arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 1,153 records · Page 64Linked to original sources

arXiv2017

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.

cs.CV↗

arXiv2017

Can Boltzmann Machines Discover Cluster Updates ?

Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms. The creative power comes from the latent representation of the Boltzmann machines, which learn to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four spin plaquette interactions. Our results endorse a fresh research paradigm where intelligent machines are designed to create or inspire human discovery of innovative algorithms.

physics.comp-ph↗

arXiv2017

VBS ${\rm W}^\pm {\rm W}^\pm {\rm H}$ production at the HL-LHC and a 100 TeV pp-collider

${\rm W}^\pm {\rm W}^\pm {\rm H}$ production at hadron colliders through vector boson scattering is a so far unconsidered process, which leads to a clean signature of two same-sign charged leptons and two widely separated jets. This process is sensitive to the ${\rm HHH}$ and ${\rm WWHH}$ couplings and any deviation of these couplings from their SM predictions serves as direct evidence of new physics beyond the SM. In this paper we perform a Monte Carlo study of this process for the $\sqrt{s}=14$ TeV LHC and a $100$ TeV pp-collider, and provide projections of the constraints on the triple-Higgs and ${\rm WWHH}$ quartic couplings for these environments. In particular, we consider the impact of pileup on the expected sensitivity in this channel. Our analysis demonstrates that although the sensitivity to the ${\rm HHH}$ coupling is rather low, the ${\rm WWHH}$ coupling can be constrained in this channel within $\sim 100\%$ and $\sim 20\%$ at 95\% confidence level around the SM prediction at the HL-LHC and a 100 TeV pp-collider, respectively.

hep-ph↗

arXiv2017

Pressure-induced superconductivity and topological quantum phase transitions in a quasi-one-dimensional topological insulator: Bi4I4

Superconductivity and topological quantum states are two frontier fields of research in modern condensed matter physics. The realization of superconductivity in topological materials is highly desired, however, superconductivity in such materials is typically limited to two- or three-dimensional materials and is far from being thoroughly investigated. In this work, we boost the electronic properties of the quasi-one-dimensional topological insulator bismuth iodide \b{eta}-Bi4I4 by applying high pressure. Superconductivity is observed in \b{eta}-Bi4I4 for pressures where the temperature dependence of the resistivity changes from a semiconducting-like behavior to that of a normal metal. The superconducting transition temperature Tc increases with applied pressure and reaches a maximum value of 6 K at 23 GPa, followed by a slow decrease. Our theoretical calculations suggest the presence of multiple pressure-induced topological quantum phase transitions as well as a structural-electronic instability.

cond-mat.supr-con↗

arXiv2017

Compositional Model based Fisher Vector Coding for Image Classification

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to depict the generation process of local features. However, the representative power of the GMM could be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes and the number of prototypes is usually small in FVC. To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions. Instead, we adopt a compositional mechanism which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as the linear combination of multiple key components and the combination weight is a latent random variable. In this way, we can greatly enhance the representative power of the generative model of FVC. To implement our idea, we designed two particular generative models with such a compositional mechanism.

cs.CV↗

arXiv2016

Frictional magneto-Coulomb drag in graphene double-layer heterostructure

Coulomb interaction between two closely spaced parallel layers of electron system can generate the frictional drag effect by interlayer Coulomb scattering. Employing graphene double layers separated by few layer hexagonal boron nitride (hBN), we investigate density tunable magneto- and Hall-drag under strong magnetic fields. The observed large magneto-drag and Hall-drag signals can be related with Laudau level (LL) filling status of the drive and drag layers. We find that the sign and magnitude of the magneto- and Hall-drag resistivity tensor can be quantitatively correlated to the variation of magneto-resistivity tensors in the drive and drag layers, confirming a theoretical formula for magneto-drag in the quantum Hall regime. The observed weak temperature dependence and $\sim B^2$ dependence of the magneto-drag are qualitatively explained by Coulomb scattering phase-space argument.

