Searcharxiv⌕ Search

arXiv subjects

Jie Chen

Publications and source records attributed to Jie Chen.

At least 685 records · Page 38Linked to original sources
arXiv2017

Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views

Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. And one of the emerging technologies are light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by a LF image as compared with a conventional 2D image of similar spatial dimension, and the compression of LF data becomes a vital part of its application. In this paper, we propose a LF codec that fully exploits the intrinsic geometry between the LF sub-views by first approximating the LF with disparity guided sparse coding over a perspective shifted light field dictionary. The sparse coding is only based on several optimized Structural Key Views (SKV); however the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information could be preserved. And to achieve optimum dictionary efficiency, the LF is divided into several Coding Regions (CR), over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF dataset, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with High Efficiency Video Coding (HEVC): with 47.87% BD-rate reduction and 1.59 dB BD-PSNR improvement achieved on average, especially with up to 4 dB improvement for low bit rate scenarios.

cs.CV↗
arXiv2017

Adaptation and learning over networks for nonlinear system modeling

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.

stat.ML↗
arXiv2017

A Complex Solar Coronal Jet with Two Phases

Jets often occur repeatedly from almost the same location. In this paper, a complex solar jet was observed with two phases to the west of NOAA AR 11513 on July 2nd, 2012. If it had been observed at only moderate resolution, the two phases and their points of origin would have been regarded as identical. However, at high resolution we find the two phases merge into one another and the accompanying footpoint brightenings occur at different locations. The phases originate from different magnetic patches rather than being one phase originating from the same patch. Photospheric line of sight (LOS) magnetograms show that the bases of the two phases lie in two different patches of magnetic flux which decrease in size during the occurrence of the two phases. Based on these observations, we suggest the driving mechanism of the two successive phases is magnetic cancellation of two separate magnetic fragments with an opposite polarity fragment between them.

astro-ph.SR↗
arXiv2017

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry groundtruth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to realworld images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at https://github.com/KevinKecc/SRN.

cs.CV↗
arXiv2017

Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks

In this paper, we study the robust consensus problem for a set of discrete-time linear agents to coordinate over an uncertain communication network, which is to achieve consensus against the transmission errors and noises resulted from the information exchange between the agents. We model the network by means of communication links subject to multiplicative stochastic uncertainties, which are susceptible to describing packet dropout, random delay, and fading phenomena. Different communication topologies, such as undirected graphs and leader-follower graphs, are considered. We derive sufficient conditions for robust consensus in the mean square sense. This results unveil intrinsic constraints on consensus attainment imposed by the network synchronizability, the unstable agent dynamics, and the channel uncertainty variances. Consensus protocols are designed based on the state information transmitted over the uncertain channels, by solving a modified algebraic Riccati equation.

eess.SY↗
arXiv2017

Event-Triggered Consensus for Linear Continuous-time Multi-agent Systems Based on a Predictor

In this paper, the problem of event-triggered consensus for linear continuous-time multi-agent systems is investigated. A new event-triggered consensus protocol based on a predictor is proposed to achieve consensus without continuous communication among agents. In the proposed consensus protocol, each agent only needs to monitor its states to determine its event-triggered instants. When an event is triggered, the agent will update its consensus protocol and sent its state information to its neighbors. In addition, the agent will also update its consensus protocol and the predictor when it receives the state information from its neighbors. A necessary and sufficient condition that the consensus problem can be solved is derived. Moreover, it is proved that Zeno behavior does not exist. Finally, a numerical example is given to illustrate that the protocol proposed in this paper can make the multi-agent systems achieve consensus through much fewer event-triggered times.

eess.SY↗
arXiv2017

Multitask diffusion adaptation over networks with common latent representations

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This work examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.

