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Wei Chen

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cond-mat.mes-hall Machine Learning quant-ph hep-ph hep-ex Information Theory math.IT Social and Information Networks cond-mat.mtrl-sci cond-mat.supr-con cond-mat.str-el physics.ins-det Computer Vision physics.soc-ph hep-lat Human-Computer Interaction eess.SP math.OC Computer Science and Game Theory physics.optics Artificial Intelligence nucl-ex nucl-th Data Structures and Algorithms Networking and Internet Architecture physics.app-ph q-fin.ST Systems and Control Computational Engineering, Finance, and Science cond-mat.dis-nn cond-mat.soft Cryptography and Security cond-mat.stat-mech hep-th math-ph math.CT math.MP math.PR Computation and Language Databases Discrete Mathematics eess.SY Graphics Information Retrieval math.CA math.KT physics.comp-ph q-fin.TR astro-ph.HE astro-ph.SR Computational Geometry cond-mat cond-mat.quant-gas physics.med-ph q-fin.PR Applications Distributed, Parallel, and Cluster Computing Logic in Computer Science physics.data-an Robotics astro-ph.CO Computational Complexity Emerging Technologies math.CV math.FA Mathematical Software nlin.CG Other Computer Science Performance physics.chem-ph physics.flu-dyn Programming Languages q-fin.RM

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preprint2026arXiv

Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts

Multimodal large language models (MLLMs) have shown considerable potential in chart understanding and reasoning tasks. However, they still struggle with high information density (HID) charts characterized by multiple subplots, legends, and dense annotations due to three major challenges: (1) limited fine-grained perception results in the omission of critical visual cues; (2) redundant or noisy visual information undermines the performance of multimodal reasoning; (3) lack of adaptive deep reasoning relative to the amount of visual information. To tackle these challenges, we present a novel focus-driven fine-grained chart reasoning model, Chart-FR1, to improve perception, focusing efficiency, and adaptive deep reasoning on HID charts. Specifically, we propose Focus-CoT, a visual focusing chain-of-thought that enhances fine-grained perception by explicitly linking reasoning steps to key visual cues, such as local image regions and OCR signals. Building on this, we introduce Focus-GRPO, a focus-driven reinforcement learning algorithm with an information-efficiency reward that compresses redundant visual information for efficient focusing, and an adaptive KL penalty mechanism that enables flexible control over reasoning depth as more visual cues are discovered. Furthermore, to fill the gap in benchmarks for HID charts, we build HID-Chart, a challenging benchmark with an information-density metric designed to evaluate fine-grained chart reasoning capabilities. Extensive experiments on multiple chart benchmarks demonstrate that Chart-FR1 outperforms state-of-the-art MLLMs in chart understanding and reasoning. Code is available at https://github.com/phkhub/Chart-FR1.

preprint2026arXiv

SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation

Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studies have incorporated multimodal signals to provide richer token-level evidence for generation. However, existing approaches largely rely on alignment-centric fusion and underexplore synergistic information across modalities. In practice, synergistic information plays a critical role in capturing emergent item properties that cannot be inferred from any single modality alone. Such properties encode intrinsic item semantics and guide user preferences, enabling models to move beyond surface-level feature matching. To address this limitation, we propose \textbf{SynGR}, a synergistic generative recommendation framework that explicitly encourages the exploitation of cross-modal dependencies during generation. By constraining overreliance on dominant modalities, SynGR enables the model to capture emergent item semantics beyond shared or modality-specific signals. Extensive experiments across three benchmark datasets demonstrate that SynGR achieves superior performance.

preprint2025arXiv

Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma

Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) essential for predictive simulations. However, the cost of uncertainty sampling, the high-dimensional phase space, and multiscale stiffness pose severe challenges to both computational efficiency and error control in traditional numerical methods. These aspects are further emphasized in presence of collisions where the high-dimensional nonlocal collision integrations and conservation properties pose severe constraints. To overcome this, we present a variance-reduced Monte Carlo framework for UQ in the Vlasov--Poisson--Landau (VPL) system, in which neural network surrogates replace the multiple costly evaluations of the Landau collision term. The method couples a high-fidelity, asymptotic-preserving VPL solver with inexpensive, strongly correlated surrogates based on the Vlasov--Poisson--Fokker--Planck (VPFP) and Euler--Poisson (EP) equations. For the surrogate models, we introduce a generalization of the separable physics-informed neural network (SPINN), developing a class of tensor neural networks based on an anisotropic micro-macro decomposition, to reduce velocity-moment costs, model complexity, and the curse of dimensionality. To further increase correlation with VPL, we calibrate the VPFP model and design an asymptotic-preserving SPINN whose small- and large-Knudsen limits recover the EP and VP systems, respectively. Numerical experiments show substantial variance reduction over standard Monte Carlo, accurate statistics with far fewer high-fidelity samples, and lower wall-clock time, while maintaining robustness to stochastic dimension.

preprint2024arXiv

Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog fashion, but it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed to efficiently quantize semantic features with trainable quantization levels. Additionally, structured pruning is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 and ImageNet dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed quantization module, structured pruning and SLL.

preprint2024arXiv

Strong decays of $T_{c\bar s0}(2900)^{++/0}$ as a fully open-flavor tetraquark state

We have studied the strong decay properties of the recently observed $T^a_{c\bar s0}(2900)^{++/0}$ by considering it as a $cu\bar{d}\bar{s}/cd\bar{u}\bar{s}$ fully open-flavor tetraquark state with $I(J^P)=1(0^+)$. In the framework of QCD sum rules, we have calculated the three-point correlation functions of the two-body strong decay processes $T^a_{c\bar s0}(2900)^{++}\rightarrow D_s^+π^+$, $D^+K^+, D_s^{\ast +}ρ^+$ and $D_{s1}^+π^+$. The full width of $T^a_{c\bar s0}(2900)^{++/0}$ is obtained as $161.7\pm94.8$ MeV, which is consistent with the experimental observation. We predict the relative branching ratios as $Γ(T\rightarrow D_sπ):Γ(T\rightarrow DK):Γ(T\rightarrow D_s^{\ast} ρ):Γ(T\rightarrow D_{s1}π)\approx1.00:1.10:0.04:0.43$, implying that the main decay modes of $T^a_{c\bar s0}(2900)^{++/0}$ state are $D_sπ$ and $DK$ channels in our calculations. However, the $P$-wave decay mode $D_{s1}π$ is also comparable and important by including the uncertainties. To further identify the nature of $T^a_{c\bar s0}(2900)^{++/0}$, we suggest confirming them in the $DK$ and $D_{s}π$ final states, and measuring the above ratios in future experiments.

preprint2023arXiv

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

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

preprint2023arXiv

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images

Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled biomedical microscopy images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework (TOWER) towards enhanced recognition of biomedical microscopy images, which works in three phases by synergizing contrastive learning and generative learning methods: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Experiments have been conducted on public datasets of biomedical microscopy images against the state-of-the-art counterparts (e.g., SimCLR and BYOL), and results demonstrate that: TOWER statistically excels in all self-supervised methods, achieving a Dice improvement of 1.38 percentage points over SimCLR. TOWER also has potential in multi-modality medical image analysis and enables label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.

preprint2023arXiv

Automated Sleep Staging via Parallel Frequency-Cut Attention

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated on the Sleep Heart Health Study dataset with new state-of-the-art results for the stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and 0.87, with only EEG signals used. The proposed method also has a high inter-rater reliability of 0.80 kappa. We also visualize the correspondence between sleep staging decisions and features extracted by the proposed method, providing strong interpretability for our model.

preprint2023arXiv

Nonreciprocal Charge and Spin Transport Induced by Non-Hermitian Skin Effect in Mesoscopic Heterojunctions

The pursuit of the non-Hermitian skin effect (NHSE) in various physical systems is of great research interest. Compared with recent progress in non-electronic systems, the implementation of the NHSE in condensed matter physics remains elusive. Here, we show that the NHSE can be engineered in the mesoscopic heterojunctions (system plus reservoir) in which electrons in two channels of the system moving towards each other have asymmetric coupling to those of the reservoir. This makes electrons in the system moving forward and in the opposite direction have unequal lifetimes, and so gives rise to a point-gap spectral topology. Accordingly, the electron eigenstates exhibit NHSE under the open boundary condition, consistent with the description of the generalized Brillouin zone. Such a reservoir-engineered NHSE visibly manifests itself as the nonreciprocal charge current that can be probed by the standard transport measurements. Further, we generalize the scenario to the spin-resolved NHSE, which can be probed by the nonreciprocal spin transport. Our work opens a new research avenue for implementing and detecting the NHSE in electronic mesoscopic systems, which will lead to interesting device applications.

preprint2023arXiv

Proximity-induced zero-energy states indistinguishable from topological edge states

When normal metals (NMs) are attached to topological insulators or topological superconductors, it is conceivable that the quantum states in these finite adjacent materials can intermix. In this case -- and because the NM usually does not possess the same symmetry as the topological material -- it is pertinent to ask whether zero-energy edge states in the topological layer are affected by the presence of the NM. To address this issue, we consider three prototype systems simulated by tight-binding models, namely a Su-Schrieffer-Heeger/NM, a Kitaev/NM, and a Chern insulator/NM. For all junctions investigated, we find that there exist trivial ``fine-tuned'' zero-energy states in the NM layer that can percolate into the topological region, thus mimicking a topological state. These zero-energy states are created by fine-tuning the NM chemical potential such that some of the NM states cross zero energy; they can occur even when the topological material is in the topologically trivial phase, and exist over a large region of the topological phase diagram. Interestingly, the true Majorana end modes of the Kitaev/NM model cannot be crossed by any NM state, as the NM metal layer in this case does not break particle-hole symmetry. On the other hand, when the chiral symmetry of the Su-Schrieffer-Heeger chain is broken by the attached NM, crossings are allowed. In addition, even in Chern insulators that do not preserve nonspatial symmetries, but the topological edge state self-generates a symmetry eigenvalue, such a fine-tuned zero-energy state can still occur. Our results indicate that when a topological material is attached to a metallic layer, one has to be cautious as to identify true topological edge states merely from their energy spectra and wave function profiles near the interface.

preprint2022arXiv

3D large-scale fused silica microfluidic chips enabled by hybrid laser microfabrication for continuous-flow UV photochemical synthesis

We demonstrate a hybrid laser microfabrication approach, which combines the technical merits of ultrafast laser-assisted chemical etching and carbon dioxide laser-induced in-situ melting, for centimeter-scale and bonding-free fabrication of 3D complex hollow microstructures in fused silica glass. With the developed approach, large-scale fused silica microfluidic chips with integrated 3D cascaded micromixing units can be reliably manufactured. High-performance on-chip mixing and continuous-flow photochemical synthesis under UV LEDs irradiation at ~280 nm were demonstrated using the manufactured chip, indicating a powerful capability for versatile fabrication of highly transparent all-glass microfluidic reactors for on-chip photochemical synthesis.

preprint2022arXiv

A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to insufficient feature interactions, or they encode aspect features and sentiment features in a parallel manner, implying that feature representation in each task is largely independent of each other except for input sharing. Both of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately, where the hierarchical interactions involve two steps: shallow-level interaction and deep-level interaction. First, we utilize cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning between two tasks in the output layer, thus the aspect input and the sentiment input are capable of encoding features of the other task via backpropagation. Extensive experiments on three real-world datasets demonstrate HI-ASA's superiority over baselines.

preprint2022arXiv

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

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

preprint2022arXiv

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

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

preprint2022arXiv

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

We consider the *adaptive influence maximization problem*: given a network and a budget $k$, iteratively select $k$ seeds in the network to maximize the expected number of adopters. In the *full-adoption feedback model*, after selecting each seed, the seed-picker observes all the resulting adoptions. In the *myopic feedback model*, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of *greedy adaptivity gap*, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a $(1-1/e)$-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a $(1-1/e)$ fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the *independent cascade model* and the *linear threshold model*. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a $(1-1/e)$-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular diffusion model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.

preprint2022arXiv

Availability Attacks Create Shortcuts

Availability attacks, which poison the training data with imperceptible perturbations, can make the data \emph{not exploitable} by machine learning algorithms so as to prevent unauthorized use of data. In this work, we investigate why these perturbations work in principle. We are the first to unveil an important population property of the perturbations of these attacks: they are almost \textbf{linearly separable} when assigned with the target labels of the corresponding samples, which hence can work as \emph{shortcuts} for the learning objective. We further verify that linear separability is indeed the workhorse for availability attacks. We synthesize linearly-separable perturbations as attacks and show that they are as powerful as the deliberately crafted attacks. Moreover, such synthetic perturbations are much easier to generate. For example, previous attacks need dozens of hours to generate perturbations for ImageNet while our algorithm only needs several seconds. Our finding also suggests that the \emph{shortcut learning} is more widely present than previously believed as deep models would rely on shortcuts even if they are of an imperceptible scale and mixed together with the normal features. Our source code is published at \url{https://github.com/dayu11/Availability-Attacks-Create-Shortcuts}.

preprint2022arXiv

Branching Reinforcement Learning

In this paper, we propose a novel Branching Reinforcement Learning (Branching RL) model, and investigate both Regret Minimization (RM) and Reward-Free Exploration (RFE) metrics for this model. Unlike standard RL where the trajectory of each episode is a single $H$-step path, branching RL allows an agent to take multiple base actions in a state such that transitions branch out to multiple successor states correspondingly, and thus it generates a tree-structured trajectory. This model finds important applications in hierarchical recommendation systems and online advertising. For branching RL, we establish new Bellman equations and key lemmas, i.e., branching value difference lemma and branching law of total variance, and also bound the total variance by only $O(H^2)$ under an exponentially-large trajectory. For RM and RFE metrics, we propose computationally efficient algorithms BranchVI and BranchRFE, respectively, and derive nearly matching upper and lower bounds. Our results are only polynomial in problem parameters despite exponentially-large trajectories.

preprint2022arXiv

Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

Neural ranking models (NRMs) have achieved promising results in information retrieval. NRMs have also been shown to be vulnerable to adversarial examples. A typical Word Substitution Ranking Attack (WSRA) against NRMs was proposed recently, in which an attacker promotes a target document in rankings by adding human-imperceptible perturbations to its text. This raises concerns when deploying NRMs in real-world applications. Therefore, it is important to develop techniques that defend against such attacks for NRMs. In empirical defenses adversarial examples are found during training and used to augment the training set. However, such methods offer no theoretical guarantee on the models' robustness and may eventually be broken by other sophisticated WSRAs. To escape this arms race, rigorous and provable certified defense methods for NRMs are needed. To this end, we first define the \textit{Certified Top-$K$ Robustness} for ranking models since users mainly care about the top ranked results in real-world scenarios. A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack. Then, we introduce a Certified Defense method, named CertDR, to achieve certified top-$K$ robustness against WSRA, based on the idea of randomized smoothing. Specifically, we first construct a smoothed ranker by applying random word substitutions on the documents, and then leverage the ranking property jointly with the statistical property of the ensemble to provably certify top-$K$ robustness. Extensive experiments on two representative web search datasets demonstrate that CertDR can significantly outperform state-of-the-art empirical defense methods for ranking models.

preprint2022arXiv

Characterization of BNL and HPK AC-LGAD sensors with a 120 GeV proton beam

We present measurements of AC-LGADs performed at the Fermilab's test beam facility using 120 GeV protons. We studied the performance of various strip and pad AC-LGAD sensors that were produced by BNL and HPK. The measurements are performed with our upgraded test beam setup that utilizes a high precision telescope tracker, and a simultaneous readout of up to 7 channels per sensor, which allows detailed studies of signal sharing characteristics. These measurements allow us to assess the differences in designs between different manufacturers, and optimize them based on experimental performance. We then study several reconstruction algorithms to optimize position and time resolutions that utilize the signal sharing properties of each sensor. We present a world's first demonstration of silicon sensors in a test beam that simultaneously achieve better than 6-10 micron position and 30 ps time resolution. This represents a substantial improvement to the spatial resolution than would be obtained with binary readout of sensors with similar pitch.

preprint2022arXiv

CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

preprint2022arXiv

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations

We study the problem of coarse-grained response selection in retrieval-based dialogue systems. The problem is equally important with fine-grained response selection, but is less explored in existing literature. In this paper, we propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. In our CFC model, dense representations of query, candidate response and corresponding context is learned based on the multi-tower architecture, and more expressive knowledge learned from the one-tower architecture (fine-grained) is distilled into the multi-tower architecture (coarse-grained) to enhance the performance of the retriever. To evaluate the performance of our proposed model, we construct two new datasets based on the Reddit comments dump and Twitter corpus. Extensive experimental results on the two datasets show that the proposed methods achieve a significant improvement over all evaluation metrics compared with traditional baseline methods.

preprint2022arXiv

Cyclotron quantization and mirror-time transition on nonreciprocal lattices

Unidirectional transport and localized cyclotron motion are two opposite physical phenomena. Here, we study the interplay effects between them on nonreciprocal lattices subject to a magnetic field. We show that, in the long-wavelength limit, the trajectories of the wave packets always form closed orbits in four-dimensional (4D) complex space. Therefore, the semiclassical quantization rules persist despite the nonreciprocity, which preserves real Landau levels. We predict a different type of non-Hermitian spectral transition induced by the spontaneous breaking of the combined mirror-time reversal ($\mathcal{MT}$) symmetry, which generally exists in such systems. An order parameter is proposed to describe the $\mathcal{MT}$ phase transition, not only to determine the $\mathcal{MT}$ phase boundary but also to quantify the degree of $\mathcal{MT}$-symmetry breaking. Such an order parameter can be generally applied to all types of non-Hermitian phase transitions.

preprint2022arXiv

D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights

A profound understanding of inter-agent relationships and motion behaviors is important to achieve high-quality planning when navigating in complex scenarios, especially at urban traffic intersections. We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) to handle the problem of discontinuous dependency in the spatial-temporal space. Specifically, the SDG is used to capture spatial interactions by reconstructing sub-graphs for different agents with dynamic and changeable characteristics during each frame. The BDG is used to infer motion tendency by modeling the implicit dependency of the current state on priors behaviors, especially the discontinuous motions corresponding to acceleration, deceleration, or turning direction. Moreover, we present a new dataset for vehicle trajectory prediction under traffic lights called VTP-TL. Our experimental results show that our model achieves more than {20.45% and 20.78% }improvement in terms of ADE and FDE, respectively, on VTP-TL as compared to other trajectory prediction algorithms. The dataset and code are available at: https://github.com/VTP-TL/D2-TPred.

preprint2022arXiv

Deep Generative Models for Geometric Design Under Uncertainty

Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplified assumptions on geometric variations, while the "real-world" uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication.

preprint2022arXiv

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. Based on the separation principle in existing communication systems, DL based CSI compression is used as source coding. However, this separate source-channel coding (SSCC) scheme is inferior to the joint source-channel coding (JSCC) scheme in the finite blocklength regime. In this paper, we propose a deep joint source-channel coding (DJSCC) based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with the wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

preprint2022arXiv

Does Momentum Change the Implicit Regularization on Separable Data?

The momentum acceleration technique is widely adopted in many optimization algorithms. However, there is no theoretical answer on how the momentum affects the generalization performance of the optimization algorithms. This paper studies this problem by analyzing the implicit regularization of momentum-based optimization. We prove that on the linear classification problem with separable data and exponential-tailed loss, gradient descent with momentum (GDM) converges to the L2 max-margin solution, which is the same as vanilla gradient descent. That means gradient descent with momentum acceleration still converges to a low-complexity model, which guarantees their generalization. We then analyze the stochastic and adaptive variants of GDM (i.e., SGDM and deterministic Adam) and show they also converge to the L2 max-margin solution. Technically, to overcome the difficulty of the error accumulation in analyzing the momentum, we construct new potential functions to analyze the gap between the model parameter and the max-margin solution. Numerical experiments are conducted and support our theoretical results.

preprint2022arXiv

Entanglement on nucleation barrier of polymer crystal

We propose a theoretical approach to quantitatively account for the role of entanglement in the nucleation of polymer melts, which is the unique feature of polymer differentiated from small molecules. By performing molecular dynamics simulations, we obtain the nucleation barriers of polymer systems with different entanglement densities, which exhibits an opposite trend compared to the prediction of the classic nucleation theory (CNT). To amend the deficiency of the CNT in polymer crystallization, we introduce the entanglement free energy to reflect the role of entanglement in polymer nucleation. Specifically, the polymer nucleation not only involves free energies of monomers inside and on the surface of a nucleus as considered in the CNT, but also affects the entanglement network around the nucleus. Our theoretical approach provides a reasonable interpretation for the unsolved nucleation phenomena of polymers in simulations and experiments.

preprint2022arXiv

Epidemic Plateau: A Phenomenon under Adaptive Prevention Strategies

Since the beginning of the COVID-19 spreading, the number of studies on the epidemic models increased dramatically. It is important for policymakers to know how the disease will spread and what are the effects of the policies and environment on the spreading. In this paper, we propose two extensions to the standard SIR model: (a) we consider the prevention measures adopted based on the current severity of the infection. Those measures are adaptive and change over time; (b) multiple cities and regions are considered, with population movements between those cities and regions, while taking into account that each region may have different prevention measures. Although the adaptive measures and mobility of the population were often observed during the pandemic, these effects are rarely explicitly modeled and studied in the classical epidemic models. The model we propose gives rise to a plateau phenomenon: the number of people infected by the disease stays at the same level during an extended period of time. We show what are conditions need to be met in order for the spreading to exhibit a plateau period in a single city. In addition, this phenomenon is interdependent when considering multiple cities. We verify from the real-world data that the plateau phenomenon does exist in many regions of the world in the current COVID-19 development. Finally, we provide theoretical analysis on the plateau phenomenon for the single-city model and derive a series of results on the emergence and the ending of the plateau, as well as on the height and length of the plateau. Our theoretical results match well with our experimental findings.

preprint2022arXiv

Event-by-event jet anisotropy and hard-soft tomography of the quark-gluon plasma

Suppression of jet spectra or jet quenching in high-energy heavy-ion collisions is caused by jet energy loss in the dense medium. The azimuthal anisotropy of jet energy loss in non-central heavy-ion collisions can lead to jet anisotropy which in turn can provide insight into the path-length dependence of jet quenching. This is investigated within the Linear Boltzmann Transport (LBT) model which simulates both elastic scattering and medium-induced gluon radiation based on perturbative QCD for jet shower and medium recoil partons as well as radiated gluons as they propagate through the quark-gluon plasma (QGP). The dynamical evolution of the QGP in each event of heavy-ion collisions is provided by the (3+1)D CLVisc hydrodynamic model with fully fluctuating initial conditions. This framework has been shown to describe the suppression of single inclusive jet spectra well. We calculate in this study the elliptic ($v_{2}^{\rm jet}$) and triangular ($v_{3}^{\rm jet}$) anisotropy coefficients of the single inclusive jet spectra in Pb+Pb collisions at the LHC energies. We investigate the colliding energy, centrality, jet transverse momentum dependence of the jet anisotropy, as well as their event-by-event correlation with the flow coefficients of the soft bulk hadrons. An approximate linear correlation between jet and bulk $v_2$ is found. Effect of the bulk $v_n$ fluctuation on $v_n^{\rm jet}$ is found negligible. The jet-induced medium excitation, which is influenced by radial flow, is shown to enhance $v_{2}^{\rm jet}$ and the enhancement increases with the jet cone size. The jet elliptic anisotropy $v_{2}^{\rm jet}$ is also found to be slightly enhanced by the shear viscosity of the bulk medium in comparison to the LBT results when jets propagate through an ideal hydrodynamic QGP medium.

preprint2022arXiv

Evidence of Spin Frustration in Vanadium Diselenide Monolayer Magnet

Monolayer VSe2, featuring both charge density wave and magnetism phenomena, represents a unique van der Waals magnet in the family of metallic two-dimensional transition-metal dichalcogenides (2D-TMDs). Herein, by means of in-situ microscopic and spectroscopic techniques, including scanning tunneling microscopy/spectroscopy, synchrotron X-ray and angle-resolved photoemission, and X-ray absorption, direct spectroscopic signatures are established, that identify the metallic 1T-phase and vanadium 3d1 electronic configuration in monolayer VSe2 grown on graphite by molecular-beam epitaxy. Element-specific X-ray magnetic circular dichroism, complemented with magnetic susceptibility measurements, further reveals monolayer VSe2 as a frustrated magnet, with its spins exhibiting subtle correlations, albeit in the absence of a long-range magnetic order down to 2 K and up to a 7 T magnetic field. This observation is attributed to the relative stability of the ferromagnetic and antiferromagnetic ground states, arising from its atomic-scale structural features, such as rotational disorders and edges. The results of this study extend the current understanding of metallic 2D-TMDs in the search for exotic low-dimensional quantum phenomena, and stimulate further theoretical and experimental studies on van der Waals monolayer magnets.

preprint2022arXiv

Fabry-Pérot interference in 2D low-density Rashba gas

In mesoscopic electronic systems, the Fabry-Pérot (FP) oscillation is observed in various 1D devices. As for higher dimensions, numerous transverse channels usually lead to dephasing that quenches the overall oscillation of the conductance. Up to now, the FP oscillation in 2D electronic systems is only reported in graphene-based devices, and very recently, the \emph{pn} junctions of inverted InAs/GaSb double quantum well [Phys. Rev. X 10, 031007 (2020)]. In the latter, the band shape of a sombrero hat plays an essential role, which introduces a novel mechanism of electron-hole hybridization for the 2D FP oscillation. In this work, we propose that such a scenario can be generalized to the 2D planar junction composed of low-density Rashba gas, where the band bottom possesses a sombrero hat shape as well. We show that the backscattering between the outer and inner Fermi circles dominates the FP interference and significantly suppresses the dephasing effect between different transverse channels, which leads to a visible oscillation of the tunneling conductance. Specially, the visibility of the oscillating pattern can be enhanced by applying interface barriers, in contrast to that in the InAs/GaSb double quantum well. Our results provide a promising way for the implementation of the FP oscillation in the 2D electron gas.

preprint2022arXiv

Failure behaviors and processing maps with failure domains for hot compression of a powder metallurgy Ni-based superalloy

Processing maps are key to guiding the thermo-mechanical processing (TMP) of superalloys. However, traditional processing maps are incapable of delimiting failure, which is an essential factor to be concerned about during the TMP of superalloys. Employing isothermal hot compression experiments and finite element analysis (FEA), the present study examined the failure behaviors of a powder metallurgy (P/M) Ni-based superalloy and constructed processing maps with failure domains based on the predicted failure threshold. The micromechanical Gurson-Tvergaard-Needleman (GTN) damage model was employed in the FEA to model the cavity-driven intergranular fracture of the superalloy. Deformation temperature and strain rate were considered in the range of 1050 ~ 1150 C and 0.001 ~ 1 s-1, respectively. The FEA results reveal that the maximum tensile stress locates at the outer budging surfaces of the samples, which causes failure initiation and subsequent propagation into longitudinal cracks, being consistent with the experiments. It is further demonstrated that the failure is strain-controlled and the critical failure strain remains nearly insensitive to the range of strain rates considered while increasing with the increase of temperature in a third-order polynomial. Finally, an optimized processing window for hot deformation of the superalloy is formulated to warrant good hot workability while avoiding flow instability and failure. The present study offers direct insights into the failure behaviors of P/M Ni-based superalloys and details a modeling strategy to delineate optimized parametric spaces for the TMP of superalloys.

preprint2022arXiv

Federated Visualization: A Privacy-preserving Strategy for Aggregated Visual Query

We present a novel privacy preservation strategy for decentralized visualization. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a prediction-based scheme, and a query-based scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.

preprint2022arXiv

From hydro to jet quenching, coalescence and hadron cascade: a coupled approach to solving the $R_{AA}\otimes v_2$ puzzle

Hydrodynamics and jet quenching are responsible for the elliptic flow $v_2$ and suppression of large transverse momentum ($p_T$) hadrons, respectively, two of the most important phenomena leading to the discovery of a strongly coupled quark-gluon plasma (QGP) in high-energy heavy-ion collisions. A consistent description of the hadron suppression factor $R_{AA}$ and $v_2$, especially at intermediate $p_T$, however, remains a challenge. We solve this long-standing $R_{AA}\otimes v_2$ puzzle by including quark coalescence for hadronization and final state hadron cascade in the coupled linear Boltzmann transport-hydro model that combines concurrent jet transport and hydrodynamic evolution of the bulk medium. We illustrate that quark coalescence and hadron cascade, two keys to solving the puzzle, also lead to a splitting of $v_2$ for pions, kaons and protons in the intermediate $p_T$ region. We demonstrate for the first time that experimental data on $R_{AA}$, $v_2$ and their hadron flavor dependence from low to intermediate and high $p_T$ in high-energy heavy-ion collisions can be understood within this coupled framework.

preprint2022arXiv

Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology

We present a novel, effective method for global point cloud registration problems by geometric topology. Based on many point cloud pairwise registration methods (e.g ICP), we focus on the problem of accumulated error for the composition of transformations along any loops. The major technical contribution of this paper is a linear method for the elimination of errors, using only solving a Poisson equation. We demonstrate the consistency of our method from Hodge-Helmhotz decomposition theorem and experiments on multiple RGBD datasets of real-world scenes. The experimental results also demonstrate that our global registration method runs quickly and provides accurate reconstructions.

preprint2022arXiv

HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis.

preprint2022arXiv

HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks

Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search space. To improve the effectiveness, a kernel density estimation-based method has been proposed to alleviate the data sparsity problem in measuring users' similarities. Additionally, we investigate the application of HFUL in terms of user prediction, time prediction, and location prediction. The extensive experiments conducted on three real-world datasets demonstrate the superiority of HFUL in terms of effectiveness, efficiency, scalability, robustness, and application compared with the state-of-the-art methods.

preprint2022arXiv

Higher order monotonicity and submodularity of influence in social networks: from local to global

Kempe, Kleinberg and Tardos (KKT) proposed the following conjecture about the general threshold model in social networks: local monotonicity and submodularity imply global monotonicity and submodularity. That is, if the threshold function of every node is monotone and submodular, then the spread function $σ(S)$ is monotone and submodular, where $S$ is a seed set and the spread function $σ(S)$ denotes the expected number of active nodes at termination of a diffusion process starting from $S$. The correctness of this conjecture has been proved by Mossel and Roch. In this paper, we first provide the concept AD-k (Alternating Difference-$k$) as a generalization of monotonicity and submodularity. Specifically, a set function $f$ is called \adk if all the $\ell$-th order differences of $f$ on all inputs have sign $(-1)^{\ell+1}$ for every $\ell\leq k$. Note that AD-1 corresponds to monotonicity and AD-2 corresponds to monotonicity and submodularity. We propose a refined version of KKT's conjecture: in the general threshold model, local AD-k implies global AD-k. The original KKT conjecture corresponds to the case for AD-2, and the case for AD-1 is the trivial one of local monotonicity implying global monotonicity. By utilizing continuous extensions of set functions as well as social graph constructions, we prove the correctness of our conjecture when the social graph is a directed acyclic graph (DAG). Furthermore, we affirm our conjecture on general social graphs when $k=\infty$.

preprint2022arXiv

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

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

preprint2022arXiv

Mass of $1^{-+}$ four-quark--hybrid mixed states

We calculate the masses of $J^{PC}=1^{-+}$ light exotic mesons by QCD sum rules; the masses are extracted from four-quark--hybrid mixing correlation functions. We construct several $1^{-+}$ four-quark currents and hybrid currents, and get two masses around $1.2\text{-}1.4\text{GeV}$ and $1.45\text{-}1.67\text{GeV}$; they can be identified as $π_1(1400)$ and $π_1(1600)$.

preprint2022arXiv

Measurement of interaction-dressed Berry curvature and quantum metric in solids by optical absorption

The quantum geometric properties of a Bloch state in momentum space are usually described by the Berry curvature and quantum metric. In realistic gapped materials where interactions and disorder render the Bloch state not a viable starting point, we generalize these concepts by introducing dressed Berry curvature and quantum metric at finite temperature, in which the effect of many-body interactions can be included perturbatively. These quantities are extracted from the charge polarization susceptibility caused by linearly or circularly polarized electric fields, whose spectral functions can be measured from momentum-resolved exciton or infrared absorption rate. As a concrete example, we investigate Chern insulators in the presence of impurity scattering, whose results suggest that the quantum geometric properties are protected by the energy gap against many-body interactions.

preprint2022arXiv

Mutual Distillation Learning Network for Trajectory-User Linking

Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextual features in check-in data, resulting in poor performance for TUL task. In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL. Specifically, MainTUL is composed of a Recurrent Neural Network (RNN) trajectory encoder that models sequential patterns of input trajectory and a temporal-aware Transformer trajectory encoder that captures long-term time dependencies for the corresponding augmented historical trajectories. Then, the knowledge learned on historical trajectories is transferred between the two trajectory encoders to guide the learning of both encoders to achieve mutual distillation of information. Experimental results on two real-world check-in mobility datasets demonstrate the superiority of MainTUL against state-of-the-art baselines. The source code of our model is available at https://github.com/Onedean/MainTUL.

preprint2022arXiv

Network Inference and Influence Maximization from Samples

Influence maximization is the task of selecting a small number of seed nodes in a social network to maximize the influence spread from these seeds. It has been widely investigated in the past two decades. In the canonical setting, the social network and its diffusion parameters are given as input. In this paper, we consider the more realistic sampling setting where the network is unknown and we only have a set of passively observed cascades that record the sets of activated nodes at each diffusion step. We study the task of influence maximization from these cascade samples (IMS) and present constant approximation algorithms for it under mild conditions on the seed set distribution. To achieve the optimization goal, we also provide a novel solution to the network inference problem, that is, learning diffusion parameters and the network structure from the cascade data. Compared with prior solutions, our network inference algorithms require weaker assumptions and do not rely on maximum-likelihood estimation and convex programming. Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.

preprint2022arXiv

Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs

Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generations of neural networks with capability of learning maps between infinite-dimensional spaces, are strong tools for solving parametric PDEs. However, they lack the ability to modeling SPDEs which usually have poor regularity due to the driving noise. As the theory of regularity structure has achieved great successes in analyzing SPDEs and provides the concept model feature vectors that well-approximate SPDEs' solutions, we propose the Neural Operator with Regularity Structure (NORS) which incorporates the feature vectors for modeling dynamics driven by SPDEs. We conduct experiments on various of SPDEs including the dynamic Phi41 model and the 2d stochastic Navier-Stokes equation, and the results demonstrate that the NORS is resolution-invariant, efficient, and achieves one order of magnitude lower error with a modest amount of data.

preprint2022arXiv

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

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

preprint2022arXiv

New hadron configuration: The double-gluon hybrid state

This is the first study on the double-gluon hybrid, which consists of one valence quark and one valence antiquark together with two valence gluons. We concentrate on the one with the exotic quantum number $J^{PC} = 2^{+-}$ that conventional $\bar q q$ mesons can not reach. We apply QCD sum rule method to evaluate its mass to be $2.26^{+0.20}_{-0.25}$ GeV, and study its possible decay patterns. Especially, its three-meson decay patterns are generally not suppressed severely compared to two-meson decay patterns, so the $S$-wave three-meson decay channels $f_1ωπ/f_1ρπ$ can be useful in identifying its nature, which is of particular importance to the direct test of QCD in the low energy sector.

preprint2022arXiv

Newly observed $a_0(1817)$ as the scaling point of constructing the scalar meson spectroscopy

Stimulated by the newly observed $a_0(1817)$ by the BESIII Collaboration, we find a perfect Regge trajectory composed of the $a_0(980)$, $a_0(1450)$, and $a_0(1817)$, which leads us to categorize the $a_0(980)$, $a_0(1450)$, and $a_0(1817)$ into the isovector scalar meson family. This scenario is supported by their two-body Okubo-Zweig-Iizuka allowed strong decay behaviors. In this scheme, we also predict the third radial excitation of the $a_0(980)$, which is denoted as the $a_0(2115)$, accessible at future experiment as a direct test of this assignment. We find another Regge trajectory which contains three isoscalar scalar states $f_0(980)$, $X(1812)$, and $f_0(2100)$. We investigate their two-body Okubo-Zweig-Iizuka allowed strong decay patterns, which are roughly consistent with the experimental data. The $f_0(980)$, $X(1812)$, and $f_0(2100)$ can be well grouped into the isoscalar scalar meson family. We want to emphasize that these two Regge trajectories have a similar slope. In summary, the present work provides a scheme of constructing the scalar meson family based on these reported scalar states. The possibility of the $f_0(1710)$ as the candidate of the scalar glueball cannot be excluded by the observation of the $a_0(1817)$ since the $a_0(1817)$ is more suitable as the isovector partner of the $X(1812)$.

