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Bin Liu

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Machine Learning Computer Vision cond-mat.soft cond-mat.supr-con physics.plasm-ph physics.flu-dyn quant-ph astro-ph.GA cond-mat.mtrl-sci cond-mat.str-el Artificial Intelligence astro-ph.HE gr-qc Information Retrieval math.OC nlin.PS cond-mat.mes-hall Cryptography and Security eess.AS hep-th physics.comp-ph astro-ph.EP astro-ph.SR Biological Physics math.CV math.FA Sound astro-ph.IM Computation Computation and Language cond-mat.other Distributed, Parallel, and Cluster Computing eess.IV hep-ph math.AP math.NA math.PR Networking and Internet Architecture physics.ins-det physics.optics Social and Information Networks Applications astro-ph.CO Computational Engineering, Finance, and Science cond-mat.stat-mech eess.SY math.CO math.DS Methodology Multimedia Neural and Evolutionary Computing Numerical Analysis physics.app-ph physics.atom-ph physics.class-ph physics.geo-ph physics.soc-ph Quantitative Methods Systems and Control

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preprint2026arXiv

Advancing Aesthetic Image Generation via Composition Transfer

Composition is a cornerstone of visual aesthetics, influencing the appeal of an image. While its principles operate independently of specific content, in practice, composition is often coupled with semantics. As a result, existing methods often enhance composition either through implicit learning or by semantics-based layout control, rather than explicitly modeling composition itself. To address this gap, we introduce Composer, a framework rooted in aesthetic theory, designed to model composition in a semantic-agnostic manner. First, it supports composition transfer by extracting key composition-aware representations from a reference image and leveraging a tailored conditional guidance module to control composition based on pre-trained diffusion models. Second, when users specify only text themes without a composition reference, Composer supports theme-driven composition retrieval by leveraging the in-context learning capabilities of Large Vision-Language Models (LVLMs), achieving explicit composition planning. To enhance composition in a reference-free mode, we conduct text-to-composition fine-tuning on the trained control module to enable implicit composition planning. Furthermore, we curated a high-quality dataset comprising 2 million image-text pairs using state-of-the-art generative models to support model training. Experimental results demonstrate that Composer significantly enhances aesthetic quality in text-to-image tasks and facilitates personalized composition control and transfer, offering users precision and flexibility in the creative process.

preprint2024arXiv

EIGER IV: The cool 10$^4$K circumgalactic environment of high-$z$ galaxies reveals remarkably efficient IGM enrichment

We report new observations of the cool diffuse gas around 29, $2.3<z<6.3$ galaxies, using deep JWST/NIRCam slitless grism spectroscopy around the sightline to the quasar J0100+2802. The galaxies span a stellar mass range of $7.1 \leq \log M_{*}/M_{sun} \leq 10.7$, and star-formation rates of $-0.1 < \log \; SFR/M_{sun}yr^{-1} \; <2.3$. We find galaxies for seven MgII absorption systems within 300 kpc of the quasar sightline. The MgII radial absorption profile falls off sharply with radii, with most of the absorption extending out to 2-3$R_{200}$ of the host galaxies. Six out of seven MgII absorption systems are detected around galaxies with $\log M_{*}/M_{sun} >$9. MgII absorption kinematics are shifted from the systemic redshift of host galaxies with a median absolute velocity of 135 km/s and standard deviation of 85 km/s. The high kinematic offset and large radial separation ($R> 1.3 R_{200}$), suggest that five out of the seven MgII absorption systems are gravitationally not bound to the galaxies. In contrast, most cool circumgalactic media at $z<1$ are gravitationally bound. The high incidence of unbound MgII gas in this work suggests that towards the end of reionization, galaxy halos are in a state of remarkable disequilibrium, and are highly efficient in enriching the intergalactic medium. Two strongest MgII absorption systems are detected at $z\sim$ 4.22 and 4.5, the former associated with a merging galaxy system and the latter associated with three kinematically close galaxies. Both these galaxies reside in local galaxy over-densities, indicating the presence of cool MgII absorption in two "proto-groups" at $z>4$.

preprint2024arXiv

Robust Dynamic Operating Envelopes via Superellipsoid-based Convex Optimisation in Unbalanced Distribution Networks

Dynamic operating envelopes (DOEs) have been introduced to integrate distributed energy resources (DER) in distribution networks via real-time management of network capacity limits. Recent research demonstrates that uncertainties in DOE calculations should be carefully considered to ensure network integrity while minimising curtailment of consumer DERs. This letter proposes a novel approach to calculating DOEs that is robust against uncertainties in the utilisation of allocated capacity limits and demonstrates that the reported solution can attain close to global optimality performance compared with existing approaches.

preprint2024arXiv

Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder

Deep generative models have been demonstrated as problematic in the unsupervised out-of-distribution (OOD) detection task, where they tend to assign higher likelihoods to OOD samples. Previous studies on this issue are usually not applicable to the Variational Autoencoder (VAE). As a popular subclass of generative models, the VAE can be effective with a relatively smaller model size and be more stable and faster in training and inference, which can be more advantageous in real-world applications. In this paper, We propose a novel VAE-based score called Error Reduction (ER) for OOD detection, which is based on a VAE that takes a lossy version of the training set as inputs and the original set as targets. Experiments are carried out on various datasets to show the effectiveness of our method, we also present the effect of design choices with ablation experiments. Our code is available at: https://github.com/ZJLAB-AMMI/VAE4OOD.

preprint2023arXiv

GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning. Code is available at https://github.com/zeroQiaoba/GCNet.

preprint2022arXiv

A DNS Tunnel Sliding Window Differential Detection Method Based on Normal Distribution Reasonable Range Filtering

A covert attack method often used by APT organizations is the DNS tunnel, which is used to pass information by constructing C2 networks. And they often use the method of frequently changing domain names and server IP addresses to evade monitoring, which makes it extremely difficult to detect them. However, they carry DNS tunnel information traffic in normal DNS communication, which inevitably brings anomalies in some statistical characteristics of DNS traffic, so that it would provide security personnel with the opportunity to find them. Based on the above considerations, this paper studies the statistical discovery methodology of typical DNS tunnel high-frequency query behavior. Firstly, we analyze the distribution of the DNS domain name length and times and finds that the DNS domain name length and times follow the normal distribution law. Secondly, based on this distribution law, we propose a method for detecting and discovering high-frequency DNS query behaviors of non-single domain names based on the statistical rules of domain name length and frequency and we also give three theorems as theoretical support. Thirdly, we design a sliding window difference scheme based on the above method. Experimental results show that our method has a higher detection rate. At the same time, since our method does not need to construct a data set, it has better practicability in detecting unknown DNS tunnels. This also shows that our detection method based on mathematical models can effectively avoid the dilemma for machine learning methods that must have useful training data sets, and has strong practical significance.

preprint2022arXiv

Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation

Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define user-item preference as the distance between their embedding representations, and 2) embedding-based Approximate Nearest Neighbor (ANN) search to tackle the efficiency problem introduced by large-scale corpus. While providing efficient retrieval, the embedding-based retrieval pattern also limits the model capacity since the form of user-item preference measure is restricted to the distance between their embedding representations. However, for other more precise user-item preference measures, e.g., preference scores directly derived from a deep neural network, they are computationally intractable because of the lack of an efficient retrieval method, and an exhaustive search for all user-item pairs is impractical. In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e.g., a deep neural network. Our main idea is to perform a greedy walk with a matching function in a similarity graph constructed from all items. To solve the problem that the similarity measures of graph construction and user-item matching function are heterogeneous, we propose a pluggable adversarial training task to ensure the graph search with arbitrary matching function can achieve fairly high precision. Experimental results in both open source and industry datasets demonstrate the effectiveness of our method. The proposed method has been fully deployed in the Taobao display advertising platform and brings a considerable advertising revenue increase. We also summarize our detailed experiences in deployment in this paper.

preprint2022arXiv

Bayesian Negative Sampling for Recommendation

How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informative instances, few has been done to discriminating false negative from true negative for unbiased negative sampling. On the basis of our order relation analysis of negatives' scores, we first derive the class conditional density of true negatives and that of false negatives. We next design a Bayesian classifier for negative classification, from which we define a model-agnostic posterior probability estimate of an instance being true negative as a quantitative negative signal measure. We also propose a Bayesian optimal sampling rule to sample high-quality negatives. The proposed Bayesian Negative Sampling (BNS) algorithm has a linear time complexity. Experimental studies validate the superiority of BNS over the peers in terms of better sampling quality and better recommendation performance.

preprint2022arXiv

Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks

In this paper, we are concerned with image classification with deep convolutional neural networks (CNNs). We focus on the following question: given a set of candidate CNN models, how to select the right one with the best generalization property for the current task? Current model selection methods all require access to a batch of labeled data for computing a pre-specified performance metric, such as the cross-entropy loss, the classification error rate and the negative log-likelihood. In many practical cases, labels are not available in time as labeling itself is a time-consuming and expensive task. To this end, we propose an approach to CNN model selection using only unlabeled data. We develop this method based on a principle termed consistent relative confidence. Experimental results on benchmark datasets demonstrate the effectiveness and efficiency of our method.

preprint2022arXiv

Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks

Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery. Grid cells (2014 Nobel Prize) in the entorhinal cortex support a periodic representation as a metric for coding space. Here, we develop a self-supervised convolutional neural network to perform visual space location, leading to the emergence of single-slit diffraction and double-slit interference patterns of waves. Our discoveries reveal the nature of CNN encoding visual space to a certain extent. CNN is no longer a black box in terms of visual spatial encoding, it is interpretable. Our findings indicate that the periodicity property of waves provides a space metric, suggesting a general role of spatial coordinate frame in artificial neural networks.

preprint2022arXiv

Evolution of Stellar Orbits Around Merging Massive Black-Hole Binary

We study the long-term orbital evolution of stars around a merging massive or supermassive black-hole (BH) binary, taking into account the general relativistic effect induced by the BH spin. When the BH spin is significant compared to and misaligned with the binary orbital angular momentum, the orbital axis ($\hat{\mathbf{l}}$) of the circumbinary star can undergo significant evolution during the binary orbital decay driven by gravitational radiation. Including the spin effect of the primary (more massive) BH, we find that starting from nearly coplanar orbital orientations, the orbital axes $\hat{\mathbf{l}}$ of circumbinary stars preferentially evolve towards the spin direction after the merger of the BH binary, regardless of the initial BH spin orientation. Such alignment phenomenon, i.e., small final misalignment angle between $\hat{\mathbf{l}}$ and the spin axis of the remanent BH $\hat{\mathbf{S}}$, can be understood analytically using the principle of adiabatic invariance. For the BH binaries with extremely mass ratio ($m_2/m_1\lesssim0.01$), $\hat{\mathbf{l}}$ may experience more complicated evolution as adiabatic invariance breaks down, but the trend of alignment still works reasonably well when the initial binary spin-orbit angle is relatively small. Our result suggests that the correlation between the orientations of stellar orbits and the spin axis of the central BH could provide a potential signature of the merger history of the massive BH.

preprint2022arXiv

Highly active hydrogen evolution facilitated by topological surface states on a Pd/SnTe metal/topological crystalline insulator heterostructure

Recently, topological quantum materials have emerged as a promising electrocatalyst for hydrogen evolution reaction (HER). However, most of their performance largely lags behind noble metals such as benchmark platinum (Pt). In this work, a Pd(20nm)/SnTe(70nm) heterostructure, fabricated by molecular beam epitaxy and electron beam evaporation, is found to display much higher electrocatalytic activity than that of a pure Pd(20nm) thin film and even higher than that of a commercial Pt foil. This heterostructure adopts an extracted turnover frequency value more than two times higher than that of the Pd(20nm) thin film at a potential of 0.2 V, indicating a much higher intrinsic activity per Pd site. Density functional theory calculations show that the conventional d-band theory, which works well for many transition metal heterostructures, cannot explain the enhancement of electrocatalytic performance. Instead, we found that the topological surface states (TSSs) of the SnTe (001) underlayer play a key role; electrons transfer from both the Pd surface and the adsorbed H atoms to the TSSs of SnTe (001), resulting in weaker Pd-H binding strength and more favorable hydrogen adsorption free energies. Our work demonstrates for the first time that a metal/topological quantum material heterostructure could be a prominent catalyst to enjoy HER activity outperforming that of a commercial Pt foil and offers a promising direction to optimize the performance of electrocatalysts based on topological quantum materials.

preprint2022arXiv

Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

The decision-making of TBM operating parameters has an important guiding significance for TBM safe and efficient construction, and it has been one of the research hotpots in the field of TBM tunneling. For this purpose, this paper introduces rock-breaking rules into machine learning method, and a rock-machine mapping dual-driven by physical-rule and data-mining is established with high accuracy. This dual-driven mappings are subsequently used as objective function and constraints to build a decision-making method for TBM operating parameters. By searching the revolution per minute and penetration corresponding to the extremum of the objective function subject to the constraints, the optimal operating parameters can be obtained. This method is verified in the field of the Second Water Source Channel of Hangzhou, China, resulting in the average penetration rate increased by 11.3%, and the total cost decreased by 10.0%, which proves the practicability and effectiveness of the developed decision-making model.

preprint2022arXiv

Linear change and minutes variability of solar wind velocity revealed by FAST

Observation of Interplanetary Scintillation (IPS) provides an important and effective way to study the solar wind and the space weather. A series of IPS observations were conducted by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The extraordinary sensitivity and the wide frequency coverage make FAST an ideal platform for IPS studies. In this paper we present some first scientific results from FAST observations of IPS with the L-band receiver. Based on the solar wind velocity fitting values of FAST observations on September 26-28, 2020, we found that the velocity decreases with increasing frequency linearly, which has not yet been reported in literature. And we have also detected a variation of solar wind velocity on a timescale of 3-5 minutes, which imply the slow change of the background solar wind, a co-existence of high- and low-speed streams, or a reflect of the quasi-periodic electron-density fluctuations.

preprint2022arXiv

Load Balancing in Low-Voltage Distribution Networks via Optimizing Residential Phase Connections

Unbalance issues in low-voltage distribution networks (LVDN) can be worsened by increasing penetration of residential PV generation if unevenly distributed among three phases. To address this issue, the phase-switching device (PSD) provides a viable and efficient method by dynamically switching customers to other phases. This paper further investigates how to optimize residential phase connections by controlling PSDs efficiently. The optimization problem is formulated as a mixed-integer non-convex programming (MINCP) problem considering relevant operational requirements of an LVDN based on the exact formulation of unbalanced three-phase optimal power flow (UTOPF). Unlike most heuristic algorithms and the linearization techniques in our previous work, this paper proposes to solve the MINCP problem via an iteration-based algorithm after exact reformulations and reasonable approximations of some constraints. The proposed method is tested in a real LVDN and compared with the approach of Zhao et al. based on the well-known linear UTOPF formulation. Case studies based on the European low-voltage test feeder demonstrate the proposed method's efficiency in mitigating the network unbalance while ensuring network security and flexibility to deal with more controllable resources.

preprint2022arXiv

Massive molecular gas reservoir in a luminous sub-millimeter galaxy during cosmic noon

We present multi-band observations of an extremely dusty star-forming lensed galaxy (HERS1) at $z=2.553$. High-resolution maps of \textit{HST}/WFC3, SMA, and ALMA show a partial Einstein-ring with a radius of $\sim$3$^{\prime\prime}$. The deeper HST observations also show the presence of a lensing arc feature associated with a second lens source, identified to be at the same redshift as the bright arc based on a detection of the [NII] 205$μ$m emission line with ALMA. A detailed model of the lensing system is constructed using the high-resolution HST/WFC3 image, which allows us to study the source plane properties and connect rest-frame optical emission with properties of the galaxy as seen in sub-millimeter and millimeter wavelengths. Corrected for lensing magnification, the spectral energy distribution fitting results yield an intrinsic star formation rate of about $1000\pm260$ ${\rm M_{\odot}}$yr$^{-1}$, a stellar mass ${\rm M_*}=4.3^{+2.2}_{-1.0}\times10^{11} {\rm M_{\odot}}$, and a dust temperature ${\rm T}_{\rm d}=35^{+2}_{-1}$ K. The intrinsic CO emission line ($J_{\rm up}=3,4,5,6,7,9$) flux densities and CO spectral line energy distribution are derived based on the velocity-dependent magnification factors. We apply a radiative transfer model using the large velocity gradient method with two excitation components to study the gas properties. The low-excitation component has a gas density $n_{\rm H_2}=10^{3.1\pm0.6}$ cm$^{-3}$ and kinetic temperature ${\rm T}_{\rm k}=19^{+7}_{-5}$ K and a high-excitation component has $n_{\rm H_2}=10^{2.8\pm0.3}$ cm$^{-3}$ and ${\rm T}_{\rm k}=550^{+260}_{-220}$ K. Additionally, HERS1 has a gas fraction of about $0.4\pm0.2$ and is expected to last 250 Myr. These properties offer a detailed view of a typical sub-millimeter galaxy during the peak epoch of star-formation activity.

