arXiv subjects

Jie Chen

Publications and source records attributed to Jie Chen.

At least 649 records · Page 36Linked to original sources

arXiv2018

Haze Density Estimation via Modeling of Scattering Coefficients of Iso-depth Regions

Vision based haze density estimation is of practical implications for the purpose of precaution alarm and emergency reactions toward disastrous hazy weathers. In this paper, we introduce a haze density estimation framework based on modeling of scattering coefficients of iso-depth regions. A haze density metric of Normalized Scattering Coefficient (NSC) is proposed to measure current haze density level with reference to two reference scales. Iso-depth regions are determined via superpixel segmentation. Efficient searching and matching of iso-depth units could be carried out for measurements via unstationary cameras. A robust dark SP selection method is used to produce reliable predictions for most out-door scenarios.

cs.CV↗

arXiv2018

A Smooth Double Proximal Primal-Dual Algorithm for a Class of Distributed Nonsmooth Optimization Problem

This technical note studies a class of distributed nonsmooth convex consensus optimization problem. The cost function is a summation of local cost functions which are convex but nonsmooth. Each of the local cost functions consists of a twice differentiable convex function and two lower semi-continuous convex functions. We call this problem as single-smooth plus double-nonsmooth (SSDN) problem. Under mild conditions, we propose a distributed double proximal primal-dual optimization algorithm. The double proximal operator is designed to deal with the difficulty caused by the unproximable property of the summation of those two nonsmooth functions. Besides, it can also guarantee that the proposed algorithm is locally Lipschitz continuous. An auxiliary variable in the double proximal operator is introduced to estimate the subgradient of the second nonsmooth function. Theoretically, we conduct the convergence analysis by employing Lyapunov stability theory. It shows that the proposed algorithm can make the states achieve consensus at the optimal point. In the end, nontrivial simulations are presented and the results demonstrate the effectiveness of the proposed algorithm.

math.OC↗

arXiv2018

Rapid Assessment of Damaged Homes in the Florida Keys after Hurricane Irma

On September 10, 2017, Hurricane Irma made landfall in the Florida Keys and caused significant damage. Informed by hydrodynamic storm surge and wave modeling and post-storm satellite imagery, a rapid damage survey was soon conducted for 1600+ residential buildings in Big Pine Key and Marathon. Damage categorizations and statistical analysis reveal distinct factors governing damage at these two locations. The distance from the coast is significant for the damage in Big Pine Key, as severely damaged buildings were located near narrow waterways connected to the ocean. Building type and size are critical in Marathon, highlighted by the near-complete destruction of trailer communities there. These observations raise issues of affordability and equity that need consideration in damage recovery and rebuilding for resilience.

stat.AP↗

arXiv2018

Saliency Integration: An Arbitrator Model

Saliency integration has attracted much attention on unifying saliency maps from multiple saliency models. Previous offline integration methods usually face two challenges: 1. if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; 2. an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model. To address these problems, in this paper, we propose an arbitrator model (AM) for saliency integration. Firstly, we incorporate the consensus of multiple saliency models and the external knowledge into a reference map to effectively rectify the misleading by candidate models. Secondly, our quest for ways of estimating the expertise of the saliency models without ground truth labels gives rise to two distinct online model-expertise estimation methods. Finally, we derive a Bayesian integration framework to reconcile the saliency models of varying expertise and the reference map. To extensively evaluate the proposed AM model, we test twenty-seven state-of-the-art saliency models, covering both traditional and deep learning ones, on various combinations over four datasets. The evaluation results show that the AM model improves the performance substantially compared to the existing state-of-the-art integration methods, regardless of the chosen candidate saliency models.

