arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 955 records · Page 53Linked to original sources

arXiv2019

Interpretable Principal Components Analysis for Multilevel Multivariate Functional Data, with Application to EEG Experiments

Many studies collect functional data from multiple subjects that have both multilevel and multivariate structures. An example of such data comes from popular neuroscience experiments where participants' brain activity is recorded using modalities such as EEG and summarized as power within multiple time-varying frequency bands within multiple electrodes, or brain regions. Summarizing the joint variation across multiple frequency bands for both whole-brain variability between subjects, as well as location-variation within subjects, can help to explain neural reactions to stimuli. This article introduces a novel approach to conducting interpretable principal components analysis on multilevel multivariate functional data that decomposes total variation into subject-level and replicate-within-subject-level (i.e. electrode-level) variation, and provides interpretable components that can be both sparse among variates (e.g. frequency bands) and have localized support over time within each frequency band. The sparsity and localization of components is achieved by solving an innovative rank-one based convex optimization problem with block Frobenius and matrix $L_1$-norm based penalties. The method is used to analyze data from a study to better understand reactions to emotional information in individuals with histories of trauma and the symptom of dissociation, revealing new neurophysiological insights into how subject- and electrode-level brain activity are associated with these phenomena.

stat.ME↗

arXiv2019

Deep Holes of Projective Reed-Solomon Codes

Projective Reed-Solomon (PRS) codes are Reed-Solomon codes of the maximum possible length q+1. The classification of deep holes --received words with maximum possible error distance-- for PRS codes is an important and difficult problem. In this paper, we use algebraic methods to explicitly construct three classes of deep holes for PRS codes. We show that these three classes completely classify all deep holes of PRS codes with redundancy at most four. Previously, the deep hole classification was only known for PRS codes with redundancy at most three in work arXiv:1612.05447

cs.IT↗

arXiv2019

Generalized collision-free velocity model for pedestrian dynamics

The collision-free velocity model is a microscopic pedestrian model, which despite its simplicity, reproduces fairly well several self-organization phenomena in pedestrian dynamics. The model consists of two components: a direction sub-model that combines individual desired moving direction and neighbor's influence to imitate the process of navigating in a two-dimensional space, and an intrinsically collision-free speed sub-model which controls the speed of the agents with respect to the distance to their neighbors. In this paper we generalize the collision-free velocity model by introducing the influence of walls and extending the distance calculations to velocity-based ellipses. Besides, we introduce enhancements to the direction sub-module that smooth the direction changes of pedestrians in the simulation; a shortcoming that was not visible in the original model due to the symmetry of the circular shapes. Moreover, the introduced improvements mitigate backward movements, leading to a more realistic distribution of pedestrians especially in bottleneck scenarios. We study by simulation the effects of the pedestrian's shape by comparing the fundamental diagram in narrow and wide corridors. Furthermore, we validate our generalized approach by investigating the flow through bottlenecks with varying exit's widths.

physics.soc-ph↗

arXiv2019

Perturbations of Stealth Black Holes in DHOST Theories

Among the Scalar-Tensor modified theories of gravity, DHOST models could play a special role for dark energy while being consistent with current observations, notably those constraining the speed of gravitational waves. Schwarzschild-de Sitter black holes were shown to be exact solutions of a particular subclass of quadratic DHOST theories, while carrying a nontrivial scalar profile that linearly evolves in time and hence potentially providing exciting new phenomenological windows to explore this model. We investigate the physical perturbations about such black holes and find that the odd-parity tensor perturbations behave in a way indistinguishable to GR. On the other hand, the effective metric for the (even-parity) scalar perturbations is singular, indicating that those exact black hole solutions are infinitely strongly coupled and cannot be trusted within the regime of validity of the DHOST effective field theory. We show how this strong coupling result is generalizable to a whole class of solutions with arbitrary manifolds both for DHOST and Horndeski.