preprint2022arXiv

Mergers prompted by dynamics in compact, multiple-star systems: a stellar-reduction case for the massive triple TIC 470710327

TIC 470710327, a massive compact hierarchical triple-star system, was recently identified by NASA's Transiting Exoplanet Survey Satellite (TESS). TIC 470710327 is comprised of a compact (1.10 d) circular eclipsing binary, with total mass $\approx 10.9-13.2\ \rm{M_{\odot}}$, and a more massive ($\approx 14-17\ \rm{M_{\odot}}$) eccentric non-eclipsing tertiary in a $52.04$ d orbit. Here we present a progenitor scenario for TIC 470710327 in which '2+2' quadruple dynamics result in Zeipel-Lidov-Kozai (ZLK) resonances that lead to a contact phase of the more massive binary. In this scenario, the two binary systems should form in a very similar manner, and dynamics trigger the merger of the more massive binary either during late phases of star formation or several Myr after the zero-age main sequence (ZAMS), when the stars begin to expand. Any evidence that the tertiary is a highly-magnetised ($\sim 1-10$ kG), slowly-rotating blue main-sequence star would hint towards a quadruple origin. Finally, our scenario suggests that the population of inclined, compact multiple-stellar systems is reduced into co-planar systems, via mergers, late during star formation or early in the main sequence. The elucidation of the origin of TIC 470710327 is crucial in our understanding of multiple massive-star formation and evolution.

preprint2022arXiv

MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays. Mongolian is the official language of the Inner Mongolia Autonomous Region and a representative low-resource language spoken by over 10 million people worldwide. However, there is a relative lack of open-source datasets for Mongolian TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnTTS2, for the benefit of related researchers. In this work, we prepare the transcription from various topics and invite three professional Mongolian announcers to form a three-speaker TTS dataset, in which each announcer records 10 hours of speeches in Mongolian, resulting 30 hours in total. Furthermore, we build the baseline system based on the state-of-the-art FastSpeech2 model and HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2 dataset is sufficient to build robust multi-speaker TTS models for real-world applications. The MnTTS2 dataset, training recipe, and pretrained models are released at: \url{https://github.com/ssmlkl/MnTTS2}

preprint2022arXiv

Notes on BIM and BFM Optimal Power Flow With Parallel Lines and Total Current Limits

The second-order cone relaxation of the branch flow model (BFM) and bus injection model (BIM) variants of optimal power flow are well-known to be equivalent for radial networks. In this work we show that in meshed networks with parallel lines, BIM dominates BFM, and propose novel constraints to make them equivalent in general. Furthermore, we develop an improvement to the second-order cone relaxations of optimal power flow, adding novel and valid linear constraints on the lifted current expressions. We develop two simple test cases to highlight the advantages of the proposed constraints. These novel constraints tighten the second-order cone relaxation gap on test cases in the `PG Lib' optimal power flow benchmark library, albeit generally in limited fashion.

preprint2022arXiv

Numerical study of three-dimensional single-mode Rayleigh-Taylor instability in turbulent mixing stage

Rayleigh-Taylor instability (RTI) as a multi-scale, strongly nonlinear physical phenomenon which plays an important role in the engineering applications and scientific research. In this paper, the mesoscopic lattice Boltzmann method is used to numerically study the late-time evolutional mechanism of three-dimensional (3D) single-mode RTI and the influences of extensive dimensionless Reynolds number and Atwood number on phase interfacial dynamics, spike and bubble growth are investigated in details. For a high Reynolds number, it is reported that the development of 3D single-mode RTI would undergo four different stages: linear growth stage, saturated velocity growth stage, reacceleration stage and turbulent mixing stage. A series of complex interfacial structures with large topological changes can be observed at the turbulent mixing stage, which always preserve the symmetries with respect to the middle axis at a low Atwood number, and the lines of symmetry within spike and bubble are broken as the Atwood number is increased. Five statistical methods for computing the spike and bubble growth rates are then analyzed to reveal the growth law of 3D single-mode RTI in turbulent mixing stage. It is found that the spike late-time growth rate shows an overall increase with the Atwood number, while the bubble growth rate seems to be independence of the Atwood number, approaching a constant of around 0.1. When the Reynolds number decreases, the later stages cannot be reached gradually and the evolution of phase interface presents a laminar flow state.

preprint2022arXiv

Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The common practice in multi-object tracking is re-identifying the missed objects after their reappearance. Though tracking performance can be boosted by the re-identification, the annotation of identity is required to train the model. In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector. In this paper, we focus on online multi-object tracking and design two novel modules, the unsupervised re-identification learning module and the occlusion estimation module, to handle these problems. Specifically, the proposed unsupervised re-identification learning module does not require any (pseudo) identity information nor suffer from the scalability issue. The proposed occlusion estimation module tries to predict the locations where occlusions happen, which are used to estimate the positions of missed objects by the detector. Our study shows that, when applied to state-of-the-art MOT methods, the proposed unsupervised re-identification learning is comparable to supervised re-identification learning, and the tracking performance is further improved by the proposed occlusion estimation module.

preprint2022arXiv

Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities

In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process. Although it has been shown that fusing heterogeneous drug and target similarities can improve the prediction ability, the existing similarity combination methods ignore the interaction consistency for neighbour entities which is more crucial for the DTI prediction model. Furthermore, area under the precision-recall curve (AUPR) that emphasizes the accuracy of top-ranked pairs and area under the receiver operating characteristic curve (AUC) that heavily punishes the existence of low ranked interacting pairs are two widely used evaluation metrics in DTI prediction. However, the two metrics are seldom considered as losses within existing DTI prediction methods. This paper first proposes two matrix factorization (MF) methods that optimize AUPR and AUC using convex surrogate losses respectively, and then develops an ensemble MF approach takes advantage of the two area under the curve metrics by combining the two single metric based MF models. Both three proposed approaches incorporate a novel local interaction consistency aware similarity interaction method to generate fused drug and target similarities that preserve vital information from the more reliable view. Experimental results over five datasets under different prediction settings show that the proposed methods outperform various competitors in terms of the metric(s) they optimize. In addition, the validation on the top ranked novel predictions confirms the ability of our methods to discover potential new DTIs.

preprint2022arXiv

Probing the Spins of Supermassive Black Holes with Gravitational Waves from Surrounding Compact Binaries

Merging compact black-hole (BH) binaries are likely to exist in the nuclear star clusters around supermassive BHs (SMBHs), such as Sgr A$^\ast$. They may also form in the accretion disks of active galactic nuclei. Such compact binaries can emit gravitational waves (GWs) in the low-frequency band (0.001-1 Hz) that are detectable by several planned space-borne GW observatories. We show that the orbital axis of the compact binary may experience significant variation due to the frame-dragging effect associated with the spin of the SMBH. The dynamical behavior of the orbital axis can be understood analytically as a resonance phenomenon. We show that rate of change of the binary orbital axis encodes the information on the spin of the SMBH. Therefore detecting GWs from compact binaries around SMBHs, particularly the modulation of the waveform associated with the variation of the binary orbital axis, can provide a new probe on the spins of SMBHs.

preprint2022arXiv

Real-time Online Multi-Object Tracking in Compressed Domain

Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance. However, the tracking speed of most existing methods is rather slow. Inspired from the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames respectively and track objects in the compressed domain. For the key frames, the RGB images are restored for detection and data association. To make data association more reliable, an appearance Convolutional Neural Network (CNN) which can be jointly trained with the detector is proposed. For the non-key frames, the objects are directly propagated by a tracking CNN based on the motion information provided in the compressed domain. Compared with the state-of-the-art online MOT methods,our tracker is about 6x faster while maintaining a comparable tracking performance.

preprint2022arXiv

Revealing sign-reversal $s^{+-}$-wave pairing by quasiparticle interference in the heavy-fermion superconductor CeCu$_2$Si$_2$

Recent observations of two nodeless gaps in superconducting CeCu$_2$Si$_2$ have raised intensive debates as to its exact gap structure of either sign-reversal ($s^{+-}$) or sign-preserving ($s^{++}$) pairing. Here we investigate the quasiparticle interference (QPI) using realistic Fermi surface topology for both weak and strong interband impurity scatterings. Our calculations of the QPI and integrated antisymmetrized local density of states reveal qualitative distinctions between $s^{+-}$ and $s^{++}$ pairing states, which include the intragap impurity resonance and a significant energy-dependence difference between two gap energies. Our predictions provide a guide for phase-sensitive QPI measurements to uncover decisively the true pairing symmetry in the heavy-fermion superconductor CeCu$_2$Si$_2$.

preprint2022arXiv

The FAST Ultra-Deep Survey (FUDS): observational strategy, calibration and data reduction

The FAST Ultra-Deep Survey (FUDS) is a blind survey that aims for the direct detection of HI in galaxies at redshifts $z<0.42$. The survey uses the multibeam receiver on the Five Hundred Meter Aperture Spherical Telescope (FAST) to map six regions, each of size 0.72 deg$^2$ at high sensitivity ($\sim 50 μ$Jy) and high frequency resolution (23 kHz). The survey will enable studies of the evolution of galaxies and their HI content with an eventual sample size of $\sim 1000$. We present the science goals, observing strategy, the effects of radio frequency interference (RFI) at the FAST site, our mitigation strategies and the methods for calibration, data reduction and imaging as applied to initial data. The observations and reductions for the first field, FUDS0, are completed, with around 128 HI galaxies detected in a preliminary analysis. Example spectra are given in this paper, including a comparison with data from the overlapping GAL2577 field of Arecibo Ultra-Deep Survey (AUDS).

preprint2022arXiv

Thermalization of fluorescent protein exciton-polaritons at room temperature

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultralow threshold room temperature exciton-polariton condensation and lasing. Our contribution investigates the thermalization of FP microcavity exciton-polaritons upon optical pumping under ambient conditions. We realize polariton cooling using a new FP molecule, called mScarlet, coupled strongly to the optical modes in a Fabry Perot cavity. Interestingly, at the threshold excitation energy (fluence) of ~ 9 nJ/pulse (15.6 mJ/cm2), we observe an effective temperature, Teff ~ 350 +/- 35 K close to the lattice temperature indicative of strongly thermalized exciton-polaritons at equilibrium. This efficient thermalization results from the interplay of radiative pumping facilitated by the energetics of the lower polariton branch and the cavity Q factor. Direct evidence for dramatic switching from an equilibrium state into a metastable state is observed for the organic cavity polariton device at room temperature via deviation from the Maxwell-Boltzmann statistics at k = 0 above the threshold. Thermalized polariton gases in organic systems at equilibrium hold substantial promise for designing room temperature polaritonic circuits, switches, and lattices for analog simulation.

preprint2022arXiv

Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning

Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features. The ideal discriminative features should be only related to the real/fake labels of facial images. However, we observe that the features learned by vanilla classification networks are correlated to unnecessary properties, such as forgery methods and facial identities. Such phenomenon would limit forgery detection performance especially for the generalization ability. Motivated by this, we propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities, which helps classification network to learn intrinsic common discriminative features for face forgery detection. To leverage data lacking ground truth label of facial identities, we design a special identity discriminator based on similarity information derived from off-the-shelf face recognition model. With the help of adversarial learning, our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities. Extensive experiments demonstrate the effectiveness of the proposed method under both intra-dataset and cross-dataset evaluation settings.

preprint2022arXiv

Vortex-ring quantum droplets in a radially-periodic potential

We establish stability and characteristics of two-dimensional (2D) vortex ring-shaped quantum droplets (QDs) formed by binary Bose-Einstein condensates (BECs). The system is modeled by the Gross-Pitaevskii (GP) equation with the cubic term multiplied by a logarithmic factor (as produced by the Lee-Huang-Yang correction to the mean-field theory) and a potential which is a periodic function of the radial coordinate. Narrow vortex rings with high values of the topological charge, trapped in particular circular troughs of the radial potential, are produced. These results suggest an experimentally relevant method for the creation of vortical QDs (thus far, only zero-vorticity ones have been reported). The 2D GP equation for the narrow rings is approximately reduced to the 1D form, which makes it possible to study the modulational stability of the rings against azimuthal perturbations. Full stability areas are delineated for these modes. The trapping capacity of the circular troughs is identified for the vortex rings with different winding numbers (WNs). Stable compound states in the form of mutually nested concentric multiple rings are constructed too, including ones with opposite signs of the WNs. Other robust compound states combine a modulationally stable narrow ring in one circular potential trough and an azimuthal soliton performing orbital motion in an adjacent one. The results may be used to design a device employing coexisting ring-shaped modes with different WNs for data storage.

preprint2022arXiv

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine along with the data processing and model execution libraries, which are exposed through a refined Python thread-level virtual machine (VM) to support diverse ML tasks and concurrent task execution. The core of MNN is the novel mechanisms of operator decomposition and semi-auto search, sharply reducing the workload in manually optimizing hundreds of operators for tens of hardware backends and further quickly identifying the best backend with runtime optimization for a computation graph. The data pipeline introduces an on-device stream processing framework to enable processing user behavior data at source. The deployment platform releases ML tasks with an efficient push-then-pull method and supports multi-granularity deployment policies. We evaluate Walle in practical e-commerce application scenarios to demonstrate its effectiveness, efficiency, and scalability. Extensive micro-benchmarks also highlight the superior performance of MNN and the Python thread-level VM. Walle has been in large-scale production use in Alibaba, while MNN has been open source with a broad impact in the community.

preprint2022arXiv

WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models

Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distinguish items in future behaviors from others based on the user's historical behaviors, have attracted a lot of interest in both industry and academic due to their substantial practical value. Though achieving many practical successes, we argue that the intrinsic {\bf incompleteness} and {\bf inaccuracy} of user behaviors in implicit feedback data is ignored and conduct preliminary experiments for supporting our claims. Motivated by the observation that model-free methods like behavioral retargeting (BR) and item-based collaborative filtering (ItemCF) hit different parts of the user-item relevance compared to neural sequential recommendation models, we propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, and fine-tuning. WSLRec resolves the incompleteness problem by pre-training models on extra weak supervisions from model-free methods like BR and ItemCF, while resolves the inaccuracy problem by leveraging the top-$k$ mining to screen out reliable user-item relevance from weak supervisions for fine-tuning. Experiments on two benchmark datasets and online A/B tests verify the rationality of our claims and demonstrate the effectiveness of WSLRec.

preprint2021arXiv

A Deep Learning Approach to Quasar Continuum Prediction

We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $\leq λ\leq$ 1600 Angstroms. We train this network using high-resolution Hubble Space Telescope/Cosmic Origin Spectrograph ultraviolet quasar spectra at low redshift ($z \sim 0.2$) from the Hubble Spectroscopic Legacy Archive, and apply it to predict quasar continua from different astronomical surveys. We utilize the HSLA quasar spectra that are well-defined in the rest-frame wavelength range [1020, 1600] Angstroms with an overall median signal-to-noise ratio of at least five. The iQNet achieves a median AFFE of 2.24% on the training quasar spectra, and 4.17% on the testing quasar spectra. We apply iQNet and predict the continua of $\sim$3200 SDSS-DR16 quasar spectra at higher redshift ($2< z \leq 5$) and measure the redshift evolution of mean transmitted flux ($< F >$) in the Ly-$α$ forest region. We measure a gradual evolution of $< F >$ with redshift, which we characterize as a power-law fit to the effective optical depth of the Ly-$α$ forest. Our measurements are broadly consistent with other estimates of $<F>$ in the literature, but provide a more accurate measurement as we are directly measuring the quasar continuum where there is minimum contamination from the Ly-$α$ forest. This work proves that the deep learning iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction.

