dependency are strongly correlated in this material. This observation indicates that the photogating effect plays an imporant role for thin InSe flakes, and it is the dominant mechanism in the ultra-high photoresponsivity of pristine InSe devices. In addition, when exposing the pristine InSe photodetectors to the ambient environment we observe a fast and irreversible change in the photoresponse, with a decrease in the photoresponsivity accompanied by an increase of the operating speed. We attribute this photodetector performance change (upon atmospheric exposure) to the decrease in the density of the traps present in InSe, due to the passivation of selenium vacancies by atmospheric oxygen species. This passivation is accompanied by a downward shift of the InSe Fermi level and by a decrease of the Fermi level pinning, which leads to an increase of the Schottky barrier between Au and InSe. Our study reveals the important role of traps induced by defects in tailoring the properties of devices based on 2D materials and offers a controllable route to design and functionalize thin InSe photodetectors to realize devices with either ultrahigh photoresposivity or fast operation speed.

preprint2020arXiv

Towards a Heavy Diquark Effective Theory for Weak Decays of Doubly Heavy Baryons

We construct a leading-order effective field theory for both scalar and axial-vector heavy diquarks, and consider its power expansion in the heavy diquark limit. By assuming the transition from QCD to diquark effective theory, we derive the most general form for the effective diquark transition currents based on the heavy diquark symmetry. The short-distance coefficients between QCD and heavy diquark effective field theory are also obtained by a tree level matching. With the effective currents in the heavy diquark limit, we perform a reduction of the form factors for semi-leptonic decays of doubly heavy baryons, and find that only one nonperturbative function is remaining. It is shown that this soft function can be related to the Isgur-Wise function in heavy meson transitions. As a phenomenological application, we take a single pole structure for the reduced form factor, and use it to calculate the semi-leptonic decay widths of doubly heavy baryons. The obtained results are consistent with others given in the literature, and can be tested in the future.

preprint2020arXiv

TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications

In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a high stakes financial application and a critical temperature forecasting application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.

preprint2020arXiv

TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator

In a generic object tracking, depth (D) information provides informative cues for foreground-background separation and target bounding box regression. However, so far, few trackers have used depth information to play the important role aforementioned due to the lack of a suitable model. In this paper, a RGB-D tracker named TSDM is proposed, which is composed of a Mask-generator (M-g), SiamRPN++ and a Depth-refiner (D-r). The M-g generates the background masks, and updates them as the target 3D position changes. The D-r optimizes the target bounding box estimated by SiamRPN++, based on the spatial depth distribution difference between the target and the surrounding background. Extensive evaluation on the Princeton Tracking Benchmark and the Visual Object Tracking challenge shows that our tracker outperforms the state-of-the-art by a large margin while achieving 23 FPS. In addition, a light-weight variant can run at 31 FPS and thus it is practical for real world applications. Code and models of TSDM are available at https://github.com/lql-team/TSDM.

preprint2020arXiv

Understanding Architectures Learnt by Cell-based Neural Architecture Search

Neural architecture search (NAS) searches architectures automatically for given tasks, e.g., image classification and language modeling. Improving the search efficiency and effectiveness have attracted increasing attention in recent years. However, few efforts have been devoted to understanding the generated architectures. In this paper, we first reveal that existing NAS algorithms (e.g., DARTS, ENAS) tend to favor architectures with wide and shallow cell structures. These favorable architectures consistently achieve fast convergence and are consequently selected by NAS algorithms. Our empirical and theoretical study further confirms that their fast convergence derives from their smooth loss landscape and accurate gradient information. Nonetheless, these architectures may not necessarily lead to better generalization performance compared with other candidate architectures in the same search space, and therefore further improvement is possible by revising existing NAS algorithms.

preprint2020arXiv

Unexpected crossovers in correlated random-diffusivity processes

The passive and active motion of micron-sized tracer particles in crowded liquids and inside living biological cells is ubiquitously characterised by "viscoelastic" anomalous diffusion, in which the increments of the motion feature long-ranged negative and positive correlations. While viscoelastic anomalous diffusion is typically modelled by a Gaussian process with correlated increments, so-called fractional Gaussian noise, an increasing number of systems are reported, in which viscoelastic anomalous diffusion is paired with non-Gaussian displacement distributions. Following recent advances in Brownian yet non-Gaussian diffusion we here introduce and discuss several possible versions of random-diffusivity models with long-ranged correlations. While all these models show a crossover from non-Gaussian to Gaussian distributions beyond some correlation time, their mean squared displacements exhibit strikingly different behaviours: depending on the model crossovers from anomalous to normal diffusion are observed, as well as unexpected dependencies of the effective diffusion coefficient on the correlation exponent. Our observations of the strong non-universality of random-diffusivity viscoelastic anomalous diffusion are important for the analysis of experiments and a better understanding of the physical origins of "viscoelastic yet non-Gaussian" diffusion.

preprint2020arXiv

Unification of Flavor SU(3) Analyses of Heavy Hadron Weak Decays

Analyses of heavy mesons and baryons hadronic charmless decays using the flavor SU(3) symemtry can be formulated in two different forms. One is to construct the SU(3) irreducible representation amplitude (IRA) by decomposing effective Hamiltonian, and the other is to draw the topological diagrams (TDA). In the flavor SU(3) limit, we study various $B/D\to PP,VP,VV$, $B_c\to DP/DV$ decays, and two-body nonleptonic decays of beauty/charm baryons, and demonstrate that when all terms are included these two ways of analyzing the decay amplitudes are completely equivalent. Furthermore we clarify some confusions in drawing topological diagrams using different ways of describing beauty/charm baryons.

preprint2020arXiv

Using UNet and PSPNet to explore the reusability principle of CNN parameters

How to reduce the requirement on training dataset size is a hot topic in deep learning community. One straightforward way is to reuse some pre-trained parameters. Some previous work like Deep transfer learning reuse the model parameters trained for the first task as the starting point for the second task, and semi-supervised learning is trained upon a combination of labeled and unlabeled data. However, the fundamental reason of the success of these methods is unclear. In this paper, the reusability of parameters in each layer of a deep convolutional neural network is experimentally quantified by using a network to do segmentation and auto-encoder task. This paper proves that network parameters can be reused for two reasons: first, the network features are general; Second, there is little difference between the pre-trained parameters and the ideal network parameters. Through the use of parameter replacement and comparison, we demonstrate that reusability is different in BN(Batch Normalization)[7] layer and Convolution layer and some observations: (1)Running mean and running variance plays an important role than Weight and Bias in BN layer.(2)The weight and bias can be reused in BN layers.( 3) The network is very sensitive to the weight of convolutional layer.(4) The bias in Convolution layers are not sensitive, and it can be reused directly.

preprint2020arXiv

WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles

This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initialization-free for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of $61.7$ cm and the attitude error of $0.92^\circ$.

preprint2020arXiv

X-ray quasi-periodic oscillations in Lense-Thirring precession model -- II. variability of relativistic iron K$α$ line

Reprocessing of primary X-ray emission in the accretion disk of black hole X-ray binaries (BHXRBs) produces the reflection spectrum, with the characteristic Fe K$α$ fluorescence line. Strong low frequency quasi-periodic oscillations (QPOs) are observed from BHXRBs, and the dependence of QPO properties (e.g., phase-lag) on the inclination angle suggests that the observed QPO may be associated with a geometrical effect, e.g., the precession of the X-ray source due to frame-dragging near the spining black hole. Here, in the scenario of Lense-Thirring precession of the X-ray source, we use a Monte-Carlo simulation of radiative transfer to study the irradiation/reflection and the resultant spectral properties including the Fe K$α$ line, as a function of precession phase (time). We found that the reflection fraction, i.e., the ratio of the incident flux towards the disk and the direct flux towards to the observer at infinity, is modulated with the precession phase, which depends on the truncation radius (i.e., the spectral state in the truncated disk model) and the inclination angle. The Fe K$α$ line profile also changes as the primary X-ray source precesses, with the line luminosity and the flux-weighted centroid energy varying with the precession phase. The periodically modulated 2-10 keV continuum flux could apparently lag the line luminosity in phase, if the truncation radius is small enough for Doppler effects due to disk orbital motion to significantly affect the observed radiation.

preprint2020arXiv

Years Delayed X-ray Afterglows of TDEs Originated from Wind-Torus Interactions

Tidal disruption events (TDEs) occurred in active galactic nuclei (AGNs) are a special class of sources with outstanding scientific significance. TDEs can generate ultrafast winds, which should almost inevitably collide with the preexisting AGN dusty tori. We perform analytical calculations and simulations on the wind-torus interactions and find such a process can generate considerable X-ray afterglow radiation several years or decades later after the TDE outburst. This provides a new origin for the years delayed X-rays in TDEs. The X-ray luminosity can reach 10^{41-42} erg/s, and the light curve characteristics depend on the parameters of winds and tori. We apply the model to two TDE candidates, and provide lower limits on the masses of the disrupted stars, as well as rigorous constraints on the gas densities of tori. Our results suggest that the observations of the time delay, spectral shape, luminosity and the light curve of the X-ray afterglow can be used to constrain the physical parameters of both TDE winds and tori, including the wind velocity, wind density and torus density.

preprint2019arXiv

Containing rumors spreading on correlated multiplex networks

Rumors flooding on rapidly-growing online social networks has geared much attention from many fronts. Individuals can transmit rumors via numerous channels since they can be active on multiple platforms. However, no systematic theoretical research of rumors containing dynamics on multiplex networks has been conducted yet. In this study, we propose a family of containing strategies based on the degree product $\mathcal{K}$ of each user on the multiplex networks. Then, we develop a heterogeneous edge-based compartmental theory to comprehend the containing dynamics. The simulation results demonstrate that strategies with preference to block users with large $\mathcal{K}$ can significantly reduce the rumor outbreak size and enlarge the threshold. Besides, better performance can be expected on heterogeneous multiplex networks with the increasing of preference intensity and degree heterogeneity. Moreover, take the inter-layer degree correlations $r_s$ into consideration, the strategy performs best on multiplex networks with $r_s=-1$, $r_s=1$ the second, and $r_s=0$ the last. On the contrary, if we prefer to block users with small $\mathcal{K}$ rather than large $\mathcal{K}$, the containing performance will be worse than that of blocking users randomly on most multiplex networks except for uncorrelated multiplex networks with uniform degree distribution. We found that the blocking preferences have no influence on the containing results on uncorrelated multiplex networks with uniform degree distribution. Our theoretical analysis can well predict the rumors containing results and performance differences in all the cases studied. The systematic theoretical research of rumors containing dynamics on multiplex networks in this study will offer inspirations for further investigations on this issue.

preprint2019arXiv

Graphene Induced Large Shift of Surface Plasmon Resonances of Gold Films: Effective Medium Theory for Atomically Thin Materials

Despite successful modeling of graphene as a 0.34-nm thick optical film synthesized by exfoliation or chemical vapor deposition (CVD), graphene induced shift of surface plasmon resonance (SPR) of gold films has remained controversial. Here we report the resolution of this controversy by developing a clean CVD graphene transfer method and extending Maxwell-Garnet effective medium theory (EMT) to 2D materials. A SPR shift of 0.24 is obtained and it agrees well with 2D EMT in which wrinkled graphene is treated as a 3-nm graphene/air layered composite, in agreement with the average roughness measured by atomic force microscope. Because the anisotropic built-in boundary condition of 2D EMT is compatible with graphene's optical anisotropy, graphene can be modelled as a film thicker than 0.34-nm without changing its optical property; however, its actual roughness, i.e., effective thickness will significantly alter its response to strong out-of-plane fields, leading to a larger SPR shift.

preprint2019arXiv

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution

In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parameter-economic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multi-supervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.

preprint2019arXiv

Mechanical characterization of cells and microspheres sorted by acoustophoresis with in-line resistive pulse sensing

Resistive Pulse Sensing (RPS) is a key label-free technology to measure particles and single-cell size distribution. As a growing corpus of evidence supports that cancer cells exhibit distinct mechanical phenotypes from healthy cells, expanding the method from size to mechanical sensing could represent a pertinent and innovative tool for cancer research. In this paper, we infer the cells compressibility by using acoustic radiation pressure to deflect flowing cells in a microchannel, and use RPS to sense the subpopulations of cells and particles at each acoustic power level. We develop and validate a linear model to analyze experimental data from a large number of particles. This high-precision linear model is complemented by a more robust (yet less detailed) statistical model to analyze datasets with fewer particles. Compared to current acoustic cell phenotyping apparatus based on video cameras, the proposed approach is not limited by the optical diffraction, frame rate, data storage or processing speed, and may ultimately constitute a step forward towards point-of-care acousto-electrical phenotyping and acoustic phenotyping of nanoscale objects such as exosomes and viruses.

preprint2019arXiv

Misinformation spreading on correlated multiplex networks

The numerous expanding online social networks offer fast channels for misinformation spreading, which could have a serious impact on socioeconomic systems. Researchers across multiple areas have paid attention to this issue with a view of addressing it. However, no systematical theoretical study has been performed to date on observing misinformation spreading on correlated multiplex networks. In this study, we propose a multiplex network-based misinformation spreading model, considering the fact that each individual can obtain misinformation from multiple platforms. Subsequently, we develop a heterogeneous edge-base compartmental theory to comprehend the spreading dynamics of our proposed model. In addition, we establish an analytical method based on stability analysis to obtain the misinformation outbreak threshold. On the basis of these theories, we finally analyze the influence of different dynamical and structural parameters on the misinformation spreading dynamics. Results show that the misinformation outbreak size $R(\infty)$ grows continuously with the effective transmission probability $β$ once $β$ exceeds a certain value, that is, the outbreak threshold $β_c$. A large average degrees, strong degree heterogeneity, or positive inter-layer correlation will reduce $β_c$, accelerating the outbreak of misinformation. Besides, increasing the degree heterogeneity or a more positive inter-layer correlation will both enlarge (reduce) $R(\infty)$ for small (large) values of $β$. Our systematic theoretical analysis results agree well with the numerical simulation results. Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks, and thereby pave the way to address this serious issue.

preprint2019arXiv

Study of Constrained Network Structures for WGANs on Numeric Data Generation

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.

preprint2019arXiv

Tensor train-Karhunen-Loève expansion for continuous-indexed random fields using higher-order cumulant functions

The goals of this work are two-fold: firstly, to propose a new theoretical framework for representing random fields on a large class of multidimensional geometrical domain in the tensor train format; secondly, to develop a new algorithm framework for accurately computing the modes and the second and third-order cumulant tensors within moderate time. The core of the new theoretical framework is the tensor train decomposition of cumulant functions. This decomposition is accurately computed with a novel rank-revealing algorithm. Compared with existing Galerkin-type and collocation-type methods, the proposed computational procedure totally removes the need of selecting the basis functions or collocation points and the quadrature points, which not only greatly enhances adaptivity, but also avoids solving large-scale eigenvalue problems. Moreover, by computing with third-order cumulant functions, the new theoretical and algorithm frameworks show great potential for representing general non-Gaussian non-homogeneous random fields. Three numerical examples, including a three-dimensional random field discretization problem, illustrate the efficiency and accuracy of the proposed algorithm framework.

preprint2019arXiv

Understanding the coexistence of spin-up and spin-down behaviors in long period X-ray pulsars

Assuming the wind-fed accretion magnetars in long period X-ray pulsars, we calculated the rotational evolution of the neutron stars. Our calculations considered the effects of the magnetic field decay in magnetars. The results show that wind-fed accretion magnetars can evolve to the long period X-ray pulsars with a spin period much longer than 1000 s. The spin-down trend observed in 4U 2206+54 like sources is expected when the young X-ray binary systems are on the way to their equilibrium period. Detailed calculations showed that its spin-down may be affected by accretion with outflows or accretion while spin-down. Due to the magnetic field decay in magnetars, wind-fed accretion magnetars will have a decreasing equilibrium period for a constant mass accretion rate. For 2S 0114+65, the spin-up rate due to magnetic field decay is one order of magnitude smaller than the observations. The spin-up rate of 2S 0114+65 may be attributed to the formation of a transient disk during wind accretion. The slowest X-ray pulsar AX J1910.7+0917 would be a link source between 4U 2206+54 and 2S 0114+65.

preprint2019arXiv

Unpolarized isovector quark distribution function from Lattice QCD: A systematic analysis of renormalization and matching

We present a detailed Lattice QCD study of the unpolarized isovector quark Parton Distribution Function (PDF) using large-momentum effective theory framework. We choose a quasi-PDF defined by a spatial correlator which is free from mixing with other operators of the same dimension. In the lattice simulation, we use a Gaussian-momentum-smeared source at $M_π=356$ MeV and $P_z \in \{1.8,2.3\}$ GeV. To control the systematics associated with the excited states, we explore {five different source-sink separations}. The nonperturbative renormalization is conducted in a regularization-independent momentum subtraction scheme, and the matching between the renormalized quasi-PDF and $\bar{\rm MS}$ PDF is calculated based on perturbative QCD up to one-loop order. Systematic errors due to renormalization and perturbative matching are also analyzed in detail. Our results for lightcone PDF are in reasonable agreement with the latest phenomenological analysis.

preprint2018arXiv

Revisiting Transmission Scheduling in RF Energy Harvesting Wireless Communications

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies in an RF-based energy harvesting system is mainly based on a classic model, in which the data transmission is scheduled in a fixed feasible energy tunnel. In this paper, we re-examine the classic energy harvesting model and show through the theoretical analysis and experimental results that the bounds of feasible energy tunnel are dynamic, which can be affected by the transmission scheduling due to the impact of residual energy on the harvested one. To describe a practical energy harvesting process more accurately, a new model is proposed by adding a feedback loop that reflects the interplay between the energy harvest and the data transmission. Furthermore, to improve network performance, we revisit the design of an optimal transmission scheduling strategy based on the new model. To handle the challenge of the endless feedback loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy reception and energy utilization regardless of the length of energy packets, achieving improved throughput performance for wireless communications.

preprint2016arXiv

A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.

preprint2016arXiv

A Comprehensive Survey on Cross-modal Retrieval

In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation learning. Real-valued representation learning methods aim to learn real-valued common representations for different modalities of data. To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space. Then, we introduce several multimodal datasets in the community, and show the experimental results on two commonly used multimodal datasets. The comparison reveals the characteristic of different kinds of cross-modal retrieval methods, which is expected to benefit both practical applications and future research. Finally, we discuss open problems and future research directions.

preprint2016arXiv

Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)

Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances. In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, and from practitioners) and (2) has evaluated a diverse range of settings, including 20 datasets, several evaluation metrics, and different query workloads. The experimental results are carefully reported and analyzed to understand the performance results. Furthermore, we propose a new method that achieves both high query efficiency and high recall empirically on majority of the datasets under a wide range of settings.

preprint2016arXiv

Beamforming for Simultaneous Wireless Information and Power Transfer in Two-Way Relay Channels

This paper studies simultaneous wireless information and power transfer (SWIPT) systems in two-way relaying (TWR) channels. Here, two source nodes receive information and energy simultaneously via power splitting (PS) from the signals sent by a multi-antenna relay node. Our objective is to maximize the weighted sum of the harvested energy at two source nodes subject to quality of service (QoS) constraints and the relay power constraints. Three well-known and practical two-way relay strategies are considered, i.e., amplify-and-forward (AF), bit level XOR based decode-and-forward (DF-XOR) and symbol level superposition coding based DF (DF-SUP). For each relaying strategy, we formulate the joint energy transmit beamforming and PS ratios optimization as a nonconvex quadratically constrained problem. To find a closed-form solution of the formulated problem, we decouple the primal problem into two subproblems. In the first problem, we intend to optimize beamforming vector for a given PS ratio. In the second subproblem, we optimize the PS ratio with a given beamforming vector. It is worth noting that although the corresponding subproblem are nonconvex, the optimal solution of each subproblem can still be found by using certain techniques. We provide numerical results that demonstrate the advantage of adapting the different relaying strategies and weighted factors to harvest energy in two-way relaying channel.

preprint2016arXiv

Calibration of LAMOST Stellar Surface Gravities Using the Kepler Asteroseismic Data

Asteroseismology is a powerful tool to precisely determine the evolutionary status and fundamental properties of stars. With the unprecedented precision and nearly continuous photometric data acquired by the NASA Kepler mission, parameters of more than 10$^4$ stars have been determined nearly consistently. However, most studies still use photometric effective temperatures (Teff) and metallicities ([Fe/H]) as inputs, which are not sufficiently accurate as suggested by previous studies. We adopted the spectroscopic Teff and [Fe/H] values based on the LAMOST low-resolution spectra (R~1,800), and combined them with the global oscillation parameters to derive the physical parameters of a large sample of stars. Clear trends were found between Δlogg(LAMOST - seismic) and spectroscopic Teff as well as logg, which may result in an overestimation of up to 0.5 dex for the logg of giants in the LAMOST catalog. We established empirical calibration relations for the logg values of dwarfs and giants. These results can be used for determining the precise distances to these stars based on their spectroscopic parameters.

preprint2016arXiv

Can $X(5568)$ be a tetraquark state?

Very recently, the D0 collaboration has reported the observation of a narrow structure, $X(5568)$, in the decay process $X(5568)\to B^0_sπ^\pm$ using the 10.4${\rm fb}^{-1}$ data of $p\bar p$ collision at $\sqrt s= 1.96$ TeV. This structure is of great interest since it is the first hadronic state with four different valence quark flavors, $b,s,u,d$. In this work, we investigate tetraquarks with four different quark flavors. Based on the diquark-antidiquark scheme, we study the spectroscopy of the tetraquarks with one heavy bottom/charm quark and three light quarks. We find that the lowest-lying S-wave state, a tetraquark with the flavor $[su][\bar b\bar d ]$ and the spin-parity $J^P=0^+$, is about 150 MeV higher than the $X(5568)$. Further detailed experimental and theoretical studies of the spectrum, production and decays of tetraquark states with four different flavors in the future are severely needed towards a better understanding its nature and the classification of hadron exotic states.

preprint2016arXiv

Deep Learning At Scale and At Ease

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort especially when the model is large and complex. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model based on the common layer abstraction of deep learning models. Good scalability is achieved through flexible distributed training architecture and specific optimization techniques. SINGA runs on GPUs as well as on CPUs, and we show that it outperforms many other state-of-the-art deep learning systems. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.

preprint2016arXiv

Deep Models for Engagement Assessment With Scarce Label Information

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition). It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training.

preprint2016arXiv

Depth and Reflection Total Variation for Single Image Dehazing

Haze removal has been a very challenging problem due to its ill-posedness, which is more ill-posed if the input data is only a single hazy image. In this paper, we present a new approach for removing haze from a single input image. The proposed method combines the model widely used to describe the formation of a haze image with the assumption in Retinex that an image is the product of the illumination and the reflection. We assume that the depth and reflection functions are spatially piecewise smooth in the model, where the total variation is used for the regularization. The proposed model is defined as a constrained optimization problem, which is solved by an alternating minimization scheme and the fast gradient projection algorithm. Some theoretic analyses are given for the proposed model and algorithm. Finally, numerical examples are presented to demonstrate that our method can restore vivid and contrastive hazy images effectively.

preprint2016arXiv

Derivative Delay Embedding: Online Modeling of Streaming Time Series

The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution. For time series modeling, most existing works make some unrealistic assumptions such as the input data is of fixed length or well aligned, which requires extra effort on segmentation or normalization of the raw streaming data. Although some literature claim their approaches to be invariant to data length and misalignment, they are too time-consuming to model a streaming time series in an online manner. We propose a novel and more practical online modeling and classification scheme, DDE-MGM, which does not make any assumptions on the time series while maintaining high efficiency and state-of-the-art performance. The derivative delay embedding (DDE) is developed to incrementally transform time series to the embedding space, where the intrinsic characteristics of data is preserved as recursive patterns regardless of the stream length and misalignment. Then, a non-parametric Markov geographic model (MGM) is proposed to both model and classify the pattern in an online manner. Experimental results demonstrate the effectiveness and superior classification accuracy of the proposed DDE-MGM in an online setting as compared to the state-of-the-art.

preprint2016arXiv

Dynamical co-existence of excitons and free carriers in perovskite probed by density-resolved fluorescent spectroscopic method

Using transient fluorescent spectra at time-zero, we develop a density-resolved fluorescent spectroscopic method for investigating photoproducts in CH3NH3PbI3 perovskite and related photophysics. The density dependent dynamical co-existence of excitons and free carriers over a wide density range is experimentally observed for the first time. The exciton binding energy (EB) and the effective mass of electron-hole pair can be estimated based on such co-existence. No ionic polarization is found contributing to photophysical behavior. It also solves the conflict between the large experimentally measured EB and the small predicted values. The spectroscopic method also helps to detect the true free carrier density under continuous illumination without the interference of ionic conductivity. Our methods and results profoundly enrich the study and understanding of the photophysics in perovskite materials for photovoltaic applications.

preprint2016arXiv

Effective information spreading based on local information in correlated networks

Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks, which will benefit the promotion of technical innovations, healthy behaviors, new products, etc. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and moderate correlation coefficients result in most efficient information spreading. Incorporating the informed density information into contact strategy, the convergence time of information spreading can be further reduced. Finally, we show that by using local informed density is more effective as compared with the global case.