cs.MA↗
arXiv2017

Phonon thermal conduction in novel 2D materials

Recently, there have been increasing interests in phonon thermal transport in low dimensional materials, due to the crucial importance for dissipating and managing heat in micro and nano electronic devices. Significant progresses have been achieved for one-dimensional (1D) systems both theoretically and experimentally. However, the study of heat conduction in two-dimensional (2D) systems is still in its infancy due to the limited availability of 2D materials and the technical challenges in fabricating suspended samples suitable for thermal measurements. In this review, we outline different experimental techniques and theoretical approaches for phonon thermal transport in 2D materials, discuss the problems and challenges in phonon thermal transport measurements and provide comparison between existing experimental data. Special focus will be given to the effects of the size, dimensionality, anisotropy and mode contributions in the novel 2D systems including graphene, boron nitride, MoS2, black phosphorous, silicene etc.

cond-mat.mes-hall↗
arXiv2016

Femtosecond laser induced structural dynamics and melting of Cu (111) single crystal. An ultrafast time-resolved x-ray diffraction study

Femtosecond, 8.04 KeV x-ray pulses are used to probe the lattice dynamics of 150 nm Cu (111) single crystal on mica substrate irradiated with 400 nm, 100 fs laser pulses. For pump fluencies below the damage and melting threshold, we observed lattice contraction due to the formation of a blast force, and coherent acoustic phonons with a period of ~69 ps. At larger pump fluence, solid to liquid phase transition, annealing, and recrystallization were measured in real time by monitoring the intensity evolution of the probing fs x-ray rocking curves and agreed with theoretical simulation results. The experimental data suggest the melting process is a purely thermal phase transition. This study provides, in real time, an ultrafast time-resolved detailed description of the significant processes that occurs as a result of a femtosecond light-pulse interacts with the Cu (111) crystal surface.

cond-mat.mtrl-sci↗
arXiv2016

Carrier emission of n-type Gallium Nitride illuminated by femtosecond laser pulses

The carrier emission efficiency of light emitting diodes is of fundamental importance for many technological applications, including the performance of GaN and other semiconductor photocathodes. We have measured the evolution of the emitted carriers and the associated transient electric field after femtosecond laser excitation of n-type GaN single crystals. These processes were studied using subpicosecond, ultrashort, electron pulses and explained by means of a three-layer analytical model. We find that for pump laser intensities on the order of 10^11 W/cm^2, the electrons that escaped from the crystal surface have a charge of about 2.7 pc and a velocity of about 1.8 um/ps. The associated transient electrical field evolves at intervals ranging from picoseconds to nanoseconds. These results provide a dynamic perspective on the photoemission properties of semiconductor photocathodes.

cond-mat.mtrl-sci↗
arXiv2016

Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model

In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations in each frame are treated as latent variables that are solved with a progressive latent model (PLM). Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based label propagation to discover harder instances in adjacent frames. With the difference of convex (DC) objective functions, PLM can be efficiently optimized with a concave-convex programming and thus guaranteeing the stability of self-learning. Extensive experiments demonstrate that even without annotation the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning and fully supervised approaches.

cs.CV↗
arXiv2016

Solving Large-scale Systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow

A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is developed to reconstruct an unknown $n$-dimensional real-/complex-valued signal $\bm{x}$ from $m$ `phaseless' quadratic equations of the form $ψ_i=|\langle\bm{a}_i,\bm{x}\rangle|$. This problem, also known as phase retrieval from magnitude-only information, is \emph{NP-hard} in general. Adopting an amplitude-based nonconvex formulation, STAF leads to an iterative solver comprising two stages: s1) Orthogonality-promoting initialization through a stochastic variance reduced gradient algorithm; and, s2) A series of iterative refinements of the initialization using stochastic truncated gradient iterations. Both stages involve a single equation per iteration, thus rendering STAF a simple, scalable, and fast approach amenable to large-scale implementations that is useful when $n$ is large. When $\{\bm{a}_i\}_{i=1}^m$ are independent Gaussian, STAF provably recovers exactly any $\bm{x}\in\mathbb{R}^n$ exponentially fast based on order of $n$ quadratic equations. STAF is also robust in the presence of additive noise of bounded support. Simulated tests involving real Gaussian $\{\bm{a}_i\}$ vectors demonstrate that STAF empirically reconstructs any $\bm{x}\in\mathbb{R}^n$ exactly from about $2.3n$ magnitude-only measurements, outperforming state-of-the-art approaches and narrowing the gap from the information-theoretic number of equations $m=2n-1$. Extensive experiments using synthetic data and real images corroborate markedly improved performance of STAF over existing alternatives.

