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Chao Zhang

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preprint2026arXiv

AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training

Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose surrogate models in scientific machine learning. However, the inherent complexity and structural diversity of PDE solution operators make multi-PDE pre-training fundamentally challenging. Existing methods mainly address this by increasing model capacity, while leaving the target solution operators unchanged. Inspired by classical numerical analysis, we instead propose to transform complex and diverse solution operators into simpler, better-aligned forms that are easier to model jointly. Since the optimal transformation varies across PDE types, it must be adaptive and input-dependent, allowing a single neural operator to approximate an entire family of operators. We instantiate this idea as AOT-POT (adaptive operator-transformation for pre-training operator transformer), which expands hidden representations into multiple parallel streams, adaptively aggregates and redistributes them before and after each sub-layer, and mixes streams through Sinkhorn-projected doubly stochastic matrices for stable training. These mechanisms together reshape diverse solution operators into a unified form that can be effectively modeled by a single architecture. Empirically, AOT-POT achieves state-of-the-art performance on 12 PDE benchmarks with only 3\% additional parameters, reducing relative L2 error by up to 77.6\% (40.9\% on average). Fine-tuning AOT-POT further reduces L2 error by up to 92\% on in-domain PDEs and 89\% on out-of-domain PDEs (unseen types during pre-training), demonstrating that adaptive operator transformation is an effective and complementary direction for advancing PDE foundation models beyond simply scaling model capacity.

preprint2026arXiv

Beyond Steering Vector: Flow-based Activation Steering for Inference-Time Intervention

Activation steering has emerged as a promising alternative for controlling language-model behavior at inference time by modifying intermediate representations while keeping model parameters frozen. However, large-scale evaluations such as AxBench show that existing steering methods are often outperformed by simple in-context prompting and generalize poorly to unseen concepts. We hypothesize that these limitations arise from unvalidated simplifying assumptions shared across prior methods, which typically restrict steering interventions to fixed, single-step, position-invariant transforms. We propose FLAS (Flow-based Activation Steering), which learns a general, concept-conditioned velocity field $v_t(h,t,c)$ that transports unsteered activations to steered ones without relying on these assumptions. On AxBench, FLAS is the first learned method to consistently outperform prompting, reaching held-out harmonic means of $1.015$ on Gemma-2-2B-IT and $1.113$ on Gemma-2-9B-IT without per-concept tuning. Analysis of the learned flow shows curved, multi-step, token-varying trajectories, which suggests that previous hypotheses on activation space geometry might be incomplete.

preprint2026arXiv

Bidirectional Decoding for Concatenated Quantum Hamming Codes

High-rate concatenated quantum codes offer a promising pathway toward fault-tolerant quantum computation, yet designing efficient decoders that fully exploit their error-correction capability remains a significant challenge. In this work, we introduce a hard-decision decoder for concatenated quantum Hamming codes with time complexity polynomial in the block length. This decoder overcomes the limitations of conventional local decoding by leveraging higher-level syndrome information to revise lower-level recovery decisions -- a strategy we refer to as bidirectional decoding. For the concatenated $[[15,7,3]]$ quantum Hamming code under independent bit-flip noise, the bidirectional decoder improves the threshold from approximately $1.56\%$ to $4.35\%$ compared with standard local decoding. Moreover, the decoder empirically preserves the full $3^{L}$ code-distance scaling for at least three levels of concatenation, resulting in substantially faster logical-error suppression than the $2^{L+1}$ scaling offered by local decoders. Our results can enhance the competitiveness of concatenated-code architectures for low-overhead fault-tolerant quantum computation.

preprint2026arXiv

DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization

Recent advances in Emotional Support Conversation (ESC) have improved emotional support generation by fine-tuning Large Language Models (LLMs) via Supervised Fine-Tuning (SFT). However, common psychological errors still persist. While Direct Preference Optimization (DPO) shows promise in reducing such errors through pairwise preference learning, its effectiveness in ESC tasks is limited by two key challenges: (1) Entangled data structure: Existing ESC data inherently entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs; and (2) Optimization ambiguity: Applying vanilla DPO to such entangled pairwise data leads to ambiguous training objectives. To address these issues, we introduce Inferential Preference Mining (IPM) to construct high-quality preference data, forming the IPM-PrefDial dataset. Building upon this data, we propose a Decoupled ESC framework inspired by Gross's Extended Process Model of Emotion Regulation, which decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. Each was trained via SFT and subsequently enhanced by DPO to align with the psychological preference. Extensive experiments demonstrate that our Decoupled ESC framework outperforms joint optimization baselines, reducing preference bias and improving response quality.

preprint2026arXiv

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training

Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed generation: long-tailed trajectories indispensable for model performance block the entire training pipeline. Asynchronous training offers a natural remedy by overlapping generation with training, but introduces a fundamental tension between efficiency and algorithmic correctness. We identify three constraints in asynchronous training to preserve convergence: intra-trajectory policy consistency, data integrity, and bounded staleness. Existing approaches fail to intrinsically address the long-tailed trajectory problem, which is further exacerbated by the imbalance characteristic of Mix-of-Experts models, or deviate from the standard RL training formulation, thereby hindering model convergence. Therefore, we propose DORA (Dynamic ORchestration for Asynchronous Rollout), which addresses this challenge through algorithm-system co-design. DORA introduces multi-version streaming rollout, a novel asynchronous paradigm that maintains multiple policy versions concurrently -- simultaneously achieving full bubble elimination without compromising algorithmic constraints. Experimental results demonstrate that our DORA system achieves substantial improvements in throughput -- up to 2--3 times higher than state-of-the-art systems on open-source benchmarks -- without compromising convergence. Furthermore, in large-scale industrial applications with tens of thousands of accelerators, DORA accelerates RL training by 2--4 times compared to synchronous training across various scenarios. The resultant open-source models, LongCat-Flash-Thinking, exhibit competitive performance on complex reasoning benchmarks, matching the capability of most advanced LLMs.

preprint2026arXiv

Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scaling

The reasoning frontier of Large Language Models (LLMs) has advanced significantly through modern post-training paradigms (e.g., Reinforcement Learning from Verifiable Rewards (RLVR)). However, the efficacy of these methods remains fundamentally constrained by the diversity and complexity of the training data. One practical solution is data synthesis; yet, prevalent methods relying on unstructured mutation or exploration suffer from homogeneity collapse, failing to systematically expand the reasoning frontier. To overcome this, we propose Evoutionary Task Discovery (EvoTD), a framework that treats data synthesis as a directed search over a dual-axis manifold of Algorithmic Skills and Complexity Attributes. We introduce structured evolutionary operators to navigate this space: a Crossover operator that synthesizes novel skill compositions to enhance diversity, and a Parametric Mutation operator that scales structural constraints (e.g., input size, tree depth) to drive robust generalization. Crucially, we integrate a dynamic Zone of Proximal Development filter, ensuring tasks lie within the learnable region of the model. Empirically, EvoTD delivers substantial reasoning gains that generalize consistently across model architectures, pretraining regimes, and scales, demonstrating that structured evolutionary curricula can effectively support reasoning improvement. We release our code on https://github.com/liqinye/EvoTD.

preprint2026arXiv

Exploration-Driven Optimization for Test-Time Large Language Model Reasoning

Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (LLMs). However, a fundamental tension arises: inference-time methods benefit from diverse sampling from a relatively flattened probability distribution, whereas reinforcement learning (RL)-based post-training inherently sharpens these distributions. To address this, we propose Exploration-Driven Optimization (EDO), which extends reward-biasing style exploration objectives to iterative post-training and integrates them into standard RL objectives, encouraging greater diversity in sampled solutions while facilitating more effective inference-time computation. We incorporate EDO into iterative Direct Preference Optimization (iDPO) and Group Relative Policy Optimization (GRPO), resulting in two variants: ED-iDPO and ED-GRPO. Extensive experiments demonstrate that both ED-iDPO and ED-GRPO exhibit greater solution diversity and improved reasoning abilities, particularly when combined with test-time computation techniques like self-consistency. Across three in-distribution reasoning benchmarks, EDO achieves a 1.0-1.3\% improvement over the strongest baselines, and delivers an additional 1.5\% average gain on five out-of-distribution tasks. Beyond accuracy, EDO preserves model entropy and stabilizes RL training dynamics, highlighting its effectiveness in preventing over-optimization collapse. Taken together, these results establish EDO as a practical framework for balancing exploration and exploitation in LLM reasoning, especially in settings that rely on test-time scaling.

preprint2026arXiv

FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration

LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this cost comes from synchronized stage execution and imbalance inside each LLM-heavy stage. We present FlashEvolve, an efficient framework that replaces synchronized execution with asynchronous workers and queues, allowing different stages and steps to overlap. To handle data staleness introduced by asynchrony, FlashEvolve tracks artifact versions and applies different policies to update, discard, or patch stale artifacts. Unlike weight-space staleness in asynchronous RL, language-space staleness is inspectable and repairable: a stale artifact is not just delayed work, but readable evidence that the LLM can reflect on, revise, and turn into useful evolution signal. FlashEvolve further improves throughput and token efficiency with speculative stage completion and adaptive workflow control. On GEPA workloads, FlashEvolve improves proposal throughput by $3.5\times$ on local vLLM and $4.9\times$ on API serving over synchronous GEPA. The same design also applies to ACE and Meta-Harness.

preprint2026arXiv

HIPPO: Accelerating Video Large Language Models Inference via Holistic-aware Parallel Speculative Decoding

Speculative decoding (SD) has emerged as a promising approach to accelerate LLM inference without sacrificing output quality. Existing SD methods tailored for video-LLMs primarily focus on pruning redundant visual tokens to mitigate the computational burden of massive visual inputs. However, existing methods do not achieve inference acceleration comparable to text-only LLMs. We observe from extensive experiments that this phenomenon mainly stems from two limitations: (i) their pruning strategies inadequately preserve visual semantic tokens, degrading draft quality and acceptance rates; (ii) even with aggressive pruning (e.g., 90% visual tokens removed), the draft model's remaining inference cost limits overall speedup. To address these limitations, we propose HIPPO, a general holistic-aware parallel speculative decoding framework. Specifically, HIPPO proposes (i) a semantic-aware token preservation method, which fuses global attention scores with local visual semantics to retain semantic information at high pruning ratios; (ii) a video parallel SD algorithm that decouples and overlaps draft generation and target verification phases. Experiments on four video-LLMs across six benchmarks demonstrate HIPPO's effectiveness, yielding up to 3.51x speedup compared to vanilla auto-regressive decoding.

preprint2026arXiv

Imaging Intermediate Melting Phases of Dual Magnetic-Field-Stabilized Wigner Crystals

The competition between Coulomb repulsion and kinetic energy in correlated systems can allow electrons to crystallize into Wigner solids. Despite researches across diverse two-dimensional Wigner platforms, the microscopic melting processes through possible intermediate phases remains largely unknown. Here, we present the visualization of electron-lattice melting in monolayer VCl3 on graphite, where two Wigner crystals coexist with markedly different critical temperatures Tc and lattice periods as stabilized by high magnetic field. One Wigner crystal possesses both record-high Tc and electron density, and undergoes melting through an intermediate nematic phase upon decreasing magnetic field. In contrast, the other Wigner crystal with a lower Tc yields a different intermediate phase during melting, exhibiting an anomalous electron liquid with an energy-independent modulation period. First-principles calculations corroborate the band-selective occupations of interface-transferred electrons in the formation of dual Wigner crystals. Our atomically resolved intermediate phases provide crucial insights into the microscopic melting pathways of Wigner crystals, enabling a phase diagram parameterized by both quantum and thermal fluctuations.

preprint2026arXiv

Interaction-Induced Quasicrystalline Order: Emergence of Quasi-Solid and Quasi-Supersolid Phases

Deterministic quasiperiodicity in quantum systems has long been associated with localization, criticality, or glassy behavior, and has therefore been believed to suppress long-range order rather than stabilize it. Here we demonstrate the opposite: quasiperiodicity in interactions--without any quasiperiodic potential, disorder, or geometric modulation--can generate coherent, ordered quantum phases. We study hard-core bosons in one dimension with quasiperiodic long-range interactions, V_{ij}=V_0 \cos(παi)\cos(παj), where n=α=(\sqrt{5}-1)/2 is the inverse golden ratio. Using large-scale path-integral quantum Monte Carlo simulations, we uncover thermodynamically stable incompressible plateaus at irrational densities tied to Fibonacci ratios. These plateaus exhibit sharp incommensurate Bragg peaks, signaling an emergent quasi-solid with long-range quasicrystalline density order. More strikingly, at nearby fillings and interaction strengths, we identify a quasi-supersolid phase that supports both Fibonacci density ordering and finite superfluid density--demonstrating that interaction-induced quasiperiodicity can stabilize supersolid coherence. Our results establish a new mechanism for realizing ordered quasicrystalline quantum matter, and provide realistic guidance for implementation in Rydberg atom arrays, multimode cavity-QED systems, and trapped-ion quantum simulators.

preprint2026arXiv

Mass Concept Erasure in Diffusion Models with Concept Hierarchy

The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general generative capabilities. However, as the number of erased concepts grows, these methods often become inefficient and ineffective, since each concept requires a separate set of fine-tuned parameters and may degrade the overall generation quality. In this work, we propose a supertype-subtype concept hierarchy that organizes erased concepts into a parent-child structure. Each erased concept is treated as a child node, and semantically related concepts (e.g., macaw, and bald eagle) are grouped under a shared parent node, referred to as a supertype concept (e.g., bird). Rather than erasing concepts individually, we introduce an effective and efficient group-wise suppression method, where semantically similar concepts are grouped and erased jointly by sharing a single set of learnable parameters. During the erasure phase, standard diffusion regularization is applied to preserve denoising process in unmasked regions. To mitigate the degradation of supertype generation caused by excessive erasure of semantically related subtypes, we propose a novel method called Supertype-Preserving Low-Rank Adaptation (SuPLoRA), which encodes the supertype concept information in the frozen down-projection matrix and updates only the up-projection matrix during erasure. Theoretical analysis demonstrates the effectiveness of SuPLoRA in mitigating generation performance degradation. We construct a more challenging benchmark that requires simultaneous erasure of concepts across diverse domains, including celebrities, objects, and pornographic content.

preprint2026arXiv

MMEDIT: A Unified Framework for Multi-Type Audio Editing via Audio Language Model

Text-guided audio editing aims to modify specific acoustic events while strictly preserving non-target content. Despite recent progress, existing approaches remain fundamentally limited. Training-free methods often suffer from signal degradation caused by diffusion inversion, while training-based methods, although achieving higher generation quality, are severely constrained by the scarcity of high-quality paired data and task formulations that cover only a narrow subset of editing operations. In addition, standard architectures typically decouple text and audio processing, limiting the ability to align instructions with specific acoustic contexts. To address these challenges, we propose MMEdit, an audio-language-model-driven framework for unified audio editing. We systematically extend task definitions to cover a comprehensive range of editing operations, including addition, replacement, removal, reordering, and attribute modification. Furthermore, we design a scalable data synthesis pipeline to construct large-scale paired datasets with fine-grained event-level annotations. To capture complex editing semantics, we integrate a Qwen2-Audio encoder with an MMDiT-based generator, enabling precise cross-modal alignment and localized editing. Experimental results demonstrate that our method achieves superior editing localization accuracy, robust instruction following, and high fidelity in non-edited regions.

preprint2026arXiv

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions

The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion-molecule interactions. Here, we assess the ability of machine-learning (ML) models to infer the MEP, based on the equivariant graph-convolutional neural network architecture PiNet2 and trained on dipole and quadrupole moments. For the established QM9 dataset, we find that including the quadrupole contribution in the ML models substantially improves their ability to recover the MEP compared to dipole-only models. This trend is confirmed on the SPICE dataset, which spans a much broader region of organic chemical space. Together, this study underscores the central role of the quadrupole moment as a fitting target for ML models aiming at rapid access to the MEP.

preprint2026arXiv

Open charm production and $Λ_{c}^{+}/D^{0}$ ratio in pp and Au+Au collisions at the RHIC

We study open charm hadrons production in pp and Au+Au collisions at $\sqrt{s_{\mathrm{NN}}} = 200$~GeV using an improved a multi-phase transport (AMPT) model. Specifically, we show the transverse-momentum spectra and nuclear modification factors $R_{\mathrm{AA}}$ of $D^{0}$ mesons and $Λ_{c}^{+}$ baryons, as well as the $Λ_{c}^{+}/D^{0}$ ratio in pp and Au+Au collisions. The results obtained from the AMPT model simulations are compared with the STAR experimental data and found to be consistent. We further investigate the $Λ_{c}^{+}/D^{0}$ ratio by evaluating contributions from coalescence, fragmentation, and the combined coalescence+fragmentation mechanisms, and we find that fragmentation alone underestimates the pronounced enhancement in Au+Au relative to pp at low and intermediate $p_{\mathrm{T}}$, whereas the coalescence+fragmentation mechanism reproduces the observed trend significantly better. These results indicate that coalescence plays a key role in charm baryon productions and helps constrain the relative importance of different hadronization mechanisms in the ultra-relativistic nuclear collisions.

preprint2026arXiv

OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond

The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a dominant memory bottleneck for efficient deployment. While the established per-channel quantization effectively accommodates intrinsic channel-wise outliers in Key tensors, its efficacy diminishes under extreme compression. In this work, we revisit the inherent limitations of the per-channel quantization paradigm from both empirical and theoretical perspectives. Our analysis identifies Token Norm Imbalance (TNI) as the primary bottleneck to quantization fidelity. We demonstrate that TNI systematically amplifies errors when shared quantization parameters are required to span token groups exhibiting substantial norm disparities. Instead of relying on intricate quantization pipelines (e.g., TurboQuant), we propose OScaR (Omni-Scaled Canalized Rotation), an accurate and lightweight KV cache compression framework for X-LLMs (i.e., text-only, multi-modal, and omni-modal LLMs). Advancing the per-channel paradigm, OScaR employs Canalized Rotation followed by Omni-Token Scaling to mitigate TNI-induced sequence-dimensional variance both effectively and efficiently, further supported by our optimized system design and CUDA kernels. Extensive evaluations across X-LLMs show that OScaR consistently outperforms existing methods and achieves near-lossless performance under INT2 quantization, establishing it as a robust, low-complexity, and universal framework that defines a new Pareto front. Compared with the BF16 FlashDecoding-v2 baseline, our OScaR implementation achieves a notable up to 3.0x speedup in decoding, reduces memory footprint by 5.3x, and increases throughput by 4.1x. The code for OScaR is publicly available at https://github.com/ZunhaiSu/OScaR-KV-Quant.

preprint2026arXiv

Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation

Pairwise preferences and pointwise ratings are the two dominant annotation protocols in image aesthetic assessment (IAA), yet existing benchmarks adopt only one, leaving their complementarity unmeasured under controlled conditions. We introduce PPaint, a matched dual-protocol benchmark in which 15 domain experts, 5 per category, annotate 150 Chinese paintings under both protocols across five aesthetic dimensions, collecting 45,900 pairwise expert judgments through a locally dense preference design alongside the matched ratings. The matched design reveals complementary strengths: preferences yield more consistent ordinal rankings, while ratings anchor the absolute score scale. Fusing both signals via two independent preference-to-score methods yields a fused expert ground truth on which the two constructions converge to nearly identical scores. The same preference-to-score principle extends to label-free VLM training. PSDistill converts VLM pairwise judgments into calibrated pseudo-scores via an Elo reference pool, and trains the same VLM with confidence-weighted ranking optimization to produce a single-pass aesthetic scorer. Trained on a single painting category, the distilled Qwen3-VL-8B improves mean SRCC from 0.504 to 0.709 across all three categories, outperforming all open-source baselines including the dedicated aesthetic model ArtiMuse and matching closed-source Gemini-3.1-Pro within 0.04 SRCC at single-pass inference cost, with cross-domain transfer further validated on APDDv2. We will release the full PPaint dataset and training code.

preprint2026arXiv

Revisiting DAgger in the Era of LLM-Agents

Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole trajectory. Existing recipes fall short in complementary ways: supervised fine-tuning provides dense teacher supervision but suffers from covariate shift because it is trained on off-policy teacher trajectories; while reinforcement learning with verifiable rewards avoids this off-policy mismatch by learning from on-policy rollouts but with only sparse outcome feedback. We address this dilemma by revisiting Dataset Aggregation (DAgger) for multi-turn LM agents: the algorithm collects trajectories through a turn-level interpolation of student and teacher policies, and the student is then trained on these trajectories using supervised labels provided by the teacher. By directly interacting with environments, we expose the model to realistic states likely to be encountered during deployment, thereby effectively mitigating covariate shift. Besides, since the student is learned by mimicking the teacher's behavior, it receives rich feedback during learning. To demonstrate DAgger enjoys the benefits of both worlds, we tested the algorithm to train a software-engineering agent with 4B- and 8B-scale student models. On SWE-bench Verified, our DAgger-style training improves over the strongest post-training baseline by +3.9 points at 4B and +3.6 points at 8B. The resulting 4B agent reaches 27.3%, outperforming representative published 8B SWE-agent systems, while the 8B agent achieves 29.8%, surpassing SWE-Gym-32B and coming within 5 points of stronger 32B-scale agents. Together with consistent gains on the held-out SWE-Gym split, these results suggest the effectiveness of DAgger for modern long-horizon LM agents.

preprint2026arXiv

Spectral Visualization of Excitonic Pair Breaking at Individual Impurities in Ta2Pd3Te5

Excitonic insulators host the condensates of bound electron-hole pairs, offering a platform for studying correlated bosonic quantum states. Yet, how macroscopic coherence emerges from locally collapsed pairing remains elusive. Here, using scanning tunnelling spectroscopy, we report the impurity-induced pair breaking in an excitonic insulator Ta2Pd3Te5. Individual Te vacancies are found to generate a pair of spectral peaks within the excitonic gap. Their energies depend sensitively on the defect configurations and are continuously tunable by tip electric field, indicating controllable impurity scatterings. Spectral mapping shows spatially anisotropic and electronically coupled electron-hole components of the subgap states. These observations, together with mean-field modelling, suggest an excitonic pair-breaking origin. In the strongly electron-hole imbalanced region, a secondary pair-breaking effect, manifesting as an additional pair of subgap states with distinctly lower energies, can emerge, presenting the interplay of pairing breakings with different excitonic order parameters. Our findings demonstrate the spectroscopic 'fingerprint' of local excitonic depairing at the atomic level, offering a crucial clue to the critical behavior across excitonic condensation.

preprint2026arXiv

Speech-based Psychological Crisis Assessment using LLMs

Psychological support hotlines provide critical support for individuals experiencing mental health emergencies, yet current assessments largely rely on human operators whose judgments may vary with professional experience and are constrained by limited staffing resources. This paper proposes a large language model (LLM)-based framework for automated crisis level classification, a key indicator that supports many downstream tasks and improves the overall quality of hotline services. To better capture emotional signals in spoken conversations, we introduce a paralinguistic injection method that inserts identified non-verbal emotional cues into speech transcripts, enabling LLM-based reasoning to incorporate critical acoustic nuances. In addition, we propose a reasoning-enhanced training strategy that trains the model to generate diagnostic reasoning chains as an auxiliary task, which serves as a regulariser to improve classification performance. Combined with data augmentation, our final system achieves a macro F1-score of 0.802 and an accuracy of 0.805 on the three-class classification task under 5-fold cross-validation.

preprint2026arXiv

The locally complexified-gentle algebras

We call an $\mathbb{R}$-algebra locally complexified-gentle if it becomes a locally gentle $\mathbb{C}$-algebra up to Morita equivalence after complexification. We use modulated quivers to introduce two types of locally complexified-gentle algebras and show that they are Morita equivalent to some semilinear clannish algebras.

