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Gang Wang

Publications and source records attributed to Gang Wang.

At least 343 records · Page 19Linked to original sources
arXiv2021

SceneRec: Scene-Based Graph Neural Networks for Recommender Systems

Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learning, which aims to project users and items into a low dimensional space to capture collaborative signals. However, the scene information, which has effectively guided many recommendation tasks, is rarely considered in existing collaborative filtering methods. To bridge this gap, we focus on scene-based collaborative recommendation and propose a novel representation model SceneRec. SceneRec formally defines a scene as a set of pre-defined item categories that occur simultaneously in real-life situations and creatively designs an item-category-scene hierarchical structure to build a scene-based graph. In the scene-based graph, we adopt graph neural networks to learn scene-specific representation on each item node, which is further aggregated with latent representation learned from collaborative interactions to make recommendations. We perform extensive experiments on real-world E-commerce datasets and the results demonstrate the effectiveness of the proposed method.

cs.IR↗
arXiv2020

Exciton-phonon coupling strength in single-layer MoSe2 at room temperature

Single-layer transition metal dichalcogenides are at the center of an ever increasing research effort both in terms of fundamental physics and applications. Exciton-phonon coupling plays a key role in determining the (opto)electronic properties of these materials. However, the exciton-phonon coupling strength has not been measured at room temperature. Here, we develop two-dimensional micro-spectroscopy to determine exciton-phonon coupling of single-layer MoSe2. We detect beating signals as a function of waiting time T, induced by the coupling between the A exciton and the A'1 optical phonon. Analysis of two-dimensional beating maps combined with simulations provides the exciton-phonon coupling. The Huang-Rhys factor of ~1 is larger than in most other inorganic semiconductor nanostructures. Our technique offers a unique tool to measure exciton-phonon coupling also in other heterogeneous semiconducting systems with a spatial resolution ~260 nm, and will provide design-relevant parameters for the development of optoelectronic devices.

cond-mat.mes-hall↗
arXiv2020

Evidence of Coalescence Sum Rule in Elliptic Flow of Identified Particles in High-energy Heavy-ion Collisions

The major goal of high-energy heavy-ion collisions is to study the properties of the deconfined quark gluon plasma (QGP), such as partonic collectivity. The collective motion of constituent quarks can be derived from the anisotropic flow measurements of identified hadrons within the coalescence framework. Based on published results of elliptic flow ($v_2$), we shall test the coalescence sum rule using $K^{\pm}$, $p$, ${\bar p}$, $Λ$ and ${\bar Λ}$, and further extract $v_2$ values for produced $u$($d$, $\bar u$, $\bar d$), $s$ and $\bar s$ quarks, as well as transported $u(d)$ quarks in 10-40\% Au+Au collisions at $\sqrt{s_{\rm NN}}=$ 7.7, 11.5, 14.5, 19.6, 27, 39 and 62.4 GeV. We also attempt to link the $v_2$ difference between $π^-$ and $π^+$ to the different numbers of $u$ and $d$ quarks in the initial gold ions, and to relate the $v_2$ measurements of multi-strange hadrons to the formation times of $ϕ$, $Ω^\pm$ and $Ξ^+$.

nucl-ex↗
arXiv2020

Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of $Aβ+$ AD and $Aβ-$ cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between $Aβ+$ AD and $Aβ-$ CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0.05$ are 116, 279 and 387 for the longitudinal $Aβ+$ AD, $Aβ+$ mild cognitive impairment (MCI) and $Aβ+$ CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD ($4.3$, $95\%$ CI=$2.3-8.2$) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.

cs.CV↗
arXiv2020

Tests of weak equivalence principle with the gravitational wave signals in the LIGO-Virgo catalogue GWTC-1

The weak equivalence principle (WEP) is the cornerstone of gravitational theories. At the local scale, WEP has been tested to high accuracy by various experiments. On the intergalactic distance scale, WEP could be tested by comparing the arrival time of different messengers emitted from the same source. The gravitational time delay caused by massive galaxies is proportional to $γ+1$, where the parameter $γ$ is unity in general relativity. The values of $γ$ for different massless particles should be different if WEP is violated, i.e., $Δγ$ is used to indicate the deviation from WEP. So far, $|Δγ|$ has been constrained with gamma-ray bursts, fast radio bursts, etc. Here we report a new constraint of $|Δγ|$ by using the gravitational wave data of binary black hole coalescences in the LIGO-Virgo catalogue GWTC-1. The best constraints imply that $|Δγ| \lesssim 10^{-15}$ at 90 per cent confidence level.