cs.IT↗
arXiv2016

On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps

Positive-definite kernel functions are fundamental elements of kernel methods and Gaussian processes. A well-known construction of such functions comes from Bochner's characterization, which connects a positive-definite function with a probability distribution. Another construction, which appears to have attracted less attention, is Polya's criterion that characterizes a subset of these functions. In this paper, we study the latter characterization and derive a number of novel kernels little known previously. In the context of large-scale kernel machines, Rahimi and Recht (2007) proposed a random feature map (random Fourier) that approximates a kernel function, through independent sampling of the probability distribution in Bochner's characterization. The authors also suggested another feature map (random binning), which, although not explicitly stated, comes from Polya's characterization. We show that with the same number of random samples, the random binning map results in an Euclidean inner product closer to the kernel than does the random Fourier map. The superiority of the random binning map is confirmed empirically through regressions and classifications in the reproducing kernel Hilbert space.

stat.ML↗
arXiv2016

Transient performance analysis of zero-attracting LMS

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and $\ell_1$-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.

cs.PF↗
arXiv2016

Nuclear in-medium effects on $η$ dynamics in proton-nucleus collisions

The dynamics of $η$ meson produced in proton-induced nuclear reactions via the decay of N$^{\ast}(1535)$ has been investigated within the Lanzhou quantum molecular dynamics transport model (LQMD). The in-medium modifications of the $η$ production in dense nuclear matter are included in the model, in which an attractive $η$-nucleon potential is implemented. The impact of the $η$ optical potential on the $η$ dynamics is investigated. It is found that the attractive potential leads to the reduction of high-momentum (kinetic energy) production from the spectra of momentum distributions and inclusive cross sections and increasing the reabsorption process by surrounding nucleons.

nucl-th↗
arXiv2016

Quantum Fisher information and symmetric logarithmic derivative via anti-commutators

Symmetric logarithmic derivative (SLD) is a key quantity to obtain quantum Fisher information (QFI) and to construct the corresponding optimal measurements. Here we develop a method to calculate the SLD and QFI via anti-commutators. This method is originated from the Lyapunov representation and would be very useful for cases that the anti-commutators among the state and its partial derivative exhibits periodic properties. As an application, we discuss a class of states, whose squares linearly depend on the states themselves, and give the corresponding analytical expressions of SLD and QFI. A noisy scenario of this class of states is also considered and discussed. Finally, we readily apply the method to the block-diagonal states and the multi-parameter estimation problems.

quant-ph↗
arXiv2016

Highly Catalytic Nanodots with Renal Clearance for Radiation Protection

Ionizing radiation (gamma and x-ray) is widely used in industry and medicine, but it can also pose a significant hazardous effect on health and induce cancer, physical deformity and even death, due to DNA damages and invasion of free radicals. There is therefore an urgent unmet demand in designing highly efficient radioprotectants with synergetic integration of effective renal clearance and low toxicity. In this study, we designed ultrasmall (sub-5 nm) highly catalytically active and cysteine-protected MoS2 dots as radioprotectants and investigated their application in protection against ionizing radiation. In vivo preclinical studies showed that the surviving fraction of MoS2-treated mice can appreciably increase to up to 79 % when they were exposed to high-energy ionizing radiation. Furthermore, MoS2 dots can contribute in cleaning up the accumulated free radicals within the body, repairing DNA damages and recovering all vital chemical and biochemical indicators, suggesting their unique role as free radical scavengers. MoS2 dots showed rapid and efficient urinary excretion with more than 80 % injected dose (I.D.) eliminated from the body after 24 hours due to their ultrasmall hydrodynamic size and did not cause any noticeable toxic responses up to 30 days.

physics.bio-ph↗