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

A Closed-loop Brain-Machine Interface SoC Featuring a 0.2$μ$J/class Multiplexer Based Neural Network

This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation. The first multiplexer with static assignment based table lookup solution (MUXnet) for multiplier-free NN processor was proposed. A state-of-the-art average accuracy of 82.4% was achieved with an energy consumption of only 0.2$μ$J/class in sleep staging task.

preprint2024arXiv

Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG

This paper proposed a Multi-Channel Multi-Domain (MCMD) based knowledge distillation algorithm for sleep staging using single-channel EEG. Both knowledge from different domains and different channels are learnt in the proposed algorithm, simultaneously. A multi-channel pre-training and single-channel fine-tuning scheme is used in the proposed work. The knowledge from different channels in the source domain is transferred to the single-channel model in the target domain. A pre-trained teacher-student model scheme is used to distill knowledge from the multi-channel teacher model to the single-channel student model combining with output transfer and intermediate feature transfer in the target domain. The proposed algorithm achieves a state-of-the-art single-channel sleep staging accuracy of 86.5%, with only 0.6% deterioration from the state-of-the-art multi-channel model. There is an improvement of 2% compared to the baseline model. The experimental results show that knowledge from multiple domains (different datasets) and multiple channels (e.g. EMG, EOG) could be transferred to single-channel sleep staging.

preprint2024arXiv

Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction

Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations. The fact that post-hoc methods can fail to reveal the actual original reasoning process of DNNs raises the need to build DNNs with built-in interpretability. Motivated by this, many self-explaining neural networks have been proposed to generate not only accurate predictions but also clear and intuitive insights into why a particular decision was made. However, existing self-explaining networks are limited in providing distribution-free uncertainty quantification for the two simultaneously generated prediction outcomes (i.e., a sample's final prediction and its corresponding explanations for interpreting that prediction). Importantly, they also fail to establish a connection between the confidence values assigned to the generated explanations in the interpretation layer and those allocated to the final predictions in the ultimate prediction layer. To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations. We perform the theoretical analysis for the proposed framework. Extensive experimental evaluation demonstrates the effectiveness of the proposed uncertainty framework.

preprint2023arXiv

Multiplayer Battle Game-Inspired Optimizer for Complex Optimization Problems

Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phases: movement and battle. Specifically, the movement phase incorporates the principles of commonly encountered ``safe zones'' to incentivize participants to relocate to areas with a higher survival potential. The battle phase simulates a range of strategies adopted by players in various situations to enhance the diversity of the population. To evaluate and analyze the performance of the proposed MBGO, we executed it alongside eight other algorithms, including three classics and five latest ones, across multiple diverse dimensions within the CEC2017 and CEC2020 benchmark functions. In addition, we employed several industrial design problems to evaluate the scalability and practicality of the proposed MBGO. The results of the statistical analysis reveal that the novel MBGO demonstrates significant competitiveness, excelling not only in convergence speed, but also in achieving high levels of convergence accuracy across both benchmark functions and real-world problems.

preprint2022arXiv

A deep machine learning potential for atomistic simulation of Fe-Si-O systems under Earth's outer core conditions

Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the long-standing challenge of accuracy versus efficiency in molecular dynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, we show that accurate and transferable ANN-ML potentials can be developed for reliable MD simulations of materials at high-pressure and high-temperature conditions of the Earth's outer core. The ANN-ML potential for Fe-Si-O system is trained by fitting to the energies and forces of related binaries and ternary liquid structures at high pressures and temperatures obtained by first-principles calculations based on density functional theory (DFT). We show that the generated ANN-ML potential describes well the structure and dynamics of liquid phases of this complex system. The efficient ANN-ML potential with DFT accuracy provides a promising scheme for accurate atomistic simulations of structures and dynamics of complex Fe-Si-O system in the Earth's outer core.

preprint2022arXiv

A Reachability Index for Recursive Label-Concatenated Graph Queries

Reachability queries checking the existence of a path from a source node to a target node are fundamental operators for querying and processing graph data. Current approaches for index-based evaluation of reachability queries either focus on plain reachability or constraint-based reachability with alternation only. In this paper, for the first time we study the problem of index-based processing for recursive label-concatenated reachability queries, referred to as RLC queries. These queries check the existence of a path that can satisfy the constraint defined by a concatenation of at most k edge labels under the Kleene plus. Many practical graph database and network analysis applications exhibit RLC queries. However, their evaluation remains prohibitive in current graph database engines. We introduce the RLC index, the first reachability index to efficiently process RLC queries. The RLC index checks whether the source vertex can reach an intermediate vertex that can also reach the target vertex under a recursive label-concatenated constraint. We propose an indexing algorithm to build the RLC index, which guarantees the soundness and the completeness of query execution and avoids recording redundant index entries. Comprehensive experiments on real-world graphs show that the RLC index can significantly reduce both the offline processing cost and the memory overhead of transitive closure while improving query processing up to six orders of magnitude over online traversals. Finally, our open-source implementation of the RLC index significantly outperforms current mainstream graph engines for evaluating RLC queries.

preprint2022arXiv

A Survey on Programmatic Weak Supervision

Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved remarkable success in easing the manual labeling bottleneck by programmatically synthesizing training labels from multiple potentially noisy supervision sources. This paper presents a comprehensive survey of recent advances in PWS. In particular, we give a brief introduction of the PWS learning paradigm, and review representative approaches for each component within PWS's learning workflow. In addition, we discuss complementary learning paradigms for tackling limited labeled data scenarios and how these related approaches can be used in conjunction with PWS. Finally, we identify several critical challenges that remain under-explored in the area to hopefully inspire future research directions in the field.

preprint2022arXiv

Abandoning the Bayer-Filter to See in the Dark

Low-light image enhancement - a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present a De-Bayer-Filter simulator based on deep neural networks to generate a monochrome raw image from the colored raw image. Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome raw data. Channel-wise attention is also introduced to the fusion process to establish a complementary interaction between features from colored and monochrome raw images. To train the convolutional networks, we propose a dataset with monochrome and color raw pairs named Mono-Colored Raw paired dataset (MCR) collected by using a monochrome camera without Bayer-Filter and a color camera with Bayer-Filter. The proposed pipeline take advantages of the fusion of the virtual monochrome and the color raw images and our extensive experiments indicate that significant improvement can be achieved by leveraging raw sensor data and data-driven learning.

preprint2022arXiv

Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience for consumers. However, accurately predicting product compatibility is difficult due to the heterogeneous product data and the lack of manually curated training data. We study the problem of discovering effective labeling rules that can enable weakly-supervised product compatibility prediction. We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions. AMRule adaptively discovers labeling rules from large-error instances via a boosting-style strategy, the high-quality rules can remedy the current model's weak spots and refine the model iteratively. For rule discovery from structured product attributes, we generate composable high-order rules from decision trees; and for rule discovery from unstructured product descriptions, we generate prompt-based rules from a pre-trained language model. Experiments on 4 real-world datasets show that AMRule outperforms the baselines by 5.98% on average and improves rule quality and rule proposal efficiency.

preprint2022arXiv

An EUV jet driven by a series of transition region micro-jets

Jets are one of the most common eruptive events in the solar atmosphere, and they are believed to be important in the context of coronal heating and solar wind acceleration. We present an observational study on a sequence of jets with the data acquired with the Solar Dynamics Observatory (SDO) and the Interface Region Imaging Spectrograph (IRIS). This sequence is peculiar in that an EUV jet, $\sim29\arcsec$ long and with a dome-like base, appears to be a consequence of a series of transition region (TR) micro-jets that are a few arcsecs in length.We find that the occurrence of any TR micro-jets is always associated with the change of geometry of micro-loops at the footpoints of the microjets. A bundle of TR flux ropes is seen to link a TR micro-jet to the dome-like structure at the base of the EUV jet. This bundle rises as a response to the TR micro-jets, with the rising motion eventually triggering the EUV jet. We propose a scenario involving a set of magnetic reconnections, in which the series of TR micro-jets are associated with the processes to remove the constraints to the TR flux ropes and thus allow them to rise and trigger the EUV jet. Our study demonstrates that small-scale dynamics in the lower solar atmosphere are crucial in understanding the energy and mass connection between the corona and the solar lower atmosphere, even though many of them might not pump mass and energy to the corona directly.

preprint2022arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VIII. A search for hot cores by using C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH lines

Hot cores characterized by rich lines of complex organic molecules are considered as ideal sites for investigating the physical and chemical environments of massive star formation. We present a search for hot cores by using typical nitrogen- and oxygen-bearing complex organic molecules (C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH), based on ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS). The angular resolutions and line sensitivities of the ALMA observations are better than 2 arcsec and 10 mJy/beam, respectively. A total of 60 hot cores are identified with 45 being newly detected, in which the complex organic molecules have high gas temperatures ($>$ 100 K) and small source sizes ($<$ 0.1 pc). So far this is the largest sample of hot cores observed with similar angular resolution and spectral coverage. The observations have also shown nitrogen and oxygen differentiation in both line emission and gas distribution in 29 hot cores. Column densities of CH$_3$OH and CH$_3$OCHO increase as rotation temperatures rise. The column density of CH$_3$OCHO correlates tightly with that of CH$_3$OH. The pathways for production of different species are discussed. Based on the spatial position difference between hot cores and UC~H{\sc ii} regions, we conclude that 24 hot cores are externally heated while the other hot cores are internally heated. The observations presented here will potentially help establish a hot core template for studying massive star formation and astrochemistry.

preprint2022arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- XI. From inflow to infall in hub-filament systems

We investigate the presence of hub-filament systems in a large sample of 146 active proto-clusters, using H$^{13}$CO$^{+}$ J=1-0 molecular line data obtained from the ATOMS survey. We find that filaments are ubiquitous in proto-clusters, and hub-filament systems are very common from dense core scales ($\sim$0.1 pc) to clump/cloud scales ($\sim$1-10 pc). The proportion of proto-clusters containing hub-filament systems decreases with increasing dust temperature ($T_d$) and luminosity-to-mass ratios ($L/M$) of clumps, indicating that stellar feedback from H{\sc ii} regions gradually destroys the hub-filament systems as proto-clusters evolve. Clear velocity gradients are seen along the longest filaments with a mean velocity gradient of 8.71 km s$^{-1}$pc$^{-1}$ and a median velocity gradient of 5.54 km s$^{-1}$pc$^{-1}$. We find that velocity gradients are small for filament lengths larger than $\sim$1~pc, probably hinting at the existence of inertial inflows, although we cannot determine whether the latter are driven by large-scale turbulence or large-scale gravitational contraction. In contrast, velocity gradients below $\sim$1~pc dramatically increase as filament lengths decrease, indicating that the gravity of the hubs or cores starts to dominate gas infall at small scales. We suggest that self-similar hub-filament systems and filamentary accretion at all scales may play a key role in high-mass star formation.

preprint2022arXiv

Boundedness of differential transforms for Poisson semigroups generated by Bessel operators

In this paper we analyze the convergence of the following type of series \begin{equation*} T_N f(x)=\sum_{j=N_1}^{N_2} v_j\Big(\mathcal{P}_{a_{j+1}} f(x)-\mathcal{P}_{a_{j}} f(x)\Big),\quad x\in \mathbb R_+, \end{equation*} where $\{\mathcal{P}_t \}_{t>0}$ is the Poisson semigroup of the Bessel operator $\displaystyle Δ_λ:=-{d^2\over dx^2}-{2λ\over x}{d\over dx}$ with $λ$ being a positive constant, $N=(N_1, N_2)\in \mathbb Z^2$ with $N_1<N_2,$ $\{v_j\}_{j\in \mathbb Z}$ is a bounded real sequences and $\{a_j\}_{j\in \mathbb Z}$ is an increasing real sequence. {Our analysis will consist in the boundedness, in $L^p(\mathbb{R}_+)$ and in $BMO(\mathbb{R}_+)$, of the operators $T_N$ and its maximal operator $ T^*f(x)= sup_N \abs{T_N f(x)}.$} It is also shown that the local size of the maximal differential transform operators is the same with the order of a singular integral for functions $f$ having local support.

preprint2022arXiv

CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general probabilistic multi-view forecasting framework CAMul, that can learn representations and uncertainty from diverse data sources. It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution. We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.

preprint2022arXiv

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.

preprint2022arXiv

Detecting electric charge with Extreme Mass Ratio Inspirals

We consider extreme mass ratio inspirals during which an electrically charged compact object with mass $m_p$ and the charge to mass ratio $q$ inspirals around a Schwarzschild black hole of mass $M$. Using the Teukolsky and generalized Sasaki-Nakamura formalisms for the gravitational and electromagnetic perturbations around a Schwarzschild black hole, we numerically calculate the energy flux of both gravitational and electromagnetic waves induced by a charged particle moving in circular orbits. With one year observation of these extreme mass ratio inspirals, we show that space-based gravitational wave detector such as the Laser Interferometer Space Antenna can detect the charge to mass ratio as small as $q\sim 10^{-3}$.

preprint2022arXiv

Detection of gravitational wave mixed polarization with single space-based detectors

General Relativity predicts only two tensor polarization modes for gravitational waves while at most six possible polarization modes are allowed in general metric theory of gravity. The number of polarization modes is determined by the specific modified theory of gravity. Therefore, the determination of polarization modes can be used to test gravitational theory. We introduce a concrete data analysis pipeline for a space-based detector such as LISA to detect the polarization modes of gravitational waves. This method can be used for monochromatic gravitational waves emitted from any compact binary system with known sky position and frequency to detect mixtures of tensor and extra polarization modes. We use the source J0806.3+1527 with one-year simulation data as an example to show that this approach is capable of probing pure and mixed polarizations without knowing the exact polarization modes. We also find that the ability of detection of extra polarization depends on the gravitational wave source location and the amplitude of non-tensorial components.

preprint2022arXiv

Detection of scalar fields by Extreme Mass Ratio Inspirals with a Kerr black hole

We study extreme mass ratio inspirals occurring in modified gravity, for which the system is modeled by a small compact object with scalar charge spiraling into a supermassive Kerr black hole. Besides the tensorial gravitational waves arising from the metric perturbations, radiations are also induced by the scalar field. The relevant metric and scalar perturbations are triggered by the orbital motion of the small object, which give rise to a system of inhomogeneous differential equations under the adiabatic approximation. Such a system of equations is then solved numerically using Green's function furnished by the solutions of the corresponding homogeneous equations. To explore the present scenario from an observational perspective, we investigate how the pertinent observables are dependent on specific spacetime configurations. In this regard, the energy fluxes and the gravitational wave dephasing accumulated during the process are evaluated, as functions of the scalar charge, mass ratio, and spin of the central supermassive black hole. In particular, the presence of additional scalar emission leads to a more significant rate of overall energy loss, which, in turn, decreases the total number of orbital cycles before the small object plunges into the central black hole. Moreover, for a central black hole with a higher spin, the imprints of the scalar charge on the resultant gravitational radiations are found to be more significant, which indicates the possibility of detecting the scalar charge.

preprint2022arXiv

Evaluation of cosmogenic production of $^{39}Ar$ and $^{42}Ar$ for rare-event physics using underground argon

Underground argon (UAr) with lower cosmogenic activities of $^{39}Ar$ and $^{42}Ar$ has been planned as a detector in detecting scintillation light and charge collection using time projection chambers for dark matter searches and as a veto detector in suppressing backgrounds for neutrinoless double beta decay (0$νββ$) experiments. Long-lived radioactive isotopes, $^{39}Ar$ and $^{42}Ar$, can also be produced on the surface when UAr is pumped out from a deep well. Understanding the production of long-lived isotopes in Ar is important for utilizing UAr for dark matter and 0$νββ$ experiments in terms of its production, transportation, and storage. Ar exposure to cosmic rays at sea-level is simulated using Geant4 for a given cosmic ray muon, neutron, and proton energy spectrum. We report the simulated cosmogenic production rates of $^{39}Ar$, $^{42}Ar$, and other long-lived isotopes at sea-level from fast neutrons, high energy muons, and high energy protons. Total production rates of 938.53/kg$_{Ar}\cdot$day and 5.81$\times$10$^{-3}$/kg$_{Ar}\cdot$day for $^{39}$Ar and $^{42}$Ar are found from our simulation. Utilizing these production rates, we set a time limit of 954 days constrained by the production of $^{39}$Ar for UAr to be on the surface before it compromises the sensitivity for a dark matter experiment. Similarly, a time limit of 1702 days constrained by the production of $^{42}$Ar is found for a 0$νββ$ experiment.

preprint2022arXiv

Example-based Color Transfer with Gaussian Mixture Modeling

Color transfer, which plays a key role in image editing, has attracted noticeable attention recently. It has remained a challenge to date due to various issues such as time-consuming manual adjustments and prior segmentation issues. In this paper, we propose to model color transfer under a probability framework and cast it as a parameter estimation problem. In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids. We employ the Expectation-Maximization (EM) algorithm (E-step and M-step) for optimization. To better preserve gradient information, we introduce a Laplacian based regularization term to the objective function at the M-step which is solved by deriving a gradient descent algorithm. Given the input of a source image and an example image, our method is able to generate continuous color transfer results with increasing EM iterations. Various experiments show that our approach generally outperforms other competitive color transfer methods, both visually and quantitatively.

preprint2022arXiv

Experimental beating the standard quantum limit under non-markovian dephasing environment

Entanglement enhanced quantum metrology has been well investigated for beating the standard quantum limit (SQL). However, the metrological advantage of entangled states becomes much more elusive in the presence of noise. Under strictly Markovian dephasing noise, the uncorrelated and maximally entangled states achieve exactly the same measurement precision. However, it was predicted that in a non-Markovian dephasing channel, the entangled probes can recover their metrological advantage. Here, by using a highly controlled photonic system, we simulate a non-Markovian dephasing channel fulfill the quadratic decay behaviour. Under such a channel, we demonstrate the GHZ states can surpass the SQL in a scaling manner, up to six photons. Since the quadratic decay behavior is quite general for short time expansion in open quantum systems (also known as the quantum Zeno effect), our results suggest a universal and scalable method to beat the SQL in the real-word metrology tasks.

preprint2022arXiv

FORCE: A Framework of Rule-Based Conversational Recommender System

The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational Recommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.

preprint2022arXiv

Gravitational axial perturbations of Schwarzschild-like black holes in dark matter halos

Gravitational waves emitted by distorted black holes, such as those arising from the coalescence of binary black holes, or a falling compact star into a supermassive black hole, carry not only information about the corresponding spacetime but also information about the environment surrounding the black holes. In this paper, we study the effects of the dark matter halos with three different density profiles on the gravitational axial perturbations of a Schwarzschild-like black hole. For this purpose, we first consider modified Schwarzschild black holes with three different dark matter profiles and derive the equation of motion of the axial perturbations of the modified Schwarzschild metric. It is shown that by ignoring the dark matter perturbations, a Regge-Wheeler-like master equation with a modified potential for the axial perturbation can be obtained explicitly. Then we calculate the complex frequencies of the quasi-normal modes of the Schwarzschild-like black hole in the dark matter halos by applying the sixth-order WKB method. The corresponding gravitational wave spectra with the effects of the dark matter halos have also been discussed.

preprint2022arXiv

Harnack inequality for the nonlocal equations with general growth

We consider a class of generalized nonlocal $p$-Laplacian equations. We find some proper structural conditions to establish a version of nonlocal Harnack inequalities of weak solutions to such nonlocal problems by using the expansion of positivity and energy estimates.

preprint2022arXiv

Hierarchical Visual Interface for Lecture Video Retrieval and Summarization

With the emergence of large-scale open online courses and online academic conferences, it has become increasingly feasible and convenient to access online educational resources. However, it is time consuming and challenging to effectively retrieve and present numerous lecture videos for common users. In this work, we propose a hierarchical visual interface for retrieving and summarizing lecture videos. Users can utilize the proposed interface to effectively explore the required video information through the results of the video summary generation in different layers. We retrieve the input keywords with the corresponding video layer with timestamps, a frame layer with slides, and the poster layer with summarization of the lecture videos. We verified the proposed interface with our user study by comparing it with other conventional interfaces. The results from our user study confirmed that the proposed interface can achieve high retrieval accuracy and good user experience.see video here https://www.youtube.com/watch?v=zrnejwsOVpc .

preprint2022arXiv

Hölder regularity for mixed local and nonlocal $p$-Laplace parabolic equations

We give a unified proof of Hölder regularity of weak solutions for mixed local and nonlocal $p$-Laplace type parabolic equations with the full range of exponents $1<p<\infty$. Our proof is based on the expansion of positivity together with the energy estimate and De Giorgi type lemma.

preprint2022arXiv

Image Deformation Estimation via Multi-Objective Optimization

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

preprint2022arXiv

Imprints of dark matter on gravitational ringing of supermassive black holes

Gravitational waves emitted from the gravitational ringing of supermassive black holes are important targets to test general relativity and probe the matter environment surrounding such black holes. The main components of the ringing waveform are black hole quasi-normal modes. In this paper, we study the effects of the dark matter halos with three different density profiles on the gravitational polar (even-parity) perturbations of a supermassive black hole. For this purpose, we first consider modified Schwarzschild spacetime with three different dark matter profiles and derive the equation of motion of the polar perturbations of the supermassive black hole. It is shown that by ignoring the dark matter perturbations, a Zerilli-like master equation with a modified potential for the polar perturbation can be obtained explicitly. Then we calculate the complex frequencies of the quasi-normal modes of the supermassive black hole in the dark matter halos. The corresponding gravitational wave spectra with the effects of the dark matter halos and their detectability have also been discussed.