preprint2021arXiv

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News. Various recommender systems, ranging from neighborhood-based, association-rule-based, matrix-factorization-based, to deep learning based, have been developed and deployed in industry. Among them, deep learning based recommender systems become increasingly popular due to their superior performance. In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems. An attacker's goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system. Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended. However, it is challenging to solve the optimization problem because it is a non-convex integer programming problem. To address the challenge, we develop multiple techniques to approximately solve the optimization problem. Our experimental results on three real-world datasets, including small and large datasets, show that our attack is effective and outperforms existing attacks. Moreover, we attempt to detect fake users via statistical analysis of the rating patterns of normal and fake users. Our results show that our attack is still effective and outperforms existing attacks even if such a detector is deployed.

preprint2021arXiv

Enhanced Lidov-Kozai migration and the formation of the transiting giant planet WD1856+534b

We investigate the possible origin of the transiting giant planet WD1856+534b, the first strong exoplanet candidate orbiting a white dwarf, through high-eccentricity migration (HEM) driven by the Lidov-Kozai (LK) effect. The host system's overall architecture is an hierarchical quadruple in the '2+2' configuration, owing to the presence of a tertiary companion system of two M-dwarfs. We show that a secular inclination resonance in 2+2 systems can significantly broaden the LK window for extreme eccentricity excitation ($e \gtrsim 0.999$), allowing the giant planet to migrate for a wide range of initial orbital inclinations. Octupole effects can also contribute to the broadening of this 'extreme' LK window. By requiring that perturbations from the companion stars be able to overcome short-range forces and excite the planet's eccentricity to $e \simeq 1$, we obtain an absolute limit of $a_{1} \gtrsim 8 \, {\rm AU} \, (a_{3} / 1500 \, {\rm AU})^{6/7}$ for the planet's semi-major axis just before migration (where $a_{3}$ is the semi-major axis of the 'outer' orbit). We suggest that, to achieve a wide LK window through the 2+2 resonance, WD1856b likely migrated from $30 \, {\rm AU} \lesssim a_{1} \lesssim 60 \, {\rm AU}$, corresponding to $\sim 10$--$20 \, {\rm AU}$ during the host's main-sequence phase. We discuss possible difficulties of all flavours of HEM affecting the occurrence rate of short-period giant planets around white dwarfs.

preprint2021arXiv

Hierarchical Black-Hole Mergers in Multiple Systems: Constrain the Formation of GW190412, GW190814 and GW190521-like events

The merging black-hole (BH) binaries GW190412, GW190814 and GW190521 from the third LIGO/VIRGO observing run exhibit some extraordinary properties, including highly asymmetric masses, significant spin, and component mass in the "mass gap". These features can be explained if one or both components of the binary are the remnants of previous mergers. In this paper, we explore hierarchical mergers in multiple stellar systems, taking into account the natal kick and mass loss due to the supernova explosion (SN) on each component, as well as the merger kick received by the merger remnant. The binaries that have survived the SNe and kicks generally have too wide orbital separations to merge by themselves, but can merge with the aid of an external companion that gives rise to Lidov-Kozai oscillations. The BH binaries that consist of second-generation BHs can also be assembled in dense star clusters through binary interactions. We characterize the parameter space of these BH binaries by merger fractions in an analytical approach. Combining the distributions of the survived binaries, we further constrain the parameters of the external companion, using the analytically formulated tertiary perturbation strength. We find that to produce the three LIGO/VIRGO O3 events, the external companions must be at least a few hundreds $M_\odot$, and fall in the intermediate-mass BH and supermassive BH range. We suggest that GW190412, GW190814 and GW190521 could all be produced via hierarchical mergers in multiples, likely in a nuclear star cluster, with the final merger induced by a massive BH.

preprint2021arXiv

INTCP: Information-centric TCP for Satellite Network

Satellite networks are booming to provide high-speed and low latency Internet access, but the transport layer becomes one of the main obstacles. Legacy end-to-end TCP is designed for terrestrial networks, not suitable for error-prone, propagation delay varying, and intermittent satellite links. It is necessary to make a clean-slate design for the satellite transport layer. This paper introduces a novel Information-centric Hop-by-Hop transport layer design, INTCP. It carries out hop-by-hop packets retransmission and hop-by-hop congestion control with the help of cache and request-response model. Hop-by-hop retransmission recovers lost packets on hop, reduces retransmission delay. INTCP controls traffic and congestion also by hop. Each hop tries its best to maximize its bandwidth utilization and improves end-to-end throughput. The capability of caching enables asynchronous multicast in transport layer. This would save precious spectrum resources in the satellite network. The performance of INTCP is evaluated with the simulated Starlink constellation. Long-distance communication with more than 1000km is carried out. The results demonstrate that, for the unicast scenario INTCP could reduce 42% one-way delay, 53% delay jitters, and improve 60% throughput compared with the legacy TCP. In multicast scenario, INTCP could achieve more than 6X throughput.

preprint2021arXiv

Semidiscrete vortex solitons

We demonstrate a possibility of the creation of stable optical solitons combining one continuous and one discrete coordinate, with embedded vorticity, in an array of planar waveguides with intrinsic cubic-quintic nonlinearity. The same system may be realized in terms of the spatiotemporal light propagation in an array of tunnel-coupled optical fibers with the cubic-quintic nonlinearity. In contrast with zero-vorticity states, semidiscrete vortex solitons do not exist without the quintic term in the nonlinearity. Two types of the solitons, \emph{viz.}, intersite- and onsite-centered ones (IC and OC, respectively), with even and odd numbers $N$ of actually excited sites in the discrete direction, are identified. We consider the modes carrying the embedded vorticity $S=1$ and $2$. In accordance with their symmetry, the vortex solitons of the OC type exhibit an intrinsic core, while the IC solitons with a small $N$ may have a coreless structure. Facilitating their creation in the experiment, the modes reported in the present work may be much more compact states than their counterparts considered in other systems, and they feature strong anisotropy. They can be set in motion in the discrete direction, provided that the coupling constant exceeds a certain minimum value. Collisions between moving vortex solitons are considered too.

preprint2020arXiv

2.5-kV AlGaN/GaN Schottky Barrier Diode on Silicon Substrate with Recessed-anode Structure

In this letter, we demonstrate high-performance lateral AlGaN/GaN Schottky barrier diodes (SBD) on Si substrate with a recessed-anode structure. The optimized rapid etch process provides results in improving etching quality with a 0.26-nm roughness of the anode recessed surface. By using the high work function metal Pt as the Schottky electrode, a low Von of 0.71 V is obtained with a high uniformity of 0.023 V for 40 devices. Supported by the flat anode recess surface and related field plate design, the SBD device with the anode-cathode spacing of 15 um show the Ron,sp of 1.53 mOhm.cm2 only, the breakdown voltage can reach 1592 V with a high power FOM (Figure-of-Merit) of 1656 MW/cm2. For the SBD device with the anode-cathode spacing of 30 um, the breakdown voltage can be as high as 2521 V and the power FOM is 1244 MW/cm2.

preprint2020arXiv

A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis

Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real applications. This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. The dataset consists of 16 scenes captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance, school gate, park, pedestrian mall, and public square. Over 200k video frames are annotated carefully, resulting in more than 3:7 million object bounding boxes and about 7:1 thousand trajectories. We further use this dataset to evaluate the performance of typical algorithms for multiple-object tracking and anomaly behavior analysis and explore the robustness of these methods in urban congested scenarios.

preprint2020arXiv

A Survey on Trust Modeling from a Bayesian Perspective

In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature, many attempts have been made to model trust with aid of Bayesian probability theory, while the field lacks a global comprehensive analysis for variants of Bayesian trust models. We present a study to fill in this gap by giving a comprehensive review of the literature. A generic Bayesian trust (GBT) modeling perspective is highlighted here. It is shown that all models under survey can cast into a GBT based computing paradigm as special cases. We discuss both capabilities and limitations of the GBT perspective and point out open questions to answer, with a hope to advance GBT to become a pragmatic infrastructure for analyzing intrinsic relationships among variants of trust models and developing novel tools for trust evaluation.

preprint2020arXiv

Adapting Active Reflector Technology for greater sensitivity and sky-coverage in FAST-like Telescopes

The Five-hundred-meter Aperture Spherical radio Telescope (FAST), the largest single dish radio telescope in the world, has implemented an innovative technology for its huge reflector, which changes the shape of the primary reflector from spherical to that of a paraboloid of 300 m aperture. Here we explore how the current FAST sensitivity can potentially be further improved by increasing the illuminated area (i.e., the aperture of the paraboloid embedded in the spherical surface). Alternatively, the maximum zenith angle can be increased to give greater sky coverage by decreasing the illuminated aperture.Different parabolic apertures within the FAST capability are analyzed in terms of how far the spherical surface would have to move to approximate a paraboloid. The sensitivity of FAST can be improved by approximately 10 % if the aperture of the paraboloid is increased from 300 m to 315 m. The parabolic aperture lies within the main spherical surface and does not extend beyond its edge. The maximum zenith angle can be increased to approximately 35 degrees from 26.4 degrees, if we decrease the aperture of the paraboloid to 220 m. This would still give a sensitivity similar to the Arecibo 305 m radio telescope. Radial deviations between paraboloids of different apertures and the spherical surfaces of differing radii are also investigated. Maximum zenith angles corresponding to different apertures of the paraboloid are further derived. A spherical surface with a different radius can provide a reference baseline for shape-changing applied through active reflector technology to FAST-like telescopes.

preprint2020arXiv

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and complicate the training process. In this work, we propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. In the \emph{search stage}, instead of searching over a discrete set of candidate feature interactions, we relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model. In the \emph{re-train stage}, we keep the architecture parameters serving as an attention unit to further boost the performance. Offline experiments on three large-scale datasets (two public benchmarks, one private) demonstrate that AutoFIS can significantly improve various FM based models. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service, where a 10-day online A/B test demonstrated that AutoFIS improved the DeepFM model by 20.3\% and 20.1\% in terms of CTR and CVR respectively.

preprint2020arXiv

Compact differences of composition operators on Bergman spaces induced by doubling weights

Bounded and compact differences of two composition operators acting from the weighted Bergman space $A^p_ω$ to the Lebesgue space $L^q_ν$, where $0<q<p<\infty$ and $ω$ belongs to the class $\mathcal{D}$ of radial weights satisfying a two-sided doubling condition, are characterized. On the way to the proofs a new description of $q$-Carleson measures for $A^p_ω$, with $p>q$ and $ω\in\mathcal{D}$, involving pseudohyperbolic discs is established. This last-mentioned result generalizes the well-known characterization of $q$-Carleson measures for the classical weighted Bergman space $A^p_α$ with $-1<α<\infty$ to the setting of doubling weights. The case $ω\in\widehat{\mathcal{D}}$ is also briefly discussed and an open problem concerning this case is posed.

preprint2020arXiv

Compact Differences of Weighted Composition Operators

Compact differences of two weighted composition operators acting from the weighted Bergman space $A^p_ω$ to another weighted Bergman space $A^q_ν$, where $0<p\le q<\infty$ and $ω,ν$ belong to the class $\mathcal{D}$ of radial weights satisfying two-sided doubling conditions, are characterized. On the way to the proof a new description of $q$-Carleson measures for $A^p_ω$, with $ω\in\mathcal{D}$, in terms of pseudohyperbolic discs is established. This last-mentioned result generalizes the well-known characterization of $q$-Carleson measures for the classical weighted Bergman space $A^p_α$ with $-1<α<\infty$ to the setting of doubling weights.

preprint2020arXiv

Cross-modality Person re-identification with Shared-Specific Feature Transfer

Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. Existing works mainly focus on learning common representation by embedding different modalities into a same feature space. However, only learning the common characteristics means great information loss, lowering the upper bound of feature distinctiveness. In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance. We model the affinities of different modality samples according to the shared features and then transfer both shared and specific features among and across modalities. We also propose a complementary feature learning strategy including modality adaption, project adversarial learning and reconstruction enhancement to learn discriminative and complementary shared and specific features of each modality, respectively. The entire cm-SSFT algorithm can be trained in an end-to-end manner. We conducted comprehensive experiments to validate the superiority of the overall algorithm and the effectiveness of each component. The proposed algorithm significantly outperforms state-of-the-arts by 22.5% and 19.3% mAP on the two mainstream benchmark datasets SYSU-MM01 and RegDB, respectively.

preprint2020arXiv

Data-Driven Model Set Design for Model Averaged Particle Filter

This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model; it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained dataset.The average performance of these model components is guaranteed since each one's parameter value is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained dataset, respectively. Computer simulations are used to demonstrate the effectiveness of the proposed method.

preprint2020arXiv

Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method

In this paper, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.

preprint2020arXiv

Deep Learning Inversion of Electrical Resistivity Data

The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.

preprint2020arXiv

Deep-Learning Inversion of Seismic Data

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution.

preprint2020arXiv

Density-Aware Graph for Deep Semi-Supervised Visual Recognition

Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, most existing SSL methods are based on common density-based cluster assumption: samples lying in the same high-density region are likely to belong to the same class, including the methods performing consistency regularization or generating pseudo-labels for the unlabelled images. Despite their impressive performance, we argue three limitations exist: 1) Though the density information is demonstrated to be an important clue, they all use it in an implicit way and have not exploited it in depth. 2) For feature learning, they often learn the feature embedding based on the single data sample and ignore the neighborhood information. 3) For label-propagation based pseudo-label generation, it is often done offline and difficult to be end-to-end trained with feature learning. Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way. Specifically, we first propose a new Density-aware Neighborhood Aggregation(DNA) module to learn more discriminative features by incorporating the neighborhood information in a density-aware manner. Then a novel Density-ascending Path based Label Propagation(DPLP) module is proposed to generate the pseudo-labels for unlabeled samples more efficiently according to the feature distribution characterized by density. Finally, the DNA module and DPLP module evolve and improve each other end-to-end.

preprint2020arXiv

Discrete solitons in zigzag waveguide arrays with different types of linear mixing between nearest-neighbor and next-nearest-neighbor couplings

We study discrete solitons in zigzag discrete waveguide arrays with different types of linear mixing between nearest-neighbor and next-nearest-neighbor couplings. The waveguide array is constructed from two layers of one-dimensional (1D) waveguide arrays arranged in zigzag form. If we alternately label the number of waveguides between the two layers, the cross-layer couplings (which couple one waveguide in one layer with two adjacent waveguides in the other layer) construct the nearestneighbor couplings, while the couplings that couple this waveguide with the two nearest-neighbor waveguides in the same layer, i.e., self-layer couplings, contribute the next-nearest-neighbor couplings. Two families of discrete solitons are found when these couplings feature different types of linear mixing. As the total power is increased, a phase transition of the second kind occurs for discrete solitons in one type of setting, which is formed when the nearest-neighbor coupling and next-nearest-neighbor coupling feature positive and negative linear mixing, respectively. The mobilities and collisions of these two families of solitons are discussed systematically throughout the paper, revealing that the width of the soliton plays an important role in its

preprint2020arXiv

Generalized weighted composition operators on Bergman spaces induced by doubling weights

Bounded and compact generalized weighted composition operators acting from the weighted Bergman space $A^p_ω$, where $0<p<\infty$ and $ω$ belongs to the class $\mathcal{D}$ of radial weights satisfying a two-sided doubling condition, to a Lebesgue space $L^q_ν$ are characterized. On the way to the proofs a new embedding theorem on weighted Bergman spaces $A^p_ω$ is established. This last-mentioned result generalizes the well-known characterization of the boundedness of the differentiation operator $D^n(f)=f^{(n)}$ from the classical weighted Bergman space $A^p_α$ to the Lebesgue space $L^q_μ$, induced by a positive Borel measure $μ$, to the setting of doubling weights.

preprint2020arXiv

Holding and transferring matter-wave solitons against gravity by spin-orbit-coupling tweezers

