arXiv subjects

Chen Wang

Publications and source records attributed to Chen Wang.

At least 685 records · Page 38Linked to original sources

arXiv2020

A local-global theorem for $p$-adic supercongruences

Let ${\mathbb Z}_p$ denote the ring of all $p$-adic integers and call $${\mathcal U}=\{(x_1,\ldots,x_n):\,a_1x_1+\ldots+a_nx_n+b=0\}$$ a hyperplane over ${\mathbb Z}_p^n$, where at least one of $a_1,\ldots,a_n$ is not divisible by $p$. We prove that if a sufficiently regular $n$-variable function is zero modulo $p^r$ over some suitable collection of $r$ hyperplanes, then it is zero modulo $p^r$ over the whole ${\mathbb Z}_p^n$. We provide various applications of this general criterion by establishing several $p$-adic analogues of hypergeometric identities.

math.NT↗

arXiv2020

On the Global Self-attention Mechanism for Graph Convolutional Networks

Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this paper, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.

cs.LG↗

arXiv2020

Motion Sensor-based Privacy Attack on Smartphones

In this paper, we build a speech privacy attack that exploits speech reverberations generated from a smartphone's in-built loudspeaker captured via a zero-permission motion sensor (accelerometer). We design our attack Spearphone2, and demonstrate that speech reverberations from inbuilt loudspeakers, at an appropriate loudness, can impact the accelerometer, leaking sensitive information about the speech. In particular, we show that by exploiting the affected accelerometer readings and carefully selecting feature sets along with off-the-shelf machine learning techniques, Spearphone can successfully perform gender classification (accuracy over 90%) and speaker identification (accuracy over 80%) for any audio/video playback on the smartphone. Our results with testing the attack on a voice call and voice assistant response were also encouraging, showcasing the impact of the proposed attack. In addition, we perform speech recognition and speech reconstruction to extract more information about the eavesdropped speech to an extent. Our work brings to light a fundamental design vulnerability in many currently-deployed smartphones, which may put people's speech privacy at risk while using the smartphone in the loudspeaker mode during phone calls, media playback or voice assistant interactions.

cs.CR↗

arXiv2020

Can Steering Wheel Detect Your Driving Fatigue?

Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the driver's anomalous behaviour is closely monitored by the system. Though many approaches have been proposed for detecting driver fatigue, they largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.

eess.SP↗

arXiv2020

Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion

The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers. Albeit powerful, the PCANet consists of basic operations such as principal components and spatial pooling, which suffers from two fundamental problems. First, the principal components obtain information by transforming it to column vectors (which we call the amalgamated view), which incurs the loss of the spatial information in the data. Second, the generalized spatial pooling utilized in the PCANet induces feature redundancy and also fails to accommodate spatial statistics of natural images. In this research, we first propose a tensor-factorization based deep network called the Tensor Factorization Network (TFNet). The TFNet extracts features from the spatial structure of the data (which we call the minutiae view). We then show that the information obtained by the PCANet and the TFNet are distinctive and non-trivial but individually insufficient. This phenomenon necessitates the development of proposed HybridNet, which integrates the information discovery with the two views of the data. To enhance the discriminability of hybrid features, we propose Attn-HybridNet, which alleviates the feature redundancy by performing attention-based feature fusion. The significance of our proposed Attn-HybridNet is demonstrated on multiple real-world datasets where the features obtained with Attn-HybridNet achieves better classification performance over other popular baseline methods, demonstrating the effectiveness of the proposed technique.

cs.CV↗

arXiv2020

Impact of counter-rotating-wave term on quantum heat transfer and phonon statistics in nonequilibrium qubit-phonon hybrid system

Counter-rotating-wave terms (CRWTs) are traditionally viewed to be crucial in open small quantum systems with strong system-bath dissipation. Here by exemplifying in a nonequilibrium qubit-phonon hybrid model, we show that CRWTs can play the significant role in quantum heat transfer even with weak system-bath dissipation. By using coherent phonon states, we obtain the quantum master equation with heat exchange rates contributed by rotating-wave-terms (RWTs) and CRWTs, respectively. We find that including only RWTs, steady state heat current and current fluctuations will be significantly suppressed at large temperature bias, whereas they are strongly enhanced by considering CRWTs in addition. Furthermore, for the phonon statistics, the average phonon number and two-phonon correlation are nearly insensitive to strong qubit-phonon hybridization with only RWTs, whereas they will be dramatically cooled down via the cooperative transitions based on CRWTs in addition. Therefore, CRWTs in quantum heat transfer system should be treated carefully.