preprint2022arXiv

Improving the fusion of acoustic and text representations in RNN-T

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR). To estimate the output distributions over subword units, RNN-T uses a fully connected layer as the joint network to fuse the acoustic representations extracted using the acoustic encoder with the text representations obtained using the prediction network based on the previous subword units. In this paper, we propose to use gating, bilinear pooling, and a combination of them in the joint network to produce more expressive representations to feed into the output layer. A regularisation method is also proposed to enable better acoustic encoder training by reducing the gradients back-propagated into the prediction network at the beginning of RNN-T training. Experimental results on a multilingual ASR setting for voice search over nine languages show that the joint use of the proposed methods can result in 4%--5% relative word error rate reductions with only a few million extra parameters.

preprint2022arXiv

Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition

End-to-end models have achieved state-of-the-art results on several automatic speech recognition tasks. However, they perform poorly when evaluated on long-form data, e.g., minutes long conversational telephony audio. One reason the model fails on long-form speech is that it has only seen short utterances during training. In this paper we study the effect of training utterance length on the word error rate (WER) for RNN-transducer (RNN-T) model. We compare two widely used training objectives, log loss (or RNN-T loss) and minimum word error rate (MWER) loss. We conduct experiments on telephony datasets in four languages. Our experiments show that for both losses, the WER on long-form speech reduces substantially as the training utterance length increases. The average relative WER gain is 15.7% for log loss and 8.8% for MWER loss. When training on short utterances, MWER loss leads to a lower WER than the log loss. Such difference between the two losses diminishes when the input length increases.

preprint2022arXiv

jTrans: Jump-Aware Transformer for Binary Code Similarity

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.

preprint2022arXiv

Learning a Structured Latent Space for Unsupervised Point Cloud Completion

Unsupervised point cloud completion aims at estimating the corresponding complete point cloud of a partial point cloud in an unpaired manner. It is a crucial but challenging problem since there is no paired partial-complete supervision that can be exploited directly. In this work, we propose a novel framework, which learns a unified and structured latent space that encoding both partial and complete point clouds. Specifically, we map a series of related partial point clouds into multiple complete shape and occlusion code pairs and fuse the codes to obtain their representations in the unified latent space. To enforce the learning of such a structured latent space, the proposed method adopts a series of constraints including structured ranking regularization, latent code swapping constraint, and distribution supervision on the related partial point clouds. By establishing such a unified and structured latent space, better partial-complete geometry consistency and shape completion accuracy can be achieved. Extensive experiments show that our proposed method consistently outperforms state-of-the-art unsupervised methods on both synthetic ShapeNet and real-world KITTI, ScanNet, and Matterport3D datasets.

preprint2022arXiv

Learning Disentangled Representations for Controllable Human Motion Prediction

Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lower-body motions. However, to achieve this, the state-of-the-art methods require either subsequently learning mapping functions to seek similar motions or training the model repetitively to enable control over the desired portion of body. In this paper, we propose a novel framework to learn disentangled representations for controllable human motion prediction. Our network involves a conditional variational auto-encoder (CVAE) architecture to model full-body human motion, and an extra CVAE path to learn only the corresponding partial-body (e.g., lower-body) motion. Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion. With a single training, our model is able to provide two types of controls for the generated human motions: (i) strictly controlling one portion of human body and (ii) adaptively controlling the other portion, by sampling from a pair of latent spaces. Additionally, we extend and adapt a sampling strategy to our trained model to diversify the controllable predictions. Our framework also potentially allows new forms of control by flexibly customizing the input for the extra CVAE path. Extensive experimental results and ablation studies demonstrate that our approach is capable of predicting state-of-the-art controllable human motions both qualitatively and quantitatively.

preprint2022arXiv

Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

In the past few years, transformers have achieved promising performances on various computer vision tasks. Unfortunately, the immense inference overhead of most existing vision transformers withholds their from being deployed on edge devices such as cell phones and smart watches. Knowledge distillation is a widely used paradigm for compressing cumbersome architectures via transferring information to a compact student. However, most of them are designed for convolutional neural networks (CNNs), which do not fully investigate the character of vision transformer (ViT). In this paper, we utilize the patch-level information and propose a fine-grained manifold distillation method. Specifically, we train a tiny student model to match a pre-trained teacher model in the patch-level manifold space. Then, we decouple the manifold matching loss into three terms with careful design to further reduce the computational costs for the patch relationship. Equipped with the proposed method, a DeiT-Tiny model containing 5M parameters achieves 76.5% top-1 accuracy on ImageNet-1k, which is +2.0% higher than previous distillation approaches. Transfer learning results on other classification benchmarks and downstream vision tasks also demonstrate the superiority of our method over the state-of-the-art algorithms.

preprint2022arXiv

Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we propose a novel approach to predicting future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce the energy-based formulation to flexibly impose prior losses over the learnable parameters of MDN to maintain motion coherence as well as improve the prediction accuracy by customizing the energy functions. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method in terms of prediction diversity and accuracy.

preprint2022arXiv

Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications

In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random variables. We obtain several types of tail inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound the expectation of the spectral norm of a matrix i.d. series. Moreover, by developing a new lower-bound function for $Q(s)=(s+1)\log(s+1)-s$ that appears in the Bennett-type inequality, we derive a tighter tail inequality of the largest eigenvalue of the matrix i.d. series than the Bernstein-type inequality when the matrix dimension is high. The resulting lower-bound function is of independent interest and can improve any Bennett-type concentration inequality that involves the function $Q(s)$. The class of i.d. probability distributions is large and includes Gaussian and Poisson distributions, among many others. Therefore, our results encompass the existing work \cite{tropp2012user} on matrix Gaussian series as a special case. Lastly, we show that the tail inequalities of a matrix i.d. series have applications in several optimization problems including the chance constrained optimization problem and the quadratic optimization problem with orthogonality constraints. In addition, we also use the resulting tail bounds to show that random matrices constructed from i.d. random variables satisfy the restricted isometry property (RIP) when it acts as a measurement matrix in compressed sensing.

preprint2022arXiv

Measuring Small Longitudinal Phase Shifts via Weak Measurement Amplification

Weak measurement amplification, which is considered as a very promising scheme in precision measurement, has been applied to various small physical quantities estimation. Since many quantities can be converted to phase signal, it is thus interesting and important to consider measuring ultra-small longitudinal phase shifts by using weak measurement. Here, we propose and experimentally demonstrate a novel weak measurement amplification based ultra-small longitudinal phase estimation, which is suitable for polarization interferometry. We realize one order of magnitude amplification measurement of small phase signal directly introduced by Liquid Crystal Variable Retarder and show its robust to finite visibility of interference. Our results may find important applications in high-precision measurements, such as gravitational waves detection.

preprint2022arXiv

Negative Chemical Pressure Effect on Superconductivity and Charge Density Wave of Cu0.5Ir1-xZrxTe2

This study demonstrates the design and synthesis of Cu0.5Ir1-xZrxTe2 system by partial substitution of Ir with Zr acting as a negative chemical pressure. With the doping of Zr, the cell parameters significantly expand, signifying an effective negative chemical pressure. The experimental results found evidence that the charge density wave (CDW)-like order is immediately quenched by subtle Zr substitution for Ir and a classical dome-shape Tc(x) that peaked at 2.80 K can be observed. The optimal Cu0.5Ir0.95Zr0.05Te2 compound is a BCS-type superconductor and exhibits type-II SC. However, high Zr concentration can provoke disorder, inducing the reappearance of CDW order. The present study shows that the Cu0.5Ir1-xZrxTe2 system may provide a new platform for further understanding of multiple electronic orders in transition metal dichalcogenides.

preprint2022arXiv

Optimizing Topological Switching in Confined 2D-Xene Nanoribbons via Finite-Size Effects

In a blueprint for topological electronics, edge state transport in a topological insulator material can be controlled by employing a gate-induced topological quantum phase transition. Here, by studying the width dependence of electronic properties, it is inferred that zigzag-Xene nanoribbons are promising materials for topological electronics with a display of unique physical characteristics associated with the intrinsic band topology and the finite-size effects on gate-induced topological switching. First, due to intertwining with intrinsic band topology-driven energy-zero modes in the pristine case, spin-filtered chiral edge states in zigzag-Xene nanoribbons remain gapless and protected against backward scattering even with finite inter-edge overlapping in ultra-narrow ribbons, i.e., a 2D quantum spin Hall material turns into a 1D topological metal. Second, mainly due to width- and momentum-dependent tunability of the gate-induced inter-edge coupling, the threshold-voltage required for switching between gapless and gapped edge states reduces as the width decreases, without any fundamental lower bound. Third, when the width of zigzag-Xene nanoribbons is smaller than a critical limit, topological switching between edge states can be attained without bulk bandgap closing and reopening. This is primarily due to the quantum confinement effect on the bulk band spectrum which increases the nontrivial bulk bandgap with decrease in width. The existence of such protected gapless edge states and reduction in threshold-voltage accompanied by enhancement in the bulk bandgap overturns the general wisdom of utilizing narrow-gap and wide channel materials for reducing the threshold-voltage in a standard field effect transistor analysis and paves the way toward low-voltage topological devices.

preprint2022arXiv

PACSan: Enforcing Memory Safety Based on ARM PA

Memory safety is a key security property that stops memory corruption vulnerabilities. Existing sanitizers enforce checks and catch such bugs during development and testing. However, they either provide partial memory safety or have overwhelmingly high performance overheads. Our novel sanitizer PACSan enforces spatial and temporal memory safety with no false positives at low performance overheads. PACSan removes the majority of the overheads involved in pointer tracking by sealing metadata in pointers through ARM PA (Pointer Authentication), and performing the memory safety checks when pointers are dereferenced. We have developed a prototype of PACSan and systematically evaluated its security and performance on the Magma, Juliet, Nginx, and SPEC CPU2017 test suites, respectively. In our evaluation, PACSan shows no false positives together with negligible false negatives, while introducing stronger security guarantees and lower performance overheads than state-of-the-art sanitizers, including HWASan, ASan, SoftBound+CETS, Memcheck, LowFat, and PTAuth. Specifically, PACSan has 0.84x runtime overhead and 1.92x memory overhead on average. Compared to the widely deployed ASan, PACSan has no false positives and much fewer false negatives and reduces 7.172% runtime overheads and 89.063%memory overheads.

preprint2022arXiv

Practical considerations for the effect of finite coverage on the azimuthal dependence of global spin alignment

The global spin alignment of vector mesons is a powerful probe to study the vorticity field of the system produced in non-central relativistic heavy-ion collisions. Since the experimental observables of global spin alignment are sensitive to many factors, proper corrections are need to be taken care of when measuring the global spin alignment of vector mesons. In this paper, we propose a method to correct the effect of finite pseudo-rapidity coverage when extract the azimuthal dependence of spin alignment parameter $ρ_{00}$. The effects of the finite pseudo-rapidity coverage on the azimuthal dependence of $ϕ$ meson spin alignment and the potentially exist intrinsic spin alignment effects are taken into consideration. The method presented in this paper is verified in a Monte-Carlo simulation, it allows the measurements of azimuthal dependence of global spin alignment to be conducted properly and accurately.

preprint2022arXiv

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning -- the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model. Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting. It uses boosting to identify large-error instances and then discovers candidate rules from them by prompting pre-trained LMs with rule templates. The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model. Experiments on four tasks show PRBoost outperforms state-of-the-art WSL baselines up to 7.1% and bridges the gaps with fully supervised models. Our Implementation is available at \url{https://github.com/rz-zhang/PRBoost}.

preprint2022arXiv

Quantum simulation of indefinite causal order induced quantum refrigeration

In the classical world, physical events always happen in a fixed causal order. However, it was recently revealed that quantum mechanics allows events to occur with indefinite causal order (ICO). In this study, we use an optical quantum switch to experimentally investigate the application of ICO in thermodynamic tasks. Specifically, we simulate the working system interacting with two identical thermal reservoirs in an ICO, observing the quantum heat extraction even though they are in thermal equilibrium where heat extraction is unaccessible by traditional thermal contact. Using such a process, we simulate an ICO refrigeration cycle and investigate its properties. We also show that by passing through the ICO channel multiple times, one can extract more heat per cycle and thus obtain a higher refrigeration performance. Our results suggest that the causal nonseparability can be a powerful resource for quantum thermodynamic tasks.

preprint2022arXiv

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems. However, there still remain some challenges in the application of PINNs: 1) the mechanism of PINNs is unsuitable (at least cannot be directly applied) to exploiting a small size of (usually very few) extra informative samples to refine the networks; and 2) the efficiency of training PINNs often becomes low for some complicated PDEs. In this paper, we propose the generative adversarial physics-informed neural network (GA-PINN), which integrates the generative adversarial (GA) mechanism with the structure of PINNs, to improve the performance of PINNs by exploiting only a small size of exact solutions to the PDEs. Inspired from the weighting strategy of the Adaboost method, we then introduce a point-weighting (PW) method to improve the training efficiency of PINNs, where the weight of each sample point is adaptively updated at each training iteration. The numerical experiments show that GA-PINNs outperform PINNs in many well-known PDEs and the PW method also improves the efficiency of training PINNs and GA-PINNs.

preprint2022arXiv

Robust mixed finite element methods for a quad-curl singular perturbation problem

Robust mixed finite element methods are developed for a quad-curl singular perturbation problem. Lower order H(grad curl)-nonconforming but H(curl)-conforming finite elements are constructed, which are extended to nonconforming finite element Stokes complexes and the associated commutative diagrams. Then H(grad curl)-nonconforming finite elements are employed to discretize the quad-curl singular perturbation problem, which possess the sharp and uniform error estimates with respect to the perturbation parameter. The Nitsche's technique is exploited to achieve the optimal convergence rate in the case of the boundary layers. Numerical results are provided to verify the theoretical convergence rates. In addition, the regularity of the quad-curl singular perturbation problem is established.

preprint2022arXiv

Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval

Unsupervised image retrieval aims to learn an efficient retrieval system without expensive data annotations, but most existing methods rely heavily on handcrafted feature descriptors or pre-trained feature extractors. To minimize human supervision, recent advance proposes deep fully unsupervised image retrieval aiming at training a deep model from scratch to jointly optimize visual features and quantization codes. However, existing approach mainly focuses on instance contrastive learning without considering underlying semantic structure information, resulting in sub-optimal performance. In this work, we propose a novel self-supervised consistent quantization approach to deep fully unsupervised image retrieval, which consists of part consistent quantization and global consistent quantization. In part consistent quantization, we devise part neighbor semantic consistency learning with codeword diversity regularization. This allows to discover underlying neighbor structure information of sub-quantized representations as self-supervision. In global consistent quantization, we employ contrastive learning for both embedding and quantized representations and fuses these representations for consistent contrastive regularization between instances. This can make up for the loss of useful representation information during quantization and regularize consistency between instances. With a unified learning objective of part and global consistent quantization, our approach exploits richer self-supervision cues to facilitate model learning. Extensive experiments on three benchmark datasets show the superiority of our approach over the state-of-the-art methods.

preprint2022arXiv

Self-Training with Differentiable Teacher

Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the teacher generates pseudo-labels, and the student makes predictions. The two models are updated alternatingly. However, such a straightforward alternating update rule leads to training instability. This is because a small change in the teacher may result in a significant change in the student. To address this issue, we propose DRIFT, short for differentiable self-training, that treats teacher-student as a Stackelberg game. In this game, a leader is always in a more advantageous position than a follower. In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels. Therefore, we treat the student as the leader and the teacher as the follower. The leader procures its advantage by acknowledging the follower's strategy, which involves differentiable pseudo-labels and differentiable sample weights. Consequently, the leader-follower interaction can be effectively captured via Stackelberg gradient, obtained by differentiating the follower's strategy. Experimental results on semi- and weakly-supervised classification and named entity recognition tasks show that our model outperforms existing approaches by large margins.

preprint2022arXiv

Semi-Integrated-Sensing-and-Communication (Semi-ISaC) Networks Assisted by NOMA

This paper investigates non-orthogonal multiple access (NOMA) assisted integrated sensing and communication (ISaC) networks. Compared to the conventional ISaC networks, where the total bandwidth is used for both the radar detection and wireless communications, the proposed Semi-ISaC networks allow that a portion of bandwidth is used for ISaC and the rest of the bandwidth is only utilized for wireless communications. We first derive the analytical expressions of the outage probability for the communication signals, including the signals for the radar target and the communication transmitter. Additionally, we derive the analytical expressions of the ergodic radar estimation information rate (REIR) for the radar echoes. The simulation results show that 1) NOMA ISaC has better spectrum efficiency than the conventional ISaC; and 2) The REIR is enhanced when we enlarge the density of pulses.

preprint2022arXiv

Shift-Robust Node Classification via Graph Adversarial Clustering

Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption methods can be adopted for data shift, yet most of them are designed to only encourage similar feature distribution between source and target data. Conditional shift on classes can still affect such adaption. Fortunately, graph yields graph homophily across different data distributions. In response, we propose Shift-Robust Node Classification (SRNC) to address these limitations. We introduce an unsupervised cluster GNN on target graph to group the similar nodes by graph homophily. An adversarial loss with label information on source graph is used upon clustering objective. Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN. We conduct experiments on both open-set shift and representation-shift, which demonstrates the superior accuracy of SRNC on generalizing to test graph with data shift. SRNC is consistently better than previous SoTA domain adaption algorithm on graph that progressively use model predictions on target graph for training.

preprint2022arXiv

SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm

Graph matching finds the correspondence of nodes across two correlated graphs and lies at the core of many applications. When graph side information is not available, the node correspondence is estimated on the sole basis of network topologies. In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure. Specifically, SI incorporates the heat diffusion wavelet to accommodate the multi-hop structure of the graphs. Based on SI, we propose a Structural Inconsistency reducing Graph Matching Algorithm (SIGMA), which improves the alignment scores of node pairs that have low SI values in each iteration. Under suitable assumptions, SIGMA can reduce SI values of true counterparts. Furthermore, we demonstrate that SIGMA can be derived by using a mirror descent method to solve the Gromov-Wasserstein distance with a novel K-hop-structure-based matching costs. Extensive experiments show that our method outperforms state-of-the-art methods.

preprint2022arXiv

Source localizations with the network of space-based gravitational wave detectors

The sky localization of the gravitational wave (GW) source is an important scientific objective for GW observations. A network of space-based GW detectors dramatically improves the sky localization accuracy compared with an individual detector not only in the inspiral stage but also in the ringdown stage. It is interesting to explore what plays an important role in the improvement. We find that the angle between the detector planes dominates the improvement, and the time delay is the next important factor. A detector network can dramatically improve the source localization for short signals and long signals with most contributions to the signal-to-noise ratio (SNR) coming from a small part of the signal in a short time, and the more SNR contributed by smaller parts, the better improvement by the network. We also find the effects of the arm length in the transfer function and higher harmonics are negligible for source localization with the detector network.

preprint2022arXiv

Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels. To work well, the label model needs to contextually identify and emphasize well-performed LFs while down-weighting the under-performers. However, evaluating the LFs is challenging due to the lack of ground truths. To address this issue, we propose the sparse conditional hidden Markov model (Sparse-CHMM). Instead of predicting the entire emission matrix as other HMM-based methods, Sparse-CHMM focuses on estimating its diagonal elements, which are considered as the reliability scores of the LFs. The sparse scores are then expanded to the full-fledged emission matrix with pre-defined expansion functions. We also augment the emission with weighted XOR scores, which track the probabilities of an LF observing incorrect entities. Sparse-CHMM is optimized through unsupervised learning with a three-stage training pipeline that reduces the training difficulty and prevents the model from falling into local optima. Compared with the baselines in the Wrench benchmark, Sparse-CHMM achieves a 3.01 average F1 score improvement on five comprehensive datasets. Experiments show that each component of Sparse-CHMM is effective, and the estimated LF reliabilities strongly correlate with true LF F1 scores.

preprint2022arXiv

Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification

Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task. In this paper, we propose to modify the structure of the cascaded-encoder-based recurrent neural network transducer (RNN-T) model by integrating a per-frame language identifier (LID) predictor. RNN-T with cascaded encoders can achieve streaming ASR with low latency using first-pass decoding with no right-context, and achieve lower word error rates (WERs) using second-pass decoding with longer right-context. By leveraging such differences in the right-contexts and a streaming implementation of statistics pooling, the proposed method can achieve accurate streaming LID prediction with little extra test-time cost. Experimental results on a voice search dataset with 9 language locales shows that the proposed method achieves an average of 96.2% LID prediction accuracy and the same second-pass WER as that obtained by including oracle LID in the input.

preprint2022arXiv

Superconductivity with the enhanced upper critical field in the Pt-Doping CuRh2Se4 spinel

We report the effect of Pt doping on the superconductivity in CuRh2Se4 spinel using a combined experimental and theoretical study. Our XRD results reveal that the Cu(Rh1-xPtx)2Se4 crystallizes in the structure with a space group of Fd3-m (No. 227), and the lattice parameter a increases with Pt doping. The resistivity and magnetic susceptibility measurement results verify that the superconducting transition temperature (Tc) forms a dome-like shape with a maximum value of 3.84 K at x = 0.06. It is also observed that the Pt-doping slightly reduces the lower critical magnetic field from 220 Oe in CuRh2Se4 to 168 Oe in Cu(Rh0.94Pt0.06)2Se4, while it significantly enhances the upper critical magnetic field, reaching the maximum of 4.93 T in the Cu(Rh0.94Pt0.06)2Se4 sample. The heat capacity result indicates that the sample Cu(Rh0.91Pt0.09)2Se4 is a bulk superconductor. First-principles calculations suggest that the Pt-doping leads to a red-shift of a density of state peak near the Fermi level, consistent with the dome-like Tc observed experimentally.

preprint2022arXiv

Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction

Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modeling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, though not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.

preprint2022arXiv

Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription

Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker diarisation, the tasks of voice activity detection (VAD) and speaker classification (SC) are required, and connectionist temporal classification (CTC) is used for ASR. The multitask framework implements VAD, SC, and ASR using an early layer, middle layer, and late layer of W2V2, which coincides with the order of segmenting the audio with VAD, clustering the segments based on speaker embeddings, and transcribing each segment with ASR. Experimental results on the augmented multi-party (AMI) dataset showed that using different W2V2 layers for VAD, SC, and ASR from the earlier to later layers for TMT not only saves computational cost, but also reduces diarisation error rates (DERs). Joint fine-tuning of VAD, SC, and ASR yielded 16%/17% relative reductions of DER with manual/automatic segmentation respectively, and consistent reductions in speaker attributed word error rate, compared to the baseline with separately fine-tuned models.