preprint2016arXiv

Electron spin and the origin of Bio-homochirality I. Extant enzymatic reaction model

In this paper, I tentatively put forward a new hypothesis that the emergence of a single chiral form of biomolecules in living organisms is specifically determined by the electron spin state during their enzyme-catalyzed synthesis processes. Specifically speaking, the electrons released from the coenzyme NAD(P)H of amino acid synthase are heterogeneous in spin states; however, when they pass through the chiral alpha-helix structure of the enzymes to the site of amino acid synthesis at the other end of the helix, their spin states are filtered and polarized, producing only spin up electrons; once the spin-polarized electrons participate in the reductive reaction between alpha-oxo acid and ammonia, only L-amino acids are formed according to the Pauli exclusion principle.

preprint2016arXiv

Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Devices on Flexible Conducting Graphene Substrates

Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were fabricated on flexible graphene substrates. Dendritic integrations in both spatial and temporal modes were successfully emulated, and spatiotemporal correlated logics were obtained. A proof-of-principle visual system model for emulating lobula giant motion detector neuron was investigated. Our results are of great interest for flexible neuromorphic cognitive systems.

preprint2016arXiv

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.

preprint2016arXiv

Hard X-ray emissions from Cassiopeia A observed by INTEGRAL

Cassiopeia A (Cas A) as the nearby young remnant of a core-collapse supernova is the best candidate for astrophysical studies in supernova explosion and its environment. We studied hard X-ray emissions from Cas A using the ten-year data of INTEGRAL observations, and first detected non-thermal continuum emission from the source up to 220 keV. The $^{44}$Ti line emissions at 68 and 78 keV are confirmed by our observations with a mean flux of $\sim (2.2\pm 0.4)\times 10^{-5}$ ph cm$^{-2}$ s$^{-1}$, corresponding to a $^{44}$Ti yield in Cas A of $(1.3\pm 0.4)\times 10^{-4}$ \ms. The continuum emission from 3 -- 500 keV can be fitted with a thermal bremsstrahlung of $kT\sim 0.79\pm 0.08$ keV plus a power-law model of $Γ\sim 3.13\pm 0.03$. The non-thermal emission from Cas A is well fitted with a power-law model without a cutoff up to 220 keV. This radiation characteristic is inconsistent with the diffusive shock acceleration models with the remnant shock velocity of only 5000km s$^{-1}$. The central compact object in Cas A cannot contribute to the emission above 80 keV significantly. Some possible physical origins of the non-thermal emission above 80 keV from the remnant shock are discussed. We deduce that the asymmetrical supernova explosion scenario of Cas A is a promising scenario to produce high energy synchrotron radiation photons, where a part of ejecta with the velocity of $\sim 0.1c$ and opening angle of $\sim10^\circ$ can account for the 100-keV emission, consistent with the "jet" observed in Cas A.

preprint2016arXiv

Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks

Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which how an individual being aware of disease responds to the disease can significantly affect the disease spreading. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of adopting one behavior is dependent on the cumulative times of the received information and the social reinforcement effect of these cumulative information. We study the impact of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects. We find that this complex adoption behavior caused from the communication layer can significantly increase the epidemic threshold and reduce the final infection rate. By defining the social cost as the sum of the cost of vaccination and the cost of treatment, we show that there exists an optimal social reinforcement effect or optimal information transmission rate allowing the minimal social cost. We also develop a mean field based theory to verify the correctness of the simulation results.

preprint2016arXiv

Makeup like a superstar: Deep Localized Makeup Transfer Network

In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the beforemakeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Qualitative and quantitative experiments show that our network performs much better than the methods of [Guo and Sim, 2009] and two variants of NerualStyle [Gatys et al., 2015a].

preprint2016arXiv

Multimodal Memory Modelling for Video Captioning

Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, e.g., convolutional neural networks (CNNs) and recurrent neural networks (RNNs), video captioning has made great progress. However, learning an effective mapping from visual sequence space to language space is still a challenging problem. In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets. Specifically, the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. First, text representation in the Long Short-Term Memory (LSTM) based text decoder is written into the memory, and the memory contents will be read out to guide an attention to select related visual targets. Then, the selected visual information is written into the memory, which will be further read out to the text decoder. To evaluate the proposed model, we perform experiments on two publicly benchmark datasets: MSVD and MSR-VTT. The experimental results demonstrate that our method outperforms the state-of-theart methods in terms of BLEU and METEOR.

preprint2016arXiv

On the $3$-representations of groups and the $2$-categorical Traces

To $2$-categorify the theory of group representations, we introduce the notions of the $3$-representation of a group in a strict $3$-category and the strict $2$-categorical action of a group on a strict $2$-category. We also $2$-categorify the concept of the trace by introducing the $2$-categorical trace of a $1$-endomorphism in a strict $3$-category. For a $3$-representation $ρ$ of a group $G$ and an element $f$ of $G$, the $2$-categorical trace $\mathbb{T}r_2 ρ_f $ is a category. Moreover, the centralizer of $f$ in $G$ acts categorically on this $2$-categorical trace. We construct the induced strict $2$-categorical action of a finite group, and show that the $2$-categorical trace $ \mathbb{T}r_2$ takes an induced strict $2$-categorical action into an induced categorical action of the initia groupoid. As a corollary, we get the $3$-character formula of the induced strict $2$-categorical action.

preprint2016arXiv

PandaX-III: Searching for Neutrinoless Double Beta Decay with High Pressure $^{136}$Xe Gas Time Projection Chambers

Searching for the Neutrinoless Double Beta Decay (NLDBD) is now regarded as the topmost promising technique to explore the nature of neutrinos after the discovery of neutrino masses in oscillation experiments. PandaX-III (Particle And Astrophysical Xenon Experiment III) will search for the NLDBD of $^{136}$Xe at the China Jin Ping underground Laboratory (CJPL). In the first phase of the experiment, a high pressure gas Time Projection Chamber (TPC) will contain 200 kg, 90% $^{136}$Xe enriched gas operated at 10 bar. Fine pitch micro-pattern gas detector (Microbulk Micromegas) will be used at both ends of the TPC for the charge readout with a cathode in the middle. Charge signals can be used to reconstruct tracks of NLDBD events and provide good energy and spatial resolution. The detector will be immersed in a large water tank to ensure $\sim$5 m of water shielding in all directions. The second phase, a ton-scale experiment, will consist of five TPCs in the same water tank, with improved energy resolution and better control over backgrounds.

preprint2016arXiv

Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval

Incompatibility of image descriptor and ranking is always neglected in image retrieval. In this paper, manifold learning and Gestalt psychology theory are involved to solve the incompatibility problem. A new holistic descriptor called Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed, which combines color and gradient direction to imitate the human visual uniformity. PUD features in the same class images distributes on one manifold in most cases because PUD improves the visual uniformity of the traditional descriptors. Thus, we use manifold ranking and PUD to realize image retrieval. Experiments were carried out on five benchmark data sets, and the proposed method can greatly improve the accuracy of image retrieval. Our experimental results in the Ukbench and Corel-1K datasets demonstrated that N-S score reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by utilizing PUD which has only 280 dimension. The results are higher than other holistic image descriptors (even some local ones) and state-of-the-arts retrieval methods.

preprint2016arXiv

Pivotal and Ribbon Entwining Datums

Let $(C,A,φ)$ be an entwining structure over $k$. In this paper, we introduce the notions of the pivotal entwined datums and ribbon entwined datums to generalize (co)pivotal Hopf algebras and (co)ribbon Hopf algebras. These notions give necessary and sufficient conditions for the category of entwined modules to be a pivotal category and ribbon category.

preprint2016arXiv

Possible singlet and triplet superconductivity on honeycomb lattice

We study the possible superconducting pairing symmetry mediated by spin and charge fluctuations on the honeycomb lattice using the extended Hubbard model and the random-phase-approximation method. From $2\%$ to $20\%$ doping levels, a spin-singlet $d_{x^{2}-y^{2}}+id_{xy}$-wave is shown to be the leading superconducting pairing symmetry when only the on-site Coulomb interaction $U$ is considered, with the gap function being a mixture of the nearest-neighbor and next-nearest-neighbor pairings. When the offset of the energy level between the two sublattices exceeds a critical value, the most favorable pairing is a spin-triplet $f$-wave which is mainly composed of the next-nearest-neighbor pairing. We show that the next-nearest-neighbor Coulomb interaction $V$ is also in favor of the spin-triplet $f$-wave pairing.

preprint2016arXiv

Production of Charmed Tetraquarks from $B_c$ and $B$ decays

Hadronic states composed of multi-quark flavors may exist in reality since they are not prohibited by QCD. Compact four quark systems of color singlet are classified as tetraquarks. To understand the properties of these states, more theoretical and experimental efforts are needed. In this work, we study charmed tetraquarks with three light flavors using flavor $SU(3)$ symmetry. States with three different light quarks must be in a ${\bf \bar 6 }$ or a ${\bf 15}$ multiplet. We investigate the production of charmed tetraquarks $X_c$ in $B\to X_c (\overline {X}_c) P$ and $B_c \to X_c P$ decays. Whether the states with three light quarks belong to ${\bf \bar 6}$ or ${\bf 15}$ can be determined by studying various tetraquark $B$ and $B_c$ decays. We demonstrate that the decay amplitudes for these decays can be parametrized by a few irreducible SU(3) invariant amplitudes. We then derive relations for decay widths and CP violating rate difference which can be examined experimentally. Although no experimental measurement is available yet, they might be accessed at the ongoing and forthcoming experiments like the LHCb and Belle-II. Measurements of these observables can not only provide useful information for the study of exotics spectroscopy but are also valuable information towards a better understanding of some non-perturbative aspects of QCD.

preprint2016arXiv

Search for the $a_0(980)-f_0(980)$ mixing in weak decays of $D_s/B_s$ mesons

Scalar mesons $a^0_0(980)$ and $f_0(980)$ can mix with each other through isospin violating effects, and the mixing intensity has been predicted at the percent level in various theoretical models. However the mixing has not been firmed established on the experimental side to date. In this work we explore the possibility to extract the $a_0-f_0$ mixing intensity using weak decays of heavy mesons: $D_s\to [π^0η, ππ] e^+ν$, $B_s\to [π^0η, ππ]\ell^+\ell^-$ and the $B_s\to J/ψ[π^0η,π^+π^-]$ decays. Based on the large amount of data accumulated by various experimental facilities including BEPC-II, LHC, Super KEKB and the future colliders, we find that the $a_0-f_0$ mixing intensity might be determined to a high precision, which will lead to a better understanding of the nature of scalar mesons.

preprint2016arXiv

Smash coproducts of bicomonads and Hom-entwining structures

Let $F,G$ be bicomonads on a monoidal category $\mathcal{C}$. The aim of this paper is to discuss the smash coproducts of $F$ and $G$. As an application, the smash coproduct of Hom-bialgebras is discussed. Further, the Hom-entwining structure and Hom-entwined modules are investigated.

preprint2016arXiv

Smoothing analysis of HLSII storage ring magnets

In order to improve the quality and stability of synchrotron light, Hefei Light Source has a major upgrade. Higher accuracy is necessary for installation and alignment of the storage ring magnets. It is not necessarily essential that the magnets are positioned exactly. In fact, the aim is to adjust neighboring magnets with a high accuracy to one another; in other words, these neighboring magnets are positioned on a smoothing curve. The paper presents an attempt to develop a reliable smoothing method based on curve fitting of least squares and iteration according to the structure characteristics of HLSII. The method significantly reduces the adjusting amount and range of the storage ring magnets. It improves productivity by a factor of one times.

preprint2016arXiv

Square-free Discriminants of Matrices and the Generalized Spectral Characterizations of Graphs

Let $S_n(\mathbb{Z})$ and $O_n(\mathbb{Q})$ denote the set of all $n\times n$ symmetric matrices over the ring of integers $\mathbb{Z}$ and the set of all $n\times n$ orthogonal matrices over the field of rational numbers $\mathbb{Q}$, respectively. The paper is mainly concerned with the following problem: Given a matrix $A\in {S_n(\mathbb{Z})}$. How can one find all rational orthogonal matrices $Q\in{O_n(\mathbb{Q})}$ such that $Q^TAQ\in {S_n(\mathbb{Z})}$, and in particular, when does $Q^TAQ\in {S_n(\mathbb{Z})}$ with $Q\in{O_n(\mathbb{Q})}$ imply that $Q$ is \emph{a signed permutation matrix} (i.e., the matrix obtained from a permutation matrix $P$ by replacing each 1 in $P$ with 1 or $-1$)? A surprisingly simple answer was given in terms of whether the discriminant of the characteristic polynomial of $A$ is odd and square-free, which partially answers the above questions. More precisely, let $Δ_A=\pm \res(ϕ,ϕ')$ be \emph{the discriminant of matrix $A$}, where $\res(ϕ,ϕ')$ is \emph{the resultant} of the characteristic polynomial $ϕ$ of $A$ and its derivative $ϕ'$. We show that if $Δ_A$ is odd and square-free, then $Q^TAQ\in {S_n(\mathbb{Z})}$ with $Q\in{O_n(\mathbb{Q})}$ implies that $Q$ is a signed permutation matrix. As an application, we present a simple and efficient method for testing whether a graph is determined by the generalized spectrum, which significantly extends our previous work.

preprint2016arXiv

Stability of half-degree point defect profiles for 2-D nematic liquid crystal

In this paper, we prove the stability of half-degree point defect profiles in $\mathbb{R}^2$ for the nematic liquid crystal within Landau-de Gennes model.

preprint2016arXiv

Suppressing disease spreading by using information diffusion on multiplex networks

Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

preprint2016arXiv

Test Flavor SU(3) symmetry in Exclusive $Λ_c$ decays

Flavor SU(3) symmetry is a powerful tool to analyze charmed baryon decays, however its applicability remains to be experimentally validated. Since there is not much data on $Ξ_c$ decays, various exclusive $Λ_c$ decays especially the ones into a neutron state are essential for the test of flavor symmetry. These decay modes are also helpful to investigate final state interactions in charmed baryon decays. In this work, we discuss the explicit roles of $Λ_c$ decays into a neutron in testing the flavor symmetry and exploring final state interactions. The involved decay modes include semileptonic decays, two-body and three-body non-leptonic decays, but all of them have not been experimentally observed to date.

preprint2016arXiv

The Distance Measurement of NGC 1313 With Cepheids

We present the detection of Cepheids in the barred spiral galaxy NGC 1313, using the Wide Field and Planetary Camera 2 on the Hubble Space Telescpoe. Twenty B (F450W) and V (F555W) epochs of observations spanning over three weeks were obtained, on which the profile-fitting photometry of all stars in the monitored field was performed using the package HSTphot. A sample of 26 variable stars have been identified to be Cepheids, with periods between 3 and 14 days. Based on the derived period-luminosity relations in B and V bands, we obtain an extinction-corrected distance modulus of mu = 28.32 +- 0.08 (random) +- 0.06 (systematic), employing the Large Magellanic Cloud as the distance zero point calibrator. The above moduli correspond to a distance of 4.61 +- 0.17 (random) +- 0.13 (systematic) Mpc, consistent with previous measurements reported in the literature with uncertainties. In addition, the reddening to NGC 1313 is found to be small.

preprint2016arXiv

The enhanced common index jump theorem for symplectic paths and non-hyperbolic closed geodesics on Finsler manifolds

In this paper, we first generalize the common index jump theorem for symplectic matrix paths proved in 2002 by Long and Zhu in [LoZ], and get an enhanced version of it. As its applications, we further prove that for a compact simply-connected manifold $(M,F)$ with a bumpy, irreversible Finsler metric $F$ and $H^*(M;{\bf Q})\cong T_{d,n+1}(x)$ for some even integer $d\ge 2$ and integer $n\ge 1$, there exist at least $\frac{dn(n+1)}{2}$ distinct non-hyperbolic closed geodesics with odd Morse indices, provided the number of distinct prime closed geodesics is finite and every prime closed geodesic satisfies $i(c)>0$. Note that the last non-zero index condition is satisfied if the flag curvature $K$ satisfies $K\ge 0$. For an odd-dimensional bumpy Finsler sphere $(S^d,F)$, there exist at least $(d+1)$ distinct prime closed geodesics with even Morse indices, and at least $(d-1)$ of which are non-hyperbolic, provided the number of distinct prime closed geodesics is finite and every prime closed geodesic $c$ satisfies $i(c)\ge 2$. Note that the last index condition $i(c)\ge 2$ is satisfied if the reversibility $λ$ and the flag curvature $K$ of $(M,F)$ satisfy $\frac{λ^2}{(1+λ)^2}<K\le 1$. Note that the first two in the above three lower bound estimates are sharp due to Katok's examples. In addition, we also prove that either there exists at least one non-hyperbolic closed geodesic, or there exist infinitely many distinct closed geodesics on a compact simply connected bumpy Finsler $(M,F)$ satisfying the above cohomological condition with some even integer $d\ge 2$ and integer $n\ge 1$.

preprint2016arXiv

What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?

Previous work has shown that feature maps of deep convolutional neural networks (CNNs) can be interpreted as feature representation of a particular image region. Features aggregated from these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in recent years. The key to the success of such methods is the feature representation. However, the different factors that impact the effectiveness of features are still not explored thoroughly. There are much less discussion about the best combination of them. The main contribution of our paper is the thorough evaluations of the various factors that affect the discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify the best choices for different factors and propose a new multi-scale image feature representation method to encode the image effectively. Finally, we show that the proposed method generalises well and outperforms the state-of-the-art methods on four typical datasets used for visual instance retrieval.

preprint2015arXiv

$a_1(1260), a_1(1420)$ and the production in heavy meson decays

The $a_1(1420)$ with $I^G(J^{PC})= 1^-(1^{++})$ observed in the $π^+ f_0(980)$ final state in the $π^-p\to π^+π^-π^- p$ process by the COMPASS collaboration seems unlikely to be an ordinary $\bar qq$ mesonic state. Available theoretical explanations include tetraquark or rescattering effects due to $a_1(1260)$ decays. If the $a_1(1420)$ were induced by the rescattering, its production rates are completely determined by those of the $a_1(1260)$. In this work, we propose to explore the ratios of branching fractions of heavy meson weak decays into the $a_1(1420)$ and $a_1(1260)$, and testing the universality of these ratios would be a straightforward way to validate/invalidate the rescattering explanation. The decay modes include in the charm sector the $D^0\to a_1^-\ell^+ν$ and $D^0\to π^\pm a_1^\mp$, and in the bottom sector $\overline B^0\to a_1^+ \ell^- \barν$, $B\to D a_1, π^\pm a_1^\mp$, $B_c\to J/ψa_1$ and $Λ_b\to Λ_c a_1$. We calculate the branching ratios for various decay modes into the $a_1(1260)$. The numerical results indicate that there is a promising prospect to study these decays on experiments including BES-III, LHCb, Babar, Belle and CLEO-c, the forthcoming Super-KEKB factory and the under-design Circular Electron-Positron Collider. Experimental analyses in future will lead to a deeper understanding of the nature of the $a_1(1420)$.

preprint2015arXiv

$S$-wave resonance contributions to the $B^0_{(s)}\to J/ψπ^+π^-$ and $B_s\toπ^+π^-μ^+μ^-$ decays

We study $S$-wave resonance contributions to the $B^0_{(s)}\to J/ψπ^+π^-$ and $B_s\toπ^+π^-\ell^+\ell^-$ decays in the perturbative QCD (PQCD) framework by introducing two-hadron distribution amplitudes for final states. The Breit-Wigner formula for the $f_0(500)$, $f_0(1500)$ and $f_0(1790)$ resonances and the Flatté model for the $f_0(980)$ resonance are adopted to parameterize the time-like scalar form factors in the two-pion distribution amplitudes, which include both resonant and nonresonant contributions. The resultant branching fraction and differential branching fraction in the pion-pair invariant mass for each resonance channel are consistent with experimental data. The determined $S$-wave two-pion distribution amplitudes, containing the information of both resonant and nonresonant rescattering phases, can be employed to predict direct CP asymmetries of other three-body hadronic $B$ meson decays in various localized regions of two-pion phase space.

preprint2015arXiv

A Robust Generalized Chinese Remainder Theorem for Two Integers

A generalized Chinese remainder theorem (CRT) for multiple integers from residue sets has been studied recently, where the correspondence between the remainders and the integers in each residue set modulo several moduli is not known. A robust CRT has also been proposed lately for robustly reconstruct a single integer from its erroneous remainders. In this paper, we consider the reconstruction problem of two integers from their residue sets, where the remainders are not only out of order but also may have errors. We prove that two integers can be robustly reconstructed if their remainder errors are less than $M/8$, where $M$ is the greatest common divisor (gcd) of all the moduli. We also propose an efficient reconstruction algorithm. Finally, we present some simulations to verify the efficiency of the proposed algorithm. The study is motivated and has applications in the determination of multiple frequencies from multiple undersampled waveforms.

preprint2015arXiv

Antenna system characteristic and solar radio burst observation

Chinese Spectral Radio Heliograph (CSRH) is an advanced aperture synthesis solar radio heliograph, developed by National Astronomical Observatories, Chinese Academy of Sciences independently. It consists of 100 reflector antennas, which are grouped into two antenna arrays (CSRH-I and CSRH-II) for low and high frequency bands respectively. The frequency band of CSRH-I is 0.4-2GHz and for CSRH-II, the frequency band is 2-15GHz. In the antenna and feed system, CSRH uses an Eleven feed to receive signals coming from the Sun, the radiation pattern with lower side lobe and back lobe of the feed is well radiated. The characteristics of gain G and antenna noise temperature T effect the quality of solar radio imaging. For CSRH, measured G is larger than 60 dBi and $ T $ is less than 120K, after CSRH-I was established, we have successfully captured a solar radio burst between 1.2-1.6GHz on November 12, 2010 through this instrument and this event was confirmed through the observation of Solar Broadband Radio Spectrometer (SBRS) at 2.84GHz and Geostationary Operational Environmental Satellite (GOES). In addition, an image obtained from CSRH-I clearly reveals the profile of the solar radio burst. The other observational work is the imaging of Fengyun-2E geosynchronous satellite which is assumed to be a point source. This data processing method indicates that, the method of deleting errors about dirty image could be used for processing other surface sources.

preprint2015arXiv

Asteroseismic based estimation of the surface gravity for the LAMOST giant stars

Asteroseismology is one of the most accurate approaches to estimate the surface gravity of a star. However, most of the data from the current spectroscopic surveys do not have asteroseismic measurements, which is very expensive and time consuming. In order to improve the spectroscopic surface gravity estimates for a large amount of survey data with the help of the small subset of the data with seismic measurements, we set up a support vector regression model for the estimation of the surface gravity supervised by 1,374 LAMOST giant stars with Kepler seismic surface gravity. The new approach can reduce the uncertainty of the estimates down to about 0.1 dex, which is better than the LAMOST pipeline by at least a factor of 2, for the spectra with signal-to-noise ratio higher than 20. Compared with the logg estimated from the LAMOST pipeline, the revised logg values provide a significantly improved match to the expected distribution of red clump and RGB stars from stellar isochrones. Moreover, even the red bump stars, which extend to only about 0.1 dex in logg, can be discriminated from the new estimated surface gravity. The method is then applied to about 350,000 LAMOST metal-rich giant stars to provide improved surface gravity estimates. In general, the uncertainty of the distance estimate based on the SVR surface gravity can be reduced to about 12% for the LAMOST data.

preprint2015arXiv

Automatic Objects Removal for Scene Completion

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.

preprint2015arXiv

Breaking Bad: Detecting malicious domains using word segmentation

In recent years, vulnerable hosts and maliciously registered domains have been frequently involved in mobile attacks. In this paper, we explore the feasibility of detecting malicious domains visited on a cellular network based solely on lexical characteristics of the domain names. In addition to using traditional quantitative features of domain names, we also use a word segmentation algorithm to segment the domain names into individual words to greatly expand the size of the feature set. Experiments on a sample of real-world data from a large cellular network show that using word segmentation improves our ability to detect malicious domains relative to approaches without segmentation, as measured by misclassification rates and areas under the ROC curve. Furthermore, the results are interpretable, allowing one to discover (with little supervision or tuning required) which words are used most often to attract users to malicious domains. Such a lightweight approach could be performed in near-real time when a device attempts to visit a domain. This approach can complement (rather than substitute) other more expensive and time-consuming approaches to similar problems that use richer feature sets.

preprint2015arXiv

Chiral Dynamics and S-wave contributions in Semileptonic $D_s/B_s$ decays into $π^+π^-$

In this work, we study the semileptonic decay modes $B_s^0\to π^+π^-\ell^+\ell^-$ and $D_s^+\to π^+π^-\ell^+ ν$ in the kinematics region where the $π^+π^-$ system has a invariant mass in the range $0.5$-$1.3$ GeV. These processes are valuable towards the determination of S-wave $π^+π^-$ light-cone distribution amplitudes whose normalizations are scalar form factors. We compare the results for scalar form factors predicted in unitarized chiral perturbation theory and extracted from the data on the $B_s\to J/ψπ^+π^-$. Then the $B_s\to π^+π^-$ and $D_s\to π^+π^-$ form factors are calculated in light-cone sum rules, based on which predictions for differential decay widths are made. The results are in good agreement with the experimental data on the $B_s$ and $D_s$ decays into $π^+π^-$. More accurate measurements at BEPC, LHC and KEKB in future will be helpful to examine our formalism and constrain the input parameters more precisely.

