arXiv subjects

Dong Wang

Publications and source records attributed to Dong Wang.

At least 703 records · Page 39Linked to original sources

arXiv2018

Strong spatial and spectral localization of surface plasmons in individual randomly disordered gold nanosponges

Porous nanosponges, percolated with a three-dimensional network of 10-nm sized ligaments, recently emerged as promising substrates for plasmon-enhanced spectroscopy and (photo-)catalysis. Experimental and theoretical work suggests surface plasmon localization in some hot-spot modes as the physical origin of their unusual optical properties, but so far the existence of such hot-spots has not been proven. Here we use scattering-type scanning near-field nano-spectroscopy on individual gold nanosponges to reveal spatially and spectrally confined modes with 10 nanometer localization lengths by mapping the local optical density of states. High quality factors of individual hot-spots of more than 40 are demonstrated. A statistical analysis of near-field intensity fluctuations unveils plasmonics in the strong localization regime. The observed field localization and enhancement make such nanosponges an appealing platform for a variety of applications ranging from nonlinear optics to strong-coupling physics.

cond-mat.mes-hall↗

arXiv2018

Learning Spatial-Aware Regressions for Visual Tracking

In this paper, we analyze the spatial information of deep features, and propose two complementary regressions for robust visual tracking. First, we propose a kernelized ridge regression model wherein the kernel value is defined as the weighted sum of similarity scores of all pairs of patches between two samples. We show that this model can be formulated as a neural network and thus can be efficiently solved. Second, we propose a fully convolutional neural network with spatially regularized kernels, through which the filter kernel corresponding to each output channel is forced to focus on a specific region of the target. Distance transform pooling is further exploited to determine the effectiveness of each output channel of the convolution layer. The outputs from the kernelized ridge regression model and the fully convolutional neural network are combined to obtain the ultimate response. Experimental results on two benchmark datasets validate the effectiveness of the proposed method.

cs.CV↗

arXiv2018

Correlation Tracking via Joint Discrimination and Reliability Learning

For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information. First, we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn the discrimination information between the target and backgrounds, and the reliability term encourages the final filter to focus on more reliable regions. Second, we introduce a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We conduct extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to evaluate the proposed tracker. Experimental results show that our tracker performs favorably against other state-of-the-art trackers.

cs.CV↗

arXiv2018

Dynamics and recovery of genuine multipartite Einstein-Podolsky-Rosen steering and genuine multipartite nonlocality for a dissipative Dirac system via Unruh effect

In this paper, we investigate the dynamics behaviors of genuine multipartite Einstein-Podolsky-Rosen steering (GMS) and genuine multipartite nonlocality (GMN), and explore how to recover the lost GMS and GMN under a mixed decoherence system. Explicitly, the decoherence system can be modeled by that a tripartite Werner-type state suffers from the non-Markovian regimes and one subsystem of the tripartite is under a non-inertial frame. The conditions for steerable and nonlocal states can be obtained with respect to the tripartite Werner-type state established initially. GMS and GMN are very fragile and vulnerable under the influence of the collective decoherence. GMS and GMN will vanish with growing intensity of the Unruh effect and the non-Markovian reservoir. Besides, all achievable GMN's states are steerable, while not every steerable state (GMS's state) can achieve nonlocality. It means that the steering-nonlocality hierarchy is still tenable and GMN's states are a strict subset of the GMS's states in such a scenario. Subsequently, we put forward an available methodology to recover the damaged GMS and GMN. It turns out that the lost GMS and GMN can be effectively restored, and the ability of GMS and GMN to suppress the collective decoherence can be enhanced.

quant-ph↗

arXiv2018

Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression

While the research on convolutional neural networks (CNNs) is progressing quickly, the real-world deployment of these models is often limited by computing resources and memory constraints. In this paper, we address this issue by proposing a novel filter pruning method to compress and accelerate CNNs. Our work is based on the linear relationship identified in different feature map subspaces via visualization of feature maps. Such linear relationship implies that the information in CNNs is redundant. Our method eliminates the redundancy in convolutional filters by applying subspace clustering to feature maps. In this way, most of the representative information in the network can be retained in each cluster. Therefore, our method provides an effective solution to filter pruning for which most existing methods directly remove filters based on simple heuristics. The proposed method is independent of the network structure, thus it can be adopted by any off-the-shelf deep learning libraries. Experiments on different networks and tasks show that our method outperforms existing techniques before fine-tuning, and achieves the state-of-the-art results after fine-tuning.