cs.CV↗

arXiv2018

Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework

Light field (LF) cameras provide perspective information of scenes by taking directional measurements of the focusing light rays. The raw outputs are usually dark with additive camera noise, which impedes subsequent processing and applications. We propose a novel LF denoising framework based on anisotropic parallax analysis (APA). Two convolutional neural networks are jointly designed for the task: first, the structural parallax synthesis network predicts the parallax details for the entire LF based on a set of anisotropic parallax features. These novel features can efficiently capture the high frequency perspective components of a LF from noisy observations. Second, the view-dependent detail compensation network restores non-Lambertian variation to each LF view by involving view-specific spatial energies. Extensive experiments show that the proposed APA LF denoiser provides a much better denoising performance than state-of-the-art methods in terms of visual quality and in preservation of parallax details.

cs.CV↗

arXiv2018

Deep Extractor Network for Target Speaker Recovery From Single Channel Speech Mixtures

Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains challenging to achieve satisfying performance provided a very short available target speaker utterance (anchor). Here we present a novel "deep extractor network" which creates an extractor point for the target speaker in a canonical high dimensional embedding space, and pulls together the time-frequency bins corresponding to the target speaker. The proposed model is different from prior works in that the canonical embedding space encodes knowledges of both the anchor and the mixture during an end-to-end training phase: First, embeddings for the anchor and mixture speech are separately constructed in a primary embedding space, and then combined as an input to feed-forward layers to transform to a canonical embedding space which we discover more stable than the primary one. Experimental results show that given a very short utterance, the proposed model can efficiently recover high quality target speech from a mixture, which outperforms various baseline models, with 5.2% and 6.6% relative improvements in SDR and PESQ respectively compared with a baseline oracle deep attracor model. Meanwhile, we show it can be generalized well to more than one interfering speaker.

cs.SD↗

arXiv2018

A Frequency-Domain Characterization of Optimal Error Covariance for the Kalman-Bucy Filter

In this paper, we discover that the trace of the division of the optimal output estimation error covariance over the noise covariance attained by the Kalman-Bucy filter can be explicitly expressed in terms of the plant dynamics and noise statistics in a frequency-domain integral characterization. Towards this end, we examine the algebraic Riccati equation associated with Kalman-Bucy filtering using analytic function theory and relate it to the Bode integral. Our approach features an alternative, frequency-domain framework for analyzing algebraic Riccati equations and reduces to various existing related results.

eess.SY↗

arXiv2018

Design of Near Infrared Sky Brightness Monitor and Test Running at Ngari Observatory in Tibet

Tibet is known as the third pole of the earth, as high as the South Pole and North Pole. The Ngari (Ali) observatory in Tibet has the advantage of plenty of photometric night, low precipitable water vapor, high transmittance, good seeing. It is a good site, and promising to be one of the best place for infrared and submillimeter observations in the world. However, there is no data available for sky background brightness in such place, which restrict the astronomical development of the sites. In the near infrared band of J, H, Ks, a NIR sky brightness monitor (NISBM) is designed based on InGaAs photoelectric diode. By using the method of chopper modulation and digital lock-in amplifier processing, the SNR (Signal Noise Ratio), detectivity and the data acquisition speed of the device is greatly improved. For each band of J, H, Ks, an independent instrument is designed and calibrated in laboratory. The NISBM has been installed in Ngari observatory in July of 2017 and obtained the first data of NIR sky brightness at Ngari observatory.

astro-ph.IM↗

arXiv2018

The operational and control software of Multi-channel Antarctic Solar Telescope

MARST (Multi-channel Antarctic Solar Telescope) aims to observe the Sun in multiple wavebands in Antarctica and will be China's first solar telescope in Antarctica. The telescope has two tubes, corresponding to Photosphere observation which uses 11 filters and Chromosphere observation in Hα waveband. The two tubes need to be coordinated to observe at the same time. The telescope will observe the Sun for a long time, so a self-guiding module is needed to improve sun tracking. Besides, performing solar specific flat-field exposure is necessary for analyzing. EPICS is introduced to control each hardware and an autonomous observation system based on RTS2 is designed under such demands. EPICS application modules are implemented for each device: telescope mount & focuser, filter wheel, Hα filter, dome with webcams, Andor CCD and PI CCD. We also integrate EPICS modules into RTS2 framework with an XML format configuration. To control these applications autonomously we have developed a RTS2 executor module where two plan classes are instantiated to control two sets of filters and CCDs, and to ensure only one could control the mount at the same time. Different types of observation plans are designed to describe different series of processes with different priorities. To improve sun tracking, we calculate the centroid of each image to get the offset, then apply the correction to the telescope during observation process. For frontend users, a GUI based on PyQt5 and QML is implemented and connects to rts2-httpd and rts2-proxy modules so that users can control devices, check images and get logs.

