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Le Zhang

Publications and source records attributed to Le Zhang.

At least 235 records · Page 13Linked to original sources
arXiv2020

Polar Coupling Enabled Nonlinear Optical Filtering at MoS$_2$/Ferroelectric Heterointerfaces

Complex oxide heterointerfaces and van der Waals heterostructures present two versatile but intrinsically different platforms for exploring emergent quantum phenomena and designing new functionalities. The rich opportunity offered by the synergy between these two classes of materials, however, is yet to be charted. Here, we report an unconventional nonlinear optical filtering effect resulting from the interfacial polar alignment between monolayer MoS$_2$ and a neighboring ferroelectric oxide thin film. The second harmonic generation response at the heterointerface is either substantially enhanced or almost entirely quenched by an underlying ferroelectric domain wall depending on its chirality, and can be further tailored by the polar domains. Unlike the extensively studied coupling mechanisms driven by charge, spin, and lattice, the interfacial tailoring effect is solely mediated by the polar symmetry, as well explained via our density functional theory calculations, pointing to a new material strategy for the functional design of nanoscale reconfigurable optical applications.

cond-mat.mes-hall↗
arXiv2020

First-Principles Study of Hydrogen Behaviors in $α$-Pu$_{2}$O$_{3}$

The in-depth understanding of hydrogen permeation through plutonium-oxide overlayers is the prerequisite to evaluate the complex hydriding induction period of Pu. In this work, the incorporation, diffusion and dissolution of hydrogen in $α$-Pu$_{2}$O$_{3}$ are investigated by the first-principles calculations and $\textit{ab initio}$ thermodynamic method based on DFT+U and DFT-D3 schemes. Our study reveals that the hydrogen incorporation is endothermic and the separated H atoms prefer to recombine as H$_{2}$ molecules rather than reacting with $α$-Pu$_{2}$O$_{3}$. The H and H$_{2}$ diffusion are both feasible, generally, H will recombine first as H$_{2}$ and then migrate. Both pressure P$_{H2}$ and temperature can promote the hydrogen dissolution in $α$-Pu$_{2}$O$_{3}$. The single H$_{2}$ molecule incorporation and (H+H$_{2}$) mixed dissolution will successively appear when increasing P$_{H2}$. Compared to PuO$_{2}$, this work indicates that Pu sesquioxide is hardly reduced by hydrogen, but the porous $α$-Pu$_{2}$O$_{3}$ facilitates hydrogen transport in Pu oxide layers. We presents the microscopic picture of hydrogen behaviors in the defect-free $α$-Pu$_{2}$O$_{3}$, which could shed some light on the study of the hydriding induction period of Pu.

cond-mat.mtrl-sci↗
arXiv2019

Fractional Dark Matter decay: cosmological imprints and observational constraints

If a fraction $f_{\rm dcdm}$ of the Dark Matter decays into invisible and massless particles (so-called "dark radiation") with the decay rate (or inverse lifetime) $Γ_{\rm dcdm}$, such decay will leave distinctive imprints on cosmological observables. With a full consideration of the Boltzmann hierarchy, we calculate the decay-induced impacts not only on the CMB but also on the redshift distortion and the kinetic Sunyaev-Zel'dovich effect, while providing detailed physical interpretations based on evaluating the evolution of gravitational potential. By using the current cosmological data with a combination of Planck 2015, Baryon Acoustic Oscillation and redshift distortion measurements which can improve the constraints, we update the $1σ$ bound on the fraction of decaying DM from $f_{\rm dcdm}\lesssim5.26\%$ to $f_{\rm dcdm}\lesssim2.73\%$ for the short-lived DM (assuming $Γ_{\rm dcdm}/H_0\gtrsim10^4$). However, no constraints are improved from RSD data ($f_{\rm dcdm}\lesssim0.94\%$) for the long-lived DM (i.e., $Γ_{\rm dcdm}/H_0\lesssim10^4$). We also find the fractional DM decay can only slightly reduce the $H_0$ and $σ_8$ tensions, which is consistent with other previous works. Furthermore, our calculations show that the kSZ effect in future would provide a further constraining power on the decaying DM.