preprint2015arXiv

Constructing New Braided $T$-Categories via Weak Monoidal Hom-Hopf Algebras

In this paper, we define and study weak monoidal Hom-Hopf algebras, which generalize both weak Hopf algebras and monoidal Hom-Hopf algebras. If $H$ is a weak monoidal Hom-Hopf algebra with bijective antipode and let $Aut_{wmHH}(H)$ be the set of all automorphisms of $H$. Then we introduce a category ${_{H}\mathcal{WMHYD}^{H}}(α,β)$ with $α,β\in Aut_{wmHH}(H)$ and construct a braided $T$-category $\mathcal{WMHYD}(H)$ that having all the categories ${_{H}\mathcal{WMHYD}^{H}}(α,β)$ as components.

preprint2015arXiv

Decipher the short-distance component of $X(3872)$ in $B_c$ decays

A foremost task in understanding the nature of the $X(3872)$ involves the discrimination of the two-quark and multiquark configurations. In this work, we propose a method to probe the short-distance component of the $X(3872)$ by measuring the ratio between the $B_c$ semileptonic and nonleptonic decays into the $X(3872)$. We demonstrate that if the $X(3872)$ production mechanism is through the $\bar cc$ component, the ratios would be universal and could be reliably predicted in theory. Measurements of these ratios at LHC and the next-generation electron-positron colliders are capable of validating/invalidating this production mechanism and providing deeper insights into the nature of the $X(3872)$.

preprint2015arXiv

Delay-Aware Uplink Fronthaul Allocation in Cloud Radio Access Networks

In cloud radio access networks (C-RANs), the baseband units and radio units of base stations are separated, which requires high-capacity fronthaul links connecting both parts. In this paper, we consider the delay-aware fronthaul allocation problem for C-RANs. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-aware fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small cross link path gains. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

preprint2015arXiv

Diffraction-free optical beam propagation with near-zero phase variation in extremely anisotropic metamaterials

Extremely anisotropic metal-dielectric multilayer metamaterials are designed to have the effective permittivity tensor of a transverse component (parallel to the interfaces of the multilayer) with zero real part and a longitudinal component (normal to the interfaces of the multilayer) with ultra-large imaginary part at the same wavelength, including the optical nonlocality analysis based on the transfer-matrix method. The diffraction-free deep-subwavelength optical beam propagation with near-zero phase variation in the designed multilayer stack due to the near-flat iso-frequency contour is demonstrated and analyzed, including the effects of the multilayer period and the material loss.

preprint2015arXiv

Direct Observation of Long Electron-Hole Diffusion Distance in CH3NH3PbI3 Perovskite Thin Film

In high performance perovskite based solar cells, CH3NH3PbI3 is the key material. We carried out a study on charge diffusion in spin-coated CH3NH3PbI3 perovskite thin film by transient fluorescent spectroscopy. A thickness-dependent fluorescent lifetime was found. By coating the film with an electron or hole transfer layer, [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) or 2,2,7,7-tetrakis(N,N-di-p-methoxyphenylamine)-9,9-spirobifluorene (Spiro-OMeTAD) respectively, we observed the charge transfer directly through the fluorescence quenching. One-dimensional diffusion model was applied to obtain long charge diffusion distances in thick films, which is ~1.7 um for electrons and up to ~6.3 um for holes. Short diffusion distance of few hundreds of nanosecond was also observed in thin films. This thickness dependent charge diffusion explained the formerly reported short charge diffusion distance (~100 nm) in films and resolved its confliction to thick working layer (300-500 nm) in real devices. This study presents direct support to the high performance perovskite solar cells and will benefit the devices design.

preprint2015arXiv

Dynamic Power Control for Delay-Aware Device-to-Device Communications

In this paper, we consider the dynamic power control for delay-aware D2D communications. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we utilize the interference filtering property of the CSMA-like MAC protocol and derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity power control algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for a sufficiently large carrier sensing distance. Finally, the proposed power control scheme is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

preprint2015arXiv

Dynamics of social contagions with heterogeneous adoption thresholds: Crossover phenomena in phase transition

Heterogeneous adoption thresholds exist widely in social contagions, but were always neglected in previous studies. We first propose a non-Markovian spreading threshold model with general adoption threshold distribution. In order to understand the effects of heterogeneous adoption thresholds quantitatively, an edge-based compartmental theory is developed for the proposed model. We use a binary spreading threshold model as a specific example, in which some individuals have a low adoption threshold (i.e., activists) while the remaining ones hold a relatively high adoption threshold (i.e., bigots), to demonstrate that heterogeneous adoption thresholds markedly affect the final adoption size and phase transition. Interestingly, the first-order, second-order and hybrid phase transitions can be found in the system. More importantly, there are two different kinds of crossover phenomena in phase transition for distinct values of bigots' adoption threshold: a change from first-order or hybrid phase transition to the second-order phase transition. The theoretical predictions based on the suggested theory agree very well with the results of numerical simulations.

preprint2015arXiv

Dynamics of social contagions with memory of non-redundant information

A key ingredient in social contagion dynamics is reinforcement, as adopting a certain social behavior requires verification of its credibility and legitimacy. Memory of non-redundant information plays an important role in reinforcement, which so far has eluded theoretical analysis. We first propose a general social contagion model with reinforcement derived from non-redundant information memory. Then, we develop a unified edge-based compartmental theory to analyze this model, and a remarkable agreement with numerics is obtained on some specific models. Using a spreading threshold model as a specific example to understand the memory effect, in which each individual adopts a social behavior only when the cumulative pieces of information that the individual received from his/her neighbors exceeds an adoption threshold. Through analysis and numerical simulations, we find that the memory characteristic markedly affects the dynamics as quantified by the final adoption size. Strikingly, we uncover a transition phenomenon in which the dependence of the final adoption size on some key parameters, such as the transmission probability, can change from being discontinuous to being continuous. The transition can be triggered by proper parameters and structural perturbations to the system, such as decreasing individuals' adoption threshold, increasing initial seed size, or enhancing the network heterogeneity.

preprint2015arXiv

Electron spin and the origin of Bio-homochirality II. Prebiotic inorganic-organic reaction model

The emergence of biomolecular homochirality is a critically important question about life phenomenon and the origins of life. In a previous paper (arXiv:1309.1229), I tentatively put forward a new hypothesis that the emergence of a single chiral form of biomolecules in living organisms is specifically determined by the electron spin state during their enzyme-catalyzed synthesis processes. However, how a homochirality world of biomolecules could have formed in the absence of enzymatic networks before the origins of life remains unanswered. Here I discussed the electron spin properties in Fe3S4, ZnS, and transition metal doped dilute magnetic ZnS, and their possible roles in the prebiotic synthesis of chiral molecules. Since the existence of these minerals in hydrothermal vent systems is matter of fact, the suggested prebiotic inorganic-organic reaction model, if can be experimentally demonstrated, may help explain where and how life originated on early Earth.

preprint2015arXiv

Entity Suggestion by Example using a Conceptual Taxonomy

Entity suggestion by example (ESbE) refers to a type of entity acquisition query in which a user provides a set of example entities as the query and obtains in return some entities that best complete the concept underlying the given query. Such entity acquisition queries can be useful in many applications such as related-entity recommendation and query expansion. A number of ESbE query processing solutions exist in the literature. However, they mostly build only on the idea of entity co-occurrences either in text or web lists, without taking advantage of the existence of many web-scale conceptual taxonomies that consist of hierarchical isA relationships between entity-concept pairs. This paper provides a query processing method based on the relevance models between entity sets and concepts. These relevance models can be used to obtain the fine-grained concepts implied by the query entity set, and the entities that belong to a given concept, thereby providing the entity suggestions. Extensive evaluations with real data sets show that the accuracy of the queries processed with this new method is significantly higher than that of existing solutions.

preprint2015arXiv

How to reveal the exotic nature of the P_c(4450)

The LHCb Collaboration announced two pentaquark-like structures in the $J/ψp$ invariant mass distribution. We show that the current information on the narrow structure at 4.45 GeV is compatible with kinematical effects of the rescattering from $χ_{c1} p$ to $J/ψp$: First, it is located exactly at the $χ_{c1} p$ threshold. Second, the mass of the four-star well-established $Λ(1890)$ is such that a leading Landau singularity from a triangle diagram can coincidentally appear at the $χ_{c1} p$ threshold, and third, there is a narrow structure at the $χ_{c1}\,p$ threshold but not at the $χ_{c0} p$ and $χ_{c2} p$ thresholds. In order to check whether that structure corresponds to a real exotic resonance, one has to measure the process $Λ_b^0\to K^-χ_{c1} p$. If the $P_c(4450)$ structure exists in the $χ_{c1} p$ invariant mass distribution as well, then the structure cannot be just a kinematical effect but is a real resonance, otherwise, one cannot conclude the $P_c(4450)$ to be another exotic hadron. In addition, it is also worthwhile to measure the decay $Υ(1S)\to J/ψp \bar p$: a narrow structure at 4.45 GeV but not at the $χ_{c0} p$ and $χ_{c2} p$ thresholds would exclude the possibility of a pure kinematical effect.

preprint2015arXiv

Long Concept Query on Conceptual Taxonomies

This paper studies the problem of finding typical entities when the concept is given as a query. For a short concept such as university, this is a well-studied problem of retrieving knowledge base such as Microsoft's Probase and Google's isA database pre-materializing entities found for the concept in Hearst patterns of the web corpus. However, we find most real-life queries are long concept queries (LCQs), such as top American private university, which cannot and should not be pre-materialized. Our goal is an online construction of entity retrieval for LCQs. We argue a naive baseline of rewriting LCQs into an intersection of an expanded set of composing short concepts leads to highly precise results with extremely low recall. Instead, we propose to augment the concept list, by identifying related concepts of the query concept. However, as such increase of recall often invites false positives and decreases precision in return, we propose the following two techniques: First, we identify concepts with different relatedness to generate linear orderings and pairwise ordering constraints. Second, we rank entities trying to avoid conflicts with these constraints, to prune out lowly ranked one (likely false positives). With these novel techniques, our approach significantly outperforms state-of-the-arts.

preprint2015arXiv

Meso-scale modeling: beyond local equilibrium assumption for multiphase flow

This is a summary of the article with the same title, accepted for publication in Advances in Chemical Engineering, 47: 193-277 (2015). Gas-solid fluidization is a typical nonlinear nonequilibrium system with multiscale structure. In particular, the mesoscale structure in terms of bubbles or clusters, which can be characterized by nonequilibrium features in terms of bimodal velocity distribution, energy non equipartition, and correlated density fluctuations, is the critical factor. Traditional two-fluid model (TFM) and relevant closures depend on local equilibrium and homogeneous distribution assumptions, and fail to predict the dynamic, nonequilibrium phenomena in circulating fluidized beds even with fine-grid resolution. In contrast, the mesoscale modeling, as exemplified by the energy-minimization multiscale (EMMS) model, is consistent with the nonequilibrium features in multiphase flows. Thus, the structure-dependent multi-fluid model conservation equations with the EMMS-based mesoscale modeling greatly improve the prediction accuracy in terms of flow, mass transfer, and reactions as well as the understanding of flow regime transitions. Such discrepancies raise the question of the applicability of the local equilibrium assumption underlying the TFM and further shed light to the necessity of mesoscale modeling.

preprint2015arXiv

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\barν$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

preprint2015arXiv

Non-hyperbolic closed characteristics on non-degenerate star-shaped hypersurfaces in ${\bf R}^{2n}$

In this paper, we prove that for every index perfect non-degenerate compact star-shaped hypersurface $Σ\subset{\bf R}^{2n}$, there exist at least $n$ non-hyperbolic closed characteristics with even Maslov-type indices on $Σ$ when $n$ is even. When $n$ is odd, there exist at least $n$ closed characteristics with odd Maslov-type indices on $Σ$ and at least $(n-1)$ of them are non-hyperbolic. Here we call a compact star-shaped hypersurface $Σ\subset {\bf R}^{2n}$ {\rm index perfect} if it carries only finitely many geometrically distinct prime closed characteristics, and every prime closed characteristic $(τ,y)$ on $Σ$ possesses positive mean index and whose Maslov-type index $i(y, m)$ of its $m$-th iterate satisfies $i(y, m)\not= -1$ when $n$ is even, and $i(y, m)\not\in \{-2,-1,0\}$ when $n$ is odd for all $m\in {\bf N}$.

preprint2015arXiv

Nonlinear stability of current-vortex sheet to the incompressible MHD equations

In this paper, we solve a long-standing open problem: nonlinear stability of current-vortex sheet in the ideal incompressible Magneto-Hydrodynamics under the linear stability condition. This result gives a first rigorous confirmation of the stabilizing effect of the magnetic field on Kelvin-Helmholtz instability.

preprint2015arXiv

On Conformal Qc Geometry, Spherical Qc Manifolds and Convex Cocompact Subgroups of ${\rm Sp}{(n+1,1)}$

Conformal qc geometry of spherical qc manifolds are investigated. We construct the qc Yamabe operators on qc manifolds, which are covariant under the conformal qc transformations. A qc manifold is scalar positive, negative or vanishing if and only if its qc Yamabe invariant is positive, negative or zero, respectively. On a scalar positive spherical qc manifold, we can construct the Green function of the qc Yamabe operator, which can be applied to construct a conformally invariant tensor. It becomes a spherical qc metric if the qc positive mass conjecture is true. Conformal qc geometry of spherical qc manifolds can be applied to study convex cocompact subgroups of ${\rm Sp}(n+1,1).$ On a spherical qc manifold constructed from such a discrete subgroup, we construct a spherical qc metric of Nayatani type. As a corollary, we prove that such a spherical qc manifold is scalar positive, negative or vanishing if and only if the Poincaré critical exponent of the discrete subgroup is less than, greater than or equal to $2n+2$, respectively.

preprint2015arXiv

On testing the equality of high dimensional mean vectors with unequal covariance matrices

In this article, we focus on the problem of testing the equality of several high dimensional mean vectors with unequal covariance matrices. This is one of the most important problem in multivariate statistical analysis and there have been various tests proposed in the literature. Motivated by \citet{BaiS96E} and \cite{ChenQ10T}, a test statistic is introduced and the asymptomatic distributions under the null hypothesis as well as the alternative hypothesis are given. In addition, it is compared with a test statistic recently proposed by \cite{SrivastavaK13Ta}. It is shown that our test statistic performs much better especially in the large dimensional case.

preprint2015arXiv

On the Hodge-type decomposition and cohomolgy groups of $k$-Cauchy-Fueter complexes over domains in the quaternionic space

The $k$-Cauchy-Fueter operator $ D_0^{(k) } $ on one dimensional quaternionic space $\mathbb{H}$ is the Euclidean version of helicity $\frac k 2$ massless field operator on the Minkowski space in physics. The $k$-Cauchy-Fueter equation for $k\geq 2$ is overdetermined and its compatibility condition is given by the $k$-Cauchy-Fueter complex. In quaternionic analysis, these complexes play the role of Dolbeault complex in several complex variables. We prove that a natural boundary value problem associated to this complex is regular. Then by using the theory of regular boundary value problems, we show the Hodge-type orthogonal decomposition, and the fact that the non-homogeneous $k$-Cauchy-Fueter equation $ D_0^{(k) } u=f$ on a smooth domain $Ω$ in $\mathbb{H}$ is solvable if and only if $f$ satisfies the compatibility condition and is orthogonal to the set $\mathscr H^1_{ (k) }(Ω)$ of Hodge-type elements. This set is isomorphic to the first cohomology group of the $k$-Cauchy-Fueter complex over $Ω$, which is finite dimensional, while the second cohomology group is always trivial.

preprint2015arXiv

On the Yamabe Problem on contact Riemannian Manifolds

Contact Riemannian manifolds, whose complex structures are not necessarily integrable, are generalization of pseudohermitian manifolds in CR geometry. The Tanaka-Webster-Tanno connection plays the role of the Tanaka-Webster connection of a pseudohermitian manifold. Conformal transformations and the Yamabe problem are also defined naturally in this setting. By constructing the special frames and the normal coordinates on a contact Riemannian manifold, we prove that if the complex structure is not integrable, its Yamabe invariant on a contact Riemannian manifold is always less than the Yamabe invariant of the Heisenberg group. So the Yamabe problem on a contact Riemannian manifold is always solvable.

preprint2015arXiv

Penguin Pollution in $B\to J/ψV$ Decays and Impact on the Extraction of the $B_s-\bar B_s$ mixing phase

We formulate the most-general time-dependent decay distributions of $B_s\to J/ψ(\to l^+l^-) ϕ(\to K^+K^-)$ in which the direct CP violation is explicitly incorporated. We then investigate the $B \to J/ψV$ decays in the perturbative QCD approach where $V$ is a light vector meson. Apart from the leading-order factorizable contributions, we also take into account various QCD corrections and the hard-spectator diagrams. With the inclusion of these sizable corrections, our theoretical results for CP-averaged branching ratios, polarization fractions, CP-violating asymmetries, and relative phases are in good consistency with the available data. Based on the global agreement, we further explore the penguin contributions and point out that the $ϕ_s$ extracted from $B_s\to J/ψϕ$ can be shifted away by ${\cal O}(10^{-3})$.

preprint2015arXiv

Predicting the epidemic threshold of the susceptible-infected-recovered model

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues---relationships among differing results and levels of accuracy---by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. When compared to the 56 real-world networks, the epidemic threshold obtained by the DMP method is closer to the actual epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in some scenarios---such as networks with positive degree-degree correlations, with an eigenvector localized on the high $k$-core nodes, or with a high level of clustering---the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input parameter, performs better than the other two methods. We also find that the performances of the three predictions are irregular versus modularity.

preprint2015arXiv

Radiative Leptonic $B_c\to γ\ell\barν$ Decay in Effective Field Theory beyond Leading Order

We study the radiative leptonic $B_c\to γ\ell\barν$ decays in the nonrelativistic QCD effective field theory, and consider a fast-moving photon. As a result the interactions with the heavy quarks can be integrated out, and thus we arrive at a factorization formula for the decay amplitude. We calculate not only the relevant short-distance coefficients at leading order and next-to-leading order in $α_s$, but also the nonrelativistic corrections at the order $|\bold{v}|^2$ in our analysis. We find out that the QCD corrections can sizably decrease the branching ratio and thus is of great importance in extracting the long-distance operator matrix elements of $B_c$. For the phenomenological application, we present our results for the photon energy, lepton energy and lepton-neutrino invariant mass distribution.

preprint2015arXiv

Spectroscopic analysis of metal-poor stars from LAMOST: early results

We report on early results from a pilot program searching for metal-poor stars with LAMOST and follow-up high-resolution observation acquired with the MIKE spectrograph attached to the Magellan~II telescope. We performed detailed abundance analysis for eight objects with iron abundances [Fe/H] < -2.0, including five extremely metal-poor (EMP; [Fe/H] < -3.0) stars with two having [Fe/H] < -3.5. Among these objects, three are newly discovered EMP stars, one of which is confirmed for the first time with high-resolution spectral observations. Three program stars are regarded as carbon-enhanced metal-poor (CEMP) stars, including two stars with no enhancement in their neutron-capture elements, which thus possibly belong to the class of CEMP-no stars; one of these objects also exhibits significant enhancement in nitrogen, and is thus a potential carbon and nitrogen-enhanced metal-poor star. The [X/Fe] ratios of the sample stars generally agree with those reported in the literature for other metal-poor stars in the same [Fe/H] range. We also compared the abundance patterns of individual program stars with the average abundance pattern of metal-poor stars, and find only one chemically peculiar object with abundances of at least two elements (other than C and N) showing deviations larger than 0.5dex. The distribution of [Sr/Ba] versus [Ba/H] agrees that an additional nucleosynthesis mechanism is needed aside from a single r-process. Two program stars with extremely low abundances of Sr and Ba support the prospect that both main and weak r-process may have operated during the early phase of Galactic chemical evolution. The distribution of [C/N] shows that there are two groups of carbon-normal giants with different degrees of mixing. However, it is difficult to explain the observed behavior of the [C/N] of the nitrogen-enhanced unevolved stars based on current data.

preprint2015arXiv

Stringent Statistical Fluctuation Analysis for Quantum Key Distribution Considering After-pulse Contributions

Statistical fluctuation problems are faced by all quantum key distribution (QKD) protocols under finite-key condition. Most of the current statistical fluctuation analysis methods work based on independent random samples, however, the precondition cannot be always satisfied on account of different choice of samples and actual parameters. As a result, proper statistical fluctuation methods are required to figure out this problem. Taking the after-pulse contributions into consideration, we give the expression of secure key rate and the mathematical model for statistical fluctuations, focusing on a decoy-state QKD protocol (Sci Rep. 3, 2453, 2013) with biased basis choice. On this basis, a classified analysis of statistical fluctuation is represented according to the mutual relationship between random samples. First for independent identical relations, we make a deviation comparison between law of large numbers and standard error analysis. Secondly, we give a sufficient condition that Chernoff bound achieves a better result than Hoeffding's inequality based on independent relations only. Thirdly, by constructing the proper martingale, for the first time we represent a stringent way to deal with statistical fluctuation issues upon dependent ones through making use of Azuma's inequality. In numerical optimization, we show the impact on secure key rate, the ones and respective deviations under various kinds of statistical fluctuation analyses.

preprint2015arXiv

The Bochner-Type Formula and The First Eigenvalue of the sub-Laplacian on a Contact Riemannian Manifold

Contact Riemannian manifolds, with not necessarily integrable complex structures, are the generalization of pseudohermitian manifolds in CR geometry. The Tanaka-Webster-Tanno connection on such a manifold plays the role of Tanaka-Webster connection in the pseudohermitian case. We prove the contact Riemannian version of the pseudohermitian Bochner-type formula, and generalize the CR Lichnerowicz theorem about the sharp lower bound for the first nonzero eigenvalue of the sub-Laplacian to the contact Riemannnian case.

preprint2015arXiv

To understand the rare decay $B_s\toπ^+π^-\ell^+\ell^-$

Motivated by the LHCb measurement, we analyze the $B_s\to π^+π^-\ell^+\ell^-$ decay in the kinematics region where the pion pairs have invariant mass in the range $0.5$-$1.3$ GeV and muon pairs do not originate from a resonance. The scalar $π^+π^-$ form factor induced by the strange $\bar ss$ current is predicted by the unitarized approach rooted in the chiral perturbation theory. Using the two-hadron light-cone distribution amplitude, we then can derive the $B_s\to π^+π^-$ transition form factor in the light-cone sum rules approach. Merging these quantities, we present our results for differential decay width which can generally agree with the experimental data. More accurate measurements at the LHC and KEKB in future are helpful to validate our formalism and determine the inputs in this approach.

preprint2014arXiv

'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

preprint2014arXiv

${\bf B_s\to K^{(*)} \ell\bar ν}$, Angular Analysis, S-wave Contributions and ${\bf |V_{ub}|}$

We analyse the $\overline B_s^0\to K^+l^-\bar ν$ and $\overline B_s^0\to K^{*+}(\to Kπ) \ell^-\bar ν$ decays that are valuable for extracting the CKM matrix element $|V_{ub}|$. We calculate the differential and integrated partial widths in units of $|V_{ub}|^2$ based on various calculations of hadronic form factors and in particular the latest Lattice QCD calculation of the $B_s\to K^*$ form factors. For the decay $\overline B_s^0 \to Kπ\ell\bar ν$, we formulate the general angular distributions with the inclusion of the various partial-wave $Kπ$ contributions. Using the results for the $Kπ$ scalar form factor calculated from unitarized chiral perturbation theory, we explore the S-wave effects on angular distribution variables and demonstrate that they may not be negligible, considering the high precision expected in future measurements. We also briefly discuss the impact of the S-wave $ππ$ contributions in the $B^-\to π^+π^-\ell \bar ν$ decay and provide estimates for the mode $B^-\to K^+K^-\ell \bar ν$. The studies of these channels in future can not only be used to determine $|V_{ub}|$, but may also provide valuable information on the $Kπ$ and $ππ$ phase shifts.

preprint2014arXiv

A global minimization algorithm for Tikhonov functionals with sparsity constraints

In this paper we present a globally convergent algorithm for the computation of a minimizer of the Tikhonov functional with sparsity promoting penalty term for nonlinear forward operators in Banach space. The dual TIGRA method uses a gradient descent iteration in the dual space at decreasing values of the regularization parameter $α_j$, where the approximation obtained with $α_j$ serves as the starting value for the dual iteration with parameter $α_{j+1}$. With the discrepancy principle as a global stopping rule the method further yields an automatic parameter choice. We prove convergence of the algorithm under suitable step-size selection and stopping rules and illustrate our theoretic results with numerical experiments for the nonlinear autoconvolution problem.