hep-th↗

arXiv2019

On the Kähler Geometry of Certain Optimal Transport Problems

Let $X$ and $Y$ be domains of $\mathbb{R}^n$ equipped with respective probability measures $μ$ and $ ν$. We consider the problem of optimal transport from $μ$ to $ν$ with respect to a cost function $c: X \times Y \to \mathbb{R}$. To ensure that the solution to this problem is smooth, it is necessary to make several assumptions about the structure of the domains and the cost function. In particular, Ma, Trudinger, and Wang established regularity estimates when the domains are strongly \textit{relatively $c$-convex} with respect to each other and cost function has non-negative \textit{MTW tensor}. For cost functions of the form $c(x,y)= Ψ(x-y)$ for some convex function $Ψ$, we find an associated Kähler manifold whose orthogonal anti-bisectional curvature is proportional to the MTW tensor. We also show that relative $c$-convexity geometrically corresponds to geodesic convexity with respect to a dual affine connection. Taken together, these results provide a geometric framework for optimal transport which is complementary to the pseudo-Riemannian theory of Kim and McCann. We provide several applications of this work. In particular, we find a complete Kähler surface with non-negative orthogonal bisectional curvature that is not a Hermitian symmetric space or biholomorphic to $\mathbb{C}^2$. We also address a question in mathematical finance raised by Pal and Wong on the regularity of \textit{pseudo-arbitrages}, or investment strategies which outperform the market.

math.OC↗

arXiv2019

Experimental quantum repeater without quantum memory

Quantum repeaters -- important components of a scalable quantum internet -- enable the entanglement to be distributed over long distances. The standard paradigm for a quantum repeater relies on a necessary demanding requirement of quantum memory. Despite significant progress, the limited performance of quantum memory makes practical quantum repeaters still a great challenge. Remarkably, a proposed all-photonic quantum repeater avoids the need for quantum memory by harnessing the graph states in the repeater nodes. Here we perform an experimental demonstration of an all-photonic quantum repeater using linear optics. By manipulating a 12-photon interferometer, we implement a 2-by-2 parallel all-photonic quantum repeater, and observe an 89% enhancement of entanglement-generation rate over the standard parallel entanglement swapping. These results open a new way towards designing repeaters with efficient single-photon sources and photonic graph states, and suggest that the all-photonic scheme represents an alternative path -- parallel to that of matter-memory-based schemes -- towards realizing practical quantum repeaters.

quant-ph↗

arXiv2019

Quantum Coherence Witness with Untrusted Measurement Devices

Coherence is a fundamental resource in quantum information processing, which can be certified by a coherence witness. Due to the imperfection of measurement devices, a conventional coherence witness may lead to fallacious results. We show that the conventional witness could mistake an incoherent state as a state with coherence due to the inaccurate settings of measurement bases. In order to make the witness result reliable, we propose a measurement-device-independent coherence witness scheme without any assumptions on the measurement settings. We introduce the decoy-state method to significantly increase the capability of recognizing states with coherence. Furthermore, we experimentally demonstrate the scheme in a time-bin encoding optical system.

quant-ph↗

arXiv2019

Experimental nonlocal measurement of a product observable

Nonlocal measurement, or instantaneous measurement of nonlocal observables, is a considerably difficult task even for a simple form of product observable since relativistic causality prohibits interaction between spacelike separate subsystems. Following a recent proposal for effectively creating the von Neumann measurement Hamiltonian of nonlocal observables [Brodutch and Cohen, Phys. Rev. Lett. 116, 070404 (2016)], here we report a proof-of-principle demonstration of nonlocally measuring a product observable using linear optics without the violation of relativistic causality. Our scheme provides a feasible approach to perform nonlocal measurements via quantum erasure with linear optics.

quant-ph↗

arXiv2019

Restricted Linearized Augmented Lagrangian Method for Euler's Elastica Model

Euler's elastica model has been extensively studied and applied to image processing tasks. However, due to the high nonlinearity and nonconvexity of the involved curvature term, conventional algorithms suffer from slow convergence and high computational cost. Various fast algorithms have been proposed, among which, the augmented Lagrangian based ones are very popular in the community. However, parameter tuning might be very challenging for these methods. In this paper, a simple cutting-off strategy is introduced into the augmented Lagrangian based algorithms for minimizing the Euler's elastica energy, which leads to easy parameter tuning and fast convergence. The cutting-off strategy is based on an observation of inconsistency inside the augmented Lagrangian based algorithms. When the weighting parameter of the curvature term goes to zero, the energy functional boils down to the ROF model. So, a natural requirement is that its augmented Lagrangian based algorithms should also approach the augmented Lagrangian based algorithms formulated directly for solving the ROF model from the very beginning. Unfortunately, this is not the case for certain existing augmented Lagrangian based algorithms. The proposed cutting-off strategy helps to decouple the tricky dependence between the auxiliary splitting variables, so as to remove the observed inconsistency. Numerical experiments suggest that the proposed algorithm enjoys easier parameter-tuning, faster convergence and even higher quality of image restorations.