preprint2022arXiv

Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization

By ensuring differential privacy in the learning algorithms, one can rigorously mitigate the risk of large models memorizing sensitive training data. In this paper, we study two algorithms for this purpose, i.e., DP-SGD and DP-NSGD, which first clip or normalize \textit{per-sample} gradients to bound the sensitivity and then add noise to obfuscate the exact information. We analyze the convergence behavior of these two algorithms in the non-convex optimization setting with two common assumptions and achieve a rate $\mathcal{O}\left(\sqrt[4]{\frac{d\log(1/δ)}{N^2ε^2}}\right)$ of the gradient norm for a $d$-dimensional model, $N$ samples and $(ε,δ)$-DP, which improves over previous bounds under much weaker assumptions. Specifically, we introduce a regularizing factor in DP-NSGD and show that it is crucial in the convergence proof and subtly controls the bias and noise trade-off. Our proof deliberately handles the per-sample gradient clipping and normalization that are specified for the private setting. Empirically, we demonstrate that these two algorithms achieve similar best accuracy while DP-NSGD is comparatively easier to tune than DP-SGD and hence may help further save the privacy budget when accounting the tuning effort.

preprint2022arXiv

On the Phases of a Semi-Sectorial Matrix

In this paper, we extend the definition of phases of sectorial matrices to those of semi-sectorial matrices, which are possibly singular. Properties of the phases are also extended, including those of the Moore-Penrose generalized inverse, compressions and Schur complements, matrix sums and products. In particular, a majorization relation is established between the phases of the nonzero eigenvalues of $AB$ and the phases of the compressions of $A$ and $B$, which leads to a generalized matrix small phase theorem. For the matrices which are not necessarily semi-sectorial, we define their (largest and smallest) essential phases via diagonal similarity transformation. An explicit expression for the essential phases of a Laplacian matrix of a directed graph is obtained.

preprint2022arXiv

Online Competitive Influence Maximization

Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., products, news stories) propagate in the same network and influence probabilities on edges are unknown. We adopt a combinatorial multi-armed bandit (CMAB) framework for OCIM, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem. We provide a nontrivial proof showing that the Triggering Probability Modulated (TPM) condition for CMAB still holds in OCIM, which is instrumental for our proposed algorithms OCIM-TS and OCIM-OFU to achieve sublinear Bayesian and frequentist regret, respectively. We also design an OCIM-ETC algorithm that requires less feedback and easier offline computation, at the expense of a worse frequentist regret bound. Experimental evaluations demonstrate the effectiveness of our algorithms.

preprint2022arXiv

Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback

We study the online influence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds. In the demand of the real world, we work with node-level feedback instead of the common edge-level feedback in the literature. The edge-level feedback reveals all edges that pass through information in a cascade, whereas the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to observe who is influenced but very difficult to observe from which relationship (edge) the influence comes. Previously, there is a nearly optimal $\tilde{O}(\sqrt{T})$-regret algorithm for OIM problem under the linear threshold (LT) diffusion model with node-level feedback. It remains unknown whether the same algorithm exists for the independent cascade (IC) diffusion model. In this paper, we resolve this open problem by presenting an $\tilde{O}(\sqrt{T})$-regret algorithm for OIM problem under the IC model with node-level feedback.

preprint2022arXiv

Optimal $(0,1)$-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)

We propose and examine two optimal $(0,1)$-matrix completion problems with majorization ordered objectives. They elevate the seminal study by Gale and Ryser from feasibility to optimality in partial order programming (POP), referring to optimization with partially ordered objectives. We showcase their applications in electric vehicle charging, portfolio optimization, and secure data storage. Solving such integer POP (iPOP) problems is challenging because of the possible non-comparability among objective values and the integer requirements. Nevertheless, we prove the essential uniqueness of all optimal objective values and identify two particular ones for each of the two inherently symmetric iPOP problems. Furthermore, for every optimal objective value, we decompose the construction of an associated optimal~$(0,1)$-matrix into a series of sorting processes, respectively agreeing with the rule of thumb "peak shaving" or "valley filling." We show that the resulting algorithms have linear time complexities and verify their empirical efficiency via numerical simulations compared to the standard order-preserving method for POP.

preprint2022arXiv

Quasi-periodic Accelerations of Energetic Particles during a Solar Flare

We report the observation of non-stationary Quasi-Periodic Pulsations (QPPs) in high-energy particles during the impulsive phase of an X4.8 flare on 2002 July 23 (SOL2002-07-23T00:35). The X4.8 flare was simultaneously measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager, Nobeyama Radio Polarimeters, and Nobeyama Radioheliograph. The quasi-period of about 50 s, determined by the wavelet transform, is detected in the Gamma-ray line emission. Using the same method, a quasi-period of about 90 s is found in Gamma-ray continuum, hard X-ray (HXR) and radio emissions during almost the same time. Our observations suggest that the flare QPPs should be associated with energetic ions and nonthermal electrons that quasi-periodically accelerated by the repetitive magnetic reconnection. The different quasi-periods between Gamma-ray line and continuum/HXR/radio emissions indicate an apparent difference in acceleration or propagation between energetic ions and nonthermal electrons of this solar flare.

preprint2022arXiv

Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending

To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversity-based freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.

preprint2022arXiv

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

The recommendation system, relying on historical observational data to model the complex relationships among the users and items, has achieved great success in real-world applications. Selection bias is one of the most important issues of the existing observational data based approaches, which is actually caused by multiple types of unobserved exposure strategies (e.g. promotions and holiday effects). Though various methods have been proposed to address this problem, they are mainly relying on the implicit debiasing techniques but not explicitly modeling the unobserved exposure strategies. By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method. In REST, we assume that the exposure of an item is controlled by the latent exposure strategies, the user, and the item. Based on the above generation process, we first provide the theoretical guarantee of our method via identification analysis. Second, we employ a variational auto-encoder to reconstruct the latent exposure strategies, with the help of the social networks and the items. Third, we devise a counterfactual reasoning based recommendation algorithm by leveraging the recovered exposure strategies. Experiments on four real-world datasets, including three published datasets and one private WeChat Official Account dataset, demonstrate significant improvements over several state-of-the-art methods.

preprint2022arXiv

Search for the elusive jet-induced diffusion wake in $Z/γ$-jets with 2D jet tomography in high-energy heavy-ion collisions

Diffusion wake is an unambiguous part of the jet-induced medium response in high-energy heavy-ion collisions that leads to a depletion of soft hadrons in the opposite direction of the jet propagation. New experimental data on $Z$-hadron correlation in Pb+Pb collisions at the Large Hadron Collider show, however, an enhancement of soft hadrons in the direction of both the $Z$ and the jet. Using a coupled linear Boltzmann transport and hydro model, we demonstrate that medium modification of partons from the initial multiple parton interaction (MPI) gives rise to a soft hadron enhancement that is uniform in azimuthal angle while jet-induced medium response and soft gluon radiation dominate the enhancement in the jet direction. After subtraction of the contributions from MPI with a mixed-event procedure, the diffusion wake becomes visible in the near-side $Z$-hadron correlation. We further employ the longitudinal and transverse gradient jet tomography for the first time to localize the initial jet production positions in $Z/γ$-jet events in which the effect of the diffusion wake is apparent in $Z/γ$-hadron correlation even without the subtraction of MPI.

preprint2022arXiv

Shape-Aware Monocular 3D Object Detection

The detection of 3D objects through a single perspective camera is a challenging issue. The anchor-free and keypoint-based models receive increasing attention recently due to their effectiveness and simplicity. However, most of these methods are vulnerable to occluded and truncated objects. In this paper, a single-stage monocular 3D object detection model is proposed. An instance-segmentation head is integrated into the model training, which allows the model to be aware of the visible shape of a target object. The detection largely avoids interference from irrelevant regions surrounding the target objects. In addition, we also reveal that the popular IoU-based evaluation metrics, which were originally designed for evaluating stereo or LiDAR-based detection methods, are insensitive to the improvement of monocular 3D object detection algorithms. A novel evaluation metric, namely average depth similarity (ADS) is proposed for the monocular 3D object detection models. Our method outperforms the baseline on both the popular and the proposed evaluation metrics while maintaining real-time efficiency.

preprint2022arXiv

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

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

preprint2022arXiv

Symmetry-enforced nodal lines in the band structures of vacancy-engineered graphene

We elaborate that single-layer graphene with periodic vacancies can have a band structure containing nodal lines or nodal loops, opening the possibility of graphene-based electronic or spintronic devices with novel functionalities. The principle is that by removing carbon atoms such that the lattice becomes nonsymmorphic, every two sublattices in the unit cell will map to each other under glide plane operation. This mapping yields degenerate eigenvalues for the glide plane operation, which guarantees that the energy bands must stick together pairwise at a boundary of the Brillouin zone. Moving away from the Brillouin zone boundary causes the symmetry-enforced nodal lines to split, resulting in accidental nodal lines caused by the crossings of the split bands. Moreover, the density of states at the Fermi level may be dramatically enhanced if the nodal lines crosses the Fermi level. The nodal lines occur a variety of vacancy configurations even in the presence of Rashba spin-orbit coupling. Finally, our theory also explains the nodal loops surrounding the entire Brillouin zone of a chevron-type nanoporous graphene fabricated in a recent experiment.

preprint2022arXiv

Thompson Sampling for Combinatorial Semi-Bandits

In this paper, we study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We first analyze the standard TS algorithm for the general CMAB model when the outcome distributions of all the base arms are independent, and obtain a distribution-dependent regret bound of $O(m\log K_{\max}\log T / Δ_{\min})$, where $m$ is the number of base arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $Δ_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. This regret upper bound is better than the $O(m(\log K_{\max})^2\log T / Δ_{\min})$ bound in prior works. Moreover, our novel analysis techniques can help to tighten the regret bounds of other existing UCB-based policies (e.g., ESCB), as we improve the method of counting the cumulative regret. Then we consider the matroid bandit setting (a special class of CMAB model), where we could remove the independence assumption across arms and achieve a regret upper bound that matches the lower bound. Except for the regret upper bounds, we also point out that one cannot directly replace the exact offline oracle (which takes the parameters of an offline problem instance as input and outputs the exact best action under this instance) with an approximation oracle in TS algorithm for even the classical MAB problem. Finally, we use some experiments to show the comparison between regrets of TS and other existing algorithms, the experimental results show that TS outperforms existing baselines.

preprint2022arXiv

Tighter monogamy relations for the Tsallis-q and Rényi-$α$ entanglement in multiqubit systems

Monogamy relations characterize the distributions of quantum entanglement in multipartite systems. In this work, we present some tighter monogamy relations in terms of the power of the Tsallis-q and Rényi-$α$ entanglement in multipartite systems. We show that these new monogamy relations of multipartite entanglement with tighter lower bounds than the existing ones. Furthermore, three examples are given to illustrate the tightness.

preprint2022arXiv

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-series data borrow the ideas from the existing methods for non-time series data to extract the domain-invariant representation. However, two peculiar difficulties to time-series data have not been solved. 1) It is not a trivial task to model the domain-invariant and complex dependence among different timestamps. 2) The domain-variant information is important but how to leverage them is almost underexploited. Fortunately, the stableness of causal structures among different domains inspires us to explore the structures behind the time-series data. Based on this inspiration, we investigate the domain-invariant unweighted sparse associative structures and the domain-variant strengths of the structures. To achieve this, we propose Sparse Associative structure alignment by learning Invariance and Variance (SASA-IV in short), a model that simultaneously aligns the invariant unweighted spare associative structures and considers the variant information for time-series unsupervised domain adaptation. Technologically, we extract the domain-invariant unweighted sparse associative structures with a unidirectional alignment restriction and embed the domain-variant strengths via a well-designed autoregressive module. Experimental results not only testify that our model yields state-of-the-art performance on three real-world datasets but also provide some insightful discoveries on knowledge transfer.

preprint2022arXiv

TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed

Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on large datasets due to the incremental complexity of the neural ODE training. Optimal Transport theory has been applied to regularize the dynamics of the ODE to speed up training in recent works. In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training. In this appoach, we optimize the network weights of the CNF alternately with evolutionary time by coordinate descent. Further with temporal regularization, stability of the evolution is ensured. This approach can be used in conjunction with the original regularization approach. We have experimentally demonstrated that the proposed approach can significantly accelerate training without sacrifying performance over baseline models.

preprint2022arXiv

Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks

A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].

preprint2022arXiv

Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models. As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical. A complementary way towards robustness is to introduce a rejection option, allowing the model to not return predictions on uncertain inputs, where confidence is a commonly used certainty proxy. Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones. This intriguing property sheds light on using coupling strategies to better detect and reject adversarial examples. We evaluate our rectified rejection (RR) module on CIFAR-10, CIFAR-10-C, and CIFAR-100 under several attacks including adaptive ones, and demonstrate that the RR module is compatible with different adversarial training frameworks on improving robustness, with little extra computation. The code is available at https://github.com/P2333/Rectified-Rejection.

preprint2022arXiv

Unbalanced-basis-misalignment tolerant measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution (MDIQKD) is a revolutionary protocol since it is physically immune to all attacks on the detection side. However, the protocol still keeps the strict assumptions on the source side that the four BB84-states must be perfectly prepared to ensure security. Some protocols release part of the assumptions in the encoding system to keep the practical security, but the performance would be dramatically reduced. In this work, we present a MDIQKD protocol that requires less knowledge of encoding system to combat the troublesome modulation errors and fluctuations. We have also experimentally demonstrated the protocol. The result indicates the high-performance and good security for its practical applications. Besides, its robustness and flexibility exhibit a good value for complex scenarios such as the QKD networks.

preprint2022arXiv

Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough

The Covid-19 pandemic has been one of the most devastating events in recent history, claiming the lives of more than 5 million people worldwide. Even with the worldwide distribution of vaccines, there is an apparent need for affordable, reliable, and accessible screening techniques to serve parts of the World that do not have access to Western medicine. Artificial Intelligence can provide a solution utilizing cough sounds as a primary screening mode for COVID-19 diagnosis. This paper presents multiple models that have achieved relatively respectable performance on the largest evaluation dataset currently presented in academic literature. Through investigation of a self-supervised learning model (Area under the ROC curve, AUC = 0.807) and a convolutional nerual network (CNN) model (AUC = 0.802), we observe the possibility of model bias with limited datasets. Moreover, we observe that performance increases with training data size, showing the need for the worldwide collection of data to help combat the Covid-19 pandemic with non-traditional means.

preprint2022arXiv

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.

preprint2021arXiv

BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational Bézier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.

preprint2021arXiv

BN-invariant sharpness regularizes the training model to better generalization

It is arguably believed that flatter minima can generalize better. However, it has been pointed out that the usual definitions of sharpness, which consider either the maxima or the integral of loss over a $δ$ ball of parameters around minima, cannot give consistent measurement for scale invariant neural networks, e.g., networks with batch normalization layer. In this paper, we first propose a measure of sharpness, BN-Sharpness, which gives consistent value for equivalent networks under BN. It achieves the property of scale invariance by connecting the integral diameter with the scale of parameter. Then we present a computation-efficient way to calculate the BN-sharpness approximately i.e., one dimensional integral along the "sharpest" direction. Furthermore, we use the BN-sharpness to regularize the training and design an algorithm to minimize the new regularized objective. Our algorithm achieves considerably better performance than vanilla SGD over various experiment settings.

preprint2021arXiv

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

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

preprint2021arXiv

Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation

In aerodynamic shape optimization, the convergence and computational cost are greatly affected by the representation capacity and compactness of the design space. Previous research has demonstrated that using a deep generative model to parameterize two-dimensional (2D) airfoils achieves high representation capacity/compactness, which significantly benefits shape optimization. In this paper, we propose a deep generative model, Free-Form Deformation Generative Adversarial Networks (FFD-GAN), that provides an efficient parameterization for three-dimensional (3D) aerodynamic/hydrodynamic shapes like aircraft wings, turbine blades, car bodies, and hulls. The learned model maps a compact set of design variables to 3D surface points representing the shape. We ensure the surface smoothness and continuity of generated geometries by incorporating an FFD layer into the generative model. We demonstrate FFD-GAN's performance using a wing shape design example. The results show that FFD-GAN can generate realistic designs and form a reasonable parameterization. We further demonstrate FFD-GAN's high representation compactness and capacity by testing its design space coverage, the feasibility ratio of the design space, and its performance in design optimization. We demonstrate that over 94% feasibility ratio is achieved among wings randomly generated by the FFD-GAN, while FFD and B-spline only achieve less than 31%. We also show that the FFD-GAN leads to an order of magnitude faster convergence in a wing shape optimization problem, compared to the FFD and the B-spline parameterizations.

preprint2021arXiv

Establishing the first hidden-charm pentaquark with strangeness

We study the $P_{cs}(4459)^0$ recently observed by LHCb using the method of QCD sum rules. Our results support its interpretation as the $\bar D^* Ξ_c$ hadronic molecular state of either $J^P=1/2^-$ or $3/2^-$. Within the hadronic molecular picture, the three LHCb experiments observing $P_c$ and $P_{cs}$ states \cite{lhcb,Aaij:2015tga,Aaij:2019vzc} can be well understood as a whole. This strongly supports the existence of hadronic molecules, whose studies can significantly improve our understanding on the construction of the subatomic world. To verify this picture, we propose to further investigate the $P_{cs}(4459)^0$ to examine whether it can be separated into two states, and to search for the $\bar D Ξ_c$ molecular state of $J^P=1/2^-$.

preprint2021arXiv

How Does Data Augmentation Affect Privacy in Machine Learning?

It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposing new MI attacks to utilize the information of augmented data. MI attack is widely used to measure the model's information leakage of the training set. We establish the optimal membership inference when the model is trained with augmented data, which inspires us to formulate the MI attack as a set classification problem, i.e., classifying a set of augmented instances instead of a single data point, and design input permutation invariant features. Empirically, we demonstrate that the proposed approach universally outperforms original methods when the model is trained with data augmentation. Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation. Notably, we achieve a 70.1% MI attack success rate on CIFAR10 against a wide residual network while the previous best approach only attains 61.9%. This suggests the privacy risk of models trained with data augmentation could be largely underestimated.

preprint2021arXiv

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

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

preprint2021arXiv

Light tetraquark states with the exotic quantum number $J^{PC} = 3^{-+}$

We apply the method of QCD sum rules to study the $s q \bar s \bar q$ tetraquark states with the exotic quantum number $J^{PC} = 3^{-+}$, and extract mass of the lowest-lying state to be $2.33^{+0.19}_{-0.16}$ GeV. To construct the relevant tetraquark currents we need to explicitly add the covariant derivative operator. Our systematical analysis on their relevant interpolating currents indicates that: a) this state well decays into the $P$-wave $ρϕ/ωϕ$ channel but not into the $ρf_2(1525)/ωf_2(1525)/ϕf_2(1270)$ channels, and b) it well decays into the $K^*(892) \bar K_2^*(1430)$ channel but not into the $P$-wave $K^*(892) \bar K^*(892)$ channel.

preprint2021arXiv

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

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

preprint2021arXiv

Mechanical Cloak via Data-Driven Aperiodic Metamaterial Design

Mechanical cloaks are materials engineered to manipulate the elastic response around objects to make them indistinguishable from their homogeneous surroundings. Typically, methods based on material-parameter transformations are used to design optical, thermal and electric cloaks. However, they are not applicable in designing mechanical cloaks, since continuum-mechanics equations are not form-invariant under general coordinate transformations. As a result, existing design methods for mechanical cloaks have so far been limited to a narrow selection of voids with simple shapes. To address this challenge, we present a systematic, data-driven design approach to create mechanical cloaks composed of aperiodic metamaterials using a large pre-computed unit cell database. Our method is flexible to allow the design of cloaks with various boundary conditions, multiple loadings, different shapes and numbers of voids, and different homogeneous surroundings. It enables a concurrent optimization of both topology and properties distribution of the cloak. Compared to conventional fixed-shape solutions, this results in an overall better cloaking performance, and offers unparalleled versatility. Experimental measurements on 3D-printed structures further confirm the validity of the proposed approach. Our research illustrates the benefits of data-driven approaches in quickly responding to new design scenarios and resolving the computational challenge associated with multiscale designs of functional structures. It could be generalized to accommodate other applications that require heterogeneous property distribution, such as soft robots and implants design.

preprint2021arXiv

On-chip integrated waveguide amplifiers on Erbium-doped thin film lithium niobate on insulator

We demonstrate on-chip light amplification with integrated optical waveguide fabricated on erbium-doped thin film lithium niobate on insulator (TFLNOI) using the photolithography assisted chemo-mechanical etching (PLACE) technique. A maximum internal net gain of 18 dB in the small-signal-gain regime is measured at the peak emission wavelength of 1530 nm for a waveguide length of 3.6 cm, indicating a differential gain per unit length of 5 dB/cm. This work paves the way to the monolithic integration of diverse active and passive photonic components on the TFLNOI platform.

preprint2021arXiv

Quantum key distribution over scattering channel

Scattering of light by cloud, haze, and fog decreases the transmission efficiency of communication channels in quantum key distribution (QKD), reduces the system's practical security, and thus constrains the deployment of free-space QKD. Here, we employ the wavefront shaping technology to compensate distorted optical signals in high-loss scattering quantum channels and fulfill a polarization-encoded BB84 QKD experiment. With this quantum channel compensation technology, we achieve a typical enhancement of about 250 in transmission efficiency and improve the secure key rate from 0 to $1.85\times10^{-6}$ per sifted key. The method and its first time validation show the great potential to expand the territory of QKD systems from lossless channels to highly scattered ones and therefore enhances the deployment ability of global quantum communication network.

preprint2021arXiv

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

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

preprint2021arXiv

Room temperature ferromagnetism of monolayer chromium telluride with perpendicular magnetic anisotropy

The realization of long-range magnetic ordering in two-dimensional (2D) systems can potentially revolutionize next-generation information technology. Here, we report the successful fabrication of crystalline Cr3Te4 monolayers with room temperature ferromagnetism. Using molecular beam epitaxy, the growth of 2D Cr3Te4 films with monolayer thickness is demonstrated at low substrate temperatures (~100C), compatible with Si CMOS technology. X-ray magnetic circular dichroism measurements reveal a Curie temperature (Tc) of ~344 K for the Cr3Te4 monolayer with an out-of-plane magnetic easy axis, which decreases to ~240 K for the thicker film (~ 7 nm) with an in-plane easy axis. The enhancement of ferromagnetic coupling and the magnetic anisotropy transition is ascribed to interfacial effects, in particular the orbital overlap at the monolayer Cr3Te4/graphite interface, supported by density-functional theory calculations. This work sheds light on the low-temperature scalable growth of 2D nonlayered materials with room temperature ferromagnetism for new magnetic and spintronic devices.

preprint2021arXiv

Security Analysis and Improvement of Source Independent Quantum Random Number Generators with Imperfect Devices

A quantum random number generator (QRNG) as a genuine source of randomness is essential in many applications, such as number simulation and cryptography. Recently, a source-independent quantum random number generator (SI-QRNG), which can generate secure random numbers with untrusted sources, has been realized. However, the measurement loopholes of the trusted but imperfect devices used in SI-QRNGs have not yet been fully explored, which will cause security problems, especially in high-speed systems. Here, we point out and evaluate the security loopholes of practical imperfect measurement devices in SI-QRNGs. We also provide corresponding countermeasures to prevent these information leakages by recalculating the conditional minimum entropy and adding a monitor. Furthermore, by taking into account the finite-size effect,we show that the influence of the afterpulse can exceed that of the finite-size effect with the large number of sampled rounds. Our protocol is simple and effective, and it promotes the security of SI-QRNG in practice as well as the compatibility with high-speed measurement devices, thus paving the way for constructing ultrafast and security-certified commercial SI-QRNG systems.

preprint2021arXiv

Toward the existence of odderon as a three-gluon bound state

Inspired by the evidence of the odderon exchange recently observed by the D0 and TOTEM Collaborations, a QCD sum rule investigation is performed to study the odderon as a three-gluon bound state. There may exist six lowest-lying three-gluon odderons with the quantum numbers $J^{PC} = 1/2/3^{\pm-}$. We systematically construct their interpolating currents, and calculate their mass spectra. To verify their existence, we propose to search for the spin-3 odderons in their $VVV$ and $VVP$ decay channels directly at LHC, with $V$ and $P$ light vector and pseudoscalar mesons respectively.

preprint2021arXiv

Two- and three-gluon glueballs of $C=+$

We study two- and three-gluon glueballs of $C=+$ using the method of QCD sum rules. We systematically construct their interpolating currents, and find that all the spin-1 currents of $C=+$ vanish. This suggests that the ``ground-state'' spin-1 glueballs of $C=+$ do not exist within the relativistic framework. We calculate masses of the two-gluon glueballs with $J^{PC} = 0^{\pm+}/2^{\pm+}$ and the three-gluon glueballs with $J^{PC} = 0^{\pm+}/2^{\pm+}$. We propose to search for the $J^{PC} = 0^{-+}/2^{-\pm}/3^{\pm-}$ three-gluon glueballs in their three-meson decay channels in future BESIII, GlueX, LHC, and PANDA experiments.

preprint2021arXiv

UAV-Assisted Over-the-Air Computation

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV's predetermined locations and flying speed, sensors' average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.

preprint2021arXiv

Vacancy-Engineered Flat-Band Superconductivity in Holey Graphene

A bipartite lattice with chiral symmetry is known to host zero energy flat bands if the numbers of the two sublattices are different. We demonstrate that this mechanism of producing flat bands can be realized on graphene by introducing periodic vacancies. Using first-principle calculations, we elaborate that even though the pristine graphene does not exactly preserve chiral symmetry, this mechanism applied to holey graphene still produces single or multiple bands as narrow as ~0.5eV near the Fermi surface throughout the entire Brillouin zone. Moreover, this mechanism can combine with vacancy-engineered nonsymmorphic symmetry to produce band structures with coexisting flat bands and nodal lines. A weak coupling mean-field treatment suggests the stabilization of superconductivity by these vacancy-engineered narrow bands. In addition, superconductivity occurs predominantly on the majority sublattices, with an amplitude that increases with the number of narrow bands.

preprint2020arXiv

(Locally) Differentially Private Combinatorial Semi-Bandits

In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential Privacy (LDP) setting. Since the server receives more information from users in CSB, it usually causes additional dependence on the dimension of data, which is a notorious side-effect for privacy preserving learning. However for CSB under two common smoothness assumptions \cite{kveton2015tight,chen2016combinatorial}, we show it is possible to remove this side-effect. In detail, for $B_{\infty}$-bounded smooth CSB under either $\varepsilon$-LDP or $\varepsilon$-DP, we prove the optimal regret bound is $Θ(\frac{mB^2_{\infty}\ln T } {Δε^2})$ or $\tildeΘ(\frac{mB^2_{\infty}\ln T} { Δε})$ respectively, where $T$ is time period, $Δ$ is the gap of rewards and $m$ is the number of base arms, by proposing novel algorithms and matching lower bounds. For $B_1$-bounded smooth CSB under $\varepsilon$-DP, we also prove the optimal regret bound is $\tildeΘ(\frac{mKB^2_1\ln T} {Δε})$ with both upper bound and lower bound, where $K$ is the maximum number of feedback in each round. All above results nearly match corresponding non-private optimal rates, which imply there is no additional price for (locally) differentially private CSB in above common settings.

preprint2020arXiv

$X_0(2900)$ and $X_1(2900)$: hadronic molecules or compact tetraquarks

Very recently the LHCb Collaboration reported their observation of the first two fully open-flavor tetraquark states, the $X_0(2900)$ of $J^P = 0^+$ and the $X_1(2900)$ of $J^P = 1^-$. We study their possible interpretations using the method of QCD sum rules, paying special attention to an interesting feature of this experiment that the higher resonance $X_1(2900)$ has a width significantly larger than the lower one $X_0(2900)$. Our results suggest that the $X_0(2900)$ can be interpreted as the $S$-wave $D^{*-}K^{*+}$ molecule state of $J^P = 0^+$, and the $X_1(2900)$ can be interpreted as the $P$-wave $\bar c \bar s u d$ compact tetraquark state of $J^P = 1^-$. Mass predictions of their bottom partners are also given.

preprint2020arXiv

A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling

Small multiples are miniature representations of visual information used generically across many domains. Handling large numbers of small multiples imposes challenges on many analytic tasks like inspection, comparison, navigation, or annotation. To address these challenges, we developed a framework and implemented a library called Piling.js for designing interactive piling interfaces. Based on the piling metaphor, such interfaces afford flexible organization, exploration, and comparison of large numbers of small multiples by interactively aggregating visual objects into piles. Based on a systematic analysis of previous work, we present a structured design space to guide the design of visual piling interfaces. To enable designers to efficiently build their own visual piling interfaces, Piling.js provides a declarative interface to avoid having to write low-level code and implements common aspects of the design space. An accompanying GUI additionally supports the dynamic configuration of the piling interface. We demonstrate the expressiveness of Piling.js with examples from machine learning, immunofluorescence microscopy, genomics, and public health.

preprint2020arXiv

A Learning-Driven Framework with Spatial Optimization For Surgical Suture Thread Reconstruction and Autonomous Grasping Under Multiple Topologies and Environmental Noises

Surgical knot tying is one of the most fundamental and important procedures in surgery, and a high-quality knot can significantly benefit the postoperative recovery of the patient. However, a longtime operation may easily cause fatigue to surgeons, especially during the tedious wound closure task. In this paper, we present a vision-based method to automate the suture thread grasping, which is a sub-task in surgical knot tying and an intermediate step between the stitching and looping manipulations. To achieve this goal, the acquisition of a suture's three-dimensional (3D) information is critical. Towards this objective, we adopt a transfer-learning strategy first to fine-tune a pre-trained model by learning the information from large legacy surgical data and images obtained by the on-site equipment. Thus, a robust suture segmentation can be achieved regardless of inherent environment noises. We further leverage a searching strategy with termination policies for a suture's sequence inference based on the analysis of multiple topologies. Exact results of the pixel-level sequence along a suture can be obtained, and they can be further applied for a 3D shape reconstruction using our optimized shortest path approach. The grasping point considering the suturing criterion can be ultimately acquired. Experiments regarding the suture 2D segmentation and ordering sequence inference under environmental noises were extensively evaluated. Results related to the automated grasping operation were demonstrated by simulations in V-REP and by robot experiments using Universal Robot (UR) together with the da Vinci Research Kit (dVRK) adopting our learning-driven framework.

preprint2020arXiv

A real-time multi-constraints obstacle avoidance method using LiDAR

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.

preprint2020arXiv

A Simultaneous Inference Procedure to Identify Subgroups from RCTs with Survival Outcomes: Application to Analysis of AMD Progression Studies

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized clinical trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with survival outcome. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which control both within- and across- marker multiplicity appropriately. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups been identified from AREDS. This simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

preprint2020arXiv

A Systematic Literature Review of Modern Software Visualization

We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013-2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.

preprint2020arXiv

Absence of equilibrium edge currents in theoretical models of topological insulators

The low energy sector of 2D and 3D topological insulators (TIs) exhibits propagating edge states, which has speculated the existence of equilibrium edge currents or edge spin currents. We demonstrate that if the low energy sector of TIs is regularized in a straightforward manner into a square or cubic lattice, then the current from the edge states is in fact canceled out exactly by that from the valence bands, rendering no edge current. This result serves as a warning that for any equilibrium property of topological insulators, the contribution from the valence bands should not be overlooked. In these regularized lattice model, there is a finite edge current only if the Dirac point of the edge states is shifted away from the chemical potential, for instance by doping, impurities, edge confining potential, surface band bending, or gate voltage. The edge current in small quantum dots as a function of the gate voltage is quantized, and the edge current can flow out of the gated region up to the decay length of the edge state.

preprint2020arXiv

Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization

Bayesian optimization is normally performed within fixed variable bounds. In cases like hyperparameter tuning for machine learning algorithms, setting the variable bounds is not trivial. It is hard to guarantee that any fixed bounds will include the true global optimum. We propose a Bayesian optimization approach that only needs to specify an initial search space that does not necessarily include the global optimum, and expands the search space when necessary. However, over-exploration may occur during the search space expansion. Our method can adaptively balance exploration and exploitation in an expanding space. Results on a range of synthetic test functions and an MLP hyperparameter optimization task show that the proposed method out-performs or at least as good as the current state-of-the-art methods.

preprint2020arXiv

Adaptive Power and Rate Control for Real-time Status Updating over Fading Channels

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information. To improve information freshness over fading channels, efficient scheduling methods are highly desired for wireless transmissions. However, due to the channel instability and arrival randomness, optimizing AoI is very challenging. In this paper, we are interested in the AoI-optimal transmissions with rate-adaptive transmission schemes in a buffer-aware system. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, a low complexity algorithm is presented to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semi-threshold-based policy, which indicates that we can achieve well real-time service with a low calculating complexity.

preprint2020arXiv

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes. We propose a deep generative model, Bézier-GAN, to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity. We use the airfoil design as an example to demonstrate the idea and analyze Bézier-GAN's representation capacity and compactness. Results show that Bézier-GAN both (1) learns smooth and realistic shape representations for a wide range of airfoils and (2) empirically accelerates optimization convergence by at least two times compared to state-of-the-art parameterization methods.

preprint2020arXiv

Asynchronous Stochastic Gradient Descent with Delay Compensation

With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely adopted to fulfill this task for its efficiency, which is, however, known to suffer from the problem of delayed gradients. That is, when a local worker adds its gradient to the global model, the global model may have been updated by other workers and this gradient becomes "delayed". We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD. This is achieved by leveraging Taylor expansion of the gradient function and efficient approximation to the Hessian matrix of the loss function. We call the new algorithm Delay Compensated ASGD (DC-ASGD). We evaluated the proposed algorithm on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly approaches the performance of sequential SGD.

preprint2020arXiv

Bubble Storytelling with Automated Animation: A Brexit Hashtag Activism Case Study

Hashtag data are common and easy to acquire. Thus, they are widely used in studies and visual data storytelling. For example, a recent story by China Central Television Europe (CCTV Europe) depicts Brexit as a hashtag movement displayed on an animated bubble chart. However, creating such a story is usually laborious and tedious, because narrators have to switch between different tools and discuss with different collaborators. To reduce the burden, we develop a prototype system to help explore the bubbles' movement by automatically inserting animations connected to the storytelling of the video creators and the interaction of viewers to those videos. We demonstrate the usability of our method through both use cases and a semi-structured user study.

preprint2020arXiv

ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit

Dance and music are two highly correlated artistic forms. Synthesizing dance motions has attracted much attention recently. Most previous works conduct music-to-dance synthesis via directly music to human skeleton keypoints mapping. Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music. Inspired by these, we systematically study such two-stage choreography approach and construct a dataset to incorporate such choreography knowledge. Based on the constructed dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to imitate human choreography procedure. Our framework firstly devises a CAU prediction model to learn the mapping relationship between music and CAU sequences. Afterwards, we devise a spatial-temporal inpainting model to convert the CAU sequence into continuous dance motions. Experimental results demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in terms of CAU BLEU score and 1.59 in terms of user study score).

preprint2020arXiv

Combinatorial Pure Exploration of Dueling Bandit

In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each round we sample a duel of two candidates on one position and observe who wins in the duel, with the goal of finding the best candidate-position matching with high probability after multiple rounds of samples. CPE-DB is an adaptation of the original combinatorial pure exploration for multi-armed bandit (CPE-MAB) problem to the dueling bandit setting. We consider both the Borda winner and the Condorcet winner cases. For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round. For Condorcet winner, we first design a fully polynomial time approximation scheme (FPTAS) for the offline problem of finding the Condorcet winner with known winning probabilities, and then use the FPTAS as an oracle to design a novel pure exploration algorithm ${\sf CAR}$-${\sf Cond}$ with sample complexity analysis. ${\sf CAR}$-${\sf Cond}$ is the first algorithm with polynomial running time per round for identifying the Condorcet winner in CPE-DB.