preprint2014arXiv

A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

preprint2014arXiv

A simple arithmetic criterion for graphs being determined by their generalized spectra

A graph $G$ is said to be determined by its generalized spectrum (DGS for short) if for any graph $H$, $H$ and $G$ are cospectral with cospectral complements implies that $H$ is isomorphic to $G$. It turns out that whether a graph $G$ is DGS is closely related to the arithmetic properties of its walk-matrix. More precisely, let $A$ be the adjacency matrix of a graph $G$, and let $W =[e, Ae, A^2e,...,A^{n-1}e]$ ($e$ is the all-one vector) be its \textit{walk-matrix}. Denote by $\mathcal{G}_n$ the set of all graphs on $n$ vertices with $\det(W)\neq 0$. In [Wang, Generalized spectral characterization of graphs revisited, The Electronic J. Combin., 20 (4),(2013), #$P_4$], the author defined a large family of graphs $$\mathcal{F}_n = \{G \in{\mathcal{G}_n}|\frac{\det(W)}{2^{\lfloor\frac{n}{2}\rfloor}}{is~ an ~odd~ square-free~ integer}\}$$ (which may have positive density among all graphs, as suggested by some numerical experiments) and conjectured every graph in $\mathcal{F}_n$ is DGS. In this paper, we show that the conjecture is actually true, thereby giving a simple arithmetic condition for determining whether a graph is DGS.

preprint2014arXiv

A Taxonomy of Hyperlink Hiding Techniques

Hidden links are designed solely for search engines rather than visitors. To get high search engine rankings, link hiding techniques are usually used for the profitability of black industries, such as illicit game servers, false medical services, illegal gambling, and less attractive high-profit industry, etc. This paper investigates hyperlink hiding techniques on the Web, and gives a detailed taxonomy. We believe the taxonomy can help develop appropriate countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that link hidden techniques are very prevalent on the Web. We also tried to explore the attitude of Google towards link hiding spam by analyzing the PageRank values of relative links. The results show that more should be done to punish the hidden link spam.

preprint2014arXiv

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

Because of the decreasing cost and high digital resolution, next-generation sequencing (NGS) is expected to replace the traditional hybridization-based microarray technology. For genetics study, the first-step analysis of NGS data is often to identify genomic variants among sequenced samples. Several statistical models and tests have been developed for variant calling in NGS study. The existing approaches, however, are based on either conventional Bayesian or frequentist methods, which are unable to address the multiplicity and testing efficiency issues simultaneously. In this paper, we derive an optimal empirical Bayes testing procedure to detect variants for NGS study. We utilize the empirical Bayes technique to exploit the across-site information among many testing sites in NGS data. We prove that our testing procedure is valid and optimal in the sense of rejecting the maximum number of nonnulls while the Bayesian false discovery rate is controlled at a given nominal level. We show by both simulation studies and real data analysis that our testing efficiency can be greatly enhanced over the existing frequentist approaches that fail to pool and utilize information across the multiple testing sites.

preprint2014arXiv

Analysis and resolution of the ground-state degeneracy of the two-component Bose-Hubbard model

We study the degeneracy of the ground-state energy $E$ of the two-component Bose-Hubbard model and of the perturbative correction $E_1$. We show that the degeneracy properties of $E$ and $E_1$ are closely related to the connectivity properties of the lattice. We determine general conditions under which $E$ is nondegenerate. This analysis is then extended to investigate the degeneracy of $E_1$. In this case, in addition to the lattice structure, the degeneracy also depends on the number of particles present in the system. After identifying the cases in which $E_1$ is degenerate and observing that the standard (degenerate) perturbation theory is not applicable, we develop a method to determine the zeroth-order correction to the ground state by exploiting the symmetry properties of the lattice. This method is used to implement the perturbative approach to the two-component Bose-Hubbard model in the case of degenerate $E_1$ and is expected to be a valid tool to perturbatively study the asymmetric character of the Mott-insulator to superfluid transition between the particle and hole side.

preprint2014arXiv

Asymmetrically interacting spreading dynamics on complex layered networks

The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.

preprint2014arXiv

Broad-band transmission spectrum and K-band thermal emission of WASP-43b as observed from the ground

(Abridged) We observed one transit and one occultation of the hot Jupiter WASP-43b simultaneously in the g'r'i'z'JHK bands using the GROND instrument on the MPG/ESO 2.2-meter telescope. From the transit event, we have independently derived WASP-43's system parameters with high precision, and improved the period to be 0.81347437(13) days. No significant variation in transit depths is detected, with the largest deviations coming from the i', H, and K bands. Given the observational uncertainties, the broad-band transmission spectrum can be explained by either a flat featureless straight line that indicates thick clouds, synthetic spectra with absorption signatures of atomic Na/K or molecular TiO/VO that indicate cloud-free atmosphere, or a Rayleigh scattering profile that indicates high-altitude hazes. From the occultation event, we have detected planetary dayside thermal emission in the K-band with a flux ratio of 0.197 +/- 0.042%, which confirms previous detections obtained in the 2.09 micron narrow band and Ks-band. The K-band brightness temperature 1878 +108/-116 K favors an atmosphere with poor day- to night-side heat redistribution. We also have a marginal detection in the i'-band (0.037 +0.023/-0.021%), which is either a false positive, a signature of non-blackbody radiation at this wavelength, or an indication of reflective hazes at high altitude.

preprint2014arXiv

Diffusion approximation for self-similarity of stochastic advection in Burgers' equation

Self-similarity of Burgers' equation with some stochastic advection is studied. In self-similar variables a stationary solution is constructed which establishes the existence of a stochastically self-similar solution for the stochastic Burgers' equation. The analysis assumes that the stochastic coefficient of advection is transformed to a white noise in the self-similar variables. Furthermore, by a diffusion approximation, the long time convergence to the self-similar solution is proved in the sense of distribution.

preprint2014arXiv

Dynamics of the Nematic-Isotropic sharp interface for the liquid crystal

In this paper, we derive the sharp interface model of the nematic-isotropic phase transition from the Landau-de Gennes theory by using the matched asymptotic expansion method. The model includes the evolution equation of the velocity and director field of the liquid crystal, the sharp interface and Young-Laplace jump condition on the interface.

preprint2014arXiv

Efficient allocation of heterogeneous response times in information spreading process

Recently, the impacts of spatiotemporal heterogeneities of human activities on spreading dynamics have attracted extensive attention. In this paper, to study heterogeneous response times on information spreading, we focus on the susceptible-infected spreading dynamics with adjustable power-law response time distribution based on uncorrelated scale-free networks. We find that the stronger the heterogeneity of response times is, the faster the information spreading is in the early and middle stages. Following a given heterogeneity, the procedure of reducing the correlation between the response times and degrees of individuals can also accelerate the spreading dynamics in the early and middle stages. However, the dynamics in the late stage is slightly more complicated, and there is an optimal value of the full prevalence time changing with the heterogeneity of response times and the response time-degree correlation, respectively. The optimal phenomena results from the efficient allocation of heterogeneous response times.

preprint2014arXiv

Electrocardiography Separation of Mother and Baby

Extraction of Electrocardiography (ECG or EKG) signals of mother and baby is a challenging task, because one single device is used and it receives a mixture of multiple heart beats. In this paper, we would like to design a filter to separate the signals from each other.

preprint2014arXiv

Epidemic spreading on complex networks with general degree and weight distributions

The spread of disease on complex networks has attracted widely attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex network with the strong heterogeneous degree and weight distributions, this approach is worked. We then propose an edge-weight based removal strategy with different biases, and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.

preprint2014arXiv

Generalized Heavy-to-Light Form Factors in Light-Cone Sum Rules

We study the form factors for a heavy meson into the S-wave $Kπ/ππ$ system with an invariant mass below 1~GeV. The mesonic final state interactions are described in terms of the scalar form factors, which are obtained from unitarized chiral perturbation theory. Employing generalized light-cone distribution amplitudes, we compute the heavy-to-light transition using light-cone sum rules. Our approach simultaneously respects constraints from analyticity and unitarity, and also takes advantage of the power expansion in the $1/m_b$ and the strong coupling constant.

preprint2014arXiv

Hard X-ray emission and $^{44}$Ti line features of Tycho Supernova Remnant

A deep hard X-ray survey of the INTEGRAL satellite first detected the non-thermal emission up to 90 keV in the Tycho supernova (SN) remnant. Its 3 -- 100 keV spectrum is fitted with a thermal bremsstrahlung of $kT\sim 0.81\pm 0.45$ keV plus a power-law model of $Γ\sim 3.01\pm 0.16$. Based on the diffusive shock acceleration theory, this non-thermal emission, together with radio measurements, implies that Tycho remnant may not accelerate protons up to $>$PeV but hundreds TeV. Only heavier nuclei may be accelerated to the cosmic ray spectral "knee". In addition, we search for soft gamma-ray lines at 67.9 and 78.4 keV coming from the decay of radioactive $^{44}$Ti in Tycho remnant by INTEGRAL. A bump feature in the 60-90 keV energy band, potentially associated with the $^{44}$Ti line emission, is found with a marginal significance level of $\sim$ 2.6 $σ$. The corresponding 3 $σ$ upper limit on the $^{44}$Ti line flux amounts to 1.5 $\times$ 10$^{-5}$ ph cm$^{-2}$ s$^{-1}$. Implications on the progenitor of Tycho SN, considered to be the prototype of type Ia SN, are discussed.

preprint2014arXiv

Harnessing the power of Social Bookmarking for improving tag-based Recommendations

Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.

preprint2014arXiv

High resolution transmission spectrum of the Earth's atmosphere -- Seeing Earth as an exoplanet using a lunar eclipse

With the rapid developments in the exoplanet field, more and more terrestrial exoplanets are being detected. Characterising their atmospheres using transit observations will become a key datum in the quest for detecting an Earth-like exoplanet. The atmospheric transmission spectrum of our Earth will be an ideal template for comparison with future exo-Earth candidates. By observing a lunar eclipse, which offers a similar configuration to that of an exoplanet transit, we have obtained a high resolution and high signal-to-noise ratio transmission spectrum of the Earth's atmosphere. This observation was performed with the High Resolution Spectrograph at Xinglong Station, China during the total lunar eclipse in December 2011. We compare the observed transmission spectrum with our atmospheric model, and determine the characteristics of the various atmospheric species in detail. In the transmission spectrum, O2, O3, O2-O2, NO2 and H2O are detected, and their column densities are measured and compared with the satellites data. The visible Chappuis band of ozone produces the most prominent absorption feature, which suggests that ozone is a promising molecule for the future exo-Earth characterization. The individual O2 lines are resolved and O2 isotopes are clearly detected. Our new observations do not confirm the absorption features of Ca II or Na I which have been reported in previous lunar eclipse observations. However, features in these and some other strong Fraunhofer line positions do occur in the observed spectrum. We propose that these are due to a Raman-scattered component in the forward-scattered sunlight appearing in the lunar umbral spectrum. Water vapour absorption is found to be rather weak in our spectrum because the atmosphere we probed is relatively dry, which prompts us to discuss the detectability of water vapour in Earth-like exoplanet atmospheres.

preprint2014arXiv

Hunting for the X_b via Radiative Decays

In this paper, we study radiative decays of X_b, the counterpart of the famous X(3872) in the bottomonium-sector as a candidate for meson-meson molecule, into the γΥ(nS) (n=1, 2, 3). Since it is likely that the X_b is below the B\bar B^* threshold and the mass difference between the neutral and charged bottom meson is small compared to the binding energy of the X_b, the isospin violating decay mode X_b\to Υ(nS)π^+π^- would be greatly suppressed. This will promote the importance of the radiative decays. We use the effective Lagrangian based on the heavy quark symmetry to explore the rescattering mechanism and calculate the partial widths. Our results show that the partial widths into γΥ(nS) are about 1 keV, and thus the branching fractions may be sizeable, considering the fact the total width may also be smaller than a few MeV like the X(3872). These radiative decay modes are of great importance in the experimental search for the X_b particularly at hadron collider. An observation of the X_b will provide a deeper insight into the exotic hadron spectroscopy and is helpful to unravel the nature of the states connected by the heavy quark symmetry.

preprint2014arXiv

Local well-posedness and small Deborah limit of a molecule-based $Q$-tensor system

In this paper, we consider a hydrodynamic $Q$-tensor system for nematic liquid crystal flow, which is derived from Doi-Onsager molecular theory by the Bingham closure. We first prove the existence and uniqueness of local strong solution. Furthermore, by taking Deborah number goes to zero and using the Hilbert expansion method, we present a rigorous derivation from the molecule-based $Q$-tensor theory to the Ericksen-Leslie theory.

preprint2014arXiv

On $3$-gauge transformations, $3$-curvature and $\mathbf{Gray}$-categories

In the $3$-gauge theory, a $3$-connection is given by a $1$-form $A$ valued in the Lie algebra $ \mathfrak g$, a $2$-form $B$ valued in the Lie algebra $\mathfrak h $ and a $3$-form $C$ valued in the Lie algebra $ \mathfrak l $, where $(\mathfrak g,\mathfrak h, \mathfrak l)$ constitutes a differential $ 2$-crossed module. We give the $3$-gauge transformations from a $3$-connection to another, and show the transformation formulae of the $1$-curvature $2$-form, the $2$-curvature $3$-form and the $3$-curvature $4$-form. The gauge configurations can be interpreted as smooth $\mathbf{Gray}$-functors between two $\mathbf{Gray}$ $3$-groupoids: the path $3$-groupoid $\mathcal{P}_3(X)$ and the $3$-gauge group $ \mathcal{G}^{\mathscr L}$ associated to the $ 2$-crossed module $\mathscr L$, whose differential is $(\mathfrak g,\mathfrak h, \mathfrak l)$. The derivatives of $\mathbf{Gray}$-functors are $3$-connections, and the derivatives of lax-natural transformations between two such $\mathbf{Gray}$-functors are $3$-gauge transformations. We give the $3$-dimensional holonomy, the lattice version of the $3$-curvature, whose derivative gives the $3$-curvature $4$-form. The covariance of $3$-curvatures easily follows from this construction. This $\mathbf{ Gray}$-categorical construction explains why $3$-gauge transformations and $3$-curvatures have the given forms. The interchanging $3$-arrows are responsible for the appearance of terms concerning the Peiffer commutator $\{,\}$.

preprint2014arXiv

Optical Character Recognition, Using K-Nearest Neighbors

The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training and run time is also very short.

preprint2014arXiv

Production of charm-strange hadronic molecules at the LHC

We explore the inclusive hadroproduction of the D_{s0}^*(2317), D_{s1}(2460) and D_{sJ}(2860) states at the Large Hadron Collider under the assumption that these hadrons are S-wave meson-meson molecules. In addition, the D_{s2}(2910), a predicted bound state of the D_2(2460)K system, is also discussed. We first derive a factorisation formula for the production rates based on effective field theory. Then we make use of two MC event generators, Herwig and Pythia, to simulate the production of pairs of charmed mesons and kaons. Using effective field theory to handle the final state interaction among the meson pairs, we give an estimate of the inclusive production rates for these particles at the order-of-magnitude accuracy. Our results show that the cross sections for the pp --> D_{s0}^*(2317) and pp --> D_{s1}(2460) at the LHC are at O(1 μb) level, while the ones for the pp --> D_{sJ}(2860) and the pp --> D_{s2}(2910) are smaller by about one order of magnitude. Such estimates suggest that these four exotic states could be copiously produced at the LHC. A study of these states at the LHC will thus provide valuable information on hadron spectroscopy as well as hadron interactions.

preprint2014arXiv

Production of the bottom analogues and the spin partner of the X(3872) at hadron colliders

Using the Monte Carlo event generator tools Pythia and Herwig, we simulate the production of bottom/charm meson and antimeson pairs at hadron colliders in proton-proton/antiproton collisions. With these results, we derive an order-of-magnitude estimate for the production rates of the bottom analogues and the spin partner of the $X(3872)$ as hadronic molecules at the LHC and Tevatron experiments. We find that the cross sections for these processes are at the nb level, so that the current and future data sets from the Tevatron and LHC experiments offer a significant discovery potential. We further point out that the $X_b/X_{b2}$ should be reconstructed in the $γΥ(nS) (n=1,2,3)$, $Υ(1S)π^+π^-π^0$, or $χ_{bJ}π^+π^-$ instead of the $Υ(nS)π^+π^-$ final states.

preprint2014arXiv

Recent Developments on the CKM Matrix

In Standard Model, CP violation arises from an irreducible complex phase in the quark mixing matrix, now under the name Cabibbo-Kobayashi-Maskawa matrix. This description has shown remarkably overall agreement with various experimental measurements. In this review, we discuss recent experimental data and theoretical developments on three quantities of CKM matrix that are most uncertain: the $V_{ub}$, including its magnitude and the phase $γ$ in standard parametrization, and the $B_s-\bar B_s$ mixing phase $β_s$.

preprint2014arXiv

Resonance Identities for Closed Characteristics on Compact Star-shaped Hypersurfaces in ${\bf R}^{2n}$

Resonance relations among periodic orbits on given energy hypersurfaces are very important for getting deeper understanding of the dynamics of the corresponding Hamiltonian systems. In this paper, we establish two new resonance identities for closed characteristics on every compact star-shaped hypersurface $Σ$ in ${\bf R}^{2n}$ when the number of geometrically distinct closed characteristics on $Σ$ is finite, which extend those identities established by C. Viterbo in 1989 for star-shaped hypersurfaces assuming in addition that all the closed characteristics and their iterates are non-degenerate, and that by W. Wang, X. Hu and Y. Long in 2007 for strictly convex hypersurfaces in ${\bf R}^{2n}$.

preprint2014arXiv

Simulated identification of epidemic threshold on finite-size networks

Epidemic threshold is one of the most important features of the epidemic dynamics. Through a lot of numerical simulations in classic Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) models on various types of networks, we study the simulated identification of epidemic thresholds on finite-size networks. We confirm that the susceptibility measure goes awry for the SIR model due to the bimodal distribution of outbreak sizes near the critical point, while the simulated thresholds of the SIS and SIR models can be accurately determined by analyzing the peak of the epidemic variability. We further verify the accuracy of theoretical predictions derived by the heterogeneous mean-field theory (HMF) and the quenched mean-field theory (QMF), by comparing them with the simulated threshold of the SIR model obtained from the variability measure. The results show that the HMF prediction agrees very well with the simulated threshold, except the case that the networks are disassortive, in which the QMF prediction is more close to the simulated threshold.

preprint2014arXiv

SN2014J gamma-rays from the 56Ni decay chain

The measurement of gamma-ray lines from the decay chain of 56Ni provides unique information about the explosion in supernovae. The 56Ni freshly-produced in the supernova powers the optical light curve, as it emits gamma-rays upon its radioactive decay to 56Co and then 56Fe. Gamma-ray lines from 56Co decay are expected to become directly visible through the overlying white dwarf material several weeks after the explosion, as they progressively penetrate the overlying material of the supernova envelope, diluted as it expands. The lines are expected to be Doppler-shifted or broadened from the kinematics of the 56Ni ejecta. With the SPI spectrometer on INTEGRAL and using an improved instrumental background method, we detect the two main lines from 56Co decay at 847 and 1238 keV from SN2014J at 3.3 Mpc, significantly Doppler-broadened, and at intensities (3.65+/-1.21) 10^-4 and (2.27+/-0.69) 10^-4 ph cm^-2s^-1, respectively, at brightness maximum. We measure their rise towards a maximum after about 60-100 days and decline thereafter. The intensity ratio of the two lines is found consistent with expectations from 56Co decay (0.62+/-0.28 at brightness maximum, expected is 0.68). We find that the broad lines seen in the late, gamma-ray transparent phase are not representative for the early gamma-ray emission, and rather notice the emission spectrum to be complex and irregular until the supernova is fully transparent to gamma-rays, with progressive uncovering of the bulk of 56Ni. We infer that the explosion morphology is not spherically symmetric, both in the distribution of 56Ni and of the unburnt material which occults the 56Co emission. Comparing light curves from different plausible models, the resulting 56Ni mass is determined as 0.49+/-0.09 M_sol.

preprint2014arXiv

Some logically weak Ramseyan theorems

We study four families of consequences of Ramsey's Theorem from the viewpoint of reverse mathematics. The first, which we call the Achromatic Ramsey Theorem, is from a partition relation introduced by Erdős, Hajnal and Rado: $ω\to [ω]^r_{c,\leq d}$, which asserts that for every $f: [ω]^r \to c$ there exists an infinite $H$ with $|f([H]^r)| \leq d$. The second and third are the Free Set Theorem and the Thin Set Theorem, which were introduced by Harvey Friedman. And the last is the Rainbow Ramsey Theorem. We show that, most theorems from these families are quite weak, i.e., they are strictly weaker than $\operatorname{ACA}_0$ over $\operatorname{RCA}_0$. Interestingly, these families turn out to be closely related. We establish the so-called strong cone avoidance property of most instances of the Achromatic Ramsey Theorem by an induction of exponents, then apply this and a similar induction to obtain the strong cone avoidance property of the Free Set Theorem. From the strong cone avoidance property of the Achromatic Ramsey Theorem and the Free Set Theorem, we derive the strong cone property of the Thin Set Theorem and the Rainbow Ramsey Theorem. It follws easily that a theorem with the strong cone avoidance property does not imply $\operatorname{ACA}_0$ over $\operatorname{RCA}_0$.

preprint2014arXiv

Suppressed epidemics in multi-relational networks

A two-state epidemic model in networks with links mimicking two kinds of relationships between connected nodes is introduced. Links of weights w1 and w0 occur with probabilities p and 1-p, respectively. The fraction of infected nodes rho(p) shows a non-monotonic behavior, with rho drops with p for small p and increases for large p. For small to moderate w1/w0 ratios, rho(p) exhibits a minimum that signifies an optimal suppression. For large w1/w0 ratios, the suppression leads to an absorbing phase consisting only of healthy nodes within a range p_L =< p =< p_R, and an active phase with mixed infected and healthy nodes for p < p_L and p>p_R. A mean field theory that ignores spatial correlation is shown to give qualitative agreement and capture all the key features. A physical picture that emphasizes the intricate interplay between infections via w0 links and within clusters formed by nodes carrying the w1 links is presented. The absorbing state at large w1/w0 ratios results when the clusters are big enough to disrupt the spread via w0 links and yet small enough to avoid an epidemic within the clusters. A theory that uses the possible local environments of a node as variables is formulated. The theory gives results in good agreement with simulation results, thereby showing the necessity of including longer spatial correlations.

preprint2014arXiv

The Links Have It: Infobox Generation by Summarization over Linked Entities

Online encyclopedia such as Wikipedia has become one of the best sources of knowledge. Much effort has been devoted to expanding and enriching the structured data by automatic information extraction from unstructured text in Wikipedia. Although remarkable progresses have been made, their effectiveness and efficiency is still limited as they try to tackle an extremely difficult natural language understanding problems and heavily relies on supervised learning approaches which require large amount effort to label the training data. In this paper, instead of performing information extraction over unstructured natural language text directly, we focus on a rich set of semi-structured data in Wikipedia articles: linked entities. The idea of this paper is the following: If we can summarize the relationship between the entity and its linked entities, we immediately harvest some of the most important information about the entity. To this end, we propose a novel rank aggregation approach to remove noise, an effective clustering and labeling algorithm to extract knowledge.

preprint2014arXiv

Variations and Correlations in Cyclotron Resonant Scattering Features of Vela X-1 Studied by INTEGRAL

Long-term hard X-ray monitoring observations on high mass X-ray binary Vela X-1 from 2003 -- 2011 have been performed by INTEGRAL. I systematically analyzed the average hard X-ray spectra of Vela X-1 from 3 -- 200 keV, with main aims to detect cyclotron resonant scattering features and study their variation patterns with accreting luminosities and orbital phases. The cyclotron scattering lines of Vela X-1 at $\sim 22 - 27$ keV and $49 - 57$ keV are confirmed in the average spectra of Vela X-1. But in the flare states with hard X-ray luminosity higher than $\sim 5\times 10^{36}$ erg s$^{-1}$ (3 -- 100 keV), the fundamental line cannot be detected. This feature suggests that a fan-like beam radiation pattern is expected in high luminosity ranges of Vela X-1. Variations of the cyclotron line energies and depths are discovered, which may change with continuum spectral properties. Both cyclotron line energies show no correlations with X-ray luminosity. The fundamental line energy shows no significant correlations with photon index and cutoff energy. While, the first harmonic energy shows a positive correlation with photon index and exponential cutoff energy. The energy ratio of two cyclotron lines always higher than 2 has the weak correlation with photon index and cutoff energy. These relations support that the X-ray spectral properties of accreting X-ray pulsars are affected by cyclotron resonance scattering. In the case of Vela X-1, the broader and deeper first harmonic would play the main role to cause the spectral variations. The positive correlations between the ratio of line width to energy and the corresponding depth for both two lines support a cylindrical column accretion geometry in Vela X-1.

preprint2013arXiv

$B\to DK^*_{0,2}$ Decays: PQCD analysis to determine CP violation phase angle $γ$