eess.IV↗

arXiv2019

Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. Simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate and energy efficiency.

eess.SP↗

arXiv2019

The dynamics of AR 12700 in its early emerging phase II: fan-shaped activities relevant to arch filament systems

The emergence of active regions (ARs) closely relates to the solar dynamo and the dynamical atmospheric phenomena.With high-resolution and long-lasting observations from the New Vacuum Solar Telescope, we report a new dynamic activity phenomenon named "fan-shaped activity (FSA)" in the emerging phase of NOAA AR 12700. The FSAs are clearly observed at Ha wavelength and are closely related to the dynamics of the adjacent arch filament system (AFS), including threads deformation and materials downward motions. On 2018 February 26, the two most representative FSAs appeared around 05:21 UT and 06:03 UT, respectively, and they firstly ascended and then decayed in around 10 minutes. At the ascending phase, accompanied by the uplifting of an adjacent AFS, each FSA launches up at one end of the AFS and extends for up to 11 Mm. At the decaying phase, the FSA gradually vanishes, and materials downflows towards the other end of the AFS are detected. After checking the evolution of the magnetic fields of AR 12700, we find that each FSA is located between the end of an AFS and an adjacent magnetic patch with the same polarity and launches at the onset of the collision and compression between these two magnetic patches. We propose that the collision lifts up the AFS, and then the initially compact AFS laterally expands, resulting in the formation of FSA. A cartoon model is proposed to depict the activities.

astro-ph.SR↗

arXiv2019

The Roadmap to 6G -- AI Empowered Wireless Networks

The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications. While 5G is being deployed around the world, efforts from industry and academia have started to look beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented transformation that will make it substantially different from the previous generations of wireless cellular systems. In particular, 6G will go beyond mobile Internet and will be required to support ubiquitous AI services from the core to the end devices of the network. Meanwhile, AI will play a critical role in designing and optimizing 6G architectures, protocols, and operations. In this article, we discuss potential technologies for 6G to enable mobile AI applications, as well as AI-enabled methodologies for 6G network design and optimization. Key trends in the evolution to 6G will also be discussed.

cs.NI↗

arXiv2019

A Graph Neural Network Approach for Scalable Wireless Power Control

Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in $K$-user interference channels. Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE. The code is available on https://github.com/yshenaw/Globecom2019.

cs.IT↗

arXiv2019

An Overview of Attacks and Defences on Intelligent Connected Vehicles

Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers' safety. Cyber security in intelligent connected vehicles is composed of in-vehicle security and security of inter-vehicle communications. Security of Electronic Control Units (ECUs) and the Control Area Network (CAN) bus are the most significant parts of in-vehicle security. Besides, with the development of 4G LTE and 5G remote communication technologies for vehicle-toeverything (V2X) communications, the security of inter-vehicle communications is another potential problem. After giving a short introduction to the architecture of next-generation vehicles including driverless and intelligent vehicles, this review paper identifies a few major security attacks on the intelligent connected vehicles. Based on these attacks, we provide a comprehensive survey of available defences against these attacks and classify them into four categories, i.e. cryptography, network security, software vulnerability detection, and malware detection. We also explore the future directions for preventing attacks on intelligent vehicle systems.