We consider possibilities to grasp and drag one-dimensional solitons in two-component Bose- Einstein condensates (BECs), under the action of gravity, by tweezers induced by spatially confined spin-orbit (SO) coupling applied to the BEC, with the help of focused laser illumination. Solitons of two types are considered, semi-dipoles and mixed modes. We find critical values of the gravity force, up to which the solitons may be held or transferred by the tweezers. The dependence of the critical force on the magnitude and spatial extension of the localized SO interaction, as well as on the solitons norm and speed (in the transfer regime), are systematically studied by means of numerical methods, and analytically with the help of a quasi-particle approximation for the soliton. In particular, a noteworthy finding is that the critical gravity force increases with the increase of the transfer speed (i.e., moving solitons are more robust than quiescent ones). Nonstationary regimes are addressed too, by considering abrupt application of gravity to solitons created in the weightless setting. In that case, solitons feature damped shuttle motion, provided that the gravity force does not exceed a dynamical critical value, which is smaller than its static counterpart. The results may help to design gravimeters based on ultracold atoms.

preprint2020arXiv

Hydrodynamic and thermal characteristics of a freely-vibrating circular cylinder in mixed convection flow

The hydrodynamic and thermal characteristics of a freely-vibrating circular cylinder in mixed convection flow are numerically investigated at low Reynolds numbers. The numerical investigations are conducted for a range of parameters, Ur = [2.0, 10], Pr = [0.7, 10] and Ri = [0.5, 2.0]. Whereas the Reynolds number, the mass ratio and the damping ratio are fixed. A secondary VIV lock-in region is found in the cases of high Richardson number Ri=2.0 for high reduced velocity values, in which the buoyancy-driven flow is non-trivial. A wide VIV lock-in region is formed with tremendous energy transfer between fluid and structure, which is extremely meaningful for hydropower harvesting. The influences of Prandtl and Richardson numbers on the hydrodynamics, structural dynamics and heat transfer are discussed in detail. The temperature contours are concentrated around cylinder for the cases of high Prandtl number, which are associated with high mean Nusselt values. The influence on heat transfer efficiency over the cylinder's surface is quantified via the calculation of mean Nusselt number and its fluctuation for different circumstances. The energy transfer coefficient is employed to quantify the energy transfer between fluid and structure in mixed convection flow. The phase angle difference between the transverse displacement of cylinder and the lift force is used to support the discussions of energy transfer. A stabilized finite element formulation in Arbitrary Lagrangian-Eulerian description is derived. The structural dynamics and vortex-induced vibration are documented for various environments, e.g., different reduced velocity, Prandtl numbers and Richardson numbers. The influence of structural dynamics on the heat transfer efficiency over a heated cylinder is recorded and discussed as well. The obtained numerical results match well with literature and the established empirical formula.

preprint2020arXiv

Merging Compact Binaries Near a Rotating Supermassive Black Hole: Eccentricity Excitation due to Apsidal Precession Resonance

We study the dynamics of merging compact binaries near a rotating supermassive black hole (SMBH) in a hierarchical triple configuration. We include various general relativistic effects that couple the inner orbit, the outer orbit and the spin of the SMBH. During the orbital decay due to gravitational radiation, the inner binary can encounter an "apsidal precession resonance" and experience eccentricity excitation. This resonance occurs when the apsidal precession rate of the inner binary matches that of the outer binary, with the precessions driven by both Newtonian interactions and various post-Newtonian effects. The eccentricity excitation requires the outer orbit to have a finite eccentricity, and is most effective for triples with small mutual inclinations, in contrast to the well-studied Lidov-Kozai effect. The resonance and the associated eccentricity growth may occur while the binary emits gravitational waves in the low-frequency band, and may be detectable by future space-based gravitational wave detectors.

preprint2020arXiv

Multi-Label Sampling based on Local Label Imbalance

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

preprint2020arXiv

Negative Margin Matters: Understanding Margin in Few-shot Classification

This paper introduces a negative margin loss to metric learning based few-shot learning methods. The negative margin loss significantly outperforms regular softmax loss, and achieves state-of-the-art accuracy on three standard few-shot classification benchmarks with few bells and whistles. These results are contrary to the common practice in the metric learning field, that the margin is zero or positive. To understand why the negative margin loss performs well for the few-shot classification, we analyze the discriminability of learned features w.r.t different margins for training and novel classes, both empirically and theoretically. We find that although negative margin reduces the feature discriminability for training classes, it may also avoid falsely mapping samples of the same novel class to multiple peaks or clusters, and thus benefit the discrimination of novel classes. Code is available at https://github.com/bl0/negative-margin.few-shot.

preprint2020arXiv

Nonlinear modes in spatially confined spin-orbit-coupled Bose-Einstein condensates with repulsive nonlinearity

It was found that spatially confined spin-orbit (SO) coupling, which can be induced by illuminating Bose-Einstein condensates (BECs) with a Gaussian laser beam, can help trap a spinor Bose gas in multi-dimensional space. Previous works on this topic were all based on a Boson gas featuring an attractive interaction. In this paper, we consider the trapping effect in the case in which the Boson gas features a repulsive interaction. After replacing the repulsive effect, stable excited modes of semi-vortex (SV) type and mixed-mode (MM) type, which cannot be created in a boson gas with attractive interactions, can be found in the current setting. The trapping ability and the capacity of the confined SO coupling versus the degree of the repulsive strength as well as the order of the excited mode are systematically discussed firstly through the paper. Moreover, the stability of the nonlinear mode trapped in this system with a moving reference frame is also discussed. Unlike the system with homogeneous SO coupling, two different types of stationary mobility modes can be stabilized when the SO coupling moves in the x- and y- directions, respectively. This finding indicates that the system with moving confined SO coupling features a typical anisotropic character that differs from the system with moving homogeneous SO coupling.

preprint2020arXiv

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

preprint2020arXiv

Numerical stability and three dimensionality of a streamline hyperbolic critical point in wake at low Reynolds number

In this work the numerical stability of a streamline singular hyperbolic/saddle critical point (HSP) and its relationship with the divergence of pressure force/fluid flux are numerically investigated at low Reynolds numbers. Three canonical configurations at different Reynolds numbers are considered: (a) an isolated circular cylinder; (b) side-by-side circular cylinders and (c) a near-wall circular cylinder. The objective is to investigate the behavior of a HSP subjecting to imbalanced shear-layer interaction and different Reynolds numbers. It is found that a HSP evolves along the shear-layer interfaces and imposes adverse pressure gradients, which potentially deteriorates near-wake stability. The inherent characteristics of a HSP is linked with net positive fluid-fluid divergence and fluid three dimensionality. A vorticity-free stagnant zone is formed around a HSP, which cut the kinetic energy supply of shear layers in wake, project third-dimensional fluid fluxes and develops three-dimensional streamwise braids. These findings are confirmed and explained via the quantification of the fluid-flux divergence, the hydrodynamic responses of cylinder(s) and the secondary enstrophy. The primary focus in this article is to reveal the subtle analytical relationships between HSP, divergence of pressure force/fluid flux, fluid three dimensionality and imbalanced shear-layer interaction. To the knowledge of authors, so far these analytical relationships have not been reported in literature.

preprint2020arXiv

Parametric Instance Classification for Unsupervised Visual Feature Learning

This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a dual-branch non-parametric fashion, PIC directly performs a one-branch parametric instance classification, revealing a simple framework similar to supervised classification and without the need to address the information leakage issue. We show that the simple PIC framework can be as effective as the state-of-the-art approaches, i.e. SimCLR and MoCo v2, by adapting several common component settings used in the state-of-the-art approaches. We also propose two novel techniques to further improve effectiveness and practicality of PIC: 1) a sliding-window data scheduler, instead of the previous epoch-based data scheduler, which addresses the extremely infrequent instance visiting issue in PIC and improves the effectiveness; 2) a negative sampling and weight update correction approach to reduce the training time and GPU memory consumption, which also enables application of PIC to almost unlimited training images. We hope that the PIC framework can serve as a simple baseline to facilitate future study.

preprint2020arXiv

Particle Filtering Methods for Stochastic Optimization with Application to Large-Scale Empirical Risk Minimization

This paper is concerned with sequential filtering based stochastic optimization (FSO) approaches that leverage a probabilistic perspective to implement the incremental proximity method (IPM). The present FSO methods are derived based on the Kalman filter (KF) and the extended KF (EKF). In contrast with typical methods such as stochastic gradient descent (SGD) and IPMs, they do not need to pre-schedule the learning rate for convergence. Nevertheless, they have limitations that inherit from the KF mechanism. As the particle filtering (PF) method outperforms KF and its variants remarkably for nonlinear non-Gaussian sequential filtering problems, it is natural to ask if FSO methods can benefit from PF to get around of their limitations. We provide an affirmative answer to this question by developing two PF based stochastic optimizers (PFSOs). For performance evaluation, we apply them to address nonlinear least-square fitting with simulated data, and empirical risk minimization for binary classification of real data sets. Experimental results demonstrate that PFSOs outperform remarkably a benchmark SGD algorithm, the vanilla IPM, and KF-type FSO methods in terms of numerical stability, convergence speed, and flexibility in handling diverse types of loss functions.

preprint2020arXiv

Polymorphism and superconductivity in the V-Nb-Mo-Al-Ga high-entropy alloys

High-entropy alloys (HEAs) are at the focus of current research for their diverse properties, including superconductivity and structural polymorphism. However, the polymorphic transition has been observed only in nonsuperconducting HEAs and mostly under high pressure. Here we report the discovery of superconductivity and temperature-driven polymorphism in the (V$_{0.5}$Nb$_{0.5}$)$_{3-x}$Mo$_{x}$Al$_{0.5}$Ga$_{0.5}$ (0.2 $\leq$ $x$ $\leq$ 1.4) HEAs. It is found that the as-cast HEA is of a single body-centered cubic (bcc) structure for $x$ = 0.2 and a mixture of the bcc and A15 structures for higher $x$ values. Upon annealing, the bcc structure undergoes a polymorphic transformation to the A15 one and all HEAs exhibits bulk superconductivity. For $x$ = 0.2, whereas the bcc polymorph is not superconducting down to 1.8 K, the A15 polymorph has a superconducting transition temperature $T_{\rm c}$ of 10.2 K and an estimated zero-temperature upper critical field $B_{\rm c2}$(0) of 20.1 T, both of which are the highest among HEA superconductors. With increasing Mo content $x$, both $T_{\rm c}$ and $B_{\rm c2}$(0) of the A15-type HEAs decrease, yet the large ratio of $B_{\rm c2}$(0)/$T_{\rm c}$ signifies a disorder-induced enhancement of the upper critical field over a wide $x$ range. The decrease in $T_{\rm c}$ is attributed to the decrease in both the electronic specific-heat coefficient and electron-phonon coupling strength. Furthermore, the valence electron count dependence of $T_{\rm c}$, which is different from both the binary A15 and other structurally different HEA superconductors, suggests that $T_{\rm c}$ may be increased further by reducing the number of valence electrons. Our results not only uncover HEA superconductors of a new structural type, but also provide the first example of polymorphism dependent superconductivity in HEAs.

preprint2020arXiv

Refined Tail Asymptotic Properties for the $M^X/G/1$ Retrial Queue

In the literature, retrial queues with batch arrivals and heavy service times have been studied and the so-called equivalence theorem has been established under the condition that the service time is heavier than the batch size. The equivalence theorem provides the distribution (or tail) equivalence between the total number of customers in the system for the retrial queue and the total number of customers in the corresponding standard (non-retrial) queue. In this paper, under the assumption of regularly varying tails, we eliminate this condition by allowing that the service time can be either heavier or lighter than the batch size. The main contribution made in this paper is an asymptotic characterization of the difference between two tail probabilities: the probability of the total number of customers in the system for the $M^X/G/1$ retrial queue and the probability of the total number of customers in the corresponding standard (non-retrial) queue. The equivalence theorem by allowing a heavier batch size is another contribution in this paper.

preprint2020arXiv

Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, based on the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter value of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points.

preprint2020arXiv

Simultaneous Denoising and Dereverberation Using Deep Embedding Features

Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the deep clustering (DC). DC is a state-of-the-art method for speech separation that includes embedding learning and K-means clustering. As for our proposed method, it contains two stages: denoising and dereverberation. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. These embedding features are generated from the anechoic speech and residual reverberation signals. They can represent the inferred spectral masking patterns of the desired signals, which are discriminative features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another supervised neural network is utilized to estimate the anechoic speech from these deep embedding features. Finally, the denoising stage and dereverberation stage are optimized by the joint training method. Experimental results show that the proposed method outperforms the WPE and BLSTM baselines, especially in the low SNR condition.

preprint2020arXiv

Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features

Multi-channel deep clustering (MDC) has acquired a good performance for speech separation. However, MDC only applies the spatial features as the additional information. So it is difficult to learn mutual relationship between spatial and spectral features. Besides, the training objective of MDC is defined at embedding vectors, rather than real separated sources, which may damage the separation performance. In this work, we propose a deep attention fusion method to dynamically control the weights of the spectral and spatial features and combine them deeply. In addition, to solve the training objective problem of MDC, the real separated sources are used as the training objectives. Specifically, we apply the deep clustering network to extract deep embedding features. Instead of using the unsupervised K-means clustering to estimate binary masks, another supervised network is utilized to learn soft masks from these deep embedding features. Our experiments are conducted on a spatialized reverberant version of WSJ0-2mix dataset. Experimental results show that the proposed method outperforms MDC baseline and even better than the oracle ideal binary mask (IBM).

preprint2020arXiv

Superconductivity in hexagonal Nb-Mo-Ru-Rh-Pd high-entropy alloys

We report the superconducting properties of new hexagonal Nb$_{10+2x}$Mo$_{35-x}$Ru$_{35-x}$Rh$_{10}$Pd$_{10}$ high-entropy alloys (HEAs) (0 $\leq$ $x$ $\leq$ 5). With increasing $x$, the superconducting transition temperature $T_{\rm c}$ shows a maximum of 6.19 K at $x$ = 2.5, while the zero-temperature upper critical field $B_{\rm c2}$(0) increases monotonically, reaching 8.3 T at $x$ = 5. For all $x$ values, the specific heat jump deviates from the Bardeen-Cooper-Schreiffer behavior. In addition, we show that $T_{\rm c}$ of these HEAs is not determined mainly by the density of states at the Fermi level and would be enhanced by lowering the valence electron concentration.

preprint2020arXiv

The Fundamental Performance of FAST with 19-beam Receiver at L Band

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) passed national acceptance and is taking pilot cycle of 'Shared-Risk' observations. The 19-beam receiver covering 1.05-1.45 GHz was used for most of these observations. The electronics gain fluctuation of the system is better than 1\% over 3.5 hours, enabling enough stability for observations. Pointing accuracy, aperture efficiency and system temperature are three key parameters of FAST. The measured standard deviation of pointing accuracy is 7.9$''$, which satisfies the initial design of FAST. When zenith angle is less than 26.4$^\circ$, the aperture efficiency and system temperature around 1.4 GHz are $\sim$ 0.63 and less than 24 K for central beam, respectively. The measured value of these two parameters are better than designed value of 0.6 and 25 K, respectively. The sensitivity and stability of the 19-beam backend are confirmed to satisfy expectation by spectral HI observations toward N672 and polarization observations toward 3C286. The performance allows FAST to take sensitive observations in various scientific goals, from studies of pulsar to galaxy evolution.

preprint2020arXiv

Two-Dimensional Rare Earth -- Gold Intermetallic Compounds on Au(111) by Surface Alloying

Surface alloying is a straightforward route to control and modify the structure and electronic properties of surfaces. Here, We present a systematical study on the structural and electronic properties of three novel rare earth-based intermetallic compounds, namely ReAu2 (Re = Tb, Ho, and Er), on Au(111) via directly depositing rare-earth metals onto the hot Au(111) surface. Scanning tunneling microscopy/spectroscopy measurements reveal the very similar atomic structures and electronic properties, e.g. electronic states, and surface work functions, for all these intermetallic compound systems due to the physical and chemical similarities between these rare earth elements. Further, these electronic properties are periodically modulated by the moiré structures caused by the lattice mismatches between ReAu2 and Au(111). These periodically modulated surfaces could serve as templates for the self-assembly of nanostructures. Besides, these two-dimensional rare earth-based intermetallic compounds provide platforms to investigate the rare earth related catalysis, magnetisms, etc., in the lower dimensions.

preprint2020arXiv

Unbalance Mitigation via Phase-switching Device and Static Var Compensator in Low-voltage Distribution Network