preprint2022arXiv

The boundedness and Hölder continuity of weak solutions to elliptic equations involving variable exponents and critical growth

In this paper we prove the boundedness and Hölder continuity of quasilinear elliptic problems involving variable exponents for a homogeneous Dirichlet and a nonhomogeneous Neumann boundary condition, respectively. The novelty of our work is the fact that we allow critical growth even on the boundary and so we close the gap in the papers of Fan-Zhao [Nonlinear Anal. 36 (1999), no. 3, 295--318.] and Winkert-Zacher [Discrete Contin. Dyn. Syst. Ser. S 5 (2012), no. 4, 865--878.] in which the critical cases are excluded. Our approach is based on a modified version of De Giorgi's iteration technique along with the localization method. As a consequence of our results, the $C^{1,α}$-regularity follows immediately.

preprint2022arXiv

Transference number in polymer electrolytes: mind the reference-frame gap

The transport coefficients, in particular the transference number, of electrolyte solutions are important design parameters for electrochemical energy storage devices. Recent observation of negative transference numbers in PEO-LiTFSI under certain conditions has generated much discussion about its molecular origins, by both experimental and theoretical means. However, one overlooked factor in these efforts is the importance of the reference frame (RF). This creates a non-negligible gap when comparing experiment and simulation, because the fluxes in the experimental measurements of transport coefficients and in the linear response theory used in the molecular dynamics simulation are defined in different RFs. In this work, we show that by applying a proper RF transformation, a much improved agreement between experimental and simulation results can be achieved. Moreover, it is revealed that the anion mass and the anion-anion correlation, rather than ion aggregates, play a crucial role for the reported negative transference numbers.

preprint2022arXiv

Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition

Incorporating biasing words obtained as contextual knowledge is critical for many automatic speech recognition (ASR) applications. This paper proposes the use of graph neural network (GNN) encodings in a tree-constrained pointer generator (TCPGen) component for end-to-end contextual ASR. By encoding the biasing words in the prefix-tree with a tree-based GNN, lookahead for future wordpieces in end-to-end ASR decoding is achieved at each tree node by incorporating information about all wordpieces on the tree branches rooted from it, which allows a more accurate prediction of the generation probability of the biasing words. Systems were evaluated on the Librispeech corpus using simulated biasing tasks, and on the AMI corpus by proposing a novel visual-grounded contextual ASR pipeline that extracts biasing words from slides alongside each meeting. Results showed that TCPGen with GNN encodings achieved about a further 15% relative WER reduction on the biasing words compared to the original TCPGen, with a negligible increase in the computation cost for decoding.

preprint2022arXiv

Turn-Taking Prediction for Natural Conversational Speech

While a streaming voice assistant system has been used in many applications, this system typically focuses on unnatural, one-shot interactions assuming input from a single voice query without hesitation or disfluency. However, a common conversational utterance often involves multiple queries with turn-taking, in addition to disfluencies. These disfluencies include pausing to think, hesitations, word lengthening, filled pauses and repeated phrases. This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task. To better model the conversational interaction, it is critical to discriminate disfluencies and end of query in order to allow the user to hold the floor for disfluencies while having the system respond as quickly as possible when the user has finished speaking. In this paper, we present a turntaking predictor built on top of the end-to-end (E2E) speech recognizer. Our best system is obtained by jointly optimizing for ASR task and detecting when the user is paused to think or finished speaking. The proposed approach demonstrates over 97% recall rate and 85% precision rate on predicting true turn-taking with only 100 ms latency on a test set designed with 4 types of disfluencies inserted in conversational utterances.

preprint2021arXiv

A FAST Survey of HINSA in PGCCs Guided by HC3N

Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we search for HI narrow-line self-absorption (HINSA) features in twelve Planck Galactic cold clumps (PGCCs), one starless core L1521B and four star forming sources. Eight of the 12 PGCCs have emission of J=2-1 of cyanoacetylene (HC3N). With an improved HINSA extraction method more robust for weaker and blended features with high velocity resolution, the detection rates of HINSA in PGCCCs are high, at 92% overall (11/12) and 87% (7/8) among sources with HC3N J=2-1 emissions. Combining the data of molecular spectra and Planck continuum maps, we studied the morphologies, abundances and excitations of HI, CO and HC3N in PGCCs. The distribution of HINSA is similar to that of CO emission. HINSA tends to be not detected in regions associated with warm dust and background ionizing radiation, as well as regions associated with stellar objects. The abundances of HI in PGCCs are approximately 3E-4, and vary within a factor of ~3. The non-thermal velocity dispersions traced by C18O J=1-0 and HINSA are consistent with each other (0.1-0.4 km/s), larger than those of HC3N (~0.1 km/s). Carbon chain molecule abundant PGCCs provide a good sample to study HINSA.

preprint2021arXiv

Auto-encoder based Model for High-dimensional Imbalanced Industrial Data

With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effectively consumed without the help of data-driven techniques. Deep learning is emerging as a promising technique to analyze these data, particularly in soft sensor modeling. The strong representational capabilities of complex data and the flexibility it offers from an architectural perspective make it a topic of active applied research in industrial settings. However, the successful applications of deep learning in soft sensing are still not widely integrated in factory control systems, because most of the research on soft sensing do not have access to large scale industrial data which are varied, noisy and incomplete. The results published in most research papers are therefore not easily reproduced when applied to the variety of data in industrial settings. Here we provide manufacturing data sets that are much larger and more complex than public open soft sensor data. Moreover, the data sets are from Seagate factories on active service with only necessary anonymization, so that they reflect the complex and noisy nature of real-world data. We introduce a variance weighted multi-headed auto-encoder classification model that fits well into the high-dimensional and highly imbalanced data. Besides the use of weighting or sampling methods to handle the highly imbalanced data, the model also simultaneously predicts multiple outputs by exploiting output-supervised representation learning and multi-task weighting.

preprint2021arXiv

Bond Bipolarons: Sign-free Monte Carlo Approach

Polarons originating from phonon displacement modulated hopping have relatively light masses and, thus, are of significant current interest as candidates for bipolaron mechanism of high-temperature superconductivity [Phys. Rev. Lett. {\bf 121}, 247001 (2018)]. We observe that the bond model, when the dominant coupling comes from atomic vibrations on lattice bonds, can be solved by efficient sign-free Monte Carlo methods based on the path-integral formulation of the particle sector in combination with either the (real-space) diagrammatic or Fock-path-integral representation of the phonon sector. We introduce the corresponding algorithms and provide illustrative results for bipolarons in two dimensions. The results suggest that the route towards high-temperature superconductivity (if any) in the multiparametric space of the model lies between the Scylla of large size of moderately light bipolarons and Charybdis of large mass of compact bipolarons. As a result, on-site repulsion is helping $s$-wave superconductivity in sharp contrast with existing expectations.

preprint2021arXiv

Certification of Genuine Multipartite Entanglement with General and Robust Device-independent Witnesses

Genuine multipartite entanglement represents the strongest type of entanglement, which is an essential resource for quantum information processing. Standard methods to detect genuine multipartite entanglement, e.g., entanglement witnesses, state tomography, or quantum state verification, require full knowledge of the Hilbert space dimension and precise calibration of measurement devices, which are usually difficult to acquire in an experiment. The most radical way to overcome these problems is to detect entanglement solely based on the Bell-like correlations of measurement outcomes collected in the experiment, namely, device-independently (DI). However, it is difficult to certify genuine entanglement of practical multipartite states in this way, and even more difficult to quantify it, due to the difficulty to identify optimal multipartite Bell inequalities and protocols tolerant to state impurity. In this work, we explore a general and robust DI method which can be applied to various realistic multipartite quantum state in arbitrary finite dimension, while merely relying on bipartite Bell inequalities. Our method allows us both to certify the presence of genuine multipartite entanglement and to quantify it. Several important classes of entangled states are tested with this method, leading to the detection of genuinely entangled states. We also certify genuine multipartite entanglement in weakly-entangled GHZ states, thus showing that the method applies equally well to less standard states.

preprint2021arXiv

Coincidence inelastic neutron scattering for detection of two-spin magnetic correlations

Inelastic neutron scattering (INS) is one powerful technique to study the low-energy single-spin dynamics of magnetic materials. A variety of quantum magnets show novel magnetic correlations such as quantum spin liquids. These novel magnetic correlations are beyond the direct detection of INS. In this paper we propose a coincidence technique, coincidence inelastic neutron scattering (cINS), which can detect the two-spin magnetic correlations of the magnetic materials. In cINS there are two neutron sources and two neutron detectors with an additional coincidence detector. Two neutrons from the two neutron sources are incident on the target magnetic material, and they are scattered by the electron spins of the magnetic material. The two scattered neutrons are detected by the two neutron detectors in coincidence with the coincidence probability described by a two-spin Bethe-Salpeter wave function. Since the two-spin Bethe-Salpeter wave function defines the momentum-resolved dynamical wave function with two spins excited, cINS can explicitly detect the two-spin magnetic correlations of the magnetic material. Thus, it can be introduced to study the various spin valence bond states of the quantum magnets.

preprint2021arXiv

Diffuse interface relaxation model for two-phase compressible flows with diffusion processes

The paper addresses a two-temperature model for simulating compressible two-phase flow taking into account diffusion processes related to the heat conduction and viscosity of the phases. This model is reduced from the two-phase Baer-Nunziato model in the limit of complete velocity relaxation and consists of the phase mass and energy balance equations, the mixture momentum equation, and a transport equation for the volume fraction.Terms describing effects of mechanical relaxation, temperature relaxation, and thermal conduction on volume fraction evolution are derived and demonstrated to be significant for heat conduction problems. The thermal conduction leads to instantaneous thermal relaxation so that the temperature equilibrium is always maintained in the interface region with meeting the entropy relations. A numerical method is developed to solve the model governing equations that ensures the pressure-velocity-temperature (PVT) equilibrium condition in its high-order extension. We solve the hyperbolic part of the governing equations with the Godunov method with the HLLC approximate Riemann solver. The non-linear parabolic part is solved with an efficient Chebyshev explicit iterative method without dealing with large sparse matrices. To verify the model and numerical methods proposed,we demonstrate numerical results of several numerical tests such as the multiphase shock tube problem, the multiphase impact problem, and the planar ablative Rayleigh-Taylor instability problem.

preprint2021arXiv

Enhanced superconductivity with possible re-appearance of charge density wave states in polycrystalline Cu$_{1-x}$Ag$_x$Ir$_2$Te$_4$ alloys

In this study, we determined the effect of doping with the noble metal Ag on Ir$_2$Te$_4$ superconductors. Based on the resistivity, magnetization, and heat capacity, we explored the changes in the superconductivity and charge density wave for Cu$_{1-x}$Ag$_x$Ir$_2$Te$_4$ as a function of isoelectric substitution. We assessed a complete set of competing states from suppressed charge density wave in the low doping region to superconductor in the middle doping region and re-entrant charge density wave in the high doping region, thereby obtaining an electronic phase diagram, where the superconducting dome was near bipartite charge density wave regions with a maximum superconducting temperature Tc of about 2.93 K at an Ag doping level of 12%. The lower H$_{c1}$ and upper H$_{c2}$ critical magnetic fields were determined for some representative samples in the Cu$_{1-x}$Ag$_x$Ir$_2$Te$_4$ series based on magnetization and resistivity measurements, respectively. We showed that H$_{c1}$ decreased whereas H$_{c2}$ increased as the doping content increased. The specific heat anomalies at the superconducting transitions $ΔC_{el}/γT_c$ for representative samples comprising Cu$_{0.92}$Ag$_{0.08}$Ir$_2$Te$_4$, Cu$_{0.88}$Ag$_{0.12}$Ir$_2$Te$_4$, and Cu$_{0.85}$Ag$_{0.15}$Ir$_2$Te$_4$ were approximately 1.40, 1.44, and 1.42, respectively, which are all near the Bardeen-Cooper-Schrieffer value of 1.43 and they indicate bulk superconductivity in these compounds.

preprint2021arXiv

Heterogeneous Millimeter Wave Wireless Power Transfer With Poisson Cluster Processes

In this paper, we analyze the energy coverage performance of heterogeneous millimeter wave (mmWave) wireless power transfer (WPT) networks, where macro base stations (MBSs) are distributed according to a Poisson point process (PPP), the location of power beacons (PBs) is modeled as a $k$-tier Poisson cluster process (PCP), and energy users (EUs) are clustered around the centers of PB clusters. Moreover, the cosine antenna gain model is adopted instead of the prevalent flap-top gain model, which is simpler in derivation but less accurate. Based on the generalized exponential distribution approximation, we propose a new technique of deriving the energy coverage probability of randomly deployed mmWave WPT networks. Specifically, taking the Thomas cluster process (TCP) for instance, we derive the energy coverage probabilities with two PB association strategies, i.e., the random PB association and the nearest PB association. Through Monte-Carlo simulations, our theoretical results are verified and the impact of system parameters, such as the array antenna size, energy threshold or average number of PBs in a cluster, are also investigated.

preprint2021arXiv

Hybrid Quantum-Classical Graph Convolutional Network

The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than $50\%$ of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields.

preprint2021arXiv

IEEE BigData 2021 Cup: Soft Sensing at Scale

IEEE BigData 2021 Cup: Soft Sensing at Scale is a data mining competition organized by Seagate Technology, in association with the IEEE BigData 2021 conference. The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques. In this paper we go into the details of the challenge and describe the data set provided to participants. We define the metrics of interest, baseline models, and describe approaches we found meaningful which may be a good starting point for further analysis. We discuss the results obtained with our approaches and give insights on what potential challenges participants may run into. Students, researchers, and anyone interested in working on a major industrial problem are welcome to participate in the challenge!

preprint2021arXiv

Long-distance entanglement purification for quantum communication

High-quality long-distance entanglement is essential for both quantum communication and scalable quantum networks. Entanglement purification is to distill high-quality entanglement from low-quality entanglement in a noisy environment and it plays a key role in quantum repeaters. The previous significant entanglement purification experiments require two pairs of low-quality entangled states and were demonstrated in table-top. Here we propose and report a high-efficiency and long-distance entanglement purification using only one pair of hyperentangled states. We also demonstrate its practical application in entanglement-based quantum key distribution (QKD). One pair of polarization spatial-mode hyperentanglement was distributed over 11 km multicore fiber (noisy channel). After purification, the fidelity of polarization entanglement arises from 0.771 to 0.887 and the effective key rate in entanglement-based QKD increases from 0 to 0.332. The values of Clauser-Horne-Shimony-Holt (CHSH) inequality of polarization entanglement arises from 1.829 to 2.128. Moreover, by using one pair of hyperentanglement and deterministic controlled-NOT gate, the total purification efficiency can be estimated as 6.6x10^3 times than the experiment using two pairs of entangled states with spontaneous parametric down-conversion (SPDC) sources. Our results offer the potential to be implemented as part of a full quantum repeater and large scale quantum network.

preprint2021arXiv

Multi-cell NOMA: Coherent Reconfigurable Intelligent Surfaces Model With Stochastic Geometry

Reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems, i.e., enhancing the channel quality and altering the SIC orders. Invoked by stochastic geometry methods, we investigate the downlink coverage performance of RIS-aided multi-cell NOMA networks. We first derive the RIS-aided channel model, concluding the direct and reflecting links. The analytical results demonstrate that the RIS-aided channel model can be closely modeled as a Gamma distribution. Additionally, interference from other cells is analyzed. Lastly, we derive closed-form coverage probability expressions for the paired NOMA users. Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.

preprint2021arXiv

Performance Analysis of Millimeter Wave Wireless Power Transfer With Imperfect Beam Alignment

In this paper, the impact of imperfect beam alignment (IBA) on millimeter wave (mmWave) wireless power transfer (WPT) is investigated. We consider a mmWave WPT network, where the location of the energy transmitters follows a Poisson point process. Instead of the mostly used flat-top antenna model, we adopt the Gaussian antenna model suggested by the 3rd Generation Partnership Project (3GPP) for better accuracy. Two beam alignment error (BAE) models, i.e., truncated Gaussian and uniform models, are introduced to represent different BAE sources. We derive the probability density functions (PDFs) of the cascaded antenna gain with both BAE models and then provide the approximated PDFs for tractability. With the help of Fox's H function, the analytic expression for the energy coverage probability with nonlinear energy harvesting model is derived. Besides, we deduce a closed-form expression of the average harvested radio frequency (RF) energy. The simulation results verify our theoretical results and demonstrate the performance degradation incurred by BAE. It also shows that the flat-top antenna model cannot always provide accurate performance evaluation in the presence of BAE.

preprint2021arXiv

Phase transition and enhanced hardness of LaB4 under pressure

The crystalline structures of lanthanum tetraboride, LaB4, under pressure are investigated using a genetic algorithm method coupled with first-principles calculations. We find two low-enthalpy phases for LaB4 as the thermodynamic ground state up to 200 GPa: a phase previously observed in experiments (P4/mbm) and a novel orthorhombic phase (Pnma-I). The P4/mbm phase transforms to the Pnma-I phase at around 106 GPa, accompanied by breakage of the B-octahedron. The P4/mbm phase of LaB4 exhibits a high hardness (30.5 GPa) at ambient conditions, which is comparable to that of the hard material B4C. The hardness of the two low-enthalpy phases increases with pressure. The electronic band structures shows that all of the competitive phases (two stable and two metastable phases) are metallic. Phonon calculations shows the P4/mbm and Pnma-I phases are thermodynamically stable in their respective pressure ranges. This elucidation of the phase transition behavior and hardness of LaB4 provides new knowledge on the interesting high-pressure behaviors of the rare-earth metal borides.

preprint2021arXiv

Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word

With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more complex, yet, the data sets remain limited: researchers are fitting million-parameter models with hundreds of data samples, which is insufficient to exercise the effectiveness of their models and thus often fail to perform when implemented in industrial applications. To solve this long-lasting problem, we are providing large scale, high dimensional time series manufacturing sensor data from Seagate Technology to the public. We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets. Transformer is used because, it has outperformed state-of-the-art techniques in Natural Language Processing, and since then has also performed well in the direct application to computer vision without introduction of image-specific inductive biases. We observe the similarity of a sentence structure to the sensor readings and process the multi-variable sensor readings in a time series in a similar manner of sentences in natural language. The high-dimensional time-series data is formatted into the same shape of embedded sentences and fed into the transformer model. The results show that transformer model outperforms the benchmark models in soft sensing field based on auto-encoder and long short-term memory (LSTM) models. To the best of our knowledge, we are the first team in academia or industry to benchmark the performance of original transformer model with large-scale numerical soft sensing data.

preprint2021arXiv

The impacts of optimization algorithm and basis size on the accuracy and efficiency of variational quantum eigensolver

Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices. However, many problems are yet to be investigated for this methodology, such as the influences of optimization algorithm and basis size on the accuracy and efficiency for quantum computing. To address these issues, five molecules (H2, LiH, HF, N2 and F2) are studied in this work based on the VQE method using unitary coupled cluster (UCC) ansatz. The performance of the gradient optimization L-BFGS-B is compared with that of the direct search method COBYLA. The former converges more quickly, but the accuracy of energy surface is a little lower. The basis set shows a vital influence on the accuracy and efficiency. A large basis set generally provides an accurate energy surface, but induces a significant increase in computing time. The 631g basis is generally required from the energy surface of the simplest H2 molecule. For practical applications of VQE, complete active space (CAS) is suggested based on limited quantum resources. With the same number of qubits, more occupied orbitals included in CAS gives a better accuracy for the energy surface and a smaller evaluation number in the VQE optimization. Additionally, the electronic structure, such as filling fraction of orbitals, the bond strength of a molecule and the maximum nuclear charge also influences the performance of optimization, where half occupation of orbitals generally requires a large computation cost.

preprint2021arXiv

Theory-based Habit Modeling for Enhancing Behavior Prediction

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users' habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions.

preprint2021arXiv

Two-stage Fourth-order Gas Kinetic Solver-based Compact Subcell Finite Volume Method for Compressible Flows over Triangular Meshes

To meet the demand for complex geometries and high resolutions of small-scale flow structures, a two-stage fourth-order subcell finite volume (SCFV) method combining the gas-kinetic solver (GKS) with subcell techniques for compressible flows over (unstructured) triangular meshes was developed to improve the compactness and efficiency. Compared to the fourth-order GKS-based traditional finite volume (FV) method, the proposed method realizes compactness effectively by subdividing each cell into a set of subcells or control volumes (CVs) and selecting only face-neighboring cells for high-order compact reconstruction. Because a set of CVs share a solution polynomial, the reconstruction is more efficient than that for traditional FV-GKS, where each CV needs to be separately reconstructed. Unlike in the single-stage third-order SCFV-GKS, both accuracy and efficiency are improved significantly by two-stage fourth-order temporal discretization, for which only a second-order gas distribution function is needed to simplify the construction of the flux function and reduce computational costs. For viscous flows, it is not necessary to compute the viscous term with GKS. Compared to the fourth-stage Runge--Kutta method, one half of the stage is saved for achieving fourth-order time accuracy, which also helps to improve the efficiency. Therefore, a new high-order method with compactness, efficiency, and robustness is proposed by combining the SCFV method with the two-stage gas-kinetic flux. Several benchmark cases were tested to demonstrate the performance of the method in compressible flow simulations.

preprint2020arXiv

A potential superhard carbon allotrope: T5-carbon

A novel stable carbon allotrope is predicted by using first-principles calculations. This allotrope is obtained by replacing one of the two atoms in the primitive cell of diamond with a carbon tetrahedron, thus it contains five atoms in one primitive cell, termed T5-carbon. The stabilities of T5-carbon are checked in structural, thermal, vibrational and energy calculations. The calculations on electronic, thermal, and mechanical properties reveal that T5-carbon is a semiconductor with an indirect band gap of 3.18 eV, and has a lattice thermal conductivity of 409 W/mK. More importantly, the Vickers hardness of T5-carbon is 76.5 GPa which is lower than that of diamond but larger than those of T-carbon and cubic boron nitride, confirming the superhard properties of T5-carbon, suggesting its wide applications in mechanical devices.

preprint2020arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- I. Survey description and a first look at G9.62+0.19