astro-ph.IM↗

arXiv2018

Off-center Rattling Triggers High-Temperature Thermal Transport in Thermoelectric lathrates: Non-Perturbative Approach

Lattice thermal conductivities $κ_{\rm L}$ of type-I clathrates encapsulating "off-center" guest atoms behave in a way that is sharply different from those of conventional crystals such as on-center type-I clathrates. The latter decreases with increasing temperature according to 1/$T$ above a few tens\,K. While, $κ_{\rm L}$ of off-center clathrates increase T-linearly above a few tens K. Further increasing temperature above $T\gtrsim$100 K, $κ_{\rm L}$ saturates without exhibiting appreciable T-dependence. In this temperature regime, the standard formulation of $κ_{\rm L}$ based on the perturbation theory is unfeasible. We herein present a theory of $κ_{\rm L}$ based on "non-perturbative" approach by taking into account the interaction between non-vibrational off-center rattling states of guest atoms and cage shells. It is remarkable that our theory shows excellent agreement with observed $κ_{\rm L}(T)$ for off-center clathrates in magnitudes, the cage volume $Ω$-dependence and T-dependence.

cond-mat.mtrl-sci↗

arXiv2018

Distributed Computation of Linear Matrix Equations: An Optimization Perspective

This paper investigates the distributed computation of the well-known linear matrix equation in the form of $AXB = F$, with the matrices A, B, X, and F of appropriate dimensions, over multi-agent networks from an optimization perspective. In this paper, we consider the standard distributed matrix-information structures, where each agent of the considered multi-agent network has access to one of the sub-block matrices of A, B, and F. To be specific, we first propose different decomposition methods to reformulate the matrix equations in standard structures as distributed constrained optimization problems by introducing substitutional variables; we show that the solutions of the reformulated distributed optimization problems are equivalent to least squares solutions to original matrix equations; and we design distributed continuous-time algorithms for the constrained optimization problems, even by using augmented matrices and a derivative feedback technique. Moreover, we prove the exponential convergence of the algorithms to a least squares solution to the matrix equation for any initial condition.

math.OC↗

arXiv2018

Explainable Neural Networks based on Additive Index Models

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples.

stat.ML↗

arXiv2018

Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)

Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods. This is especially true with large data sets (millions or more observations and hundreds to thousands of predictors). However, the complexity of the SML models makes them opaque and hard to interpret without additional tools. There has been a lot of interest recently in developing global and local diagnostics for interpreting and explaining SML models. In this paper, we propose locally interpretable models and effects based on supervised partitioning (trees) referred to as LIME-SUP. This is in contrast with the KLIME approach that is based on clustering the predictor space. We describe LIME-SUP based on fitting trees to the fitted response (LIM-SUP-R) as well as the derivatives of the fitted response (LIME-SUP-D). We compare the results with KLIME and describe its advantages using simulation and real data.

stat.ML↗

arXiv2018

Thermal Conductivity of Polymers and Their Nanocomposites

Polymers are usually considered as thermal insulators and their applications are limited by their low thermal conductivity. However, recent studies showed that certain polymers have surprisingly high thermal conductivity, some of which are comparable to that in poor metals or even silicon. In this review, we outline the experimental achievements and theoretical progress of thermal transport in polymers and their nanocomposites. The open questions and challenges of existing theories are discussed. Special attention is given to the mechanism of thermal transport, the enhancement of thermal conductivity in polymer nanocomposites/fibers, and their potential application as thermal interface materials.