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be effectively addressed. To mitigate unbalances in LVDN, dynamic switching devices (PSDs) and static var compensator (SVC) are two equipment that are cost-effective and efficient. However, most existing research on operating PSDs are based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper, after presenting the dispatch model of SVC, formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem considering all practical operational requirements. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on a modified IEEE system and a practical system in Australia demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN.

preprint2020arXiv

Uncovering Insurance Fraud Conspiracy with Network Learning

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.

preprint2019arXiv

A general criterion for solid instability and its application to creases

A general force-perturbation-based criterion for solid instability is proposed, which can predict instability including crease without priori knowledge of instability configuration. The crease instability is analyzed in detail, we found that the occurrence of solid instability does not always correspond to the non-positive definiteness of global stiffness matrix. An element stiffness-based criterion based on material stiffness is proposed as a stronger criterion in order to fast determine the occurrence of instability. This criterion has been shown to degenerate into the criterion for judging instability of certain known phenomena, such as necking and shear band phenomena. Besides, instability in strongly anisotropic materials is also predicted by the element stiffness-based criterion.

preprint2019arXiv

The GBT Diffuse Ionized Gas Survey: Tracing the Diffuse Ionized Gas around the Giant HII Region W43

The Green Bank Telescope (GBT) Diffuse Ionized Gas Survey (GDIGS) is a fully-sampled radio recombination line (RRL) survey of the inner Galaxy at C-band (4-8 GHz). We average together ~15 Hn$α$ RRLs within the receiver bandpass to improve the spectral signal-to-noise ratio. The average beam size for the RRL observations at these frequencies is ~2'. We grid these data to have spatial and velocity spacings of 30" and 0.5 km/s, respectively. Here we discuss the first RRL data from GDIGS: a six square-degree-area surrounding the Galactic HII region complex W43. We attempt to create a map devoid of emission from discrete HII regions and detect RRL emission from the diffuse ionized gas (DIG) across nearly the entire mapped area. We estimate the intensity of the DIG emission by a simple empirical model, taking only the HII region locations, angular sizes, and RRL intensities into account. The DIG emission is predominantly found at two distinct velocities: ~40 km/s and ~100 km/s. While the 100 km/s component is associated with W43 at a distance of ~6 kpc, the origin of the 40 km/s component is less clear. Since the distribution of the 40 km/s emission cannot be adequately explained by ionizing sources at the same velocity, we hypothesize that the plasma at the two velocity components is interacting, placing the 40 km/s DIG at a similar distance as the 100 km/s emission. We find a correlation between dust temperature and integrated RRL intensity, suggesting that the same radiation field that heats the dust also maintains the ionization of the DIG.

preprint2018arXiv

A projection-based numerical integration scheme for embedded interface: Application to fluid-structure interaction

We present a projection-based numerical integration technique to deal with embedded interface in finite element (FE) framework. The element cut by an embedded interface is denoted as a cut cell. We recognize elemental matrices of a cut cell can be reconstructed from the elemental matrices of its sub-divided cells, via projection at matrix level. These sub-divided cells are termed as integration cells. The proposed technique possesses following characteristics (1) no change in FE formulation and quadrature rule; (2) consistency with the derivation of FE formulation in variational principle. It can be considered as a re-projection of the residuals of equation system in the test function space or a reduced-order modeling (ROM) technique. These characteristics significantly improves its scalability, easy-to-implementation and robustness to deal with problems involving embedded discontinuities in FE framework. Numerical examples, e.g., vortex-induced vibration (VIV), rotation, free fall and rigid-body contact in which the proposed technique is implemented to integrate the variational form of Navier-Stokes equations in cut cells, are presented.

preprint2016arXiv

Collusive Attacks to "Circle-Type" Multi-party Quantum Key Agreement Protocols

We find that existing multi-party quantum key agreement (MQKA) protocols designed for fairness of the key are, in fact, unfair. Our analysis shows that these protocols are sensitive to collusive attacks; that is, dishonest participants can collaborate to predetermine the key without being detected. In fact, the transmission structures of the quantum particles in those unfair MQKA protocols, three of which have already been analyzed, have much in common. We call these unfair MQKA protocols circle-type MQKA protocols. Likewise, the transmission structures of the quantum particles in MQKA protocols that can resist collusive attacks are also similar. We call such protocols complete-graph-type MQKA protocols. A MQKA protocol also exists that can resist the above attacks but is still not fair, and we call it the tree-type MQKA protocol. We first point out a common, easily missed loophole that severely compromises the fairness of present circle-type MQKA protocols. Then we show that two dishonest participants at special positions can totally predetermine the key generated by circle-type MQKA protocols. We anticipate that our observations will contribute to secure and fair MQKA protocols, especially circle-type protocols.

preprint2016arXiv

Existence of stochastic entropy solutions for stochastic scalar balance laws with Lipschitz vector fields

In this paper, we consider a scalar stochastic balance law and gain the existence for stochastic entropy solutions. Our proof relies on the BGK approximation and the generalized Itô formula. Moreover, as an application, we derive the existence of stochastic entropy solutions for stochastic Buckley-Leverett type equations.

preprint2016arXiv

Invariant curves of almost periodic twist mappings

In this paper we are concerned with the existence of invariant curves of planar twist mappings which are almost periodic in a spatial variable. As an application of this result to differential equations we will discuss the existence of almost periodic solutions and the boundedness of all solutions for superlinear Duffing's equation with an almost periodic external force.

preprint2016arXiv

Online Fault-Tolerant Dynamic Event Region Detection in Sensor Networks via Trust Model

This paper proposes a Bayesian modeling approach to address the problem of online fault-tolerant dynamic event region detection in wireless sensor networks. In our model every network node is associated with a virtual community and a trust index, which quantitatively measures the trustworthiness of this node in its community. If a sensor node's trust value is smaller than a threshold, it suggests that this node encounters a fault and thus its sensor reading can not be trusted at this moment. This concept of sensor node trust discriminates our model with the other alternatives, e.g.,the Markov random fields. The practical issues, including spatiotemporal correlations of neighbor nodes' sensor readings, the presence of sensor faults and the requirement of online processing are linked together by the concept trust and are all taken into account in the modeling stage. Based on the proposed model, the trust value of each node is updated online by a particle filter algorithm upon the arrival of new observations. The decision on whether a node is located in the event region is made based upon the current estimate of this node's trust value. Experimental results demonstrate that the proposed solution can provide striking better performance than existent methods in terms of error rate in detecting the event region.

preprint2016arXiv

Posterior Exploration based Sequential Monte Carlo for Global Optimization

We propose a global optimization algorithm based on the Sequential Monte Carlo (SMC) sampling framework. In this framework, the objective function is normalized to be a probabilistic density function (pdf), based on which a sequence of annealed target pdfs is designed to asymptotically converge on the set of global optima. A sequential importance sampling (SIS) procedure is performed to simulate the resulting targets, and the maxima of the objective function is assessed from the yielded samples. The disturbing issue lies in the design of the importance sampling (IS) pdf, which crucially influences the IS efficiency. We propose an approach to design the IS pdf online by embedding a posterior exploration (PE) procedure into each iteration of the SMC framework. The PE procedure can also explore the important regions of the parameter space supported by the target pdf. A byproduct of the PE procedure is an adaptive mechanism to design the annealing temperature schedule online. We compare the performance of the proposed algorithm with those of several existing related alternatives by applying them to over a dozen standard benchmark functions. The result demonstrates the appealing properties of our algorithm.

preprint2016arXiv

Robust Particle Filter by Dynamic Averaging of Multiple Noise Models

State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable. To this end, a robust particle filter (PF) algorithm is proposed, in which the heavier tailed Student's t distributions are employed together with the Gaussian distribution to model the measurement noise. The effect of each model is automatically and dynamically adjusted via a Bayesian model averaging mechanism. The validity of the proposed algorithm is evaluated by illustrative simulations.

preprint2016arXiv

Robust video object tracking via Bayesian model averaging based feature fusion

In this article, we are concerned with tracking an object of interest in video stream. We propose an algorithm that is robust against occlusion, the presence of confusing colors, abrupt changes in the object feature space and changes in object size. We develop the algorithm within a Bayesian modeling framework. The state space model is used for capturing the temporal correlation in the sequence of frame images by modeling the underlying dynamics of the tracking system. The Bayesian model averaging (BMA) strategy is proposed for fusing multi-clue information in the observations. Any number of object features are allowed to be involved in the proposed framework. Every feature represents one source of information to be fused and is associated with an observation model. The state inference is performed by employing the particle filter methods. In comparison with related approaches, the BMA based tracker is shown to have robustness, expressivity, and comprehensibility.

preprint2016arXiv

Spatial modulation of a unitary impurity-induced resonances in superconducting CeCoIn$_{5}$

Motivated by recent experimental progress in high-resolution scanning tunneling microscopy (STM) techniques, we propose to investigate the local quasiparticle density of states around a unitary impurity in the heavy fermion superconductor CeCoIn$_{5}$. Based on the T-matrix approach we obtain a sharp nearly zero-energy resonance state (ZERS) in the strong impurity potential scattering localized around the impurity, and find qualitative differences in the spatial pattern of the tunneling conductance modulated by the nodal structure of the superconducting gap. These unique features may be used as a probe of the superconducting gap symmetry and in combination with the further STM measurements, may help to confirm the $d_{x^{2}-y^{2}}$ pairing in CeCoIn$_{5}$ at ambient pressure.

preprint2016arXiv

Spin-dependent Seebeck Effect in Aharonov-Bohm Rings with Rashba and Dresselhaus Spin-orbit Interactions

We theoretically investigate the spin-dependent Seebeck effect in an Aharonov-Bohm mesoscopic ring in the presence of both Rashba and Dresselhaus spin-orbit interactions under magnetic flux perpendicular to the ring. We apply the Green's function method to calculate the spin Seebeck coefficient employing the tight-binding Hamiltonian. It is found that the spin Seebeck coefficient is proportional to the slope of the energy-dependent transmission coefficients. We study the strong dependence of spin Seebeck coefficient on the Fermi energy, magnetic flux, strength of spin-orbit coupling, and temperature. Maximum spin Seebeck coefficients can be obtained when the strengths of Rashba and Dresselhaus spin-orbit couplings are slightly different. The spin Seebeck coefficient can be reduced by increasing temperature and disorder.

preprint2016arXiv

Structural Analysis of User Choices for Mobile App Recommendation

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. While there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this paper, we provide a systematic study for addressing these challenges. Specifically, we develop a Structural User Choice Model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption data set collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art top-N recommendation methods by a significant margin.

preprint2016arXiv

Student's t Distribution based Estimation of Distribution Algorithms for Derivative-free Global Optimization

In this paper, we are concerned with a branch of evolutionary algorithms termed estimation of distribution (EDA), which has been successfully used to tackle derivative-free global optimization problems. For existent EDA algorithms, it is a common practice to use a Gaussian distribution or a mixture of Gaussian components to represent the statistical property of available promising solutions found so far. Observing that the Student's t distribution has heavier and longer tails than the Gaussian, which may be beneficial for exploring the solution space, we propose a novel EDA algorithm termed ESTDA, in which the Student's t distribution, rather than Gaussian, is employed. To address hard multimodal and deceptive problems, we extend ESTDA further by substituting a single Student's t distribution with a mixture of Student's t distributions. The resulting algorithm is named as estimation of mixture of Student's t distribution algorithm (EMSTDA). Both ESTDA and EMSTDA are evaluated through extensive and in-depth numerical experiments using over a dozen of benchmark objective functions. Empirical results demonstrate that the proposed algorithms provide remarkably better performance than their Gaussian counterparts.

preprint2016arXiv

Tensor Decomposition via Variational Auto-Encoder

Tensor decomposition is an important technique for capturing the high-order interactions among multiway data. Multi-linear tensor composition methods, such as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the complex interactions among objects are multi-linear, and are thus insufficient to represent nonlinear relationships in data. Another assumption of these methods is that a predefined rank should be known. However, the rank of tensors is hard to estimate, especially for cases with missing values. To address these issues, we design a Bayesian generative model for tensor decomposition. Different from the traditional Bayesian methods, the high-order interactions of tensor entries are modeled with variational auto-encoder. The proposed model takes advantages of Neural Networks and nonparametric Bayesian models, by replacing the multi-linear product in traditional Bayesian tensor decomposition with a complex nonlinear function (via Neural Networks) whose parameters can be learned from data. Experimental results on synthetic data and real-world chemometrics tensor data have demonstrated that our new model can achieve significantly higher prediction performance than the state-of-the-art tensor decomposition approaches.

preprint2016arXiv

The formation efficiency of close-in planets via Lidov-Kozai migration: analytic calculations

Lidov-Kozai oscillations of planets in stellar binaries, combined with tidal dissipation, can lead to the formation of hot Jupiters (HJs) or tidal disruption of planets. Recent population synthesis studies have found that the fraction of systems resulting in HJs (F_HJ) depends strongly on the planet mass, host stellar type and tidal dissipation strength, while the total migration fraction F_mig = F_ HJ + F_dis (including both HJ formation and tidal disruption) exhibits much weaker dependence. We present an analytical method for calculating F_HJ and F_mig in the Lidov-Kozai migration scenario. The key ingredient of our method is to determine the critical initial planet-binary inclination angle that drives the planet to reach sufficiently large eccentricity for efficient tidal dissipation or disruption. This calculation includes the effects of the octupole potential and short-range forces on the planet. Our analytical method reproduces the resulting planet migration/disruption fractions from population synthesis, and can be easily implemented for various planet, stellar/companion types, and for different distributions of initial planetary semi-major axes, binary separations and eccentricities. We extend our calculations to planets in the super-Earth mass range and discuss the conditions for such planets to survive Lidov-Kozai migration and form close-in rocky planets.

preprint2016arXiv

The surface-forming energy release rate versus the local energy release rate

This paper identifies two ways to extract the energy (or power) flowing into a crack tip during propagation based on the power balance of areas enclosed by a stationary contour and a comoving contour. It is very interesting to find a contradiction that two corresponding energy release rates (ERRs), a surface-forming ERR and a local ERR, are different when stress singularity exists at a crack tip. Besides a rigorous mathematical interpretation, we deduce that the stress singularity leads to an accompanying kinetic energy at the crack tip. The local ERR represents the driving force to overcome the surface energy and the accompanying kinetic energy, while the surface-forming ERR represents the driving force to overcome the surface energy only. Their advantages and disadvantages are discussed. We recommend using the surface-forming ERR based fracture criterion for a crack propagation in elastic-plastic materials, since it has a wide applicability and concise formulae which are easy to compute among all energy based criteria.

preprint2016arXiv

Topological mechanics of origami and kirigami

Origami and kirigami have emerged as potential tools for the design of mechanical metamaterials whose properties such as curvature, Poisson ratio, and existence of metastable states can be tuned using purely geometric criteria. A major obstacle to exploiting this property is the scarcity of tools to identify and program the flexibility of fold patterns. We exploit a recent connection between spring networks and quantum topological states to design origami with localized folding motions at boundaries and study them both experimentally and theoretically. These folding motions exist due to an underlying topological invariant rather than a local imbalance between constraints and degrees of freedom. We give a simple example of a quasi-1D folding pattern that realizes such topological states. We also demonstrate how to generalize these topological design principles to two dimensions. A striking consequence is that a domain wall between two topologically distinct, mechanically rigid structures is deformable even when constraints locally match the degrees of freedom.

preprint2016arXiv

Uniqueness of stochastic entropy solutions for stochastic balance laws with Lipschitz fluxes

In this paper, we consider a stochastic balance law with a Lipschitz flux and gain the uniqueness for stochastic entropy solutions. The argument is supported by the stochastic kinetic formulation, the Itô formula and the regularization techniques. Furthermore, as an application, we derive the uniqueness of stochastic entropy solutions for stochastic porous media type equations.

preprint2016arXiv

Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process

In this paper, we describe work in progress towards a real-time vision-based traffic flow prediction (TFP) system. The proposed method consists of three elemental operators, that are dynamic texture model based motion segmentation, feature extraction and Gaussian process (GP) regression. The objective of motion segmentation is to recognize the target regions covering the moving vehicles in the sequence of visual processes. The feature extraction operator aims to extract useful features from the target regions. The extracted features are then mapped to the number of vehicles through the operator of GP regression. A training stage using historical visual data is required for determining the parameter values of the GP. Using a low-resolution visual data set, we performed preliminary evaluations on the performance of the proposed method. The results show that our method beats a benchmark solution based on Gaussian mixture model, and has the potential to be developed into qualified and practical solutions to real-time TFP.