$B^\pm \to (D^0, \bar D^0, D_{CP})K^{*\pm}_{0,2}$ decays are helpful in determining the CP violation angle $γ$, and we analyze these decay processes within the perturbative QCD approach based on $k_T$ factorization. We found that the branching ratio of $B^-\to D^0 K^{*-}_0$ can reach the order of $10^{-4}$, due to the enhancement of nonfactorizable contributions in color-suppressed $D^0$-emission, while the branching ratio of $B^-\to \bar D^0 K^{*-}_0$ is of the order $10^{-5}$. The ratio of decay amplitudes is about 3 times larger than the one in the channel $B^\pm\to D K^\pm$. Large branching ratios provide a good opportunity to observe $ B^\pm \to D K^{*\pm}_2$ on the ongoing and forthcoming experimental facilities and consequently these channels may be of valuable avail in reducing the errors in the CP violation phase angle $γ$. We also explore the possible time-dependent CP asymmetries of $B_s$ decay into a scalar meson to determine the phase angle $γ$.

preprint2013arXiv

An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection

Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.

preprint2013arXiv

Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities

Information Extraction (IE) is the task of automatically extracting structured information from unstructured/semi-structured machine-readable documents. Among various IE tasks, extracting actionable intelligence from ever-increasing amount of data depends critically upon Cross-Document Coreference Resolution (CDCR) - the task of identifying entity mentions across multiple documents that refer to the same underlying entity. Recently, document datasets of the order of peta-/tera-bytes has raised many challenges for performing effective CDCR such as scaling to large numbers of mentions and limited representational power. The problem of analysing such datasets is called "big data". The aim of this paper is to provide readers with an understanding of the central concepts, subtasks, and the current state-of-the-art in CDCR process. We provide assessment of existing tools/techniques for CDCR subtasks and highlight big data challenges in each of them to help readers identify important and outstanding issues for further investigation. Finally, we provide concluding remarks and discuss possible directions for future work.

preprint2013arXiv

Chiral Dynamics and S-wave Contributions in Semileptonic B decays

The flavor-changing neutral current process $b\to s l^+l^-$ is beneficial to testing the standard model and hunting for new physics scenarios. In exclusive decay modes like $B\to K^*(892)l^+l^-$, the S-wave effects may not be negligible and thus have to be reliably estimated. Using the scalar form factors derived from dispersion relations in two channels and matched to Chiral Perturbation Theory, we investigate the S-wave contributions in $\bar B^0\to K^- π^+ l^+l^-$, with the $Kπ$ invariant mass lying in the vicinity of the mass of $K^*(892)$, and the $B_s\to K^- K^+ l^+l^-$ with $m_{KK}\sim m_ϕ$. We find that the S-wave will modify differential decay widths by about 10% in the process of $\bar B^0\to K^- π^+ l^+l^-$ and about 5% in $B_s\to K^- K^+ l^+l^-$. A forward-backward asymmetry for the charged kaon in the final state arises from the interference between the S-wave and P-wave contributions. The measurement of this asymmetry offers a new way to determine the variation of the $Kπ$ S-wave phase versus the invariant mass.

preprint2013arXiv

Closed characteristics on compact convex hypersurfaces in $\R^8$

In this paper, we prove there exist at least four geometrically distinct closed characteristics on every compact convex hypersurface $\Sg$ in $\R^8$. This gives a confirmed answer in the case $n=4$ to a long standing conjecture in Hamiltonian analysis since the time of A. M. Liapounov in 1892 (cf. P. 235 of \cite{Eke3}).

preprint2013arXiv

Cohesive sets and rainbows

We study the strength of $\RRT^3_2$, Rainbow Ramsey Theorem for colorings of triples, and prove that $\RCA + \RRT^3_2$ implies neither $\WKL$ nor $\RRT^4_2$. To this end, we establish some recursion theoretic properties of cohesive sets and rainbows for colorings of pairs. We show that every sequence (2-bounded coloring of pairs) admits a cohesive set (infinite rainbow) of non-PA Turing degree; and that every $\emptyset'$-recursive sequence (2-bounded coloring of pairs) admits a $\low_3$ cohesive set (infinite rainbow).

preprint2013arXiv

Conformal metrics with constant curvature one and finite conical singularities on compact Riemann surfaces

A conformal metric $g$ with constant curvature one and finite conical singularities on a compact Riemann surface $Σ$ can be thought of as the pullback of the standard metric on the 2-sphere by a multi-valued locally univalent meromorphic function $f$ on $Σ\backslash \{{\rm singularities}\}$, called the {\it developing map} of the metric $g$. When the developing map $f$ of such a metric $g$ on the compact Riemann surface $Σ$ has reducible monodromy, we show that, up to some M{\" o}bius transformation on $f$, the logarithmic differential $d\,(\log\, f)$ of $f$ turns out to be an abelian differential of 3rd kind on $Σ$, which satisfies some properties and is called a {\it character 1-form of} $g$. Conversely, given such an abelian differential $ω$ of 3rd kind satisfying the above properties, we prove that there exists a unique conformal metric $g$ on $Σ$ with constant curvature one and conical singularities such that one of its character 1-forms coincides with $ω$. This provides new examples of conformal metrics on compact Riemann surfaces of constant curvature one and with singularities. Moreover, we prove that the developing map is a rational function for a conformal metric $g$ with constant curvature one and finite conical singularities with angles in $2π\,{\Bbb Z}_{>1}$ on the two-sphere.

preprint2013arXiv

CP violation effects on the measurement of $γ$ from $B\to DK$

Inspired by the unexpectedly large difference between the CP violation of $D$ decays into $K^+K^-$ and $π^+π^-$, we explore the impact on the extraction of $γ$ via the $B\to DK$ process with the $D$ meson reconstructed in the $K^+K^-,π^+π^-$ final state. We show that the extracted results for $γ$ can be shifted by ${\cal O}(A_{CP} / r_B^K)$, where $A_{CP}$ is the direct CP asymmetry in $D$ decays and $r_B^K$ is the ratio of the decay amplitudes of $B^-\to \bar D^0K^-$ and $B^-\to D^0K^-$. Using the recent data on CP asymmetry, we demonstrate the correction to physical observables in $B\to DK$ can reach 6%, which corresponds to the shift of $γ$ by roughly $5^\circ$. The remanent corrections depend on the strong phase of the $D$ decays, but are less than $0.5^\circ$. With the increasing precision in the $γ$ determination on the LHCb experiment and Super B factories, the inclusion of CP violation of $D$ decays will therefore soon become important.

preprint2013arXiv

Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers

We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.

preprint2013arXiv

Equitable chromatic threshold of Kronecker products of complete graphs

A proper vertex coloring of a graph is equitable if the sizes of color classes differ by at most 1. The equitable chromatic threshold of a graph $G$, denoted by $χ_=^*(G)$, is the minimum $k$ such that $G$ is equitably $k^\prime$-colorable for all $k^\prime \ge k$. Let $G\times H$ denote the direct product of graphs $G$ and $H$. For $n\ge m\ge 2$ we prove that $χ_=^*(K_{m} \times K_n)$ equals $\lceil\frac{mn}{m+1}\rceil$ if $n\equiv 2,...,m (\textup{mod} m+1)$, and equals $m\lceil\frac{n}{s^\star}\rceil$ if $n\equiv 0,1 (\textup{mod} m+1)$, where $s^\star$ is the minimum positive integer such that $s^\star \nmid n$ and $s^\star\ge m+2.$

preprint2013arXiv

Exploring The Contribution of Unlabeled Data in Financial Sentiment Analysis

With the proliferation of its applications in various industries, sentiment analysis by using publicly available web data has become an active research area in text classification during these years. It is argued by researchers that semi-supervised learning is an effective approach to this problem since it is capable to mitigate the manual labeling effort which is usually expensive and time-consuming. However, there was a long-term debate on the effectiveness of unlabeled data in text classification. This was partially caused by the fact that many assumptions in theoretic analysis often do not hold in practice. We argue that this problem may be further understood by adding an additional dimension in the experiment. This allows us to address this problem in the perspective of bias and variance in a broader view. We show that the well-known performance degradation issue caused by unlabeled data can be reproduced as a subset of the whole scenario. We argue that if the bias-variance trade-off is to be better balanced by a more effective feature selection method unlabeled data is very likely to boost the classification performance. We then propose a feature selection framework in which labeled and unlabeled training samples are both considered. We discuss its potential in achieving such a balance. Besides, the application in financial sentiment analysis is chosen because it not only exemplifies an important application, the data possesses better illustrative power as well. The implications of this study in text classification and financial sentiment analysis are both discussed.

preprint2013arXiv

From microscopic theory to macroscopic theory: a systematic study on static modeling for liquid crystals

In this paper, we propose a systematic way of liquid crystal modeling to build connection between microscopic theory and macroscopic theory. A new Q-tensor theory based on Onsager's molecular theory which leads to liquid crystals with certain shape has been proposed. Making uniaxial assumption, we can recover the Oseen-Frank theory from the derived $Q$-tensor theory, and the Oseen-Frank model coefficients can be examined. In addition, the smectic-A phase can also be characterized by the derived macroscopic model.

preprint2013arXiv

From microscopic theory to macroscopic theory: dynamics of the rod-like liquid crystal molecules

Starting from Doi-Onsager equation for the liquid crystal, we first derive the Q-tensor equation by the Bingham closure. Then we derive the Ericksen-Leslie equation from the Q-tensor equation by taking the small Deborah number limit.

preprint2013arXiv

Generalized Spectral Characterization of Graphs: Revisited

A graph $G$ is said to be \textit{determined by its generalized spectrum} (DGS for short) if for any graph $H$, $H$ and $G$ are cospectral with cospectral complements implies that $H$ is isomorphic to $G$. In \cite{WX,WX1}, Wang and Xu gave some methods for determining whether a family of graphs are DGS. In this paper, we shall review some of the old results and present some new ones along this line of research.

preprint2013arXiv

Ground-based detections of thermal emission from the dense hot Jupiter WASP-43b in H and Ks-bands

We report new detections of thermal emission from the transiting hot Jupiter WASP-43b in the H and Ks-bands as observed at secondary eclipses. The observations were made with the WIRCam instrument on the CFHT. We obtained a secondary eclipse depth of 0.103$_{-0.017}^{+0.017}%$ and 0.194$_{-0.029}^{+0.029}%$ in the H and Ks-bands, respectively. The Ks band depth is consistent with previous measurement in the narrow band centered at 2.09um by Gillon et al. (2012). Our eclipse depths in both bands are consistent with a blackbody spectrum with a temperature of ~1850 K, slightly higher than the dayside equilibrium temperature without day-night energy redistribution. Based on theoretical models of the dayside atmosphere of WASP-43b, our data constrain the day-night energy redistribution in the planet to be $\lesssim 15-25$%, depending on the metal content in the atmosphere. Combined with energy balance arguments our data suggest that a strong temperature inversion is unlikely in the dayside atmosphere of WASP-43b. However, a weak inversion cannot be strictly ruled out at the current time. Future observations are required to place detailed constraints on the chemical composition of the atmosphere.

preprint2013arXiv

Hadroproduction of $Υ(nS)$ above $B\bar B$ Thresholds and Implications for $Y_b(10890)$

Based on the non-relativistic QCD factorization scheme, we study the hadroproduction of the bottomonium states $Υ(5S)$ and $Υ(6S)$. We argue to search for them in the final states $Υ(1S,2S,3S)π^+π^-$, which are found to have anomalously large production rates at $Υ(5S)$. The enhanced rates for the dipionic transitions in the $Υ(5S)$-energy region could, besides $Υ(5S)$, be ascribed to $Y_b(10890)$, a state reported by the Belle collaboration, which may be interpreted as a tetraquark. The LHC/Tevatron measurements are capable of making a case in favor of or against the existence of $Y_b(10890)$, as demonstrated here. Dalitz analysis of the $Υ(1S,2S,3S)π^+π^-$ states from the $Υ(5S)/Y_b(10890)$ decays also impacts directly on the interpretation of the charged bottomonium-like states, $Z_b(10600)$ and $Z_b(10650)$, discovered by Belle in these puzzling decays.

preprint2013arXiv

Hard X-ray emission cutoff in anomalous X-ray pulsar 4U 0142+61 detected by INTEGRAL

The anomalous X-ray pulsar 4U 0142+61 was studied by the INTEGRAL observations. The hard X-ray spectrum of 18 -- 500 keV for 4U 0142+61 was derived using near 9 years of INTEGRAL/IBIS data. We obtained the average hard X-ray spectrum of 4U 0142+61 with all available data. The spectrum of 4U 0142+61 can be fitted with a power-law with an exponential high energy cutoff. This average spectrum is well fitted with a power-law of $Γ\sim 0.51\pm 0.11$ plus a cutoff energy at $128.6\pm 17.2$ keV. The hard X-ray flux of the source from 20 -- 150 keV showed no significant variations (within 20$\%$) from 2003 -- 2011. The spectral profiles have some variability in nine years: photon index varied from 0.3 -- 1.5, and cutoff energies of 110 -- 250 keV. The detection of the high energy cutoff around 130 keV shows some constraints on the radiation mechanisms of magnetars and possibly probes the differences between magnetar and accretion models for these special class of neutron stars. Future HXMT observations could provide stronger constraints on the hard X-ray spectral properties of this source and other magnetar candidates.

preprint2013arXiv

High-precision measurement of atmospheric mass-squared splitting with T2K and NOvA

A precise measurement of the atmospheric mass-squared splitting |Δm^2_{μμ}| is crucial to establish the three-flavor paradigm and to constrain the neutrino mass models. In addition, a precise value of |Δm^2_{μμ}| will significantly enhance the hierarchy reach of future medium-baseline reactor experiments like JUNO and RENO-50. In this work, we explore the precision in |Δm^2_{μμ}| that will be available after the full runs of T2K and NOvA. We find that the combined data will be able to improve the precision in |Δm^2_{μμ}| to sub-percent level for maximal 2-3 mixing. Depending on the true value of \sin^2θ_{23} in the currently-allowed 3 sigma range, the precision in |Δm^2_{μμ}| will vary from 0.87% to 1.24%. We further demonstrate that this is a robust measurement as it remains almost unaffected by the present uncertainties in θ_{13}, δ_{CP}, the choice of mass hierarchy, and the systematic errors.

preprint2013arXiv

Hunting for a scalar glueball in exclusive B decays

Using flavor SU(3) symmetry for the light quarks validated by the available experimental data, we propose an intuitive way to hunt for a scalar glueball in B decays. In the presence of mixing between the glueball and ordinary scalar mesons, we explore the extraction of the mixing parameters. In particular, we discuss the implication from the recently available experimental data and show the sensitivities of B decays as a probe to the scalar structures. The future Super KEKB factory would allow access to establishing the mixing pattern among the scalars, and possibly allow one to disentangle the long-standing puzzle concerning the existence and mixings of the scalar glueball predicted by QCD.

preprint2013arXiv

Kinematics of massive star ejecta in the Milky Way as traced by 26Al

Massive stars form in groups and their winds and supernova explosions create superbubbles up to kpc in size. Their ejecta are important for the dynamics of the interstellar medium and chemical evolution models. However, ejecta kinematics and the characteristic scales in space and time are rather unexplored beyond ~10 ka. Through measurement of radioactive 26Al with its decay time constant of ~10^6 years, we aim to trace the kinematics of massive-star and supernova ejecta over million-year time scales. Our goal is to identify the mixing time scale and the spatio-kinematics of such ejecta from the pc to kpc scale. We use the SPI spectrometer on INTEGRAL and its observations along the Galactic ridge to trace the line shape of the 1808.63 keV gamma-ray line from 26Al decay. We determine line centroids and compare these to Doppler shift expectations from large-scale systematic rotation around the Galaxy's center, as observed in other Galactic objects. We measure the radial velocities of gas traced by 26Al, averaged over the line of sight, as a function of Galactic longitude. We find substantially higher velocities than expected from Galactic rotation, the average bulk velocity being ~200 km s^-1 larger than the Galactic-rotation prediction. The observed radial velocity spread implies a Doppler-broadening of the gamma-ray line that is consistent with our measurements of the overall line width. We can reproduce the observed characteristics with 26Al sources located along the inner spiral arms, when we add a global blow-out into the forward direction away from arms, such as expected from massive stars offset towards the spiral-arm leading edge. The superbubbles-halo connection implies angular-momentum transfer in the disk-halo system and also radial gas flows.

preprint2013arXiv

Landweber iteration of Kaczmarz type with general non-smooth convex penalty functionals

The determination of solutions of many inverse problems usually requires a set of measurements which leads to solving systems of ill-posed equations. In this paper we propose the Landweber iteration of Kaczmarz type with general uniformly convex penalty functional. The method is formulated by using tools from convex analysis. The penalty term is allowed to be non-smooth to include the $L^1$ and total variation (TV) like penalty functionals, which are significant in reconstructing special features of solutions such as sparsity and piecewise constancy in practical applications. Under reasonable conditions, we establish the convergence of the method. Finally we present numerical simulations on tomography problems and parameter identification in partial differential equations to indicate the performance.

preprint2013arXiv

Omitting cohesive sets

We prove that if $\vec{R}$ is a computable sequence of subsets of $ω$ which admits no computable cohesive set, then no 3-generic computes any $\vec{R}$-cohesive set; and there exists a Martin-Löf random which computes no $\vec{R}$-cohesive set.

preprint2013arXiv

On r-equitable chromatic threshold of Kronecker products of complete graphs

A graph $G$ is $r$-equitably $k$-colorable if its vertex set can be partitioned into $k$ independent sets, any two of which differ in size by at most $r$. The $r$-equitable chromatic threshold of a graph $G$, denoted by $χ_{r=}^*(G)$, is the minimum $k$ such that $G$ is $r$-equitably $k'$-colorable for all $k'\ge k$. Let $G\times H$ denote the Kronecker product of graphs $G$ and $H$. In this paper, we completely determine the exact value of $χ_{r=}^*(K_m\times K_n)$ for general $m,n$ and $r$. As a consequence, we show that for $r\ge 2$, if $n\ge \frac{1}{r-1}(m+r)(m+2r-1)$ then $K_m\times K_n$ and its spanning supergraph $K_{m(n)}$ have the same $r$-equitable colorability, and in particular $χ_{r=}^*(K_m\times K_n)=χ_{r=}^*(K_{m(n)})$, where $K_{m(n)}$ is the complete $m$-partite graph with $n$ vertices in each part.

preprint2013arXiv

Optimal Control Theory in Intelligent Transportation Systems Research - A Review

Continuous motorization and urbanization around the globe leads to an expansion of population in major cities. Therefore, ever-growing pressure imposed on the existing mass transit systems calls for a better technology, Intelligent Transportation Systems (ITS), to solve many new and demanding management issues. Many studies in the extant ITS literature attempted to address these issues within which various research methodologies were adopted. However, there is very few paper summarized what does optimal control theory (OCT), one of the sharpest tools to tackle management issues in engineering, do in solving these issues. It{\textquoteright}s both important and interesting to answer the following two questions. (1) How does OCT contribute to ITS research objectives? (2) What are the research gaps and possible future research directions? We searched 11 top transportation and control journals and reviewed 41 research articles in ITS area in which OCT was used as the main research methodology. We categorized the articles by four different ways to address our research questions. We can conclude from the review that OCT is widely used to address various aspects of management issues in ITS within which a large portion of the studies aimed to reduce traffic congestion. We also critically discussed these studies and pointed out some possible future research directions towards which OCT can be used.

preprint2013arXiv

Production of charged heavy quarkonium-like states at the LHC and the Tevatron

We study prompt hadroproduction of the charged bottomonium-like states $Z_b^\pm (10610)$ and $Z_b^\pm (10650)$, and the charged charmonium-like states $Z_c^\pm (3900)$ and $Z_c^\pm (4020)$, at the Tevatron and the LHC, provided that these states are $S$-wave hadronic molecules. Using two Monte Carlo event generators, Herwig and Pythia, to simulate the production of heavy meson pairs, we derive an order-of-magnitude estimate of the production rates for these four particles. Our estimates yield a cross section at the nb level for the $Z_b(10610)$ and $Z_b(10650)$. The results for the $Z_c(3900)$ and $Z_c(4020)$ are larger by a factor of 20-30. These cross sections are large enough to be observed, and measurements at hadron colliders in the future will supplement the study using electron-positron collisions, and therefore allow to explore the mysterious nature of these exotic states.

preprint2013arXiv

Rainbow Ramsey theorem for triples is strictly weaker than the Arithmetic Comprehension Axiom

We prove that $\RCA + \RRT^3_2 \not\vdash \ACA$ where $\RRT^3_2$ is the Rainbow Ramsey Theorem for 2-bounded colorings of triples. This reverse mathematical result is based on a cone avoidance theorem, that every 2-bounded coloring of pairs admits a cone-avoiding infinite rainbow, regardless of the complexity of the given coloring. We also apply the proof of the cone avoidance theorem to the question whether $\RCA + \RRT^4_2 \vdash \ACA$ and obtain some partial answer.

preprint2013arXiv

Rigorous derivation from Landau-de Gennes theory to Ericksen-Leslie theory

Starting from Beris-Edwards system for the liquid crystal, we present a rigorous derivation of Ericksen-Leslie system with general Ericksen stress and Leslie stress by using the Hilbert expansion method.

preprint2013arXiv

Socially-Aware Venue Recommendation for Conference Participants

Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences. This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration. In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation. Our proposed algorithm, Social Aware Recommendation of Venues and Environments (SARVE), computes the Pearson Correlation and social characteristic information of conference participants. SARVE further incorporates the current context of both the smart conference community and participants in order to model a recommendation process using distributed community detection. Through the integration of the above computations and techniques, we are able to recommend presentation sessions of active participant presenters that may be of high interest to a particular participant. We evaluate SARVE using a real world dataset. Our experimental results demonstrate that SARVE outperforms other state-of-the-art methods.

preprint2013arXiv

SPI Measurements of Nucleosynthesis Gamma-Rays

Studies based on the gamma-ray lines from radioactive decay of unstable isotopes produced in massive-star and supernova nucleosynthesis have been among INTEGRAL's prominent science achievements. 26Al has become a tool to study specific source regions, such as massive-star groups and associations in nearby regions which can be discriminated from the galactic-plane background, and the inner Galaxy where Doppler shifted lines add to the astronomical information. 60Fe is co-produced by the sources of 26Al, and the isotopic ratio from their nucleosynthesis encodes stellar-structure information. Here we summarize latest results using the accumulated multi-year database of observations, point to the relevant publications, and discuss their astrophysical implications.

preprint2013arXiv

Spin and Spectral Variations of Peculiar High-Mass X-ray Binary 4U 2206+54

Spin properties and spectral variations of high mass X-ray binary 4U 2206+54 are studied with long-term hard X-ray monitoring observations by INTEGRAL. A long-period X-ray pulsar of P_spin\sim 5558 s has been identified in 4U 2206+54. The spin evolution of the neutron star in 4U 2206+54 is detected with the INTEGRAL/IBIS data. From 2005 to 2011, the spin period of the neutron star in 4U 2206+54 varies from \sim 5558 s to 5588 s. The average spin-down rate in the last 20 years is derived as \sim 5\times 10^{-7} s s^{-1}. 4U 2206+54 is a variable source with luminosities of \sim 10^{35} - 10^{36} erg s^{-1} in the range of 3 -- 100 keV. Its spectrum can be described by an absorbed power-law model with exponential rolloff. The hydrogen column density and photon index show the anti-correlations with hard X-ray luminosity: low column density and small photon index at maximum of luminosity. This spectral variation pattern suggests that 4U 2206+54 would be a highly obscured binary system. Furthermore, the possible cyclotron line features are searched for in the spectra of different luminosities. The possible cyclotron absorption feature around 30 keV is not confirmed in our work. The physical origin and evolutional track of this very slow pulsation neutron star are still unclear. We suggest that 4U 2206+54 would be a young system with the long-term spin-down trend, would evolve to a longer spin period range and transit to the spin-up phase similar to 2S 0114+65. These very slow pulsation X-ray pulsars would belong to a new class of compact objects -- accreting magnetars.

preprint2013arXiv

State Transition Analysis of Time-Frequency Resource Conversion-based Call Admission Control for LTE-Type Cellular Network

To address network congestion stemmed from traffic generated by advanced user equipments, in [1] we propose a novel network resource allocation strategy, time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. Considering an LTE-type cellular network, a call admission control policy called double-threshold guard channel policy is proposed there to facilitate the implementation of TFRC. In this report, we present state transition analysis of this TFRC-based call admission control policy for an LTE-type cellular network. Overall, there are five categories of events that can trigger a transition of the system state: 1) a new call arrival; 2) a handoff user arrival; 3) a handoff user departure; 4) a call termination; and 5) a periodic time-frequency resource conversion. We analyze them case by case in this report and the validation of the analysis has been provided in [1].

preprint2013arXiv

Temporal Variations And Spectral Properties of Be/X-ray Pulsar GRO J1008-57 Studied by INTEGRAL