cond-mat.mes-hall↗

arXiv2016

Phase-locked array of quantum cascade lasers with an intracavity spatial filter

Phase-locking an array of quantum cascade lasers is an effective way to achieve higher output power and beam shaping. In this article, based on Talbot effect, we show a new-type phase-locked array of mid-infrared quantum cascade lasers with an integrated spatial- filtering Talbot cavity. All the arrays show stable in-phase operation from the threshold current to full power current. The beam divergence of the array device is smaller than that of a single-ridge laser. We use the multi-slit Fraunhofer diffraction mode to interpret the far-field radiation profile and give a solution to get better beam quality. The maximum power is just about 5 times that of a single-ridge laser for eleven-laser array device and 3 times for seven-laser array device. Considering the great modal selection ability, simple fabricating process and the potential for achieving better beam quality and smaller cavity loss, this new-type phase-locked array may be a hopeful and elegant solution to get high power or beam shaping.

physics.optics↗

arXiv2016

Phase-locked array of quantum cascade lasers with an integrated Talbot cavity

We show a phase-locked array of three quantum cascade lasers with an integrated Talbot cavity at one side of the laser array. The coupling scheme is called diffraction coupling. By controlling the length of Talbot to be a quarter of Talbot distance (Zt/4), in-phase mode operation can be selected. The in-phase operation shows great modal stability under different injection currents, from the threshold current to the full power current. The far-field radiation pattern of the in-phase operation contains three lobes, one central maximum lobe and two side lobes. The interval between adjacent lobes is about 10.5°. The output power is about 1.5 times that of a single-ridge laser. Further studies should be taken to achieve better beam performance and reduce optical losses brought by the integrated Talbot cavity.

physics.optics↗

arXiv2016

Unusual magneto-transport from Si-square nets in topological semimetal HfSiS

The class of topological semimetals comprises a large pool of compounds. Together they provide a wide platform to realize exotic quasiparticles for example Dirac, nodal line Dirac and Weyl fermions. In this letter, we report the Berry phase, Fermi surface topology and anisotropic magnetoresistance of HfSiS which has recently been predicted to be a nodal line semimetal. This compound contains large carrier density, higher than most of the known semimetals. Massive amplitudes of de Haas-van Alphen and Shubnikov-de Haas oscillations up to 20 K in 7 T assist us in witnessing nontrivial pi-Berry phase which is a consequence of topological Dirac-type dispersion of bands originating from the hybridization of px + py and dx2-y2 orbitals of square-net plane of Si and Hf atoms, respectively. Furthermore, we establish the 3D Fermi-surface which consists of very asymmetric water caltrop-like electron and barley seed-like hole pockets which account for the anisotropic magnetoresistance in HfSiS.

cond-mat.mtrl-sci↗

arXiv2016

Recommender Engine for Continuous Time Quantum Monte Carlo Methods

Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accuracy. Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions. We then utilize powerful molecular simulation techniques to propose efficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers.

cond-mat.str-el↗

arXiv2016

A robust optimal control problem with moment constraints on distribution: theoretical analysis and an algorithm

We study an optimal control problem in which both the objective function and the dynamic constraint contain an uncertain parameter. Since the distribution of this uncertain parameter is not exactly known, the objective function is taken as the worst-case expectation over a set of possible distributions of the uncertain parameter. This ambiguity set of distributions is, in turn, defined by the first two moments of the random variables involved. The optimal control is found by minimizing the worst-case expectation over all possible distributions in this set. If the distributions are discrete, the stochastic min-max optimal control problem can be converted into a convensional optimal control problem via duality, which is then approximated as a finite-dimensional optimization problem via the control parametrization. We derive necessary conditions of optimality and propose an algorithm to solve the approximation optimization problem. The results of discrete probability distribution are then extended to the case with one dimensional continuous stochastic variable by applying the control parametrization methodology on the continuous stochastic variable, and the convergence results are derived. A numerical example is present to illustrate the potential application of the proposed model and the effectiveness of the algorithm.

math.OC↗

arXiv2016

Exploiting Structure Sparsity for Covariance-based Visual Representation

The past few years have witnessed increasing research interest on covariance-based feature representation. A variety of methods have been proposed to boost its efficacy, with some recent ones resorting to nonlinear kernel technique. Noting that the essence of this feature representation is to characterise the underlying structure of visual features, this paper argues that an equally, if not more, important approach to boosting its efficacy shall be to improve the quality of this characterisation. Following this idea, we propose to exploit the structure sparsity of visual features in skeletal human action recognition, and compute sparse inverse covariance estimate (SICE) as feature representation. We discuss the advantage of this new representation on dealing with small sample, high dimensionality, and modelling capability. Furthermore, utilising the monotonicity property of SICE, we efficiently generate a hierarchy of SICE matrices to characterise the structure of visual features at different sparsity levels, and two discriminative learning algorithms are then developed to adaptively integrate them to perform recognition. As demonstrated by extensive experiments, the proposed representation leads to significantly improved recognition performance over the state-of-the-art comparable methods. In particular, as a method fully based on linear technique, it is comparable or even better than those employing nonlinear kernel technique. This result well demonstrates the value of exploiting structure sparsity for covariance-based feature representation.