The "ATOMS," standing for {\it ALMA Three-millimeter Observations of Massive Star-forming regions}, survey has observed 146 active star forming regions with ALMA Band 3, aiming to systematically investigate the spatial distribution of various dense gas tracers in a large sample of Galactic massive clumps, to study the roles of stellar feedback in star formation, and to characterize filamentary structures inside massive clumps. In this work, the observations, data analysis, and example science of the "ATOMS" survey are presented, using a case study for the G9.62+0.19 complex. Toward this source, some transitions, commonly assumed to trace dense gas, including CS $J = 2-1$, HCO$^+$ $J = 1-0$ and HCN $J = 1-0$, are found to show extended gas emission in low density regions within the clump; less than 25\% of their emission is from dense cores. SO, CH$_3$OH, H$^{13}$CN and HC$_3$N show similar morphologies in their spatial distributions and reveal well the dense cores. Widespread narrow SiO emission is present (over $\sim$1 pc), which may be caused by slow shocks from large--scale colliding flows or H{\sc ii} regions. Stellar feedback from an expanding H{\sc ii} region has greatly reshaped the natal clump, significantly changed the spatial distribution of gas, and may also account for the sequential high-mass star formation in the G9.62+0.19 complex. The ATOMS survey data can be jointly analyzed with other survey data, e.g., "MALT90", "Orion B", "EMPIRE", "ALMA\_IMF", and "ALMAGAL", to deepen our understandings of "dense gas" star formation scaling relations and massive proto-cluster formation.

preprint2020arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- II. Compact objects in ACA observations and star formation scaling relations

We report studies of the relationships between the total bolometric luminosity ($L_{\rm bol}$ or $L_{\rm TIR}$) and the molecular line luminosities of $J=1-0$ transitions of H$^{13}$CN, H$^{13}$CO$^+$, HCN, and HCO$^+$ with data obtained from ACA observations in the "ATOMS" survey of 146 active Galactic star forming regions. The correlations between $L_{\rm bol}$ and molecular line luminosities $L'_{\rm mol}$ of the four transitions all appear to be approximately linear. Line emission of isotopologues shows as large scatters in $L_{\rm bol}$-$L'_{\rm mol}$ relations as their main line emission. The log($L_{\rm bol}$/$L'_{\rm mol}$) for different molecular line tracers have similar distributions. The $L_{\rm bol}$-to-$L'_{\rm mol}$ ratios do not change with galactocentric distances ($R_{\rm GC}$) and clump masses ($M_{\rm clump}$). The molecular line luminosity ratios (HCN-to-HCO$^+$, H$^{13}$CN-to-H$^{13}$CO$^+$, HCN-to-H$^{13}$CN and HCO$^+$-to-H$^{13}$CO$^+$) all appear constant against $L_{\rm bol}$, dust temperature ($T_{\rm d}$), $M_{\rm clump}$ and $R_{\rm GC}$. Our studies suggest that both the main lines and isotopologue lines are good tracers of the total masses of dense gas in Galactic molecular clumps. The large optical depths of main lines do not affect the interpretation of the slopes in star formation relations. We find that the mean star formation efficiency (SFE) of massive Galactic clumps in the "ATOMS" survey is reasonably consistent with other measures of the SFE for dense gas, even those using very different tracers or examining very different spatial scales.

preprint2020arXiv

BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision

We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly incomplete and noisy distant labels via external knowledge bases. To address this challenge, we propose a new computational framework -- BOND, which leverages the power of pre-trained language models (e.g., BERT and RoBERTa) to improve the prediction performance of NER models. Specifically, we propose a two-stage training algorithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels, which can significantly improve the recall and precision; In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance. Thorough experiments on 5 benchmark datasets demonstrate the superiority of BOND over existing distantly supervised NER methods. The code and distantly labeled data have been released in https://github.com/cliang1453/BOND.

preprint2020arXiv

Coincidence angle-resolved photoemission spectroscopy: Proposal for detection of two-particle correlations

The angle-resolved photoemission spectroscopy (ARPES) is one powerful experimental technique to study the electronic structure of materials. As many electron materials show unusual many-body correlations, the technique to detect directly these many-body correlations will play important roles in the study of their many-body physics. In this article, we propose a technique to detect directly the two-particle correlations, a coincidence ARPES (cARPES) where two incident photons excite two respective photoelectrons which are detected in coincidence. While the one-photon-absorption and one-photoelectron-emission ARPES provides the single-particle spectrum function, the proposed cARPES with two-photon absorption and two-photoelectron emission is relevant to a two-particle Bethe-Salpeter wave function. Examples of the coincidence detection probability of the cARPES for a free Fermi gas and a Bardeen-Cooper-Schrieffer (BCS) superconducting state are studied in detail. We also propose another two experimental techniques, a coincidence angle-resolved photoemission and inverse-photoemission spectroscopy (cARP/IPES) and a coincidence angle-resolved inverse-photoemission spectroscopy (cARIPES). As all of these proposed coincidence techniques can provide the two-particle frequency Bethe-Salpeter wave functions, they can show the momentum and energy dependent two-particle dynamical physics of the material electrons in the particle-particle or particle-hole channel. Thus, they can be introduced to study the Cooper-pair physics in the superconductor, the itinerant magnetism in the metallic ferromagnet/antiferromagnet, and the particle-hole pair physics in the metallic nematic state. Moreover, as the two-particle Bethe-Salpeter wave functions also involve the inner-pair dynamical physics, these proposed coincidence techniques can be used to study the inner-pair time-retarded physics.

preprint2020arXiv

Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square

We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely "combined Neyman-Pearson chi-square" ($χ^2_{\mathrm{CNP}}$). Through analytical derivations and toy model simulations, we show that $χ^2_\mathrm{CNP}$ leads to a significantly smaller bias on the best-fit model parameters compared to those using either Neyman's or Pearson's chi-square. When the computational cost of using the Poisson-likelihood chi-square is high, $χ^2_\mathrm{CNP}$ provides a good alternative given its natural connection to the covariance matrix formalism.

preprint2020arXiv

Constraints of general screened modified gravities from comprehensive analysis of binary pulsars

Testing gravity by binary pulsars nowadays becomes a key issue. Screened modified gravity is a kind of scalar-tensor theory with screening mechanism in order to satisfy the tight Solar System tests. In this paper, we investigate how the screening mechanism affects the orbital dynamics of binary pulsars, and calculate in detail the five post-Keplerian (PK) parameters in this theory. These parameters differ from those of general relativity (GR), and the differences are quantified by the scalar charges, which lead to the dipole radiation in this theory. We combine the observables of PK parameters for the ten binary pulsars, respectively, to place the constraints on the scalar charges and possible deviations from GR. The dipole radiation in the neutron star (NS) - white dwarf (WD) binaries leads to more stringent constraints on deviations from GR. The most constraining systems for the scalar charges of NS and WD are PSR~B1913$+$16 and PSR~J1738$+$0333, respectively. The results of all tests exclude significant strong-field deviations and show good agreement with GR.

preprint2020arXiv

Deep Patch-based Human Segmentation

3D human segmentation has seen noticeable progress in re-cent years. It, however, still remains a challenge to date. In this paper, weintroduce a deep patch-based method for 3D human segmentation. Wefirst extract a local surface patch for each vertex and then parameterizeit into a 2D grid (or image). We then embed identified shape descriptorsinto the 2D grids which are further fed into the powerful 2D Convolu-tional Neural Network for regressing corresponding semantic labels (e.g.,head, torso). Experiments demonstrate that our method is effective inhuman segmentation, and achieves state-of-the-art accuracy.

preprint2020arXiv

Discriminative Topic Mining via Category-Name Guided Text Embedding

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users' particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification.

preprint2020arXiv

Downlink Analysis for Reconfigurable Intelligent Surfaces Aided NOMA Networks

preprint2020arXiv

Equivalence between distributional and viscosity solutions for the double-phase equation

We investigate the different notions of solutions to the double-phase equation $$ -\dive(|Du|^{p-2}Du+a(x)|Du|^{q-2}Du)=0, $$ which is characterized by the fact that both ellipticity and growth switch between two different types of polynomial according to the position. We introduce the $\mathcal{A}_{H(\cdot)}$-harmonic functions of nonlinear potential theory, and then show that $\mathcal{A}_{H(\cdot)}$-harmonic functions coincide with the distributional and viscosity solutions, respectively. This implies that the distributional and viscosity solutions are exactly the same.

preprint2020arXiv

Evolution of Ethereum: A Temporal Graph Perspective

Ethereum is one of the most popular blockchain systems that supports more than half a million transactions every day and fosters miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this paper, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytics platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract and user-contract graphs, are constructed according to trading relationship and are segmented with an appropriate time window. We observe a strong correlation between the size of user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions is validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, "the rich is already very rich".

preprint2020arXiv

Exploring the Hierarchy in Relation Labels for Scene Graph Generation

By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem. Under this formulation, predicate categories are treated as completely different classes. However, different from the object labels where different classes have explicit boundaries, predicates usually have overlaps in their semantic meanings. For example, sit\_on and stand\_on have common meanings in vertical relationships but different details of how these two objects are vertically placed. In order to leverage the inherent structures of the predicate categories, we propose to first build the language hierarchy and then utilize the Hierarchy Guided Feature Learning (HGFL) strategy to learn better region features of both the coarse-grained level and the fine-grained level. Besides, we also propose the Hierarchy Guided Module (HGM) to utilize the coarse-grained level to guide the learning of fine-grained level features. Experiments show that the proposed simple yet effective method can improve several state-of-the-art baselines by a large margin (up to $33\%$ relative gain) in terms of Recall@50 on the task of Scene Graph Generation in different datasets.

preprint2020arXiv

Filament Intersections and Cold Dense Cores in Orion A North

We studied the filament structures and dense cores in OMC-2,3 region in Orion A North molecular cloud using the high-resolution N2H+ (1-0) spectral cube observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). The filament network over a total length of 2 pc is found to contain 170 intersections and 128 candidate dense cores. The dense cores are all displaced from the infrared point sources (possible young stars), and the major fraction of cores (103) are located around the intersections. Towards the intersections, there is also an increasing trend for the total column density Ntot as well as the the power-law index of the column-density Probability Distribution Function (N-PDF), suggesting that the intersections would in general have more significant gas assembly than the other part of the filament paths. The virial analysis shows that the dense cores mostly have virial mass ratio of alpha_vir=M_vir/M_gas<1.0, suggesting them to be bounded by the self gravity. In the mean time, only about 23 percent of the cores have critical mass ratio of alpha_crit=M_crit/M_gas<1.0, suggesting them to be unstable against core collapse. Combining these results, it shows that the major fraction of the cold starless and possible prestellar cores in OMC-2,3 are being assembled around the intersections, and currently in a gravitationally bound state. But more extensive core collapse and star formation may still require continuous core-mass growth or other perturbatio

preprint2020arXiv

Fragility of quantum correlations and coherence in a multipartite photonic system

Certain quantum states are well-known to be particularly fragile in the presence of decoherence, as illustrated by Schrodinger's famous gedanken cat experiment. It has been better appreciated more recently that quantum states can be characterized in a hierarchy of quantum quantities such entanglement, quantum correlations, and quantum coherence. It has been conjectured that each of these quantities have various degrees of fragility in the presence of decoherence. Here we experimentally confirm this conjecture by preparing tripartite photonic states and subjecting them to controlled amounts of dephasing. When the dephasing is applied to all the qubits, we find that the entanglement is the most fragile quantity, followed by the quantum coherence, then mutual information. This is in agreement with the widely held expectation that multipartite quantum correlations are a highly fragile manifestation of quantumness. We also perform dephasing on one out of the three qubits on star and $ W \bar{W} $ states. Here the distribution of the correlations and coherence in the state becomes more important in relation to the dephasing location.

preprint2020arXiv

Full analytical formulas for frequency response of space-based gravitational wave detectors

The discovery of gravitational waves, which are ripples of space-time itself, opened a new window to test general relativity, because it predicts that there are only plus and cross polarizations for gravitational waves. For alternative theories of gravity, there may be up to six polarizations. The measurement of the polarization is one of the major scientific goals for future gravitational wave detectors. To evaluate the capability of the detector, we need to use the frequency dependent response functions averaged over the source direction and polarization angle. We derive the full analytical formulas of the averaged response functions for all six possible polarizations and present their asymptotic behaviors based on these analytical formulas. Compared with the numerical simulation, the full analytical formulas are more efficient and valid for any equal-arm interferometric gravitational wave detector without optical cavities in the arms and for a time-delay-interferometry Michelson combination.

preprint2020arXiv

Full Counting Statistics of Phonon Transport in Disordered Systems

The coherent potential approximation (CPA) within full counting statistics (FCS) formalism is shown to be a suitable method to investigate average electric conductance, shot noise as well as higher order cumulants in disordered systems. We develop a similar FCS-CPA formalism for phonon transport through disordered systems. As a byproduct, we derive relations among coefficients of different phonon current cumulants. We apply the FCS-CPA method to investigate phonon transport properties of graphene systems in the presence of disorders. For binary disorders as well as Anderson disorders, we calculate up to the $8$-th phonon transmission moments and demonstrate that the numerical results of the FCS-CPA method agree very well with that of the brute force method. The benchmark shows that the FCS-CPA method achieves $20$ times more speedup ratio. Collective features of phonon current cumulants are also revealed.

preprint2020arXiv

FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams

Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {"Canon", "Sony", "Nikon"}, previous models return one mixed set of entities that are either Camera Brands or Japanese Companies. In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, and (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing a masked language model with pre-trained BERT models. Extensive experiments demonstrate that FUSE can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet.

preprint2020arXiv

G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data

In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of the WA data to boost the multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that the WA data annotated with the same weak label has a high probability of being assigned to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lie on/near the latent model are likely to connect together and further form a subset/cluster for effective proposals generation. With the proposals generated, the $α$-expansion is adopted for labeling, and our method in return updates the proposals. This works in an iterative way. Extensive experiments validate our method and show that the proposed method produces noticeably better results than state-of-the-art techniques in most cases.

preprint2020arXiv

Gradient Holder regularity for parabolic normalized p(x,t)-Laplace equation

We consider interior Hölder regularity of the spatial gradient of viscosity solutions to the normalized $p(x,t)$-Laplace equation $$ u_t=\left(δ_{ij}+(p(x,t)-2)\frac{u_i u_j}{|Du|^2}\right)u_{ij} $$ with some suitable assumptions on $p(x,t)$, which arises naturally from a two-player zero-sum stochastic differential game with probabilities depending on space and time.

preprint2020arXiv

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hierarchical topic structures, hierarchical topic models generalize flat topic models by incorporating latent topic hierarchies into their generative modeling process. However, due to their purely unsupervised nature, the learned topic hierarchy often deviates from users' particular needs or interests. To guide the hierarchical topic discovery process with minimal user supervision, we propose a new task, Hierarchical Topic Mining, which takes a category tree described by category names only, and aims to mine a set of representative terms for each category from a text corpus to help a user comprehend his/her interested topics. We develop a novel joint tree and text embedding method along with a principled optimization procedure that allows simultaneous modeling of the category tree structure and the corpus generative process in the spherical space for effective category-representative term discovery. Our comprehensive experiments show that our model, named JoSH, mines a high-quality set of hierarchical topics with high efficiency and benefits weakly-supervised hierarchical text classification tasks.

preprint2020arXiv

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4\% mAP on COCO minival set with 27M parameters. Our implementation is available at https://github.com/ggjy/HitDet.pytorch.

preprint2020arXiv

Improved Large-margin Softmax Loss for Speaker Diarisation

Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers. It is well-known that large-margin training can improve the generalisation ability to unseen data, and its use in such open-set problems has been widespread. Therefore, this paper introduces a general approach to the large-margin softmax loss without any approximations to improve the quality of speaker embeddings for diarisation. Furthermore, a novel and simple way to stabilise training, when large-margin softmax is used, is proposed. Finally, to combat the effect of overlapping speech, different training margins are used to reduce the negative effect overlapping speech has on creating discriminative embeddings. Experiments on the AMI meeting corpus show that the use of large-margin softmax significantly improves the speaker error rate (SER). By using all hyper parameters of the loss in a unified way, further improvements were achieved which reached a relative SER reduction of 24.6% over the baseline. However, by training overlapping and single speaker speech samples with different margins, the best result was achieved, giving overall a 29.5% SER reduction relative to the baseline.

preprint2020arXiv

Infall in massive clumps harboring bright infrared sources

Thirty massive clumps associated with bright infrared sources were observed to detect the infall signatures and characterize infall properties in the envelope of the massive clumps by APEX telescope in CO(4-3) and C$^{17}$O(3-2) lines. Eighteen objects have "blue profile" in CO(4-3) line with virial parameters less than 2, suggesting that global collapse is taking place in these massive clumps. The CO(4-3) lines were fitted by the two-layer model in order to obtain infall velocities and mass infall rates. Derived mass infall rates are from 10$^{-3}$ to 10$^{-1}$ M$_{\odot}$yr$^{-1}$. A positive relationship between clump mass and infall rate appears to indicate that gravity plays a dominant role in the collapsing process. Higher luminosity clump has larger mass infall rate, implying that the clump with higher mass infall rate has higher star formation rate.

preprint2020arXiv

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

Deep learning methods have revolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities. Therefore, it is of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. The main focus of this review is the combination of vision and natural language modalities, which has become an important topic in both the computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications. Regarding multimodal representation learning, we review the key concepts of embedding, which unify multimodal signals into a single vector space and thereby enable cross-modality signal processing. We also review the properties of many types of embeddings that are constructed and learned for general downstream tasks. Regarding multimodal fusion, this review focuses on special architectures for the integration of representations of unimodal signals for a particular task. Regarding applications, selected areas of a broad interest in the current literature are covered, including image-to-text caption generation, text-to-image generation, and visual question answering. We believe that this review will facilitate future studies in the emerging field of multimodal intelligence for related communities.

preprint2020arXiv

No static regular black holes in Einstein-complex-scalar-Gauss-Bonnet gravity

In this brief report, we investigate the existence of 4-dimensional static spherically symmetric black holes (BHs) in the Einstein-complex-scalar-Gauss-Bonnet (EcsGB) gravity with an arbitrary potential $V(ϕ)$ and a coupling $f(ϕ)$ between the scalar field $ϕ$ and the Gauss-Bonnet (GB) term. We find that static regular BH solutions with complex scalar hairs do not exist. This conclusion does not depend on the coupling between the GB term and the scalar field, nor on the scalar potential $V(ϕ)$ and the presence of a cosmological constant $Λ$ (which can be either positive or negative), as longer as the scalar field remains complex and is regular across the horizon.

preprint2020arXiv

Optical capillary-based interferometric sensor for detection of gas refractive index

In this paper, we report a capillary-based M-Z interferometer that could be used for precise detection of variations in refractive indices of gaseous samples. This sensing mechanism is quite straightforward. Cladding and core modes of a capillary are simultaneously excited by coupling coherent laser beams to the capillary cladding and core, respectively. Interferogram would be generated as the light transmitted from the core interferes with the light transmitted from the cladding. Variations in refractive index of the air filling the core lead to variations in phase difference between the core and cladding modes, thus shifting the interference fringes. Using a photodiode together with a narrow slit, we could analyze the fringe shifts. The resolution of the sensor was found to be 1*10-8 RIU, that is comparable to the highest resolution obtained by other interferometric sensors reported in previous literatures. Finally, we also analyze the temperature cross sensitivity of the sensor. The advantages of our sensor include very low cost, high sensitivity, straightforward sensing mechanism, and ease of fabrication.

preprint2020arXiv

paper2repo: GitHub Repository Recommendation for Academic Papers

GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order to attract more people to follow their work. Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic. The key challenge is to identify the similarity between an input paper and its related repositories across the two platforms, $\textit{without the benefit of human labeling}$. Towards that end, paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn and map the embeddings of papers and repositories into the same space, where proximity offers the basis for recommendation. To make our method more practical in real life systems, labels used for model training are computed automatically from features of user actions on GitHub. In machine learning, such automatic labeling is often called {\em distant supervision\/}. To the authors' knowledge, this is the first distant-supervised cross-platform (paper to repository) matching system. We evaluate the performance of paper2repo on real-world data sets collected from GitHub and Microsoft Academic. Results demonstrate that it outperforms other state of the art recommendation methods.

preprint2020arXiv

Partially-Typed NER Datasets Integration: Connecting Practice to Theory

While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead of relying on fully-typed NER datasets, many efforts have been made to leverage multiple partially-typed ones for training and allow the resulting model to cover a full type set. However, there is neither guarantee on the quality of integrated datasets, nor guidance on the design of training algorithms. Here, we conduct a systematic analysis and comparison between partially-typed NER datasets and fully-typed ones, in both theoretical and empirical manner. Firstly, we derive a bound to establish that models trained with partially-typed annotations can reach a similar performance with the ones trained with fully-typed annotations, which also provides guidance on the algorithm design. Moreover, we conduct controlled experiments, which shows partially-typed datasets leads to similar performance with the model trained with the same amount of fully-typed annotations

preprint2020arXiv

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello high-dimensional neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs, provides analytical stress tensor calculations and interfaces to both the Atomic Simulation Environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials.

preprint2020arXiv

Primordial black holes and secondary gravitational waves from k/G inflation

The possibility that in the mass range around $10^{-12}\ M_\odot$ most of dark matter constitutes of primordial black holes (PBHs) is a very interesting topic. To produce PBHs with this mass, the primordial scalar power spectrum needs to be enhanced to the order of 0.01 at the scale $k\sim 10^{12}\ \text{Mpc}^{-1}$. The enhanced power spectrum also produces large secondary gravitational waves at the mHz band. A phenomenological delta function power spectrum is usually used to discuss the production of PBHs and secondary gravitational waves. Based on G and k inflations, we propose a new mechanism to enhance the power spectrum at small scales by introducing a non-canonical kinetic term $[1-2G(ϕ)]X$ with the function $G(ϕ)$ having a peak. Away from the peak, $G(ϕ)$ is negligible and we recover the usual slow-roll inflation which is constrained by the cosmic microwave background anisotrpy observations. Around the peak, the slow-roll inflation transiently turns to ultra slow-roll inflation. The enhancement of the power spectrum can be obtained with generic potentials, and there is no need to fine tune the parameters in $G(ϕ)$. The energy spectrum $Ω_{GW}(f)$ of secondary gravitational waves have the characteristic power law behaviour $Ω_{GW}(f)\sim f^{n}$ and is testable by pulsar timing array and space based gravitational wave detectors.

preprint2020arXiv

Reconfigurable Intelligent Surfaces Aided Multi-Cell NOMA Networks: A Stochastic Geometry Model