Spin period variations and hard X-ray spectral properties of Be/X-ray pulsar GRO J1008-57 are studied with INTEGRAL observations. The pulsation periods at 93.66 s in 2004 and at 93.73 s in 2009 are determined. Pulse profiles of GRO J1008-57 during outbursts are energy dependent: double-peak profile in soft bands of 3-7 keV and single-peak profile in hard X-rays above 7 keV. GRO J1008-57 has undergone a spin-down trend from 1993-2009 with a rate of 4.1x10^-5 s day^-1, and might transfer into a spin-up trend after 2009. The spectra from 3-100 keV of GRO J1008-57 during outbursts are fitted with photon index of 1.4 and cutoff energies of 23-29 keV. We find a relatively soft spectrum in early phase of the 2009 outburst with cutoff energy 13 keV. Above a hard X-ray flux of 10^-9 erg cm^-2 s^-1, the spectra of GRO J1008-57 during outbursts need an enhanced hydrogen absorption of column density 6x10^22 cm^-2. The observed dip-like pulse profile of GRO J1008-57 in soft X-ray bands should be caused by this intrinsic absorption. Around the outburst peaks, a possible cyclotron resonance scattering feature at 74 keV is detected in the spectra of GRO J1008-57, making the source as the neutron star of highest magnetic field (6.6x10^12 G) in known accreting X-ray pulsars. Two modulations periods at 124.38 and 248.78 days are found using RXTE/ASM light curves of GRO J1008-57. Two flare peaks appearing in the folded light curve have different spectral properties. The normal outburst lasting 0.1 orbital phase has a hard spectrum and cannot be detected below 3 keV. The second flare lasting ten days shows a very soft spectrum without significant detections above 5 keV. Lack of soft X-ray emissions in outbursts should be caused by the enhanced hydrogen absorption near the periastron passage. Soft spectral origins in the second flares still need further detailed studies with soft X-ray spectroscopies.

preprint2013arXiv

To Reserve or Not to Reserve: Optimal Online Multi-Instance Acquisition in IaaS Clouds

Infrastructure-as-a-Service (IaaS) clouds offer diverse instance purchasing options. A user can either run instances on demand and pay only for what it uses, or it can prepay to reserve instances for a long period, during which a usage discount is entitled. An important problem facing a user is how these two instance options can be dynamically combined to serve time-varying demands at minimum cost. Existing strategies in the literature, however, require either exact knowledge or the distribution of demands in the long-term future, which significantly limits their use in practice. Unlike existing works, we propose two practical online algorithms, one deterministic and another randomized, that dynamically combine the two instance options online without any knowledge of the future. We show that the proposed deterministic (resp., randomized) algorithm incurs no more than 2-alpha (resp., e/(e-1+alpha)) times the minimum cost obtained by an optimal offline algorithm that knows the exact future a priori, where alpha is the entitled discount after reservation. Our online algorithms achieve the best possible competitive ratios in both the deterministic and randomized cases, and can be easily extended to cases when short-term predictions are reliable. Simulations driven by a large volume of real-world traces show that significant cost savings can be achieved with prevalent IaaS prices.

preprint2013arXiv

Y Chromosomes of 40% Chinese Are Descendants of Three Neolithic Super-grandfathers

Demographic change of human populations is one of the central questions for delving into the past of human beings. To identify major population expansions related to male lineages, we sequenced 78 East Asian Y chromosomes at 3.9 Mbp of the non-recombining region (NRY), discovered >4,000 new SNPs, and identified many new clades. The relative divergence dates can be estimated much more precisely using molecular clock. We found that all the Paleolithic divergences were binary; however, three strong star-like Neolithic expansions at ~6 kya (thousand years ago) (assuming a constant substitution rate of 1e-9/bp/year) indicates that ~40% of modern Chinese are patrilineal descendants of only three super-grandfathers at that time. This observation suggests that the main patrilineal expansion in China occurred in the Neolithic Era and might be related to the development of agriculture.

preprint2012arXiv

A generalization of Montgomery-Yang correspondence

In this paper, we want to construct a one-to-one correspondence from the set of diffeomorphism classes of spin $d$-twisted homology $\mc P^3$ to the set of isotopy classes of the embedding from $S^3$ to $S^6$, which is a generalization of the Montgomery-Yang correspondence. Furthermore, we will apply this generalized correspondence to prove the existence of free involution on these $d$-twisted homology $\mc P^3$.

preprint2012arXiv

Analysis of $B\to K^*_J (\to K π) μ^+μ^-$ in the higher kaon resonance region

We study the resonant contributions in the process $\bar{B}^0\to K^- π^+μ^+μ^-$ with the $K^-π^+$ invariant mass square $m_{Kπ}^2\in [1, 5] {\rm GeV}^2$. Width effects of the involved strange mesons, $K^*(1410), K_0^*(1430), K_2^*(1430), K^*(1680), K_3^*(1780)$ and $K_4^*(2045)$, are incorporated. In terms of the helicity amplitudes, we derive a compact form for the full angular distributions, through which the branching ratios, forward-backward asymmetries and polarizations are attained. We propose that the uncertainties in the $B\to K^*_J$ form factors can be pinned down by the measurements of a set of SU(3)-related processes. Using results from the large energy limit, we derive the dependence of branching fractions on the $m_{Kπ}$, and find that the $K^*_2(1430)$ resonance has a clear signature, in particular, in the transverse polarizations.

preprint2012arXiv

Approximation of the random inertial manifold of singularly perturbed stochastic wave equations

By applying Rohlin's result on the classification of homomorphisms of Lebesgue space, the random inertial manifold of a stochastic damped nonlinear wave equations with singular perturbation is proved to be approximated almost surely by that of a stochastic nonlinear heat equation which is driven by a new Wiener process depending on the singular perturbation parameter. This approximation can be seen as the Smolukowski--Kramers approximation as time goes to infinity. However, as time goes infinity, the approximation changes with the small parameter, which is different from the approximation on a finite interval.

preprint2012arXiv

B decays into a scalar/tensor meson in pursuit of determining the CKM angle $γ$

In this work, I suggest a new way for determining the CKM angle $γ$ via B decays into a scalar/tensor meson without any hadronic uncertainty. The proposed idea makes profits of the two triangles formed by the $B^\pm\to (D^0,\bar D^0,D_{CP}^0) K^{*\pm}_{0(2)}(1430)$ decay amplitudes. The advantages in it are large CP asymmetries and the avoidance of the use of doubly Cabibbo-suppressed D decays. Branching ratios of $B^\pm\to (D^0,\bar D^0,D_{CP}^0) K^{*\pm}_{0(2)}(1430)$ are estimated to have the order $10^{-6}-10^{-5}$ and therefore measurable by the ongoing LHCb experiment and future experimental facilities. The usefulness of other related channels, for instance the neutral $B_d$ decays into $DK^{*}_{0(2)}(1430)$ and $B_s\to (D^0, \bar D^0) M$ ($M=f_0(980),f_0(1370), f_2'(1525), f_1(1285), f_1(1420), h_1(1180))$, the $B\to D^{\mp} a_{0,2}^{\pm}$ for the extraction of $γ+2β$ and the $B_s\to D^{\mp} K_{0,2}^{*\pm}$ to access $γ+2β_s$, is also discussed in brevity.

preprint2012arXiv

CP violation in $D^0 \to (K^- K^+, π^- π^+)$ from diquarks

The explanation of the large CP asymmetries in $D^0\to (π^+π^-, K^+K^-)$ decays observed by the LHCb collaboration is likely to call for new physics beyond the CKM paradigm. We explore new contributions caused by the color-sextet scalar diquark, and demonstrate that the diquark with the mass of order 1 TeV and nominal couplings with quarks can generate the CP asymmetries at the percent level. Using the experimental data on branching ratios and CP asymmetries of $D^0\to (π^+π^-, K^+K^-)$, we derive the constraints on the diquark mass and couplings, which can be further examined on hadron colliders in the dijet final states.

preprint2012arXiv

Cyclotron Resonance Energies and Orbital Elements of Accretion Pulsar 4U 0115+63 During the Giant Outburst in 2008

We present both timing and spectral analysis of the outburst of 4U 0115+63 in April -- May 2008 with INTEGRAL and RXTE observations. We have determined the spin period of the neutron star at $\sim 3.61430 \pm 0.00003$ s, and a spin up rate during the outburst of $\dot{P}=(-7.24 \pm 0.03)\times10^{-6} {\rm s d^{-1}}$, the angle of periapsis $ω=48.67^\circ \pm 0.04^\circ$ in 2008 and its variation (apsidal motion) $\dotω = 0.048^\circ \pm 0.003^\circ {\rm yr}^{-1}$. We also confirm the relation of spin-up torque versus luminosity in this source during the giant outburst. The hard X-ray spectral properties of 4U 0115+63 during the outburst are studied with INTEGRAL and RXTE. Four cyclotron absorption lines are detected using the spectra from combined data of IBIS and JEM-X aboard INTEGRAL in the energy range of 3 -- 100 keV. The 5 -- 50 keV luminosities at an assumed distance of 7 kpc are determined to be in the range of $(1.5-12)\times 10^{37} {\rm ergs s^{-1}}$ during the outburst. The fundamental absorption line energy varies during the outburst: around 15 keV during the rising phase, and transiting to $\sim 10$ keV during the peak of the outburst, and further coming back to $\sim 15$ keV during the decreasing phase. The variations of photon index show the correlation with the fundamental line energy changes: the source becomes harder around the peak of the outburst and softer in both rising and decreasing phases. This correlation and transition processes during the outburst need further studies in both observations and theoretical work. The known relation of the fundamental line energy and X-ray luminosity is confirmed by our results, however, our discoveries suggest that some other factors besides luminosity play the important role in fundamental line energy variations and spectral transitions.

preprint2012arXiv

Determining CP violation angle $γ$ with B decays into a scalar/tensor meson

We propose a new way for determining the CP violation angle $γ$ without any hadronic uncertainty. The suggested method is to use the two triangles formed by the decay amplitudes of $B^\pm\to (D^0,\bar D^0,D_{CP}^0) K^{*\pm}_{0(2)}(1430)$. The advantages are that large CP asymmetries are expected in these processes and only singly Cabibbo-suppressed $D$ decay modes are involved. Measurements of the branching fractions of the neutral $B_d$ decays into $DK^{*}_{0(2)}(1430)$ and the time-dependent CP asymmetries in $B_s\to (D^0, \bar D^0) M$ ($M=f_0(980),f_0(1370), f_2'(1525), f_1(1285), f_1(1420), h_1(1180))$ provide an alternative way to extract the angle $γ$, which will increase the statistical significance.

preprint2012arXiv

Direct CP Violation in Charm Decays due to Left-Right Mixing

Motivated by the $3.8σ$ deviation from no CP violation hypothesis for the CP asymmetry (CPA) difference between $D^0\to K^+ K^-$ and $D^0\to π^+ π^-$, reported recently by LHCb and CDF, we investigate the CP violating effect due to the left-right (LR) mixing in the general LR symmetric model. In particular, in the non-manifest LR model we show that the large CPA difference could be explained, while the constraints from $(ε'/ε)_K$ and $D^0$-$\bar D^0$ are satisfied.

preprint2012arXiv

Ensemble Averaging for Dynamical Systems under Fast Oscillating Random Boundary Conditions

This paper is devoted to provide a theoretical underpinning for ensemble forecasting with rapid fluctuations in body forcing and in boundary conditions. Ensemble averaging principles are proved under suitable `mixing' conditions on random boundary conditions and on random body forcing. The ensemble averaged model is a nonlinear stochastic partial differential equation, with the deviation process (i.e., the approximation error process) quantified as the solution of a linear stochastic partial differential equation.

preprint2012arXiv

Equitable chromatic threshold of complete multipartite graphs

A proper vertex coloring of a graph is equitable if the sizes of color classes differ by at most one. The equitable chromatic number of a graph $G$, denoted by $χ_=(G)$, is the minimum $k$ such that $G$ is equitably $k$-colorable. The equitable chromatic threshold of a graph $G$, denoted by $χ_=^*(G)$, is the minimum $t$ such that $G$ is equitably $k$-colorable for $k\ge t$. We develop a formula and a linear-time algorithm which compute the equitable chromatic threshold of an arbitrary complete multipartite graph.

preprint2012arXiv

Equitable coloring of Kronecker products of complete multipartite graphs and complete graphs

A proper vertex coloring of a graph is equitable if the sizes of color classes differ by at most 1. The equitable chromatic number of a graph $G$, denoted by $χ_=(G)$, is the minimum $k$ such that $G$ is equitably $k$-colorable. The equitable chromatic threshold of a graph $G$, denoted by $χ_=^*(G)$, is the minimum $t$ such that $G$ is equitably $k$-colorable for $k \ge t$. In this paper, we give the exact values of $χ_=(K_{m_1,..., m_r} \times K_n)$ and $χ_=^*(K_{m_1,..., m_r} \times K_n)$ for $\sum_{i = 1}^r m_i \leq n$.

preprint2012arXiv

Factorization of Heavy-to-Light Baryonic Transitions in SCET

In the framework of the soft-collinear effective theory, we demonstrate that the leading-power heavy-to-light baryonic form factors at large recoil obey the heavy quark and large energy symmetries. Symmetry breaking effects are suppressed by $Λ/m_{b}$ or $Λ/E$, where $Λ$ is the hadronic scale, $m_b$ is the $b$ quark mass and $E\sim m_b$ is the energy of light baryon in the final state. At leading order, the leading power baryonic form factor $ξ_{Λ,p}(E)$, in which two hard-collinear gluons are exchanged in the baryon constituents, can factorize into the soft and collinear matrix elements convoluted with a hard-kernel of order $α_s^2$. Including the energy release dependence, we derive the scaling law $ξ_{Λ,p}(E)\sim Λ^2 /E^2$. We also find that this form factor $ξ_Λ(E)$ is numerically smaller than the form factor governed by soft processes, although the latter is formally power-suppressed.

preprint2012arXiv

Forward-backward asymmetry of $B\to K_J l^+l^-$ decays in SM and new physics models

We report on our studies of $B\to K_Jl^+l^-$ in the standard model and several new physics variations, with $K_J$ denoting a kaonic resoanance. In terms of helicity amplitudes, we derive a compact form for the full angular distributions, and use them to calculate the branching ratios, forward-backward asymmetries and polarizations. We have updated the constraints on effective Wilson coefficients and/or free parameters in these new physics scenarios by making use of the $B\to K^*l^+l^-$ and $b\to s l^+l^-$ experimental data. Their impact on $B\to K_J^*l^+l^-$ is subsequently explored and in particular the zero-crossing point for forward-backward asymmetry in new physics scenarios can sizably deviate from the standard model.

preprint2012arXiv

Lie bialgebra structures on the Lie algebra $\widetilde{\frak{sl}_2(C_q[x,y])}$

In the present paper we shall investigate the Lie bialgebra structures on the Lie algebra $\widetilde{\frak{sl}_2(C_q[x,y])}$, which are shown to be triangular coboundary.

preprint2012arXiv

Light-Cone Distribution Amplitudes of the Ground State Bottom Baryons in HQET

We provide the definition of the complete set of light-cone distribution amplitudes (LCDAs) for the ground state heavy bottom baryons with the spin-parities J^P = 1/2^+ and J^P = 3/2^+ in the heavy quark limit. We present the renormalization effects on the twist-2 light-cone distribution amplitudes and use the QCD sum rules to compute the moments of twist-2, twist-3, and twist-4 LCDAs. Simple models for the heavy baryon distribution amplitudes are analyzed with account of their scale dependence.

preprint2012arXiv

On Cauchy-Szegö kernel for quaternionic Siegel upper half space

The work is dedicated to the construction of the Cauchy-Szegö kernel for the Cauchy-Szegö projection integral operator from the space of $L^2$-integrable functions defined on the boundary of the quaternionic Siegel upper half space to the space of boundary values of the quaternionic regular functions of the Hardy space over the quaternionic Siegel upper half space.

preprint2012arXiv

On the excess of ultra-high energy cosmic rays in the direction of Centaurus A

A posteriori anisotropy study of ultra-high energy cosmic rays (UHECRs) with the Pierre Auger Observatory (PAO) has shown evidence of excess of cosmic ray particles above 55 EeV within $18^{\circ}$ of the direction of the radio galaxy Centaurus A. However, the origin of the excess remains elusive. We simulate the propagation of different species of particles coming from the direction of Centaurus A in the Galactic magnetic fields, and find that only particles of nuclear charge $Z\la 10$ can avoid being deflected outside of the $18^{\circ}$ window of Centaurus A. On the other hand, considering the increasingly heavy composition of UHECRs at the highest energies measured by PAO, a plausible scenario for cosmic rays from the direction of Centaurus A can be found if they consist of intermediate-mass nuclei. The chemical composition of cosmic rays can be further constrained by lower-energy cosmic rays of the same rigidity. We find that cosmic ray acceleration in the lobes of Centaurus A is not favored, while acceleration in the stellar winds that are rich in intermediate-mass nuclei, could meet the requirement. This suggests that the observed excess may originate from cosmic ray accelerators induced by stellar explosions in the star-forming regions of Centaurus A and/or the Centaurus cluster located behind Centaurus A.

preprint2012arXiv

Radio-to-TeV Phase-resolved Emission from the Crab Pulsar: The Annular Gap Model

In the framework of the three-dimensional (3D) annular gap model with reasonable parameters (the magnetic inclination angle α= 45 deg and the view angle ζ= 63 deg), we first use the latest hight energy data to self-consistently calculate radio, X-ray, gamma-ray and TeV (MAGIC and VERITAS) light curves, phase-averaged spectrum and phase-resolved spectra for the Crab pulsar. It is found that the acceleration electric field and potential in the annular gap and core gap are huge enough in the several tens of neutron star radii. The pulsed emission of radio, X-ray, gamma-ray and TeV are mainly generated from the emission of primary particles or secondary particles with different emission mechanisms in the nearly similar region of the annular gap located in the only one magnetic pole, which leads to the nearly "phase-aligned" multi-wavelength light curves. The emission of peak 1 (P1) and peak 2 (P2) is originated from the annular gap region near the null charge surface, while the emission of bridge is mainly originated from the core gap region. The phase-averaged spectrum and phase-resolved spectra of the Crab pulsar from soft X-ray to TeV band are produced by four components: synchrotron radiation from CR-induced and ICS-induced pairs dominates the X-ray band to soft gamma-ray band (100 eV to 10 MeV); curvature radiation and synchrotron radiation from the primary particles mainly contribute to gamma-ray band (10 MeV to \sim 20 GeV); ICS from the pairs significantly contributes to the TeV gamma-ray band (\sim 20 GeV to 400 GeV). The multi-wavelength pulsed emission from the Crab pulsar has been well modeled with the annular gap and core gap model. To distinguish our single magnetic pole model from two-pole models, the convincing values of the magnetic inclination angle and the viewing angle will play a key role.

preprint2012arXiv

Tetraquark Interpretation of the Charged Bottomonium-like states Z_b^+-(10610) and Z_b^+-(10650) and Implications

We present a tetraquark interpretation of the charged bottomonium-like states Z+-_b(10610) and Z+-_b(10650), observed by the Belle collaboration in the pi+- Upsilon(nS) (n=1,2,3) and pi+- h_b(mP) (m=1,2) invariant mass spectra from the data taken near the peak of the Upsilon(5S). In this framework, the underlying processes involve the production and decays of a vector tetraquark Y_b(10890), e+e- --> Y_b(10890) --> [Z+-_b(10610)pi-+, Z+-_b(10650)pi-+] followed by the decays [Z+-_b (10610), Z+-_b (10650)] --> pi+- Upsilon(nS), pi+- h_b(mP). Combining the contributions from the meson loops and an effective Hamiltonian, we are able to reproduce the observed masses of the Z+-_b(10610) and Z+-_b(10650). The analysis presented here is in agreement with the Belle data and provides crucial tests of the tetraquark hypothesis. We also calculate the corresponding meson loop effects in the charm sector and find them dynamically suppressed. The charged charmonium-like states Z+-_c(3752) and Z+-_c(3882) can be searched for in the decays of the J^{PC}=1^{--} tetraquark state Y(4260) via Y(4260) --> Z+-_c(3752)pi-+ and Y(4260) --> Z+-_c(3882)pi-+, with the subsequent decays (Z+-_c(3752),Z+-_c(3882)) --> (J/psi, h_c)pi+-.

preprint2012arXiv

The small Deborah number limit of the Doi-Onsager equation to the Ericksen-Leslie equation

We present a rigorous derivation of the Ericksen-Leslie equation starting from the Doi-Onsager equation. As in the fluid dynamic limit of the Boltzmann equation, we first make the Hilbert expansion for the solution of the Doi-Onsager equation. The existence of the Hilbert expansion is connected to an open question whether the energy of the Ericksen-Leslie equation is dissipated. We show that the energy is dissipated for the Ericksen-Leslie equation derived from the Doi-Onsager equation. The most difficult step is to prove a uniform bound for the remainder in the Hilbert expansion. This question is connected to the spectral stability of the linearized Doi-Onsager operator around a critical point. By introducing two important auxiliary operators, the detailed spectral information is obtained for the linearized operator around all critical points. However, these are not enough to justify the small Deborah number limit for the inhomogeneous Doi-Onsager equation, since the elastic stress in the velocity equation is also strongly singular. For this, we need to establish a precise lower bound for a bilinear form associated with the linearized operator. In the bilinear form, the interactions between the part inside the kernel and the part outside the kernel of the linearized operator are very complicated. We find a coordinate transform and introduce a five dimensional space called the Maier-Saupe space such that the interactions between two parts can been seen explicitly by a delicate argument of completing the square. However, the lower bound is very weak for the part inside the Maier-Saupe space. In order to apply them to the error estimates, we have to analyze the structure of the singular terms and introduce a suitable energy functional.

preprint2012arXiv

Transverse Energy-Energy Correlations in Next-to-Leading Order in $α_s$ at the LHC

We compute the transverse energy-energy correlation (EEC) and its asymmetry (AEEC) in next-to-leading order (NLO) in $α_s$ in proton-proton collisions at the LHC with the center-of-mass energy $E_{\rm c.m.}=7$ TeV. We show that the transverse EEC and the AEEC distributions are insensitive to the QCD factorization- and the renormalization-scales, structure functions of the proton, and for a judicious choice of the jet-size, also the underlying minimum bias events. Hence they can be used to precisely test QCD in hadron colliders and determine the strong coupling $α_s$. We illustrate these features by defining the hadron jets using the anti-$k_T$ jet algorithm and an event selection procedure employed in the analysis of jets at the LHC and show the $α_s(M_Z)$-dependence of the transverse EEC and the AEEC in the anticipated range $0.11 \leq α_s(M_Z) \leq 0.13$.

preprint2012arXiv

Well-posedness of Hydrodynamics on the Moving Elastic Surface

The dynamics of a membrane is a coupled system comprising a moving elastic surface and an incompressible membrane fluid. We will consider a reduced elastic surface model, which involves the evolution equations of the moving surface, the dynamic equations of the two-dimensional fluid, and the incompressible equation, all of which operate within a curved geometry. In this paper, we prove the local existence and uniqueness of the solution to the reduced elastic surface model by reformulating the model into a new system in the isothermal coordinates. One major difficulty is that of constructing an appropriate iterative scheme such that the limit system is consistent with the original system.

preprint2012arXiv

Well-posedness of the Ericksen-Leslie system

In this paper, we prove the local well-posedness of the Ericksen-Leslie system, and the global well-posednss for small initial data under the physical constrain condition on the Leslie coefficients, which ensures that the energy of the system is dissipated. Instead of the Ginzburg-Landau approximation, we construct an approximate system with the dissipated energy based on a new formulation of the system.

preprint2011arXiv

A Failure Self-recovery Strategy with Balanced Energy Consumption for Wireless Ad Hoc Networks

In energy constrained wireless sensor networks, it is significant to make full use of the limited energy and maximize the network lifetime even when facing some unexpected situation. In this paper, all sensor nodes are grouped into clusters, and for each cluster, it has a mobile cluster head to manage the whole cluster. We consider an emergent situation that one of the mobile cluster heads is broken down, and hence the whole cluster is consequently out of work. An efficient approach is proposed for recovering the failure cluster by selecting multiple static sensor nodes as the cluster heads to collect packets and transmit them to the sink node. Improved simulated annealing algorithm is utilized to achieve the uniform deployment of the cluster heads. The new cluster heads are dynamically changed in order to keep balanced energy consumption. Among the new cluster heads, packets are transmitted through multi-hop forwarding path which is cost-lowest path found by Dijkstra's algorithm. A balanced energy consumption model is provided to help find the cost-lowest path and prolong the lifetime of the network. The forwarding path is updated dynamically according to the cost of the path and residual energy of the node in that path. The experimental results show that the failure cluster is recovered and the lifetime of the cluster is prolonged.