astro-ph.CO↗
arXiv2019

Ordered or Orderless: A Revisit for Video based Person Re-Identification

Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common practice of employing recurrent neural networks (RNNs) to aggregate temporal spatial features may not be optimal. Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation. Based on this observation, we then present a simple yet surprisingly powerful approach for VPRe-id, where we treat VPRe-id as an efficient orderless ensemble of image based person re-identification problem. More specifically, we divide videos into individual images and re-identify person with ensemble of image based rankers. Under the i.i.d. assumption, we provide an error bound that sheds light upon how could we improve VPRe-id. Our work also presents a promising way to bridge the gap between video and image based person re-identification. Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets (iLIDS-VID, PRID 2011, and MARS).

cs.CV↗
arXiv2019

AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning

Triplet loss has been widely employed in a wide range of computer vision tasks, including local descriptor learning. The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets. For high-informativeness triplet collection, researchers mostly focus on mining hard negatives in the second stage, while paying relatively less attention to constructing informative batches. To alleviate this issue, we propose AdaSample, an adaptive online batch sampler, in this paper. Specifically, hard positives are sampled based on their informativeness. In this way, we formulate a hardness-aware positive mining pipeline within a novel maximum loss minimization training protocol. The efficacy of the proposed method is evaluated on several standard benchmarks, where it demonstrates a significant and consistent performance gain on top of the existing strong baselines.

cs.CV↗
arXiv2019

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Apparent personality and emotion analysis are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. Furthermore, an adversarial-like loss function is employed to promote representation coherence among heterogeneous dataset sources. Based on this, we also explore the emotion-to-apparent-personality relationship. Extensive experiments demonstrate the effectiveness of PersEmoN.

cs.CV↗
arXiv2019

Superpixel Tensor Pooling for Visual Tracking using Multiple Midlevel Visual Cues Fusion

In this paper, we propose a method called superpixel tensor pooling tracker which can fuse multiple midlevel cues captured by superpixels into sparse pooled tensor features. Our method first adopts the superpixel method to generate different patches (superpixels) from the target template or candidates. Then for each superpixel, it encodes different midlevel cues including HSI color, RGB color, and spatial coordinates into a histogram matrix to construct a new feature space. Next, these matrices are formed to a third order tensor. After that, the tensor is pooled into the sparse representation. Then the incremental positive and negative subspaces learning is performed. Our method has both good characteristics of midlevel cues and sparse representation hence is more robust to large appearance variations and can capture compact and informative appearance of the target object. To validate the proposed method, we compare it with state-of-the-art methods on 24 sequences with multiple visual tracking challenges. Experiment results demonstrate that our method outperforms them significantly.

eess.IV↗
arXiv2019

Gluon emission from heavy quarks in dense nuclear matter

We study the medium-induced gluon emission process experienced by a hard jet parton propagating through the dense nuclear matter in the framework of deep inelastic scattering off a large nucleus. We work beyond the collinear rescattering expansion and the soft gluon emission limit, and derive a closed formula for the medium-induced single gluon emission spectrum from a heavy or light quark jet interacting with the dense nuclear medium via transverse and longitudinal scatterings. Without performing the collinear rescattering expansion, the medium-induced gluon emission spectrum is controlled by the full distribution of the differential elastic scattering rates between the propagating partons and the medium constituents. We further show that if one utilizes heavy static scattering centers for the traversed nuclear matter and takes the soft gluon emission limit, our result can reduce to the first order in opacity Djordjevic-Gyulassy-Levai-Vitev formula.

hep-ph↗
arXiv2019

Holographic Complexity from the Crofton's Formula in Lorentzian AdS$_3$

We study the Crofton's formula in the Lorentzian AdS$_3$ and find that the area of a generic space-like two dimensional surface is given by the flux of space-like geodesics. The "complexity=volume" conjecture then implies a new holographic representation of the complexity in terms of the number of geodesics. Finally, we explore the possible explanation of this result from the standpoint of information theory.

hep-th↗
arXiv2019

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.