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. The downlink RIS-aided NOMA networks are investigated via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) model, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Additionally, we derive closed-form analytical and asymptotic expressions for coverage probabilities of the paired NOMA users. Lastly, we derive the analytical expressions of the ergodic rate for both of the paired NOMA users and calculate the asymptotic expressions for the typical user. The analytical results indicate that 1) the achievable rates reach an upper limit when the length of RIS increases; 2) exploiting RISs can enhance the path loss intercept to improve the performance without influencing the bandwidth. Our results show that 1) RIS-aided NOMA networks have superior performance than the conventional NOMA networks, and 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel quality of NOMA users.

preprint2020arXiv

Regularity property of some operators on Lipschitz spaces and homogeneous Lipschitz spaces related to biharmonic operator

In this paper, we consider the characterizations of the Lipschitz spaces and homogeneous Lipschitz spaces associated to the biharmonic operator $Δ^2.$ With this characterizations, we prove the boundedness of the Bessel potentials, fractional integrals, fractional powers, Riesz transforms and multipliers of Laplace transforms type associated to $Δ^2$ on Lipschitz spaces and homogeneous Lipschitz spaces. The proofs of these results need the language of semigroups in an essential way.

preprint2020arXiv

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates

Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets (DNNs) that have a large number of parameters. Non-Bayesian methods are simple to implement but often conflate different sources of uncertainties and require huge computing resources. We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective. The core of our method is to view DNN transformations as state evolution of a stochastic dynamical system and introduce a Brownian motion term for capturing epistemic uncertainty. Based on this perspective, we propose a neural stochastic differential equation model (SDE-Net) which consists of (1) a drift net that controls the system to fit the predictive function; and (2) a diffusion net that captures epistemic uncertainty. We theoretically analyze the existence and uniqueness of the solution to SDE-Net. Our experiments demonstrate that the SDE-Net model can outperform existing uncertainty estimation methods across a series of tasks where uncertainty plays a fundamental role.

preprint2020arXiv

Semi-Grant-Free NOMA: A Stochastic Geometry Model

Grant-free (GF) transmission holds promise in terms of low latency communication by directly transmitting messages without waiting for any permissions. However, collision situations may frequently happen when limited spectrum is occupied by numerous GF users. The non-orthogonal multiple access (NOMA) technique can be a promising solution to achieve massive connectivity and fewer collisions for GF transmission by multiplexing users in power domain. We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network connectivity and spectral efficiency by enabling grant-based (GB) and GF users to share the same spectrum resources. With the aid of semi-GF protocols, uplink NOMA networks are investigated by invoking stochastic geometry techniques. We propose a novel \textit{dynamic protocol} to interpret which part of the GF users are paired in NOMA transmissions via transmitting various channel quality thresholds by an added handshake. We utilize open-loop protocol with a fixed average threshold as the benchmark to investigate performance improvement. It is observed that dynamic protocol provides more accurate channel quality thresholds than open-loop protocol, thereby the interference from the GF users is reduced to a large extent. We analyze the outage performance and diversity gains under two protocols. Numerical results demonstrate that dynamic protocol is capable of enhancing the outage performance than open-loop protocol.

preprint2020arXiv

SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of crossover in real-coded genetic algorithm (RCGA), in this paper we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 4 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of accuracy.

preprint2020arXiv

Spherically symmetric static black holes in Einstein-aether theory

In this paper, we systematically study spherically symmetric static spacetimes in the framework of Einstein-aether theory, and pay particular attention to the existence of black holes (BHs). In the present studies we first clarify several subtle issues. In particular, we find that, out of the five non-trivial field equations, only three are independent, so the problem is well-posed, as now generically there are only three unknown functions, {$F(r), B(r), A(r)$, where $F$ and $B$ are metric coefficients, and $A$ describes the aether field.} In addition, the two second-order differential equations for $A$ and $F$ are independent of $B$, and once they are found, $B$ is given simply by an algebraic expression of $F,\; A$ and their derivatives. To simplify the problem further, we explore the symmetry of field redefinitions, and work first with the redefined metric and aether field, and then obtain the physical ones by the inverse transformations. These clarifications significantly simplify the computational labor, which is important, as the problem is highly involved mathematically. In fact, it is exactly because of these, we find various numerical BH solutions with an accuracy that is at least two orders higher than previous ones. More important, these BH solutions are the only ones that satisfy the self-consistent conditions and meantime are consistent with all the observational constraints obtained so far. The locations of universal horizons are also identified, together with several other observationally interesting quantities, such as the innermost stable circular orbits (ISCO), the ISCO frequency, and the maximum redshift $z_{max}$ of a photon emitted by a source orbiting the ISCO. All of these quantities are found to be quite close to their relativistic limits.

preprint2020arXiv

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms. We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion. To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and query terms. To solve the node attachment task, it learns feature representations for query-anchor pairs from multiple views and performs multi-view co-training for prediction. Extensive experiments show that STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6\% in accuracy and 7.0\% in mean reciprocal rank on three public benchmarks. The implementation of STEAM can be found at \url{https://github.com/yueyu1030/STEAM}.

preprint2020arXiv

Wasserstein Autoregressive Models for Density Time Series

Data consisting of time-indexed distributions of cross-sectional or intraday returns have been extensively studied in finance, and provide one example in which the data atoms consist of serially dependent probability distributions. Motivated by such data, we propose an autoregressive model for density time series by exploiting the tangent space structure on the space of distributions that is induced by the Wasserstein metric. The densities themselves are not assumed to have any specific parametric form, leading to flexible forecasting of future unobserved densities. The main estimation targets in the order-$p$ Wasserstein autoregressive model are Wasserstein autocorrelations and the vector-valued autoregressive parameter. We propose suitable estimators and establish their asymptotic normality, which is verified in a simulation study. The new order-$p$ Wasserstein autoregressive model leads to a prediction algorithm, which includes a data driven order selection procedure. Its performance is compared to existing prediction procedures via application to four financial return data sets, where a variety of metrics are used to quantify forecasting accuracy. For most metrics, the proposed model outperforms existing methods in two of the data sets, while the best empirical performance in the other two data sets is attained by existing methods based on functional transformations of the densities.

preprint2019arXiv

An arbitrary two-particle high-dimensional Bell state measurement by auxiliary entanglement

Bell state measurement (BSM) plays a vital role in quantum information. There are many researches on BSM of qubit Bell state, however, there is no definite solution of how to realize high-dimensional Bell state measurement (HDBSM). In this paper, We present a scheme for realizing arbitrary high-dimensional two-particle Bell state measurement by auxiliary entanglement. In our scheme, the maximal entangled states with different degrees of freedom (DOF) are used as auxiliary states, and then the HDBSM can be achieved by implementing Bell measurement with different degrees of freedom. We present the detailed HDBSM scheme for three and four-dimensional Bell state, and the general formula for arbitrary high-dimensional Bell state. Furthermore, We have designed the experimental scheme for three-dimensional BSM, and given the method of arbitrary dimension. This method can promote the high-dimensional quantum information task.

preprint2019arXiv

Chemical properties of two dense cores in a Planck Galactic Cold Clump G168.72-15.48

To deepen our understanding of the chemical properties of the Planck Galactic Cold Clump (PGCC) G168.72-15.48, we performed observations of nine molecular species, namely, \ce{c-C3H}, \ce{H2CO}, \ce{HC5N}, \ce{HC7N}, \ce{SO}, \ce{CCH}, \ce{N2H+}, \ce{CH3OH}, and \ce{CH3CCH}, toward two dense cores in PGCC G168.72-15.48 using the Tianma Radio Telescope and Purple Mountain Observatory Telescope. We detected \ce{c-C3H}, \ce{H2CO}, \ce{HC5N}, \ce{N2H+}, \ce{CCH}, and \ce{CH3OH} in both G168-H1 and G168-H2 cores, whereas \ce{HC7N} and \ce{CH3CCH} were detected only in G168-H1 and SO was detected only in G168-H2. Mapping observations reveal that the \ce{CCH}, \ce{N2H+}, \ce{CH3OH}, and \ce{CH3CCH} emissions are well coupled with the dust emission in G168-H1. Additionally, \ce{N2H+} exhibits an exceptionally weak emission in the denser and more evolved G168-H2 core, which may be attributed to the \ce{N2H+} depletion. We suggest that the \ce{N2H+} depletion in G168-H2 is dominated by \ce{N2} depletion, rather than the destruction by CO. The local thermodynamic equilibrium calculations indicate that the carbon-chain molecules of \ce{CCH}, \ce{HC5N}, \ce{HC7N}, and \ce{CH3CCH} are more abundant in the younger G168-H1 core. We found that starless core G168-H1 may have the properties of cold dark clouds based on its abundances of carbon-chain molecules. While, the prestellar core G168-H2 exhibits lower carbon-chain molecular abundances than the general cold dark clouds. With our gas-grain astrochemical model calculations, we attribute the observed chemical differences between G168-H1 and G168-H2 to their different gas densities and different evolutionary stages.

preprint2019arXiv

Electromechanics of the liquid water vapour interface

Two collective properties distinguishing the thin liquid water vapour interface from the bulk liquid are the anisotropy of the pressure tensor giving rise to surface tension and the orientational alignment of the molecules leading to a finite dipolar surface potential. Both properties can be regarded as capillary phenomena and are likely to be coupled. We have investigated this coupling by determining the response of the tangential component of the surface tension to the application of an electric field normal to the surface using finite field molecular dynamics simulations. We find an upside down parabola with a maximum shifted away from zero field. Comparing the molecular dynamics results to an elementary electromechanical continuum model we relate the zero field derivative of the tangential part of the surface tension to the electrostatic potential generated by the spontaneous dipole alignment. The calculations show that these quantities have similar values but are not and in fact need not be identical. The electromechanical model also allows us to convert the absolute curvature of the quadratic field dependence to an effective dielectric constant of the water interface which is found to be much lower compared to the bulk value as expected.

preprint2019arXiv

Fragmentation of shells: An analogy with the crack formation in tree bark

How does a shell explode into a series of fragments upon impact? The well accepted explanation is Mott's theory, which considers the fragmentation of shells as a random process controlled by defects. However, Mott's theory is inadequate due to its assumption of energy conversion, and it is incapable of explaining the lack of change in saturation fragment length with the increase in expansion velocity. In this paper, we present a theory to explain the physical mechanism for fragmentation of shells and propose a highly efficient model for predicting the number of necks after fragmentation. We recognize that the fragmentation problem in shells is analogous to the cracking behavior of tree bark, and closed form solutions is obtained to describe the relationship between the expansion velocity and the number of necks with consideration of the strain rate dependent strength of the shell material. The theoretical results show excellent correlation with the experimental results.

preprint2016arXiv

Accelerated Variance Reduced Block Coordinate Descent

Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space. Existing algorithms lack at least one of these qualities, and thus are inefficient in handling such big data challenge. In this paper, we propose a method enjoying all these merits with an accelerated convergence rate $O(\frac{1}{k^2})$. Empirical studies on large scale datasets with more than one million features are conducted to show the effectiveness of our methods in practice.

preprint2016arXiv

Capture cross sections of 15N(n, γ)16N at astrophysical energies

We have reanalyzed reaction cross sections of 16N on 12C target. The nucleon density distribution of 16N, especially surface density distribution, was extracted using the modified Glauber model. On the basis of dilute surface densities, the discussion of 15N(n, γ)16N reaction was performed within the framework of the direct capture reaction mechanism. The calculations agreed quite well with the experimental data.

preprint2016arXiv

Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis

Subspace recovery from corrupted and missing data is crucial for various applications in signal processing and information theory. To complete missing values and detect column corruptions, existing robust Matrix Completion (MC) methods mostly concentrate on recovering a low-rank matrix from few corrupted coefficients w.r.t. standard basis, which, however, does not apply to more general basis, e.g., Fourier basis. In this paper, we prove that the range space of an $m\times n$ matrix with rank $r$ can be exactly recovered from few coefficients w.r.t. general basis, though $r$ and the number of corrupted samples are both as high as $O(\min\{m,n\}/\log^3 (m+n))$. Our model covers previous ones as special cases, and robust MC can recover the intrinsic matrix with a higher rank. Moreover, we suggest a universal choice of the regularization parameter, which is $λ=1/\sqrt{\log n}$. By our $\ell_{2,1}$ filtering algorithm, which has theoretical guarantees, we can further reduce the computational cost of our model. As an application, we also find that the solutions to extended robust Low-Rank Representation and to our extended robust MC are mutually expressible, so both our theory and algorithm can be applied to the subspace clustering problem with missing values under certain conditions. Experiments verify our theories.

preprint2016arXiv

Computing the dielectric constant of liquid water at constant dielectric displacement

The static dielectric constant of liquid water is computed using classical force field based molecular dynamics simulation at fixed electric displacement D. The method to constrain the electric displacement is the finite temperature classical variant of the constant-D method developed by Stengel, Spaldin and Vanderbilt (Nat. Phys. 2009, 5: 304). There is also a modification of this scheme imposing fixed values of the macroscopic field E. The method is applied to the popular SPC/E model of liquid water. We compare four different estimates of the dielectric constant, two obtained from fluctuations of the polarization at D = 0 and E = 0 and two from the variation of polarization with finite D and E. It is found that all four estimates agree when properly converged. The computational effort to achieve convergence varies however, with constant D calculations being substantially more efficient. We attribute this difference to the much shorter relaxation time of longitudinal polarization compared to transverse polarization accelerating constant D calculations.

preprint2016arXiv

Experimental Detection of Entanglement Polytopes via Local Filters

Entanglement polytopes result in finitely many types of entanglement that can be detected by only measuring single-particle spectra. With high probability, however, the local spectra lie in more than one polytope, hence providing no information about the entanglement type. To overcome this problem, we propose to additionally use local filters. We experimentally demonstrate the detection of entanglement polytopes in a four-qubit system. Using local filters we can distinguish the entanglement type of states with the same single particle spectra, but which belong to different polytopes.

preprint2016arXiv

Experimental generation of a high-fidelity four-photon linear cluster state

Cluster state plays a crucial role in the one-way quantum computation. Here, we propose and experimentally demonstrate a new scheme to prepare an ultrahigh-fidelity four-photon linear cluster state via spontaneous parametric down-conversion process. The state fidelity is measured to be $0.9517\pm0.0027$. Our scheme can be directly extended to more photons to generate N-qubit linear cluster state. Furthermore, our scheme is optimal for generating photonic linear cluster states in the sense of achieving the maximal success probability and having the simplest strategy. The key idea is that the photon pairs are prepared in some special non-maximally entangled states instead of the normal Bell states. To generate a 2N-qubit linear cluster state from N pairs of entangled photons, only (N-1) Hong-Ou-Mandel interferences are needed and a success probability of $(\frac{1}{4})^{N-1}$ is achieved.

preprint2016arXiv

Finite Field Methods for the Supercell Modelling of Charged Insulator-Electrolyte Interfaces

Surfaces of ionic solids interacting with an ionic solution can build up charge by exchange of ions. The surface charge is compensated by a strip of excess charge at the border of the electrolyte forming an electric double layer. These electric double layers are very hard to model using the supercells methods of computational condensed phase science. The problem arises when the solid is an electric insulator (as most ionic solids are) permitting a finite interior electric field over the width of the slab representing the solid in the supercell. The slab acts as a capacitor. The stored charge is a deficit in the solution failing to compensate fully for the solid surface charge. Here we show how these problems can be overcome using the finite field methods developed by Stengel, Spaldin and Vanderbilt [Nat. Phys. {\bf 5}, 304, (2009)]. We also show how the capacitance of the double layer can be computed once overall electric neutrality of the double layer is restored by application of a finite macroscopic field $\mathbf{E}$ or alternatively by zero electric displacement $\mathbf{D}$. The method is validated for a classical model of a solid-electrolyte interface using the finite temperature molecular dynamics adaptation of the constant field method presented previously [Phys. Rev. B, 2016, 93, 144201]. Because ions in electrolytes can diffuse across supercell boundaries, this application turns out to be a critical illustration of the multivaluedness of polarization in periodic systems.

preprint2016arXiv

Frequency-Domain Response Based Timing Synchronization: A Near Optimal Sampling Phase Criterion for TDS-OFDM

In time-domain synchronous OFDM (TDS-OFDM) system for digital television terrestrial multimedia broadcasting (DTMB) standard, the baseband OFDM signal is upsampled and shaping filtered by square root raised cosine (SRRC) filter before digital-to-analog converter (DAC). Much of the work in the area of timing synchronization for TDS-OFDM focuses on frame synchronization and sampling clock frequency offset recovery, which does not consider the sampling clock phase offset due to the upsampling and SRRC filter. This paper evaluates the bit-error-rate (BER) effect of sampling clock phase offset in TDS-OFDM system. First, we provide the BER for M-order quadrature amplitude modulation (M-QAM) in uncoded TDS-OFDM system. Second, under the condition of the optimal BER criterion and additive white Gaussian noise (AWGN) channel, we propose a near optimal sampling phase estimation criterion based on frequency-domain response. Simulations demonstrate that the proposed criterion also has good performance in actual TDS-OFDM system with channel coding over multipath channels, and it is superior to the conventional symbol timing recovery methods for TDS-OFDM system.

preprint2016arXiv

Multiple-View Spectral Clustering for Group-wise Functional Community Detection

Functional connectivity analysis yields powerful insights into our understanding of the human brain. Group-wise functional community detection aims to partition the brain into clusters, or communities, in which functional activity is inter-regionally correlated in a common manner across a group of subjects. In this article, we show how to use multiple-view spectral clustering to perform group-wise functional community detection. In a series of experiments on 291 subjects from the Human Connectome Project, we compare three versions of multiple-view spectral clustering: MVSC (uniform weights), MVSCW (weights based on subject-specific embedding quality), and AASC (weights optimized along with the embedding) with the competing technique of Joint Diagonalization of Laplacians (JDL). Results show that multiple-view spectral clustering not only yields group-wise functional communities that are more consistent than JDL when using randomly selected subsets of individual brains, but it is several orders of magnitude faster than JDL.

preprint2016arXiv

Perspectives of Racetrack Memory for Large-Capacity On-Chip Memory: From Device to System

Current-induced domain wall motion (CIDWM) is regarded as a promising way towards achieving emerging high-density, high-speed and low-power non-volatile devices. Racetrack memory is an attractive spintronic memory based on this phenomenon, which can store and transfer a series of data along a magnetic nanowire. However, storage capacity issue is always one of the most serious bottlenecks hindering its application for practical systems. This paper focuses on the potential of racetrack memory towards large capacity. The investigations covering from device level to system level have been carried out. Various alternative mechanisms to improve the capacity of racetrack memory have been proposed and elucidated, e.g. magnetic field assistance, chiral DW motion and voltage-controlled flexible DW pinning. All of them can increase nanowire length, allowing enhanced feasibility of large-capacity racetrack memory. By using SPICE compatible racetrack memory electrical model and commercial CMOS 28 nm design kit, mixed simulations are performed to validate their functionalities and analyze their performance. System level evaluations demonstrate the impact of capacity improvement on overall system. Compared with traditional SRAM based cache, racetrack memory based cache shows its advantages in terms of execution time and energy consumption.

preprint2016arXiv

Reversible tuning of superconductivity in pressurized qausi-one-dimensional A2Cr3As3 (A=K and Rb)

In-situ hydrostatic and uniaxial high pressure studies were performed on recently discovered CrAs-based qausi-one-dimensional superconductors A2Cr3As3 (A=K and Rb). The established Pressure-Temperature phase diagram in this study clearly demonstrates that either hydrostatic pressure or uniaxial pressure globally suppresses the superconducting transition temperature (Tc), and the latter is more effective than the former. Interestingly, in the same hydrostatic pressure environment, the suppressing rate of Tc in Rb2Cr3As3 is nearly twice as that of K2Cr3As3. Significantly, the reduced Tc in these superconductors can fully recover to its ambient-pressure value after the applied pressure is entirely released. Our results suggest that the bonding distance and angle between Cr-Cr in the Cr3As3 chains are the key factor in determining Tc and that the optimal lattice for superconductivity is hosted in the pristine K2Cr3As3.

preprint2016arXiv

Time-invariant entanglement and sudden death of non-locality

We investigate both theoretically and experimentally the dynamics of entanglement and non-locality for two qubits immersed in a global pure dephasing environment. We demonstrate the existence of a class of states for which entanglement is forever frozen during the dynamics, even if the state of the system does evolve. At the same time non-local correlations, quantified by the violation of the Clauser-Horne-Shimony-Holt (CHSH) inequality, either undergo sudden death or are trapped during the dynamics.

preprint2016arXiv

UDBMS: Road to Unification for Multi-model Data Management

A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data models against a single, integrated backend. For example, semi-structured, graph and relational models are examples of data models that may be supported by a new system. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach significantly reduces integration, migration, development, maintenance and operational issues. The problem is challenging: the existing database principles mainly work for a single model and the research on multi-model data management is still at an early stage. In this paper, we envision a UDBMS (Unified Database Management System) for multi-model data management in one platform. UDBMS will provide several new features such as unified data model and flexible schema, unified query processing, unified index structure and cross-model transaction guarantees. We discuss our vision as well as present multiple research challenges that we need to address.

preprint2015arXiv

Breakdown of Three-dimensional Dirac Semimetal State in pressurized Cd3As2

We report the first observation of a pressure-induced breakdown of the 3D-DSM state in Cd3As2, evidenced by a series of in-situ high-pressure synchrotron X-ray diffraction (XRD) and single crystal transport measurements. We find that Cd3As2 undergoes a structural phase transition from a metallic tetragonal (T) phase in space group I41/acd to a semiconducting monoclinic (M) phase in space group P21/c at critical pressure 2.57 GPa, above this pressure, an activation energy gap appears, accompanied by distinct switches in Hall resistivity slope and electron mobility. These changes of crystal symmetry and corresponding transport properties manifest the breakdown of the 3D-DSM state in pressurized Cd3As2.

preprint2015arXiv

Characterization and Modeling of a Water-based Liquid Scintillator

We have characterised Water-based Liquid Scintillator (WbLS) using low energy protons, UV-VIS absorbance, and fluorescence spectroscopy. We have also developed and validated a simulation model that describes the behaviour of WbLS in our detector configurations for proton beam energies of 2 GeV, 475 MeV, and 210 MeV and for two WbLS compositions. Our results have enabled us to estimate the light yield and ionisation quenching of WbLS, as well as to understand the influence of the wavelength shifting of Cerenkov light on our measurements. These results are relevant to the suitability of water-based liquid scintillator materials for next generation intensity frontier experiments.