cs.CV↗

arXiv2018

The enhancement of quantum entanglement under an open Dirac system with Hawking effect in Schwarzschild space-time

In this letter, we mainly investigate how to enhance the damaged quantum entanglement under an open Dirac system with Hawking effect within Schwarzschild space-time. We consider that particle A hold by Alice undergoes generalized amplitude damping noise in a flat space-time and another particle B by Bob entangled with A is under a Schwarzschild space-time. Subsequently, we put forward a physical scheme to recover the damaged quantum entanglement by prior weak measurement on subsystem A before the interaction with the decoherence noise followed by post-measurement filtering operation. The results indicate that our scheme can effectively recover the damaged quantum entanglement affected by the Hawking effect and the noisy channel. Thus, our work might be beneficial to understand the dynamic behavior of quantum state and recover the damaged quantum entanglement with open Dirac systems under Hawking effect in the background of Schwarzschild black hole.

quant-ph↗

arXiv2018

RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection

Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk is largely ignored when optimizing portfolios, which may lead to unreliable profits, particularly in volatile markets. We present a risk-aversion CORN-K algorithm, RACORN-K, that penalizes risk when searching for optimal portfolios. Experiments on four datasets (DJIA, MSCI, SP500(N), HSI) demonstrate that the new algorithm can deliver notable and reliable improvements in terms of return, Sharp ratio and maximum drawdown, especially on volatile markets.

q-fin.RM↗

arXiv2018

Full-info Training for Deep Speaker Feature Learning

In recent studies, it has shown that speaker patterns can be learned from very short speech segments (e.g., 0.3 seconds) by a carefully designed convolutional & time-delay deep neural network (CT-DNN) model. By enforcing the model to discriminate the speakers in the training data, frame-level speaker features can be derived from the last hidden layer. In spite of its good performance, a potential problem of the present model is that it involves a parametric classifier, i.e., the last affine layer, which may consume some discriminative knowledge, thus leading to `information leak' for the feature learning. This paper presents a full-info training approach that discards the parametric classifier and enforces all the discriminative knowledge learned by the feature net. Our experiments on the Fisher database demonstrate that this new training scheme can produce more coherent features, leading to consistent and notable performance improvement on the speaker verification task.

cs.SD↗

arXiv2018

Deep factorization for speech signal

Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper. The proposed framework infers speech factors in a sequential way, where factors previously inferred are used as conditional variables when inferring other factors. We will show that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with a high accuracy. This factorization and reconstruction approach provides potential values for many speech processing tasks, e.g., speaker recognition and emotion recognition, as will be demonstrated in the paper.

eess.AS↗

arXiv2018

Agile Amulet: Real-Time Salient Object Detection with Contextual Attention

This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile Amulet) for salient object detection. The Agile Amulet builds on previous works to predict saliency maps using multi-level convolutional features. Compared to previous works, Agile Amulet employs some key innovations to improve training and testing speed while also increase prediction accuracy. More specifically, we first introduce a contextual attention module that can rapidly highlight most salient objects or regions with contextual pyramids. Thus, it effectively guides the learning of low-layer convolutional features and tells the backbone network where to look. The contextual attention module is a fully convolutional mechanism that simultaneously learns complementary features and predicts saliency scores at each pixel. In addition, we propose a novel method to aggregate multi-level deep convolutional features. As a result, we are able to use the integrated side-output features of pre-trained convolutional networks alone, which significantly reduces the model parameters leading to a model size of 67 MB, about half of Amulet. Compared to other deep learning based saliency methods, Agile Amulet is of much lighter-weight, runs faster (30 fps in real-time) and achieves higher performance on seven public benchmarks in terms of both quantitative and qualitative evaluation.