preprint2011arXiv

B to tensor meson form factors in the perturbative QCD approach

We calculate the $B_{u,d,s}\to T$ form factors within the framework of the perturbative QCD approach, where $T$ denotes a light tensor meson with $J^P=2^+$. Due to the similarities between the wave functions of a vector and a tensor meson, the factorization formulas of $B\to T$ form factors can be obtained from the $B\to V$ transition through a replacement rule. As a consequence, we find that these two sets of form factors have the same signs and correlated $q^2$-dependence behaviors. At $q^2=0$ point, the $B\to T$ form factors are smaller than the $B\to V$ ones, in accordance with the experimental data of radiative B decays. In addition, we use our results for the form factors to explore semilteptonic $B\to Tl\bar ν_l$ decays and the branching fractions can reach the order $10^{-4}$.

preprint2011arXiv

Branching ratios, forward-backward asymmetries and angular distributions of $B\to K_2^*l^+l^-$ in the standard model and new physics scenarios

We analyze the $B\to K_2^*(\to Kπ)l^+l^-$ (with $l=e,μ,τ$) decay in the standard model and two new physics scenarios: vector-like quark model and family non-universal $Z'$ model. We derive its differential angular distributions, using the recently calculated form factors in the perturbative QCD approach. Branching ratios, polarizations, forward-backward asymmetries and transversity amplitudes are predicted, from which we find a promising prospective to observe this channel on the future experiment. We also update the constraints on effective Wilson coefficients and/or free parameters in these two new physics scenarios by making use of the experimental data of $B\to K^*l^+l^-$ and $b\to sl^+l^-$. Their impact on $B\to K_2^*l^+l^-$ is subsequently explored and in particular the zero-crossing point for the forward-backward asymmetry in these new physics scenarios can sizably deviate from the SM scenario. In addition we also generalize the analysis to a similar mode $B_s\to f_2'(1525)(\to K^+K^-) l^+l^-$.

preprint2011arXiv

Connectivity of Direct Products of Graphs

Let $κ(G)$ be the connectivity of $G$ and $G\times H$ the direct product of $G$ and $H$. We prove that for any graphs $G$ and $K_n$ with $n\ge 3$, $κ(G\times K_n)=min\{nκ(G),(n-1)δ(G)\}$, which was conjectured by Guji and Vumar.

preprint2011arXiv

Connectivity of Kronecker products by K2

Let $κ(G)$ be the connectivity of $G$. The Kronecker product $G_1\times G_2$ of graphs $G_1$ and $G_2$ has vertex set $V(G_1\times G_2)=V(G_1)\times V(G_2)$ and edge set $E(G_1\times G_2)=\{(u_1,v_1)(u_2,v_2):u_1u_2\in E(G_1),v_1v_2\in E(G_2)\}$. In this paper, we prove that $κ(G\times K_2)=\textup{min}\{2κ(G), \textup{min}\{|X|+2|Y|\}\}$, where the second minimum is taken over all disjoint sets $X,Y\subseteq V(G)$ satisfying (1)$G-(X\cup Y)$ has a bipartite component $C$, and (2) $G[V(C)\cup \{x\}]$ is also bipartite for each $x\in X$.

preprint2011arXiv

Effects of a weakly interacting light U boson on the nuclear equation of state and properties of neutron stars in relativistic models

We investigate the effects of the light vector U-boson that couples weakly to nucleons in relativistic mean-field models on the equation of state and subsequently the consequence in neutron stars. It is analyzed that the U-boson can lead to a much clearer rise of the neutron star maximum mass in models with the much softer equation of state. The inclusion of the U-boson may thus allow the existence of the non-nucleonic degrees of freedom in the interior of large mass neutron stars initiated with the favorably soft EOS of normal nuclear matter. In addition, the sensitive role of the U-boson in the neutron star radius and its relation to the test of the non-Newtonian gravity that is herein addressed by the light U-boson are discussed.

preprint2011arXiv

Existence of closed characteristics on compact convex hypersurfaces in $\R^{2n}

In this paper, we prove there exist at least $[\frac{n+1}{2}]+1$ geometrically distinct closed characteristics on every compact convex hypersurface $\Sg$ in $\R^{2n}$. Moreover, there exist at least $[\frac{n}{2}]+1$ geometrically distinct non-hyperbolic closed characteristics on $\Sg$ in $\R^{2n}$ provided the number of geometrically distinct closed characteristics on $\Sg$ is finite.

preprint2011arXiv

Global well-posedness of compressible Navier-Stokes equations for some classes of large initial data

We prove the global well-posedness of three dimensional compressible Navier-Stokes equations for some classes of large initial data, which is of large oscillation for the density and large energy for the velocity. The structure of the system (especially, the effective viscous flux) is fully exploited.

preprint2011arXiv

On a conjecture of Anosov

In this paper, we prove that for every bumpy Finsler $n$-sphere $(S^n,\,F)$ with reversibility $λ$ and flag curvature $K$ satisfying $(\fracλ{λ+1})^2<K\le 1$, there exist $2[\frac{n+1}{2}]$ prime closed geodesics. This gives a confirmed answer to a conjecture of D. V. Anosov \cite{Ano} in 1974 for a generic case.

preprint2011arXiv

On Nilpotence of a Kind of Circulant Matrices over Zp

We investigate the nilpotence of a kind of circulant matrices $T_{n,m}$ over field $Z_p$ where $T_{n,m}= \sum_{i = 0}^{m - 1} {S_n^i}$ and $S_n$ is the fundamental circulant matrix of order $n$. The necessary and sufficient condition on $n$ and $m$ for determining nilpotence of $T_{n,m}$ over $Z_p$ is presented. Moreover, we obtain a formula for nilpotent index of $T_{n,m}$ when the condition is satisfied. As an application, we give a complete solution of a conjecture of C.Y.Zhang.

preprint2011arXiv

On the approximation for singularly perturbed stochastic wave equations

We explore the relation between fast waves, damping and imposed noise for different scalings by considering the singularly perturbed stochastic nonlinear wave equations νu_{tt}+u_t=\D u+f(u)+ν^α\dot{W} on a bounded spatial domain. An asymptotic approximation to the stochastic wave equation is constructed by a special transformation and splitting of $νu_{t}$. This splitting gives a clear description of the structure of $u$. The approximating model, for small $ν>0$\,, is a stochastic nonlinear heat equation for exponent $0\leqα<1$\,, and is a deterministic nonlinear wave equation for exponent $α>1$\,.

preprint2011arXiv

On the edge connectivity of direct products with dense graphs

Let $κ'(G)$ be the edge connectivity of $G$ and $G\times H$ the direct product of $G$ and $H$. Let $H$ be an arbitrary dense graph with minimal degree $δ(H)>|H|/2$. We prove that for any graph $G$, $κ'(G\times H)=\textup{min}\{2κ'(G)e(H),δ(G)δ(H)\}$, where $e(H)$ denotes the number of edges in $H$. In addition, the structure of minimum edge cuts is described. As an application, we present a necessary and sufficient condition for $G\times K_n(n\ge3)$ to be super edge connected.

preprint2011arXiv

On the topological decomposition of the hypersurfaces in projective toric manifolds

In this paper, we want to discuss the topology of the non-singular hypersurface $Y^{n}$ with complex dimension $n$ in a projective toric manifold $X^{n+1}$. When $n$ is odd, our main results are a decomposition of $Y^{n}\cong Y'\sharp \ s(S^n \times S^n) $ as a connected sum of $s$ copies of $S^n \times S^n$ with a differential manifold $Y'$ such that $b_n (Y')=0$ or 2. When $n$ is even and the degree of $Y$ in $X$ is big enough, we find that $Y$ also admits such a decomposition $Y'\sharp \ s(S^n \times S^n)$, where $Y'$ satisfy $b_n(Y')-|sign(Y')|=b_n(X)\pm sign(H^n(X))$, where $sign(H^n(X))$ is the signature of a certain bilinear form defined on $H^n(X,\mz)$.

preprint2011arXiv

Production of the Exotic $1^{--}$ Hadrons $ϕ(2170)$, X(4260) and $Y_b(10890)$ at the LHC and Tevatron via the Drell-Yan Mechanism

We calculate the Drell-Yan production cross sections and differential distributions in the transverse momentum and rapidity of the $J^{PC}=1^{--}$ exotic hadrons $ϕ(2170)$, X(4260) and $Y_b(10890)$ at the hadron colliders LHC and the Tevatron. These hadrons are tetraquark (four-quark) candidates, with a hidden $s\bar{s}$, $c\bar{c}$ and $b\bar{b}$ quark pair, respectively. In deriving the distributions and cross sections, we include the order $α_s$ QCD corrections, resum the large logarithms in the small transverse momentum region in the impact-parameter formalism, and use the state of the art parton distribution functions. Taking into account the data on the production and decays of these vector hadrons from the $e^+e^-$ experiments, we present the production rates for the processes $pp(\bar{p}) \to ϕ(2170)(\to ϕ(1020) π^+π^- \to K^+K^- π^+π^-)+...$, $pp(\bar{p}) \to X(4260)(\to J/ψπ^+π^- \to μ^+μ^-π^+π^-)+...$, and $pp(\bar{p}) \to Y_b(10890)(\to (Υ(1S), Υ(2S), Υ(3S)) π^+π^- \to μ^+μ^-π^+π^-)+...$. Their measurements at the hadron colliders will provide new experimental avenues to explore the underlying dynamics of these hadrons.

preprint2011arXiv

Robustness of Complex Networks against Attacks Guided by Damage

Extensive researches have been dedicated to investigating the performance of real networks and synthetic networks against random failures or intentional attack guided by degree (degree attack). Degree is one of straightforward measures to characterize the vitality of a vertex in maintaining the integrity of the network but not the only one. Damage, the decrease of the largest component size that was caused by the removal of a vertex, intuitively is a more destructive guide for intentional attack on networks since the network functionality is usually measured by the largest component size. However, it is surprising to find that little is known about behaviors of real networks or synthetic networks against intentional attack guided by damage (damage attack), in which adversaries always choose the vertex with the largest damage to attack. In this article, we dedicate our efforts to understanding damage attack and behaviors of real networks as well as synthetic networks against this attack. To this end, existing attacking models, statistical properties of damage in complex networks are first revisited. Then, we present the empirical analysis results about behaviors of complex networks against damage attack with the comparisons to degree attack. It is surprising to find a cross-point for diverse networks before which damage attack is more destructive than degree attack. Further investigation shows that the existence of cross-point can be attributed to the fact that: degree attack tends produce networks with more heterogenous damage distribution than damage attack. Results in this article strongly suggest that damage attack is one of most destructive attacks and deserves our research efforts.Our understandings about damage attack may also shed light on efficient solutions to protect real networks against damage attack.

preprint2011arXiv

Self similarity and attraction in stochastic nonlinear reaction-diffusion systems

Similarity solutions play an important role in many fields of science: we consider here similarity in stochastic dynamics. Important issues are not only the existence of stochastic similarity, but also whether a similarity solution is dynamically attractive, and if it is, to what particular solution does the system evolve. By recasting a class of stochastic PDEs in a form to which stochastic centre manifold theory may be applied we resolve these issues in this class. For definiteness, a first example of self-similarity of the Burgers' equation driven by some stochastic forced is studied. Under suitable assumptions, a stationary solution is constructed which yields the existence of a stochastic self-similar solution for the stochastic Burgers' equation. Furthermore, the asymptotic convergence to the self-similar solution is proved. Second, in more general stochastic reaction-diffusion systems stochastic centre manifold theory provides a framework to construct the similarity solution, confirm its relevance, and determines the correct solution for any compact initial condition. Third, we argue that dynamically moving the spatial origin and dynamically stretching time improves the description of the stochastic similarity. Lastly, an application to an extremely simple model of turbulent mixing shows how anomalous fluctuations may arise in eddy diffusivities. The techniques and results we discuss should be applicable to a wide range of stochastic similarity problems.

preprint2011arXiv

The substellar mass function in the central region of the open cluster Praesepe from deep LBT observations

Studies of the mass function (MF) of open clusters of different ages allow us to probe the efficiency with which brown dwarfs evaporate from clusters to populate the field. Surveys of older clusters (age >100 Myr) are not affected so severely by several problems encountered in young clusters, such as intra-cluster extinction and large uncertainties in brown dwarf models. We present the results of a deep photometric survey to study the MF of the central region of the old open cluster Praesepe (age~590$^{+150}_{-120}$ Myr, distance ~190$^{+6.0}_{-5.8}$ pc), down to the substellar regime. We performed an optical ($riz$ and $Y$-band) photometric survey of Praesepe using the Large Binocular Telescope Camera covering an area of ~0.59 deg$^2$ in the cluster centre from $i$~19.0 mag (~100 M_Jup) down to a 5$σ$ detection limit at $i$~25.6mag (~40 M_Jup). The survey is approximately 95% complete at $i=23.8$ mag and $z=22.0$ mag (~55 M_Jup). We identify 59 cluster member candidates, of which 37 are substellar, by comparing with the predictions of a dusty atmosphere model. The MF of those candidates rises from the substellar boundary until ~67 M_Jup and then declines. This is quite different from the form inferred for other open clusters older than 50 Myr, but seems to be similar to those found in very young open clusters, the MFs of which peak at ~10 M_Jup. Either Praesepe really does have a different MF from other clusters or they had similar initial MFs but a different dynamical evolution. Since most of the candidates are faint, we lack astrometric or spectroscopic follow-ups to test their memberships. However, the contaminations by field dwarfs, galaxies, or giants are found to have little effect on the shape of MF and therefore the MF of `real' cluster members should have similar characteristics.

preprint2011arXiv

Using nonlocal coherence to quantify quantum correlation

We reexamine quantum correlation from the fundamental perspective of its consanguineous quantum property, the coherence. We emphasize the importance of specifying the tensor product structure of the total state space before discussing quantum correlation. A measure of quantum correlation for arbitrary dimension bipartite states using nonlocal coherence is proposed, and it can be easily generalized to the multipartite case. The quantification of non-entangled component within quantum correlation is investigated for certain states.

preprint2010arXiv

$|V_{ub}|$ and $B\toη^{(')}$ Form Factors in Covariant Light Front Approach

$B\to (π, η, η')$ transition form factors are investigated in the covariant light-front approach. With theoretical uncertainties, we find that $B\to (π, η, η')$ form factors at $q^2=0$ are $f^{(π, η, η')}_{+}(0)=(0.245^{+0.000}_{-0.001}\pm 0.011, 0.220 \pm 0.009\pm0.009, 0.180\pm 0.008^{+0.008}_{-0.007})$ for vector current and $f^{(π, η, η')}_{T}(0)=(0.239^{+0.002+0.020}_{-0.003-0.018}, 0.211\pm 0.009^{+0.017}_{-0.015}, 0.173\pm 0.007^{+0.014}_{-0.013})$ for tensor current, respectively. With the obtained $q^2$-dependent $f^π_{+}(q^2)$ and observed branching ratio (BR) for $\bar B_d\to π^+ \ell \bar ν_{\ell}$, the $V_{ub}$ is found as $|V_{ub}|_{LF}= (3.99 \pm 0.13)\times 10^{-3}$. As a result, the predicted BRs for $\bar B\to (η, η') \ell \barν_{\ell}$ decays with $\ell=e,μ$ are given by $(0.49^{+0.02+0.10}_{-0.04- 0.07}, 0.24^{+0.01+0.04}_{-0.02-0.03})\times 10^{-4}$, while the BRs for $D^-\to (η,η')\ell\barν_{\ell}$ are $(11.1^{+0.5+0.9}_{-0.6-0.9}, 1.79^{+0.07+0.12}_{-0.08-0.12})\times 10^{-4}$. In addition, we also study the integrated lepton angular asymmetries for $\bar B\to (π,η,η')τ\barν_τ$:$(0.277^{+0.001+0.005}_{-0.001-0.007},0.290^{+0.002+0.003}_{-0.000-0.003},0.312^{+0.004+0.005}_{-0.000-0.006})$.

preprint2010arXiv

$B_s\to D_s(3040)$ form factors and $B_s$ decays into $D_s(3040)$

Under the assignment of $D_s(3040)$ as a radially excited p-wave $\bar cs$ state with $J^P=1^+$, we compute the $B_s\to D_s (3040)$ form factors within the covariant light-front quark model. Two classification schemes for the p-wave $\bar cs$ meson are adopted. We also use our results to predict the branching ratios (BRs), polarization fractions and angular asymmetries in semileptonic $B_s\to D_s(3040)\ell\barν(\ell=e,μ,τ)$. The BRs are found to have the order of $10^{-3}$ for $\ell=e,μ$ and $10^{-5}$ for $\ell=τ$. We find that the polarization fractions and the angular asymmetries could be useful to pin down the ambiguities of the quantum numbers for $D_s(3040)$. In addition, we investigate the nonleptonic $B_s\to D_s(3040)M$ decays under the factorization method, where $M$ denotes a charged pseudoscalar or a vector meson. The BRs of $B_s\to D_s(3040)ρ$ and $B_s\to D_s(3040)D_s^*$ reach the order of $10^{-3}$, while the other channels are typically smaller by 1-2 orders.

preprint2010arXiv

$B_s\to f_0(980)$ form factors and $B_s$ decays into $f_0(980)$

We compute the $B_s\to f_0(980)$ transition form factors using light-cone QCD sum rules at leading order in the strong coupling constant, and also including an estimate of next-to-leading order corrections. We use the results to predict the branching fractions of the rare decay modes $B_s \to f_0 \ell^+ \ell^-$ and $B_s \to f_0 ν\bar ν$, which turn out to be ${\cal O}(10^{-7})$ ($B_s\to f_0(980)\ell^+\ell^-$, with $\ell=e,μ$), ${\cal O}(10^{-8})$ ($B_s\to f_0(980)τ^+τ^-$) and ${\cal O}(10^{-6})$($B_s\to f_0(980)ν\barν$). We also predict the branching ratio of $B_s\to J/ψf_0(980)$ decay under the factorization assumption, and discuss the role of this channel for the determination of the $B_s$ mixing phase compared to the golden mode $B_s \to J/ψϕ$. As a last application, we consider $D_s \to f_0$ form factors, providing a determination of the branching ratio of $D_s \to f_0 e^+ ν_e$.

preprint2010arXiv

$B$-to-Glueball form factor and Glueball production in $B$ decays

We investigate transition form factors of $B$ meson decays into a scalar glueball in the light-cone formalism. Compared with form factors of $B$ to ordinary scalar mesons, the $B$-to-glueball form factors have the same power in the expansion of $1/m_B$. Taking into account the leading twist light-cone distribution amplitude, we find that they are numerically smaller than those form factors of $B$ to ordinary scalar mesons. Semileptonic $B\to Gl\barν$, $B\to Gl^+l^-$ and $B_s\to Gl^+l^-$ decays are subsequently investigated. We also analyze the production rates of scalar mesons in semileptonic $B$ decays in the presence of mixing between scalar $\bar qq$ and glueball states. The glueball production in $B_c$ meson decays is also investigated and the LHCb experiment may discover this channel. The sizable branching fraction in $B_c\to (π^+π^-)l^-\barν$, $B_c\to (K^+K^-)l^-\barν$ or $B_c\to (π^+π^-π^+π^-)l^-\barν$ could be a clear signal for a scalar glueball state.

preprint2010arXiv

A general cloak to shift the scattering of different objects

By introducing a concept of "minifying layer", we propose a universal device to hide the position of an object by shifting its scattering without scaling effect for a distance, independent of the direction of incident wave and observation. Unlike the previously reported similar cloaks whose configurations are relevant to the objects to be cloaked. The design here is able to shift the scattering of various objects by one cloak. Moreover, via adjusting the parameter of the "minifying layer", tunable magnifying or even minifying effect on the shifted scattering can also be achieved. The performances of the devices are demonstrated by finite element simulations. This design provides a more flexible way to shift the scattering of an object, and thus can be expected to have wide applications.

preprint2010arXiv

A more general interacting model of holographic dark energy

So far, there have been no theories or observational data that deny the presence of interaction between dark energy and dark matter. We extend naturally the holographic dark energy (HDE) model, proposed by Granda and Oliveros, in which the dark energy density includes not only the square of the Hubble scale, but also the time derivative of the Hubble scale to the case with interaction and the analytic forms for the cosmic parameters are obtained under the specific boundary conditions. The various behaviors concerning the cosmic expansion depend on the introduced numerical parameters which are also constrained. The more general interacting model inherits the features of the previous ones of HDE, keeping the consistency of the theory.

preprint2010arXiv

A near-perfect invisibility cloak constructed with homogeneous materials

A near-perfect, non-singular cylindrical invisibility cloak with diamond cross section is achieved by a two-step coordinate transformation. A small line segment is stretched and then blown up into a diamond space, and finally the cloak consisting of four kinds and eight blocks of homogeneous transformation media is obtained. Numerical simulations confirm the well performance of the cloak. The operation bandwidth of the cloak is also investigated. Our scheme is promising to create a simple and well-performed cloak in practice.

preprint2010arXiv

Distinguishing two kinds of scalar mesons from heavy meson decays

In the SU(3) symmetry limit, semileptonic $D^+\to Sl^+ν$ and $B^-\to Sl^-\barν$ decays, with $S=a_0(980)$, $f_0(980)$ and $f_0(600)$, are found to obey different sum rules in the $\bar qq$ and the tetra-quark descriptions for scalar mesons. Thus these sum rules can distinguish the two scenarios for light scalar mesons model-independently. This method also applies to the $\bar B^0\to J/ψ(η_c) S$ decays. Two kinds of SU(3) symmetry breaking effects are found to be under control, which will not spoil our method. The branching fractions of the $D^+\to Sl^+ν$, $B^-\to Sl^-\barν$ and $\bar B^0\to J/ψ(η_c) S$ decays roughly have the order $10^{-4}$, $10^{-5}$ and $10^{-6}$, respectively. The ongoing BES-III and the forthcoming Super B experiments are able to measure these channels and accordingly to provide detailed information of the scalar meson inner structure.

preprint2010arXiv

Do Ultrahigh Energy Cosmic Rays Come from Active Galactic Nuclei and Fermi $γ$-ray Sources?