cond-mat.mes-hall↗

arXiv2020

Protecting a Bosonic Qubit with Autonomous Quantum Error Correction

To build a universal quantum computer from fragile physical qubits, effective implementation of quantum error correction (QEC) is an essential requirement and a central challenge. Existing demonstrations of QEC are based on a schedule of discrete error syndrome measurements and adaptive recovery operations. These active routines are hardware intensive, prone to introducing and propagating errors, and expected to consume a vast majority of the processing power in a large-scale quantum computer. In principle, QEC can be realized autonomously and continuously by tailoring dissipation within the quantum system, but this strategy has remained challenging so far. Here we encode a logical qubit in Schrödinger cat-like multiphoton states of a superconducting cavity, and demonstrate a corrective dissipation process that directly stabilizes an error syndrome operator: the photon number parity. Implemented with continuous-wave control fields only, this passive protocol realizes autonomous correction against single-photon loss and boosts the coherence time of the multiphoton qubit by over a factor of two. Notably, QEC is realized in a modest hardware setup with neither high-fidelity readout nor fast digital feedback, in contrast to the technological sophistication required for prior QEC demonstrations. Compatible with other error suppression and phase stabilization techniques, our experiment suggests reservoir engineering as a resource-efficient alternative or supplement to active QEC in future quantum computing architectures.

quant-ph↗

arXiv2020

Timing analysis of the black hole candidate EXO 1846-031 with Insight-HXMT monitoring

We present the observational results from a detailed timing analysis of the black hole candidate EXO 1846-031 during its outburst in 2019 with the observations of Insight-HXMT, NICER and MAXI. This outburst can be classfied roughly into four different states. Type-C quasi-periodic oscillations (QPOs) observed by NICER (about 0.1-6Hz) and Insight-HXMT (about 0.7-8Hz) are also reported in this work. Meanwhile, we study various physical quantities related to QPO frequency.The QPO rms-frequency relationship in three energy band 1-10 keV indicates that there is a turning pointing in frequency around 2 Hz,which is similar to that of GRS 1915+105. A possible hypothesis for the relationship above may be related to the inclination of the source, which may require a high inclination to explain it. The relationships between QPO frequency and QPO rms,hardness,total fractional rms and count rate have also been found in other transient sources, which can indicate that the origin of type-C QPOs is non-thermal.

astro-ph.HE↗

arXiv2020

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly found in black hole X-ray binaries, and their origin is still under debate. The properties of LFQPOs at high energies (above 30 keV) are closely related to the nature of the accretion flow in the innermost regions, and thus play a crucial role in critically testing various theoretical models. The Hard X-ray Modulation Telescope (Insight-HXMT) is capable of detecting emissions above 30 keV, and is therefore an ideal instrument to do so. Here we report the discovery of LFQPOs above 200 keV in the new black hole MAXI J1820+070 in the X-ray hard state, which allows us to understand the behaviours of LFQPOs at hundreds of kiloelectronvolts. The phase lag of the LFQPO is constant around zero below 30 keV, and becomes a soft lag (that is, the high-energy photons arrive first) above 30 keV. The soft lag gradually increases with energy and reaches ~0.9s in the 150-200 keV band. The detection at energies above 200 keV, the large soft lag and the energy-related behaviors of the LFQPO pose a great challenge for most currently existing models, but suggest that the LFQPO probably originates from the precession of a small-scale jet.

astro-ph.HE↗

arXiv2020

Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies. In this paper, we propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we configure a multi-agent historical order book simulation environment for execution tasks built on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [arXiv:1904.12066]. Second, we formulate the problem of optimal execution in an RL setting where an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in High Frequency Trading (HFT). Third, we develop and train an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment. In some scenarios, our RL agent converges towards a Time-Weighted Average Price (TWAP) strategy. Finally, we evaluate the simulation with our RL agent by comparing it with a market replay simulation using real market Limit Order Book (LOB) data.

q-fin.TR↗

arXiv2020

Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer

Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. In this study, we aimed to evaluate technical feasibility of using digital pathological approaches to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. Based on Groovy scripts and QuPath, a novel informatics system was developed to facilitate interactive annotation and imaging data exchange for machine learning purposes. Through this developed system, cellular boundaries were detected and expanded set of cellular features were extracted to represent cell- and tissue-level characteristics. According to our evaluation, cell-level classification was accurately achieved for both tumor and stroma cells with greater than 90% accuracy. Upon further re-examinations, 44.2% of the misclassified cells were due to over-/under-segmentations or low-quality of imaging areas. For a total number of 6,485 imaging patches with sufficient tumor and stroma cells (ten of each at least), we achieved 91-95% accuracy to differentiate HGSOC v. SBOT. When all the patches were considered for a WSI to make consensus prediction, 97% accuracy was achieved for accurately classifying all patients, indicating that cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications.

eess.IV↗

arXiv2020

Radio Frequency Interference Mitigation based on the ArPLS and SumThreshold Method

As radio telescopes become sensitive, radio frequency interference (RFI) is more and more serious for interesting signals of radio astronomy. There exist demands for developing an automatic, accurate and efficient RFI mitigation method. Therefore, this work investigated the RFI detection algorithm. Firstly, we introduced an Asymmetrically Reweighted Penalized Least Squares (ArPLS) method to estimate baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in the SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human interventions and the online RFI processing pipeline. Applications to FAST (Five-hundred-meter Aperture Spherical Telescope) data show that the proposed scheme based on the ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.