cs.CV↗

arXiv2018

A diffusion generated method for computing Dirichlet partitions

A Dirichlet $k$-partition of a closed $d$-dimensional surface is a collection of $k$ pairwise disjoint open subsets such that the sum of their first Laplace-Beltrami-Dirichlet eigenvalues is minimal. In this paper, we develop a simple and efficient diffusion generated method to compute Dirichlet $k$-partitions for $d$-dimensional flat tori and spheres. For the $2d$ flat torus, for most values of $k=3$-9,11,12,15,16, and 20, we obtain hexagonal honeycombs. For the $3d$ flat torus and $k=2,4,8,16$, we obtain the rhombic dodecahedral honeycomb, the Weaire-Phelan honeycomb, and Kelvin's tessellation by truncated octahedra. For the $4d$ flat torus, for $k=4$, we obtain a constant extension of the rhombic dodecahedral honeycomb along the fourth direction and for $k=8$, we obtain a 24-cell honeycomb. For the $2d$ sphere, we also compute Dirichlet partitions for $k=3$-7,9,10,12,14,20. Our computational results agree with previous studies when a comparison is available. As far as we are aware, these are the first published results for Dirichlet partitions of the $4d$ flat torus.

math.OC↗

arXiv2018

Human and Machine Speaker Recognition Based on Short Trivial Events

Trivial events are ubiquitous in human to human conversations, e.g., cough, laugh and sniff. Compared to regular speech, these trivial events are usually short and unclear, thus generally regarded as not speaker discriminative and so are largely ignored by present speaker recognition research. However, these trivial events are highly valuable in some particular circumstances such as forensic examination, as they are less subjected to intentional change, so can be used to discover the genuine speaker from disguised speech. In this paper, we collect a trivial event speech database that involves 75 speakers and 6 types of events, and report preliminary speaker recognition results on this database, by both human listeners and machines. Particularly, the deep feature learning technique recently proposed by our group is utilized to analyze and recognize the trivial events, which leads to acceptable equal error rates (EERs) despite the extremely short durations (0.2-0.5 seconds) of these events. Comparing different types of events, 'hmm' seems more speaker discriminative.

cs.SD↗

arXiv2018

The Age of Social Sensing

Online social media, such as Twitter and Instagram, democratized information broadcast, allowing anyone to share information about themselves and their surroundings at an unprecedented scale. The large volume of information thus posted on these media offer a new lens into the physical world through the eyes of the social network. The exploitation of this lens to inspect aspects of world state has recently been termed social sensing. The power of manipulating reality via the use (or intentional misuse) of social media opened concerns with issues ranging from radicalization by terror propaganda to potential manipulation of elections in mature democracies. Many important challenges and open research questions arise in this emerging field that aims to better understand how information can be extracted from the medium and what properties characterize the extracted information and the world it represents. Addressing the above challenges requires multi-disciplinary research at the intersection of computer science and social sciences that combines cyber-physical computing, sociology, sensor networks, social networks, cognition, data mining, estimation theory, data fusion, information theory, linguistics, machine learning, behavioral economics, and possibly others. This paper surveys important directions in social sensing, identifies current research challenges, and outlines avenues for future research.

cs.SI↗

arXiv2018

Watching charge separation in nanoantennas by ultrafast point-projection electron microscopy

Watching the motion of electrons on their natural nanometre length- and femtosecond time scales is a fundamental goal and an open challenge of contemporary ultrafast science. Optical techniques and electron microscopy currently mostly provide either ultrahigh temporal or spatial resolution, yet, microscopy techniques with combined space-time resolution need further development. Here we create an ultrafast electron source by plasmon nanofocusing on a sharp gold taper and implement this source in an ultrafast point-projection electron microscope. This source is used, in an optical pump - electron probe experiment, to study ultrafast photoemission from a nanometer-sized plasmonic antenna. We show that the real space motion of the photoemitted electrons and residual holes in the metal is probed with 20-nm spatial resolution and 25-fs time resolution. This is a step forward towards time-resolved microscopy of electronic motion in nanostructures.