preprint2020arXiv

Computation Offloading in Heterogeneous Mobile Edge Computing with Energy Harvesting

Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. In this paper, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs and MEC-ss. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of an MD are taken into consideration. A noncooperative computation offloading game is formulated. We give the definition and existence analysis of the Nash equilibrium (NE). Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. We can obtain the optimal solution of the sub-problem by seeking the finite solutions of its Karush-Kuhn-Tucker (KKT) conditions. Thereafter, a distributive NE-orienting iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and parameter effect.

preprint2020arXiv

ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

preprint2020arXiv

Constructing Basis Path Set by Eliminating Path Dependency

The way the basis path set works in neural network remains mysterious, and the generalization of newly appeared G-SGD algorithm to more practical network is hindered. The Basis Path Set Searching problem is formulated from the perspective of graph theory, to find the basis path set in a regular complicated neural network. Our paper aims to discover the underlying cause of path dependency between two independent substructures. Algorithm DEAH is designed to solve the Basis Path Set Searching problem by eliminating such path dependency. The path subdivision chain is proposed to effectively eliminate the path dependency inside the chain and between chains. The theoretical proofs and analysis of polynomial time complexity are presented. The paper therefore provides one methodology to find the basis path set in a more general neural network, which offers theoretical and algorithmic support for the application of G-SGD algorithm in more practical scenarios.

preprint2020arXiv

Controlling a Networked SIS Model via a Single Input over Undirected Graphs

This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition on the values of curing and infection rates for the healthy state to be exponentially stable is obtained via the analysis of signed Laplacians when the control input is the curing budget of a single agent. In the case when the healthy state is stabilizable, an explicit expression for the minimum curing budget is provided. The utility of the algorithm is demonstrated using a simulation over a network of cities in the northeastern United States.

preprint2020arXiv

Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data

Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and disjointly stored on different machines, it becomes imperative to distribute the implementation of such variance reduced methods. In this paper, we consider a general framework that directly distributes popular stochastic variance reduced methods in the master/slave model, by assigning outer loops to the parameter server, and inner loops to worker machines. This framework is natural and friendly to implement, but its theoretical convergence is not well understood. We obtain a comprehensive understanding of algorithmic convergence with respect to data homogeneity by measuring the smoothness of the discrepancy between the local and global loss functions. We establish the linear convergence of distributed versions of a family of stochastic variance reduced algorithms, including those using accelerated and recursive gradient updates, for minimizing strongly convex losses. Our theory captures how the convergence of distributed algorithms behaves as the number of machines and the size of local data vary. Furthermore, we show that when the data are less balanced, regularization can be used to ensure convergence at a slower rate. We also demonstrate that our analysis can be further extended to handle nonconvex loss functions.

preprint2020arXiv

Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process

The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or distance measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures.

preprint2020arXiv

Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima, and high computational cost. To address these hurdles, we propose a novel data-driven metamaterial design framework based on deep generative modeling. A variational autoencoder (VAE) and a regressor for property prediction are simultaneously trained on a large metamaterial database to map complex microstructures into a low-dimensional, continuous, and organized latent space. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity, enable interpolation between microstructures and encode meaningful patterns of variation in geometries and properties. Based on these insights, systematic data-driven methods are proposed for the design of microstructure, graded family, and multiscale system. For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space. The vector operation is further extended to generate metamaterial families with a controlled gradation of mechanical properties by searching on a constructed graph model. For multiscale metamaterial systems design, a diverse set of microstructures can be rapidly generated using VAE for target properties at different locations and then assembled by an efficient graph-based optimization method to ensure compatibility between adjacent microstructures. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems that achieve desired distortion behaviors.

preprint2020arXiv

Detection of Fermi Arcs in Weyl Semimetals through Surface Negative Refraction

One of the main features of Weyl semimetals is the existence of Fermi arc surface states at their surface, which cannot be realized in pure two-dimensional systems in the absence of many-body interactions. Due to the gapless bulk of the semimetal, it is, however, challenging to observe clear signatures from the Fermi arc surface states. Here, we propose to detect such novel surface states via perfect negative refraction that occurs between two adjacent open surfaces with properly orientated Fermi arcs. Specifically, this phenomenon visibly manifests in non-local transport measurement, where the negative refraction generates a return peak in the real-space conductance. This provides a unique signature of the Fermi arc surface states. We discuss the appearance of this peak both in inversion and time-reversal symmetric Weyl semimetals, where the latter exhibits conductance oscillations due to multiple negative refraction scattering events.

preprint2020arXiv

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.

preprint2020arXiv

Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person re-identification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets.

preprint2020arXiv

Effects of Structural Distortions on the Electronic Structure of T-type Transition Metal Dichalcogenides

Single-layer transition metal dichalcogenides (TMDCs) can adopt two distinct structures corresponding to different coordination of the metal atoms. TMDCs adopting the T-type structure exhibit a rich and diverse set of phenomena, including charge density waves (CDW) in a $\sqrt{13}\times\sqrt{13}$ supercell pattern in TaS$_2$ and TaSe$_2$, and a possible excitonic insulating phase in TiSe$_2$. These properties make the T-TMDCs desirable components of layered heterostructure devices. In order to predict the emergent properties of combinations of different layered materials, one needs simple and accurate models for the constituent layers which can take into account potential effects of lattice mismatch, relaxation, strain, and structural distortion. Previous studies have developed ab initio tight-binding Hamiltonians for H-type TMDCs [arXiv:1709.07510]. Here we extend this work to include T-type TMDCs. We demonstrate the capabilities of our model using three example systems: a 1-dimensional sinusoidal ripple, the 2$\times$2 CDW in TiSe$_2$, and the $\sqrt{13}\times\sqrt{13}$ CDW in TaS$_2$. Using the technique of band unfolding we compare the electronic structure of the distorted crystals to the pristine band structure and find excellent agreement with direct DFT calculations, provided the magnitude of the distortions remains in the linear regime.

preprint2020arXiv

Efficient Approximation Algorithms for Adaptive Influence Maximization

Given a social network $G$ and an integer $k$, the influence maximization (IM) problem asks for a seed set $S$ of $k$ nodes from $G$ to maximize the expected number of nodes influenced via a propagation model. The majority of the existing algorithms for the IM problem are developed only under the non-adaptive setting, i.e., where all $k$ seed nodes are selected in one batch without observing how they influence other users in real world. In this paper, we study the adaptive IM problem where the $k$ seed nodes are selected in batches of equal size $b$, such that the $i$-th batch is identified after the actual influence results of the former $i-1$ batches are observed. In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{ρ_b(\varepsilon-1)}$, where $ρ_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter. In particular, we propose a general framework AdaptGreedy that could be instantiated by any existing non-adaptive IM algorithms with expected approximation guarantee. Our approach is based on a novel randomized policy that is applicable to the general adaptive stochastic maximization problem, which may be of independent interest. In addition, we propose a novel non-adaptive IM algorithm called EPIC which not only provides strong expected approximation guarantee, but also presents superior performance compared with the existing IM algorithms. Meanwhile, we clarify some existing misunderstandings in recent work and shed light on further study of the adaptive IM problem. We conduct experiments on real social networks to evaluate our proposed algorithms comprehensively, and the experimental results strongly corroborate the superiorities and effectiveness of our approach.

preprint2020arXiv

Efficient decoy-states for the reference-frame-independent measurement-device-independent quantum key distribution

Reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) is a novel protocol which eliminates all possible attacks on detector side and necessity of reference-frame alignment in source sides. However, its performance may degrade notably due to statistical fluctuations, since more parameters, e.g. yields and error rates for mismatched-basis events, must be accumulated to monitor the security. In this work, we find that the original decoy-states method estimates these yields over pessimistically since it ignores the potential relations between different bases. Through processing parameters of different bases jointly, the performance of RFI-MDI-QKD is greatly improved in terms of secret key rate and achievable distance when statistical fluctuations are considered. Our results pave an avenue towards practical RFI-MDI-QKD.

preprint2020arXiv

EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural idea of an ensemble model of the two, existing ensemble methods only focused on leveraging one approach to enhance another, we argue however that they can be further mutually enhanced with a proper training strategy. In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario. It consists of a language-model-like generator, a ranker generator, and one ranker discriminator. Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts. The experimental results on a large short-text conversation data demonstrate the effectiveness of the ensembleGAN by the amelioration on both human and automatic evaluation metrics.

preprint2020arXiv

Exemplar-based Layout Fine-tuning for Node-link Diagrams

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the whole graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.

preprint2020arXiv

Federated Learning via Intelligent Reflecting Surface

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex-functions programming algorithm for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results will demonstrate that our proposed algorithm and the deployment of an IRS can achieve a lower training loss and higher FL prediction accuracy than the baseline algorithms.

preprint2020arXiv

Ferroelectricity and multiferroicity in anti-Ruddlesden-Popper structures

Combining ferroelectricity with other properties such as visible light absorption or long-range magnetic order requires the discovery of new families of ferroelectric materials. Here, through the analysis of a high-throughput database of phonon band structures, we identify a new structural family of anti-Ruddlesden-Popper phases A$_4$X$_2$O (A=Ca, Sr, Ba, Eu, X=Sb, P, As, Bi) showing ferroelectric and anti-ferroelectric behaviors. The discovered ferroelectrics belong to the new class of hyperferroelectrics which polarize even under open-circuit boundary conditions. The polar distortion involves the movement of O anions against apical A cations and is driven by geometric effects resulting from internal chemical strains. Within this new structural family, we show that Eu$_4$Sb$_2$O combines coupled ferromagnetic and ferroelectric order at the same atomic site, a very rare occurrence in materials physics.

preprint2020arXiv

Finite-key analysis for twin-field quantum key distribution based on generalized operator dominance condition

Quantum key distribution (QKD) can help two distant peers to share secret key bits, whose security is guaranteed by the law of physics. In practice, the secret key rate of a QKD protocol is always lowered with the increasing of channel distance, which severely limits the applications of QKD. Recently, twin-field (TF) QKD has been proposed and intensively studied, since it can beat the rate-distance limit and greatly increase the achievable distance of QKD. Remarkalebly, K. Maeda et. al. proposed a simple finite-key analysis for TF-QKD based on operator dominance condition. Although they showed that their method is sufficient to beat the rate-distance limit, their operator dominance condition is not general, i.e. it can be only applied in three decoy states scenarios, which implies that its key rate cannot be increased by introducing more decoy states, and also cannot reach the asymptotic bound even in case of preparing infinite decoy states and optical pulses. Here, to bridge this gap, we propose an improved finite-key analysis of TF-QKD through devising new operator dominance condition. We show that by adding the number of decoy states, the secret key rate can be furtherly improved and approach the asymptotic bound. Our theory can be directly used in TF-QKD experiment to obtain higher secret key rate. Our results can be directly used in experiments to obtain higher key rates.

preprint2020arXiv

Fractional Fourier transforms on $L^p$ and applications

This paper is devoted to the $L^p(\mathbb R)$ theory of the fractional Fourier transform (FRFT) for $1\le p < 2$. In view of the special structure of the FRFT, we study FRFT properties of $L^1$ functions, via the introduction of a suitable chirp operator. However, in the $L^1(\mathbb{R})$ setting, problems of convergence arise even when basic manipulations of functions are performed. We overcome such issues and study the FRFT inversion problem via approximation by suitable means, such as the fractional Gauss and Abel means. We also obtain the regularity of fractional convolution and results on pointwise convergence of FRFT means. Finally we discuss $L^p$ multiplier results and a Littlewood-Paley theorem associated with FRFT.

preprint2020arXiv

Fully open-flavor tetraquark states $bc\bar{q}\bar{s}$ and $sc\bar{q}\bar{b}$ with $J^{P}=0^{+},1^{+}$

We have studied the masses for fully open-flavor tetraquark states $bc\bar{q}\bar{s}$ and $sc\bar{q}\bar{b}$ with quantum numbers $J^{P}=0^{+},1^{+}$. We systematically construct all diquark-antiquark interpolating currents and calculate the two-point correlation functions and spectral densities in the framework of QCD sum rule method. Our calculations show that the masses are about $7.1-7.2$ GeV for the $bc\bar{q}\bar{s}$ tetraquark states and $7.0-7.1$ GeV for the $sc\bar{q}\bar{b}$ tetraquarks. The masses of $bc\bar{q}\bar{s}$ tetraquarks are below the thresholds of $\bar{B}_{s}D$ and $\bar{B}_{s}^{*}D$ final states for the scalar and axial-vector channels respectively. The $sc\bar{q}\bar{b}$ tetraquark states with $J^{P}=1^{+}$ lie below the $B_{c}^{+}K^{*}$ and $B_{s}^{*}D$ thresholds. Such low masses for these possible tetraquark states indicate that they can only decay via weak interaction and thus are very narrow and stable.

preprint2020arXiv

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. Specifically, our network consists of three steps. First, we extract the coarse object point cloud from the RGB-D image by 2D detection. Second, we feed the coarse object point cloud to a translation localization network to perform 3D segmentation and object translation prediction. Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation. In the third step, we define point-wise embedding vector features to capture viewpoint-aware information. To calculate more accurate rotation, we adopt a rotation residual estimator to estimate the residual between initial rotation and ground truth, which can boost initial pose estimation performance. Our proposed G2L-Net is real-time despite the fact multiple steps are stacked via the proposed coarse-to-fine framework. Extensive experiments on two benchmark datasets show that G2L-Net achieves state-of-the-art performance in terms of both accuracy and speed.

preprint2020arXiv

Global Context Aware Convolutions for 3D Point Cloud Understanding

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point clouds directly in a neural network. Point cloud data, however, could have arbitrary rotations, especially those acquired from 3D scanning. Recent works show that it is possible to design point cloud convolutions with rotation invariance property, but such methods generally do not perform as well as translation-invariant only convolution. We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive. To address this problem, we propose a novel convolution operator that enhances feature distinction by integrating global context information from the input point cloud to the convolution. To this end, a globally weighted local reference frame is constructed in each point neighborhood in which the local point set is decomposed into bins. Anchor points are generated in each bin to represent global shape features. A convolution can then be performed to transform the points and anchor features into final rotation-invariant features. We conduct several experiments on point cloud classification, part segmentation, shape retrieval, and normals estimation to evaluate our convolution, which achieves state-of-the-art accuracy under challenging rotations.

preprint2020arXiv

GraphFederator: Federated Visual Analysis for Multi-party Graphs

This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for joint modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a federated graph representation model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization views for joint visualization, exploration, and analysis of multi-party graphs. Experimental results with two datasets demonstrate the effectiveness of our approach.

preprint2020arXiv

Influence Maximization with Spontaneous User Adoption

We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes. Self activation reflects the real-world scenarios such as people naturally share product recommendations with their friends even without marketing intervention. It also leads to two new forms of optimization problems: (a) {\em preemptive influence maximization (PIM)}, which aims to find $k$ nodes that, if self-activated, can reach the most number of nodes before other self-activated nodes; and (b) {\em boosted preemptive influence maximization (BPIM)}, which aims to select $k$ seeds that are guaranteed to be activated and can reach the most number of nodes before other self-activated nodes. We propose scalable algorithms for PIM and BPIM and prove that they achieve $1-\varepsilon$ approximation for PIM and $1-1/e-\varepsilon$ approximation for BPIM, for any $\varepsilon > 0$. Through extensive tests on real-world graphs, we demonstrate that our algorithms outperform the baseline algorithms significantly for the PIM problem in solution quality, and also outperform the baselines for BPIM when self-activation behaviors are non-uniform across nodes.

preprint2020arXiv

InGaAs/InP single-photon detectors with 60% detection efficiency at 1550 nm

InGaAs/InP single-photon detectors (SPDs) are widely used for near-infrared photon counting in practical applications. Photon detection efficiency (PDE) is one of the most important parameters for SPD characterization, and therefore increasing PDE consistently plays a central role in both industrial development and academic research. Here we present the implementation of high-frequency gating InGaAs/InP SPD with a PDE as high as 60% at 1550 nm. On one hand, we optimize the structure design and device fabrication of InGaAs/InP single-photon avalanche diode with an additional dielectric-metal reflection layer to relatively increase the absorption efficiency of incident photons by ~ 20%. On the other hand, we develop a monolithic readout circuit of weak avalanche extraction to minimize the parasitic capacitance for the suppression of the afterpulsing effect. With 1.25 GHz sine wave gating and optimized gate amplitude and operation temperature, the SPD is characterized to reach a PDE of 60% with a dark count rate (DCR) of 340 kcps. For practical use, given 3 kcps DCR as a reference the PDE reaches ~ 40% PDE with an afterpulse probability of 5.5%, which can significantly improve the performance for the near-infrared SPD based applications.

preprint2020arXiv

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence. By enforcing the NMT model to predict source context, we want the model to learn "contextualized" source sentence representations that capture document-level dependencies on the source side. We further propose two different methods to learn and integrate such contextualized sentence embeddings into NMT: a joint training method that jointly trains an NMT model with the source context prediction model and a pre-training & fine-tuning method that pretrains the source context prediction model on a large-scale monolingual document corpus and then fine-tunes it with the NMT model. Experiments on Chinese-English and English-German translation show that both methods can substantially improve the translation quality over a strong document-level Transformer baseline.

preprint2020arXiv

Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems

Resonant Beam Charging (RBC) is a wireless charging technology which supports multi-watt power transfer over meter-level distance. The features of safety, mobility and simultaneous charging capability enable RBC to charge multiple mobile devices safely at the same time. To detect the devices that need to be charged, a Mask R-CNN based dection model is proposed in previous work. However, considering the constraints of the RBC system, it's not easy to apply Mask R-CNN in lightweight hardware-embedded devices because of its heavy model and huge computation. Thus, we propose a machine learning detection approach which provides a lighter and faster model based on traditional Mask R-CNN. The proposed approach makes the object detection much easier to be transplanted on mobile devices and reduce the burden of hardware computation. By adjusting the structure of the backbone and the head part of Mask R-CNN, we reduce the average detection time from $1.02\mbox{s}$ per image to $0.6132\mbox{s}$, and reduce the model size from $245\mbox{MB}$ to $47.1\mbox{MB}$. The improved model is much more suitable for the application in the RBC system.

preprint2020arXiv

Medium modification of $γ$-jet fragmentation functions in Pb+Pb collisions at LHC

Coupled linear Boltzmann transport and hydrodynamic (CoLBT-hydro) model has been developed for simultaneous simulations of jet propagation and jet-induced medium excitation in heavy-ion collisions. Within this coupled approach, the final reconstructed jets in heavy-ion collisions include not only hadrons from the hadronization of medium modified jet shower partons from the linear Boltzmann transport (LBT) but also hadrons from the freeze-out of the jet-induced medium excitation in the hydrodynamic evolution of the bulk medium. Using the CoLBT-hydro model, we study medium modification of the fragmentation functions of $γ$-triggered jets in high-energy heavy-ion collisions at the Large Hadron Collider. The CoLBT-hydro model is shown to describe the experimental data not only on the suppression of leading hadrons within the jet cone at large momentum fraction $z_γ=p_T^h/p_T^γ$ relative to the transverse momentum of the trigger photon due to parton energy loss but also the enhancement of soft hadrons at small $z_γ$ and $z_{\rm jet}=p_T^h/p_T^{\rm jet}$ due to jet-induced medium excitation. There is no suppression of the fragmentation function, however, at large momentum fraction $z_{\rm jet}$ relative to the transverse momentum of the reconstructed jet due to trigger bias and medium modification of quark to gluon jet fraction. For jets whose final transverse momenta are comparable to or larger than that of the trigger photon, the trigger bias can lead to enhancement of the jet fragmentation function at large $z_{\rm jet}$.

preprint2020arXiv

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects. The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4\% to 17.7\% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

preprint2020arXiv

MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement

Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing models generally do not generate high-performance novel designs, outside the domain of the training data. To address these challenges, we propose MO-PaDGAN, which contains a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances. Through a real-world airfoil design example, we demonstrate that MO-PaDGAN expands the existing boundary of the design space towards high-performance regions and generates new designs with high diversity and performances exceeding training data.

preprint2020arXiv

Molecular Latent Space Simulators

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.

preprint2020arXiv

New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design

In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We then provide a unified view targeting three research directions that naturally emerge from the above challenges: (1) Federated learning for training deep networks, (2) Data-independent deployment of learning algorithms, and (3) Communication-aware distributed inference. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.

preprint2020arXiv

Normal family of meromorphic mappings and Big Picard's theorem

In this paper, we prove some results in normal family of meromorphic mappings intersecting with moving hypersurfaces. As some applications, we establish some results for normal mapping and extension of holomorphic mappings. A our result is strongly extended the Montel's normal criterion in the case several variables which is due to Tu in [Proc. Amer. Math. Soc. 127,1039-1049, 1999]. Our results are also strongly extended the results of Tu-Li in [Sci. China Ser. A. 48, 355-364, 2005] and [J. Math. Anal. Appl. 342, 629-638, 2006].

preprint2020arXiv

On Spectral Properties of Signed Laplacians with Connections to Eventual Positivity

Signed graphs have appeared in a broad variety of applications, ranging from social networks to biological networks, from distributed control and computation to power systems. In this paper, we investigate spectral properties of signed Laplacians for undirected signed graphs. We find conditions on the negative weights under which a signed Laplacian is positive semidefinite via the Kron reduction and multiport network theory. For signed Laplacians that are indefinite, we characterize their inertias with the same framework. Furthermore, we build connections between signed Laplacians, generalized M-matrices, and eventually exponentially positive matrices.

preprint2020arXiv

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS-based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

preprint2020arXiv

Optimization from Structured Samples for Coverage Functions

We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomially many independent samples of the form $\{S_i, f(S_i)\}_{i=1}^t$ (Balkanski et al., 2017), even if coverage functions are $(1 - ε)$-PMAC learnable using these samples (Badanidiyuru et al., 2012), which means most of the function values can be approximately learned very well with high probability. In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS) for coverage functions, where the data samples encode the structural information of the functions. We show that under three general assumptions on the sample distributions, we can design efficient OPSS algorithms that achieve a constant approximation for the maximum coverage problem. We further prove a constant lower bound under these assumptions, which is tight when not considering computational efficiency. Moreover, we also show that if we remove any one of the three assumptions, OPSS for the maximum coverage problem has no constant approximation.

preprint2020arXiv

Optimized protocol for twin-field quantum key distribution

Twin-field quantum key distribution (TF-QKD) and its variant protocols are highly attractive due to the advantage of overcoming the rate-loss limit for secret key rates of point-to-point QKD protocols. For variations of TF-QKD, the key point to ensure security is switching randomly between a code mode and a test mode. Among all TF-QKD protocols, their code modes are very different, e.g. modulating continuous phases, modulating only two opposite phases, and sending or not sending signal pulses. Here we show that, by discretizing the number of global phases in the code mode, we can give a unified view on the first two types of TF-QKD protocols, and demonstrate that increasing the number of discrete phases extends the achievable distance, and as a trade-off, lowers the secret key rate at short distances due to the phase post-selection.

preprint2020arXiv

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a team's employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a team's passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.

preprint2020arXiv

Persistent currents and spin torque caused by percolated quantum spin Hall state

Motivated by recent experiments, we investigate the quantum spin Hall state in 2D topological insulator/ferromagnetic metal planar junctions by means of a tight-binding model and linear response theory. We demonstrate that whether the edge state Dirac cone is submerged into the ferromagnetic subbands and the direction of the magnetization dramatically affect (i) how the edge state percolates into the ferromagnet, and (ii) the spin-momentum locking of the edge state. Laminar flows of room temperature persistent charge and spin currents near the interface are uncovered. In addition, the current-induced spin polarization at the edge of the 2D topological insulator is found to be dramatically enhanced near the impurities. The current-induced spin polarization in the ferromagnet is mainly polarized in the out-of-plane direction ${\hat{\bf z}}$, rendering a current-induced spin torque that is predominantly field-like $\propto {\bf S}\times{\hat{\bf z}}$.

preprint2020arXiv

Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling

This paper proposes a learning algorithm to find a scheduling policy that achieves an optimal delay-power trade-off in communication systems. Reinforcement learning (RL) is used to minimize the expected latency for a given energy constraint where the environments such as traffic arrival rates or channel conditions can change over time. For this purpose, this problem is formulated as an infinite-horizon Markov Decision Process (MDP) with constraints. To handle the constrained optimization problem, we adopt the Lagrangian relaxation technique to solve it. Then, we propose a variant of Q-learning, Q-greedyUCB that combines Q-learning for \emph{average} reward algorithm and Upper Confidence Bound (UCB) policy to solve this decision-making problem. We prove that the Q-greedyUCB algorithm is convergent through mathematical analysis. Simulation results show that Q-greedyUCB finds an optimal scheduling strategy, and is more efficient than Q-learning with the $\varepsilon$-greedy and Average-payoff RL algorithm in terms of the cumulative reward (i.e., the weighted sum of delay and energy) and the convergence speed. We also show that our algorithm can reduce the regret by up to 12% compared to the Q-learning with the $\varepsilon$-greedy and Average-payoff RL algorithm.

preprint2020arXiv

Quantum key distribution with dissipative Kerr soliton generated by on-chip microresonators

Quantum key distribution (QKD) can distribute symmetric key bits between remote legitimate users with the guarantee of quantum mechanics principles. For practical applications, the compact and robust photonic components for QKD are essential, and there are increasing attention to integrate the source, detector and modulators on a photonic chip. However, the massive and parallel QKD based on wavelength multiplexing are still challenge, due to the limited coherent light sources on the chip. Here, we introduce the Kerr dissipative soliton in a microresonator, which provides the locked coherent frequency comb with 49GHz frequency spacing, for QKD. We demonstrate the parallel QKD by demulplexing the coherent comb lines form the soliton, and showing the potential of Gbps secret key rate if the hundreds of channels covering C and L bands are fully exploited. The demonstrated soliton based QKD architecture are compatible with the efforts of quantum photonic integrated circuits, which are compact, robust and low-cost, and provides a competitive platform of practical QKD chip.

preprint2020arXiv

Reconfigurable Intelligent Surface Assisted Massive MIMO with Antenna Selection

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS- assisted massive MIMO systems with antenna selection.

preprint2020arXiv

Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs. This issue can significantly destabilize the feature embeddings and the predictions, rendering deep networks much less useful for the experts. This paper approaches this challenge by leveraging unit visualization techniques that enable the investigation of many-to-many relationships between dynamically varied multi-scalar aggregations of urban traffic data and neural network predictions. Through regular exchanges with a domain expert, we design and develop a visual analytics solution that integrates 1) a Bivariate Map equipped with an advanced bivariate colormap to simultaneously depict input traffic and prediction errors across space, 2) a Morans I Scatterplot that provides local indicators of spatial association analysis, and 3) a Multi-scale Attribution View that arranges non-linear dot plots in a tree layout to promote model analysis and comparison across scales. We evaluate our approach through a series of case studies involving a real-world dataset of Shenzhen taxi trips, and through interviews with domain experts. We observe that geographical scale variations have important impact on prediction performances, and interactive visual exploration of dynamically varying inputs and outputs benefit experts in the development of deep traffic prediction models.

preprint2020arXiv

SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.

preprint2020arXiv

Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

preprint2020arXiv

The influence of the Insight-HXMT/LE time response on timing analysis

LE is the low energy telescope of Insight-HXMT. It uses swept charge devices (SCDs) to detect soft X-ray photons. The time response of LE is caused by the structure of SCDs. With theoretical analysis and Monte Carlo simulations we discuss the influence of LE time response (LTR) on the timing analysis from three aspects: the power spectral density, the pulse profile and the time lag. After the LTR, the value of power spectral density monotonously decreases with the increasing frequency. The power spectral density of a sinusoidal signal reduces by a half at frequency 536 Hz. The corresponding frequency for QPO signals is 458 Hz. The Root mean square (RMS) of QPOs holds the similar behaviour. After the LTR, the centroid frequency and full width at half maxima (FWHM) of QPOs signals do not change. The LTR reduces the RMS of pulse profiles and shifts the pulse phase. In the time domain, the LTR only reduces the peak value of the crosscorrelation function while it does not change the peak position. Thus it will not affect the result of the time lag. When considering the time lag obtained from two instruments and one among them is LE, a 1.18 ms lag is expected caused by the LTR. The time lag calculated in the frequency domain is the same as that in the time domain.

preprint2020arXiv

Weighted estimates for the bilinear maximal operator on filtered measure spaces

Assuming the bilinear reverse Holder's condition, we character weighted inequalities for the bilinear maximal operator on filtered measure spaces. We also obtain Hytonen-Perez type weighted estimates for the bilinear maximal operator. Our approaches are mainly based on the new construction of bilinear versions of principal sets and the new Carleson embedding theorem on filtered measure spaces. In particular, we find a new property of the construction and we call it the conditional sparsity of principal sets.

preprint2019arXiv

Designing Anisotropic Microstructures with Spectral Density Function

Materials' microstructure strongly influences its performance and is thus a critical aspect in design of functional materials. Previous efforts on microstructure mediated design mostly assume isotropy, which is not ideal when material performance is dependent on an underlying transport phenomenon. In this article, we propose an anisotropic microstructure design strategy that leverages Spectral Density Function (SDF) for rapid reconstruction of high resolution, two phase, isotropic or anisotropic microstructures in 2D and 3D. We demonstrate that SDF microstructure representation provides an intuitive method for quantifying anisotropy through a dimensionless scalar variable termed anisotropy index. The computational efficiency and low dimensional microstructure representation enabled by our method is demonstrated through an active layer design case study for Bulk Heterojunction Organic Photovoltaic Cells (OPVCs). Results indicate that optimized design, exhibiting strong anisotropy, outperforms isotropic active layer designs. Further, we show that Cross-sectional Scanning Tunneling Microscopy and Spectroscopy (XSTM/S) is as an effective tool for characterization of anisotropic microstructures.

preprint2019arXiv

Electron-Hole Interference in an Inverted-Band Semiconductor Bilayer

Electron optics in the solid state promises new functionality in electronics through the possibility of realizing micrometer-sized interferometers, lenses, collimators and beam splitters that manipulate electrons instead of light. Until now, however, such functionality has been demonstrated exclusively in one-dimensional devices, such as in nanotubes, and in graphene-based devices operating with p-n junctions. In this work, we describe a novel mechanism for realizing electron optics in two dimensions. By studying a two-dimensional Fabry-Pérot interferometer based on a resonant cavity formed in an InAs/GaSb double quantum well using p-n junctions, we establish that electron-hole hybridization in band-inverted systems can facilitate coherent interference. With this discovery, we expand the field of electron optics to encompass materials that exhibit band inversion and hybridization, with the promise to surpass the performance of current state-of-the-art devices.

preprint2019arXiv

Elongated Nano Domains and Molecular Intermixing induced Doping in Organic Photovoltaic Active Layers with Electric Field Treatment

The effects of the electric-field-assisted annealing on the bulk heterojunction nano-morphology in the P3HT/PCBM active layer of the organic photovoltaic cells (OPVCs) are presented here. It was widely accepted that the electric-field-assisted annealing will facilitate the P3HT, the polar polymer, to be better crystalline to enhance the charge mobility, hence the improvement of the OPVC performance. The influences on the nano-morphology of the electron donor and accepter domains are not well understood. Here, using the cross-sectional scanning tunneling microscopy and spectroscopy (XSTM/S), the electric-field-assisted annealing treatment is found to influence the molecular domains to be elongated with the orientation near the direction of the external electric field. The elongation of the molecular domains is believed to facilitate the domain percolation, which causes higher charge mobility, hence the higher short-circuit current density (Jsc). On the other hand, it was also observed that the electronic properties of the P3HT-rich and PCBM-rich domains in the electric-field-assisted annealed samples showed smaller energy band gaps and smaller molecular orbital offset between the two domains, which is argued to decrease the open circuit voltage (Voc) and negatively impact the OPVC performance. Based on the X-ray diffraction (XRD) and small angle X-ray scattering (SAXS) results, the altered electronic properties are argued to be due to the molecular intermixing induced doping effects. These results point out competing factors affecting the OPVC performance with the electric-field-assisted annealing treatment.

preprint2019arXiv

Fabrication and performance of AC-coupled LGADs

Detectors that can simultaneously provide fine time and spatial resolution have attracted wide-spread interest for applications in several fields such as high-energy and nuclear physics as well as in low-energy electron detection, photon science, photonics and imaging. Low-Gain Avalanche Diodes (LGADs), being fabricated on thin silicon substrates and featuring a charge gain of up to 100, exhibit excellent timing performance. Since pads much larger than the substrate thickness are necessary to achieve a spatially uniform multiplication, a fine pad pixelation is difficult. To overcome this limitation, the AC-coupled LGAD approach was introduced. In this type of device, metal electrodes are placed over an insulator at a fine pitch, and signals are capacitively induced on these electrodes. At Brookhaven National Laboratory, we have designed and fabricated prototypes of AC-coupled LGAD sensors. The performance of small test structures with different particle beams from radioactive sources are shown.

preprint2019arXiv

Field-Effect Transistor based on Surface Negative Refraction in Weyl Nanowires

Weyl semimetals are characterized by their bulk Weyl points -- conical band touching points that carry a topological monopole charge -- and Fermi arc states that span between the Weyl points on the surface of the material. Recently, significant progress has been made towards understanding and measuring the physical properties of Weyl semimetals. Yet, potential applications remain relatively sparse. Here, we propose Weyl semimetal nanowires as field-effect transistors, dubbed WEYLFETs. Specifically, applying gradient gate voltage along the nanowire, an electrical field is generated that effectively tilts the open surfaces, thus, varying the relative orientation between Fermi arcs on different surfaces. As a result, perfect negative refraction between adjacent surfaces can occur and longitudinal conductance along the wire is suppressed. The WEYLFET offers a high on/off ratio with low power consumption. Adverse effects due to dispersive Fermi arcs and surface disorder are studied.

preprint2019arXiv

Generating quantum multi-criticality in topological insulators by periodic driving

We demonstrate that the prototypical two-dimensional Chern insulator hosts exotic quantum multi-criticality in the presence of an appropriate periodic driving: a linear Dirac-like transition coexists with a nodal loop-like transition caused by emerging symmetries. The existence of multiple universality classes and scaling laws can be unambiguously captured by a single renormalization group approach based on the stroboscopic Floquet Hamiltonian, regardless of whether the topological transition is associated with the anomalous edge modes or not.

preprint2019arXiv

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.

preprint2019arXiv

Searching for Neutrino-less Double Beta Decay of $^{136}$Xe with PandaX-II Liquid Xenon Detector

We report the Neutrino-less Double Beta Decay (NLDBD) search results from PandaX-II dual-phase liquid xenon time projection chamber. The total live time used in this analysis is 403.1 days from June 2016 to August 2018. With NLDBD-optimized event selection criteria, we obtain a fiducial mass of 219 kg of natural xenon. The accumulated xenon exposure is 242 kg$\cdot$yr, or equivalently 22.2 kg$\cdot$yr of $^{136}$Xe exposure. At the region around $^{136}$Xe decay Q-value of 2458 keV, the energy resolution of PandaX-II is 4.2%. We find no evidence of NLDBD in PandaX-II and establish a lower limit for decay half-life of 2.4 $ \times 10^{23} $ yr at the 90% confidence level, which corresponds to an effective Majorana neutrino mass $m_{ββ} < (1.3 - 3.5)$ eV. This is the first NLDBD result reported from a dual-phase xenon experiment.

preprint2019arXiv

Unifying topological phase transitions in noninteracting, interacting, and periodically driven systems

Topological phase transitions track changes in topological properties of a system and occur in real materials as well as quantum engineered systems, all of which differ greatly in terms of dimensionality, symmetries, interactions, and driving, and hence require a variety of techniques and concepts to describe their topological properties. For instance, depending on the system, topology may be accessed from single-particle Bloch wave functions, Green's functions, or many-body wave functions. We demonstrate that despite this diversity, all topological phase transitions display a universal feature: namely, a divergence of the curvature function that composes the topological invariant at the critical point. This feature can be exploited via a renormalization-group-like methodology to describe topological phase transitions. This approach serves to extend notions of correlation function, critical exponents, scaling laws and universality classes used in Landau theory to characterize topological phase transitions in a unified manner.

preprint2017arXiv

An empirical behavioural order-driven model with price limit rules

We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.

