arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 469 records · Page 26Linked to original sources

arXiv2016

Chiral Magnetic Effect Task Force Report

In this report, we briefly examine the current status of the study of the chiral magnetic effect including theory and experimental progress. We recommend future strategies for resolving uncertainties in interpretation including recommendations for theoretical work, recommendations for measurements based on data collected in the past five years, and recommendations for beam use in the coming years of RHIC. We have specifically investigated the case for colliding nuclear isobars (nuclei with the same mass but different charge) and find the case compelling. We recommend that a program of nuclear isobar collisions to isolate the chiral magnetic effect from background sources be placed as a high priority item in the strategy for completing the RHIC mission.

nucl-th↗

arXiv2016

Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and develop a new structure for deconvolution of multiple modalities. We propose a novel feature transformation network to bridge the convolutional networks and deconvolutional networks. In the feature transformation network, we correlate the two modalities by discovering common features between them, as well as characterize each modality by discovering modality specific features. With the common features, we not only closely correlate the two modalities, but also allow them to borrow features from each other to enhance the representation of shared information. With specific features, we capture the visual patterns that are only visible in one modality. The proposed network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2.

cs.CV↗

arXiv2016

A Siamese Long Short-Term Memory Architecture for Human Re-Identification

Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance. In the existing works concentrating on feature extraction, representations are formed locally and independent of other regions. We present a novel siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information. The feedback connections and internal gating mechanism of the LSTM cells enable our model to memorize the spatial dependencies and selectively propagate relevant contextual information through the network. We demonstrate improved performance compared to the baseline algorithm with no LSTM units and promising results compared to state-of-the-art methods on Market-1501, CUHK03 and VIPeR datasets. Visualization of the internal mechanism of LSTM cells shows meaningful patterns can be learned by our method.

cs.CV↗

arXiv2016

Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition

3D action recognition - analysis of human actions based on 3D skeleton data - becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods to model the contextual dependency in the temporal domain. In this paper, we extend this idea to spatio-temporal domains to analyze the hidden sources of action-related information within the input data over both domains concurrently. Inspired by the graphical structure of the human skeleton, we further propose a more powerful tree-structure based traversal method. To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell. Our method achieves state-of-the-art performance on 4 challenging benchmark datasets for 3D human action analysis.

cs.CV↗

arXiv2016

Test the chiral magnetic effect with isobaric collisions

The quark-gluon matter produced in relativistic heavy-ion collisions may contain local domains in which P and CP symmetries are not preserved. When coupled with an external magnetic field, such P- and CP-odd domains will generate electric currents along the magnetic field --- a phenomenon called the chiral magnetic effect (CME). Recently, the STAR Collaboration at RHIC and the ALICE Collaboration at the LHC released data of charge-dependent azimuthal-angle correlators with features consistent with the CME expectation. However, the experimental observable is contaminated with significant background contributions from elliptic-flow-driven effects, which makes the interpretation of the data ambiguous. In this Letter, we show that the collisions of isobaric nuclei, $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr, provide an ideal tool to disentangle the CME signal from the background effects. Our simulation demonstrates that the two collision types at $\sqrt{s_{\rm NN}}=200$ GeV have more than $10\%$ difference in the CME signal and less than $2\%$ difference in the elliptic-flow-driven backgrounds for the centrality range of $20-60\%$.

nucl-th↗

arXiv2016

Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images

Recognizing actions from still images is popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside sub-images and models the correlation of sub-images via extra layers of SPN. Our method is shown to be effective on two benchmark datasets.

cs.CV↗

arXiv2016

Ultra-low power threshold for laser induced changes in optical properties of 2D Molybdenum dichalcogenides

The optical response of traditional semiconductors depends on the laser excitation power used in experiments. For two-dimensional (2D) semiconductors, laser excitation effects are anticipated to be vastly different due to complexity added by their ultimate thinness, high surface to volume ratio, and laser-membrane interaction effects. We show in this article that under laser excitation the optical properties of 2D materials undergo irreversible changes. Most surprisingly these effects take place even at low steady state excitation, which is commonly thought to be non-intrusive. In low temperature photoluminescence (PL) we show for monolayer (ML) MoSe2 samples grown by different techniques that laser treatment increases significantly the trion (i.e. charged exciton) contribution to the emission compared to the neutral exciton emission. Comparison between samples exfoliated onto different substrates shows that laser induced doping is more efficient for ML MoSe2 on SiO2/Si compared to h-BN and gold. For ML MoS2 we show that exposure to laser radiation with an average power in the $μ$W/$μ$m$^2$ range does not just increase the trion-to-exciton PL emission ratio, but may result in the irreversible disappearance of the neutral exciton PL emission and a shift of the main PL peak to lower energy.