cs.CR↗

arXiv2019

DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks

Computational color constancy refers to the estimation of the scene illumination and makes the perceived color relatively stable under varying illumination. In the past few years, deep Convolutional Neural Networks (CNNs) have delivered superior performance in illuminant estimation. Several representative methods formulate it as a multi-label prediction problem by learning the local appearance of image patches using CNNs. However, these approaches inevitably make incorrect estimations for the ambiguous patches affected by their neighborhood contexts. Inaccurate local estimates are likely to bring in degraded performance when combining into a global prediction. To address the above issues, we propose a contextual deep network for patch-based illuminant estimation equipped with refinement. First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches. The patches are sampled based on the observation that pixels with large color differences describe the illumination well. Then, the refinement net integrates the input patches with the corrected patches in conjunction with the use of intermediate features to improve the performance. To train such a network with numerous parameters, we propose a stage-wise training strategy, in which the features and the predicted illuminant from previous stages are provided to the next learning stage with more finer estimates recovered. Experiments show that our approach obtains competitive performance on two illuminant estimation benchmarks.

cs.CV↗

arXiv2019

Two Types of Solar Confined Flares

With the aim of understanding the physical mechanisms of confined flares, we selected 18 confined flares during 2011-2017, and classified the confined flares into two types based on their different dynamic properties and magnetic configurations. "Type I" of confined flares are characterized by slipping reconnection, strong shear, and stable filament. "Type II" flares have nearly no slipping reconnection, and have a configuration in potential state after the flare. Filament erupts but is confined by strong strapping field. "Type II" flares could be explained by 2D MHD models while "type I" flares need 3D MHD models. 7 flares of 18 ($\sim$39 \%) belong to "type I" and 11 ($\sim$61 \%) are "type II" confined flares. The post-flare loops (PFLs) of "type I" flares have a stronger non-potentiality, however, the PFLs in "type II" flares are weakly sheared. All the "type I" flares exhibit the ribbon elongations parallel to the polarity inversion line (PIL) at speeds of several tens of km s$^{-1}$. For "type II" flares, only a small proportion shows the ribbon elongations along the PIL. We suggest that different magnetic topologies and reconnection scenarios dictate the distinct properties for the two types of flares. Slipping agnetic reconnections between multiple magnetic systems result in "type I" flares. For "type II" flares, magnetic reconnections occur in anti-parallel magnetic fields underlying the erupting filament. Our study shows that "type I" flares account for more than one third of the overall large confined flares, which should not be neglected in further studies.

astro-ph.SR↗

arXiv2019

Chromospheric cannonballs on the Sun

In the highly dynamic chromosphere, there exist many kinds of small-scale activities, such as spicules, surges, and Ellerman bombs. Here, we report the discovery of a new phenomenon in the chromosphere observed with the New Vacuum Solar Telescope at the Fuxian Solar Observatory. In the high tempo-spatial resolution H$α$ images, some dark or bright structures are found to fly along the curved trajectory, looking like cannonballs. Their average size, mass, and velocity are about 1.5 $\times$ 10$^{9}$ km$^{3}$, 1.5 $\times$ 10$^{8}$ kg, and 56 km s$^{-1}$, respectively. In the simultaneous (extreme-)ultraviolet images obtained by the Solar Dynamics Observatory, these cannonballs appear as brighter features compared to the surrounding area, implying that there exists some kind of heating during this process. The photospheric magnetograms show the magnetic flux emergence and interaction with the pre-existing fields. These observations reveal that the cannonballs are chromospheric material blobs launched due to the magnetic reconnection between emerging magnetic flux and the pre-existing loops.

astro-ph.SR↗

arXiv2019

Relating microswimmer synthesis to rheotactic tunability

We explore the behavior of micron-scale autophoretic Janus (Au/Pt) rods, having various Au/Pt length ratios, swimming near a wall in an imposed background flow. We find that their ability to robustly orient and move upstream, i.e. to rheotax, depends strongly on the Au/Pt ratio, which is easily tunable in synthesis. Numerical simulations of swimming rods actuated by a surface slip show a similar rheotactic tunability when varying the location of the surface slip versus surface drag. Slip location determines whether swimmers are Pushers (rear-actuated), Pullers (front-actuated), or in between. Our simulations and modeling show that Pullers rheotax most robustly due to their larger tilt angle to the wall, which makes them responsive to flow gradients. Thus, rheotactic response infers the nature of difficult to measure flow-fields of an active particle, establishes its dependence on swimmer type, and shows how Janus rods can be tuned for flow responsiveness. We demonstrate the effectiveness of a simple geometric sieve for rheotactic ability.

cond-mat.soft↗