preprint2017arXiv

Proactive Caching for Energy-Efficiency in Wireless Networks: A Markov Decision Process Approach

Content caching in wireless networks provides a substantial opportunity to trade off low cost memory storage with energy consumption, yet finding the optimal causal policy with low computational complexity remains a challenge. This paper models the Joint Pushing and Caching (JPC) problem as a Markov Decision Process (MDP) and provides a solution to determine the optimal randomized policy. A novel approach to decouple the influence from buffer occupancy and user requests is proposed to turn the high-dimensional optimization problem into three low-dimensional ones. Furthermore, a non-iterative algorithm to solve one of the sub-problems is presented, exploiting a structural property we found as \textit{generalized monotonicity}, and hence significantly reduces the computational complexity. The result attains close performance in comparison with theoretical bounds from non-practical policies, while benefiting from higher time efficiency than the unadapted MDP solution.

preprint2016arXiv

$π$ Spin Berry Phase in a Quantum-Spin-Hall-Insulator-Based Interferometer: Evidence for the Helical Spin Texture of the Edge States

Quantum spin Hall insulator is characterized by the helical edge states, with the spin polarization of electron being locked to its direction of motion. Although the edge-state conduction has been observed, unambiguous evidence of the helical spin texture is still lacking. Here, we investigate the coherent edge-state transport in an interference loop pinched by two point contacts. Due to the helical character, the forward inter-edge scattering enforces a $π$ spin rotation. Two successive processes can only produce a nontrivial $2π$ or trivial $0$ spin rotation, which can be controlled by the Rashba spin-orbit coupling. The nontrivial spin rotation results in a geometric $π$ Berry phase, which can be detected by a $π$ phase shift of the conductance oscillation relative to the trivial case. Our results provide a smoking gun evidence for the helical spin texture of the edge states. Moreover, it also provides the opportunity to all-electrically explore the trajectory-dependent spin Berry phase in condensed matter.

preprint2016arXiv

A Candidate Young Massive Planet in Orbit around the Classical T Tauri Star CI Tau

The ~2 Myr old classical T Tauri star CI Tau shows periodic variability in its radial velocity (RV) variations measured at infrared (IR) and optical wavelengths. We find that these observations are consistent with a massive planet in a ~9-day period orbit. These results are based on 71 IR RV measurements of this system obtained over 5 years, and on 26 optical RV measurements obtained over 9 years. CI Tau was also observed photometrically in the optical on 34 nights over ~one month in 2012. The optical RV data alone are inadequate to identify an orbital period, likely the result of star spot and activity induced noise for this relatively small dataset. The infrared RV measurements reveal significant periodicity at ~9 days. In addition, the full set of optical and IR RV measurements taken together phase coherently and with equal amplitudes to the ~9 day period. Periodic radial velocity signals can in principle be produced by cool spots, hot spots, and reflection of the stellar spectrum off the inner disk, in addition to resulting from a planetary companion. We have considered each of these and find the planet hypothesis most consistent with the data. The radial velocity amplitude yields an Msin(i) of ~8.1 M_Jup; in conjunction with a 1.3 mm continuum emission measurement of the circumstellar disk inclination from the literature, we find a planet mass of ~11.3 M_Jup, assuming alignment of the planetary orbit with the disk.

preprint2016arXiv

A Communication-Efficient Parallel Algorithm for Decision Tree

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication costs. In this paper, we propose a new algorithm, called \emph{Parallel Voting Decision Tree (PV-Tree)}, to tackle this challenge. After partitioning the training data onto a number of (e.g., $M$) machines, this algorithm performs both local voting and global voting in each iteration. For local voting, the top-$k$ attributes are selected from each machine according to its local data. Then, globally top-$2k$ attributes are determined by a majority voting among these local candidates. Finally, the full-grained histograms of the globally top-$2k$ attributes are collected from local machines in order to identify the best (most informative) attribute and its split point. PV-Tree can achieve a very low communication cost (independent of the total number of attributes) and thus can scale out very well. Furthermore, theoretical analysis shows that this algorithm can learn a near optimal decision tree, since it can find the best attribute with a large probability. Our experiments on real-world datasets show that PV-Tree significantly outperforms the existing parallel decision tree algorithms in the trade-off between accuracy and efficiency.

preprint2016arXiv

An approximate message passing approach for compressive hyperspectral imaging using a simultaneous low-rank and joint-sparsity prior

This paper considers a compressive sensing (CS) approach for hyperspectral data acquisition, which results in a practical compression ratio substantially higher than the state-of-the-art. Applying simultaneous low-rank and joint-sparse (L&S) model to the hyperspectral data, we propose a novel algorithm to joint reconstruction of hyperspectral data based on loopy belief propagation that enables the exploitation of both structured sparsity and amplitude correlations in the data. Experimental results with real hyperspectral datasets demonstrate that the proposed algorithm outperforms the state-of-the-art CS-based solutions with substantial reductions in reconstruction error.

preprint2016arXiv

Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction

Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent (ProxSCD) have been widely used to solve the R-ERM problem. Recently, variance reduction technique was proposed to improve ProxSGD and ProxSCD, and the corresponding ProxSVRG and ProxSVRCD have better convergence rate. These proximal algorithms with variance reduction technique have also achieved great success in applications at small and moderate scales. However, in order to solve large-scale R-ERM problems and make more practical impacts, the parallel version of these algorithms are sorely needed. In this paper, we propose asynchronous ProxSVRG (Async-ProxSVRG) and asynchronous ProxSVRCD (Async-ProxSVRCD) algorithms, and prove that Async-ProxSVRG can achieve near linear speedup when the training data is sparse, while Async-ProxSVRCD can achieve near linear speedup regardless of the sparse condition, as long as the number of block partitions are appropriately set. We have conducted experiments on a regularized logistic regression task. The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.

preprint2016arXiv

Coherence and incoherence collective behavior in financial market

Financial markets have been extensively studied as highly complex evolving systems. In this paper, we quantify financial price fluctuations through a coupled dynamical system composed of phase oscillators. We find a Financial Coherence and Incoherence (FCI) coexistence collective behavior emerges as the system evolves into the stable state, in which the stocks split into two groups: one is represented by coherent, phase-locked oscillators, the other is composed of incoherent, drifting oscillators. It is demonstrated that the size of the coherent stock groups fluctuates during the economic periods according to real-world financial instabilities or shocks. Further, we introduce the coherent characteristic matrix to characterize the involvement dynamics of stocks in the coherent groups. Clustering results on the matrix provides a novel manifestation of the correlations among stocks in the economic periods. Our analysis for components of the groups is consistent with the Global Industry Classification Standard (GICS) classification and can also figure out features for newly developed industries. These results can provide potentially implications on characterizing inner dynamical structure of financial markets and making optimal investment tragedies.

preprint2016arXiv

Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms

We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where subsets of base arms with unknown distributions form super arms. In each round, a super arm is played and the base arms contained in the super arm are played and their outcomes are observed. We further consider the extension in which more based arms could be probabilistically triggered based on the outcomes of already triggered arms. The reward of the super arm depends on the outcomes of all played arms, and it only needs to satisfy two mild assumptions, which allow a large class of nonlinear reward instances. We assume the availability of an offline (α,β)-approximation oracle that takes the means of the outcome distributions of arms and outputs a super arm that with probability β generates an α fraction of the optimal expected reward. The objective of an online learning algorithm for CMAB is to minimize (α,β)-approximation regret, which is the difference between the αβ fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm. We provide CUCB algorithm that achieves O(log n) distribution-dependent regret, where n is the number of rounds played, and we further provide distribution-independent bounds for a large class of reward functions. Our regret analysis is tight in that it matches the bound of UCB1 algorithm (up to a constant factor) for the classical MAB problem, and it significantly improves the regret bound in a earlier paper on combinatorial bandits with linear rewards. We apply our CMAB framework to two new applications, probabilistic maximum coverage and social influence maximization, both having nonlinear reward structures. In particular, application to social influence maximization requires our extension on probabilistically triggered arms.

preprint2016arXiv

Cournot-Nash Equilibria for Bandwidth Allocation under Base-Station Cooperation

-In this paper, a novel resource allocation scheme based on discrete Cournot-Nash equilibria and optimal transport theory is proposed. The originality of this framework lies in the joint optimization of downlink bandwidth allocation and cooperation between base stations. A tractable formalization is given in the form of a quadratic optimization problem. A low complexity approximate solution is derived and theoretically characterized. Simulations highlight the existence of an optimal working point, that maximizes user satisfaction ratio and network load. The impact of the network deployment on the optimum is numerically investigated, thanks to the $β$-Ginibre model. Indeed, base stations are assumed to be drawn according to $β$-Ginibre point processes. Numerical analysis shows that the network performance increases with $β$ going to one.

preprint2016arXiv

Decoding the $X(5568)$ as a fully open-flavor $su\bar b\bar d$ tetraquark state

We investigate the recent evidence for a charged $X(5568)$ meson as an exotic open-flavor tetraquark state $su\bar b\bar d$ with $J^P=0^+/1^+$ in the framework of QCD sum rules. We use the color antisymmetric $[\mathbf{\bar 3_c}]_{su} \otimes [\mathbf{3_c}]_{\bar{b}\bar d}$ tetraquark currents in both scalar and axial-vector channels to perform evaluations and numerical analyses. Our results imply that the $X(5568)$ can be interpreted as both the scalar $su\bar b\bar d$ tetraquark state and the axial-vector one, which are in good agreement with the experimental measurement. We also discuss the possible decay patterns of the $X(5568)$ and suggest to search for its neutral partner in the radiative decay into $B_s^0 γ$ and $B_s^* γ$, which can be used to determine its spin-parity quantum numbers. Moreover, we predict its charmed partner state around $2.55$ GeV with the quark content $su\bar c\bar d$ and $J^P=0^+/1^+$.

preprint2016arXiv

Delay Optimal Scheduling of Arbitrarily Bursty Traffic over Multi-State Time-Varying Channels

In this paper, we study joint queue-aware and channel-aware scheduling of arbitrarily bursty traffic over multi-state time-varying channels, where the bursty packet arrival in the network layer, the backlogged queue in the data link layer, and the power adaptive transmission with fixed modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve minimum queueing delay given a power constraint, a probabilistic cross-layer scheduling policy is proposed, and characterized by a Markov chain model. To describe the delay-power tradeoff, we formulate a non-linear optimization problem, which however is very challenging to solve. To handle with this issue, we convert the optimization problem into an equivalent Linear Programming (LP) problem, which allows us to obtain the optimal threshold-based scheduling policy with an optimal threshold imposed on the queue length in accordance with each channel state.

preprint2016arXiv

Delay-Optimal Buffer-Aware Scheduling with Adaptive Transmission

In this work, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process and are buffered at the transmitter. The transmitter determines the scheduling policy of how many packets to transmit under an average power constraint. The power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot to capture the realism in communication systems. We also consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. This problem is modelled as a Constrained Markov Decision Process (CMDP). We first prove that the optimal policy of the (Lagrangian) relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, where each of the vertices are obtained by the optimal solution to the relaxed problem. This allows us to show the optimal policies of the CMDP are threshold-based, and hence can be implemented by a proposed efficient algorithm. The theoretical results and the algorithm are validated by Linear Programming and simulations.

preprint2016arXiv

Discrete Scale Invariance in Supercritical Percolation

Recently it has been demonstrated that the connectivity transition from microscopic connectivity to macroscopic connectedness, known as percolation, is generically announced by a cascade of microtransitions of the percolation order parameter [Chen et al., Phys. Rev. Lett. 112, 155701 (2014)]. Here we report the discovery of macrotransition cascades which follow percolation. The order parameter grows in discrete macroscopic steps with positions that can be randomly distributed even in the thermodynamic limit. These transition positions are, however, correlated and follow scaling laws which arise from discrete scale invariance and non self-averaging, both traditionally unrelated to percolation. We reveal the discrete scale invariance in ensemble measurements of these non self-averaging systems by rescaling of the individual realizations before averaging.

preprint2016arXiv

Distinct Reconstruction Patterns and Spin-Resolved Electronic States along the Zigzag Edges of Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides represent an emerging class of materials exhibiting various intriguing properties, and integration of such materials for potential device applications will necessarily encounter creation of different boundaries. Using first-principles approaches, here we investigate the structural, electronic, and magnetic properties along two inequivalent zigzag M and X edges of MX$_{2}$ (M=Mo, W; X=S, Se). Along the M edges, we reveal a previously unrecognized but energetically strongly preferred (2x1) reconstruction pattern, which is universally operative for all the MX$_{2}$, characterized by a self-passivation mechanism through place exchanges of the outmost X and M edge atoms. In contrast, the X edges undergo a more moderate (2x1) or (3x1) reconstruction for MoX$_{2}$ or WX$_{2}$, respectively. We further use the prototypical zigzag MoX$_{2}$ nanoribbons to demonstrate that the M and X edges possess distinctly different electronic and magnetic properties, which are discussed for spintronic and catalytic applications.

preprint2016arXiv

Effect of Quantum Tunneling on Spin Hall Magnetoresistance

We present a formalism that simultaneously incorporates the effect of quantum tunneling and spin diffusion on spin Hall magnetoresistance observed in normal metal/ferromagnetic insulator bilayers (such as Pt/YIG) and normal metal/ferromagnetic metal bilayers (such as Pt/Co), in which the angle of magnetization influences the magnetoresistance of the normal metal. In the normal metal side the spin diffusion is known to affect the landscape of the spin accumulation caused by spin Hall effect and subsequently the magnetoresistance, while on the ferromagnet side the quantum tunneling effect is detrimental to the interface spin current which also affects the spin accumulation. The influence of generic material properties such as spin diffusion length, layer thickness, interface coupling, and insulating gap can be quantified in a unified manner, and experiments that reveal the quantum feature of the magnetoresistance are suggested.

preprint2016arXiv

Generalization Error Bounds for Optimization Algorithms via Stability

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG). Conventional analysis on these optimization algorithms focuses on their convergence rates during the training process, however, people in the machine learning community may care more about the generalization performance of the learned model on unseen test data. In this paper, we investigate on this issue, by using stability as a tool. In particular, we decompose the generalization error for R-ERM, and derive its upper bound for both convex and non-convex cases. In convex cases, we prove that the generalization error can be bounded by the convergence rate of the optimization algorithm and the stability of the R-ERM process, both in expectation (in the order of $\mathcal{O}((1/n)+\mathbb{E}ρ(T))$, where $ρ(T)$ is the convergence error and $T$ is the number of iterations) and in high probability (in the order of $\mathcal{O}\left(\frac{\log{1/δ}}{\sqrt{n}}+ρ(T)\right)$ with probability $1-δ$). For non-convex cases, we can also obtain a similar expected generalization error bound. Our theorems indicate that 1) along with the training process, the generalization error will decrease for all the optimization algorithms under our investigation; 2) Comparatively speaking, SVRG has better generalization ability than GD and SGD. We have conducted experiments on both convex and non-convex problems, and the experimental results verify our theoretical findings.

preprint2016arXiv

H-alpha Variability in PTFO8-8695 and the Possible Direct Detection of Emission from a 2 Million Year Old Evaporating Hot Jupiter

We use high time cadence, high spectral resolution optical observations to detect excess H-alpha emission from the 2 - 3 Myr old weak lined T Tauri star PTFO8-8695. This excess emission appears to move in velocity as expected if it were produced by the suspected planetary companion to this young star. The excess emission is not always present, but when it is, the predicted velocity motion is often observed. We have considered the possibility that the observed excess emission is produced by stellar activity (flares), accretion from a disk, or a planetary companion; we find the planetary companion to be the most likely explanation. If this is the case, the strength of the H-alpha line indicates that the emission comes from an extended volume around the planet, likely fed by mass loss from the planet which is expected to be overflowing its Roche lobe.

preprint2016arXiv

Hidden-symmetry-protected quantum pseudo-spin Hall effect in optical lattices

We propose a scheme to realize a new Z_2 topological insulator in a square optical lattice. Different from the conventional topological insulator protected by the time-reversal symmetry, here, the optical lattice possesses a novel hidden symmetry, which is responsible for the present Z_2 topological order. With a properly defined pseudo-spin, such a topological insulator is characterized by the helical edge states which exhibits pseudo-spin-momentum locking, so that it can be considered as a quantum pseudo-spin Hall insulator. The Z_2 topological invariant is derived and its experimental detection is discussed as well.

preprint2016arXiv

High-flux water desalination with interfacial salt sieving effect in nanoporous carbon composite membranes

Nanoporous carbon composite membranes, comprising a layer of porous carbon fiber structures with an average channel width of 30-60 nm grown on a porous ceramic substrate, are found to exhibit robust desalination effect with high freshwater flux. In three different membrane processes of vacuum membrane distillation, reverse osmosis and forward osmosis, the carbon composite membrane showed 100% salt rejection with 3.5 to 20 times higher freshwater flux compared to existing polymeric membranes. Thermal accounting experiments found that at least 80% of the freshwater pass through the carbon composite membrane with no phase change. Molecular dynamics simulations revealed a unique salt rejection mechanism. When seawater is interfaced with either vapor or the surface of carbon, one to three interfacial atomic layers contain no salt ions. Below the liquid entry pressure, the salt solution is stopped at the openings to the porous channels and forms a meniscus, while the surface layer of freshwater can feed the surface diffusion flux that is fast-transported on the surfaces of the carbon fibers, driven by the chemical potential gradient. As the surface-transported water does not involve a phase change, hence that component involves no energy expenditure in the form of latent heat.

preprint2016arXiv

Holder's inequalities involving the infinite product and their applications in martingale spaces

We give Hölder's inequalities for integral and conditional expectation involving the infinite product. Moreover, a generalized Doob maximal operator is introduced and weighted inequalities for the operator are established.

preprint2016arXiv

Joint Pushing and Caching with a Finite Receiver Buffer: Optimal Policies and Throughput Analysis

Pushing and caching hold the promise of significantly increasing the throughput of content-centric wireless networks. However, the throughput gain of these techniques is limited by the buffer size of the receiver. To overcome this, this paper presents a Joint Pushing and Caching (JPC) method that jointly determines the contents to be pushed to, and to be removed from, the receiver buffer in each timeslot. An offline and two online JPC policies are proposed respectively based on noncausal, statistical, and causal content Request Delay Information (RDI), which predicts a user's request time for certain content. It is shown that the effective throughput of JPC is increased with the receiver buffer size and the pushing channel capacity. Furthermore, the causal feedback of user requests is found to greatly enhance the performance of online JPC without inducing much signalling overhead in practice.

preprint2016arXiv

Kondo Effect in Alkali-Earth Atomic Gases with Confinement-induced Resonance

Alkali-earth atoms have a long-lived electronic excited state, which can be localized in the Fermi sea of ground state atoms by an external potential and serve as magnetic impurities, due to the spin-exchange interaction between the excited and the ground state atoms. This can give rise to the Kondo effect. However, in order to achieve this effect in current atomic gas experiment, it requires the Kondo temperature to be increased to a sizable portion of the Fermi temperature. In this letter we propose that the spin-exchange interaction can be strongly enhanced by utilizing the confinement-induced resonance (CIR). We analyze this system by the renormalization group approach, and we show that nearby a CIR, the Kondo temperature can indeed be increased to the regime attainable by current experiments.

preprint2016arXiv

Magnetic Impurity in a Tonks Gas of Fermions

In this letter we consider a magnetic impurity in a one-dimensional spin-$1/2$ Fermi gas with infinitely strong repulsive interaction between fermions. We rigorously prove that, independent of whether the magnetic coupling between impurity and fermions is ferromagnetic or anti-ferromagnetic, the ground state is always a fully polarized ferromagnetic state for the itinerant fermions. This ferromagnetism can be understood as a cooperative effect of avoiding frustration of magnetic coupling during fermion hopping and the large spin degeneracy of a fermion Tonks gas. By numerically diagonalizing a finite size system, we show that the spin gap first increases linearly with magnetic coupling strength in the weak coupling regime, while decreases in the strong coupling regime. Our results show that a magnetic impurity in a strongly correlated gas can exhibit effect different from the Kondo effect as in a weakly correlated Fermi liquid.

preprint2016arXiv

Martingale Inequalities in Variable Exponent Hardy spaces with $0<p^-\leq p^+<\infty$

We investigate the properties of the variable Lebesgue spaces with quasi-norm on a probability space, and give the atomic decompositions suited to the variable exponent martingale Hardy spaces. Using the decompositions and the harmonic mean of a variable exponent, we obtain several continuous embedding relations between martingale Hardy spaces with small exponent. Finally, we extend these results to the cases $0<p^-\leq p^+<\infty.$

preprint2016arXiv

Method for identifying electromagnetically induced transparency in a tunable circuit quantum electrodynamics system

Electromagnetically induced transparency (EIT) has been realized in atomic systems, but fulfilling the EIT conditions for artificial atoms made from superconducting circuits is a more difficult task. Here we report an experimental observation of the EIT in a tunable three-dimensional transmon by probing the cavity transmission. To fulfill the EIT conditions, we tune the transmon to adjust its damping rates by utilizing the effect of the cavity on the transmon states. From the experimental observations, we clearly identify the EIT and Autler-Townes splitting (ATS) regimes as well as the transition regime in between. Also, the experimental data demonstrate that the threshold $Ω_{\rm AIC}$ determined by the Akaike information criterion can describe the EIT-ATS transition better than the threshold $Ω_{\rm EIT}$ given by the EIT theory.

preprint2016arXiv

Non-Markov property of afterpulsing effect in single-photon avalanche detector

Single-photon avalanche photodiode(SPAD) has been widely used in researching of quantum optics. Afterpulsing effect, which is an intrinsic character of SPAD, affects the system performance in most of the experiments and needs to be carefully handled. For a long time, afterpulsing has been presumed to be determined by the pre-ignition avalanche. We studied the afterpulsing effect of a commercial InGaAs/InP SPAD (APD: Princeton Lightwave PGA-300) and demonstrated that its afterpulsing is non-Markov, which has memory effect of the avalanching history. Theoretical analysis and the experimental results clearly indicate that the embodiment of this memory effect is the afterpulsing probability, which increases as the number of ignition-avalanche pulses increase. The conclusion makes the principle of afterpulsing effect clearer and is instructive to the manufacturing processes and afterpulsing evaluation of high-count-rate SPADs. It can also be regarded as an fundamental premise to handle the afterpulsing signals in many applications, such as quantum communication and quantum random number generator.

preprint2016arXiv

Non-Markovian property of afterpulsing effect in single-photon avalanche detector

The single-photon avalanche photodiode(SPAD) has been widely used in research on quantum optics. The afterpulsing effect, which is an intrinsic character of SPAD, affects the system performance in most experiments and needs to be carefully handled. For a long time, afterpulsing has been presumed to be determined by the pre-ignition avalanche. We studied the afterpulsing effect of a commercial InGaAs/InP SPAD (The avalanche photodiode model is: Princeton Lightwave PGA-300) and demonstrated that its afterpulsing is non-Markovian, with a memory effect in the avalanching history. Theoretical analysis and experimental results clearly indicate that the embodiment of this memory effect is the afterpulsing probability, which increases as the number of ignition-avalanche pulses increase. This conclusion makes the principle of the afterpulsing effect clearer and is instructive to the manufacturing processes and afterpulsing evaluation of high-count-rate SPADs. It can also be regarded as a fundamental premise to handle the afterpulsing signals in many applications, such as quantum communication and quantum random number generation.

preprint2016arXiv

Partitioned Sampling of Public Opinions Based on Their Social Dynamics

Public opinion polling is usually done by random sampling from the entire population, treating individual opinions as independent. In the real world, individuals' opinions are often correlated, e.g., among friends in a social network. In this paper, we explore the idea of partitioned sampling, which partitions individuals with high opinion similarities into groups and then samples every group separately to obtain an accurate estimate of the population opinion. We rigorously formulate the above idea as an optimization problem. We then show that the simple partitions which contain only one sample in each group are always better, and reduce finding the optimal simple partition to a well-studied Min-r-Partition problem. We adapt an approximation algorithm and a heuristic algorithm to solve the optimization problem. Moreover, to obtain opinion similarity efficiently, we adapt a well-known opinion evolution model to characterize social interactions, and provide an exact computation of opinion similarities based on the model. We use both synthetic and real-world datasets to demonstrate that the partitioned sampling method results in significant improvement in sampling quality and it is robust when some opinion similarities are inaccurate or even missing.

preprint2016arXiv

Polar Codes for Broadcast Channels with Receiver Message Side Information and Noncausal State Available at the Encoder

In this paper polar codes are proposed for two receiver broadcast channels with receiver message side information (BCSI) and noncausal state available at the encoder, referred to as BCSI with noncausal state for short, where the two receivers know a priori the private messages intended for each other. This channel generalizes BCSI with common message and Gelfand-Pinsker problem and has applications in cellular communication systems. We establish an achievable rate region for BCSI with noncausal state and show that it is strictly larger than the straightforward extension of the Gelfand-Pinsker result. To achieve the established rate region with polar coding, we present polar codes for the general Gelfand-Pinsker problem, which adopts chaining construction and utilizes causal information to pre-transmit the frozen bits. It is also shown that causal information is necessary to pre-transmit the frozen bits. Based on the result of Gelfand-Pinsker problem, we use the chaining construction method to design polar codes for BCSI with noncausal state. The difficulty is that there are multiple chains sharing common information bit indices. To avoid value assignment conflicts, a nontrivial polarization alignment scheme is presented. It is shown that the proposed rate region is tight for degraded BCSI with noncausal state.

preprint2016arXiv

QCD sum rule study of hidden-charm pentaquarks

We study the mass spectra of hidden-charm pentaquarks having spin $J = {1\over2}/{3\over2}/{5\over2}$ and quark contents $uud c \bar c$. We systematically construct all the relevant local hidden-charm pentaquark currents, and select some of them to perform QCD sum rule analyses. We find that the $P_c(4380)$ and $P_c(4450)$ can be identified as hidden-charm pentaquark states composed of an anti-charmed meson and a charmed baryon. Besides them, we also find a) the lowest-lying hidden-charm pentaquark state of $J^P = 1/2^-$ has the mass $4.33^{+0.17}_{-0.13}$ GeV, while the one of $J^P = 1/2^+$ is significantly higher, that is around $4.7-4.9$ GeV; b) the lowest-lying hidden-charm pentaquark state of $J^P = 3/2^-$ has the mass $4.37^{+0.18}_{-0.13}$ GeV, consistent with the $P_c(4380)$ of $J^P = 3/2^-$, while the one of $J^P = 3/2^+$ is also significantly higher, that is above $4.6$ GeV; c) the hidden-charm pentaquark state of $J^P = 5/2^-$ has a mass around $4.5-4.6$ GeV, slightly larger than the $P_c(4450)$ of $J^P = 5/2^+$.

preprint2016arXiv

Quantifying immediate price impact of trades based on the $k$-shell decomposition of stock trading networks

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into $k$ classes using the $k$-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell.

preprint2016arXiv

Quantum coherence between cavity and artificial atom in a superconducting circuit QED ladder system

We have created a quantum three-level ladder system with the cavity dispersive energy level in a superconducting circuit quantum electrodynamics system consisting of a transmon qubit and a cavity, and have directly observed the Autler-Townes splitting effect instead of representing it by the probability of the qubit being at each level. A coupler tone is applied on the transition between the second excited state of transmon and cavity dispersive level, while the cavity spectrum is probed. A doublet transmission and anormalous dispersion spectrum of the cavity level is clearly shown. The inverse Fourier transform of cavity spectrum indicates that there is a quantum coherence Rabi oscillation of the populations between cavity and qubit.

preprint2016arXiv

Robust AC Quantum Spin Hall Effect without Participation of Edge States

The quantum spin Hall (QSH) effect in the DC regime, which has been intensively researched, relies on the existence of symmetry-protected edge states. Here, we demonstrate that a QSH system behaves quite differently in response to an applied AC electric field, and put forward the idea of AC QSH effect. The AC QSH effect can occur in the bulk without involving the fragile edge states, hence being robust against time-reversal symmetry breaking and disorder. It lays a more solid foundation for practical applications of the two-dimensional topological insulators, in the emerging field of AC spintronics.

preprint2016arXiv

ROSIE: Runtime Optimization of SPARQL Queries Using Incremental Evaluation

Relational databases are wildly adopted in RDF (Resource Description Framework) data management. For efficient SPARQL query evaluation, the legacy query optimizer needs reconsiderations. One vital problem is how to tackle the suboptimal query plan caused by error-prone cardinality estimation. Consider the schema-free nature of RDF data and the Join-intensive characteristic of SPARQL query, determine an optimal execution order before the query actually evaluated is costly or even infeasible, especially for complex queries on large-scale data. In this paper, we propose ROSIE, a Runtime Optimization framework that iteratively re-optimize SPARQL query plan according to the actual cardinality derived from Incremental partial query Evaluation. By introducing an approach for heuristic-based plan generation, as well as a mechanism to detect cardinality estimation error at runtime, ROSIE relieves the problem of biased cardinality propagation in an efficient way, and thus is more resilient to complex query evaluation. Extensive experiments on real and benchmark data show that compared to the state-of-the-arts, ROSIE consistently outperformed on complex queries by orders of magnitude.

preprint2016arXiv

Scalable orbital-angular-momentum sorting without destroying photon states

Single photons with orbital angular momentum (OAM) have attracted substantial attention from researchers. A single photon can carry infinite OAM values theoretically. Thus, OAM photon states have been widely used in quantum information and fundamental quantum mechanics. Although there have been many methods for sorting quantum states with different OAM values, the nondestructive and efficient sorter of high-dimensional OAM remains a fundamental challenge. Here, we propose a scalable OAM sorter which can categorize different OAM states simultaneously, meanwhile, preserving both OAM and spin angular momentum. Fundamental elements of the sorter are composed of symmetric multiport beam splitters (BSs) and Dove prisms with cascading structure, which in principle can be flexibly and effectively combined to sort arbitrarily high-dimensional OAM photons. The scalable structures proposed here greatly reduce the number of BSs required for sorting high-dimensional OAMstates. In view of the nondestructive and extensible features, the sorters can be used as fundamental devices not only for high-dimensional quantum information processing, but also for traditional optics.

preprint2016arXiv

Scaling Theory of ${\mathbb Z}_{2}$ Topological Invariants

For inversion-symmetric topological insulators and superconductors characterized by ${\mathbb Z}_{2}$ topological invariants, two scaling schemes are proposed to judge topological phase transitions driven by an energy parameter. The scaling schemes renormalize either the phase gradient or the second derivative of the Pfaffian of the time-reversal operator, through which the renormalization group flow of the driving energy parameter can be obtained. The Pfaffian near the time-reversal invariant momentum is revealed to display a universal critical behavior for a great variety of models examined.

preprint2016arXiv

Scaling Theory of Topological Phase Transitions

Topologically ordered systems are characterized by topological invariants that are often calculated from the momentum space integration of a certain function that represents the curvature of the many-body state. The curvature function may be Berry curvature, Berry connection, or other quantities depending on the system. Akin to stretching a messy string to reveal the number of knots it contains, a scaling procedure is proposed for the curvature function in inversion symmetric systems, from which the topological phase transition can be identified from the flow of the driving energy parameters that control the topology (hopping, chemical potential, etc.) under scaling. At an infinitesimal operation, one obtains the renormalization group (RG) equations for the driving energy parameters. A length scale defined from the curvature function near the gap-closing momentum is suggested to characterize the scale invariance at critical points and fixed points, and displays a universal critical behavior in a variety of systems examined.

preprint2016arXiv

Searching for hidden-charm baryonium signals in QCD sum rules

We give an explicit QCD sum rule investigation to hidden-charm baryonium states with the quark content $u\bar u d\bar d c\bar c$, spin $J=0/1/2/3$, and of both positive and negative parities. We systematically construct the relevant local hidden-charm baryonium interpolating currents, which can actually couple to various structures, including hidden-charm baryonium states, charmonium states plus two pions, and hidden-charm tetraquark states plus one pion, etc. We do not know which structure these currents couple to at the beginning, but after sum rule analyses we can obtain some information. We find some of them can couple to hidden-charm baryonium states, using which we evaluate the masses of the lowest-lying hidden-charm baryonium states with quantum numbers $J^P=2^-/3^-/0^+/1^+/2^+$ to be around 5.0 GeV. We suggest to search for hidden-charm baryonium states, especially the one of $J=3^-$, in the $D$-wave $J/ψππ$ and $P$-wave $J/ψρ$ and $J/ψω$ channels in this energy region.

preprint2016arXiv

Spin Hall Effect Induced Spin Transfer Through an Insulator

When charge current passes through a normal metal that exhibits spin Hall effect, spin accumulates at the edge of the sample in the transverse direction. We predict that this spin accumulation, or spin voltage, enables quantum tunneling of spin through an insulator or vacuum to reach a ferromagnet without transferring charge. In a normal metal/insulator/ferromagnetic insulator trilayer (such as Pt/oxide/YIG), the quantum tunneling explains the spin-transfer torque and spin pumping that exponentially decay with the thickness of the insulator. In a normal metal/insulator/ferromagnetic metal trilayer (such as Pt/oxide/Co), the spin transfer in general does not decay monotonically with the thickness of the insulator. Combining with the spin Hall magnetoresistance, this tunneling mechanism points to the possibility of a new type of tunneling spectroscopy that can probe the magnon density of states of a ferromagnetic insulator in an all-electrical and noninvasive manner.

preprint2016arXiv

Strongly Modulated Ambipolar Characteristics of Few-layer Black Phosphorus in Oxygen

Two-dimensional black phosphorus has been configured as field-effect transistors, showing an intrinsic symmetric ambipolar transport characteristic. Here, we demonstrate the strongly modulated ambipolar characteristics of few-layer black phosphorus in oxygen. Pure oxygen exposure can dramatically decrease the electron mobility of black phosphorus without degrading the hole transport. The transport characteristics can be nearly recovered upon annealing in Argon. This reveals that oxygen molecules are physisorbed on black phosphorus. In contrast, oxygen exposure upon light illumination exhibits a significant attenuation for both electron and hole transport, originating from the photoactivated oxidation of black phosphorus, which is corroborated by in situ X-ray photoelectron spectroscopy characterization. Our findings clarify the predominant role of oxygen in modulating ambipolar characteristics of black phosphorus, thereby providing deeper insight to the design of black phosphorus based complementary electronics.

preprint2016arXiv

The hidden-charm pentaquark and tetraquark states

In the past decade many charmonium-like states were observed experimentally. Especially those charged charmonium-like $Z_c$ states and bottomonium-like $Z_b$ states can not be accommodated within the naive quark model. These charged $Z_c$ states are good candidates of either the hidden-charm tetraquark states or molecules composed of a pair of charmed mesons. Recently, the LHCb Collaboration discovered two hidden-charm pentaquark states, which are also beyond the quark model. In this work, we review the current experimental progress and investigate various theoretical interpretations of these candidates of the multiquark states. We list the puzzles and theoretical challenges of these models when confronted with the experimental data. We also discuss possible future measurements which may distinguish the theoretical schemes on the underlying structures of the hidden-charm multiquark states.

preprint2016arXiv

The Routing of Complex Contagion in Kleinberg's Small-World Networks

In Kleinberg's small-world network model, strong ties are modeled as deterministic edges in the underlying base grid and weak ties are modeled as random edges connecting remote nodes. The probability of connecting a node $u$ with node $v$ through a weak tie is proportional to $1/|uv|^α$, where $|uv|$ is the grid distance between $u$ and $v$ and $α\ge 0$ is the parameter of the model. Complex contagion refers to the propagation mechanism in a network where each node is activated only after $k \ge 2$ neighbors of the node are activated. In this paper, we propose the concept of routing of complex contagion (or complex routing), where we can activate one node at one time step with the goal of activating the targeted node in the end. We consider decentralized routing scheme where only the weak ties from the activated nodes are revealed. We study the routing time of complex contagion and compare the result with simple routing and complex diffusion (the diffusion of complex contagion, where all nodes that could be activated are activated immediately in the same step with the goal of activating all nodes in the end). We show that for decentralized complex routing, the routing time is lower bounded by a polynomial in $n$ (the number of nodes in the network) for all range of $α$ both in expectation and with high probability (in particular, $Ω(n^{\frac{1}{α+2}})$ for $α\le 2$ and $Ω(n^{\fracα{2(α+2)}})$ for $α> 2$ in expectation), while the routing time of simple contagion has polylogarithmic upper bound when $α= 2$. Our results indicate that complex routing is harder than complex diffusion and the routing time of complex contagion differs exponentially compared to simple contagion at sweetspot.