preprint2016arXiv

You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors

We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users, via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.

preprint2015arXiv

A novel self-locked energy absorbing system

Metallic thin-walled round tubes are widely used as energy absorption elements. However, lateral splash of the round tubes under impact loadings reduces the energy absorption efficiency and may cause secondary damages. Therefore, it is necessary to assemble and fasten round tubes together by boundary constraints and/or fasteners between tubes, which increases the time and labor cost and affects the mechanical performance of round tubes. In an effort to break through this limitation, a novel self-locked energy-absorbing system has been proposed in this paper. The proposed system is made up of thin-walled tubes with dumbbell-shaped cross section, which are specially designed to interlock with each other and thus provide lateral constraint under impact loadings. Both finite element simulations and impact experiment demonstrated that without boundary constraints or fasteners between tubes, the proposed self-locked energy-absorbing system can still effectively attenuate impact loads while the round tube systems fail to carry load due to the lateral splashing of tubes. Furthermore, the optimal geometric design for a single dumbbell-shaped tube and the optimal stacking arrangement for the system are discussed, and a general guideline on the structural design of the proposed self-locked energy absorbing system is provided.

preprint2015arXiv

Intrinsic-to-extrinsic transition in fracture toughness through structural design: A lesson from nature

Catastrophic failure of materials and structures due to unstable crack growth could be prevented if facture toughness could be enhanced at will through structural design, but how can this be possible if fracture toughness is a material constant related to energy dissipation in the vicinity of a propagating crack tip. Here we draw inspiration from the deformation behavior of biomolecules in load bearing biological materials, which have been evolved with a large extensibility and a high breaking strength beyond their elastic limit, and introduce an effective biomimetic strategy to enhance fracture toughness of a structure through an intrinsic to extrinsic (ITE) transition. In the ITE transition, toughness starts as an intrinsic parameter at the basic material level, but by designing a protein-like effective stress-strain behavior the toughness at the system level becomes an extrinsic parameter that increases with the system size without bound. This phenomenon is demonstrated through a combination of numerical simulations, analytic modeling and experiments, and leads to a biomimetic strategy which can be broadly adopted to enhance fracture toughness in engineering systems.

preprint2015arXiv

Mixture Modeling based Probabilistic Situation Awareness

The problem of situational awareness (SAW) is investigated from the probabilistic modeling point of view. Taking the situation as a hidden variable, we introduce a hidden Markov model (HMM) and an extended state space model (ESSM) to mathematically express the dynamic evolution law of the situation and the relationships between the situation and the observable quantities. We use the Gaussian mixture model (GMM) to formulate expert knowledge, which is needed in building the HMM and ESSM. We show that the ESSM model is preferable as compared with HMM, since using ESSM, we can also get a real time estimate of the pivot variable that connects the situation with the observable quantities. The effectiveness and efficiency of both models are tested through a simulated experiment about threat surveillance.

preprint2015arXiv

Multi-user quantum key distribution with collective eavesdropping detection over collective-noise channels

A multi-user quantum key distribution protocol is proposed with single particles and the collective eavesdropping detection strategy on a star network. By utilizing this protocol, any two users of the network can accomplish quantum key distribution with the help of a serving center. Due to the utilization of collective eavesdropping detection strategy, the users of the protocol just need have the ability of performing certain unitary operations. Furthermore, we present three fault-tolerant versions of the proposed protocol, which can combat with the errors over different collective-noise channels. The security of all the proposed protocols is guaranteed by the theorems on quantum operation discrimination.

preprint2015arXiv

QKD-based quantum private query without a failure probability

In this paper, we present a quantum-key-distribution (QKD)-based quantum private query (QPQ) protocol utilizing single-photon signal of multiple optical pulses. It maintains the advantages of the QKD-based QPQ, i.e., easy to implement and loss tolerant. In addition, different from the situations in the previous QKD-based QPQ protocols, in our protocol, the number of the items an honest user will obtain is always one and the failure probability is always zero. This characteristic not only improves the stability (in the sense that, ignoring the noise and the attack, the protocol would always succeed), but also benefits the privacy of the database (since the database will no more reveal additional secrets to the honest users). Furthermore, for the user's privacy, the proposed protocol is cheat sensitive, and for security of the database, we obtain an upper bound for the leaked information of the database in theory.

preprint2015arXiv

Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating

A robust algorithm solution is proposed for tracking an object in complex video scenes. In this solution, the bootstrap particle filter (PF) is initialized by an object detector, which models the time-evolving background of the video signal by an adaptive Gaussian mixture. The motion of the object is expressed by a Markov model, which defines the state transition prior. The color and texture features are used to represent the object, and a marginal likelihood based feature fusion approach is proposed. A corresponding object template model updating procedure is developed to account for possible scale changes of the object in the tracking process. Experimental results show that our algorithm beats several existing alternatives in tackling challenging scenarios in video tracking tasks.

preprint2015arXiv

Suppression of extreme orbital evolution in triple systems with short range forces

The Lidov-Kozai (LK) mechanism plays an important role in the secular evolution of many hierarchical triple systems. The standard LK mechanism consists of large-amplitude oscillations in eccentricity and inclination of a binary subject to the quadrupole potential from an outer perturber. Recent work has shown that when the octupole terms are included in the potential, the inner binary can reach more extreme eccentricities as well as undergo orientation flips. It is known that pericenter precessions due to short-range effects, such as General Relativity and tidal and rotational distortions, can limit the growth of eccentricity and even suppress standard (quadrupolar) LK oscillations, but their effect on the octupole-level LK mechanism has not been fully explored. In this paper, we systematically study how these short-range forces affect the extreme orbital behaviour found in octupole LK cycles. In general, the influence of the octupole potential is confined to a range of initial mutual inclinations Itot centered around 90deg (when the inner binary mass ratio is <<1), with this range expanding with increasing octupole strength. We find that, while the short-range forces do not change the width and location of this "window of influence", they impose a strict upper limit on the maximum achievable eccentricity. This limiting eccentricity can be calculated analytically, and its value holds even for strong octupole potential and for the general case of three comparable masses. Short-range forces also affect orbital flips, progressively reducing the range of Itot within which flips are possible as the intensity of these forces increases.

preprint2014arXiv

Fermi surface topology and impurity-induced resonance state in BiS$_{2}$-based superconductors

Within the two-orbital model for BiS$_{2}$-based superconductors, the effect from a single nonmagnetic impurity scattering on the superconducting-state is investigated in terms of the T-matrix approach. By considering three kinds of the typical Fermi surface topology which characterize the essential features of the doping dependence of the electronic structure in BiS$_{2}$-based superconductors, it is found that the impurity scattering on the superconducting-state with the conventional $s$-wave, the extended $s$-wave, the $\pm s$-wave, and the $d_{x^{2}-y^{2}}$-wave symmetries induces \textit{qualitatively} different resonance states due to the evolution of unique nodal structures on the Fermi surface with doping. These impurity-induced resonance states can be verified directly by the scanning tunneling microscopy experiments, and therefore they are proposed as a probe of the superconducting pairing symmetry in BiS$_{2}$-based superconductors.

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

Mechanics of nanoscale wrinkling of graphene on a non-developable spherical surface

The configuration of graphene (GE) sheet conforming to the spherical surface substrate is studied through theoretical model and molecular simulations. Two basic configurations are observed: fully conformation and wrinkling. The final configuration of the adsorbed GE results from the competition between two energy terms: the adhesion energy between GE and substrate, the strain energy stored in the GE due to the deformations. Here, we derive theoretical solutions by accounting for two energy terms, and predict the final morphology of GE on the spherical surface (a special kind of nano-developable curved surface) substrate with using the phase diagram. A critical cone angle of the absorbed GE for an arbitrary spherical surface substrate is obtained. Fully conformation of GE is observed when the cone angle of absorbed GE is below the critical value, otherwise wrinkles appear. Molecular simulations are implemented to verify the theoretical model with results agree well with theoretical predictions. Results from our present work can offer a guide for designing new functional graphene electronical devices (such as nanoswithes) and fabricating high quality nanostructured coating (Fig. 13).

preprint2014arXiv

Mobility in a strongly coupled dusty plasma with gas

The mobility of a charged projectile in a strongly coupled dusty plasma is simulated. A net force $F$, opposed by a combination of collisional scattering and gas friction, causes projectiles to drift at a mobility-limited velocity $u_p$. The mobility $μ_p=u_p/F$ of the projectile's motion is obtained. Two regimes depending on $F$ are identified. In the high force regime, $μ_p \propto F^{0.23}$, and the scattering cross section $σ_s$ diminishes as $u_p^{-6/5}$. Results for $σ_s$ are compared with those for a weakly coupled plasma and for two-body collisions in a Yukawa potential. The simulation parameters are based on microgravity plasma experiments.

preprint2014arXiv

Post-processing of the oblivious key in quantum private queries

Quantum private query (QPQ) is a kind of quantum protocols to protect both users' privacy in their communication. There is an interesting example, that is, Alice wants to buy one item from Bob's database, which is composed of a quantity of valuable messages. QPQ protocol is the communication procedure ensuring that Alice can get only one item from Bob, and at the same time, Bob cannot know which one was taken by Alice. Owing to its practicability, quantum-key-distribution-based QPQ has draw much attention in recent years. However, the post-processing of the key in such protocols, called oblivious key, remains far from being satisfactorily known. Especially, the error correction method for such special key is still missing. Here we focus on the post-processing of the oblivious key, including both dilution and error correction. On the one hand, we demonstrate that the previous dilution method, which greatly reduces the communication complexity, will bring Alice the chance to illegally obtain much additional information about Bob's database. Simulations show that by very limited queries Alice can obtain the whole database. On the other hand, we present an effective error-correction method for the oblivious key, which completes its post-processing and makes such QPQ more practical.

preprint2014arXiv

Quantitative Law of Diffusion Induced Stress and Fracture

In diffusion processes of solid materials, such as in the classical thermal shock problem and the recent lithium ion battery, the maximum diffusion induced stress (DIS) is a very important quantity. However a widely accepted, accurate and easy-to-use quantitative formula on it still lacks. In this paper, by normalizing the governing equations, an almost analytical model is developed, except a single-variable function of the dimensionless Biot number which cannot be determined analytically and is then given by a curve. Formulae for various typical geometries and working conditions are presented. If the stress and the diffusion process are fully coupled (i.e. stress-dependent diffusion), as in lithium ion diffusion, the normalized maximum DIS can be characterized by a two-variable function of a dimensionless coupling parameter and the Biot number, which is obtained numerically and presented in contour plots. Moreover, it is interesting to note that these two parameters, within a wide range, can be further approximately combined into a single dimensionless parameter to characterize the maximum DIS. These formulae together with curves/contours provide engineers and materialists a simple and easy way to quickly obtain the stress and verify the reliability of materials under various typical diffusion conditions. Via energy balance analysis, the model of diffusion induced fracture is also developed. It interestingly predicts that the spacing of diffusion induced cracks is constant, independent of the thickness of specimen and the concentration difference. Our thermal shock experiments on alumina plates validate these qualitative and quantitative theoretical predictions, such as the constant crack spacing and the predicted critical temperature difference at which the cracks initiate. Furthermore, the proposed model can interpret the observed hierarchical crack patterns.

preprint2014arXiv

Superdiffusion of 2D Yukawa liquids due to a perpendicular magnetic field

Stochastic transport of a two-dimensional (2D) dusty plasma liquid with a perpendicular magnetic field is studied. Superdiffusion, which is a type of non-Fickian transport, is found to occur especially at higher magnetic fields with $β$ of order unity. Here, $β= ω_c / ω_{pd}$ is the ratio of the cyclotron and plasma frequencies for dust particles. The mean-square displacement ${\rm {MSD}} = 4 D_αt^α$ is found to have an exponent $α> 1$, indicating superdiffusion, with $α$ increasing monotonically to $1.1$ as $β$ increases to unity. The 2D Langevin molecular dynamics simulation used here also reveals that another indicator of random particle motion, the velocity autocorrelation function (VACF), has a dominant peak frequency $ω_{peak}$ that empirically obeys $ω_{peak}^2 = ω_c^2+ ω_{pd}^2/4$.

preprint2014arXiv

The surface-forming energy release rate based fracture criterion for elastic-plastic crack propagation

J integral based criterion is widely used in elastic-plastic fracture mechanics. However, it is not rigorously applicable when plastic unloading appears during crack propagation. One difficulty is that the energy density with plastic unloading in J integral cannot be defined unambiguously. In this paper, we alternatively start from the analysis on the power balance, and propose a surface-forming energy release rate (ERR), which represents the energy directly dissipated on the surface-forming during the crack propagation and excludes the loading-mode-dependent plastic dissipation. Therefore the surface-forming ERR based fracture criterion has wider applicability, including elastic-plastic crack propagation problems. Several formulae have been derived for calculating the surface-forming ERR. From the most concise formula, it is interesting to note that the surface-forming ERR can be computed only by the stress and deformation of the current moment, and the definition of the energy density or work density is avoided. When an infinitesimal contour is chosen, the expression can be further simplified. For any fracture behaviors, the surface-forming ERR is proven to be path-independent, and the path-independence of its constituent term, so-called integral, is also investigated. The physical meanings and applicability of the proposed surface-forming ERR, traditional ERR, Js integral and J integral are compared and discussed.

preprint2013arXiv

A hybrid molecular dynamics/atomic-scale finite element method for quasi-static atomistic simulations at finite temperature

In this paper, a hybrid quasi-static atomistic simulation method at finite temperature is developed, which combines the advantages of MD for thermal equilibrium and atomic-scale finite element method (AFEM) for efficient equilibration. Some temperature effects are embedded in static AFEM simulation by applying the virtual and equivalent thermal disturbance forces extracted from MD. Alternatively performing MD and AFEM can quickly obtain a series of thermodynamic equilibrium configurations such that a quasi-static process is modeled. Moreover, a stirring-accelerated MD/AFEM fast relaxation approach is proposed, in which the atomic forces and velocities are randomly exchanged to artificially accelerate the "slow processes" such as mechanical wave propagation and thermal diffusion. The efficiency of the proposed methods is demonstrated by numerical examples on single wall carbon nanotubes.

preprint2013arXiv

Cryptanalysis of a multi-party quantum key agreement protocol with single particles

Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x] presented an efficient multi-party quantum key agreement (QKA) protocol by employing single particles and unitary operations. The aim of this protocol is to fairly and securely negotiate a secret session key among $N$ parties with a high qubit efficiency. In addition, the authors claimed that no participant can learn anything more than his/her prescribed output in this protocol, i.e., the sub-secret keys of the participants can be kept secret during the protocol. However, here we points out that the sub-secret of a participant in Sun et al.'s protocol can be eavesdropped by the two participants next to him/her. In addition, a certain number of dishonest participants can fully determine the final shared key in this protocol. Finally, we discuss the factors that should be considered when designing a really fair and secure QKA protocol.

preprint2013arXiv

Electron correlation and impurity-induced quasiparticle resonance states in cuprate superconductors

We theoretically study the quasiparticle resonance states around a nonmagnetic impurity in cuprate superconductors based on t-t'-J-U model. The purpose of introducing the Coulomb repulsive interaction U is to partially impose the double occupancy constraint by employing the Gutzwiller projected mean-field approximation. We determine the spatial variation of the order parameter and the local density of states (LDOS) by self-consistently solving Bogoliubov-de Gennes equations. We find that in the large U limit, a zero-energy resonance peak in the LDOS indeed appears for the impurity potential in the unitary limit, at the same time the asymmetric superconducting coherence peaks are strongly suppressed. As U decreases the electron double occupancy d is permitted and gradually increases, leading to the decreasing of order parameter. In particular the above zero-energy resonance peak begins to evolve into a double-peaked structure since a critical value $d_{c}$. These important feathers are qualitatively agreement with the scanning tunneling spectroscopy experiments, and uncover the essential role played by the electron correlation in cuprate superconductors.