cond-mat.mes-hall↗

arXiv2018

Optimal Weight Allocation of Dynamic Distribution Networks and Positive Semi-definiteness of Signed Laplacians

In this paper, we consider the robustness of a basic model of a dynamical distribution network. In the first problem, i.e., optimal weight allocation, we minimize the H-inf- norm of the dynamical distribution network subject to allocation of the weights on the edges. It is shown that this optimization problem can be formulated as a semi-definite program. Next we consider the semi-definiteness of the weighted graph Laplacian matrix with negative weights on the edges. A necessary and sufficient condition, using the effective resistance matrix, is established to guarantee the positive semi-definiteness of the Laplacian matrix. Furthermore, the bounded real lemma is derived for state-space symmetric systems.

math.OC↗

arXiv2018

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Outdoor vision-based systems suffer from atmospheric turbulences, and rain is one of the worst factors for vision degradation. Current rain removal methods show limitations either for complex dynamic scenes, or under torrential rain with opaque occlusions. We propose a novel derain framework which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust against rain occlusion interferences and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for the location of rain streaks and the occluded background contents. Different classical and novel methods such as Robust Principle Component Analysis and Convolutional Neural Networks are applied and compared for their respective advantages in efficiently exploiting the rich spatial-temporal features provided by the two tensors. Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes. Visual evaluations show that the proposed framework is not only able to suppress heavy and opaque occluding rain streaks, but also large semi-transparent regional fluctuations and distortions.

cs.CV↗

arXiv2018

Room Temperature Continuous-wave Excited Biexciton Emission in CsPbBr3 Nanocrystals

Biexcitons are a manifestation of many-body excitonic interactions crucial for quantum information and quantum computation in the construction of coherent combinations of quantum states. However, due to their small binding energy and low transition efficiency, most biexcitons in conventional semiconductors exist either at cryogenic temperature or under femtosecond pulse laser excitation. Here we demonstrate room temperature, continuous wave driven biexciton states in CsPbBr3 perovskite nanocrystals through coupling with a plasmonic nanogap. The room temperature CsPbBr3 biexciton excitation fluence (~100 mW/cm2) is reduced by ~10^13 times in the Ag nanowire-film nanogaps. The giant enhancement of biexciton emission is driven by coherent biexciton-plasmon Fano interference. These results provide new pathways to develop high efficiency non-blinking single photon sources, entangled light sources and lasers based on biexciton states.

cond-mat.mes-hall↗

arXiv2018

Insight into the origin of Lithium/Nickel ions exchange in layered Li(NixMnyCoz)O2 cathode materials

In layered LiNixMnyCozO2 cathode material for lithium-ion batteries, the spins of transition metal (TM) ions construct a two-dimensional triangular networks, which can be considered as a simple case of geometrical frustration. By performing neutron powder diffraction experiments and magnetization measurements, we find that long-range magnetic order cannot be established in LiNixMnyCozO2 even at low temperature of 3 K. Remarkably, the frustration parameters of these compounds are estimated to be larger than 30, indicating the existence of strongly frustrated magnetic interactions between spins of TM ions. As frustration will inevitably give rise to lattice instability, the formation of Li/Ni exchange in LiNixMnyCozO2 will help to partially relieve the degeneracy of the frustrated magnetic lattice by forming a stable antiferromagnetic state in hexagonal sublattice with nonmagnetic ions located in centers of the hexagons. Moreover, Li/Ni exchange will introduce 180° superexchange interaction, which further relieves the magnetic frustration through bringing in new exchange paths. Thus, the variation of Li/Ni exchange ratio vs. TM mole fraction in LiNixMnyCozO2 with different compositions can be well understood and predicted in terms of magnetic frustration and superexchange interactions. This provides a unique viewpoint to study the Li/Ni ions exchange in layered Li(NixMnyCoz)O2 cathode materials.

cond-mat.mtrl-sci↗