preprint2016arXiv

Time and Frequency Injection into a Stabilized Fiber Link for Multi-clock Dissemination Network

Owing to the characteristics of ultra-low loss and anti-electromagnetic interference, using optical fiber to deliver time and frequency signal has been a preferred choice for high precise clock dissemination and comparison. As a brilliant idea, one has been able to reproduce ultra-stable signals from one local station to multiple users. In this paper, we take a step further. A concept of multi-clock (in different locations) dissemination for multi-terminals is presented. By injecting frequency signals into one stabilized ring-like fiber network, the relative stabilities of 3.4e-14@1s for a master clock dissemination and 5.1e-14@1s for a slave clock dissemination have been achieved. The proposed scheme can greatly simplify the future 'N' to 'N' time and frequency dissemination network, especially facing a multi-clock comparison situation.

preprint2016arXiv

Universal anyons at the irradiated surface of topological insulator

Anyons have recently received great attention due to their promising application in topological quantum computation. The best validated system that enjoys the anyonic excitations are the Laughlin states. The quasi-particles in Laughlin states are neither fermions nor bosons but possess the discrete statistical angle ? = ?=m, with m being an integer. Here we report a possible realization of the universal Abelian anyons, whose statistical angle can be tuned continuously by external parameters and can take any arbitrary values interpolating ? = 0 and ? = ?. The proposed setup is the surface state of a three dimensional topological insulator driven by an amplitude-modulated circularly-polarized light. It is found that the external field leads to a particular Floquet phase, which is a two-spatial-dimensional analogy of the Weyl semimetal phase in the Floquet first Brillouin zone. The chiral anomaly of this phase results in a U(1) Chern-Simons gauge theory with a tunable Floquet Chern number. Owing to this underlying gauge field theory, the irradiated surface of topological insulator constitutes a promising platform for the observation of the universal anyons.

preprint2016arXiv

Unusual Fe-H bonding associated with oxygen vacancies at the (001) surface of Fe3O4

An unusual Fe-H bonding rather than conventional OH bonding is identified at Fe3O4 (001) surface. This abnormal behavior is associated with the oxygen vacancies which exist on the surface region but also penetrate deep into the bulk Fe3O4. In contrast, OH bonding becomes preferential as generally expected on an ozone processed surface, which has appreciably less oxygen vacancies. Such bonding site selective behavior, depending on oxygen vacancy concentrations, is further confirmed with DFT calculations. The results demonstrate an opportunity for tuning the chemical properties of oxide surfaces or oxide clusters.

preprint2015arXiv

$a_1(1420)$ resonance as a tetraquark state and its isospin partner

We systematically construct tetraquark currents of $I^GJ^{PC}=1^-1^{++}$ and classify them into types $\mathbf{A}$ (antisymmetric), $\mathbf{S}$ (symmetric) and $\mathbf{M}$ (mixed), based on flavor symmetries of diquarks and antidiquarks composing the tetra quark currents. We use tetraquark currents of type $\mathbf{M}$ to perform QCD sum rule analyses, and find a tetraquark current $η^M_{5μ}$ with quark contents $q s\bar q \bar s$($q=u$ or $d$) leading to a mass of $1.44 \pm 0.08$ GeV consistent with the $a_1(1420)$ state recently observed by the COMPASS collaboration. Our results support tetraquark explanations for both $a_1(1420)$ and $f_1(1420)$, assuming that they are isospin partners. We also study their possible decay patterns. As tetraquark candidates, the possible decay modes of $a_1(1420)$ are $S$-wave $a_1(1420) \rightarrow K^*(892)K$ and $P$-wave $a_1(1420)\rightarrow f_0(980) π$ while the possible decay patterns of $f_1(1420)$ are $S$-wave $f_1(1420) \rightarrow K^*(892)K$ and $P$-wave $f_1(1420) \rightarrow a_0(980) π$. We speculate that $a_1(1420)$ is partly responsible for the large isospin violation in the $η(1405)\to f_0(980)π_0$ decay mode which is reported by BESIII collaboration in the $J/ψ\toγ3π$ process.

preprint2015arXiv

A case study on regularity in cellular network deployment

This paper aims to validate the $β$-Ginibre point process as a model for the distribution of base station locations in a cellular network. The $β$-Ginibre is a repulsive point process in which repulsion is controlled by the $β$ parameter. When $β$ tends to zero, the point process converges in law towards a Poisson point process. If $β$ equals to one it becomes a Ginibre point process. Simulations on real data collected in Paris (France) show that base station locations can be fitted with a $β$-Ginibre point process. Moreover we prove that their superposition tends to a Poisson point process as it can be seen from real data. Qualitative interpretations on deployment strategies are derived from the model fitting of the raw data.

preprint2015arXiv

A partition-based Summary-Graph-Driven Method for Efficient RDF Query Processing

RDF query optimization is a challenging problem. Although considerable factors and their impacts on query efficiency have been investigated, this problem still needs further investigation. We identify that decomposing query into a series of light-weight operations is also effective in boosting query processing. Considering the linked nature of RDF data, the correlations among operations should be carefully handled. In this paper, we present SGDQ, a novel framework that features a partition-based Summary Graph Driven Query for efficient query processing. Basically, SGDQ partitions data and models partitions as a summary graph. A query is decomposed into subqueries that can be answered without inter-partition processing. The final results are derived by perform summary graph matching and join the results generated by all matched subqueries. In essence, SGDQ combines the merits of graph match processing and relational join-based query implementation. It intentionally avoids maintain huge intermediate results by organizing sub-query processing in a summary graph driven fashion. Our extensive evaluations show that SGDQ is an effective framework for efficient RDF query processing. Its query performance consistently outperforms the representative state-of-the-art systems.

preprint2015arXiv

A simple implementation of quantum key distribution based on single-photon Bell state measurement

Recently some alternatives of the measurement device independent quantum key distribution(MDI-QKD) based on the single-photon Bell state measurement (SBSM) have been proposed. Although these alternatives are not precisely as secure as MDI-QKD, they possess the advantage of high key rate of traditional BB84-like protocol and avoid the technical complexity of two-photon interference required in the MDI-QKD. However, the setups of these proposed schemes are rather complicated compared to commonly used BB84 systems. Here we propose a simple implementation of SBSM-based QKD which is directly built on the existing realization of BB84 QKD. Our proposal exhibits the hidden connection between SBSM-based QKD and traditional phase-coding QKD protocols. This finding discloses the physics behind these two different types of QKD protocols. In addition, we experimentally demonstrate the feasibility of our protocol.

preprint2015arXiv

Ab-initio Studies of (Li$_{0.8}$Fe$_{0.2}$)OHFeSe Superconductors: Revealing the Dual Roles of Fe$_{0.2}$ in Structural Stability and Charge Transfer

The recently discovered (Li$_{0.8}$Fe$_{0.2}$)OHFeSe superconductor provides a new platform for exploiting the microscopic mechanisms of high-$T_c$ superconductivity in FeSe-derived systems. Using density functional theory calculations, we first show that substitution of Li by Fe not only significantly strengthens the attraction between the (Li$_{0.8}$Fe$_{0.2}$)OH spacing layers and the FeSe superconducting layers along the \emph{c} axis, but also minimizes the lattice mismatch between the two in the \emph{ab} plane, both favorable for stabilizing the overall structure. Next we explore the electron injection into FeSe from the spacing layers, and unambiguously identify the Fe$_{0.2}$ components to be the dominant atomic origin of the dramatically enhanced interlayer charge transfer. We further reveal that the system strongly favors collinear antiferromagnetic ordering in the FeSe layers, but the spacing layers can be either antiferromagnetic or ferromagnetic depending on the Fe$_{0.2}$ spatial distribution. Based on these understandings, we also predict (Li$_{0.8}$Co$_{0.2}$)OHFeSe to be structurally stable with even larger electron injection and potentially higher $T_c$.

preprint2015arXiv

Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-ε)^3$ for any (polynomially small) $ε> 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.

preprint2015arXiv

Delay-Optimal Buffer-Aware Probabilistic Scheduling with Adaptive Transmission

Cross-layer scheduling is a promising way to improve Quality of Service (QoS) given a power constraint. In this paper, we investigate the system with random data arrival and adaptive transmission. Probabilistic scheduling strategies aware of the buffer state are applied to generalize conventional deterministic scheduling. Based on this, the average delay and power consumption are analysed by Markov reward process. The optimal delay-power tradeoff curve is the Pareto frontier of the feasible delay-power region. It is proved that the optimal delay-power tradeoff is piecewise-linear, whose vertices are obtained by deterministic strategies. Moreover, the corresponding strategies of the optimal tradeoff curve are threshold-based, hence can be obtained by a proposed effective algorithm. On the other hand, we formulate a linear programming to minimize the average delay given a fixed power constraint. By varying the power constraint, the optimal delay-power tradeoff curve can also be obtained. It is demonstrated that the algorithm result and the optimization result match each other, and are further validated by Monte-Carlo simulation.

preprint2015arXiv

Delay-optimal Data Transmission in Renewable Energy Aided Cognitive Radio Networks

Renewable energy powered cognitive radio (CR) network has gained much attention due to its combination of the CR's spectrum efficiency and the renewable energy's "green" nature. In the paper, we investigate the delay-optimal data transmission in the renewable energy aided CR networks. Specifically, a primary user (PU) and a secondary user (SU) share the same frequency in an area. The SU's interference to the PU is controlled by interference-signal-ratio (ISR) constraint, which means that the ISR at the PU receiver (Rx) should be less than a threshold. Under this constraint, the renewable energy powered SU aims to minimize the average data buffer delay by scheduling the renewable allocations in each slot. A constrained stochastic optimization problem is formulated when the randomness of the renewable arrival, the uncertainty of the SU's data generation, and the variability of the fading channel are taken into account. By analyzing the formulated problem, we propose two practical algorithms that is optimal for two special scenarios. And the two algorithms respectively give an upper and a lower bound for the general scenario. In addition, the availability of the PU's private information at the SU is discussed. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

preprint2015arXiv

Dissipationless Multiferroic Magnonics

We propose that the magnetoelectric effect in multiferroic insulators with coplanar antiferromagnetic spiral order, such as BiFeO$_{3}$, enables electrically controlled dissipationless magnonics. Applying an oscillating electric field in these materials with frequency as low as household frequency can activate Goldstone modes that manifests fast planar rotations of spins, whose motion is not obstructed by crystalline anisotropy. Combining with spin ejection mechanisms, such a fast planar rotation can deliver electricity at room temperature over a distance of the magnetic domain, which is free from the energy loss due to Gilbert damping.

preprint2015arXiv

Experimental demonstration of quantum key distribution without monitoring of the signal disturbance

Quantum key distribution (QKD) enables two distant users, Alice and Bob, to share secret keys. In existing QKD protocols, an eavesdropper's intervention will inevitably disturb the quantum signals; thus, Alice and Bob must monitor the signal disturbance to place a bound on the potential information leakage. However, T. Sasaki et al. proposed a quite different protocol, named round-robin differential phase shift (RRDPS), in which the amount of eavesdropped information is bounded without monitoring the signal disturbance. Here, we present the first active implementation of the RRDPS protocol. In our experiment, Alice prepares packets of pulses, with each packet being a train with 65 pulses, and the global phase of each packet is randomized. Bob uses a 1-GHz, 1-64-bit actively controlled variable-delay interferometer to realize random switching of the different delays. Benefiting from the large pulse number of each packet, the high stability and low insertion loss of the interferometer, the system can distribute secret key over a distance of 90 km. Our results confirm the feasibility of performing practical QKD with this novel protocol.

preprint2015arXiv

Experimental round-robin differential phase-shift quantum key distribution

In conventional quantum key distribution (QKD) protocols, security is guaranteed by estimating the amount of leaked information through monitoring signal disturbance, which, in practice, is generally caused by environmental noise and device imperfections rather than eavesdropping. Such estimation therefore tends to overrate the amount of leaked information in practice, leads to a fundamental threshold of the bit error rate. The threshold becomes a bottleneck of the development of practical QKD systems. In classical communication, according to Shannon's communication theory, information can transform through a noisy channel even if the background noise is very strong compare to the signal and hence the threshold of the bit error rate tends to 50%. One might wonder whether a QKD scheme can also tolerate error rate as high as 50%. The question is answered affirmatively with the recent work of round-robin differential phase-shift (RRDPS) protocol, which breaks through the fundamental threshold of the bit error rate and indicates another potential direction in the field of quantum cryptography. The key challenge to realize the RRDPS scheme lies on the measurement device, which requires a variable-delay interferometer. The delay needs to be chosen from a set of predetermined values randomly. Such measurement can be realized by switching between many interferometers with different delays at a high speed in accordance with the system repetition rate. The more delay values can be chosen from, the higher error rate can be tolerated. By designing an optical system with multiple switches and employing an active phase stabilization technology, we successfully construct a variable-delay interferometer with 128 actively selectable delays. With this measurement, we experimentally demonstrate the RRDPS QKD protocol and obtain a final key rate of 15.54 bps via a total loss of 18 dB and 8.9% error rate.

preprint2015arXiv

From Competition to Complementarity: Comparative Influence Diffusion and Maximization

Influence maximization is a well-studied problem that asks for a small set of influential users from a social network, such that by targeting them as early adopters, the expected total adoption through influence cascades over the network is maximized. However, almost all prior work focuses on cascades of a single propagating entity or purely-competitive entities. In this work, we propose the Comparative Independent Cascade (Com-IC) model that covers the full spectrum of entity interactions from competition to complementarity. In Com-IC, users' adoption decisions depend not only on edge-level information propagation, but also on a node-level automaton whose behavior is governed by a set of model parameters, enabling our model to capture not only competition, but also complementarity, to any possible degree. We study two natural optimization problems, Self Influence Maximization and Complementary Influence Maximization, in a novel setting with complementary entities. Both problems are NP-hard, and we devise efficient and effective approximation algorithms via non-trivial techniques based on reverse-reachable sets and a novel "sandwich approximation". The applicability of both techniques extends beyond our model and problems. Our experiments show that the proposed algorithms consistently outperform intuitive baselines in four real-world social networks, often by a significant margin. In addition, we learn model parameters from real user action logs.

preprint2015arXiv

GreenDelivery: Proactive Content Caching and Push with Energy-Harvesting-based Small Cells

The explosive growth of mobile multimedia traffic calls for scalable wireless access with high quality of service and low energy cost. Motivated by the emerging energy harvesting communications, and the trend of caching multimedia contents at the access edge and user terminals, we propose a paradigm-shift framework, namely GreenDelivery, enabling efficient content delivery with energy harvesting based small cells. To resolve the two-dimensional randomness of energy harvesting and content request arrivals, proactive caching and push are jointly optimized, with respect to the content popularity distribution and battery states. We thus develop a novel way of understanding the interplay between content and energy over time and space. Case studies are provided to show the substantial reduction of macro BS activities, and thus the related energy consumption from the power grid is reduced. Research issues of the proposed GreenDelivery framework are also discussed.

preprint2015arXiv

Joint Channel and Queue Aware Scheduling for Wireless Links with Multiple Fading States

In this work, we address the delay optimal scheduling problem for wireless transmission with fixed modulation over multi-state fading channels. We propose a stochastic scheduling policy which schedules the source to transmit with probability jointly based on the buffer and channel states, with an average power constraint at the transmitter. Our objective is to minimize the average queueing delay by choosing the optimal transmission probabilities. Using Markov chain modeling, we formulate a power-constrained delay minimization problem, and then transform it into a Linear Programming (LP) one. By analyzing its property, we can derive the optimal threshold-based scheduling policy together with the corresponding transmission probabilities. Our theoretical analysis is corroborated by simulation results.

preprint2015arXiv

Joint time and frequency dissemination network over delay-stabilized fiber optic links

A precise fiber-based time and frequency dissemination scheme for multiple users with a tree-like branching topology is proposed. Through this scheme, ultra-stable signals can be easily accessed online anywhere along the fiber without affecting other sites. The scheme is tested through an experiment, in which a modulated frequency signal and a synchronized time signal are transferred to multiple remote sites over a delay-stabilized fiber optic links that are over 50 km long. Results show that the relative stabilities are 5E-14@1s and 2E-17@10000s. Meanwhile, compared with each site, time synchronization precision is less than 80 ps. These results can pave the way to practical applications in joint time and frequency dissemination network systems.

preprint2015arXiv

Large-Scale Convex Optimization for Ultra-Dense Cloud-RAN

The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network adaptation. Thus, it can resolve the main challenges for next-generation wireless networks, including higher energy efficiency and spectral efficiency, higher cost efficiency, scalable connectivity, and low latency. In this article, we shall provide an algorithmic thinking on the new design challenges for the dense heterogeneous Cloud-RAN based on convex optimization. As problem sizes scale up with the network size, we will demonstrate that it is critical to take unique structures of design problems and inherent characteristics of wireless channels into consideration, while convex optimization will serve as a powerful tool for such purposes. Network power minimization and channel state information acquisition will be used as two typical examples to demonstrate the effectiveness of convex optimization methods. We will then present a two-stage framework to solve general large-scale convex optimization problems, which is amenable to parallel implementation in the cloud data center.

preprint2015arXiv

Long-range Cooper pair splitter with high entanglement production rate

Cooper pairs in the superconductor are a natural source of spin entanglement. The existing proposals of the Cooper pair splitter can only realize a low efficiency of entanglement production, and its size is constrained by the superconducting coherence length. Here we show that a long-range Cooper pair splitter can be implemented in a normal metal-superconductor-normal metal (NSN) junction by driving a supercurrent in the S. The supercurrent results in a band gap modification of the S, which significantly enhances the crossed Andreev reflection (CAR) of the NSN junction and simultaneously quenches its elastic cotunneling. Therefore, a high entanglement production rate close to its saturation value can be achieved by the inverse CAR. Interestingly, in addition to the conventional entangled electron states between opposite energy levels, novel entangled states with equal energy can also be induced in our proposal.

preprint2015arXiv

Majorana Edge States in Superconductor/Noncollinear Magnet Interfaces

Through $s-d$ coupling, a superconducting thin film interfaced to a noncollinear magnetic insulator inherits its magnetic order, which may induce unconventional superconductivity that hosts Majorana edge states. From the cycloidal, helical, or (tilted) conical magnetic order of multiferroics, or the Bloch and Neel domain walls of ferromagnetic insulators, the induced pairing is ($p_{x}+p_{y}$)-wave, a pairing state that supports Majorana edge modes without adjusting the chemical potential. In this setup, the Majorana states can be separated over the distance of the long range magnetic order, which may reach macroscopic scale. A skyrmion spin texture, on the other hand, induces a ($p_{r}+ip_φ$)-wave-like state, which albeit nonuniform and influenced by an emergent electromagnetic field, hosts both a bulk persistent current and a topological edge current.

preprint2015arXiv

Mass spectra of Zc and Zb exotic states as hadron molecules

We construct charmonium-like and bottomonium-like molecular interpolating currents with quantum numbers J(PC)=1(+-) in a systematic way, including both color singlet-singlet and color octet-octet structures. Using these interpolating currents, we calculate two-point correlation functions and perform QCD sum rule analyses to obtain mass spectra of the charmonium-like and bottomonium-like molecular states. Masses of the charmonium-like q_bar c c_bar q molecular states for these various currents are extracted in the range 3.85-4.22 GeV, which are in good agreement with observed masses of the Zc resonances. Our numerical results suggest a possible landscape of hadronic molecule interpretations of the newly-observed Zc states. Mass spectra of the bottomonium-like q_bar b b_bar q molecular states are similarly obtained in the range 9.92-10.48 GeV, which support the interpretation of the Zb(10610) meson as a molecular state within theoretical uncertainties. Possible decay channels of these molecular states are also discussed.

preprint2015arXiv

Measurement-Device-Independent Quantum Coin Tossing

Quantum coin tossing (QCT) is an important primitive of quantum cryptography and has received continuous interest. However, in practical QCT, Bob's detectors can be subjected to detector-side channel attacks launched by dishonest Alice, which will possibly make the protocol completely insecure. Here, we report a simple strategy of a detector-blinding attack based on a recent experiment. To remove all the detector side channels, we present a solution of measurement-device-independent QCT (MDI-QCT). This method is similar to the idea of MDI quantum key distribution (QKD). MDI-QCT is loss tolerant with single-photon sources and has the same bias as the original loss-tolerant QCT under a coherent attack. Moreover, it provides the potential advantage of doubling the secure distance for some special cases. Finally, MDI-QCT can also be modified to fit the weak coherent-state sources. Thus, based on the rapid development of practical MDI-QKD, our proposal can be implemented easily.

preprint2015arXiv

Minimal Model of Spin-Transfer Torque and Spin Pumping caused by Spin Hall Effect

In the normal metal/ferromagnetic insulator bilayer (such as Pt/Y$_{3}$Fe$_{5}$O$_{12}$) and the normal metal/ferromagnetic metal/oxide trilayer (such as Pt/Co/AlO$_{x}$) where spin injection and ejection are achieved by the spin Hall effect in the normal metal, we propose a minimal model based on quantum tunneling of spins to explain the spin-transfer torque and spin pumping caused by the spin Hall effect. The ratio of their damping-like to field-like component depends on the tunneling wave function that is strongly influenced by generic material properties such as interface $s-d$ coupling, insulating gap, and layer thickness, yet the spin relaxation plays a minor role. The quantified result renders our minimal model an inexpensive tool for searching for appropriate materials.

preprint2015arXiv

On the Depth of Deep Neural Networks: A Theoretical View

People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.

preprint2015arXiv

Orientationally Misaligned Zipping of Lateral Graphene and Boron Nitride Nanoribbons with Minimized Strain Energy and Enhanced Half-Metallicity

Lateral heterostructures of two-dimensional materials may exhibit various intriguing emergent properties. Yet when specified to the orientationally aligned heterojunctions of zigzag graphene and hexagonal boron nitride (hBN) nanoribbons, realizations of the high expectations on their properties encounter two standing hurtles. First, the rapid accumulation of strain energy prevents large- scale fabrication. Secondly, the pronounced half-metallicity predicted for freestanding graphene nanoribbons is severely suppressed. By properly tailoring orientational misalignment between zigzag graphene and chiral hBN nanoribbons, here we present a facile approach to overcome both obstacles. Our first-principles calculations show that the strain energy accumulation in such heterojunctions is significantly diminished for a range of misalignments. More strikingly, the half-metallicity is substantially enhanced from the orientationally aligned case, back to be comparable in magnitude with the freestanding case. The restored half-metallicity is largely attributed to the recovered superexchange interaction between the opposite heterojunction interfaces. The present findings may have important implications in eventual realization of graphene-based spintronics.

preprint2015arXiv

Outage Minimization for a Fading Wireless Link with Energy Harvesting Transmitter and Receiver

This paper studies online power control policies for outage minimization in a fading wireless link with energy harvesting transmitter and receiver. The outage occurs when either the transmitter or the receiver does not have enough energy, or the channel is in outage, where the transmitter only has the channel distribution information. Under infinite battery capacity and without retransmission, we prove that threshold-based power control policies are optimal. We thus propose disjoint/joint threshold-based policies with and without battery state sharing between the transmitter and receiver, respectively. We also analyze the impact of practical receiver detection and processing on the outage performance. When retransmission is considered, policy with linear power levels is adopted to adapt the power thresholds per retransmission. With finite battery capacity, a three dimensional finite state Markov chain is formulated to calculate the optimal parameters and corresponding performance of proposed policies. The energy arrival correlation between the transmitter and receiver is addressed for both finite and infinite battery cases. Numerical results show the impact of battery capacity, energy arrival correlation and detection cost on the outage performance of the proposed policies, as well as the tradeoff between the outage probability and the average transmission times.

preprint2015arXiv

P-wave charmed baryons from QCD sum rules

We study the P-wave charmed baryons using the method of QCD sum rule in the framework of heavy quark effective theory (HQET). We consider systematically all possible baryon currents with a derivative for internal rho- and lambda-mode excitations. We have found a good working window for the currents corresponding to the rho-mode excitations for Lambda_c(2595), Lambda_c(2625), Xi_c(2790) and Xi_c(2815) which complete two SU(3) 3F_bar multiplets of J(P)=1/2(-) and 3/2(-), while the currents corresponding to the lambda-mode excitations seem also consistent with the data. Our results also suggest that there are two Sigma_c(2800) states of J(P)=1/2(-) and 3/2(-) whose mass splitting is 14 \pm 7 MeV, and two Xi_c(2980) states whose mass splitting is 12 \pm 7 MeV. They have two Omega_c partners of J(P) = 1/2(-) and 3/2(-), whose masses are around 3.25 \pm 0.20 GeV with mass splitting 10 \pm 6 MeV. All of them together complete two SU(3) 6F multiplets of J(P)=1/2(-) and 3/2(-). They may also have J(P)=5/2(-) partners. Xi_c(3080) may be one of them, and the other two are Sigma_c(5/2(-)) and Omega_c(5/2(-)), whose masses are 85 \pm 23 and 50 \pm 27 MeV larger.

preprint2015arXiv

QCD sum rule calculation for P-wave bottom baryons

We study the P-wave bottom baryons using the method of QCD sum rule and heavy quark effective theory. Our results suggest that Lambda_b(5912) and Lambda_b(5920) can be well described by the baryon doublet [3F_bar, 1, 1, rho], and they belong to the SU(3) 3F_bar multiplets of J(P)=1/2(-) and 3/2(-). Their SU(3) flavor partners, Xi_b(1/2-) and Xi_b(3/2-), have masses 6.06 \pm 0.13 GeV and 6.07 \pm 0.13 GeV, respectively, with mass splitting 9 \pm 4 MeV. The results obtained using baryon doublet [3F_bar, 1, 0, lambda] are similar and also consistent with the experimental data. We also study the SU(3) 6F multiplets by using the baryon multiplets [6F, 0, 1, lambda], [6_F, 1, 0, rho] and [6F, 2, 1, lambda], and our results suggest that the P-wave bottom baryons Sigma_b, Xi'_b and Omega_b have (averaged) masses about 6.0 GeV, 6.2 GeV and 6.4 GeV, respectively.

preprint2015arXiv

QCD sum rule Study of the $d^*(2380)$

We systematically construct $I(J^P)=0(3^+)$ six-quark local interpolating currents without derivative operators. We discuss the best choice of operator, and select three $Δ$-$Δ$ like operators to perform QCD sum rule analyses to calculate the mass of the $d^*(2380)$. The mass extracted from this analysis is $M_{d^*} = 2.4\pm0.2$ GeV, consistent with the $d^*(2380)$ mass observed by the WASA detector at COSY. We also obtain a sum-rule lower mass bound $M_{d^*} > 2.25$ GeV. We also consider the effect of mixing of singlet dibaryon fields with the same quantum numbers, and perform the QCD sum rule analysis of the mixed interpolating current and extract the mass of the $d^*(2380)$ and its lower mass bound. With optimized mixing parameters, we find that the mixed current does not change the numerical result significantly.

preprint2015arXiv

Randomness determines practical security of BB84 quantum key distribution

Unconditional security of the BB84 quantum key distribution protocol has been proved by exploiting the fundamental laws of quantum mechanics, but the practical quantum key distribution system maybe hacked by considering the imperfect state preparation and measurement respectively. Until now, different attacking schemes have been proposed by utilizing imperfect devices, but the general security analysis model against all of the practical attacking schemes has not been proposed. Here, we demonstrate that the general practical attacking schemes can be divided into the Trojan horse attack, strong randomness attack and weak randomness attack respectively. We prove security of BB84 protocol under randomness attacking models, and these results can be applied to guarantee the security of the practical quantum key distribution system.

preprint2015arXiv

Robust quantum random number generator based on avalanche photodiodes

We propose and demonstrate a scheme to realize a high-efficiency truly quantum random number generator (RNG) at room temperature (RT). Using an effective extractor with simple time bin encoding method, the avalanche pulses of avalanche photodiode (APD) are converted into high-quality random numbers (RNs) that are robust to slow varying noise such as fluctuations of pulse intensity and temperature. A light source is compatible but not necessary in this scheme. Therefor the robustness of the system is effective enhanced. The random bits generation rate of this proof-of-principle system is 0.69 Mbps with double APDs and 0.34 Mbps with single APD. The results indicate that a high-speed RNG chip based on the scheme is potentially available with an integrable APD array.

preprint2015arXiv

Simple rate-adaptive LDPC coding for quantum key distribution

Although quantum key distribution (QKD) comes from the development of quantum theory, the implementation of a practical QKD system does involve a lot of classical process, such as key reconciliation and privacy amplification, which is called post-processing. Post-processing has been a crucial element to high speed QKD systems, even the bottleneck of it because of its relatively high time consumption. Low density parity check (LDPC) is now becoming a promising approach of overcoming the bottleneck due to its good performance in processing throughput. In this article we propose and simulate an easily implemented but efficiently rate-adaptive LDPC coding approach of reconciliation, different from the previously proposed puncturing- and shortening-based approach. We also give a measure for choosing the optimal LDPC parameter for our rate-adaptive approach according to error rates.

preprint2015arXiv

Smoothed Lp-Minimization for Green Cloud-RAN with User Admission Control

The cloud radio access network (Cloud-RAN) has recently been proposed as one cost-effective and energy-efficient technique for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in Cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green Cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the l0-norm based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but non-smooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed l1/l2-norm relaxation [1], we adopt the nonconvex but smoothed lp-minimization (0 < p <= 1) approach to promote sparsity in the multicast setting, thereby enabling efficient algorithm design based on the principle of the majorization-minimization (MM) algorithm and the semidefinite relaxation (SDR) technique. In particular, an iterative reweighted-l2 algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed lp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast Cloud-RAN.

preprint2015arXiv

Statistical Properties and Pre-hit Dynamics of Price Limit Hits in the Chinese Stock Markets

Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners.

preprint2015arXiv

Stick-Slip Control in Nanoscale Boundary Lubrication by Surface Wettability

We study the effect of atomic scale surface-lubricant interactions on nanoscale boundary-lubricated friction, by considering two example surfaces - hydrophilic mica and hydrophobic graphene - confining thin layers of water in molecular dynamics simulations. We observe stick-slip dynamics for thin water films confined by mica sheets, involving periodic breaking-reforming transitions of atomic scale capillary water bridges formed around the potassium ions of mica. However, only smooth sliding without stick-slip events is observed for water confined by graphene, as well as for thicker water layers confined by mica. Thus, our results illustrate how atomic scale details affect the wettability of the confining surfaces, and consequently control the presence or absence of stick-slip dynamics in nanoscale friction.

preprint2015arXiv

The effect of heavy metal in CMOS on neutron induced single event upset simulated with Geant4

Local metal interconnection is widely used in modern complementary metal oxide semiconductor (CMOS) technology. The most frequently used local materials are some heavy metals, such as tungsten (W) or copper (Cu). It's well known that single event upset (SEU) could occur in a CMOS under neutron exposure. In this paper the rectangular parallelepiped (RPP) method is used to investigate the SEU response of a typical CMOS. SEU induced by 1$\sim$14\:MeV neutrons are simulated with Geant4 and the cross sections are calculated. The results show that only in the structure with W, secondary particle $α$ is created and SEUs are generated when the energy of neutron is less than $4$\:MeV.

preprint2015arXiv

The Rationale for Second Level Adaptation

Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second level adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $α\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

preprint2015arXiv

Theory of Graphene Raman Spectroscopy

Raman spectroscopy plays a key role in studies of graphene and related carbon systems. Graphene is perhaps the most promising material of recent times for many novel applications, including electronics. In this paper, the traditional and well established Kramers-Heisenberg-Dirac (KHD) Raman scattering theory (1925-1927) is extended to crystalline graphene for the first time. It demands different phonon production mechanisms and phonon energies than does the popular "double resonance" Raman scattering model. The latter has never been compared to KHD. Within KHD, phonons are produced instantly along with electrons and holes, in what we term an electron-hole-phonon triplet, which does not suffer Pauli blocking. A new mechanism for double phonon production we name "transition sliding" explains the brightness of the 2D mode and other overtones, as a result of linear (Dirac cone) electron dispersion. Direct evidence for sliding resides in hole doping experiments performed in 2011 \cite{chenCrommie}. Whole ranges of electronic transitions are permitted and may even constructively interfere for the same laser energy and phonon q, explaining the dispersion, bandwidth, and strength of many two phonon Raman bands. Graphene's entire Raman spectrum, including dispersive and fixed bands, missing bands not forbidden by symmetries, weak bands, overtone bands, Stokes anti-Stokes anomalies, individual bandwidths, trends with doping, and D-2D band spacing anomalies emerge naturally and directly in KHD theory.

preprint2015arXiv

Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

Influence maximization is a problem of finding a small set of highly influential users, also known as seeds, in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks. We show that time-critical influence maximization under the time-delayed IC and LT models maintains desired properties such as submodularity, which allows a greedy approximation algorithm to achieve an approximation ratio of $1-1/e$. To overcome the inefficiency of the greedy algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures and directed acyclic subgraphs, while the second one converts the problem to one in the original models and then applies existing fast heuristic algorithms to it. Our simulation results demonstrate that our algorithms achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing fast heuristics that disregard the deadline constraint and delays in diffusion.

preprint2015arXiv

Towards exotic hidden-charm pentaquarks in QCD

Inspired by $P_c(4380)$ and $P_c(4450)$ recently observed by LHCb, a QCD sum rule investigation is performed, by which they can be identified as exotic hidden-charm pentaquarks composed of an anti-charmed meson and a charmed baryon. Our results suggest that $P_c(4380)$ and $P_c(4450)$ have quantum numbers $J^P=3/2^-$ and $5/2^+$, respectively. Furthermore, two extra hidden-charm pentaqurks with configurations $\bar{D}Σ_c^*$ and $\bar{D}^*Σ_c^*$ are predicted, which have spin-parity quantum numbers $J^P=3/2^-$ and $J^P=5/2^+$, respectively. As an important extension, the mass predictions of hidden-bottom pentaquarks are also given. Searches for these partners of $P_c(4380)$ and $P_c(4450)$ are especially accessible at future experiments like LHCb and BelleII.

preprint2015arXiv

Tunable room-temperature ferromagnet using an iron-oxide and graphene oxide nanocomposite

Magnetic materials have found wide application ranging from electronics and memories to medicine. Essential to these advances is the control of the magnetic order. To date, most room-temperature applications have a fixed magnetic moment whose orientation is manipulated for functionality. Here we demonstrate an iron-oxide and graphene oxide nanocomposite based device that acts as a tunable ferromagnet at room temperature. Not only can we tune its transition temperature in a wide range of temperatures around room temperature, but the magnetization can also be tuned from zero to 0.011 A.m$^2$/kg through an initialization process with two readily accessible knobs (magnetic field and electric current), after which the system retains its magnetic properties semi-permanently until the next initialization process. We construct a theoretical model to illustrate that this tunability originates from an indirect exchange interaction mediated by spin-imbalanced electrons inside the nanocomposite.

preprint2015arXiv

Weighted norm inequalities for multisublinear maximal operator in martingale spaces

Let $v,~ω_1, ~ω_2$ be weights and $1<p_1, ~p_2<\infty.$ Suppose that $\frac{1}{p}=\frac{1}{p_1}+\frac{1}{p_2}$ and $(ω_1, ω_2)\in RH(p_1, p_2).$ For the multisublinear maximal operator $\mathfrak{M}$ in martingale spaces, we characterize the weights for which $\mathfrak{M}$ is bounded from $L^{p_1}(ω_1)\times L^{p_2}(ω_2)$ to $L^{p,\infty}(v)\hbox{or}L^p(v).$ If $v=ω_2^{\frac{p}{p_2}}ω_2^{\frac{p}{p_2}},$ we partially give the bilinear version of one-weight theory.

preprint2015arXiv

Zc(4200)+ decay width as a charmonium-like tetraquark state

To identify the nature of the newly observed charged resonance Zc(4200), we study its hadronic decays Zc(4200) -> J/psi pi, Zc(4200) -> eta_c rho and Zc(4200) -> D bar D* as a charmonium-like tetraquark state. In the framework of the QCD sum rules, we calculate the three-point functions and extract the coupling constants and decay widths for these interaction vertices. Including all these channels, the full decay width of the Zc(4200) state is consistent with the experimental value reported by the Belle Collaboration, supporting the tetraquark interpretation of this state.

preprint2014arXiv

$D^\bar D^$ molecule interpretation of $Z_c(4025)$

We have used QCD sum rules to study the newly observed charged state $Z_c(4025)$ as a hidden-charm $D^*\bar D^*$ molecular state with the quantum numbers $I^G(J^{P})=1^+(1^{+})$. Using a $D^*\bar D^*$ molecular interpolating current, we have calculated the two-point correlation function and the spectral density up to dimension eight at leading order in $α_s$. The extracted mass is $m_X=(4.04\pm0.24)$ GeV. This result is compatible with the observed mass of $Z_c(4025)$ within the errors, which implies a possible molecule interpretation of this new resonance. We also predict the mass of the corresponding hidden-bottom $B^*\bar B^*$ molecular state: $m_{Z_b}=(9.98\pm0.21)$ GeV.