preprint2013arXiv

Enhanced No-Go Theorem for Quantum Position Verification

Based on the instantaneous nonlocal quantum computation (INQC), Buhrman et al. proposed an excellent attack strategy to quantum position verification (QPV) protocols in 2011, and showed that, if the colluding adversaries are allowed to previously share unlimited entangled states, it is impossible to design an unconditionally secure QPV protocol in the previous model. Here, trying to overcome this no-go theorem, we find some assumptions in the INQC attack, which are implicit but essential for the success of this attack, and present three different QPV protocols where these assumptions are not satisfied. We show that for the general adversaries, who execute the attack operations at every common time slot or the time when they detect the arrival of the challenge signals from the verifiers, secure QPV is achievable. This implies practically secure QPV can be obtained even if the adversaries is allowed to share unlimited entanglement previously. Here by "practically" we mean that in a successful attack the adversaries need launch a new round of attack on the coming qubits with extremely high frequency so that none of the possible qubits, which may be sent at random time, will be missed. On the other side, using such Superdense INQC (SINQC) attack, the adversaries can still attack the proposed protocols successfully in theory. The particular attack strategies to our protocols are presented respectively. On this basis, we demonstrate the impossibility of secure QPV with looser assumptions, i.e. the enhanced no-go theorem for QPV.

preprint2013arXiv

Helical swimming in Stokes flow using a novel boundary-element method

We apply the boundary-element method to Stokes flows with helical symmetry, such as the flow driven by an immersed rotating helical flagellum. We show that the two-dimensional boundary integral method can be reduced to one dimension using the helical symmetry. The computational cost is thus much reduced while spatial resolution is maintained. We review the robustness of this method by comparing the simulation results with the experimental measurement of the motility of model helical flagella of various ratios of pitch to radius, along with predictions from resistive-force theory and slender-body theory. We also show that the modified boundary integral method provides reliable convergence if the singularities in the kernel of the integral are treated appropriately.

preprint2013arXiv

Locomotion of helical bodies in viscoelastic fluids: enhanced swimming at large helical amplitudes

The motion of a rotating helical body in a viscoelastic fluid is considered. In the case of force-free swimming, the introduction of viscoelasticity can either enhance or retard the swimming speed and locomotive efficiency, depending on the body geometry, fluid properties, and the body rotation rate. Numerical solutions of the Oldroyd-B equations show how previous theoretical predictions break down with increasing helical radius or with decreasing filament thickness. Helices of large pitch angle show an increase in swimming speed to a local maximum at a Deborah number of order unity. The numerical results show how the small-amplitude theoretical calculations connect smoothly to the large-amplitude experimental measurements.

preprint2013arXiv

Longitudinal viscosity of 2D Yukawa liquids

The longitudinal viscosity $η_l$ is obtained for a two-dimensional (2D) liquid using a Green-Kubo method with a molecular dynamics simulation. The interparticle potential used has the Debye-Hückel or Yukawa form, which models a 2D dusty plasma. The longitudinal $η_l$ and shear $η_s$ viscosities are found to have values that match very closely, with only negligible differences for the entire range of temperatures that is considered. The bulk viscosity $η_b$ is determined to be either negligibly small or not a meaningful transport coefficient, for a 2D Yukawa liquid.

preprint2013arXiv

Nonmagnetic impurity resonance states as a test of superconducting pairing symmetry in CeCoIn$_{5}$

We theoretically study the effect of a nonmagnetic impurity in heavy fermion superconductor CeCoIn$_{5}$ within a realistic band model and the T-matrix approximation approach. By considering two known possible pairing symmetry candidates $d_{x^{2}-y^{2}}$ and $d_{xy}$, we find that although both total density of states exhibit a similar V-shaped gaplike feature, only the $d_{x^{2}-y^{2}}$-wave pairing symmetry gives rise to robust intragap impurity resonance states reflected by the resonance peaks near the Fermi energy in the local density of states. These features can be readily probed by scanning tunneling microscopy experiments, and are proposed to shed light on the pairing symmetry and provide hints on the microscopic mechanism of unconventional superconductivity in the Ce-based heavy fermion superconductors.

preprint2013arXiv

Propulsion by a Helical Flagellum in a Capillary Tube

We study the microscale propulsion of a rotating helical filament confined by a cylindrical tube, using a boundary-element method for Stokes flow that accounts for helical symmetry. We determine the effect of confinement on swimming speed and power consumption. Except for a small range of tube radii at the tightest confinements, the swimming speed at fixed rotation rate increases monotonically as the confinement becomes tighter. At fixed torque, the swimming speed and power consumption depend only on the geometry of the filament centerline, except at the smallest pitch angles for which the filament thickness plays a role. We find that the `normal' geometry of \textit{Escherichia coli} flagella is optimized for swimming efficiency, independent of the degree of confinement. The efficiency peaks when the arc length of the helix within a pitch matches the circumference of the cylindrical wall. We also show that a swimming helix in a tube induces a net flow of fluid along the tube.

preprint2012arXiv

Dynamical spin response of electron doped Mott insulators on a triangular lattice

The spin dynamics of electron doped Mott insulators on a triangular lattice is studied based on the $t-J$ model. It is found that the particularly universal behaviors of integrated dynamical spin structure factor seen in the doped Mott insulators on a square lattice, are absent in the doped Mott insulators on a triangular lattice, indicating the presence of the normal state gap. As a result, the spin-lattice relaxation rate 1/$T_{1}$ divided by $T$ reduces with decreasing temperatures in the temperature region above 0.2$J$$\approx$50K, then follows a Curie-Weiss-like behavior at the temperature less than 50K, in qualitative agreement with experimental observations.

preprint2012arXiv

Energy Transport in a Shear Flow of Particles in a 2D Dusty Plasma

A shear flow of particles in a laser-driven two-dimensional (2D) dusty plasma are observed in a further study of viscous heating and thermal conduction. Video imaging and particle tracking yields particle velocity data, which we convert into continuum data, presented as three spatial profiles: mean particle velocity (i.e., flow velocity), mean-square particle velocity, and mean-square fluctuations of particle velocity. These profiles and their derivatives allow a spatially-resolved determination of each term in the energy and momentum continuity equations, which we use for two purposes. First, by balancing these terms so that their sum (i.e., residual) is minimized while varying viscosity $η$ and thermal conductivity $κ$ as free parameters, we simultaneously obtain values for $η$ and $κ$ in the same experiment. Second, by comparing the viscous heating and thermal conduction terms, we obtain a spatially-resolved characterization of the viscous heating.

preprint2012arXiv

Frequency-dependent shear viscosity of a liquid 2D dusty plasma

The viscoelasticity of a two-dimensional liquid strongly-coupled dusty plasma is studied experimentally, without macroscopic shear. Positions and velocities of the dust particles, measured by video microscopy, are used as the inputs to the generalized Green-Kubo relation to obtain the complex viscosity $η(ω)$. The real part of $η(ω)$ (which corresponds to dissipation) diminishes gradually with frequency, while the imaginary part (which corresponds to elasticity) is peaked at a frequency below the 2D dusty plasma frequency. The viscoelastic approximation is found to accurately describe the 2D experimental results for $η(ω)$, yielding the Maxwell relaxation time $τ_M = 0.10 s$. Results for $η(ω)$ are compared to 2D molecular dynamics Yukawa simulations and to a previous experiment that was performed using an oscillating macroscopic shear.

preprint2012arXiv

Local Unitary Classification of Arbitrary Dimensional Multipartite Pure States

We propose a practical entanglement classification scheme for general multipartite pure states in arbitrary dimensions under local unitary equivalence by exploiting the high order singular value decomposition technique and local symmetries of the states. By virtue of this scheme, the method of determining the local unitary equivalence of $n$-qubit states proposed by Kraus is extended to the case for arbitrary dimensional multipartite states.

preprint2012arXiv

Many-to-many disjoint paths in hypercubes with faulty vertices

This paper considers the problem of many-to-many disjoint paths in the hypercube $Q_n$ with $f$ faulty vertices and obtains the following result. For any integer $k$ with $1\leq k\leq n-2$, any two sets $S$ and $T$ of $k$ fault-free vertices in different parts of $Q_n\ (n\geq 3)$, if $f\leq 2n-2k-3$ and each fault-free vertex has at least two fault-free neighbors, then there exist $k$ fully disjoint fault-free paths linking $S$ and $T$ which contain at least $2^n-2f$ vertices. This result improves some known results in a sense.

preprint2012arXiv

Observation of temperature peaks due to strong viscous heating in a dusty plasma flow

Profound temperature peaks are observed in regions of high velocity shear in a 2D dusty plasma experiment with laser-driven flow. These are attributed to viscous heating, which occurs due to collisional scattering in a shear flow. Using measurements of viscosity, thermal conductivity, and spatial profiles of flow velocity and temperature, we determine three dimensionless numbers: Brinkman Br = 0.5, Prandtl Pr = 0.09, and Eckert Ec = 5.7. The large value of Br indicates significant viscous heating that is consistent with the observed temperature peaks.

preprint2012arXiv

Scalable hierarchical parallel algorithm for the solution of super large-scale sparse linear equations

The parallel linear equations solver capable of effectively using 1000+ processors becomes the bottleneck of large-scale implicit engineering simulations. In this paper, we present a new hierarchical parallel master-slave-structural iterative algorithm for the solution of super large-scale sparse linear equations in distributed memory computer cluster. Through alternatively performing global equilibrium computation and local relaxation, our proposed algorithm will reach the specific accuracy requirement in a few of iterative steps. Moreover, each set/slave-processor majorly communicate with its nearest neighbors, and the transferring data between sets/slave-processors and master is always far below the set-neighbor communication. The corresponding algorithm for implicit finite element analysis has been implemented based on MPI library, and a super large 2-dimension square system of triangle-lattice truss structure under random static loads is simulated with over one billion degrees of freedom and up to 2001 processors on "Exploration 100" cluster in Tsinghua University. The numerical experiments demonstrate that this algorithm has excellent parallel efficiency and high scalability, and it may have broad application in other implicit simulations.

preprint2012arXiv

Superconductivity and magnetic fluctuations in electron-doped cobaltate superconductors

We study the interplay between superconductivity and antiferromagnetic spin fluctuations in electron-doped cobaltate Na$_{x}$CoO$_{2}\cdot y$H$_{2}$O based on the kinetic energy driven superconductivity mechanism. We show that the superconducting state is governed by both charge carrier pairing and quasiparticle coherent, and displays a common dome-shaped phase diagram in agreement with experimental results. By calculating the dynamical spin structure factor, we theoretically find that the magnetic excitation shows a commensurate resonance peak, which locates at antiferromagnetic wave vector $Q (\frac{2π}{3},\frac{2π}{\sqrt{3}})$ for a broad range of low energies, then evolves outward into six incommensurate magnetic scattering peaks with increasing energy. Such commensurate-incommensurate spin resonance excitation should be measured by the inelastic neutron scattering technique (INS). Our present results strongly suggest that magnetic resonance can indeed be one of the fundamental features in doped Mott insulators.

preprint2012arXiv

Waves and instability in a one-dimensional microfluidic array

Motion in a one-dimensional (1D) microfluidic array is simulated. Water droplets, dragged by flowing oil, are arranged in a single row, and due to their hydrodynamic interactions spacing between these droplets oscillates with a wave-like motion that is longitudinal or transverse. The simulation yields wave spectra that agree well with experiment. The wave-like motion has an instability which is confirmed to arise from nonlinearities in the interaction potential. The instability's growth is spatially localized. By selecting an appropriate correlation function, the interaction between the longitudinal and transverse waves is described.

preprint2011arXiv

Accurate particle position measurement from images

The moment method is an image analysis technique for sub-pixel estimation of particle positions. The total error in the calculated particle position includes effects of pixel locking and random noise in each pixel. Pixel locking, also known as peak locking, is an artifact where calculated particle positions are concentrated at certain locations relative to pixel edges. We report simulations to gain an understanding of the sources of error and their dependence on parameters the experimenter can control. We suggest an algorithm, and we find optimal parameters an experimenter can use to minimize total error and pixel locking. Simulating a dusty plasma experiment, we find that a sub-pixel accuracy of 0.017 pixel or better can be attained. These results are also useful for improving particle position measurement and particle tracking velocimetry (PTV) using video microscopy, in fields including colloids, biology, and fluid mechanics.

preprint2011arXiv

Errors in particle tracking velocimetry with high-speed cameras

Velocity errors in particle tracking velocimetry (PTV) are studied. When using high-speed video cameras, the velocity error may increase at a high camera frame rate. This increase in velocity error is due to particle-position uncertainty, which is one of two sources of velocity errors studied here. The other source of error is particle acceleration, which has the opposite trend of diminishing at higher frame rates. Both kinds of errors can propagate into quantities calculated from velocity, such as the kinetic temperature of particles or correlation functions. As demonstrated in a dusty plasma experiment, the kinetic temperature of particles has no unique value when measured using PTV, but depends on the sampling time interval or frame rate. It is also shown that an artifact appears in an autocorrelation function computed from particle positions and velocities, and it becomes more severe when a small sampling-time interval is used. Schemes to reduce these errors are demonstrated.

preprint2011arXiv

Flexible quantum private queries based on quantum key distribution

We present a flexible quantum-key-distribution-based protocol for quantum private queries. Similar to M. Jakobi et al's protocol [Phys. Rev. A 83, 022301 (2011)], it is loss tolerant, practical and robust against quantum memory attack. Furthermore, our protocol is more flexible and controllable. We show that, by adjusting the value of $θ$, the average number of the key bits Alice obtains can be located on any fixed value the users wanted for any database size. And the parameter $k$ is generally smaller (even $k=1$ can be achieved) when $θ<π/4$, which implies lower complexity of both quantum and classical communications. Furthermore, the users can choose a smaller $θ$ to get better database security, or a larger $θ$ to obtain a lower probability with which Bob can correctly guess the address of Alice's query.

preprint2011arXiv

Green-Kubo relation for viscosity tested using experimental data for a 2D dusty plasma

The theoretical Green-Kubo relation for viscosity is tested using experimentally obtained data. In a dusty plasma experiment, micron-size dust particles are introduced into a partially-ionized argon plasma, where they become negatively charged. They are electrically levitated to form a single-layer Wigner crystal, which is subsequently melted using laser heating. In the liquid phase, these dust particles experience interparticle electric repulsion, laser heating, and friction from the ambient neutral argon gas, and they can be considered to be in a nonequilibrium steady state. Direct measurements of the positions and velocities of individual dust particles are then used to obtain a time series for an off-diagonal element of the stress tensor and its time autocorrelation function. This calculation also requires the interparticle potential, which was not measured experimentally, but was obtained using a Debye-Hückel-type model with experimentally determined parameters. Integrating the autocorrelation function over time yields the viscosity for shearing motion amongst dust particles. The viscosity so obtained is found to agree with results from a previous experiment using a hydrodynamical Navier-Stokes equation. This comparison serves as a test of the Green-Kubo relation for viscosity. Our result is also compared to the predictions of several simulations.

preprint2011arXiv

Identifying anomalous diffusion and melting in dusty plasmas

Anomalous diffusion in liquids and the solid-liquid phase transition (melting) are studied in two-dimensional Yukawa systems. The self-intermediate scattering function (self-ISF), calculated from simulation data, exhibits a temporal decay, or relaxation, with a characteristic relaxation time. This decay is found to be useful for distinguishing normal and anomalous diffusion in a liquid, and for identifying the solid-liquid phase transition. For liquids, a scaling of the relaxation time with length scale is found. For the solid-liquid phase transition, the shape of the self-ISF curve is found to be a sensitive indicator of phase. Friction has a significant effect on the timing of relaxation, but not the melting point.

preprint2011arXiv

Rapid heating and cooling in two-dimensional Yukawa systems

Simulations are reported to investigate solid superheating and liquid supercooling of two-dimensional (2D) systems with a Yukawa interparticle potential. Motivated by experiments where a dusty plasma is heated and then cooled suddenly, we track particle motion using a simulation with Langevin dynamics. Hysteresis is observed when the temperature is varied rapidly in a heating and cooling cycle. As in the experiment, transient solid superheating, but not liquid supercooling, is observed. Solid superheating, which is characterized by solid structure above the melting point, is found to be promoted by a higher rate of temperature increase.