cs.CV↗
arXiv2019

Robust Regression via Deep Negative Correlation Learning

Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear regression to a robust loss function which is jointly optimizable with the deep convolutional network, and ii) utilizing ensemble of deep networks. Although some improved performance is achieved, the former may be lacking due to the intrinsic limitation of choosing a single hypothesis and the latter usually suffers from much larger computational complexity. To cope with those issues, we propose to regress via an efficient "divide and conquer" manner. The core of our approach is the generalization of negative correlation learning that has been shown, both theoretically and empirically, to work well for non-deep regression problems. Without extra parameters, the proposed method controls the bias-variance-covariance trade-off systematically and usually yields a deep regression ensemble where each base model is both "accurate" and "diversified". Moreover, we show that each sub-problem in the proposed method has less Rademacher Complexity and thus is easier to optimize. Extensive experiments on several diverse and challenging tasks including crowd counting, personality analysis, age estimation, and image super-resolution demonstrate the superiority over challenging baselines as well as the versatility of the proposed method.

cs.CV↗
arXiv2019

A Deep Framework for Bone Age Assessment based on Finger Joint Localization

Bone age assessment is an important clinical trial to measure skeletal child maturity and diagnose of growth disorders. Conventional approaches such as the Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to their large inter-observer and intra-observer variations. In this paper, we propose a finger joint localization strategy to filter out most non-informative parts of images. When combining with the conventional full image-based deep network, we observe a much-improved performance. % Our approach utilizes full hand and specific joints images for skeletal maturity prediction. In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.

cs.CV↗
arXiv2019

Salient Object Detection via High-to-Low Hierarchical Context Aggregation

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate convolutional side-output features in convolutional neural networks (CNN). Based on this, most of the existing state-of-the-art saliency detectors design complex network structures to fuse the side-output features of the backbone feature extraction networks. However, should the fusion strategies be more and more complex for accurate salient object detection? In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features. As we know, the contexts of an image usually refer to the global structures, and the top layers of CNN usually learn to convey global information. On the other hand, it is difficult for the intermediate side-output features to express contextual information. Here, we design an hourglass network with intermediate supervision to learn contextual features in a high-to-low manner. The learned hierarchical contexts are aggregated to generate the hybrid contextual expression for an input image. At last, the hybrid contextual features can be used for accurate saliency estimation. We extensively evaluate our method on six challenging saliency datasets, and our simple method achieves state-of-the-art performance under various evaluation metrics. Code will be released upon paper acceptance.

cs.CV↗
arXiv2019

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.

cs.CV↗
arXiv2019

High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge,this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework.

eess.IV↗
arXiv2019

ABS: an Analytical method of Blind Separation of CMB from foregrounds

Extracting CMB B-mode polarization from complicated foregrounds is a challenging task in searching for inflationary gravitational waves. We propose the ABS method as a blind and analytical solution to this problem. It applies to the measured cross bandpower between different frequency bands and obtains the CMB B-mode bandpower analytically. It does not rely on assumptions of foregrounds and does not require multiple parameter fitting. Testing against a variety of foregrounds, survey frequency configurations and instrument noise, we verify its applicability and numerical stability. The ABS method also applies to CMB temperature, E-mode polarization, the thermal Sunyaev Zel'dovich effect, spectral distortion, and even significantly different problems such as cosmic magnification.

astro-ph.CO↗
arXiv2019

Can Conformal and Disformal Couplings Between Dark Sectors Explain the EDGES 21cm Anomaly?

The recently announced result by EDGES points an unexpected excess in the 21 cm global brightness temperature from cosmic dawn at $z\sim 17$, potentially indicating new phenomena beyond the $Λ$CDM model. A generic cosmological model which allows conformal and disformal couplings between dark matter and dark energy is employed to investigate the impact on the 21cm absorption signal and understand the EDGES anomaly. After exploring a wide range of parameter space for couplings, we find that the coupling effects can lead to a moderate change in the Hubble parameter while a negligible change in the spin temperature in the early Universe. Consequently, the decrease of the Hubble parameter from the mixed conformal and disformal couplings can reproduce the 21cm absorption approximately in consistent with the EDGES result at $z=17.5$. However, there is still tension in corresponding parameter space between EDGES and other cosmological observations for this model.

astro-ph.CO↗