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 Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel \emph{game-theoretic machine learning} approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.

preprint2014arXiv

Agent Behavior Prediction and Its Generalization Analysis

Machine learning algorithms have been applied to predict agent behaviors in real-world dynamic systems, such as advertiser behaviors in sponsored search and worker behaviors in crowdsourcing. The behavior data in these systems are generated by live agents: once the systems change due to the adoption of the prediction models learnt from the behavior data, agents will observe and respond to these changes by changing their own behaviors accordingly. As a result, the behavior data will evolve and will not be identically and independently distributed, posing great challenges to the theoretical analysis on the machine learning algorithms for behavior prediction. To tackle this challenge, in this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of the machine learning algorithms on its basis. Since the one-step transition probability matrix of MCRE depends on both previous states and the random environment, conventional techniques for generalization analysis cannot be directly applied. To address this issue, we propose a novel technique that transforms the original MCRE into a higher-dimensional time-homogeneous Markov chain. The new Markov chain involves more variables but is more regular, and thus easier to deal with. We prove the convergence of the new Markov chain when time approaches infinity. Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model. To the best of our knowledge, this is the first work that performs the generalization analysis on data generated by complex processes in real-world dynamic systems.

preprint2014arXiv

Büchi Types for Infinite Traces and Liveness

We develop a new type and effect system based on Büchi automata to capture finite and infinite traces produced by programs in a small language which allows non-deterministic choices and infinite recursions. There are two key technical contributions: (a) an abstraction based on equivalence relations defined by the policy Büchi automaton, the Büchi abstraction; (b) a novel type and effect system to correctly capture infinite traces. We show how the Büchi abstraction fits into the abstract interpretation framework and show soundness and completeness.

preprint2014arXiv

Compositional Structure Learning for Action Understanding

The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile robotics and video search, with solutions to classification, localization and detection. In this paper, we propose a compositional model that leverages a new mid-level representation called compositional trajectories and a locally articulated spatiotemporal deformable parts model (LALSDPM) for fully action understanding. Our methods is advantageous in capturing the variable structure of dynamic human activity over a long range. First, the compositional trajectories capture long-ranging, frequently co-occurring groups of trajectories in space time and represent them in discriminative hierarchies, where human motion is largely separated from camera motion; second, LASTDPM learns a structured model with multi-layer deformable parts to capture multiple levels of articulated motion. We implement our methods and demonstrate state of the art performance on all three problems: action detection, localization, and recognition.

preprint2014arXiv

Decoy state measurement-device-independent quantum key distribution based on the Clauser-Horne-Shimony-Holt inequality

The measurement-device-independent quantum key distribution (MDI-QKD) protocol is proposed to remove the detector side channel attacks, while its security relies on the assumption that the encoding systems are perfectly characterized. In contrast, the MDI-QKD protocol based on the Clauser-Horne-Shimony-Holt inequality (CHSH-MDI-QKD) weakens this assumption, which only requires the quantum state to be prepared in the two-dimensional Hilbert space and the devices are independent. In experimental realizations, the weak coherent state, which is always used in QKD systems due to the lack of an ideal single photon source, may be prepared in the high-dimensional space. In this paper, we investigate the decoy-state CHSH-MDI-QKD protocol with $s(3 \le s \le 5)$ intensities, including one signal state and $s-1$ decoy states, and we also consider the finite-size effect on the decoy-state CHSH-MDI-QKD protocol with five intensities. Simulation results show that this scheme is very practical.

preprint2014arXiv

Delay-Energy lower bound on Two-Way Relay Wireless Network Coding

Network coding is a novel solution that significantly improve the throughput and energy consumed of wireless networks by mixing traffic flows through algebraic operations. In conventional network coding scheme, a packet has to wait for packets from other sources to be coded before transmitting. The wait-and-code scheme will naturally result in packet loss rate in a finite buffer. We will propose Enhanced Network Coding (ENC), an extension to ONC in continuous time domain. In ENC, the relay transmits both coded and uncoded packets to reduce delay. In exchange, more energy is consumed in transmitting uncoded packets. ENC is a practical algorithm to achieve minimal average delay and zero packet-loss rate under given energy constraint. The system model for ENC on a general renewal process queuing is presented. In particular, we will show that there exists a fundamental trade-off between average delay and energy. We will also present the analytic result of lower bound for this trade-off curve, which can be achieved by ENC.

preprint2014arXiv

Delta-Complete Analysis for Bounded Reachability of Hybrid Systems

We present the framework of delta-complete analysis for bounded reachability problems of general hybrid systems. We perform bounded reachability checking through solving delta-decision problems over the reals. The techniques take into account of robustness properties of the systems under numerical perturbations. We prove that the verification problems become much more mathematically tractable in this new framework. Our implementation of the techniques, an open-source tool dReach, scales well on several highly nonlinear hybrid system models that arise in biomedical and robotics applications.

preprint2014arXiv

Detecting Fulde-Ferrell superconductors by an Andreev interferometer

We propose an Andreev interferometer, based on a branched Y-junction, to detect the finite momentum pairing in Fulde-Ferrell (FF) superconductors. In this interferometer, the oscillation of subgap conductance is a unique function of phase difference between the two channels of the Y-junction, which is determined by the phase modulation of the order parameter in the FF superconductors. This interferometer has the potential not only to determine the magnitude but also the direction of the momentum of Cooper pairs in the FF superconductor. The possible applications of the interferometer in the identification of the finite momentum pairing in non-centrosymmetric superconductors are also discussed.

preprint2014arXiv

Dissipationless Spin Current between Two Coupled Ferromagnets

We demonstrate the general principle which states that a dissipationless spin current flows between two coupled ferromagnets if their magnetic orders are misaligned. This principle applies regardless the two ferromagnets are metallic or insulating, and also generally applies to bulk magnetic insulators. On a phenomenological level, this principle is analogous to Josephson effect, and yields a dissipationless spin current that is independent from scattering. The microscopic mechanisms for the dissipationless spin current depend on the systems, which are elaborated in details. A uniform, static magnetic field is further proposed to be an efficient handle to create the misaligned configuration and stabilize the dissipationless spin current.

preprint2014arXiv

Exotic hadron states

Many charmonium-like and bottomonium-like $XYZ$ resonances have been observed by the Belle, Babar, CLEO and BESIII collaborations in the past decade. They are difficult to fit in the conventional quark model and thus are considered as candidates of exotic hadrons, such as multi-quark states, meson molecules, and hybrids. In this talk, we first briefly introduce the method of QCD sum rules and then provide a short review of the mass spectra of the quarkonium-like tetraquark states and the heavy quarkonium hybrids in the QCD sum rules approach. Possible interpretations of the $XYZ$ resonances are briefly discussed.

preprint2014arXiv

Exotic open-flavor $bc\bar{q}\bar{q}$, $bc\bar{s}\bar{s}$ and $qc\bar{q}\bar{b}$, $sc\bar{s}\bar{b}$ tetraquark states

We study the exotic $bc\bar{q}\bar{q}$, $bc\bar{s}\bar{s}$ and $qc\bar{q}\bar{b}$, $sc\bar{s}\bar{b}$ systems by constructing the corresponding tetraquark currents with $J^P=0^+$ and $1^+$. After investigating the two-point correlation functions and the spectral densities, we perform QCD sum rule analysis and extract the masses of these open-flavor tetraquark states. Our results indicate that the masses of both the scalar and axial vector tetraquark states are about $7.1-7.2$ GeV for the $bc\bar{q}\bar{q}$ system and $7.2-7.3$ GeV for the $bc\bar{s}\bar{s}$ system. For the $qc\bar{q}\bar{b}$ tetraquark states with $J^P=0^+$ and $1^+$, their masses are extracted to be around $7.1$ GeV. The masses for the scalar and axial vector $sc\bar{s}\bar{b}$ states are $7.1$ GeV and $6.9-7.1$ GeV, respectively. The tetraquark states $qc\bar{q}\bar{b}$ and $sc\bar{s}\bar{b}$ lie below the thresholds of $D^{(\ast)}B^{(\ast)}$ and $D_s^{(\ast)}B_s^{(\ast)}$ respectively, but they can decay into $B_c$ plus a light meson. However, the tetraquark states $bc\bar{q}\bar{q}$ and $bc\bar{s}\bar{s}$ lie below the $D^{(\ast)}\bar B^{(\ast)}$ and $D_s^{(\ast)}\bar B_s^{(\ast)}$ thresholds, suggesting dominantly weak decay mechanisms.

preprint2014arXiv

Exploring the Spectrum of Heavy Quarkonium Hybrids with QCD Sum Rules

QCD Laplace sum rules are used to calculate heavy quarkonium (charmonium and bottomonium) hybrid masses in several distinct $J^{PC}$ channels. Previous studies of heavy quarkonium hybrids did not include the effects of dimension-six condensates, leading to unstable sum rules and unreliable mass predictions in some channels. We have updated these sum rules to include dimension-six condensates, providing new mass predictions for the spectra of heavy quarkonium hybrids. We confirm the finding of other approaches that the negative-parity $J^{PC}=(0,1,2)^{-+},\,1^{--}$ states form the lightest hybrid supermultiplet and the positive-parity $J^{PC}=(0,1)^{+-},\,(0,1,2)^{++}$ states are members of a heavier supermultiplet. Our results disfavor a pure charmonium hybrid interpretation of the $X(3872)$, in agreement with previous work.

preprint2014arXiv

Field and long-term demonstration of a wide area quantum key distribution network

A wide area quantum key distribution (QKD) network deployed on communication infrastructures provided by China Mobile Ltd. is demonstrated. Three cities and two metropolitan area QKD networks were linked up to form the Hefei-Chaohu-Wuhu wide area QKD network with over 150 kilometers coverage area, in which Hefei metropolitan area QKD network was a typical full-mesh core network to offer all-to-all interconnections, and Wuhu metropolitan area QKD network was a representative quantum access network with point-to-multipoint configuration. The whole wide area QKD network ran for more than 5000 hours, from 21 December 2011 to 19 July 2012, and part of the network stopped until last December. To adapt to the complex and volatile field environment, the Faraday-Michelson QKD system with several stability measures was adopted when we designed QKD devices. Through standardized design of QKD devices, resolution of symmetry problem of QKD devices, and seamless switching in dynamic QKD network, we realized the effective integration between point-to-point QKD techniques and networking schemes.

preprint2014arXiv

Generalization Analysis for Game-Theoretic Machine Learning

For Internet applications like sponsored search, cautions need to be taken when using machine learning to optimize their mechanisms (e.g., auction) since self-interested agents in these applications may change their behaviors (and thus the data distribution) in response to the mechanisms. To tackle this problem, a framework called game-theoretic machine learning (GTML) was recently proposed, which first learns a Markov behavior model to characterize agents' behaviors, and then learns the optimal mechanism by simulating agents' behavior changes in response to the mechanism. While GTML has demonstrated practical success, its generalization analysis is challenging because the behavior data are non-i.i.d. and dependent on the mechanism. To address this challenge, first, we decompose the generalization error for GTML into the behavior learning error and the mechanism learning error; second, for the behavior learning error, we obtain novel non-asymptotic error bounds for both parametric and non-parametric behavior learning methods; third, for the mechanism learning error, we derive a uniform convergence bound based on a new concept called nested covering number of the mechanism space and the generalization analysis techniques developed for mixing sequences. To the best of our knowledge, this is the first work on the generalization analysis of GTML, and we believe it has general implications to the theoretical analysis of other complicated machine learning problems.

preprint2014arXiv

Generalized Second Price Auction with Probabilistic Broad Match

Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported broad match between queries and bid keywords when executing GSP auctions, however, it has been revealed that GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call the Probabilistic Broad-Match (PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting.

preprint2014arXiv

GRB 131231A: Implications of the GeV emission

GRB 131231A was detected by the Large Area Telescope onboard Fermi Space Gamma-ray Telescope. The high energy gamma-ray ($> 100$ MeV) afterglow emission spectrum is $F_ν\propto ν^{-0.54\pm0.15}$ in the first $\sim 1300$ s after the trigger and the most energetic photon has an energy $\sim 62$ GeV arriving at $t\sim 520$ s. With reasonable parameters of the GRB outflow as well as the density of the circum-burst medium, the synchrotron radiation of electrons or protons accelerated at an external forward shock have difficulty accounting for the data. The synchrotron self-Compton radiation of the forward shock-accelerated electrons, instead, can account for both the spectrum and temporal behavior of the GeV afterglow emission. We also show that the prospect for detecting GRB 131231A$-$like GRBs with Cherenkov Telescope Array (CTA) is promising.

preprint2014arXiv

Heavy tetraquark states and quarkonium hybrids

Many of the $XYZ$ resonances observed by the Belle, Babar, CLEO and BESIII collaborations in the past decade are difficult to interpret as conventional quark-antiquark mesons, motivating the consideration of scenarios such as multi-quark states, meson molecules, and hybrids. After a brief introduction to QCD sum-rule methods, we provide a brief but comprehensive review of the mass spectra of the quarkonium-like tetraquark states $qQ\bar q\bar Q$, doubly charmed/bottomed tetraquark states $QQ\bar q\bar q$ and the heavy quarkonium hybrid states $\bar QGQ$ in the QCD sum rules approach. Possible interpretations of the $XYZ$ resonances are briefly discussed.

preprint2014arXiv

Hidden symmetry and protection of Dirac points on the honeycomb lattice

The honeycomb lattice possesses a novel energy band structure, which is characterized by two distinct Dirac points in the Brillouin zone, dominating most of the physical properties of the honeycomb structure materials. However, up till now, the origin of the Dirac points is unclear yet. Here, we discover a hidden symmetry on the honeycomb lattice and prove that the existence of Dirac points is exactly protected by such hidden symmetry. Furthermore, the moving and merging of the Dirac points and a quantum phase transition, which have been theoretically predicted and experimentally observed on the honeycomb lattice, can also be perfectly explained by the parameter dependent evolution of the hidden symmetry.

preprint2014arXiv

How Much Frequency Can Be Reused in 5G Cellular Networks---A Matrix Graph Model

The 5th Generation cellular network may have the key feature of smaller cell size and denser resource employment, resulted from diminishing resource and increasing communication demands. However, small cell may result in high interference between cells. Moreover, the random geographic patterns of small cell networks make them hard to analyze, at least excluding schemes in the well-accepted hexagonal grid model. In this paper, a new model---the matrix graph is proposed which takes advantage of the small cell size and high inter-cell interference to reduce computation complexity. This model can simulate real world networks accurately and offers convenience in frequency allocation problems which are usually NP-complete. An algorithm dealing with this model is also given, which asymptotically achieves the theoretical limit of frequency allocation, and has a complexity which decreases with cell size and grows linearly with the network size. This new model is specifically proposed to characterize the next-generation cellular networks.

preprint2014arXiv

IMRank: Influence Maximization via Finding Self-Consistent Ranking

Influence maximization, fundamental for word-of-mouth marketing and viral marketing, aims to find a set of seed nodes maximizing influence spread on social network. Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy. We first point out that greedy algorithms are essentially finding a self-consistent ranking, where nodes' ranks are consistent with their ranking-based marginal influence spread. This insight motivates us to develop an iterative ranking framework, i.e., IMRank, to efficiently solve influence maximization problem under independent cascade model. Starting from an initial ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a self-consistent ranking by reordering nodes iteratively in terms of their ranking-based marginal influence spread computed according to current ranking. We also prove that IMRank definitely converges to a self-consistent ranking starting from any initial ranking. Furthermore, within this framework, a last-to-first allocating strategy and a generalization of this strategy are proposed to improve the efficiency of estimating ranking-based marginal influence spread for a given ranking. In this way, IMRank achieves both remarkable efficiency and high accuracy by leveraging simultaneously the benefits of greedy algorithms and heuristic algorithms. As demonstrated by extensive experiments on large scale real-world social networks, IMRank always achieves high accuracy comparable to greedy algorithms, with computational cost reduced dramatically, even about $10-100$ times faster than other scalable heuristics.

preprint2014arXiv

Inductance Due to Spin Current

The inductance of spintronic devices that transport charge neutral spin currents is discussed. It is known that in a media that contains charge neutral spins, a time-varying electric field induces a spin current. We show that since the spin current itself produces an electric field, this implies existence of inductance and electromotive force when the spin current changes with time. The relations between the electromotive force and the corresponding flux, which is a vector calculated by the cross product of electric field and the trajectory of the device, are clarified. The relativistic origin generally renders an extremely small inductance, which indicates the advantage of spin current in building low inductance devices. The same argument also explains the inductance due to electric dipole current, and applies to physical dipoles consist of polarized bound charges.

preprint2014arXiv

Joint Network and Gelfand-Pinsker Coding for 3-Receiver Gaussian Broadcast Channels with Receiver Message Side Information

The problem of characterizing the capacity region for Gaussian broadcast channels with receiver message side information appears difficult and remains open for N >= 3 receivers. This paper proposes a joint network and Gelfand-Pinsker coding method for 3-receiver cases. Using the method, we establish a unified inner bound on the capacity region of 3-receiver Gaussian broadcast channels under general message side information configuration. The achievability proof of the inner bound uses an idea of joint interference cancelation, where interference is canceled by using both dirty-paper coding at the encoder and successive decoding at some of the decoders. We show that the inner bound is larger than that achieved by state of the art coding schemes. An outer bound is also established and shown to be tight in 46 out of all 64 possible cases.

preprint2014arXiv

Majorana and fractionally charged bound states in 1-D Rashba nanowire under spatially varying Zeeman fields

We study topological phase transitions in one dimensional (1-D) Rashba nanowire under a spatially varying Zeeman field when coupled to an $s$-wave superconductor substrate. We show that this system supports both Majorana bound states (MBS) and fractionally charged bound states (FBS) of Jackiw-Rebbi type. By disassembling Zeeman Hamiltonian into multiple helical components, we find that each helical component is relating to a corresponding topological region, characterized by the emergence of MBS. FBS arises in the overlapping gapped area created by any two helical components with topologically differing configuration, analogous to those formed at the knot in SSH model. We then develop a general criteria for the occurrence conditions of MBS and FBS. Our results suggest that systems with large Rashba spin orbit couple amplitude or in presence of weak Zeeman fields favor MBS, and otherwise FBS are more favorable. In the end, we demonstrate that spin components of zero energy bound states in topological phases are polarized in the plane perpendicular to Rashba vector, and the polarization oscillates with the variance of phases of the Zeeman field.

preprint2014arXiv

Masses of the Bottom-Charm Hybrid $\bar bGc$ States

QCD sum rules are used to study the mass spectrum of bottom-charm hybrid $\bar bGc$ systems. The correlation functions and the spectral densities are calculated up to dimension six condensates at leading order of $α_s$ for several $J^P$ quantum numbers. After performing the QCD sum rule analysis, we predict the masses of the $J^P=0^{-}, 0^{+}, 1^{-}, 1^{+}, 2^{-}, 2^{+}$ bottom-charm hybrids. These mass predictions show a similar supermultiplet structure as the bottomonium and charmonium hybrids. Using the QCD sum-rule mass predictions we analyze the possible hadronic decay patterns of the $\bar cGc$, $\bar bGc$ and $\bar bGb$ hybrids including the open-flavour and hidden-flavour mechanisms.

preprint2014arXiv

Masses of the tensor mesons with $J^{P}=2^{-}$

We calculate the two-point correlation function using the interpolating current with $J^{PC}=2^{-}$. After performing the Borel sum rule analysis, the extracted masses of the $2^{--}$ tensor charmonium and bottomonium are $3.97\pm0.25$ GeV and $10.13\pm0.34$ GeV respectively. For comparison, we also perform the moment sum rule analysis for the charmonium and bottomonium systems. We extend the same analysis to study the $\bar qq, \bar qs, \bar ss, \bar qc, \bar sc, \bar qb, \bar sb$ and $\bar cb$ systems. Their masses are $1.78\pm 0.12, 1.85\pm 0.14, 2.00\pm 0.16, 2.86\pm 0.14, 3.01\pm 0.21, 5.66\pm0.33, 6.40\pm 0.25$, and $7.08\pm 0.34$ GeV respectively.

preprint2014arXiv

Micro-transition cascades to percolation

We report the discovery of a discrete hierarchy of micro-transitions occurring in models of continuous and discontinuous percolation. The precursory micro-transitions allow us to target almost deterministically the location of the transition point to global connectivity. This extends to the class of intrinsically stochastic processes the possibility to use warning signals anticipating phase transitions in complex systems.

preprint2014arXiv

Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network

A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum size so that with a guaranteed probability the number of users discussing the topic would reach a given threshold. We formulate the task as an optimization problem called seed minimization with probabilistic coverage guarantee (SM-PCG). This problem departs from the previous studies on social influence maximization or seed minimization because it considers influence coverage with probabilistic guarantees instead of guarantees on expected influence coverage. We show that the problem is not submodular, and thus is harder than previously studied problems based on submodular function optimization. We provide an approximation algorithm and show that it approximates the optimal solution with both a multiplicative ratio and an additive error. The multiplicative ratio is tight while the additive error would be small if influence coverage distributions of certain seed sets are well concentrated. For one-way bipartite graphs we analytically prove the concentration condition and obtain an approximation algorithm with an $O(\log n)$ multiplicative ratio and an $O(\sqrt{n})$ additive error, where $n$ is the total number of nodes in the social graph. Moreover, we empirically verify the concentration condition in real-world networks and experimentally demonstrate the effectiveness of our proposed algorithm comparing to commonly adopted benchmark algorithms.

preprint2014arXiv

Mismatched-basis statistics enable quantum key distribution with uncharacterized qubit sources

In the postprocessing of quantum key distribution, the raw key bits from the mismatched-basis measurements, where two parties use different bases, are normally discarded. Here, we propose a postprocessing method that exploits measurement statistics from mismatched-basis cases, and prove that incorporating these statistics enables uncharacterized qubit sources to be used in the measurement-device-independent quantum key distribution protocol and the Bennett-Brassard 1984 protocol, a case which is otherwise impossible.

preprint2014arXiv

Phase-encoded measurement device independent quantum key distribution with practical spontaneous parametric-down-conversion sources

Measurement-device-independent quantum key distribution (MDI-QKD) with weak coherent sources has been widely and meticulously analyzed. However, the analysis for MDI-QKD with spontaneous parametric-down-conversion sources (SPDCS) is incomplete. In this paper, by accounting for practical parameters of SPDCS with thermal distribution, we presents an investigation on the performances of MDI-QKD under the active three-intensity decoy protocol and the passive one-intensity decoy protocol respectively. Phase randomization, inherently prerequisite for decoy protocol, is taken into consideration for evaluating the overall quantum bit gain and quantum bit error rate. The numerical simulations show that MDI-QKD using SPDCS with practical decoy protocols can be demonstrated comparable to the asymptotical case with infinite decoy states and has apparent superiority both in transmission distance and key generation rate compared to the MDI-QKD using weak coherent sources. Our results also indicate that MDI-QKD using thermal distributed SPDCS with active three-intensity decoy protocol performs better than the one with passive one-intensity decoy protocol.

preprint2014arXiv

Photon propagation in a one-dimensional optomechanical lattice

We consider a one-dimensional optomechanical lattice where each site is strongly driven by a control laser to enhance the basic optomechanical interaction. We then study the propagation of photons injected by an additional probe laser beam; this is the lattice-generalization of the well-known optomechanically-induced transparency (OMIT) effect in a single optomechanical cavity. We find an interesting interplay between OMIT-type physics and geometric, Fabry-Perot type resonances. In particular, phonon-like polaritons can give rise to high-amplitude transmission resonances which are much narrower than the scale set by internal photon losses. We also find that the local photon density of states in the lattice exhibits OMIT-style interference features. It is thus far richer than would be expected by just looking at the band structure of the dissipation-free coherent system.

preprint2014arXiv

Probing spin entanglement by gate-voltage-controlled interference of current correlation in quantum spin Hall insulators

We propose an entanglement detector composed of two quantum spin Hall insulators and a side gate deposited on one of the edge channels. For an ac gate voltage, the differential noise contributed from the entangled electron pairs exhibits the nontrivial step structures, from which the spin entanglement concurrence can be easily obtained. The possible spin dephasing effects in the quantum spin Hall insulators are also included.

preprint2014arXiv

Proof-of-principle experiment of reference-frame-independent quantum key distribution with phase coding

We have demonstrated a proof-of-principle experiment of reference-frame-independent phase coding quantum key distribution (RFI-QKD) over an 80-km optical fiber. After considering the finite-key bound, we still achieve a distance of 50 km. In this scenario, the phases of the basis states are related by a slowly time-varying transformation. Furthermore, we developed and realized a new decoy state method for RFI-QKD systems with weak coherent sources to counteract the photon-number-splitting attack. With the help of a reference-frame-independent protocol and a Michelson interferometer with Faraday rotator mirrors, our system is rendered immune to the slow phase changes of the interferometer and the polarization disturbances of the channel, making the procedure very robust.

preprint2014arXiv

Quantum computing through electron propagation in the edge states of quantum spin Hall systems

We propose to implement quantum computing based on electronic spin qubits by controlling the propagation of the electron wave packets through the helical edge states of quantum spin Hall systems (QSHs). Specfically, two non-commutative single-qubit gates, which rotate a qubit around z and y axes, can be realized by utilizing gate voltages either on a single QSH edge channel or on a quantum point contact structure. The more challenging two-qubit controlled phase gate can be implemented through the on-demand capacitive Coulomb interaction between two adjacent edge channels from two parallel QSHs. As a result, a universal set of quantum gates can be achieved in an all-electrical way. The fidelity and purity of the two-qubit gate are calculated with both time delay and finite width of the wave packets taken into consideration, which can reach high values with the existing high-quality single electron source.

preprint2014arXiv

Quantum Hacking on Quantum Key Distribution using Homodyne Detection

Imperfect devices in commercial quantum key distribution systems open security loopholes that an eavesdropper may exploit. An example of one such imperfection is the wavelength dependent coupling ratio of the fiber beam splitter. Utilizing this loophole, the eavesdropper can vary the transmittances of the fiber beam splitter at the receiver's side by inserting lights with wavelengths different from what is normally used. Here, we propose a wavelength attack on a practical continuous-variable quantum key distribution system using homodyne detection. By inserting light pulses at different wavelengths, this attack allows the eavesdropper to bias the shot noise estimation even if it is done in real time. Based on experimental data, we discuss the feasibility of this attack and suggest a prevention scheme by improving the previously proposed countermeasures.

preprint2014arXiv

Quantum key distribution based on quantum dimension and independent devices

In this paper, we propose a quantum key distribution (QKD) protocol based on only a two-dimensional Hilbert space encoding a quantum system and independent devices between the equipment for state preparation and measurement. Our protocol is inspired by the fully device-independent quantum key distribution (FDI-QKD) protocol and the measurement-device-independent quantum key distribution (MDI-QKD) protocol. Our protocol only requires the state to be prepared in the two dimensional Hilbert space, which weakens the state preparation assumption in the original MDI-QKD protocol. More interestingly, our protocol can overcome the detection loophole problem in the FDI-QKD protocol, which greatly limits the application of FDI-QKD. Hence our protocol can be implemented with practical optical components.

preprint2014arXiv

Real-time Topic-aware Influence Maximization Using Preprocessing

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks (referred collectively as items in this paper) are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods for these topics to avoid redoing influence maximization for each item from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

preprint2014arXiv

Security of modified Ping-Pong protocol in noisy and lossy channel

The "Ping-Pong" (PP) protocol is a two-way quantum key protocol based on entanglement. In this protocol, Bob prepares one maximally entangled pair of qubits, and sends one qubit to Alice. Then, Alice performs some necessary operations on this qubit and sends it back to Bob. Although this protocol was proposed in 2002, its security in the noisy and lossy channel has not been proven. In this report, we add a simple and experimentally feasible modification to the original PP protocol, and prove the security of this modified PP protocol against collective attacks when the noisy and lossy channel is taken into account. Simulation results show that our protocol is practical.

preprint2014arXiv

Spin Superfluidity in Coplanar Multiferroics

Multiferroics with coplanar magnetic order are discussed in terms of a superfluid condensate, with special emphasis on spin supercurrents created by phase gradients of the condensate and the effect of external electric fields. By drawing the analogy to a superconducting condensate, phenomena such as persistent currents in rings, the Little-Parks effect, fluxoid quantization, the Josephson-like effect through spin domain walls, and interference behavior in a SQUID-like geometry are analyzed for coplanar multiferroics.

preprint2014arXiv

Tight finite-key analysis for passive decoy-state quantum key distribution under general attacks

For quantum key distribution (QKD) using spontaneous parametric-down-conversion sources (SPDCSs), the passive decoy-state protocol has been proved to be efficiently close to the theoretical limit of an infinite decoy-state protocol. In this paper, we apply a tight finite-key analysis for the passive decoy-state QKD using SPDCSs. Combining the security bound based on the uncertainty principle with the passive decoy-state protocol, a concise and stringent formula for calculating the key generation rate for QKD using SPDCSs is presented. The simulation shows that the secure distance under our formula can reach up to 182 km when the number of sifted data is $10^{10}$. Our results also indicate that, under the same deviation of statistical fluctuation due to finite-size effects, the passive decoy-state QKD with SPDCSs can perform as well as the active decoy-state QKD with a weak coherent source.

preprint2014arXiv

Towards Efficient Path Query on Social Network with Hybrid RDF Management

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance.

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.

preprint2014arXiv

When MIMO Control Meets MIMO Communication: A Majorization Condition for Networked Stabilizability

In this paper, we initiate the study of networked stabilization via a MIMO communication scheme between the controller and the plant. Specifically, the communication system is modeled as a MIMO transceiver, which consists of three parts: an encoder, a MIMO channel, and a decoder. In the spirit of MIMO communication, the number of SISO subchannels in the transceiver is often greater than the number of data streams to be transmitted. Moreover, the subchannel capacities are assumed to be fixed a priori. In this case, the encoder/decoder pair gives an additional design freedom on top of the controller, leading to a stabilization problem via coding/control co-design. It turns out that how to take the best advantage of the coding mechanism is quite crucial. From a demand/supply perspective, the design of the coding mechanism boils down to reshaping the demands for communication resource from different control inputs to match the given supplies. We study the problem for the case of AWGN subchannels and fading subchannels, respectively. In both cases, we arrive at a unified necessary and sufficient condition on the capacities of the subchannels under which the coding/control co-design problem is solvable. The condition is given in terms of a majorization type relation. As we go along, systematic procedures are also put forward to implement the coding/control co-design. A numerical example is presented to illustrate our results.

preprint2013arXiv

A Cross-layer Perspective on Energy Harvesting Aided Green Communications over Fading Channels

We consider the power allocation of the physical layer and the buffer delay of the upper application layer in energy harvesting green networks. The total power required for reliable transmission includes the transmission power and the circuit power. The harvested power (which is stored in a battery) and the grid power constitute the power resource. The uncertainty of data generated from the upper layer, the intermittence of the harvested energy, and the variation of the fading channel are taken into account and described as independent Markov processes. In each transmission, the transmitter decides the transmission rate as well as the allocated power from the battery, and the rest of the required power will be supplied by the power grid. The objective is to find an allocation sequence of transmission rate and battery power to minimize the long-term average buffer delay under the average grid power constraint. A stochastic optimization problem is formulated accordingly to find such transmission rate and battery power sequence. Furthermore, the optimization problem is reformulated as a constrained MDP problem whose policy is a two-dimensional vector with the transmission rate and the power allocation of the battery as its elements. We prove that the optimal policy of the constrained MDP can be obtained by solving the unconstrained MDP. Then we focus on the analysis of the unconstrained average-cost MDP. The structural properties of the average optimal policy are derived. Moreover, we discuss the relations between elements of the two-dimensional policy. Next, based on the theoretical analysis, the algorithm to find the constrained optimal policy is presented for the finite state space scenario. In addition, heuristic policies with low-complexity are given for the general state space. Finally, simulations are performed under these policies to demonstrate the effectiveness.

preprint2013arXiv

A Theoretical Analysis of NDCG Type Ranking Measures

A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.

preprint2013arXiv

A Variational Principle for Improving 2D Triangle Meshes based on Hyperbolic Volume

In this paper, we consider the problem of improving 2D triangle meshes tessellating planar regions. We propose a new variational principle for improving 2D triangle meshes where the energy functional is a convex function over the angle structures whose maximizer is unique and consists only of equilateral triangles. This energy functional is related to hyperbolic volume of ideal 3-simplex. Even with extra constraints on the angles for embedding the mesh into the plane and preserving the boundary, the energy functional remains well-behaved. We devise an efficient algorithm for maximizing the energy functional over these extra constraints. We apply our algorithm to various datasets and compare its performance with that of CVT. The experimental results show that our algorithm produces the meshes with both the angles and the aspect ratios of triangles lying in tighter intervals.

preprint2013arXiv

All-electrically reading out and initializing topological qubits with quantum dots

We analyze the reading and initialization of a topological qubit encoded by Majorana fermions in one-dimensional semiconducting nanowires, weakly coupled to a single level quantum dot (QD). It is shown that when the Majorana fermions are fused by tuning gate voltage, the topological qubit can be read out directly through the occupation of the QD in an energy window. The initialization of the qubit can also be realized via adjusting the gate voltage on the QD, with the total fermion parity conserved. As a result, both reading and initialization processes can be achieved in an all-electrical way.

preprint2013arXiv

An Outage Exponent Region based Coded f-Matching Framework for Channel Allocation in Multi-carrier Multi-access Channels

The multi-carrier multi-access technique is widely adopt in future wireless communication systems, such as IEEE 802.16m and 3GPP LTE-A. The channel resources allocation in multi-carrier multi-access channel, which can greatly improve the system throughput with QoS assurance, thus attracted much attention from both academia and industry. There lacks, however, an analytic framework with a comprehensive performance metric, such that it is difficult to fully exploit the potentials of channel allocation. This paper will propose an analytic coded fmatching framework, where the outage exponent region (OER) will be defined as the performance metric. The OER determines the relationship of the outage performance among all of the users in the full SNR range, and converges to the diversity-multiplexing region (DMR) when SNR tends to infinity. To achieve the optimal OER and DMR, the random bipartite graph (RBG) approach, only depending on 1 bit CSI, will be proposed to formulate this problem. Based on the RBG formulation, the optimal frequency-domain coding based maximum f-matching method is then proposed. By analyzing the combinatorial structure of the RBG based coded f-matching with the help of saddlepoint approximation, the outage probability of each user, OER, and DMR will be derived in closed-form formulas. It will be shown that all of the users share the total multiplexing gain according to their rate requirements, while achieving the full frequency diversity, i.e., the optimal OER and DMR. Based on the principle of parallel computations, the parallel vertices expansion & random rotation based Hopcroft-Karp (PVER2HK) algorithm, which enjoys a logarithmic polynomial complexity, will be proposed. The simulation results will not only verify the theoretical derivations, but also show the significant performance gains.

preprint2013arXiv

Approximate dynamic programming using fluid and diffusion approximations with applications to power management

Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach using the solutions to associated fluid and diffusion approximations. In order to illustrate this approach, the paper focuses on an application to dynamic speed scaling for power management in computer processors.

preprint2013arXiv

Charge and spin supercurrents in triplet superconductor--ferromagnet--singlet superconductor Josephson junctions