astro-ph.IM↗

arXiv2020

Reduction of PDF uncertainty in the measurement of the weak mixing angle at the ATLAS experiment

We investigate the parton distribution function (PDF) uncertainty in the measurement of the effective weak mixing angle $\sin^2θ_{\text{eff}}^{\ell}$ at the CERN Large Hadron Collider (LHC). The PDF-induced uncertainty is large in the proton-proton collisions at the LHC due to the dilution effect. The measurement of the Drell-Yan forward-backward asymmetry ($A_{FB}$) at the LHC can be used to reduce the PDF uncertainty in the $\sin^2θ_{\text{eff}}^{\ell}$ measurement. However, when including the full mass range of lepton pairs in the $A_{FB}$ data analysis, the correlation between the PDF updating procedure and the $\sin^2θ_{\text{eff}}^{\ell}$ extraction leads to a sizable bias in the obtained $\sin^2θ_{\text{eff}}^{\ell}$ value. From our studies, we find that the bias can be significantly reduced by removing Drell-Yan events with invariant mass around the $Z$ pole region, while most of the sensitivity in reducing the PDF uncertainty remains. Furthermore, the lepton charge asymmetry in the $W$ boson events as a function of the rapidity of the charged leptons, $A_\pm(η_\ell)$, is known to be another observable which can be used to reduce the PDF uncertainty in the $\sin^2θ_{\text{eff}}^{\ell}$ measurement. The constraint from $A_\pm(η_\ell)$ is complementary to that from the $A_{FB}$, thus no bias affects the $\sin^2θ_{\text{eff}}^{\ell}$ extraction. The studies are performed using the Error PDF Updating Method Package ({\sc ePump}), which is based on the Hessian updating methods. In this article, the CT14HERA2 PDF set is used as an example.

hep-ex↗

arXiv2020

TartanAir: A Dataset to Push the Limits of Visual SLAM

We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The data is collected in photo-realistic simulation environments with the presence of moving objects, changing light and various weather conditions. By collecting data in simulations, we are able to obtain multi-modal sensor data and precise ground truth labels such as the stereo RGB image, depth image, segmentation, optical flow, camera poses, and LiDAR point cloud. We set up large numbers of environments with various styles and scenes, covering challenging viewpoints and diverse motion patterns that are difficult to achieve by using physical data collection platforms. In order to enable data collection at such a large scale, we develop an automatic pipeline, including mapping, trajectory sampling, data processing, and data verification. We evaluate the impact of various factors on visual SLAM algorithms using our data. The results of state-of-the-art algorithms reveal that the visual SLAM problem is far from solved. Methods that show good performance on established datasets such as KITTI do not perform well in more difficult scenarios. Although we use the simulation, our goal is to push the limits of Visual SLAM algorithms in the real world by providing a challenging benchmark for testing new methods, while also using a large diverse training data for learning-based methods. Our dataset is available at \url{http://theairlab.org/tartanair-dataset}.

cs.RO↗

arXiv2020

The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)

We present a new video understanding pentathlon challenge, an open competition held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020. The objective of the challenge was to explore and evaluate new methods for text-to-video retrieval-the task of searching for content within a corpus of videos using natural language queries. This report summarizes the results of the first edition of the challenge together with the findings of the participants.

cs.CV↗

arXiv2020

Deformed relativistic Hartree-Bogoliubov theory in continuum with point coupling functional: examples of even-even Nd isotopes

The aim of this work is to develop the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) theory based on the point-coupling density functionals and extend it to provide a unified description for all even-even nuclei in the nuclear chart by overcoming all possible challenges. The nuclear superfluidity is considered via Bogoliubov transformation. Densities and potentials are expanded in terms of Legendre polynomials to include the axial deformation degrees of freedom. Sophisticated relativistic Hartree-Bogoliubov equations in coordinate space are solved in the DiracWoods-Saxon basis to consider the continuum effects. Numerical checks are performed from light nuclei to heavy nuclei. The techniques to construct the DRHBc mass table for even-even nuclei are explored. The DRHBc theory is extended to study heavier nuclei beyond magnesium isotopes. Taking Nd isotopes as examples, the experimental binding energies, two-neutron separation energies, quadrupole deformations, and charge radii are reproduced rather well. The deformation and continuum play essential roles in the description of nuclear masses and prediction of drip-line nuclei. By examining the single-particle levels in the canonical basis and their contributions to the total density, the thickness of the neutron skin, the particles number in continuum, and the Coulomb barrier, the exotic structures including the neutron skin and the proton radioactivity are predicted.

nucl-th↗

arXiv2020

Online Visual Place Recognition via Saliency Re-identification

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency re-identification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.

cs.CV↗

arXiv2020

Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs' vast view scope. However, due to UAVs flight altitude limitations, it may be impossible to observe the global map, and motion planning in the local map is a POMDP (Partially Observable Markov Decision Process) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned system under partial observation from UAV without reconstruction of global maps, which consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV's perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate.

cs.RO↗