preprint2011arXiv

The Friedmann equation in modified entropy-area relation from entropy force

According to the formal holographic principle, a modification to the assumption of holographic principle in Verlinder's investigation of entropy force is obtained. A more precise relation between entropy and area in the holographic system is proposed. With the entropy corrections to the area-relation, we derivate Newton's laws and Einstein equation with a static spherically symmetric holographic screen. Furthermore we derived the correction terms to the modified Friedmann equation of the FRW universe starting from the holographic principle and the Debye model.

preprint2011arXiv

The Friedmann equations in deformed Hořava-Lifshitz gravity and Debye model

Considering the results from Hořava-Lifshitz (HL) theory, a more precise relation between the number of bits and area in the holographic system is proposed. With this corrected relation and Debye model, two modified Friedmann equations are derived from the Hawking temperature and the Unruh temperature separately in entropic force. These equations could be better in describing the whole evolution of the universe.

preprint2011arXiv

The Klein-Gordon equation in Machian model

The non-local Machian model is regarded as an alternative theory of gravitation which states that all particles in the Universe as a 'gravitationally entangled' statistical ensemble. It is shown that the Klein-Gordon equation can be derived within this Machian model of the universe. The crucial point of the derivation is the activity of the Machian energy background field which causing a fluctuation about the average momentum of a particle, the non-locality problem in quantum theory is addressed in this framework.

preprint2011arXiv

Thermodynamical properties of interacting holographic dark energy model with apparent horizon

We have investigated the thermodynamical properties of the universe with dark energy. It is demonstrated that in a universe with spacial curvature the natural choice for IR cutoff could be the apparent horizon radius. We shown that any interaction of pressureless dark matter with holographic dark energy, whose infrared cutoff is set by the apparent horizon radius, implying a constant effective equation of state of dark component in a universe. In addition we found that for the static observer in space, the comoving distance has a faster expansion than the apparent horizon radius with any spatial curvature. We also verify that in some conditions the modified first law of thermodynamics could return to the classic form at apparent horizon for a universe filled with dark energy and dark matter. Besides, the generalized second law of thermodynamics is discussed in a region enclosed by the apparent horizon.

preprint2011arXiv

Viscosity calculated in simulations of strongly-coupled dusty plasmas with gas friction

A two-dimensional strongly-coupled dusty plasma is modeled using Langevin and frictionless molecular dynamical simulations. The static viscosity $η$ and the wave-number-dependent viscosity $η(k)$ are calculated from the microscopic shear in the random motion of particles. A recently developed method of calculating the wave-number-dependent viscosity $η(k)$ is validated by comparing the results of $η(k)$ from the two simulations. It is also verified that the Green-Kubo relation can still yield an accurate measure of the static viscosity $η$ in the presence of a modest level of friction as in dusty plasma experiments.

preprint2010arXiv

Evolution of shear-induced melting in dusty plasma

The spatiotemporal development of melting is studied experimentally in a 2D dusty plasma suspension. Starting with an ordered lattice, and then suddenly applying localized shear, a pair of counter-propagating flow regions develop. A transition between two melting stages is observed before a steady state is reached. Melting spreads with a front that propagates at the transverse sound speed. Unexpectedly, coherent longitudinal waves are excited in the flow region.

preprint2010arXiv

Extinction of quasiparticle scattering interference in cuprate superconductors

The quasiparticle scattering interference phenomenon characterized by the peaks in the local density of states is studied within the kinetic energy driven superconducting mechanism in the presence of a single impurity. By calculation of the Fourier transformed ratio of the local density of states at opposite energy, it is shown that the quasiparticle scattering interference phenomenon can be described qualitatively by a single impurity in the kinetic energy driven homogeneous d-wave superconducting state. The amplitude of the peak increases with increasing energy at the low energy, and reaches a maximum at the intermediate energy, then diminishes to zero at the high energy. The theory also predicts that with increasing doping, the position of the peak along the nodal direction moves towards to the center of the Brillouin zone, while the position of the peak along the antinodal direction is shifted to large momentum region.

preprint2010arXiv

Local electronic structures on the superconducting interface $LaAlO_{3}/SrTiO_{3}$

Motivated by the recent discovery of superconductivity on the heterointerface $LaAlO_{3}/SrTiO_{3}$, we theoretically investigate its local electronic structures near an impurity considering the influence of Rashba-type spin-orbit interaction (RSOI) originated in the lack of inversion symmetry. We find that local density of states near an impurity exhibits the in-gap resonance peaks due to the quasiparticle scattering on the Fermi surface with the reversal sign of the pairing gap caused by the mixed singlet and RSOI-induced triplet superconducting state. We also analyze the evolutions of density of states and local density of states with the weight of triplet pairing component determined by the strength of RSOI, which will be widely observed in thin films of superconductors with surface or interface-induced RSOI, or various noncentrosymmetric superconductors in terms of point contact tunneling and scanning tunneling microscopy, and thus reveal an admixture of the spin singlet and RSOI-induced triplet superconducting states.

preprint2010arXiv

Optimizing Information Credibility in Social Swarming Applications

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications: search and rescue, coordinated fire-fighting, and the DARPA balloon hunt challenge. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event. We first propose a model, based on common intuitions of believability, about the credibility of information. We then cast the problem posed above as a discrete optimization problem, and introduce optimal centralized solutions and an approximate solution amenable to decentralized implementation whose performance is about 20% off on average from the optimal (on real-world datasets derived from Google News) while being 3 orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions. To our knowledge, we are the first to propose and explore the problem of extracting credible information from a network of smartphones.

preprint2010arXiv

The modified wave function of test particle approaching holographic screen from entropy force

In this note we generalize entropy based on the quantum mechanical probability density distribution. Motivated by J. Munkhammar and the uncertainty of entropy we modified the origin wave function of the test particle. The corrected one acting on the quantum particle is subject to the uncertainty principle. Considering the uncertain relation, the corrected probability of the particle for measurement on holographic screen has been proposed. We also derivate the speed of information transfer described by wave function. Our quantum approach to entropy stress the information in a physical system is directly associated with its quantum mechanical entropy defined by the equality of the partition function.

preprint2010arXiv

The Parametric Symmetry and Numbers of the Entangled Class of 2 \times M \times N System

We present in the work two intriguing results in the entanglement classification of pure and true tripartite entangled state of $2\times M\times N$ under stochastic local operation and classical communication. (i) the internal symmetric properties of the nonlocal parameters in the continuous entangled class; (ii) the analytic expression for the total numbers of the true and pure entangled class $2\times M \times N$ states. These properties help people to know more of the nature of the $2\times M\times N$ entangled system.

preprint2010arXiv

Viscoelasticity of 2D liquids quantified in a dusty plasma experiment

The viscoelasticity of two-dimensional liquids is quantified in an experiment using a dusty plasma. An experimental method is demonstrated for measuring the wavenumber-dependent viscosity, $η(k)$, which is a quantitative indicator of viscoelasticity. Using an expression generalized here to include friction, $η(k)$ is computed from the transverse current autocorrelation function (TCAF), which is found by tracking random particle motion. The TCAF exhibits an oscillation that is a signature of elastic contributions to viscoelasticity. Simulations of a Yukawa liquid are consistent with the experiment.

preprint2009arXiv

Shapiro-like Resonance in Ultracold Molecule Production via an Oscillating Magnetic Field

We study the process of production of ultracold molecules from ultracold atoms using a sinusoidally oscillating magnetic field modulation. When the magnetic field is resonant roughly with the molecular binding energy, Shapiro-like resonances are observed. Their resonance profiles are well fitted by the Lorentzian functions. The line widths depend on both the amplitude and the duration of the applied modulations, and are found to be dramatically broadened by thermal dephasing effect. The resonance centers shift due to both many-body effect and finite temperature effect. Our theory is consistent with recent experiment (S. T. Thompson, E. Hodby, and C. E. Wieman, Phys. Rev. Lett. 95, 190404 (2005)). Our model predicts a 1/3 ceiling for the molecular production yield in uncondensed ultracold atomic clouds for a long coupling time, while for the condensed atoms the optimal conversion yield could be beyond the limit.

preprint2009arXiv

Spin-resolved impurity resonance states in electron-doped cuprate superconductors

With the aim at understanding the non-monotonic $d_{x^{2}-y^{2}}$-wave gap, we analyze the local electronic structure near impurities in the electron-doped cuprate superconductors. We find that the local density of states near a non-magnetic impurity in the scenario of $d_{x^{2}-y^{2}}$-wave superconductivity with higher harmonics is qualitatively different from that obtained from the $d_{x^{2}-y^{2}}$-wave superconductivity coexisting with antiferromagnetic spin density wave order. We propose that spin-polarized scanning tunneling microscopy measurements can distinguish the two scenarios and shed light on the real physical origin of a non-monotonic $d_{x^{2}-y^{2}}$-wave gap.

preprint2008arXiv

Solid superheating observed in two-dimensional strongly-coupled dusty plasma

It is demonstrated experimentally that strongly-coupled plasma exhibits solid superheating. A 2D suspension of microspheres in dusty plasma, initially self-organized in a solid lattice, was heated and then cooled rapidly by turning laser heating on and off. Particles were tracked using video microscopy, allowing atomistic-scale observation during melting and solidification. During rapid heating, the suspension remained in a solid structure at temperatures above the melting point, demonstrating solid superheating. Hysteresis diagrams did not indicate liquid supercooling in this 2D system.

preprint2006arXiv

Heat transport of electron-doped Cobaltates

Within the t-J model, the heat transport of electron-doped cobaltates is studied based on the fermion-spin theory. It is shown that the temperature dependent thermal conductivity is characterized by the low temperature peak located at a finite temperature. The thermal conductivity increases monotonously with increasing temperature at low temperatures T $<$ 0.1$J$, and then decreases with increasing temperature for higher temperatures T $>$ 0.1$J$, in qualitative agreement with experimental result observed from Na$_{x}$CoO$_{2}$ .

preprint2005arXiv

Kinetic energy driven superconductivity in the electron doped cobaltate Na$_{x}$CoO$_{2}\cdot y$H$_{2}$O

Within the charge-spin separation fermion-spin theory, we have shown that the mechanism of superconductivity in the electron doped cobaltate Na$_{x}$CoO$_{2}\cdot y$H$_{2}$O is ascribed to its kinetic energy. The dressed fermions interact occurring directly through the kinetic energy by exchanging magnetic excitations. This interaction leads to a net attractive force between dressed fermions, then the electron Cooper pairs originating from the dressed fermion pairing state are due to the charge-spin recombination, and their condensation reveals the superconducting ground state. The superconducting transition temperature is identical to the dressed fermion pair transition temperature, and is suppressed to a lower temperature due to the strong magnetic frustration. The optimal superconducting transition temperature occurs in the electron doping concentration $δ\approx 0.29$, and then decreases for both underdoped and overdoped regimes, in qualitative agreement with the experimental results.

preprint2005arXiv

Shear viscosity of two-dimensional Yukawa systems in liquid state

The shear viscosity of a two-dimensional (2D) liquid was calculated using equilibrium molecular dynamics simulations with a Yukawa potential. The shear viscosity has a minimum, at a Coulomb coupling parameter of about 17, arising from the temperature dependence of the kinetic and potential contributions. Previous calculations of 2D viscosity were less extensive, and for a different potential. The stress autocorrelation function was found to decay rapidly, contrary to some earlier work. These results are useful for 2D condensed matter systems and are compared to a recent dusty plasma experiment.

Bin Liu

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150 published item(s)

Advancing Aesthetic Image Generation via Composition Transfer

EIGER IV: The cool 10$^4$K circumgalactic environment of high-$z$ galaxies reveals remarkably efficient IGM enrichment

Robust Dynamic Operating Envelopes via Superellipsoid-based Convex Optimisation in Unbalanced Distribution Networks

Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder

GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

A DNS Tunnel Sliding Window Differential Detection Method Based on Normal Distribution Reasonable Range Filtering

Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation

Bayesian Negative Sampling for Recommendation

Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks

Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks

Evolution of Stellar Orbits Around Merging Massive Black-Hole Binary

Highly active hydrogen evolution facilitated by topological surface states on a Pd/SnTe metal/topological crystalline insulator heterostructure

Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

Linear change and minutes variability of solar wind velocity revealed by FAST

Load Balancing in Low-Voltage Distribution Networks via Optimizing Residential Phase Connections

Massive molecular gas reservoir in a luminous sub-millimeter galaxy during cosmic noon

Mergers prompted by dynamics in compact, multiple-star systems: a stellar-reduction case for the massive triple TIC 470710327

MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

Notes on BIM and BFM Optimal Power Flow With Parallel Lines and Total Current Limits

Numerical study of three-dimensional single-mode Rayleigh-Taylor instability in turbulent mixing stage

Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation

Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities

Probing the Spins of Supermassive Black Holes with Gravitational Waves from Surrounding Compact Binaries

Real-time Online Multi-Object Tracking in Compressed Domain

Revealing sign-reversal $s^{+-}$-wave pairing by quasiparticle interference in the heavy-fermion superconductor CeCu$_2$Si$_2$

The FAST Ultra-Deep Survey (FUDS): observational strategy, calibration and data reduction

Thermalization of fluorescent protein exciton-polaritons at room temperature

Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning

Vortex-ring quantum droplets in a radially-periodic potential

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models

A Deep Learning Approach to Quasar Continuum Prediction

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Enhanced Lidov-Kozai migration and the formation of the transiting giant planet WD1856+534b

Hierarchical Black-Hole Mergers in Multiple Systems: Constrain the Formation of GW190412, GW190814 and GW190521-like events

INTCP: Information-centric TCP for Satellite Network

Semidiscrete vortex solitons

2.5-kV AlGaN/GaN Schottky Barrier Diode on Silicon Substrate with Recessed-anode Structure

A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis

A Survey on Trust Modeling from a Bayesian Perspective

Adapting Active Reflector Technology for greater sensitivity and sky-coverage in FAST-like Telescopes

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Compact differences of composition operators on Bergman spaces induced by doubling weights

Compact Differences of Weighted Composition Operators

Cross-modality Person re-identification with Shared-Specific Feature Transfer

Data-Driven Model Set Design for Model Averaged Particle Filter

Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method

Deep Learning Inversion of Electrical Resistivity Data

Deep-Learning Inversion of Seismic Data

Density-Aware Graph for Deep Semi-Supervised Visual Recognition

Discrete solitons in zigzag waveguide arrays with different types of linear mixing between nearest-neighbor and next-nearest-neighbor couplings

Generalized weighted composition operators on Bergman spaces induced by doubling weights

Holding and transferring matter-wave solitons against gravity by spin-orbit-coupling tweezers

Hydrodynamic and thermal characteristics of a freely-vibrating circular cylinder in mixed convection flow

Merging Compact Binaries Near a Rotating Supermassive Black Hole: Eccentricity Excitation due to Apsidal Precession Resonance

Multi-Label Sampling based on Local Label Imbalance

Negative Margin Matters: Understanding Margin in Few-shot Classification

Nonlinear modes in spatially confined spin-orbit-coupled Bose-Einstein condensates with repulsive nonlinearity

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Numerical stability and three dimensionality of a streamline hyperbolic critical point in wake at low Reynolds number

Parametric Instance Classification for Unsupervised Visual Feature Learning

Particle Filtering Methods for Stochastic Optimization with Application to Large-Scale Empirical Risk Minimization

Polymorphism and superconductivity in the V-Nb-Mo-Al-Ga high-entropy alloys

Refined Tail Asymptotic Properties for the $M^X/G/1$ Retrial Queue

Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach

Simultaneous Denoising and Dereverberation Using Deep Embedding Features

Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features

Superconductivity in hexagonal Nb-Mo-Ru-Rh-Pd high-entropy alloys

The Fundamental Performance of FAST with 19-beam Receiver at L Band

Two-Dimensional Rare Earth -- Gold Intermetallic Compounds on Au(111) by Surface Alloying

Unbalance Mitigation via Phase-switching Device and Static Var Compensator in Low-voltage Distribution Network

Uncovering Insurance Fraud Conspiracy with Network Learning

A general criterion for solid instability and its application to creases

The GBT Diffuse Ionized Gas Survey: Tracing the Diffuse Ionized Gas around the Giant HII Region W43