We study the Josephson effect in a triplet superconductor--ferromagnet--singlet superconductor junction. We show that the interaction of tunneling Cooper pairs with the interface magnetization can permit a Josephson current at the lowest order of a tunneling Hamiltonian perturbation theory. Two conditions must be satisfied for this to occur: the magnetization of the ferromagnet has a component parallel to the ${\bf d}$-vector of the triplet superconductor, and the gaps of the superconductors have the same parity with respect to the interface momentum. The resulting charge current displays an unconventional dependence on the orientation of the magnetic moment and the phase difference. This is accompanied by a phase-dependent spin current in the triplet superconductor, while a phase-independent spin current is always present. The tunneling perturbation theory predictions are confirmed using a numerical Green's function method. An analytical treatment of a one-dimensional junction demonstrates that our conclusions are robust far away from the tunneling regime, and reveals signatures of the unconventional Josephson effect in the critical currents.

preprint2013arXiv

Charge-SQUID and Tunable Phase-slip Flux Qubit

A phase-slip flux qubit, exactly dual to a charge qubit, is composed of a superconducting loop interrupted by a phase-slip junction. Here we propose a tunable phase-slip flux qubit by replacing the phase-slip junction with a charge-related superconducting quantum interference device (SQUID) consisting of two phase-slip junctions connected in series with a superconducting island. This charge-SQUID acts as an effective phase-slip junction controlled by the applied gate voltage and can be used to tune the energy-level splitting of the qubit. Also, we show that a large inductance inserted in the loop can reduce the inductance energy and consequently suppress the dominating flux noise of the phase-slip flux qubit. This enhanced phase-slip flux qubit is exactly dual to a transmon qubit.

preprint2013arXiv

Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price

In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grid. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the charging energy for each EV is random. The goal is to minimize the mean waiting time of EVs under the long term constraint on the cost. We propose queue mapping to convert the EV queue to the charge demand queue and prove the equivalence between the minimization of the two queues' average length. Then we focus on the minimization for the average length of the charge demand queue under long term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this scheduling problem. The system state includes the charge demand queue length, the charge demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally the number of charging demands and the allocated energy from the storage battery compose the two-dimensional policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the two-dimensional policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally we investigate the proposed radical policy and conservative policy numerically.

preprint2013arXiv

Coalitional Game Theoretic Approach for Cooperative Transmission in Vehicular Networks

Cooperative transmission in vehicular networks is studied by using coalitional game and pricing in this paper. There are several vehicles and roadside units (RSUs) in the networks. Each vehicle has a desire to transmit with a certain probability, which represents its data burtiness. The RSUs can enhance the vehicles' transmissions by cooperatively relaying the vehicles' data. We consider two kinds of cooperations: cooperation among the vehicles and cooperation between the vehicle and RSU. First, vehicles cooperate to avoid interfering transmissions by scheduling the transmissions of the vehicles in each coalition. Second, a RSU can join some coalition to cooperate the transmissions of the vehicles in that coalition. Moreover, due to the mobility of the vehicles, we introduce the notion of encounter between the vehicle and RSU to indicate the availability of the relay in space. To stimulate the RSU's cooperative relaying for the vehicles, the pricing mechanism is applied. A non-transferable utility (NTU) game is developed to analyze the behaviors of the vehicles and RSUs. The stability of the formulated game is studied. Finally, we present and discuss the numerical results for the 2-vehicle and 2-RSU scenario, and the numerical results verify the theoretical analysis.

preprint2013arXiv

Correlated diffusion of colloidal particles near a liquid-liquid interface

Optical microscopy and multi-particle tracking are used to investigate the cross-correlated diffusion of quasi two-dimensional (2D) colloidal particles near an oil-water interface. It is shown that the effect of the interface on correlated diffusion is asymmetric. Along the line joining the centers of particles, the amplitude of correlated diffusion coefficient ${D}_{\|}(r)$ is enhanced by the interface, while the decay rate of ${D}_{\|}(r)$ is hardly affected. At the direction perpendicular to the line, the decay rate of ${D}_{\bot}(r)$ is enhanced at short inter-particle separation $r$. This enhancing effect fades at the long $r$. In addition, both $D_{\|}(r)$ and $D_{\bot}(r)$ are independent of the colloidal area fraction $n$ at long $r$, which indicates that the hydrodynamic interactions (HIs) among the particles are dominated by that through the surrounding fluid at this region. However, at short $r$, $D_{\bot}(r)$ is dependent on $n$, which suggests the HIs are more contributed from the 2D particle monolayer self.

preprint2013arXiv

Cumulative Effect in Information Diffusion: A Comprehensive Empirical Study on Microblogging Network

Cumulative effect in social contagions underlies many studies on the spread of innovation, behaviors, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in the information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of each message, where nodes are the involved users and links are the following relationships among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both the structural motif of the diffusion network and the temporal pattern of information diffusion process among users. The patterns provide vital insight for understanding the variation of message popularity and explain the characteristics of diffusion networks.

preprint2013arXiv

Delay Optimal Scheduling for Energy Harvesting Based Communications

Green communication attracts increasing research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through twodimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.

preprint2013arXiv

Deriving an underlying mechanism for discontinuous percolation

Understanding what types of phenomena lead to discontinuous phase transitions in the connectivity of random networks is an outstanding challenge. Here we show that a simple stochastic model of graph evolution leads to a discontinuous percolation transition and we derive the underlying mechanism responsible: growth by overtaking. Starting from a collection of $n$ isolated nodes, potential edges chosen uniformly at random from the complete graph are examined one at a time while a cap, $k$, on the maximum allowed component size is enforced. Edges whose addition would exceed $k$ can be simply rejected provided the accepted fraction of edges never becomes smaller than a function which decreases with $k$ as $g(k) = 1/2 + (2k)^{-β}$. We show that if $β< 1$ it is always possible to reject a sampled edge and the growth in the largest component is dominated by an overtaking mechanism leading to a discontinuous transition. If $β> 1$, once $k \ge n^{1/β}$, there are situations when a sampled edge must be accepted leading to direct growth dominated by stochastic fluctuations and a "weakly" discontinuous transition. We also show that the distribution of component sizes and the evolution of component sizes are distinct from those previously observed and show no finite size effects for the range of $β$ studied.

preprint2013arXiv

Exotic $QQ\bar{q}\bar{q}$, $QQ\bar{q}\bar{s}$ and $QQ\bar{s}\bar{s}$ states

After constructing the possible $J^P=0^-, 0^+, 1^-$ and $1^+$ $QQ\bar{q}\bar{q}$ tetraquark interpolating currents in a systematic way, we investigate the two-point correlation functions and extract the corresponding masses with the QCD sum rule approach. We study the $QQ\bar{q}\bar{q}$, $QQ\bar{q}\bar{s}$ and $QQ\bar{s}\bar{s}$ systems with various isospins $I=0, 1/2, 1$. Our numerical analysis indicates that the masses of doubly-bottomed tetraquark states are below the threshold of the two bottom mesons, two bottom baryons and one doubly bottomed baryon plus one anti-nucleon. Very probably these doubly-bottomed tetraquark states are stable.

preprint2013arXiv

Flux Quantization Due to Monopole and Dipole Currents

By discussing field-induced quantum interference effects due to monopole moments and those due to dipole moments on equal footing, their similarities and differences are clarified. First, we demonstrate the general principle for flux quantization. For particles carrying a monopole moment, the interference causes monopole current to oscillate periodically with flux defined as inner product of field and area, whereas for particles carrying a fixed dipole moment, the dipole current oscillates periodically with flux vector defined as cross product of field and trajectory. Our analysis unifies the oscillation of monopole or dipole currents in various devices, such as SQUID and spin-FET, into the same physical picture. Second, we show that interference effects can also happen in open trajectory devices that transport dipole currents, such as spin Josephson effect, based on the non-gauge field nature of the interference effects of dipole moments. In addition, we propose that the interference effect of electric dipoles, known as He-McKellar-Wilkens effect, can be realized by the bilayer exciton condensates observed in semiconductor heterostructure and bilayer graphene.

preprint2013arXiv

Fractional G-White Noise Theory, Wavelet Decomposition for Fractional G-Brownian Motion, and Bid-Ask Pricing Application to Finance Under Uncertainty

G-framework is presented by Peng [41] for measure risk under uncertainty. In this paper, we define fractional G-Brownian motion (fGBm). Fractional G-Brownian motion is a centered G-Gaussian process with zero mean and stationary increments in the sense of sub-linearity with Hurst index $H\in (0,1)$. This process has stationary increments, self-similarity, and long rang dependence properties in the sense of sub-linearity. These properties make the fractional G-Brownian motion a suitable driven process in mathematical finance. We construct wavelet decomposition of the fGBm by wavelet with compactly support. We develop fractional G-white noise theory, define G-Itô-Wick stochastic integral, establish the fractional G-Itô formula and the fractional G-Clark-Ocone formula, and derive the G-Girsanov's Theorem. For application the G-white noise theory, we consider the financial market modelled by G-Wick-Itô type of SDE driven by fGBm. The financial asset price modelled by fGBm has volatility uncertainty, using G-Girsanov's Theorem and G-Clark-Ocone Theorem, we derive that sublinear expectation of the discounted European contingent claim is the bid-ask price of the claim.

preprint2013arXiv

G-consistent price system and bid-ask pricing for European contingent claims under Knightian uncertainty

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the European contingent claims under Knightian uncertain financial market. Firstly, we consider the basic risky asset price model on the uncertain financial market, which we construct here is the model with drift uncertain and volatility uncertain. We describe such model by using generalized G-Brownian motion and call it as G-asset price system. We present the uncertain risk premium which is uncertain and distributed with maximum distribution. We derive the closed form of bid-ask price of the European contingent claim against the underlying risky asset with G-asset price system as the discounted conditional G-expecation of the claim, and the bid and ask prices are the viscosity solutions to the nonlinear HJB equations.Furthermore, we consider the main part of this paper, i.e., consider the risky asset on the Knightian uncertain financial market with the price fluctuation shows as continuous trajectories. We propose the G-conditional full support condition by using uncertain capacity, and the risky asset price path satisfying the G-conditional full support condition could be approximated by its G-consistent asset price systems. We derive that the bid and ask prices of the European contingent claim against such risky asset under uncertain can be expressed by discounted of some conditional G-expectation of the claim. We give examples, such as G-Markovian processes and the geometric fractional G-Brownian motion [9], satisfying the G-conditional full support condition.

preprint2013arXiv

Implications of Resonant Inelastic X-ray Scattering Data for Theoretical Models of Cuprates

There are two commonly discussed points of view in theoretical description of cuprate superconductors, (i) Cuprates can be described by the modified t-J model. (ii) Overdoped cuprates are close to the regime of normal Fermi liquid (NFL). We argue that recent resonant inelastic X-ray scattering data challenge both points. While the modified t-J model describes well the strongly underdoped regime, it fails to describe high energy magnetic excitations when approaching optimal doping. This probably indicates failure of the Zhang-Rice singlet picture. In the overdoped regime the momentum-integrated spin structure factor S(ω) has the same intensity and energy distribution as that in an undoped parent compound. This implies that the entire spin spectral sum rule is saturated at ω~ 2J, while in a NFL the spectral weight should saturate only at the total bandwidth which is much larger than 2J.

preprint2013arXiv

Josephson Effect and Triplet--Singlet Ratio of Non--centrosymmetric Superconductors

We calculate the Andreev bound states and the corresponding Josephson current for an asymmetric 2-dimensional Josephson junction by solving Bogoliubov-de-Gennes equations. The junction consists of a non-centrosymmetric superconductor (NCS) separated by a tunneling barrier with a variable height to a conventional s-wave superconductor. In addition to the antisymmetric spin-orbit coupling in the NCS on the one side, this asymmetric junction gives rise to a Rashba spin-orbit coupling at the interface. We explore the rich parameter space and recover various limiting cases such as s-wave/p-wave junction and the asymmetric s-wave junctions. In addition, we report a transition from a 0-junction to a pi/2-junction with increasing triplet-singlet pairing ratio of the NCS, which serves as a novel mechanism to determine the unknown ratio in a variety of NCS's.

preprint2013arXiv

Mass Spectrum of Heavy Quarkonium Hybrids

We have extended the calculation of the correlation functions of heavy quarkonium hybrid operators with various $J^{PC}$ quantum numbers to include QCD condensates up to dimension six. In contrast to previous analyses which were unable to optimize the QCD sum-rules for certain $J^{PC}$, recent work has shown that inclusion of dimension six condensates stabilizes the hybrid sum-rules and permits reliable mass predictions. In this work we have investigated the effects of the dimension six condensates on the remaining channels. After performing the QCD sum-rule analysis, we update the mass spectra of charmonium and bottomonium hybrids with exotic and non-exotic quantum numbers. We identify that the negative-parity states with $J^{PC}=(0, 1, 2)^{-+}, 1^{--}$ form the lightest hybrid supermultiplet while the positive-parity states with $J^{PC}=(0, 1)^{+-}, (0, 1, 2)^{++}$ belong to a heavier hybrid supermultiplet, confirming the supermultiplet structure found in other approaches. The hybrid with $J^{PC}=0^{--}$ has a much higher mass which may suggest a different excitation of the gluonic field compared to other channels. In agreement with previous results, we find that the $J^{PC}=1^{++}$ charmonium hybrid is substantially heavier than the X(3872), which seems to preclude a pure charmonium hybrid interpretation for this state.

preprint2013arXiv

Maximizing Acceptance Probability for Active Friending in On-Line Social Networks

Friending recommendation has successfully contributed to the explosive growth of on-line social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target, has not been explored in existing on-line social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, \emph{Acceptance Probability Maximization (APM)}, and develop a polynomial time algorithm, called \emph{Selective Invitation with Tree and In-Node Aggregation (SITINA)}, to find the optimal solution. We implement an active friending service with SITINA in Facebook to validate our idea. Our user study and experimental results manifest that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.

preprint2013arXiv

Measurement of the electronic thermal conductance channels and heat capacity of graphene at low temperature

The ability to transport energy is a fundamental property of the two-dimensional Dirac fermions in graphene. Electronic thermal transport in this system is relatively unexplored and is expected to show unique fundamental properties and to play an important role in future applications of graphene, including opto-electronics, plasmonics, and ultra-sensitive bolometry. Here we present measurements of bipolar, electron-diffusion and electron-phonon thermal conductances, and infer the electronic specific heat, with a minimum value of 10 $k_{\rm{B}}$ ($10^{-22}$ JK$^{-1}$) per square micron. We test the validity of the Wiedemann-Franz law and find the Lorenz number equals $1.32\times(π^2/3)(k_{\rm{B}}/e)^2$. The electron-phonon thermal conductance has a temperature power law $T^2$ at high doping levels, and the coupling parameter is consistent with recent theory, indicating its enhancement by impurity scattering. We demonstrate control of the thermal conductance by electrical gating and by suppressing the diffusion channel using superconducting electrodes, which sets the stage for future graphene-based single microwave photon detection.

preprint2013arXiv

Measurement-device-independent quantum key distribution with uncharacterized qubit sources

Measurement-device-independent quantum key distribution (MDIQKD) is proposed to be secure against any possible detection attacks. The security of the original proposal relies on the assumption that the legitimate users can fully characterize the encoding systems including sources. Here, we propose a MDIQKD protocol where we allow uncharacterized encoding systems as long as qubit sources are used. A security proof of the MDIQKD protocol is presented that does not need the knowledge of the encoding states. Simulation results show that the scheme is practical.

preprint2013arXiv

Microstrip filters for measurement and control of superconducting qubits

Careful filtering is necessary for observations of quantum phenomena in superconducting circuits at low temperatures. Measurements of coherence between quantum states requires extensive filtering to protect against noise coupled from room temperature electronics. We demonstrate distributed transmission line filters which cut off exponentially at GHz frequencies and can be anchored at the base temperature of a dilution refrigerator. The compact design makes them suitable to filter many different bias lines in the same setup, necessary for the control and measurement of superconducting qubits.

preprint2013arXiv

On the Hyperbolicity of Small-World and Tree-Like Random Graphs

Hyperbolicity is a property of a graph that may be viewed as being a "soft" version of a tree, and recent empirical and theoretical work has suggested that many graphs arising in Internet and related data applications have hyperbolic properties. We consider Gromov's notion of δ-hyperbolicity, and establish several results for small-world and tree-like random graph models. First, we study the hyperbolicity of Kleinberg small-world random graphs and show that the hyperbolicity of these random graphs is not significantly improved comparing to graph diameter even when it greatly improves decentralized navigation. Next we study a class of tree-like graphs called ringed trees that have constant hyperbolicity. We show that adding random links among the leaves similar to the small-world graph constructions may easily destroy the hyperbolicity of the graphs, except for a class of random edges added using an exponentially decaying probability function based on the ring distance among the leaves. Our study provides one of the first significant analytical results on the hyperbolicity of a rich class of random graphs, which shed light on the relationship between hyperbolicity and navigability of random graphs, as well as on the sensitivity of hyperbolic δ to noises in random graphs.

preprint2013arXiv

Opportunistic DF-AF Selection Relaying with Optimal Relay Selection in Nakagami-m Fading Environments

An opportunistic DF-AF selection relaying scheme with maximal received signal-to-noise ratio (SNR) at the destination is investigated in this paper. The outage probability of the opportunistic DF-AF selection relaying scheme over Nakagami-m fading channels is analyzed, and a closed-form solution is obtained. We perform asymptotic analysis of the outage probability in high SNR domain. The coding gain and the diversity order are obtained. For the purpose of comparison, the asymptotic analysis of opportunistic AF scheme in Nakagami-m fading channels is also performed by using the Squeeze Theorem. In addition, we prove that compared with the opportunistic DF scheme and opportunistic AF scheme, the opportunistic DF-AF selection relaying scheme has better outage performance.

preprint2013arXiv

Outage Exponent: A Unified Performance Metric for Parallel Fading Channels

The parallel fading channel, which consists of finite number of subchannels, is very important, because it can be used to formulate many practical communication systems. The outage probability, on the other hand, is widely used to analyze the relationship among the communication efficiency, reliability, SNR, and channel fading. To the best of our knowledge, the previous works only studied the asymptotic outage performance of the parallel fading channel which are only valid for a large number of subchannels or high SNRs. In this paper, a unified performance metric, which we shall refer to as the outage exponent, will be proposed. Our approach is mainly based on the large deviations theory and the Meijer's G-function. It is shown that the proposed outage exponent is not only an accurate estimation of the outage probability for any number of subchannels, any SNR, and any target transmission rate, but also provides an easy way to compute the outage capacity, finite-SNR diversity-multiplexing tradeoff, and SNR gain. The asymptotic performance metrics, such as the delay-limited capacity, ergodic capacity, and diversity-multiplexing tradeoff can be directly obtained by letting the number of subchannels or SNR tends to infinity. Similar to Gallager's error exponent, a reliable function for parallel fading channels, which illustrates a fundamental relationship between the transmission reliability and efficiency, can also be defined from the outage exponent. Therefore, the proposed outage exponent provides a complete and comprehensive performance measure for parallel fading channels.

preprint2013arXiv

Projection Design For Statistical Compressive Sensing: A Tight Frame Based Approach

In this paper, we develop a framework to design sensing matrices for compressive sensing applications that lead to good mean squared error (MSE) performance subject to sensing cost constraints. By capitalizing on the MSE of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we use the fact that a Parseval tight frame is the closest design - in the Frobenius norm sense - to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracle estimator with respect to the equivalent sensing matrix, subject to sensing energy constraints. Based on this result, we then propose two sensing matrix designs that exhibit two key properties: i) the designs are closed form rather than iterative; ii) the designs exhibit superior performance in relation to other designs in the literature, which is revealed by our numerical investigation in various scenarios with different sparse recovery algorithms including basis pursuit de-noise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP).

preprint2013arXiv

QCD Sum Rule Analysis of Heavy Quarkonium Hybrids

We have studied the charmonium and bottomonium hybrid states with various $J^{PC}$ quantum numbers in QCD sum rules. At leading order in $α_s$, the two-point correlation functions have been calculated up to dimension six including the tri-gluon condensate and four-quark condensate. After performing the QCD sum rule analysis, we have confirmed that the dimension six condensates can stabilize the hybrid sum rules and allow the reliable mass predictions. We have updated the mass spectra of the charmonium and bottomonium hybrid states and identified that the negative-parity states with $J^{PC}=(0, 1, 2)^{-+}, 1^{--}$ form the lightest hybrid supermultiplet while the positive-parity states with $J^{PC}=(0, 1)^{+-}, (0, 1, 2)^{++}$ belong to a heavier hybrid supermultiplet.

preprint2013arXiv

QCD Sum-Rule Interpretation of X(3872) with $J^{PC}=1^{++}$ Mixtures of Hybrid Charmonium and $\bar D D^*$ Molecular Currents

QCD sum-rules are employed to determine whether the X(3872) can be described as a mixed state that couples to $J^{PC}=1^{++}$ charmonium hybrid and $\bar D D^*$ molecular currents. After calculating the mixed correlator of hybrid and molecular currents, we formulate the sum-rule in terms of a mixing parameter that interpolates between the pure molecular and hybrid scenarios. As the mixing parameter is increased from the pure molecular case, the predicted mass increases until it reaches a maximum value in good agreement with the X(3872) and the resulting sum-rule analysis appears more robust than the pure molecular case.

preprint2013arXiv

Quantum discord in quantum random access codes and its connection with dimension witness

We exploit quantum discord (and geometric discord) to detect quantum correlations present in a well-known communication model called quantum random access codes (QRACs), which has a variety of applications in areas ranging form quantum communication complexity, network coding, information causality, to security proof of QKD protocol. In spite of the fact that there is no entanglement between the two parts involved in this model, analytical derivation shows that the quantum discord is nonzero and highlights that quantum discord might be regarded as a figure of merit to characterize the quantum feature of QRACs, since this model has no classical counterparts. To gain further insight, we also investigate the dynamical behavior of quantum discord under state rotations. As a comparison, the connection between quantum discord and dimension witness is graphically discussed. Interestingly, our results illustrate that these two quantities are monotonically related to each other, which implies that the dimension witness and nonclassical correlations may be intrinsically linked.

preprint2013arXiv

Quantum discord of ensemble of quantum states

We highlight an information-theoretic meaning of quantum discord as the gap between the ac- cessible information and the Holevo bound in the framework of ensemble of quantum states. This complementary relationship implies that a large amount of pre-existing arguments about the evaluation of quantum discord can be directly applied to the accessible information and vice versa. For an ensemble of two pure qubit states, we show that one can evade the optimization problem with the help of the Koashi-Winter relation. Further, for the general case (two mixed qubit states), we recover the main results presented by Fuchs and Caves [Phys. Rev. Lett. 73, 3047 (1994)], but totally from the perspective of quantum discord. Following this line of thought, we also investigate the geometric discord as an indicator of quantumness of ensembles in detail. Finally, we give an example to elucidate the difference between quantum discord and geometric discord with respect to optimal measurement strategies.

preprint2013arXiv

Quantum Hacking on Continuous-Variable Quantum Key Distribution System using a Wavelength Attack

The security proofs of continuous-variable quantum key distribution are based on the assumptions that the eavesdropper can neither act on the local oscillator nor control Bob's beam splitter. These assumptions may be invalid in practice due to potential imperfections in the implementations of such protocols. In this paper, we consider the problem of transmitting the local oscillator in a public channel and propose a wavelength attack which can allow the eavesdropper to control the intensity transmission of Bob's beam splitter by switching the wavelength of the input light. Specifically we target continuous-variable quantum key distribution systems that use the heterodyne detection protocol using either direct or reverse reconciliation. Our attack is proved to be feasible and renders all of the final key shared between the legitimate parties insecure, even if they have monitored the intensity of the local oscillator. To prevent our attack on commercial systems, a simple wavelength filter should be added before performing the monitoring detection.

preprint2013arXiv

Relation between semi- and fully-device-independent protocols

We study the relation between semi and fully device independent protocols. As a tool, we use the correspondence between Bell inequalities and dimension witnesses. We present a method for converting the former into the latter and vice versa. This relation provides us with interesting results for both scenarios. First, we find new random number generation protocols with higher bit rates for both the semi and fully device independent cases. As a byproduct, we obtain whole new classes of Bell inequalities and dimension witnesses. Then, we show how optimization methods used in studies on Bell inequalities can be adopted for dimension witnesses.

preprint2013arXiv

Specular Andreev Reflection in Inversion-symmetric Weyl-semimetals

The electron-hole conversion at the normal-metal superconductor interface in inversion-symmetric Weyl semimetals is investigated with an effective two-band model. We find that the specular Andreev reflection of Weyl fermions has two unusual features. The Andreev conductance for s-wave BCS pairing states is anisotropic, depending on the angle between the line connecting a pair of Weyl points and the normal of the junction, due to opposite chirality carried by the paired electrons. For the Fulde-Ferrell-Larkin-Ovchinnikov pairing states, the Andreev reflection spectrum is isotropic and is independent of the finite momentum of the Cooper pairs.

preprint2013arXiv

Sybil-proof Mechanisms in Query Incentive Networks

In this paper, we study incentive mechanisms for retrieving information from networked agents. Following the model in [Kleinberg and Raghavan 2005], the agents are represented as nodes in an infinite tree, which is generated by a random branching process. A query is issued by the root, and each node possesses an answer with an independent probability $p=1/n$. Further, each node in the tree acts strategically to maximize its own payoff. In order to encourage the agents to participate in the information acquisition process, an incentive mechanism is needed to reward agents who provide the information as well as agents who help to facilitate such acquisition. We focus on designing efficient sybil-proof incentive mechanisms, i.e., which are robust to fake identity attacks. %We consider incentive mechanisms which are sybil-proof, i.e., robust to fake identity attacks. We propose a family of mechanisms, called the direct referral (DR) mechanisms, which allocate most reward to the information holder as well as its direct parent (or direct referral). We show that, when designed properly, the direct referral mechanism is sybil-proof and efficient. In particular, we show that we may achieve an expected cost of $O(h^2)$ for propagating the query down $h$ levels for any branching factor $b>1$. This result exponentially improves on previous work when requiring to find an answer with high probability. When the underlying network is a deterministic chain, our mechanism is optimal under some mild assumptions. In addition, due to its simple reward structure, the DR mechanism might have good chance to be adopted in practice.

preprint2013arXiv

Time Consistent Bid-Ask Dynamic Pricing Mechanisms for Contingent Claims and Its Numerical Simulations Under Uncertainty

We study time consistent dynamic pricing mechanisms of European contingent claims under uncertainty by using G framework introduced by Peng ([24]). We consider a financial market consisting of a riskless asset and a risky stock with price process modelled by a geometric generalized G-Brownian motion, which features the drift uncertainty and volatility uncertainty of the stock price process. Using the techniques on G-framework we show that the risk premium of the asset is uncertain and distributed with maximum distribution. A time consistent G-expectation is defined by the viscosity solution of the G-heat equation. Using the time consistent G-expectation we define the G dynamic pricing mechanism for the claim. We prove that G dynamic pricing mechanism is the bid-ask Markovian dynamic pricing mechanism. The full nonlinear PDE is derived to describe the bid (resp. ask) price process of the claim. Monotone implicit characteristic finite difference schemes for the nonlinear PDE are given, nonlinear iterative schemes are constructed, and the simulations of the bid (resp. ask) prices of contingent claims under uncertainty are implemented.

preprint2013arXiv

Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing?

This paper advocates the use of the emerging distributed compressive sensing (DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor networks (WSN) with practical network lifetime and data gathering rates that are substantially higher than the state-of-the-art. In particular, we argue that there are two fundamental mechanisms in an EH WSN: i) the energy diversity associated with the EH process that entails that the harvested energy can vary from sensor node to sensor node, and ii) the sensing diversity associated with the DCS process that entails that the energy consumption can also vary across the sensor nodes without compromising data recovery. We also argue that such mechanisms offer the means to match closely the energy demand to the energy supply in order to unlock the possibility for energy-neutral WSNs that leverage EH capability. A number of analytic and simulation results are presented in order to illustrate the potential of the approach.

preprint2013arXiv

Tunable optical absorption and interactions in graphene via oxygen plasma

We report significant changes of optical conductivity in single layer graphene induced by mild oxygen plasma exposure, and explore the interplay between carrier doping, disorder, and many-body interactions from their signatures in the absorption spectrum. The first distinctive effect is the reduction of the excitonic binding energy that can be extracted from the renormalized saddle point resonance at 4.64 eV. Secondly, the real part of the frequency-dependent conductivity is nearly completely suppressed below an exposure-dependent threshold in the near infrared range. The clear step-like suppression follows the Pauli blocking behaviour expected for doped monolayer graphene. The nearly zero residual conductivity at frequencies below 2Ef can be interpreted as arising from the weakening of the electronic self-energy. Our data shows that mild oxygen exposure can be used to controlably dope graphene without introducing the strong physical and chemical changes that are common in other approaches to oxidized graphene, allowing a controllable manipulation of the optical properties of graphene.

preprint2013arXiv

Unstable supercritical discontinuous percolation transitions

The location and nature of the percolation transition in random networks is a subject of intense interest. Recently, a series of graph evolution processes have been introduced that lead to discontinuous percolation transitions where the addition of a single edge causes the size of the largest component to exhibit a significant macroscopic jump in the thermodynamic limit. These processes can have additional exotic behaviors, such as displaying a `Devil's staircase' of discrete jumps in the supercritical regime. Here we investigate whether the location of the largest jump coincides with the percolation threshold for a range of processes, such as Erdos-Renyi percolation, percolation via edge competition and via growth by overtaking. We find that the largest jump asymptotically occurs at the percolation transition for Erdos-Renyi and other processes exhibiting global continuity, including models exhibiting an `explosive' transition. However, for percolation processes exhibiting genuine discontinuities, the behavior is substantially richer. In percolation models where the order parameter exhibits a staircase, the largest discontinuity generically does not coincide with the percolation transition. For the generalized Bohman-Frieze-Wormald model, it depends on the model parameter. Distinct parameter regimes well in the supercritical regime feature unstable discontinuous transitions, which is a novel and unexpected phenomenon in percolation. We thus demonstrate that seemingly and genuinely discontinuous percolation transitions can involve a rich behavior in supercriticality, a regime that has been largely ignored in percolation.

preprint2013arXiv

Wavelength attack scheme on continuous-variable quantum key distribution system using heterodyne detection protocol

The security proof of continuous variable quantum key distribution(CV QKD) based on two assumptions that the eavesdropper can neither act on the local oscillator nor control Bob's beam splitter. These assumptions maybe invalid in practice due to some potential imperfections of the implementations of the protocol. In [Phys.Rev.A, 84.062308], we found that one can control the intensity transmission of the beam splitter by switching the wavelength of the input light. In this paper, we revisit the problem of transmitting the local oscillator in public channel, and propose a wavelength attack targeting the CV QKD systems using heterodyne protocol. Our attack is proved to be feasible and renders all final key shared between the legitimate parties insecure even they have monitored the intensity. To prevent our attack, a wavelength filter should be added before performing the heterodyne detection.

preprint2013arXiv

Weighted estimates for the multilinear maximal function

A formulation of the Carleson embedding theorem in the multilinear setting is proved which allows to obtain a multilinear analogue of Sawyer's two weight theorem for the multisublinear maximal function \mathcal{M} introduced in Lerner et al. A multilinear version of the B_p theorem from Hytönen and Pérez is also obtained and a mixed A_{\vec{P}}-W_{\vec{P}}^{\infty} bound for \mathcal{M} is proved as well.

preprint2013arXiv

XYZ States

In the past decade, many new charmonium (or charmonium-like) and bottomonium (or bottomonium-like) states were observed experimentally. I will review these XYZ states which do not fit into the quark model spectrum easily.

preprint2012arXiv

2-GHz clock quantum key distribution over 260 km of standard telecom fiber

We report a demonstration of quantum key distribution (QKD) over a standard telecom fiber exceeding 50 dB in loss and 250 km in length. The differential phase shift QKD protocol was chosen and implemented with 2 GHz system clock rate. By careful optimization of the 1-bit delayed Faraday-Michelson interferometer and the use of the super-conducting single photon detector (SSPD), we achieved a quantum bit error rate below 2% when the fiber length was no more than 205 km, and of 3.45% for the 260 km length fiber with 52.9 dB loss. We also improved the quantum efficiency of SSPD to obtain high key rate for 50 km length.

preprint2012arXiv

Correlated dynamics of weakly charged silica spheres at an air-water interface

Optical microscopy and multi-particle tracking are used to investigate the spatially correlated motion of weakly charged silica spheres at an air-water interface for different area fraction $n$ occupied by the particles. When the area fraction is very small, e.g. $n=0.03$, the correlation function along the line joining the centers of particles $D_{rr}$ decays with inter-particle distance $R$ as $1/R^{0.86\pm0.02}$, and the function perpendicular to this line $D_{θθ}$ decays with $R$ as $1/R^{1.45\pm0.03}$, which differs from the results of [Phys. Rev. Lett. 97, 176001 (2006)] with low surface viscosity (where $D_{rr}\propto 1/R$, $D_{θθ}\propto 1/R^2$). We argue that the differences arise from the Coulomb interaction between particles. The Coulomb interaction enhances the correlated motion of particles. Experimental results show that with the increase of $n$, the decay rate of $D_{rr}$ and $D_{θθ}$ with $R$ decreases and the cross-correlation enhances for the Coulomb interaction increases. The Coulomb interaction between colloidal particles should serve as an effective surface viscoelastical role in our system. With the scaled separation $\frac R {d}(\frac {η_{w}d} {η_{es,2p}})^{3/2}$, the correlated motions for various values of $n$ and different particles can be scaled onto a single master curve, where $d$ is particles' diameter, $η_{w}$ is the viscosity of the water, and $η_{es,2p}$ is the effective surface viscosity whose measurements agree well with that of one-particle surface viscosity $η_{es,1p}$. The effective surface viscosity $η_{es,2p}$ as a function of the area fraction $n$ for different silica spheres is presented.

preprint2012arXiv

Distributed Consensus Resilient to Both Crash Failures and Strategic Manipulations

In this paper, we study distributed consensus in synchronous systems subject to both unexpected crash failures and strategic manipulations by rational agents in the system. We adapt the concept of collusion-resistant Nash equilibrium to model protocols that are resilient to both crash failures and strategic manipulations of a group of colluding agents. For a system with $n$ distributed agents, we design a deterministic protocol that tolerates 2 colluding agents and a randomized protocol that tolerates $n - 1$ colluding agents, and both tolerate any number of failures. We also show that if colluders are allowed an extra communication round after each synchronous round, there is no protocol that can tolerate even 2 colluding agents and 1 crash failure.

preprint2012arXiv

Effect of Intensity Modulator Extinction on Practical Quantum Key Distribution System

We study how the imperfection of intensity modulator effects on the security of a practical quantum key distribution system. The extinction ratio of the realistic intensity modulator is considered in our security analysis. We show that the secret key rate increases, under the practical assumption that the indeterminable noise introduced by the imperfect intensity modulator can not be controlled by the eavesdropper.

preprint2012arXiv

Electron-phonon mediated heat flow in disordered graphene

We calculate the heat flux and electron-phonon thermal conductance in a disordered graphene sheet, going beyond a Fermi's Golden rule approach to fully account for the modification of the electron-phonon interaction by disorder. Using the Keldysh technique combined with standard impurity averaging methods in the regime $k_F l >> 1$ (where $k_F$ is the Fermi wavevector, l the mean free path), we consider both scalar potential (i.e. deformation potential) and vector potential couplings between electrons and phonons. We also consider the effects of electronic screening at the Thomas-Fermi level. We find that the temperature dependence of the heat flux and thermal conductance is sensitive to the presence of disorder and screening, and reflects the underlying chiral nature of electrons in graphene and the corresponding modification of their diffusive behaviour. In the case of weak screening, disorder enhances the low-temperature heat flux over the clean system (changing the associated power law from $T^4$ to $T^3$), and the deformation potential dominates. For strong screening, both the deformation potential and vector potential couplings make comparable contributions, and the low-temperature heat flux obeys a $T^5$ power law.