gr-qc↗
arXiv2020

SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval

Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching. In this paper, we study the problem of transferring the deep knowledge of the pre-trained language model (PLM) to Term-based Sparse representations, aiming to improve the representation capacity of bag-of-words(BoW) method for semantic-level matching, while still keeping its advantages. Specifically, we propose a novel framework SparTerm to directly learn sparse text representations in the full vocabulary space. The proposed SparTerm comprises an importance predictor to predict the importance for each term in the vocabulary, and a gating controller to control the term activation. These two modules cooperatively ensure the sparsity and flexibility of the final text representation, which unifies the term-weighting and expansion in the same framework. Evaluated on MSMARCO dataset, SparTerm significantly outperforms traditional sparse methods and achieves state of the art ranking performance among all the PLM-based sparse models.

cs.IR↗
arXiv2020

PDLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Pressure and Dynamic Light Duration

Existing ineffective and inflexible traffic light control at urban intersections can often lead to congestion in traffic flows and cause numerous problems, such as long delay and waste of energy. How to find the optimal signal timing strategy is a significant challenge in urban traffic management. In this paper, we propose PDlight, a deep reinforcement learning (DRL) traffic light control algorithm with a novel reward as PRCOL (Pressure with Remaining Capacity of Outgoing Lane). Serving as an improvement over the pressure used in traffic control algorithms, PRCOL considers not only the number of vehicles on the incoming lane but also the remaining capacity of the outgoing lane. Simulation results using both synthetic and real-world data-sets show that the proposed PDlight yields lower average travel time compared with several state-of-the-art algorithms, PressLight and Colight, under both fixed and dynamic green light duration.

cs.LG↗
arXiv2020

A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction

Today's intelligent traffic light control system is based on the current road traffic conditions for traffic regulation. However, these approaches cannot exploit the future traffic information in advance. In this paper, we propose GPlight, a deep reinforcement learning (DRL) algorithm integrated with graph neural network (GNN) , to relieve the traffic congestion for multi-intersection intelligent traffic control system. In GPlight, the graph neural network (GNN) is first used to predict the future short-term traffic flow at the intersections. Then, the results of traffic flow prediction are used in traffic light control, and the agent combines the predicted results with the observed current traffic conditions to dynamically control the phase and duration of the traffic lights at the intersection. Experiments on both synthetic and two real-world data-sets of Hangzhou and New-York verify the effectiveness and rationality of the GPlight algorithm.

cs.LG↗
arXiv2020

On the clustering properties of produced particles in high-energy $pp$ collisions

Minijets provide useful information on parton interactions in the low transverse-momentum (low-$p_T$) region. Because minijets produce clusters, we study the clustering properties of produced particles in high-energy $pp$ collisions as a first step to identify minijets. We develop an algorithm to find clusters by using the k-means clustering method, in conjunction with a k-number (cluster number) selection principle in the space of pseudorapidity and azimuthal angles. We test the clustering algorithm using events generated by PYTHIA 8.1, for $pp$ collision at $\sqrt{s}=200$ GeV. We find that clustering of low-$p_T$ hadrons occurs in high multiplicity events. However similar clustering properties are also present for particles produced randomly in a finite pseudorapidity and azimuthal angle space. To distinguish the dynamics from random generations of events, it is necessary to examine the correlation between particles and between clusters. We find that the correlations between clusters may provide a useful tool to distinguish the underlying dynamics of the reaction mechanism.

hep-ph↗
arXiv2020

A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation

Motivated by the widespread use of temporal-difference (TD-) and Q-learning algorithms in reinforcement learning, this paper studies a class of biased stochastic approximation (SA) procedures under a mild "ergodic-like" assumption on the underlying stochastic noise sequence. Building upon a carefully designed multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (for control of the gradient bias), we prove a general result on the convergence of the iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel looking-ahead viewpoint renders finite-time analysis of biased SA algorithms under a large family of stochastic perturbations possible. For direct comparison with existing contributions, we also demonstrate these bounds by applying them to TD- and Q-learning with linear function approximation, under the practical Markov chain observation model. The resultant finite-time error bound for both the TD- as well as the Q-learning algorithms is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without making any modifications to the parameter updates) using even nonlinear function approximators; as well as for Markov chains ii) under general mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable.

stat.ML↗
arXiv2020

Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges

Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, monitoring people's interactions, whether counting them at fixed locations or tracking them by generating origin-destination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

cs.CY↗
arXiv2020

Effect of Spatial and Temporal Traffic Statistics on the Performance of Wireless Networks

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

cs.NI↗
arXiv2020

DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving

We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving.