preprint2015arXiv

Correlation between intercalated magnetic layers and superconductivity in pressurized EuFe2(As0.81P0.19)2

We report comprehensive high pressure studies on correlation between intercalated magnetic layers and superconductivity in EuFe2(As0.81P0.19)2 single crystal through in-situ high pressure resistance, specific heat, X-ray diffraction and X-ray absorption measurements. We find that an unconfirmed magnetic order of the intercalated layers coexists with superconductivity in a narrow pressure range 0-0.5GPa, and then it converts to a ferromagnetic (FM) order at pressure above 0.5 GPa, where its superconductivity is absent. The obtained temperature-pressure phase diagram clearly demonstrates that the unconfirmed magnetic order can emerge from the superconducting state. In stark contrast, the superconductivity cannot develop from the FM state that is evolved from the unconfirmed magnetic state. High pressure X-ray absorption (XAS) measurements reveal that the pressure-induced enhancement of Eu's mean valence plays an important role in suppressing the superconductivity and tuning the transition from the unconfirmed magnetic state to a FM state. The unusual interplay among valence state of Eu ions, magnetism and superconductivity under pressure may shed new light on understanding the role of the intercalated magnetic layers in Fe-based superconductors.

preprint2015arXiv

Dynamic Power Splitting Policies for AF Relay Networks with Wireless Energy Harvesting

Wireless energy harvesting (WEH) provides an exciting way to supply energy for relay nodes to forward information for the source-destination pairs. In this paper, we investigate the problem on how the relay node dynamically adjusts the power splitting ratio of information transmission (IT) and energy harvesting (EH) in order to achieve the optimal outage performance. According to the knowledge of channel state information (CSI) at the relay, optimal dynamic power splitting policy with full CSI and partial CSI are both provided. Finally, through simulations, the proposed power splitting policies can improve the outage performances and the policy with full CSI achieves the best performance. It is also shown that the policy with partial CSI can approach the policy with full CSI closely and incurs far less system overhead.

preprint2015arXiv

Geometrical and electronic structures of tripotassium-doped hydrocarbon superconductors: Density functional calculations

A systemically theoretical study has been presented to explored the crystal structures and electronic characteristics of polycyclic aromatic hydrocarbons (PAHs), such as solid phenanthrene, picene, 1,2;8,9-dibenzopentacene, and 7-phenacenes, since these PAHs exhibited the superconductivity when potassium doping into. For tripotassium-doped phenanthrene and picene, we demonstrate the K atomic positions to fit the experimental lattice parameters, and analyze the distinction between the stablest configuration and the fitted experimental one. Based on the first-principles calculations, for the first time, we predict the possible crystal configurations of pristine and tripotassium-doped 1,2;8,9-dibenzopentacene and 7-phenacenes, respectively. For these four PAHs, the electronic structures after doping are investigated in details. The results show that the electronic characters near the Fermi level are high sensitive to structure. Because of the change of the benzene rings arrangement, the 1,2;8,9-dibenzopentacene exhibits visibly different band structures from other three PAHs. In these metallic PAHs, two bands cross the Fermi level which results in the complicated multiband feature of Fermi surfaces. Fascinatingly, we find that the electronic states of potassium contribute to the Fermi surfaces especially for K-3$d$ electrons, which improves a way to understand this superconductivity. As a result, we suggest that the rigid-band picture is invalidated due to the hybridization between K atoms and PAH molecules as well as the rearrangement and distortion of PAH molecules.

preprint2015arXiv

Measurement of Radiation Damage of Water-based Liquid Scintillator and Liquid Scintillator

Liquid scintillating phantoms have been proposed as a means to perform real-time 3D dosimetry for proton therapy treatment plan verification. We have studied what effect radiation damage to the scintillator will have upon this application. We have performed measurements of the degradation of the light yield and optical attenuation length of liquid scintillator and water-based liquid scintillator after irradiation by 201 MeV proton beams that deposited doses of approximately 52 Gy, 300 Gy, and 800 Gy in the scintillator. Liquid scintillator and water-based liquid scintillator (composed of $5\%$ scintillating phase) exhibit light yield reductions of $1.74 \pm 0.55 \%$ and $1.31 \pm 0.59 \%$ after $\approx$ 800 Gy of proton dose, respectively. Whilst some increased optical attenuation was observed in the irradiated samples, the measured reduction to the light yield is also due to damage to the scintillation light production. Based on our results and conservative estimates of the expected dose in a clinical context, a scintillating phantom used for proton therapy treatment plan verification would exhibit a systematic light yield reduction of approximately $0.1\%$ after a year of operation.

preprint2015arXiv

Mechanical Strain Effects on Black Phosphorus Nanoresonators

We perform classical molecular dynamics to investigate the effects of mechanical strain on single-layer black phosphorus nanoresonators at different temperatures. We find that the resonant frequency is highly anisotropic in black phosphorus due to its intrinsic puckered configuration, and that the quality factor in the armchair direction is higher than in the zigzag direction at room temperature. The quality factors are also found to be intrinsically larger than graphene and MoS2 nanoresonators. The quality factors can be increased by more than a factor of two by applying tensile strain, with uniaxial strain in the armchair direction being most effective. However, there is an upper bound for the quality factor increase due to nonlinear effects at large strains, after which the quality factor decreases. The tension induced nonlinear effect is stronger along the zigzag direction, resulting in a smaller maximum strain for quality factor enhancement.

preprint2015arXiv

Metal-Enhanced Near-Infrared Fluorescence by Micropatterned Gold Nanocages

In metal-enhanced fluorescence (MEF), the localized surface plasmon resonances of metallic nanostructures amplify the absorption of excitation light and assist in radiating the consequent fluorescence of nearby molecules to the far-field. This effect is at the base of various technologies that have strong impact on fields such as optics, medical diagnostics and biotechnology. Among possible emission bands, those in the near-infrared (NIR) are particularly intriguing and widely used in proteomics and genomics due to its noninvasive character for biomolecules, living cells, and tissues, which greatly motivates the development of effective, and eventually multifunctional NIR-MEF platforms. Here we demonstrate NIR-MEF substrates based on Au nanocages micropatterned with a tight spatial control. The dependence of the fluorescence enhancement on the distance between the nanocage and the radiating dipoles is investigated experimentally and modeled by taking into account the local electric field enhancement and the modified radiation and absorption rates of the emitting molecules. At a distance around 80 nm, a maximum enhancement up to 2-7 times with respect to the emission from pristine dyes (in the region 660 nm-740 nm) is estimated for films and electrospun nanofibers. Due to their chemical stability, finely tunable plasmon resonances, and large light absorption cross sections, Au nanocages are ideal NIR-MEF agents. When these properties are integrated with the hollow interior and controllable surface porosity, it is feasible to develop a nanoscale system for targeted drug delivery with the diagnostic information encoded in the fluorophore.

preprint2015arXiv

Neutrino Oscillation Studies with Reactors

Nuclear reactors are one of the most intense, pure, controllable, cost-effective, and well-understood sources of neutrinos. Reactors have played a major role in the study of neutrino oscillations, a phenomenon that indicates that neutrinos have mass and that neutrino flavors are quantum mechanical mixtures. Over the past several decades reactors were used in the discovery of neutrinos, were crucial in solving the solar neutrino puzzle, and allowed the determination of the smallest mixing angle $θ_{13}$. In the near future, reactors will help to determine the neutrino mass hierarchy and to solve the puzzling issue of sterile neutrinos.

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

Nonlinear Image Formation by Optical Superlattices

The angular spectrum theory is applied to the nonlinear harmonic generation process in optical superlattices. Several explicit and analytical structure functions are deduced to design optical superlattices for various purposes. Employing this method, nonlinear image formation is achieved during the second-harmonic generation process in a properly designed optical superlattice. This method is universal for both nonlinear beam shaping and nonlinear arbitrary image formation. The theory has been experimentally validated in two-dimensional optical superlattice of LiTaO3 crystals and the results agree well with the theoretical prediction. This work not only extends the application of optical superlattices, but also opens a new area for imaging technologies.

preprint2015arXiv

On algebras of strongly derived unbounded type

Let $A$ be a finite-dimensional algebra over an algebraically closed field. We prove $A$ is a strongly derived unbounded algebra if and only if there exists an integer $m$, such that $C_m(\proj A)$, the category of all minimal projective complexes with degree concentrated in $[0, m]$, is of strongly unbounded type, which is also equivalent to the statement the repetitive algebra $\hat{A}$ is of strongly unbounded representation type. As a corollary, we can establish the dichotomy on the representation type of $C_m(\proj A)$, the homotopy category $K^b(\proj A)$ and the repetitive algebra $\hat{A}$.

preprint2015arXiv

Priori-Information Aided Iterative Hard Threshold: A Low-Complexity High-Accuracy Compressive Sensing Based Channel Estimation for TDS-OFDM

This paper develops a low-complexity channel estimation (CE) scheme based on compressive sensing (CS) for time-domain synchronous (TDS) orthogonal frequency-division multiplexing (OFDM) to overcome the performance loss under doubly selective fading channels. Specifically, an overlap-add method of the time-domain training sequence is first proposed to obtain the coarse estimates of the channel length, path delays and path gains of the wireless channel, by exploiting the channel's temporal correlation to improve the robustness of the coarse CE under the severe fading channel with long delay spread. We then propose the priori-information aided (PA) iterative hard threshold (IHT) algorithm, which utilizes the priori information of the acquired coarse estimate for the wireless channel and therefore is capable of obtaining an accurate channel estimate of the doubly selective fading channel. Compared with the classical IHT algorithm whose convergence requires the $l_2$ norm of the measurement matrix being less than 1, the proposed PA-IHT algorithm exploits the priori information acquired to remove such a limitation as well as to reduce the number of required iterations. Compared with the existing CS based CE method for TDS-OFDM, the proposed PA-IHT algorithm significantly reduces the computational complexity of CE as well as enhances the CE accuracy. Simulation results demonstrate that, without sacrificing spectral efficiency and changing the current TDS-OFDM signal structure, the proposed scheme performs better than the existing CE schemes for TDS-OFDM in various scenarios, especially under severely doubly selective fading channels.

preprint2015arXiv

Recent Results From The Daya Bay Experiment

The Daya Bay reactor neutrino experiment has observed the disappearance of electron antineutrinos from nuclear reactors at $\sim$kilometer baselines. The relative measurement of the $\barν_e$ rate and spectrum between near and far detectors allows for a precision measurement of the oscillation parameters $\sin^22θ_{13}$ and $|Δ{m}^2_{ee}|$. Two new antineutrino detectors (ADs) were installed in summer 2012, bringing the experiment to the final 8-AD configuration. With 621 days of data, Daya Bay has measured $\sin^22θ_{13} = 0.084 \pm 0.005$ and $|Δ{m}^2_{ee}| = 2.44^{+0.10}_{-0.11} \times 10^{-3}$ eV$^2$. This is the most precise measurement of $\sin^22θ_{13}$ to date and the most precise measurement of $|Δ{m}^2_{ee}|$ in this channel. Several other analyses are also performed, including an independent measurement of $\sin^22θ_{13}$ using $\barν_e$ samples tagged by neutron capture on hydrogen, a search for light sterile neutrinos, and a measurement of the absolute reactor antineutrino flux.

preprint2015arXiv

Robust Preamble Design for Synchronization, Signaling Transmission and Channel Estimation

The European second generation digital video broadcasting standard (DVB-T2) introduces a P1 symbol. This P1 symbol facilitates the coarse synchronization and carries 7-bit transmission parameter signaling (TPS), including the fast Fourier transform size, single-input/single-output and multiple-input/single-output transmission modes, etc. However, this P1 symbol suffers from obvious performance loss over fading channels. In this paper, an improved preamble scheme is proposed, where a pair of optimal m sequences are inserted into the frequency domain. One sequence is used for carrier frequency offset (CFO) estimation, and the other carries TPS to inform the receiver about the transmission configuration parameters. Compared with the conventional preamble scheme, the proposed preamble improves CFO estimation performance and the signaling capacity. Meanwhile, without additional overhead, the proposed scheme exploits more active pilots than the conventional schemes. In this way, it can facilitate the channel estimation, improve the frame synchronization accuracy as well as enhance its robustness to frequency selective fading channels.

preprint2015arXiv

Stratifications and foliations for good reductions of Shimura varieties of Hodge type

Level $m$-stratifications on PEL Shimura varieties are defined and studied by Wedhorn using BT-$m$s with PEL structure, and then by Vasiu for general Hodge type Shimura varieties using Shimura $F$-crystals. The theory of foliations is established by Oort for Siegel modular varieties, and by Mantovan for PEL Shimura varieties. It plays an important role in Hamacher's work to compute the dimension of Newton strata of PEL Shimura varieties. We study level $m$ stratifications on good reductions at $p>2$ of Shimura varieties of Hodge type by constructing certain torsors together with equivariant morphisms, and relating them to truncated displays. We then use the results obtained to extend the theory of foliations to these reductions. As a consequence, combined with results of Nie and Zhu, we get a dimension formula for Newton strata.

preprint2015arXiv

Unsupervised Feature Learning for Dense Correspondences across Scenes

We propose a fast, accurate matching method for estimating dense pixel correspondences across scenes. It is a challenging problem to estimate dense pixel correspondences between images depicting different scenes or instances of the same object category. While most such matching methods rely on hand-crafted features such as SIFT, we learn features from a large amount of unlabeled image patches using unsupervised learning. Pixel-layer features are obtained by encoding over the dictionary, followed by spatial pooling to obtain patch-layer features. The learned features are then seamlessly embedded into a multi-layer match- ing framework. We experimentally demonstrate that the learned features, together with our matching model, outperforms state-of-the-art methods such as the SIFT flow, coherency sensitive hashing and the recent deformable spatial pyramid matching methods both in terms of accuracy and computation efficiency. Furthermore, we evaluate the performance of a few different dictionary learning and feature encoding methods in the proposed pixel correspondences estimation framework, and analyse the impact of dictionary learning and feature encoding with respect to the final matching performance.

preprint2014arXiv

A Characterization of BMO^α-martingale spaces by fractional Carleson measures

We give a characterization of $BMO^α$-martingale spaces by using fractional Carleson measures. We get the boudedness of martingale transform and square function on $BMO^α$-martingale spaces easily by using this characterization. We also proved the martingale version of Carleson's inequality related with $BMO^α$-martingales.

preprint2014arXiv

A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm

A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters $δ$ and c in the classification function. That is to say,to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path.In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters.

preprint2014arXiv

Connection between ambient-pressure and pressure-induced superconducting phases in alkaline iron selenide superconductors

A unique platform for investigating the correlation between the antiferromagnetic (AFM) and superconducting (SC) states in high temperature superconductors is created by the discovery of alkaline iron selenide superconductors which are composed of an AFM insulating phase and a SC phase separated spatially. Our previous studies showed that pressure can fully suppress the superconductivity of ambient-pressure superconducting phase (SC-I) and AFM order simultaneously, then induce another superconducting phase (SC-II) at higher pressure. Consequently, the connection between the two superconducting phases becomes an intriguing issue. In this study, on the basis of observing pressure-induced reemergence of superconductivity in Rb0.8Fe2-ySe2-xTex (x=0, 0.19 and 0.28) superconductors, we find that the superconductivity of the SC-I and SC-II phases as well as the AFM ordered state can be synchronously tuned by Te doping and disappear together at the doping level of x=0.4. We propose that the two superconducting phases are connected by the AFM phase, in other words, the state of long-ranged AFM order plays a role in giving rise to superconductivity of the SC-I phase, while the fluctuation state of the suppressed AFM phase drives the emergence of SC-II phase. These results comprehensively demonstrate the versatile roles of AFM states in stabilizing and developing superconductivity in the alkaline iron selenide superconductors.

preprint2014arXiv

Distributed Relay Selection Protocols for Simultaneous Wireless Information and Power Transfer

Harvesting energy from the radio-frequency (RF) signal is an exciting solution to replenish energy in energy-constrained wireless networks. In this paper, an amplify-and-forward (AF) based wireless relay network is considered, where the relay nodes need to harvest energy from the source's RF signal to forward information to the destination. To improve the performance of information transmission, we propose two distributed relay selection protocols, Maximum Harvested Energy (MHE) protocol and Maximum Signal-to-Noise Ratio (MSNR) protocol. Then, we derive the outage probabilities of the system with our proposed relay selection protocols and prove that the proposed selection protocols indeed can improve the system performances and the MSNR protocol outperforms the MHE protocol. Simulation results verify the analysis and theorems. In addition, the effects of key system parameters are also investigated via simulations.

preprint2014arXiv

Generalization Bounds for Representative Domain Adaptation

In this paper, we propose a novel framework to analyze the theoretical properties of the learning process for a representative type of domain adaptation, which combines data from multiple sources and one target (or briefly called representative domain adaptation). In particular, we use the integral probability metric to measure the difference between the distributions of two domains and meanwhile compare it with the H-divergence and the discrepancy distance. We develop the Hoeffding-type, the Bennett-type and the McDiarmid-type deviation inequalities for multiple domains respectively, and then present the symmetrization inequality for representative domain adaptation. Next, we use the derived inequalities to obtain the Hoeffding-type and the Bennett-type generalization bounds respectively, both of which are based on the uniform entropy number. Moreover, we present the generalization bounds based on the Rademacher complexity. Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation. We discuss the factors that affect the asymptotic behavior of the learning process and the numerical experiments support our theoretical findings as well. Meanwhile, we give a comparison with the existing results of domain adaptation and the classical results under the same-distribution assumption.

preprint2014arXiv

Relations among Some Low Rank Subspace Recovery Models

Recovering intrinsic low dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, there has been a lot of work that models subspace recovery as low rank minimization problems. We find that some representative models, such as Robust Principal Component Analysis (R-PCA), Robust Low Rank Representation (R-LRR), and Robust Latent Low Rank Representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find better solutions to these low rank models at overwhelming probabilities, although these models are non-convex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low complexity randomized algorithms, e.g., our novel $\ell_{2,1}$ filtering algorithm, to R-PCA. Experiments verify the advantages of our algorithms over other state-of-the-art ones that are based on the alternating direction method.

preprint2014arXiv

Remarks on Ekedahl-Oort stratifications

This short paper is a continuation of the author's Ph.D thesis, where Ekedahl-Oort strata are defined and studied for Shimura varieties of Hodge type. The main results here are as follows. 1. The Ekedahl-Oort stratification is independent of the choices of symplectic embeddings. 2. Under certain reasonable assumptions, there is certain functoriality for Ekedahl-Oort stratifications with respect to morphisms of Shimura varieties.

preprint2014arXiv

Robust antiferromagnetism preventing superconductivity in pressurized Ba0.61K0.39Mn2Bi2

Here, we report that K-doped BaMn2Bi2 shows no experimental evidence of superconductivity down to 1.5 K under pressures up to 35.6 GPa, however, a tetragonal to an orthorhombic phase transition is observed at pressure of 20 GPa. Theoretical calculations for the tetragonal and orthorhombic phases, on basis of our high-pressure XRD data, find that the AFM order is robust in both of the phases in pressurized Ba0.61K0.39Mn2Bi2. Our experimental and theoretical results suggest that the K-doped BaMn2Bi2 belongs to a strong Hunds AFM metal with a hybridization of localized spin electrons and itinerant electrons, and that its robust AFM order essentially prevents the emergence of superconductivity.

preprint2014arXiv

Simultaneous Wireless Information and Power Transfer with Co-Channel Interference

Simultaneous wireless information and power transfer (SWIPT) is an appealing solution to balance the energy distribution in wireless networks and improve the energy-efficiency of the entire network. In this paper, we study the optimal policies for SWIPT over the flat-fading channel subject to the co-channel interference. The SWIPT receiver using dynamic power splitting (DPS) is expected to achieve the maximum information rate given an average harvested energy constraint. In the case that channel state information at the transmitter (CSIT) is not available, the optimal power splitting is derived. For the case with CSIT, the joint optimal policy of the transmit power and the power splitting are also provided. Through simulations, we show the optimal rate-energy (R-E) trade-offs of our proposed policies. The case with CSIT achieves better R-E trade-off than the case with no CSIT.

preprint2014arXiv

Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning

In this paper, we study the generalization performance of regularized multi-task learning (RMTL) in a vector-valued framework, where MTL is considered as a learning process for vector-valued functions. We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL? 2) under what conditions is RMTL generalizable and can guarantee the consistency of each task during simultaneous learning? In particular, we investigate two types of task-group relatedness: the observed discrepancy-dependence measure (ODDM) and the empirical discrepancy-dependence measure (EDDM), both of which detect the dependence between two groups of multiple related tasks (MRTs). We then introduce the Cartesian product-based uniform entropy number (CPUEN) to measure the complexities of vector-valued function classes. By applying the specific deviation and the symmetrization inequalities to the vector-valued framework, we obtain the generalization bound for RMTL, which is the upper bound of the joint probability of the event that there is at least one task with a large empirical discrepancy between the expected and empirical risks. Finally, we present a sufficient condition to guarantee the consistency of each task in the simultaneous learning process, and we discuss how task relatedness affects the generalization performance of RMTL. Our theoretical findings answer the aforementioned two questions.

preprint2014arXiv

The role of 245 phase in alkaline iron selenide superconductors revealed by high pressure studies

Here we show that a pressure of about 8 GPa suppresses both the vacancy order and the insulating phase, and a further increase of the pressure to about 18 GPa induces a second transition or crossover. No superconductivity has been found in compressed insulating 245 phase. The metallic phase in the intermediate pressure range has a distinct behavior in the transport property, which is also observed in the superconducting sample. We interpret this intermediate metal as an orbital selective Mott phase (OSMP). Our results suggest that the OSMP provides the physical pathway connecting the insulating and superconducting phases of these iron selenide materials.

preprint2014arXiv

Weighted inequlities for a generalized dyadic maximal operator involving the infinite product

We define a generalized dyadic maximal operator involving the infinite product and discuss weighted inequalities for the operator. A formulation of the Carleson embedding theorem is proved. Our results depend heavily on a generalized Hölder's inequalities.

preprint2013arXiv

A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank

Rank minimization has attracted a lot of attention due to its robustness in data recovery. To overcome the computational difficulty, rank is often replaced with nuclear norm. For several rank minimization problems, such a replacement has been theoretically proven to be valid, i.e., the solution to nuclear norm minimization problem is also the solution to rank minimization problem. Although it is easy to believe that such a replacement may not always be valid, no concrete example has ever been found. We argue that such a validity checking cannot be done by numerical computation and show, by analyzing the noiseless latent low rank representation (LatLRR) model, that even for very simple rank minimization problems the validity may still break down. As a by-product, we find that the solution to the nuclear norm minimization formulation of LatLRR is non-unique. Hence the results of LatLRR reported in the literature may be questionable.