cond-mat.mtrl-sci↗

arXiv2016

Selective Term Proximity Scoring Via BP-ANN

When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all types of queries. Here we propose a document ranking model which decides for which queries it would be beneficial to use a proximity-based ranking, based on a collection of features of the query. We use a machine learning approach in determining whether utilizing TP will be beneficial. Experiments show that the proposed model returns improved rankings while also reducing the overhead incurred as a result of using TP statistics.

cs.IR↗

arXiv2016

Understanding doping, vacancy, lattice stability and superconductivity in KxFe2-ySe2

Metal-intercalated iron selenides are a class of superconductors that have received much attention but are less understood in comparison with their FeAs-based counterparts. Here, the controversial issues such as Fe vacancy, the real phase responsible for superconductivity, and lattice stability have been addressed based on first-principles calculations. New insights into the distinct features in terms of carrier doping have been revealed.

cond-mat.supr-con↗

arXiv2016

High Throughput Opportunistic Cooperative Device-to-Device Communications With Caching

To achieve the potential in providing high throughput for cellular networks by device-to-device (D2D) communications, the interference among D2D links should be carefully managed. In this paper, we propose an opportunistic cooperation strategy for D2D transmission by exploiting the caching capability at the users to control the interference among D2D links. We consider overlay inband D2D, divide the D2D users into clusters, and assign different frequency bands to cooperative and non-cooperative D2D links. To provide high opportunity for cooperative transmission, we introduce a caching policy. To maximize the network throughput, we jointly optimize the cluster size and bandwidth allocation, where the closed-form expression of the bandwidth allocation factor is obtained. Simulation results demonstrate that the proposed strategy can provide 400%-500% throughput gain over traditional D2D communications when the content popularity distribution is skewed, and can provide 60%-80% gain even when the content popularity distribution is uniform.

cs.IT↗

arXiv2016

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.

cs.SI↗

arXiv2016

Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e. during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.

cs.CV↗

arXiv2016

Well separated trion and neutral excitons on superacid treated MoS2 monolayers

Developments in optoelectronics and spin-optronics based on transition metal dichalcogenide monolayers (MLs) need materials with efficient optical emission and well-defined transition energies. In as-exfoliated MoS2 MLs the photoluminescence (PL) spectra even at low temperature consists typically of broad, overlapping contributions from neutral, charged excitons (trions) and localized states. Here we show that in superacid treated MoS2 MLs the PL intensity increases by up to 60 times at room temperature. The neutral and charged exciton transitions are spectrally well separated in PL and reflectivity at T=4 K, with linewidth for the neutral exciton of 15 meV, but with similar intensities compared to the ones in as-exfoliated MLs at the same temperature. Time resolved experiments uncover picoseconds recombination dynamics analyzed separately for charged and neutral exciton emission. Using the chiral interband selection rules, we demonstrate optically induced valley polarization for both complexes and valley coherence for only the neutral exciton.

cond-mat.mtrl-sci↗

arXiv2016

Scene Parsing with Integration of Parametric and Non-parametric Models

We adopt Convolutional Neural Networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.

cs.CV↗

arXiv2016

Recurrent Attentional Networks for Saliency Detection

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.

cs.CV↗

arXiv2016

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art hand-crafted features on the suggested cross-subject and cross-view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.

cs.CV↗

arXiv2016

Energy-saving Pushing Based on Personal Interest and Context Information

Pushing files to users based on predicting the personal interest of each user may provide higher throughput gain than broadcasting popular files to users based on their common interests. However, the energy consumed at base station for pushing files individually to each user is also higher than broadcast. In this paper, we propose an energy-saving transmission strategy for pre-downloading the files to each user by exploiting the excess resources in the network during off-peak time. Specifically, a power allocation and scheduling algorithm is designed aimed to minimize the extra energy consumed for pushing, where network and user level context information are exploited. Simulation results show that when the energy of both content placement and content delivery is taken into account, the proposed unicast strategy consumes less energy and achieves higher throughput than broadcasting when the files popularity is not uniform and the personal interest prediction is with less uncertainty.

cs.IT↗

arXiv2016

Cooperative Device-to-Device Communications With Caching

Device-to-Device (D2D) communications can increase the throughput of cellular networks significantly, where the interference among D2D links should be properly managed. In this paper, we propose an opportunistic cooperative D2D transmission strategy by exploiting the caching capability at the users to deal with the interference among D2D links. To increase the cooperative opportunity and improve spatial reuse gain, we divide the D2D users into clusters and cache different popular files at the users within a cluster, and then find the optimal cluster size. To maximize the network throughput, we assign different frequency bands to cooperative and non-cooperative D2D links and optimize the bandwidth partition. Simulation results demonstrate that the proposed strategy can provide 500%-600% throughput gain over existing cache-enabled D2D communications when the popularity distribution is skewed, and can provide 40%-80% gain even when the popularity distribution is uniform.

cs.IT↗