arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 469 records · Page 26Linked to original sources

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↗

arXiv2016

Discrete quantum dot like emitters in monolayer MoSe2: Spatial mapping, Magneto-optics and Charge tuning

Transition metal dichalcogenide monolayers such as MoSe2,MoS2 and WSe2 are direct bandgap semiconductors with original optoelectronic and spin-valley properties. Here we report spectrally sharp, spatially localized emission in monolayer MoSe2. We find this quantum dot like emission in samples exfoliated onto gold substrates and also suspended flakes. Spatial mapping shows a correlation between the location of emitters and the existence of wrinkles (strained regions) in the flake. We tune the emission properties in magnetic and electric fields applied perpendicular to the monolayer plane. We extract an exciton g-factor of the discrete emitters close to -4, as for 2D excitons in this material. In a charge tunable sample we record discrete jumps on the meV scale as charges are added to the emitter when changing the applied voltage. The control of the emission properties of these quantum dot like emitters paves the way for further engineering of the light matter interaction in these atomically thin materials.

cond-mat.mtrl-sci↗

arXiv2016

Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks

Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs). C-HRNNs not only make use of the representation power of CNNs, but also efficiently encodes spatial and scale dependencies among different image regions. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve state-of-the-art results on Places 205, SUN 397, MIT indoor, and competitive results on ILSVRC 2012.

cs.CV↗

arXiv2016

Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.

eess.SY↗

arXiv2016

Optimal Scheduling of Electric Vehicles Charging in low-Voltage Distribution Systems

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.

eess.SY↗

arXiv2016

Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.

cs.CV↗

arXiv2015

Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification

This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification. Variations in lighting conditions, environment and pose changes across camera views make re-identification a challenging problem. Previous methods address these challenges by designing specific features or by learning a distance function. We propose a hierarchical feature learning framework that learns invariant representations from labeled image pairs. A mapping is learned such that the extracted features are invariant for images belonging to same individual across views. To learn robust representations and to achieve better generalization to unseen data, the system has to be trained with a large amount of data. Critically, most of the person re-identification datasets are small. Manually augmenting the dataset by partial corruption of input data introduces additional computational burden as it requires several training epochs to converge. We propose a hierarchical network which incorporates a marginalization technique that can reap the benefits of training on large datasets without explicit augmentation. We compare our approach with several baseline algorithms as well as popular linear and non-linear metric learning algorithms and demonstrate improved performance on challenging publicly available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also achieves the stateof-the-art results on these datasets.

cs.CV↗