cs.CV↗
arXiv2020

Efficient phonon cascades in hot photoluminescence of WSe$_2$ monolayers

Energy relaxation of photo-excited charge carriers is of significant fundamental interest and crucial for the performance of monolayer (1L) transition metal dichaclogenides (TMDs) in optoelectronics. We measure light scattering and emission in 1L-WSe$_2$ close to the laser excitation energy (down to~$\sim$0.6meV). We detect a series of periodic maxima in the hot photoluminescence intensity, stemming from energy states higher than the A-exciton state, in addition to sharp, non-periodic Raman lines related to the phonon modes. We find a period $\sim$15meV for peaks both below (Stokes) and above (anti-Stokes) the laser excitation energy. We detect 7 maxima from 78K to room temperature in the Stokes signal and 5 in the anti-Stokes, of increasing intensity with temperature. We assign these to phonon cascades, whereby carriers undergo phonon-induced transitions between real states in the free-carrier gap with a probability of radiative recombination at each step. We infer that intermediate states in the conduction band at the $Λ$-valley of the Brillouin zone participate in the cascade process of 1L-WSe$_2$. The observations explain the primary stages of carrier relaxation, not accessible so far in time-resolved experiments. This is important for optoelectronic applications, such as photodetectors and lasers, because these determine the recovery rate and, as a consequence, the devices' speed and efficiency.

cond-mat.mes-hall↗
arXiv2020

Numerical simulation of sky localization for LISA-TAIJI joint observation

LISA is considered to be launched alongside the Athena to probe the energetic astrophysical processes. LISA can determine the direction of sources for Athena's follow-up observation. As another space gravitational wave mission, TAIJI is expected to be launched in the 2030s. The LISA-TAIJI network would provide abundant merits for sources understanding. In this work, we simulate the joint LISA-TAIJI observations for gravitational waves from coalescing supermassive black hole binaries and monochromatic sources. By using the numerical mission orbits, we evaluate the performances of sky localization for various time-delay interferometry channels. For 30 days observation until coalescence, the LISA-TAIJI network in optimal operation can localize all simulated binary sources, $(10^7,\ 3.3 \times 10^6)\ M_\odot$, $(10^6,\ 3.3 \times 10^5)\ M_\odot$ and $(10^5,\ 3.3 \times 10^4)\ M_\odot$ at redshift $z=2$, in 0.4 deg$^2$ (field of view of Wide Field Imager on Athena). The angular resolution can be improved by more than 10 times comparing to LISA or TAIJI single detector at a given percentage of population. The improvements for monochromatic sources at 3 mHz and 10 mHz are relatively moderate in one-year observation. The precision of sky localization could be improved by around 1 to 3 times comparing to single LISA at a given percentage of sources. For a simulated 90 days observation for monochromatic waves, the LISA-TAIJI network still represents a considerable localization advantage which could be more than 10 times better.

gr-qc↗
arXiv2020

Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data

Data used to train machine learning models can be adversarial--maliciously constructed by adversaries to fool the model. Challenge also arises by privacy, confidentiality, or due to legal constraints when data are geographically gathered and stored across multiple learners, some of which may hold even an "anonymized" or unreliable dataset. In this context, the distributionally robust optimization framework is considered for training a parametric model, both in centralized and federated learning settings. The objective is to endow the trained model with robustness against adversarially manipulated input data, or, distributional uncertainties, such as mismatches between training and testing data distributions, or among datasets stored at different workers. To this aim, the data distribution is assumed unknown, and lies within a Wasserstein ball centered around the empirical data distribution. This robust learning task entails an infinite-dimensional optimization problem, which is challenging. Leveraging a strong duality result, a surrogate is obtained, for which three stochastic primal-dual algorithms are developed: i) stochastic proximal gradient descent with an $ε$-accurate oracle, which invokes an oracle to solve the convex sub-problems; ii) stochastic proximal gradient descent-ascent, which approximates the solution of the convex sub-problems via a single gradient ascent step; and, iii) a distributionally robust federated learning algorithm, which solves the sub-problems locally at different workers where data are stored. Compared to the empirical risk minimization and federated learning methods, the proposed algorithms offer robustness with little computation overhead. Numerical tests using image datasets showcase the merits of the proposed algorithms under several existing adversarial attacks and distributional uncertainties.

cs.LG↗
arXiv2020

Ultralong carrier lifetime of topological edge states in a-Bi4Br4

The rising of quantum spin Hall insulators (QSHI) in two-dimensional (2D) systems has been attracting significant interest in current research, for which the 1D helical edge states, a hallmark of QSHI, are widely expected to be a promising platform for next-generation optoelectronics. However, the dynamics of the 1D edge states has not yet been experimentally addressed. Here, we report the observation of optical response of the topological helical edge states in a-Bi4Br4, using the infrared-pump infrared-probe microscopic spectroscopy. Remarkably, we observe that the carrier lifetime of the helical edge states reaches nanosecond-scale at room temperature, which is about 2 - 3 orders longer than that of most 2D topological surface states and is even comparable with that of the well developed optoelectronics semiconductors used in modern industry. The ultralong carrier lifetime of the topological edge states may be attributed to their helical and 1D nature. Our findings not only provide an ideal material for further investigations of the carrier dynamics of 1D helical edge states but also pave the way for its application in optoelectronics.

cond-mat.mtrl-sci↗