cs.CV↗

arXiv2016

Probing a pseudoscalar at the LHC in light of $R(D^{(*)})$ and muon g-2 excesses

We study the excesses of $R(D^{(*)})$ and muon $g-2$ in the framework of a two-Higgs-doublet model with top quark flavor-changing neutral-current (FCNC) couplings. Considering the relevant theoretical and experimental constraints, we find that the $R(D^{(*)})$ and muon $g-2$ excesses can be simultaneously explained in a parameter space allowed by the constraints. In such a parameter space the pseudoscalar ($A$) has a mass between 20 GeV and 150 GeV so that it can be produced from the top quark FCNC decay $t\to A c$. Focusing on its dominant decay $A\to τ\barτ$, we perform a detailed simulation on $pp\to t\bar{t}\to Wb Ac\to jjbcτ\barτ$ and find that the $2σ$ upper limits from a data set of 30 (100) fb$^{-1}$ at the 13 TeV LHC can mostly (entirely) exclude such a parameter space.

hep-ph↗

arXiv2016

Tungsten doping of Ta3N5-Nanotubes for Band Gap Narrowing and Enhanced Photoelectrochemical Water Splitting Efficiency

Ordered W-doped Ta2O5 nanotube arrays were grown by self-organizing electrochemical anodization of TaW alloys with different tungsten concentrations and by a suitable high temperature ammonia treatment, fully converted to W:Ta3N5 tubular structures. A main effect found is that W doping can decrease the band gap from 2 eV (bare Ta3N5) down to 1.75 eV. Ta3N5 nanotubes grown on 0.5 at% W alloy and modified with (CoOH)x as co-catalyst show ~33% higher photocurrents in photoelectrochemical (PEC) water splitting than pure Ta3N5.

cond-mat.mtrl-sci↗

arXiv2016

Highly controlled coating of a biomimetic polymer in TiO2 nanotubes

Highly controlled coating of biomimetic polydopamine (PDA) was achieved on titanium dioxide nanotubes (TiO2 NTs) by exposing TiO2 NT arrays to a slightly alkaline dopamine solution. The thin films act as photonic sensitizers (enhancing photocurrents and photodegradation) in the visible light range. The PDA coatings can furthermore be used as a platform for decorating the TiO2 NTs with different co-catalysts and metal nanoparticles (NPs).

cond-mat.mtrl-sci↗

arXiv2016

Tantalum nitride nanotube photoanodes: establishing a beneficial back-contact by lift-off and transfer to titanium nitride layer

In this work we introduce the use of TiN/Ti2 N layers as a back contact for lifted-off membranes of anodic Ta3N5 nanotube layers. In photoelectrochemical H2 generation experiments under simulated AM 1.5G light, shift of the onset potential for anodic photocurrents to lower potentials is observed, as well as a higher magnitude of the photocurrents compared to conventional Ta3N5 nanotubes (~ 0.5 V RHE ). We ascribe this beneficial effect to the improved conductive properties of the TiNx -based back contact layer that enables a facilitated electron-transport for tantalum-nitride based materials to the conductive substrate.

cond-mat.mtrl-sci↗

arXiv2016

Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines

Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems. We present a general strategy to overcome this difficulty by adopting ideas and techniques from the machine learning community. We fit the unnormalized probability of the physical model to a feedforward neural network and reinterpret the architecture as a restricted Boltzmann machine. Then, exploiting its feature detection ability, we utilize the restricted Boltzmann machine for efficient Monte Carlo updates and to speed up the simulation of the original physical system. We implement these ideas for the Falicov-Kimball model and demonstrate improved acceptance ratio and autocorrelation time near the phase transition point.

physics.comp-ph↗

arXiv2016

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.

cs.LG↗