preprint2013arXiv

Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence

In this paper, we present the Bennett-type generalization bounds of the learning process for i.i.d. samples, and then show that the generalization bounds have a faster rate of convergence than the traditional results. In particular, we first develop two types of Bennett-type deviation inequality for the i.i.d. learning process: one provides the generalization bounds based on the uniform entropy number; the other leads to the bounds based on the Rademacher complexity. We then adopt a new method to obtain the alternative expressions of the Bennett-type generalization bounds, which imply that the bounds have a faster rate o(N^{-1/2}) of convergence than the traditional results O(N^{-1/2}). Additionally, we find that the rate of the bounds will become faster in the large-deviation case, which refers to a situation where the empirical risk is far away from (at least not close to) the expected risk. Finally, we analyze the asymptotical convergence of the learning process and compare our analysis with the existing results.

preprint2013arXiv

Constrain on superconductivity of potassium intercalated phenanthrene

Raman-scattering measurements are performed in K${_x}$phenanthrene with the nominal value ${x}$ ranging from 0 to 6.0 at room temperature. Based on the Raman spectra, two phases (${x}$ = 3.0 and 4.0) are observed among the K${_x}$phenanthrene, and the immediate phase (${x}$ = 3.5) is produced. Only the ${x}$ = 3.0 phase is found to exhibit the superconducting transition at 5 K. The C-C stretching mods are observed to broaden and become disorder in K${_x}$phenanthrene (nominal value ${x}$ =2.0, 2.5, 6.0), indicating some molecular disorder in the metal intercalation process. This disorder is expected to influence the nonmetallic nature of these materials. The absence of metallic characteristics in those nonsuperconducting phases is provided from the calculated electronic structures based on the local density approximation.

preprint2013arXiv

Generalization Bounds for Domain Adaptation

In this paper, we provide a new framework to obtain the generalization bounds of the learning process for domain adaptation, and then apply the derived bounds to analyze the asymptotical convergence of the learning process. Without loss of generality, we consider two kinds of representative domain adaptation: one is with multiple sources and the other is combining source and target data. In particular, we use the integral probability metric to measure the difference between two domains. For either kind of domain adaptation, we develop a related Hoeffding-type deviation inequality and a symmetrization inequality to achieve the corresponding generalization bound based on the uniform entropy number. We also generalized the classical McDiarmid's inequality to a more general setting where independent random variables can take values from different domains. By using this inequality, we then obtain generalization bounds based on the Rademacher complexity. Afterwards, we analyze the asymptotic convergence and the rate of convergence of the learning process for such kind of domain adaptation. Meanwhile, we discuss the factors that affect the asymptotic behavior of the learning process and the numerical experiments support our theoretical findings as well.

preprint2013arXiv

Klein tunneling and cone transport in AA-stacked bilayer graphene

We investigate the quantum tunneling of electrons in an AA-stacked bilayer graphene (BLG) $n$-$p$ junction and $n$-$p$-$n$ junction. We show that Klein tunneling of an electron can occur in this system. The quasiparticles are not only chiral but are additionally described by a `cone index'. Due to the orthogonality of states with different cone indexes, electron transport across a potential barrier must strictly conserve the cone index and this leads to the protected cone transport which is unique in AA-stacked BLG. Together with the negative refraction of electrons, electrons residing in different cones can be spatially separated according to their cone index when transmitted across an $n$-$p$ junction. This suggests the possibility of `cone-tronic' devices based on AA-stacked BLG. Finally, we calculate the junction conductance of the system.

preprint2013arXiv

Observation of antiferromagnetic order collapse in the pressurized insulator LaMnPO

The emergence of superconductivity in the iron pnictide and cuprate high temperature superconductors usually accompanies the suppression of an antiferromagnetically (AFM) ordered state in a corresponding parent compound through the use of chemical doping or external pressure1-5. A great deal of effort has been made to find superconductivity in Mn-based compounds6-14, which are thought to bridge the gap between the two families of high temperature superconductors7,15,16, but long-ranged AFM order was not successfully suppressed via chemical doping in these investigations. Here we report the first observation of the pressure-induced elimination of long-ranged AFM order in LaMnPO single crystals that are iso-structural to the LaFeAsO superconductor15,17. By combining in-situ high pressure resistance and ac susceptibility measurements, we found that LaMnPO undergoes a crossover from an AFM insulating to an AFM metallic state at a pressure ~20 GPa and that the long-ranged AFM order collapses at a higher pressure ~32 GPa. Our findings are of importance to explore potential superconductivity in Mn-based compounds and to shed new light on the underlying mechanism of high temperature superconductivity.

preprint2013arXiv

On characterization of Poisson integrals of Schrodinger operators with BMO traces

Let L be a Schrodinger operator of the form L=-Δ+V acting on L^2(Rn) where the nonnegative potential V belongs to the reverse Holder class Bq for some q>= n. Let BMO_L(Rn) denote the BMO space on Rn associated to the Schrodinger operator L. In this article we will show that a function f in BMO_L(Rn) is the trace of the solution of L'u=-u_tt+Lu=0, u(x,0)= f(x), where u satisfies a Carleson condition. Conversely, this Carleson condition characterizes all the L-harmonic functions whose traces belong to the space BMO_L(Rn). This result extends the analogous characterization founded by Fabes, Johnson and Neri for the classical BMO space of John and Nirenberg.

preprint2013arXiv

Structural and vibrational properties of phenanthrene under pressure

The structural and vibrational properties of phenanthrene are measured at high pressures up to 30.2 GPa by Raman spectroscopy and synchrotron x-ray diffraction techniques. Two phase transitions are observed in raman spectra under pressures of 2.3 GPa and 5.4 GPa with significant changes of intermolecular and intramolecular vibrational modes, and all the raman peaks disappeared in the luminesce background above 10.2 GPa. Upon further compression above 20.0 GPa, three broad bands are observed respectively around 1600, 2993 and 3181 cm$^{-1}$ in Raman spectra, strongly indicating that phenanthrene has transformed into amorphous hydrogenated carbon. Based on x-ray diffraction, the structures of corresponding phases observed in Raman spectra are identified with space group of $P_{\rm}2_{1}$ for phase I at 0-2.2 GPa, $P_{\rm}$2/\emph{m} for phase II at 2.2-5.6 GPa and $P_{\rm}$2/\emph{m}+\emph{Pmmm} for phase III at 5.6-11.4 GPa, and the structure above 11.4 GPa is identified with space group of \emph{Pmmm}. Although phenanthrene has amorphized above 20.0 GPa, the amorphous hydrogenated carbon clusters still remain the crystalline characteristic based on x-ray diffraction patterns. Our results suggest that the long-range periodicity and the local disorder state coexist in phenanthrene at high pressures.

preprint2012arXiv

Harnack's inequality for fractional nonlocal equations

We prove interior Harnack's inequalities for solutions of fractional nonlocal equations. Our examples include fractional powers of divergence form elliptic operators with potentials, operators arising in classical orthogonal expansions and the radial Laplacian. To get the results we use an analytic method based on a generalization of the Caffarelli--Silvestre extension problem, the Harnack's inequality for degenerate Schrödinger operators proved by C. E. Gutiérrez, and a transference method. In this manner we apply local PDE techniques to nonlocal operators. On the way a maximum principle and a Liouville theorem for some fractional nonlocal equations are obtained.

preprint2012arXiv

High-pressure phases of a hydrogen-rich compound: tetramethylgermane

The vibrational and structural properties of a hydrogen-rich Group IVa hydride, Ge(CH$_3$)$_4$, are studied by combining Raman spectroscopy and synchrotron X-ray diffraction measurements at room temperature and at pressures up to 30.2 GPa. Both techniques allow the obtaining of complementary information on the high-pressure behaviors and yield consistent phase transitions at 1.4 GPa for the liquid to solid and 3.0, 5.4, and 20.3 GPa for the solid to solid. The four high-pressure solid phases are identified to have the cubic, orthorhombic, monoclinic and monoclinic crystal structures with space groups of Pa-3 for phase I, Pnma for phase II, P2$_1$/c for phase III, and P2$_1$ for phase IV, respectively. These transitions are suggested to result from the changes in the inter- and intra-molecular bonding of this compound. The softening of some Raman modes on CH$_3$ groups and their sudden disappearance indicate that Ge(CH$_3$)$_4$ might be an ideal compound to realize metallization and even high-temperature superconductivity at modest static pressure for laboratory capability.

preprint2012arXiv

Pressure-Driven Quantum Criticality in An Iron-Selenide Superconductor

The discovery of superconductivity of about 30 K in iron selenides with very large magnetic moments simulates the examination of completing orders. Here we report a finding of pressure- induced suppression of the superconducting transition temperature Tc and enhancement of the temperature of the resistance hump TH through charge transfer between two iron sites with different occupancies. The activation energy for the electric transport of the high-temperature resistance is observed to go to zero at a critical pressure of 8.7 GPa, at which superconductivity tends to disappear and the semiconductor-to-metal transition takes place. Beyond the critical point, the resistance exhibits a metallic behavior over the whole temperature range studied. All these features indicate the existence of quantum criticality in iron-selenide superconductors.

preprint2012arXiv

Reemerging superconductivity at 48 K across quantum criticality in iron chalcogenides

Pressure plays an essential role in the induction1 and control2,3 of superconductivity in iron-based superconductors. Substitution of a smaller rare-earth ion for the bigger one to simulate the pressure effects has surprisingly raised the superconducting transition temperature Tc to the record high 55 K in these materials4,5. However, Tc always goes down after passing through a maximum at some pressure and the superconductivity eventually tends to disappear at sufficiently high pressures1-3. Here we show that the superconductivity can reemerge with a much higher Tc after its destruction upon compression from the ambient-condition value of around 31 K in newly discovered iron chalcogenide superconductors. We find that in the second superconducting phase the maximum Tc is as high as 48.7 K for K0.8Fe1.70Se2 and 48 K for (Tl0.6Rb0.4)Fe1.67Se2, setting the new Tc record in chalcogenide superconductors. The presence of the second superconducting phase is proposed to be related to pressure-induced quantum criticality. Our findings point to the potential route to the further achievement of high-Tc superconductivity in iron-based and other superconductors.

preprint2012arXiv

Regularity estimates in Hölder spaces for Schrödinger operators via a T1 theorem

We derive Hölder regularity estimates for operators associated with a time independent Schrödinger operator of the form $-Δ+V$. The results are obtained by checking a certain condition on the function $T1$. Our general method applies to get regularity estimates for maximal operators and square functions of the heat and Poisson semigroups, for Laplace transform type multipliers and also for Riesz transforms and negative powers $(-Δ+V)^{-γ/2}$, all of them in a unified way.

preprint2012arXiv

Risk Bounds for Infinitely Divisible Distribution

In this paper, we study the risk bounds for samples independently drawn from an infinitely divisible (ID) distribution. In particular, based on a martingale method, we develop two deviation inequalities for a sequence of random variables of an ID distribution with zero Gaussian component. By applying the deviation inequalities, we obtain the risk bounds based on the covering number for the ID distribution. Finally, we analyze the asymptotic convergence of the risk bound derived from one of the two deviation inequalities and show that the convergence rate of the bound is faster than the result for the generic i.i.d. empirical process (Mendelson, 2003).

preprint2011arXiv

A Multi-Channel Diversity Based MAC Protocol for Power-Constrained Cognitive Ad Hoc Networks

One of the major challenges in the medium access control (MAC) protocol design over cognitive Ad Hoc networks (CAHNs) is how to efficiently utilize multiple opportunistic channels, which vary dynamically and are subject to limited power resources. To overcome this challenge, in this paper we first propose a novel diversity technology called \emph{Multi-Channel Diversity} (MCD), allowing each secondary node to use multiple channels simultaneously with only one radio per node under the upperbounded power. Using the proposed MCD, we develop a MCD based MAC (MCD-MAC) protocol, which can efficiently utilize available channel resources through joint power-channel allocation. Particularly, we convert the joint power-channel allocation to the Multiple-Choice Knapsack Problem, such that we can obtain the optimal transmission strategy to maximize the network throughput through dynamic programming. Simulation results show that our proposed MCD-MAC protocol can significantly increase the network throughput as compared to the existing protocols.

preprint2011arXiv

Correlation between superconductivity and antiferromagnetism in Rb0.8Fe2Se2-xTex single crystals

We report the first experimental evidence for the intimate connection between superconductivity and antiferromagnetism in Rb0.8Fe2Se2-xTex single crystal under negative chemical pressure by substituting Se with isovalent Te atoms. Electrical resistance measurements in the temperature range from 4 K to 550 K demonstrate that both superconducting transition temperature (Tc) and Neel temperature (TN) were suppressed continuously with the lattice expansion. When the Te concentration x in Rb0.8Fe2Se2-xTex approaches 0.3, the superconducting transition temperature Tc is completely suppressed and the sample behaves like a semiconductor, meanwhile the characteristic peak of antiferromagnetic transition on resistance curve disappears. Our observation suggests that the pressure-induced lattice expansion can be used to tune the correlativity of superconductivity and antiferromagnetism.

preprint2011arXiv

Fractional Vector-Valued Littlewood-Paley-Stein Theory for Semigroups

We consider the fractional derivative of a general Poisson semigroup. With this fractional derivative we define the generalized fractional Littlewood-Paley $g$-function for semigroups acting on $L^p$-spaces of functions with values in Banach spaces. We give a characterization of the classes of Banach spaces for which the fractional Litlewood-Paley $g$-function is bounded on $L^p$-spaces. We show that the class of Banach spaces is independent of the order of derivation and coincides with the classical (Lusin type/cotype) case. It is also shown that the same kind of results exist for the case of the fractional area function and the fractional $g^*_λ$-function on $\mathbb{R}^n$. At last, we consider the relationship of the almost sure finiteness of the fractional Littlewood-Paley $g$-function, area function and $g^*_λ$-function with the Lusin cotype property of the underlying Banach space. As a byproduct of the techniques developed, one can get some results of independent interest for vector-valued Calderón--Zygmund operators. For example, one can get the following characterization, a Banach space $\mathbb{B}$ is UMD if and only if for some (or, equivalently, for every) $p\in [1,\infty)$, $\displaystyle \lim_{ε\rightarrow 0} \int_{|x-y|> ε} \frac{f(y)}{x-y}dy $ exists \textup{a.e.} $x\in \mathbb{R}$ for every $f\in L^p_\mathbb{B}(\mathbb{R}).$

preprint2011arXiv

More counterexamples to Happel's question and Snashall-Solberg's conjecture

In this paper we provide more counterexamples to Happel's question and Snashall-Solberg's conjecture which generalize many counterexamples to these conjectures studied in the literature. In particular, we show that a family of $\mathbb{Z}_n\times \mathbb{Z}_n$-Galois covering algebras of quantized exterior algebra $A_q$ in two variables answer negatively to Happel's question, and meanwhile, the one-point coextensions of $\mathbb{Z}_n$ and $\mathbb{Z}_n\times \mathbb{Z}_m$-Galois covering algebras of $A_q$ negate the Snashall-Solberg's conjecture.

preprint2011arXiv

Optimal Relay Power Allocation for Amplify-and-Forward Relay Networks with Non-linear Power Amplifiers

In this paper, we propose an optimal relay power allocation of an Amplify-and-Forward relay networks with non-linear power amplifiers. Based on Bussgang Linearization Theory, we depict the non-linear amplifying process into a linear system, which lets analyzing system performance easier. To obtain spatial diversity, we design a complete practical framework of a non-linear distortion aware receiver. Consider a total relay power constraint, we propose an optimal power allocation scheme to maximum the receiver signal-to-noise ratio. Simulation results show that proposed optimal relay power allocation indeed can improve the system capacity and resist the non-linear distortion. It is also verified that the proposed transmission scheme outperforms other transmission schemes without considering non-linear distortion.

preprint2011arXiv

Regularity properties of Schrödinger operators

Let L be a Schrödinger operator of the form L=-Δ+V, where the nonnegative potential V satisfies a reverse Hölder inequality. Using the method of L-harmonic extensions we study regularity estimates at the scale of adapted Hölder spaces. We give a pointwise description of L-Hölder spaces and provide some characterizations in terms of the growth of fractional derivatives of any order and Carleson measures. Applications to fractional powers of L and multipliers of Laplace transform type developed.

preprint2011arXiv

Statistical Delay Control and QoS-Driven Power Allocation Over Two-Hop Wireless Relay Links

The time-varying feature of wireless channels usually makes the hard delay bound for data transmissions unrealistic to guarantee. In contrast, the statistically-bounded delay with a small violation probability has been widely used for delay quality-of-service (QoS) characterization and evaluation. While existing research mainly focused on the statistical-delay control in single-hop links, in this paper we propose the QoS-driven power-allocation scheme over two-hop wireless relay links to statistically upper-bound the end-to-end delay under the decodeand- forward (DF) relay transmissions. Specifically, by applying the effective capacity and effective bandwidth theories, we first analyze the delay-bound violation probability over two tops each with independent service processes. Then, we show that an efficient approach for statistical-delay guarantees is to make the delay distributions of both hops identical, which, however, needs to be obtained through asymmetric resource allocations over the two hops. Motivated by this fact, we formulate and solve an optimization problem aiming at minimizing the average power consumptions to satisfy the specified end-to-end delay-bound violation probability over two-hop relay links. Also conducted is a set of simulations results to show the impact of the QoS requirements, traffic load, and position of the relay node on the power allocation under our proposed optimal scheme.

preprint2011arXiv

The Effects of Narrowband Interference on Finite-Resolution IR-UWB Digital Receiver

Finite-resolution digital receiver is recently considered as a potential way to Ultra Wide Band (UWB) communication systems due to its ability of mitigating the challenge of Analog-Digital Converter (ADC). In this paper, the effects of narrowband interference (NBI) are investigated when finite-resolution digital receiver is used for Impulse Radio-UWB (IR-UWB) system. It is shown that finite-resolution receiver enlarges the impact of NBI. The lower resolution of the UWB receiver is, the more degradations NBI causes.

preprint2011arXiv

Universal geometric classification of armchair graphene nanoribbons by their properties in a staggered sublattice potential

We demonstrate the topological properties of the band-gap of armchair graphene nanoribbons in a spatially varying staggered sublattice potential. Several general scaling laws are presented to quantify the band gap variation. It is found that all armchair nanoribbons are described by one of three distinct classes depending on their width, one of which is the well known massless Dirac condition, and the other two we call potentially gapless, and gapless-superlattice. We construct an effective theory which faithfully reproduces these results, and makes explicit the nature of the competing masses and overlap integrals across a particular sample. Finally we propose several systems on which these results should shed considerable light, and which have all already been experimentally realized.

preprint2010arXiv

A quantum model for the stock market

Beginning with several basic hypotheses of quantum mechanics, we give a new quantum model in econophysics. In this model, we define wave functions and operators of the stock market to establish the Schrödinger equation for the stock price. Based on this theoretical framework, an example of a driven infinite quantum well is considered, in which we use a cosine distribution to simulate the state of stock price in equilibrium. After adding an external field into the Hamiltonian to analytically calculate the wave function, the distribution and the average value of the rate of return are shown.

preprint2010arXiv

Evidence for Competition between Superconductivity and Kondo Effect in CeFeAsO0.7F0.3 under High Pressure

We discover a pressure induced quantum phase transition from the superconducting state to the non-superconducting Kondo screened phase associated with a 2% volume collapse in CeFeAsO0.7F0.3 through measurements of high-pressure resistance, synchrotron x-ray diffraction, and x-ray absorption spectroscopy (XAS) in a diamond anvil cell. Our XAS data of Ce-L3 in CeFeAsO0.7F0.3 clearly show a spectral weight transfer from the main line to the satellite line after the transition, demonstrating the formation of the Kondo singlets under pressure in CeFeAsO1-xFx. Our results have revealed a physical picture of a pressure-induce competition between Kondo singlets and BCS singlets in the Ce-pnictide superconductors.

preprint2010arXiv

Phase Diagram of Pressure-induced Superconductivity and its Relation to Hall Coefficient in Bi2Te3 Single Crystal

Pressure-induced superconductivity and its relation to corresponding Hall coefficient (RH) have been reported for Bi2Te3, one of known topological insulators. A full phase diagram is presented which shows a complex dependence of the superconducting transition temperature as a function of pressure over an extensive range. High-pressure RH measurements reveal a close relation of these complex behaviors, particularly, a dramatic change of dRH/dP before structural phase transition and a pressure-induced crossover on RH in the high pressure phase were observed.

preprint2010arXiv

Physical origin of superconductivity in EuFe2As1.4P0.6 and EuFe2As2: pressure-induce valence change of europium

Superconductivity can be realized in Eu-containing pnictides by application of chemical (internal) and physical (external) pressure, the intrinsic physical mechanism of which attracts much attention in physics community. Here we present the experimental evidence for the valence change of europium in compounds of EuFe2As1.4P0.6 exposed to ambient pressure and EuFe2As2 to high pressure by x-ray absorption measurements on L3-Eu edge. We find that the absorption spectrum of EuFe2As1.4P0.6 at ambient pressure shows clear spectra weight transfer from a divalent to a trivalent state. Furthermore, application of pressure on EuFe2As2 using a diamond anvil cell shows a similar behavior of valence transition as EuFe2As1.4P0.6. These findings are the first observation of superconductivity mechanized by valence change in pnictides superconductors and elucidate the intrinsic physical origin of superconductivity in EuFe2As1.4P0.6 and compressed EuFe2As2.

preprint2008arXiv

High pressure study on LaFeAsO with different Tc

We report studies on pressure dependence of superconducting transition temperature (Tc) of LaFeAsO, LaFeAs(O0.5F0.5) and LaFeAs(O0.89F0.11) samples. In-situ resistance measurements under high pressure showed that the Tc of these three compounds increases with pressure initially, reaches a maximum value and then decreases with further increasing pressure, although the Tc at ambient pressure are different. The onset Tc of LaFeAsO is ~50 K at 1.5 GPa, which is the highest record in La-based oxypnicited system. The significant change in Tc induced by pressure is attributed to the orbital degeneracy and the electron density of state at the Fermi level.

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arxivconfidence 95%

external id: arxiv:2410.08988:author:278:chao-zhang

Imported May 21, 2026Synced May 21, 2026

arxivconfidence 95%

external id: arxiv:2605.05892:author:5:chao-zhang