We study possible correlations between ultrahigh energy cosmic rays (UHECRs), observed by Auger, AGASA and Yakutsk, and nearby active galactic nuclei (AGNs) and $Fermi$ sources. We consider the deflection effects by a Galactic magnetic field (GMF) model constrained by the most updated measurements. We found that the average deflection angles of UHECRs by the Galactic magnetic fields are less than $4^\circ$. A correlation between the Auger cosmic-ray events and nearby AGNs with a significance level of $\sim 4σ$ was found for the Auger UHECR data sets with or without deflection correction. No correlation was found between the AGASA/Yakutsk events with nearby AGNs. Marginal correlations between the Auger events and the $Fermi$ sources, and between AGASA events and $Fermi$ AGNs were found when the deflections calculated by the GMF model were considered. However, no correlation was found between the Yakutsk data and $Fermi$ sources. Some $Fermi$ sources are close to the arrival directions of UHECR events detected by Auger, AGASA, and Yakutsk, most of which are probably chance coincidence rather than objects producing UHECRs in the nearby Universe. Four $Fermi$ sources, NGC 4945, ESO 323-G77, NGC 6951, and Cen A, within 100~Mpc have UHECR events within $3.1^{\circ}$ from their positions, which could potentially be cosmic ray accelerators. However, the association can only be confirmed if more UHECRs are preferably detected in these directions.

preprint2010arXiv

Field-effect and frequency dependent transport in semiconductor-enriched single-wall carbon nanotube network device

The electrical and optical response of a field-effect device comprising a network of semiconductor-enriched single-wall carbon nanotubes, gated with sodium chloride solution is investigated. Field-effect is demonstrated in a device that uses facile fabrication techniques along with a small-ion as the gate electrolyte - and this is accomplished as a result of the semiconductor enhancement of the tubes. The optical transparency and electrical resistance of the device are modulated with gate voltage. A time-response study of the modulation of optical transparency and electrical resistance upon application of gate voltage suggests the percolative charge transport in the network. Also the ac response in the network is investigated as a function of frequency and temperature down to 5 K. An empirical relation between onset frequency and temperature is determined.

preprint2010arXiv

Half-metallic ferromagnetism in transition-metal doped germanium nitride: A first-principles study

The electronic and magnetic properties of transition-metal doped $β$-Ge3N4 have been studied using first-principles calculations. The results show that the substitutional transition-metal impurities tend to cluster. The V and Cr doped Ge3N4 compounds are ferromagnetic semiconductors, while the compounds with Mn and Fe doping show a half-metallic ferromagnetic character.

preprint2010arXiv

Hindered magnetic dipole transition in the covariant light-front approach

Hindered magnetic dipole transitions $Υ(nS)\to γη_b(n'S)$ are studied in the covariant light-front approach. Compared with the allowed magnetic dipole transitions, we find that results for hindered magnetic dipole transitions are sensitive to heavy quark mass and shape parameters of the light-front wave functions. It is possible to tune the parameters so that the predictions of branching fractions of $Υ(2S,3S)\toγη_b$ are consistent with the recent experimental data, but the relevant decay constant of $η_b$ is much smaller than that of $Υ(1S)$. We also generalize the investigation to the charmonium sector and find the the same conclusion.

preprint2010arXiv

Ion irradiation and biomolecular radiation damage II. Indirect effect

It has been reported that damage of genome in a living cell by ionizing radiation is about one-third direct and two-thirds indirect. The former which has been introduced in our last paper, concerns direct energy deposition and ionizing reactions in the biomolecules; the latter results from radiation induced reactive species (mainly radicals) in the medium (mainly water) surrounding the biomolecules. In this review, a short description of ion implantation induced radical formation in water is presented. Then we summarize the aqueous radical reaction chemistry of DNA, protein and their components, followed by a brief introduction of biomolecular damage induced by secondary particles (ions and electron). Some downstream biological effects are also discussed.

preprint2010arXiv

Large deviations for slow-fast stochastic partial differential equations

A large deviation principle is derived for stochastic partial differential equations with slow-fast components. The result shows that the rate function is exactly that of the averaged equation plus the fluctuating deviation which is a stochastic partial differential equation with small Gaussian perturbation. This also confirms the effectiveness of the approximation of the averaged equation plus the fluctuating deviation to the slow-fast stochastic partial differential equations.

preprint2010arXiv

Long-term Hard X-ray Monitoring of 2S 0114+65 with INTEGRAL/IBIS

(abridged) We present the results of the long-term hard X-ray monitoring of the high mass X-ray binary 2S 0114+65 with INTEGRAL/IBIS from 2003 to 2008. 2S 0114+65 is a variable hard X-ray source, when 2S 0114+65 was bright, we found a pulse period evolution of $\sim 2.67$ hour to 2.63 hour from 2003 -- 2008, with a spin-up rate of the neutron star $\sim 1.09\times 10^{-6}$ s s$^{-1}$. Compared with the previous reported spin-up rate, the spin-up rate of the neutron star in 2S 0114+65 is accelerating. The spectral properties of 2S 0114+65 in the band of 18 -- 100 keV which changed with the orbital phases. The variation of the power-law photon index over orbital phase anticorrelates with hard X-ray flux, and the variation of $E_{\rm cut}$ has a positive correlation with the hard X-ray flux, implying that the harder spectrum at the maximum of the light curve. The variations of spectral properties over orbital phase suggested 2S 0114+65 as a highly obscured binary system. In some observational revolutions, hard X-ray tails above 70 keV are detected. We study the characteristics of the hard X-ray tails combining JEM-X and IBIS data in the energy range of 3 -- 100 keV. The 3 -- 100 keV spectra of 2S 0114+65 are generally fitted by an absorbed power-law model with high energy cutoff. We discover that the hard X-ray tails are only detected when column density is very low. Thus, high column density leads to disappearance of the hard X-ray tails in this wind-fed neutron star accretion binary.

preprint2010arXiv

Macroscopic discrete modelling of stochastic reaction-diffusion equations on a periodic domain

Dynamical systems theory provides powerful methods to extract effective macroscopic dynamics from complex systems with slow modes and fast modes. Here we derive and theoretically support a macroscopic, spatially discrete, model for a class of stochastic reaction-diffusion partial differential equations with cubic nonlinearity. Dividing space into overlapping finite elements, a special coupling condition between neighbouring elements preserves the self-adjoint dynamics and controls interelement interactions. When the interelement coupling parameter is small, an averaging method and an asymptotic expansion of the slow modes show that the macroscopic discrete model will be a family of coupled stochastic ordinary differential equations which describe the evolution of the grid values. This modelling shows the importance of subgrid scale interaction between noise and spatial diffusion and provides a new rigourous approach to constructing semi-discrete approximations to stochastic reaction-diffusion partial differential equations.

preprint2010arXiv

Multi-View Active Learning in the Non-Realizable Case

The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prove that, with unbounded Tsybakov noise, the sample complexity of multi-view active learning can be $\widetilde{O}(\log\frac{1}ε)$, contrasting to single-view setting where the polynomial improvement is the best possible achievement. We also prove that in general multi-view setting the sample complexity of active learning with unbounded Tsybakov noise is $\widetilde{O}(\frac{1}ε)$, where the order of $1/ε$ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of $1/ε$ is related to the parameter in Tsybakov noise.

preprint2010arXiv

Nonleptonic $B_s$ to charmonium decays and their role in the determination of the $β_s$

This talk consists of two parts. We first present a light-cone QCD sum rule computation of the $B_s\to f_0(980)$ form factors which are necessary inputs in semileptonic and nonleptonic $B_s$ decays into $f_0(980)$. Then we analyze nonleptonic $B_s$ decays into a charmonium state and a light meson, which are potentially useful to access the $B_s$-${\bar B}_s$ mixing phase $β_s$. We explore the experimental feasibility of measuring these various channels, paying attention to different determinations of $β_s$ in view of the hints of new physics recently emerged in the $B_s$ sector.

preprint2010arXiv

Nonleptonic $B_s$ to charmonium decays: analyses in pursuit of determining the weak phase $β_s$

We analyze nonleptonic $B_s$ decays to a charmonium state and a light meson, induced by the $b \to c {\bar c}s$ transition, which are useful to access the $B_s$-${\bar B}_s$ mixing phase $β_s$. We use generalized factorization and $SU(3)_F$ symmetry to relate such modes to correspondent $B$ decay channels. We discuss the feasibility of the measurements in the various channels, stressing the importance of comparing different determinations of $β_s$ in view of the hints of new physics effects (NP) recently emerged in the $B_s$ sector. Finally, adopting a general parametrization of NP contributions to the decay amplitudes, we discuss how to experimentally constrain new physics parameters.

preprint2010arXiv

Radioactive 26Al from the Scorpius-Centaurus Association

The Scorpius-Centaurus association is the most-nearby group of massive and young stars. As nuclear-fusion products are ejected by massive stars and supernovae into the surrounding interstellar medium, the search for characteristic gamma-rays from radioactivity is one way to probe the history of activity of such nearby massive stars on a My time scale through their nucleosynthesis. 26Al decays within ~1 My, 1809 keV gamma-rays from its decay can be measured with current gamma-ray telescopes, such as INTEGRAL's gamma-ray spectrometer SPI. Following earlier 26Al gamma-ray mapping with NASA's Compton observatory, we test spatial emission skymaps of 26Al for a component which could be attributed to ejecta from massive stars in the Scorpius-Centaurus group of stars. Such a model fit of spatial distributions for large-scale and local components is able to discriminate 26Al emission associated with Scorpius-Centaurus, in spite of the strong underlying nucleosynthesis signal from the Galaxy at large. We find an 26Al signal above 5 sigma significance, which we associate with the Sco-Cen group. The observed flux of 6 *10^{-5}ph cm^{-2} s^{-1} corresponds to 1.1 *10^{-4} M_sol of 26Al. This traces the nucleosynthesis ejecta of several massive stars within the past several million years. We confirm through direct detection of radioactive 26Al the recent ejection of massive-star nucleosynthesis products from the Sco-Cen association. Its youngest subgroup in Upper Scorpius appears to dominate 26Al contributions from this association. Our 26Al signal can be interpreted as a measure of the age and richness of this youngest subgroup. We also estimate a kinematic imprint of these nearby massive-star ejecta from the bulk motion of 26Al and compare this to other indications of Scorpius-Centaurus massive-star activity .

preprint2010arXiv

Study light scalar meson property from heavy meson decays

In the SU(3) symmetry limit, the ratio $R\equiv\frac{{\cal B}(D^+\to f_0l^+ν)+ {\cal B}(D^+\to σl^+ν)}{{\cal B}(D^+\to a_0^0l^+ν)}$ is equal to 1 if the scalar mesons are $\bar qq$ states, while it is 3 if these mesons are tentraquark states. This ratio provides a model-independent way to distinguish the descriptions for light scalar mesons . It also applies to the $B^-\to Sl^-\barν$ and $\bar B^0\to J/ψ(η_c) S$ decays. The SU(3) symmetry breaking effect is found to be under control, which will not spoil our method. The branching fractions of the $D^+\to S l^+ν$, $B^-\to S l^-\barν$ and $\bar B^0\to J/ψ(η_c) S$ decays roughly have the order $10^{-4}$, $10^{-5}$ and $10^{-6}$, respectively. The B factory experiments and ongoing BEPC-II experiments are able to measure these channels and accordingly to provide the detailed information of the scalar meson inner structure.

preprint2010arXiv

Vertex-deleted subgraphs and regular factors from regular graph

Let $k$, $m$ and $r$ be three integers such that $2\leq k\leq m\leq r$. Let $G$ be a $2r$-regular, $2m$-edge-connected graph of odd order. We obtain some sufficient conditions, which guarantee $G-v$ contains a $k$-factor for all $v\in V(G)$.

preprint2010arXiv

Z-mediated charge and CP asymmetries and FCNCs in $B_{d,s}$ processes

We show model-independently that the negative like-sign charge asymmetry $(-A^b_{s\ell})$ is less than $ 3.16\times 10^{-3}$ when the constraints from the $B_q-\bar B_q$ mixings and the time-dependent CP asymmetries (CPAs) for $B_q\to J/ΨM_q$ with $M_q=K,ϕ$ and $q=d,s$ are taken into account. Although the result is smaller than the measured value by the DØ Collaboration at Fermilab, there is still plenty of room to have new physics, which is sensitive to new CP violating effects, as the standard model (SM) prediction is $(2.3_{-0.5}^{+0.6})\times 10^{-4}$. To illustrate the potential large $|A^{b}_{s\ell}|$, we show the influence of new $SU(2)_L$ singlet exotic quarks in the vector-like quark model, where the $Z$-mediated flavor changing neutral currents (FCNCs) are generated at tree level. In particular, we demonstrate that (a) the like-sign charge asymmetry could be enhanced by a factor of two in magnitude; (b) the CPA of $\sin2β^{J/Ψϕ}_s$ could reach to $-15%$; (c) the CPA of $\sin2β_{ϕK_S}$ could be higher than $\sin2β_{J/ΨK_S}$ when $|A^b_{s\ell}|$ is larger than the SM prediction; and (d) the branching ratio for $B_s\to μ^+ μ^-$ could be as large as $0.6\times 10^{-8}$.

preprint2009arXiv

Confinement Effects on the Kinetics and Thermodynamics of Protein Dimerization

In the cell, protein complexes form relying on specific interactions between their monomers. Excluded volume effects due to molecular crowding would lead to correlations between molecules even without specific interactions. What is the interplay of these effects in the crowded cellular environment? We study dimerization of a model homodimer both when the mondimers are free or tethered to each other. We consider a structured environment: Two monomers first diffuse into a cavity of size $L$ and then fold and bind within the cavity. The folding and binding are simulated using molecular dynamics based on a simplified topology based model. The {\it confinement} in the cell is described by an effective molecular concentration $C \sim L^{-3}$. A two-state coupled folding and binding behavior is found. We show the maximal rate of dimerization occurred at an effective molecular concentration $C^{op}\simeq 1m$M which is a relevant cellular concentration. In contrast, for tethered chains the rate keeps at a plateau when $C<C^{op}$ but then decreases sharply when $C>C^{op}$. For both the free and tethered cases, the simulated variation of the rate of dimerization and thermodynamic stability with effective molecular concentration agrees well with experimental observations. In addition, a theoretical argument for the effects of confinement on dimerization is also made.

preprint2009arXiv

Covariant Light-Front Approach for $B_c$ transition form factors

In the covariant light-front quark model, we investigate the form factors of $B_c$ decays into $D, D^*, D_s, D_s^*, η_c, J/ψ, B, B^*, B_s, B_s^*$ mesons. The form factors in the spacelike region $q^2<0$ are directly evaluated. To extrapolate the form factors to the full kinematic region, we fit the form factors by adopting a three-parameter form from the spacelike region. $b\to u,d,s$ transition form factors at maximally recoiling point ($q^2=0$) are smaller than $b\to c$ and $c\to d,s$ transition form factors, while the $b\to d,s,c$ form factors at zero recoiling point are close to each other. In the fitting procedure, we find the parameters for the form factors $A_2(B_c\to B^*)$ and $A_2(B_c\to B^*_s)$ strongly depend on the decay constants of $B^*$ and $B_s^*$ mesons. Fortunately, the semileptonic and nonleptonic $B_c$ decays are not sensitive to these form factors. With the form factors, we also investigate the branching fractions, polarizations of the semileptonic $B_c\to Mlν$ decays. Semileptonic $B_c\to (η_c,J/ψ)lν$ and $B_c\to (B_s,B_s^*)lν$ decays have much larger branching fractions than $B_c\to (D,D^*,B,B^*)lν$. In the three kinds of $B_c\to Vlν$ decays, contributions from the longitudinal polarized vector is comparable with those from the transversely polarized vector. These predictions will be tested at the ongoing and forthcoming hadron colliders.

preprint2009arXiv

Study of axial strain induced torsion of single wall carbon nanotubes by 2D continuum anharmonic anisotropic elastic model

Recent molecular dynamic simulations have found chiral single wall carbon nanotubes (SWNTs) twist during stretching, which is similar to the motion of a screw. Obviously this phenomenon, as a type of curvature-chirality effect, can not be explained by usual isotropic elastic theory of SWNT. More interestingly, with larger axial strains (before buckling), the axial strain induced torsion (a-SIT) shows asymmetric behaviors for axial tensile and compressing strains, which suggests anharmonic elasticity of SWNTs plays an important role in real a-SIT responses. In order to study the a-SIT of chiral SWNTs with actual sizes, and avoid possible deviations of computer simulation results due to the finite-size effect, we propose a 2D analytical continuum model which can be used to describe the the SWNTs of arbitrary chiralities, curvatures, and lengths, with the concerning of anisotropic and anharmonic elasticity of SWNTs. This elastic energy of present model comes from the continuum limit of lattice energy based on Second Generation Reactive Empirical Bond Order potential (REBO-II), a well-established empirical potential for solid carbons. Our model has no adjustable parameters, except for those presented in REBO-II, and all the coefficients in the model can be calculated analytically. Using our method, we obtain a-SIT responses of chiral SWNTs with arbitrary radius, chiralities and lengthes. Our results are in reasonable agreement with recent molecular dynamic simulations. [Liang {\it et. al}, Phys. Rev. Lett, ${\bf 96}$, 165501 (2006).] Our approach can also be used to calculate other curvature-chirality dependent anharmonic mechanic responses of SWNTs.

preprint2009arXiv

Study of Decay Modes B -->K_0^*(1430) phi

Within the framework of perturbative QCD approach based on $\mathbf{k_T}$ factorization, we investigate the charmless decay mode $B \to K_0^*(1430)ϕ$. Under two different scenarios (S1 and S2) for the description of scalar meson $K_0^*(1430)$, we explore the branching fractions and related CP asymmetries. Besides the dominant contributions from the factorizable emission diagrams, penguin operators in the annihilation diagrams could also provide considerable contributions. The central values of our predictions are larger than those from the QCD factorization in both scenarios. Compared with the experimental measurements of the BaBar collaboration, the result of neutral channel in the S1 agrees with experimental data, while the result of the charged one is a bit smaller than the data. In the S2 scenario, although the central value for the branching fractions of both channels are much larger than the data, the predictions could agree with the data due to the large uncertainties to the branching fractions from the hadronic input parameters. The CP asymmetry in the charged channel is small and not sensitive to CKM angle $γ$. With the accurate data in near future from the various $B$ factories, these predictions will be under stringent tests.

preprint2008arXiv

Ion irradiation induced direct damage to DNA

Ion beams have been widely applied in a few biological research fields such as radioactive breeding, health protection, and tumor therapy. Up to now many interesting and impressive achievements in biology and agriculture have been made. Over the past several decades, scientists in biology, physics, and chemistry have pursued investigations focused on understanding the mechanisms of these radiobiological effects of ion beams. From the chemical point of view, these effects are due to the ion irradiation induced biomolecular damage, direct or indirect. In this review, we will present a chemical overview of the direct effects of ion irradiation upon DNA and its components, based on a review of literature combined with recent experimental results. It is suggested that, under ion bombardment, a DNA molecule undergoes a variety of processes, including radical formation, atomic displacement, intramolecular bond-scissions, emission of fragments, fragment recombination and molecular crosslink, which may lead to genetic mutations or cell death. This may help to understand the mechanisms of the radiobiological effects caused by ion irradiation, such as radiation breeding and tumor therapy.

preprint2008arXiv

Measuring $D^0-\bar D^0$ mixing in $D^0(\bar D^0)\to f_0(980) K^{*}$ and more

We investigate the $D^0-\bar D^0$ mixing through the doubly Cabibbo suppressed (DCS) channel $D^0\to f_0(980)K^{*0}$ and its charge conjugate channel, in which the $K^{*0}$ meson is reconstructed in both $K^+π^-$ and $K_Sπ^0$ final state. Although the decay $D^0\to f_0(980)K^{*}$ has a small branching ratio, the final state mesons are relatively easy to identify. The $f_0(980)$ meson can be replaced by $π^+π^-$ in which $π^+π^-$ form an $S$-wave state, or a longitudinally polarized vector meson $ρ^0,ω$. All mixing parameters, including the mass difference and decay width difference, can be extracted by studying the time-dependent decay width of these channels. We show that the method is valid in all regions for mixing parameters and it does not depend on the strong phase difference.

preprint2006arXiv

Massive scalar field quasinormal modes of a Schwarzschild black hole surrounded by quintessence

We present the quasinormal frequencies of the massive scalar field in the background of a Schwarzchild black hole surrounded by quintessence with the third-order WKB method. The mass of the scalar field $u$ plays an important role in studying the quasinormal frequencies, the real part of the frequencies increases linearly as mass $u$ increases, while the imaginary part in absolute value decreases linearly which leads to damping more slowly and the frequencies having a limited value. Moreover, owing to the presence of the quintessence, the massive scalar field damps more slowly.

preprint2006arXiv

Radioactive 26Al and massive stars in the Galaxy

Gamma-rays from radioactive 26Al (half life ~7.2 10^5 yr) provide a 'snapshot' view of ongoing nucleosynthesis in the Galaxy. The Galaxy is relatively transparent to such gamma-rays, and emission has been found concentrated along the plane of the Galaxy. This led to the conclusion1 that massive stars throughout the Galaxy dominate the production of 26Al. On the other hand, meteoritic data show locally-produced 26Al, perhaps from spallation reactions in the protosolar disk. Furthermore, prominent gamma-ray emission from the Cygnus region suggests that a substantial fraction of Galactic 26Al could originate in localized star-forming regions. Here we report high spectral resolution measurements of 26Al emission at 1808.65 keV, which demonstrate that the 26Al source regions corotate with the Galaxy, supporting its Galaxy-wide origin. We determine a present-day equilibrium mass of 2.8 (+/-0.8) M_sol of 26Al. We use this to estimate that the frequency of core collapse (i.e. type Ib/c and type II) supernovae to be 1.9(+/- 1.1) events per century.

preprint2005arXiv

Early Tracking Behavior in Small-field Quintessence Models

We study several quintessence models which are singular at Q=0, and use a simple initial constraint $Q_i\ge H_{inflation}/2π$ to see when they enter tracking regime, disregarding the details of inflation. We find it can give strong constraints for the inverse power-law potential $V=V_0Q^{-α}$, which has to enter tracking regime for ${\rm ln}z \sim 10$. While for the supergravity model $V=V_0Q^{-α}{\rm exp}(kQ^2/2)$, the constraint is much weakened. For another kind inverse power-law potential $V=V_0{\rm exp}(λ/Q)$, it exhibits no constraints.

preprint2005arXiv

Probing the gauge bosons $Z'$ and $B'$ from the Littlest Higgs model in the high-energy linear $e^{+}e^{-}$ colliders

The littlest Higgs (LH) model predicts the existence of the new gauge bosons $Z'$ and $B'$. We calculate the contributions of these new particles to the processes $e^{+}e^{-}\to\bar{f}f$ with $f=τ, μ, b,$ or $c$ and study the possibility of detecting these new particles via these processes in the future high-energy linear $e^{+}e^{-}$ collider (LC) experiments with $\sqrt{s}=500GeV$ and $£_{int}=340fb^{-1}$. We find that, with reasonable values of the parameter preferred by the electroweak precision data, the possible signals of these new particles might be detected. The $Z'$ mass $M_{Z'}$ can be explored up to $2.8TeV$ via the process $e^{+}e^{-}\to\bar{b}b $ for $0.3\leq c \leq 0.5 $ and the $B'$ mass $M_{B'}$ can be explored up to $1.26TeV$ via the process $e^{+}e^{-}\to\bar{l}l $ for $0.64\leq c' \leq 0.73 $.

preprint2003arXiv

The Real Scalar Field in Schwarzschild-de Sitter Spacetime

In this paper, the real scalar field equation in Schwarzschild-de Sitter spacetime is solved numerically with high precision. A method called polynomial approximation is introduced to derive the relation between the tortoise coordinate x and the radius r. This method is diﬀerent from the tangent approximation [1] and leads to more accurate result. The Nariai black hole is then discussed in details. We find that the wave function is harmonic only near the horizons as I. Brevik and B. Simonsen [1] found. Howerver the wave function is not harmonic in the region of the potential peak, with amplitude increasing instead. Furthermore, we also find that, when cosmological constant decreases, the potential peak increases, and the maximum wave amplitude increases.

preprint1995arXiv

Impurity Energy Level Within The Haldane Gap

An impurity bond $J{'}$ in a periodic 1D antiferromagnetic, spin 1 chain with exchange $J$ is considered. Using the numerical density matrix renormalization group method, we find an impurity energy level in the Haldane gap, corresponding to a bound state near the impurity bond. When $J{'}<J$ the level changes gradually from the edge of the Haldane gap to the ground state energy as the deviation $dev=(J-J{'})/J$ changes from 0 to 1. It seems that there is no threshold. Yet, there is a threshold when $J{'}>J$. The impurity level appears only when the deviation $dev=(J{'}-J)/J{'}$ is greater than $B_{c}$, which is near 0.3 in our calculation.

Wei Wang

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498 published item(s)

Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents

DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization

METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution

Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend

Parametric amplification of continuous variable entangled state for loss-tolerant multi-phase estimation

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

Quark masses and low energy constants in the continuum from the tadpole improved clover ensembles

Rigorous uniaxial limit of the Qian--Sheng inertial Q-tensor hydrodynamics for liquid crystals

When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

A First Search for Solar $^8$B Neutrino in the PandaX-4T Experiment using Neutrino-Nucleus Coherent Scattering

Computing Shor's algorithmic steps with classical light beams

Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization

From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning

Integer-Based Pattern Synthesis for Asymmetric Multi-Reflection RIS

Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction

Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Sparsity Exploitation via Joint Receive Processing and Transmit Beamforming Design for MIMO-OFDM ISAC Systems

Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

$μ$AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware

3D-Aware Semantic-Guided Generative Model for Human Synthesis

A Novel Mobility Model to Support the Routing of Mobile Energy Resources

A review on vision-based analysis for automatic dietary assessment

A Search for the Cosmic Ray Boosted Sub-GeV Dark Matter at the PandaX-II Experiment

A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction

A van der Waals Interface Hosting Two Groups of Magnetic Skyrmions

Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach

Afterpulse measurement of JUNO 20-inch PMTs

An Asymmetrical Model for High Energy Radiation of Cassiopeia A

Approximating the ground state eigenvalue via the effective potential

Batch-efficient EigenDecomposition for Small and Medium Matrices

Computer Vision Based Parking Optimization System

Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations

Constructing graphs having Laplacian pair state transfer by an edge perturbation

Content-Variant Reference Image Quality Assessment via Knowledge Distillation

COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas

Could TDE outflows produce the PeV neutrino events?

Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

Cybertwin-enabled 6G Space-air-ground Integrated Networks: Architecture, Open Issue, and Challenges

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Did bio-homochirality arise from spin-polarized electron?

Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling

Disorder-induced broadening of the spin waves in a triangular-lattice quantum-spin-liquid candidate YbZnGaO$_4$

Disproof of a conjecture on the main spectrum of generalized Bethe trees

Disproof of a conjecture on the minimum Wiener index of signed trees

Distributed Eco-Driving Algorithm of Vehicle Platoon Using Traffic Light and Road Slope Information

Distribution Amplitudes of $K^*$ and $ϕ$ at Physical Pion Mass from Lattice QCD

DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation

Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification

Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices

Efficient, High-purity, Robust Sound Frequency Conversion with a Linear Metasurface

Emotion Recognition From Gait Analyses: Current Research and Future Directions

Enhancing Security of Memristor Computing System Through Secure Weight Mapping

Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition

Evidence for magnetic fractional excitations in a Kitaev quantum-spin-liquid candidate $α$-RuCl$_3$

Evidence for strong correlations at finite temperatures in the dimerized magnet Na$_2$Cu$_2$TeO$_6$

Exact ground state and elementary excitations of a competing spin chain with twisted boundary condition

Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences

Exploring neutrinos from proton decays catalyzed by GUT monopoles in the Sun

FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate

Fast Differentiable Matrix Square Root

FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space

Field-Dependent Magnetic Domain Behavior in van der Waals Fe$_3$GeTe$_2$

First Lattice QCD determination of semileptonic decays of charmed-strange baryons $Ξ_c$

Fluorination Increases Hydrophobicity at the Macroscopic Level but not at the Microscopic Level

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

Generalized Dynamic Junction Theory to Resolve the Mechanism of Direct Current Generation in Liquid-Solid Interfaces