physics.optics↗

arXiv2018

Distributed Estimation of Principal Eigenspaces

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data are stored across multiple machines, however, communication cost can prohibit the computation of PCA in a central location and distributed algorithms for PCA are thus needed. This paper proposes and studies a distributed PCA algorithm: each node machine computes the top $K$ eigenvectors and transmits them to the central server; the central server then aggregates the information from all the node machines and conducts a PCA based on the aggregated information. We investigate the bias and variance for the resulting distributed estimator of the top $K$ eigenvectors. In particular, we show that for distributions with symmetric innovation, the empirical top eigenspaces are unbiased and hence the distributed PCA is "unbiased". We derive the rate of convergence for distributed PCA estimators, which depends explicitly on the effective rank of covariance, eigen-gap, and the number of machines. We show that when the number of machines is not unreasonably large, the distributed PCA performs as well as the whole sample PCA, even without full access of whole data. The theoretical results are verified by an extensive simulation study. We also extend our analysis to the heterogeneous case where the population covariance matrices are different across local machines but share similar top eigen-structures.

stat.CO↗

arXiv2018

Intrinsic Charge Transport in Stanene: Roles of Bucklings and Electron-Phonon Couplings

The intrinsic charge transport of stanene is investigated by using density function theory and density function perturbation theory coupled with Boltzmann transport equations from first principles. The accurate Wannier interpolations are applied to calculate the charge carrier scatterings with all branches of phonons with dispersion contribution. The intrinsic carrier mobilities are predicted to be 2~3$\times10^3$ cm$^2$/(V s) at 300 K, and we find that the intervalley scatterings from the out-of-plane and transverse acoustic phonon modes dominate the carrier relaxation. In contrast, the intrinsic carrier mobilities obtained by the conventional deformation potential approach (Long et al., J. Am. Chem. Soc. 2009, 131, 17728) are found to as large as 2~3$\times$10$^6$ cm$^2$/(V s) at 300 K, in which the longitudinal acoustic phonons are assumed to be the only scattering mechanism. The inadequacy of the deformation potential approximation in stanene is attributed to the buckling of the honeycomb structure, which originates from the $sp^2-sp^3$ orbital hybridization and results in broken mirror symmetry as compared to graphene. The high carrier mobility of stanene renders it a promising candidate in nanoelectronics and spintronics applications and we propose to enhance its carrier mobilities by suppressing the out-of-plane vibrations by substrate suspension or clamping.

physics.chem-ph↗

arXiv2017

Structure Re-determination and Superconductivity Observation of Bulk 1T MoS2

2H MoS2 has been intensively studied because of layer-dependent electronic structures and novel physical properties. Though the metastable 1T MoS2 with the [MoS6] octahedron was observed from the microscopic area, the true crystal structure of 1T phase has not been determined strictly. Moreover, the true physical properties have not been demonstrated from experiments due to the challenge for the preparation of pure 1T MoS2 crystals. Here, we successfully synthesized the 1T MoS2 single crystals and re-determined the crystal structure of 1T MoS2 from single-crystal X-ray diffraction. 1T MoS2 crystalizes in space group P-3m1 with a cell of a = b = 3.190(3) Å and c = 5.945(6) Å. The individual MoS2 layer consists of MoS6 octahedron sharing edge with each other. More surprisingly, the bulk 1T MoS2 crystals undergo a superconducting transition of Tc = 4 K, which is the first observation of superconductivity in pure 1T MoS2 phase.

cond-mat.supr-con↗

arXiv2017

Software Engineering Solutions To Support Vertical Transportation

In this paper we introduce the core results of the project on visualisation and analysis of data collected from the vertical transport facilities. The aim of the project was to provide better user experience as well as to help building maintenance staff to increase productivity of their work. We elaborated a web-based system for vertical transportation, to cover the needs of (1) staff working on building maintenance, (2) people who are regularly using the facilities in the corresponding buildings.

cs.HC↗