preprint2012arXiv

Fermi Surface Reconstruction by Dynamic Magnetic Fluctuations

We demonstrate that nearly critical quantum magnetic fluctuations in strongly correlated electron systems can change the Fermi surface topology and also lead to spin charge separation (SCS) in two dimensions. To demonstrate these effects we consider a small number of holes injected into the bilayer antiferromagnet. The system has a quantum critical point (QCP) which separates magnetically ordered and disordered phases. We demonstrate that in the physically interesting regime there is a magnetically driven Lifshitz point (LP) inside the magnetically disordered phase. At the LP the topology of the hole Fermi surface is changed. We also demonstrate that in this regime the hole spin and charge necessarily separate when approaching the QCP. The considered model sheds light on generic problems concerning the physics of the cuprates.

preprint2012arXiv

Fermi Surface Reconstruction by Dynamic Magnetic Fluctuations and Spin-Charge Separation Near an O(3) Quantum Critical Point

Stimulated by the small/large Fermi surface controversy in the cuprates we consider a small number of holes injected into the bilayer antiferromagnet. The system has an O(3) quantum critical point (QCP) separating the magnetically ordered and the magnetically disordered phases. We demonstrate that nearly critical quantum magnetic fluctuations can change the Fermi surface topology and also lead to spin charge separation (SCS) in two dimensions. We demonstrate that in the physically interesting regime there is a magnetically driven Lifshitz point (LP) inside the magnetically disordered phase. At the LP the topology of the hole Fermi surface is changed. The position of the LP, while being close to the position of the QCP is generally different. Dependent on the additional hole hopping integrals $t^{\prime}$ and $t^{\prime\prime}$, the LP can be located either in the magnetically ordered phase and/or in the magnetically disordered phase. We also demonstrate that in this regime the hole spin and charge necessarily separate when approaching the QCP. The considered model sheds light on generic problems concerning the physics of the cuprates.

preprint2012arXiv

Field test of the wavelength-saving quantum key distribution network

We propose a wavelength-saving topology of quantum key distribution(QKD) network based on passive optical elements, and report the field test of this network on the commercial telecom optical fiber. In this network, 5 nodes are supported with 2 wavelengths, and every two nodes can share secure keys directly at the same time. All QKD links in the network operate at the frequency of 20 MHz. We also characterized the insertion loss and crosstalk effects on the point-to-point QKD system after introducing this QKD network.

preprint2012arXiv

Hierarchic Power Allocation for Spectrum Sharing in OFDM-Based Cognitive Radio Networks

In this paper, a Stackelberg game is built to model the hierarchic power allocation of primary user (PU) network and secondary user (SU) network in OFDM-based cognitive radio (CR) networks. We formulate the PU and the SUs as the leader and the followers, respectively. We consider two constraints: the total power constraint and the interference-to-signal ratio (ISR) constraint, in which the ratio between the accumulated interference and the received signal power at each PU should not exceed certain threshold. Firstly, we focus on the single-PU and multi-SU scenario. Based on the analysis of the Stackelberg Equilibrium (SE) for the proposed Stackelberg game, an analytical hierarchic power allocation method is proposed when the PU can acquire the additional information to anticipate SUs' reaction. The analytical algorithm has two steps: 1) The PU optimizes its power allocation with considering the reaction of SUs to its action. In the power optimization of the PU, there is a sub-game for power allocation of SUs given fixed transmit power of the PU. The existence and uniqueness for the Nash Equilibrium (NE) of the sub-game are investigated. We also propose an iterative algorithm to obtain the NE, and derive the closed-form solutions of NE for the perfectly symmetric channel. 2) The SUs allocate the power according to the NE of the sub-game given PU's optimal power allocation. Furthermore, we design two distributed iterative algorithms for the general channel even when private information of the SUs is unavailable at the PU. The first iterative algorithm has a guaranteed convergence performance, and the second iterative algorithm employs asynchronous power update to improve time efficiency. Finally, we extend to the multi-PU and multi-SU scenario, and a distributed iterative algorithm is presented.

preprint2012arXiv

Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships

Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate the efficacy of our influence maximization algorithm over other heuristic algorithms.

preprint2012arXiv

IRIE: Scalable and Robust Influence Maximization in Social Networks

Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory comparing with PMIA.

preprint2012arXiv

Optimal partial-arcs in VMAT treatment planning

Purpose: To improve the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. Methods and materials: A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial arc with the lowest treatment time. The complete algorithm is called pmerge. Results: Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 seconds. Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose quality is maintained across the initial, vmerge, and pmerge plans to within 5% of the mean doses to the critical organs-at-risk and with target coverage above 98%. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. Conclusions: The pmerge algorithm is an extension to vmerge that automatically finds the partial-arc plan that minimizes the treatment time. VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality. Partial arcs are most applicable to cases with non-centralized targets, where the time savings is greatest.

preprint2012arXiv

Possible $J^{PC} = 0^{+-}$ Exotic State

We study the possible exotic states with $J^{PC} = 0^{+-}$ using the tetraquark interpolating currents with the QCD sum rule approach. The extracted masses are around 4.85 GeV for the charmonium-like states and 11.25 GeV for the bottomomium-like states. There is no working region for the light tetraquark currents, which implies the light $0^{+-}$ state may not exist below 2 GeV.

preprint2012arXiv

Spin-1 charmonium-like states in QCD sum rule

We study the possible spin-1 charmonium-like states by using QCD sum rule approach. We calculate the two-point correlation functions for all the local form tetraquark interpolating currents with $J^{PC}=1^{--}, 1^{-+}, 1^{++}$ and $1^{+-}$ and extract the masses of the tetraquark charmonium-like states. The mass of the $1^{--}$ $qc\bar q\bar c$ state is $4.6\sim4.7$ GeV, which implies a possible tetraquark interpretation for Y(4660) meson. The masses for both the $1^{++}$ $qc\bar q\bar c$ and $sc\bar s\bar c$ states are $4.0\sim 4.2$ GeV, which is slightly above the mass of X(3872). For the $1^{-+}$ and $1^{+-}$ $qc\bar q\bar c$ states, the extracted masses are $4.5\sim4.7$ GeV and $4.0\sim 4.2$ GeV respectively.

preprint2011arXiv

A Novel VSWR-Protected and Controllable CMOS Class E Power Amplifier for Bluetooth Applications

This paper describes the design of a differential class-E PA for Bluetooth applications in 0.18um CMOS technology with load mismatch protection and power control features. The breakdown induced by load mismatch can be avoided by attenuating the RF power to the final stage during over voltage conditions. Power control is realized by means of "open loop" techniques to regulate the power supply voltage, and a novel controllable bias network with temperature compensated is proposed, which allows a moderate power control slope (dB/V) to be achieved. Post-layout Simulation results show that the level of output power can be controlled in 2dBm steps; especially the output power in every step is quite insensitive to temperature variations.

preprint2011arXiv

ARPES Spectral Function in Lightly Doped and Antiferromagnetically Ordered YBa2Cu3O{6+y}

At doping below 6% the bilayer cuprate YBa2Cu3O{6+y} is a collinear antiferromagnet. Independent of doping the value of the staggered magnetization at zero temperature is about 0.6μ_B. This is the maximum value of the magnetization allowed by quantum fluctuations of localized spins. In this low doping regime the compound is a normal conductor with a finite resistivity at zero temperature. These experimental observations create a unique opportunity for theory to perform a controlled calculation of the electron spectral function. In the present work we perform this calculation within the framework of the extended t-J model. As one expects the Fermi surface consists of small hole pockets centered at (π/2,π/2). The electron spectral function is very strongly anisotropic with maximum of intensity located at the inner parts of the pockets and with very small intensity at the outer parts. We also found that the antiferromagnetic correlations act against the bilayer bonding-antibonding splitting destroying it. The bilayer Fermi surface splitting is practically zero.

preprint2011arXiv

Attacking practical quantum key distribution system with wavelength dependent beam splitter and multi-wavelength sources

Unconditional security of quantum key distribution protocol can be guaranteed by the basic property of quantum mechanics. Unfortunately, the practical quantum key distribution system always have some imperfections, and the practical system may be attacked if the imperfection can be controlled by the eavesdropper Eve. Applying the fatal security loophole introduced by the imperfect beam splitter's wavelength dependent optical property, we propose wavelength-dependent attacking model, which can be applied to almost all practical quantum key distribution systems with the passive state modulation and photon state detection after the practical beam splitter. Utilizing our attacking model, we experimentally demonstrate the attacking system based on practical polarization encoding quantum key distribution system with almost 100% success probability. Our result demonstrate that all practical devices require tightened security inspection for avoiding side channel attacks in practical quantum key distribution experimental realizations.

preprint2011arXiv

Bulk and Surface Nanoscale Hole Density Inhomogeneity in HgBa$_2$CuO$_{4+δ}$ and Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ Cuprates

It is well established that the hole density in the prototypical superconductor La$_{2-x}$Sr$_x$CuO$_4$ is very inhomogeneous due to Sr-dopant induced disorder. On the other hand, until recently it is widely believed that the hole distribution in HgBa$_2$CuO$_{4+δ}$ and Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ doped by interstitial oxygen is much more uniform. Recent nuclear magnetic resonance measurements indicate, however, that the charge inhomogeneity in HgBa$_2$CuO$_{4+δ}$ is close to that in La$_{2-x}$Sr$_x$CuO$_4$. Calculations performed in the present paper confirm this observation. We also show that the charge inhomogeneity is most pronounced at the surface layer that can be probed by scanning tunneling microscope. Our simulations demonstrate that, despite having similar amplitudes of charge inhomogeneity, the hole mean free path in HgBa$_2$CuO$_{4+δ}$ is substantially longer than that in La$_{2-x}$Sr$_x$CuO$_4$. The screening of Coulomb repulsion in HgBa$_2$CuO$_{4+δ}$ is also stronger. These two reasons might explain the difference in superconducting critical temperatures between these two compounds.

preprint2011arXiv

Data sets of very large linear feasibility problems solved by projection methods

We give a link to a page on the Web on which we deposited a set of eight huge Linear Programming (LP) problems for Intensity-Modulated Proton Therapy (IMPT) treatment planning. These huge LP problems were employed in our recent research and we were asked to make them public.

preprint2011arXiv

Doping Dependence of Gap Inhomogeneities at Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+δ}$ Surfaces

We study the inhomogeneity of the electronic pairing gap observed by STM near the surface of Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+δ}$ to be correlated with interstitial O defects. We treat the problem in a slave boson mean field theory of a disordered $t-t^{\prime}-J$ model, and identify three aspects of the O defects related to the inhomogeneity: (1)the superexchange interaction is locally enhanced in their vicinity, which enhances the local pairing gap and reduces the coherence peak; (2)they donate holes into CuO$_2$ plane, which reduces the spinon density of states of and hence the average gap at large doping; (3)holes are locally attracted to the vicinity of oxygen defects, which causes impurity bound state and further reduces the coherence peak. The interplay of these mechanisms explains simultaneously the locally enhanced pairing gap around oxygen defects, and the reduction of average gap as increasing oxygen concentration.

preprint2011arXiv

Including robustness in multi-criteria optimization for intensity-modulated proton therapy

We present a method to include robustness into a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimizationmethod reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved tradeoffs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer, making a computationally friendly interface possible to the planner.

preprint2011arXiv

Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model Technical Report

In many real-world situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model. Under the CLT model, we focus on the problem that one entity tries to block the influence propagation of its competing entity as much as possible by strategically selecting a number of seed nodes that could initiate its own influence propagation. We call this problem the influence blocking maximization (IBM) problem. We prove that the objective function of IBM in the CLT model is submodular, and thus a greedy algorithm could achieve 1-1/e approximation ratio. However, the greedy algorithm requires Monte-Carlo simulations of competitive influence propagation, which makes the algorithm not efficient. We design an efficient algorithm CLDAG, which utilizes the properties of the CLT model, to address this issue. We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on real-world and synthetic datasets. Our results show that CLDAG is able to provide best accuracy in par with the greedy algorithm and often better than other algorithms, while it is two orders of magnitude faster than the greedy algorithm.

preprint2011arXiv

Multicriteria VMAT optimization

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems.

preprint2011arXiv

Optimal Pricing in Social Networks with Incomplete Information

In revenue maximization of selling a digital product in a social network, the utility of an agent is often considered to have two parts: a private valuation, and linearly additive influences from other agents. We study the incomplete information case where agents know a common distribution about others' private valuations, and make decisions simultaneously. The "rational behavior" of agents in this case is captured by the well-known Bayesian Nash equilibrium. Two challenging questions arise: how to compute an equilibrium and how to optimize a pricing strategy accordingly to maximize the revenue assuming agents follow the equilibrium? In this paper, we mainly focus on the natural model where the private valuation of each agent is sampled from a uniform distribution, which turns out to be already challenging. Our main result is a polynomial-time algorithm that can exactly compute the equilibrium and the optimal price, when pairwise influences are non-negative. If negative influences are allowed, computing any equilibrium even approximately is PPAD-hard. Our algorithm can also be used to design an FPTAS for optimizing discriminative price profile.

preprint2011arXiv

Organic Molecules on Wide-Gap Insulators: Electronic Excitations

The electronic excitation of a conjugated molecule-insulator interface, as exemplified by the adsorption of benzoic acid and its phenolic derivative on NaCl(001) surface, is addressed by many-body Green's function methods. By solving the two-particle Bethe-Salpeter equation on top of the $GW$ quasiparticle energies, it turns out that instead of the intramolecular $π-π^\ast$ transition of the adsorbate, the lowest singlet excited state of the adsorbate system, being a charge transfer excitonic effect, is essentially assigned to the transition from the surface valence band maximum to the $π-π^\ast$ state of the molecule. An accurate description of this lowest electronic excitations confined at the interface requires the knowledge of a full excitonic Hamiltonian due to the sizable electron-hole exchange interaction.

preprint2011arXiv

Phase transition behavior in a cellular automaton model with different initial configurations

We investigate the dynamical transition from free-flow to jammed traffic, which is related to the divergence of the relaxation time and susceptibility of the energy dissipation rate $E_d$, in the Nagel-Schreckenberg (NS) model with two different initial configurations. Different initial configurations give rise to distinct phase transition. We argue that the phase transition of the deterministic NS model with megajam and random initial configuration is first- and second-order phase transition, respectively. The energy dissipation rate $E_d$ and relaxation time follow power-law behavior in some cases. The associated dynamic exponents have also been presented.

preprint2011arXiv

Quantum criticality in a Mott pn-junction in an armchair carbon nanotube

In an armchair carbon nanotube pn junction the p- and n- regions are separated by a region of a Mott insulator, which can backscatter electrons only in pairs. We predict a quantum-critical behavior in such a pn junction. Depending on the junction's built-in electric field E, its conductance G scales either to zero or to the ideal value G=4e^2/h as the temperature T is lowered. The two types of the G(T) dependence indicate the existence, at some special value of E, of an intermediate quantum critical point with a finite conductance G<4e^2/h. This makes the pn junction drastically different from a simple barrier in a Luttinger liquid.

preprint2011arXiv

Semi-device independent random number expansion without entanglement

By testing the classical correlation violation between two systems, the random number can be expanded and certified without applying classical statistical method. In this work, we propose a new random number expansion protocol without entanglement, and the randomness can be guaranteed only by the 2-dimension quantum witness violation. Furthermore, we only assume that the dimensionality of the system used in the protocol has a tight bound, and the whole protocol can be regarded as a semi-device independent black-box scenario. Comparing with the device independent random number expansion protocol based on entanglement, our protocol is much easier to implement and test.

preprint2011arXiv

Strategies used as spectroscopy of financial markets reveal new stylized facts

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This enormous dataset allows us to compare (i) a closed national market (A-shares) with an international market (B-shares), (ii) individuals and institutions and (iii) real investors to random strategies with respect to timing that share otherwise all other characteristics. We find that more trading results in smaller net return due to trading frictions. We unveiled quantitative power laws with non-trivial exponents, that quantify the deterioration of performance with frequency and with holding period of the strategies used by investors. Random strategies are found to perform much better than real ones, both for winners and losers. Surprising large arbitrage opportunities exist, especially when using zero-intelligence strategies. This is a diagnostic of possible inefficiencies of these financial markets.

preprint2011arXiv

Strongly discontinuous explosive percolation with multiple giant components

We generalize the random graph evolution process of Bohman, Frieze, and Wormald [T. Bohman, A. Frieze, and N. C. Wormald, Random Struct. Algorithms, 25, 432 (2004)]. Potential edges, sampled uniformly at random from the complete graph, are considered one at a time and either added to the graph or rejected provided that the fraction of accepted edges is never smaller than a decreasing function asymptotically approaching the value alpha = 1/2. We show that multiple giant components appear simultaneously in a strongly discontinuous percolation transition and remain distinct. Furthermore, tuning the value of alpha determines the number of such components with smaller alpha leading to an increasingly delayed and more explosive transition. The location of the critical point and strongly discontinuous nature are not affected if only edges which span components are sampled.

preprint2011arXiv

Suppression of Grain Boundaries in Graphene Growth on Superstructured Mn-Cu(111) Surface

As undesirable defects, grain boundaries (GBs) are widespread in epitaxial graphene using existing growth methods on metal substrates. Employing density functional theory calculations, we first identify that the misorientations of carbon islands nucleated on a Cu(111) surface lead to the formation of GBs as the islands coalesce. We then propose a two-step kinetic pathway to effectively suppress the formation of GBs. In the first step, large aromatic hydrocarbon molecules are deposited onto a $\sqrt{3}\times\sqrt{3}$ superstructured Cu-Mn alloyed surface to seed the initial carbon clusters of a single orientation; in the second step, the seeded islands are enlarged through normal chemical vapor deposition of methane to form a complete graphene sheet. The present approach promises to overcome a standing obstacle in large scale single-crystal graphene fabrication.

preprint2011arXiv

The 7-channel FIR HCN Interferometer on J-TEXT Tokamak

A seven-channel far-infrared hydrogen cyanide (HCN) laser interferometer has been established aiming to provide the line integrated plasma density for the J-TEXT experimental scenarios. A continuous wave glow discharge HCN laser designed with a cavity length 3.4 m is used as the laser source with a wavelength of 337 μm and an output power up to 100 mW. The system is configured as a Mach-Zehnder type interferometer. Phase modulation is achieved by a rotating grating, with a modulation frequency of 10 kHz which corresponds to the temporal resolution of 0.1 ms. The beat signal is detected by TGS detector. The phase shift induced by the plasma is derived by the comparator with a phase sensitivity of 0.06 fringe. The experimental results measured by the J-TEXT interferometer are presented in details. In addition, the inversed electron density profile done by a conventional approach is also given. The kinematic viscosity of dimethyl silicone and vibration control is key issues for the system performance. The laser power stability under different kinematic viscosity of silicone oil is presented. A visible improvement of measured result on vibration reduction is shown in the paper.

preprint2011arXiv

The Vector and Axial-Vector Charmonium-like States

After constructing all the tetraquark interpolating currents with $J^{PC}=1^{-+}, 1^{--}, 1^{++}$ and $1^{+-}$ in a systematic way, we investigate the two-point correlation functions to extract the masses of the charmonium-like states with QCD sum rule. For the $1^{--}$ $qc\bar q\bar c$ charmonium-like state, $m_X=4.6\sim4.7$ GeV, which implies a possible tetraquark interpretation for the state Y(4660). The masses for both the $1^{++}$ $qc\bar q\bar c$ and $sc\bar s\bar c$ charmonium-like states are around $4.0\sim 4.2$ GeV, which are slightly above the mass of X(3872). For the $1^{-+}$ $qc\bar q\bar c$ charmonium-like state, the extracted mass is $4.5\sim 4.7$ GeV. We also discuss the possible decay modes and experimental search of the $1^{-+}$ charmonium-like states.

preprint2010arXiv

2-Modules and the Representation of 2-Rings

In this paper, we develop 2-dimensional algebraic theory which closely follows the classical theory of modules. The main results are giving definitions of 2-module and the representation of 2-ring. Moreover, for a 2-ring $\cR$, we prove that its modules form a 2-Abelian category.

preprint2010arXiv

Anomalous molecular orbital variation upon adsorption on wide band gap insulator

It is commonly believed that organic molecules are physisorbed on the ideal non-polar surfaces of wide band gap insulators with limited variation of the electronic properties of the adsorbate molecule. On the basis of first principles calculations within density functional theory (DFT) and $GW$ approximation, we show that this is not generally true. We find that the molecular frontier orbitals undergo significant changes when a hydroxy acid (here we chose gluconic acid) is adsorbed on MgSO$_4$$\cdot$H$_2$O(100) surface due to the complex interaction between the molecule and the insulating surface. The predicted trend of the adsorption effect on the energy gap obtained by DFT is reversed when the surface polarization effect is taken into account via the many-body corrections.

preprint2010arXiv

Color centers in NaCl by hybrid functionals

We present in this work the electronic structure and transition energies (both thermodynamic and optical) of Cl vacancies in NaCl by hybrid density functionals. The underestimated transition energies by the semi-local functional inherited from the band gap problem are recovered by the PBE0 hybrid functional through the non-local exact exchange, whose amount is adjusted to reproduce the experimental band gap. The hybrid functional also gives a better account of the lattice relaxation for the defect systems arising from the reduced self-interaction. On the other hand, the quantitative agreement with experimental vertical transition energy cannot be achieved with hybrid functionals due to the inaccurate descriptions of the ionization energies of the localized defect and the positions of the band edges.

preprint2010arXiv

Decay of a plasmon into neutral modes in a carbon nanotube

We evaluate the rate of energy loss of a plasmon in a disorder-free carbon nanotube. The plasmon decays into neutral bosonic excitations of the electron liquid. The process is mediated either by phonon-assisted backscattering of a single electron, or Umklapp backscattering of two electrons. To lowest order in the backscattering interactions the partial decay rates are additive. At zero doping the corresponding decay rates scale as power-laws of the temperature with positive and negative exponents for the two mechanisms, respectively. The precise values of the exponents depend on the Luttinger liquid parameter. At finite doping the decay rates are described by universal crossover functions of frequency and chemical potential measured in units of temperature. In the evaluation of the plasmon decay, we concentrate on a finite-length geometry allowing excitation of plasma resonances.

preprint2010arXiv

Detection of bottom ferromagnetic electrode oxidation in magnetic tunnel junctions by magnetometry measurements

Surface oxidation of the bottom ferromagnetic (FM) electrode, one of the major detrimental factors to the performance of a Magnetic Tunnel Junction (MTJ), is difficult to avoid during the fabrication process of the MTJ's tunnel barrier. Since Co rich alloys are commonly used for the FM electrodes in MTJs, over-oxidation of the tunnel barrier results in the formation of a CoO antiferromagnetic (AF) interface layer which couples with the bottom FM electrode to form a typical AF/FM exchange bias (EB) system. In this work, surface oxidation of the CoFe and CoFeB bottom electrodes was detected via magnetometry measurements of exchange-bias characterizations including the EB field, training effect, uncompensated spin density, and coercivity. Variations of these parameters were found to be related to the surface oxidation of the bottom electrode, among them the change of coercivity is most sensitive. Annealed samples show evidence for an oxygen migration back to the MgO tunnel barrier by annealing.

preprint2010arXiv

Higher Dimensional Homology Algebra II:Projectivity

In this paper, we will prove that the 2-category (2-SGp) of symmetric 2-groups and 2-category ($\cR$-2-Mod) of $\cR$-2-modules(\cite{5}) have enough projective objects, respectively.

preprint2010arXiv

Higher Dimensional Homology Algebra III:Projective Resolutions and Derived 2-Functors in (2-SGp)

In this paper, we will define the derived 2-functor by projective resolution of any symmetric 2-group, and give some related properties of the derived 2-functor.

preprint2010arXiv

Higher Dimensional Homology Algebra IV:Projective Resolutions and Derived 2-Functors in ($\cR$-2-Mod)

In this paper, we will construct the projective resolution of any $\cR$-2-module, define the derived 2-functor and give some related properties of the derived 2-functor.

preprint2010arXiv

Higher Dimensional Homology Algebra V:Injective Resolutions and Derived 2-Functors in ($\cR$-2-Mod)

In this paper, we will construct the injective resolution of any $\cR$-2-module, define the right derived 2-functor, and give some related properties of the derived 2-functor in ($\cR$-2-Mod).

preprint2010arXiv

Magnetic quantum oscillations and multiple holon pockets in underdoped YBa$_2$Cu$_3$O$_{6+y}$

We show that lightly doped YBa$_2$Cu$_3$O$_{6+y}$ has multiple holon pockets with different areas, which lead to multiple frequencies of magnetic quantum oscillations. Using neutron scattering data on incommensurate spin ordering we determine these areas, which yields frequencies in good agreement with experiments. Divergence of the effective mass observed in magnetic quantum oscillations indicates a quantum phase transition at the oxygen content $y\approx 0.48$. We argue that the transition is the onset of quasistatic incommensurate magnetic order predicted by theory and observed in neutron scattering.

preprint2010arXiv

Order flow dynamics around extreme price changes on an emerging stock market

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.

preprint2010arXiv

TCP Reno over Adaptive CSMA

An interesting distributed adaptive CSMA MAC protocol, called adaptive CSMA, was proposed recently to schedule any strictly feasible achievable rates inside the capacity region. Of particular interest is the fact that the adaptive CSMA can achieve a system utility arbitrarily close to that is achievable under a central scheduler. However, a specially designed transport-layer rate controller is needed for this result. An outstanding question is whether the widely-installed TCP Reno is compatible with adaptive CSMA and can achieve the same result. The answer to this question will determine how close to practical deployment adaptive CSMA is. Our answer is yes and no. First, we observe that running TCP Reno directly over adaptive CSMA results in severe starvation problems. Effectively, its performance is no better than that of TCP Reno over legacy CSMA (IEEE 802.11), and the potentials of adaptive CSMA cannot be realized. Fortunately, we find that multi-connection TCP Reno over adaptive CSMA with active queue management can materialize the advantages of adaptive CSMA. NS-2 simulations demonstrate that our solution can alleviate starvation and achieve fair and efficient rate allocation. Multi-connection TCP can be implemented at either application or transport layer. Application-layer implementation requires no kernel modification, making the solution readily deployable in networks running adaptive CSMA.

preprint2009arXiv

Conductance of a single-atom carbon chain with graphene leads

We study the conductance of an interconnect between two graphene leads formed by a single-atom carbon chain. Its dependence on the chemical potential and the number of atoms in the chain is qualitatively different from that in the case of normal metal leads. Electron transport proceeds via narrow resonant states in the wire. The latter arise due to strong reflection at the junctions between the chain and the leads, which is caused by the small density of states in the leads at low energy. The energy dependence of the transmission coefficient near resonance is asymmetric and acquires a universal form at small energies. We find that in the case of leads with the zigzag edges the dispersion of the edge states has a significant effect on the device conductance.

preprint2009arXiv

Long-term correlations and multifractal analysis of trading volumes for Chinese stocks

We investigate the temporal correlations and multifractal nature of trading volume of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. We find that the trading volume exhibit size-dependent non-universal long memory and multifractal nature. No crossover in the power-law dependence of the detrended fluctuation functions is observed. Our results show that the intraday pattern in the trading volume has negligible impact on the long memory and multifractality.

preprint2009arXiv

Metal-insulator transition in vanadium dioxide nanobeams: probing sub-domain properties of strongly correlated materials

Many strongly correlated electronic materials, including high-temperature superconductors, colossal magnetoresistance and metal-insulator-transition (MIT) materials, are inhomogeneous on a microscopic scale as a result of domain structure or compositional variations. An important potential advantage of nanoscale samples is that they exhibit the homogeneous properties, which can differ greatly from those of the bulk. We demonstrate this principle using vanadium dioxide, which has domain structure associated with its dramatic MIT at 68 degrees C. Our studies of single-domain vanadium dioxide nanobeams reveal new aspects of this famous MIT, including supercooling of the metallic phase by 50 degrees C; an activation energy in the insulating phase consistent with the optical gap; and a connection between the transition and the equilibrium carrier density in the insulating phase. Our devices also provide a nanomechanical method of determining the transition temperature, enable measurements on individual metal-insulator interphase walls, and allow general investigations of a phase transition in quasi-one-dimensional geometry.

preprint2009arXiv

Phase-resolved spectral analysis of 4U 1901+03 during its outburst

The high mass X-ray binary 4U 1901+03 was reported to have the pulse profile evolving with the X-ray luminosity and energy during its outburst in February-July 2003: the pulse peak changed from double to single along with the decreasing luminosity. We have carried out a detailed analysis on the contemporary phase-resolved energy spectrum of 4U 1901+03 as observed by Rossi X-ray Timing Explorer (RXTE). We find that, both the continuum and the pulse spectra are phase dependent. The optical depth derived from the pulse spectrum is in general larger than that from the continuum. Fe Ka emission line is only detected in the spectrum of the continuum and is missing in the pulse spectrum. This suggests an origin of Fe emission from the accretion disk but not the surface of the neutron star.

preprint2009arXiv

Scaling and memory in the non-poisson process of limit order cancelation

The order submission and cancelation processes are two crucial aspects in the price formation of stocks traded in order-driven markets. We investigate the dynamics of order cancelation by studying the statistical properties of inter-cancelation durations defined as the waiting times between consecutive order cancelations of 22 liquid stocks traded on the Shenzhen Stock Exchange of China in year 2003. Three types of cancelations are considered including cancelation of any limit orders, of buy limit orders and of sell limit orders. We find that the distributions of the inter-cancelation durations of individual stocks can be well modeled by Weibulls for each type of cancelation and the distributions of rescaled durations of each type of cancelations exhibit a scaling behavior for different stocks. Complex intraday patterns are also unveiled in the inter-cancelation durations. The detrended fluctuation analysis (DFA) and the multifractal DFA show that the inter-cancelation durations possess long-term memory and multifractal nature, which are not influenced by the intraday patterns. No clear crossover phenomenon is observed in the detrended fluctuation functions with respect to the time scale. These findings indicate that the cancelation of limit orders is a non-Poisson process, which has potential worth in the construction of order-driven market models.

preprint2009arXiv

X-ray absorption signatures of the molecular environment in water and ice

The x-ray absorption spectra of water and ice are calculated with a many-body approach for electron-hole excitations. The experimental features, including the small effects of temperature change in the liquid, are quantitatively reproduced from molecular configurations generated by ab-initio molecular dynamics. The spectral difference between the solid and the liquid is due to two major short range order effects. One, due to breaking of hydrogen bonds, enhances the pre-edge intensity in the liquid. The other, due to a non-bonded molecular fraction in the first coordination shell, affects the main spectral edge in the conversion of ice to water. This effect may not involve hydrogen bond breaking as shown by experiment in high-density amorphous ice.

preprint2008arXiv

Decoherence in rf SQUID Qubits

We report measurements of coherence times of an rf SQUID qubit using pulsed microwaves and rapid flux pulses. The modified rf SQUID, described by an double-well potential, has independent, in situ, controls for the tilt and barrier height of the potential. The decay of coherent oscillations is dominated by the lifetime of the excited state and low frequency flux noise and is consistent with independent measurement of these quantities obtained by microwave spectroscopy, resonant tunneling between fluxoid wells and decay of the excited state. The oscillation's waveform is compared to analytical results obtained for finite decay rates and detuning and averaged over low frequency flux noise.

preprint2003arXiv

Pion susceptibility of the QCD vacuum from an effective quark-quark interaction

A modified method for calculating the pion vacuum susceptibility from an effective quark-quark interaction model is derived. Within this approach it is shown that the pion vacuum susceptibility is free of ultraviolet divergence and is much smaller than the previous estimations.

preprint1998arXiv

Ground State Properties of One Dimensional S=1/2 Heisenberg Model with Dimerization and Quadrumerization

The one dimensional S=1/2 Heisenberg model with dimerization and quadrumerization is studied by means of the numerical exact diagonalization of finite size systems. Using the phenomenological renormalization group and finite size scaling law, the ground state phase diagram is obtained in the isotropic case. It exhibits a variety of the ground states which contains the S=1 Haldane state, S=1 dimer state and S=1/2 dimer state as limiting cases. The gap exponent $ν$ is also calculated which coincides with the value for the dimerization transition of the isotropic Heisenberg chain. In the XY limit, the phase diagram is obtained analytically and the comparison is made with the isotropic case.

preprint1995arXiv

A Generalization of Quantum Statistics

We propose a new fractional statistics for arbitrary dimensions, based on an extension of Pauli's exclusion principle, to allow for finite multi-occupancies of a single quantum state. By explicitly constructing the many-body Hilbert space, we obtain a new algebra of operators and a new thermodynamics. The new statistics is different from fractional exclusion statistics; and in a certain limit, it reduces to the case of parafermi statistics.

preprint1995arXiv

Thermodynamics of an Anyon System

We examine the thermal behavior of a relativistic anyon system, dynamically realized by coupling a charged massive spin-1 field to a Chern-Simons gauge field. We calculate the free energy (to the next leading order), from which all thermodynamic quantities can be determined. As examples, the dependence of particle density on the anyon statistics and the anyon anti-anyon interference in the ideal gas are exhibited. We also calculate two and three-point correlation functions, and uncover certain physical features of the system in thermal equilibrium.

preprint1994arXiv

Renormalization Group Flow of Four-fermi with Chern-Simons Interaction

We introduce Chern-Simons interaction into the three dimensional four-fermi theory, ad suggest a possible line of non-Gaussian infrared stable fixed points of the four-fermi operator, this line is characterized by the Chern-Simons coupling.

Wei Chen

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390 published item(s)

Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts

SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation

Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma

Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization

Strong decays of $T_{c\bar s0}(2900)^{++/0}$ as a fully open-flavor tetraquark state

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

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images

Automated Sleep Staging via Parallel Frequency-Cut Attention

Nonreciprocal Charge and Spin Transport Induced by Non-Hermitian Skin Effect in Mesoscopic Heterojunctions

Proximity-induced zero-energy states indistinguishable from topological edge states

3D large-scale fused silica microfluidic chips enabled by hybrid laser microfabrication for continuous-flow UV photochemical synthesis

A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

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

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

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

Availability Attacks Create Shortcuts

Branching Reinforcement Learning

Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

Characterization of BNL and HPK AC-LGAD sensors with a 120 GeV proton beam

CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations

Cyclotron quantization and mirror-time transition on nonreciprocal lattices

D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights

Deep Generative Models for Geometric Design Under Uncertainty

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

Does Momentum Change the Implicit Regularization on Separable Data?

Entanglement on nucleation barrier of polymer crystal

Epidemic Plateau: A Phenomenon under Adaptive Prevention Strategies

Event-by-event jet anisotropy and hard-soft tomography of the quark-gluon plasma

Evidence of Spin Frustration in Vanadium Diselenide Monolayer Magnet

Fabry-Pérot interference in 2D low-density Rashba gas

Failure behaviors and processing maps with failure domains for hot compression of a powder metallurgy Ni-based superalloy

Federated Visualization: A Privacy-preserving Strategy for Aggregated Visual Query

From hydro to jet quenching, coalescence and hadron cascade: a coupled approach to solving the $R_{AA}\otimes v_2$ puzzle

Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology

HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks

Higher order monotonicity and submodularity of influence in social networks: from local to global

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

Mass of $1^{-+}$ four-quark--hybrid mixed states

Measurement of interaction-dressed Berry curvature and quantum metric in solids by optical absorption

Mutual Distillation Learning Network for Trajectory-User Linking

Network Inference and Influence Maximization from Samples

Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs

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

New hadron configuration: The double-gluon hybrid state

Newly observed $a_0(1817)$ as the scaling point of constructing the scalar meson spectroscopy

Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization

On the Phases of a Semi-Sectorial Matrix

Online Competitive Influence Maximization

Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback

Optimal $(0,1)$-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)

Quasi-periodic Accelerations of Energetic Particles during a Solar Flare

Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

Search for the elusive jet-induced diffusion wake in $Z/γ$-jets with 2D jet tomography in high-energy heavy-ion collisions

Shape-Aware Monocular 3D Object Detection

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

Symmetry-enforced nodal lines in the band structures of vacancy-engineered graphene

Thompson Sampling for Combinatorial Semi-Bandits

Tighter monogamy relations for the Tsallis-q and Rényi-$α$ entanglement in multiqubit systems

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed

Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks

Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Unbalanced-basis-misalignment tolerant measurement-device-independent quantum key distribution

Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

BN-invariant sharpness regularizes the training model to better generalization

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

Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation

Establishing the first hidden-charm pentaquark with strangeness

How Does Data Augmentation Affect Privacy in Machine Learning?