arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 1,117 records · Page 62Linked to original sources

arXiv2018

The Influences of Edge Asymmetry on Network Robustness

Asymmetry of in/out-degree distribution is a widespread phenomenon in real-world complex networks. This paper put forward the concept of Edge Asymmetry(EA) to quantify this feature. We designed an EA-based strategy to attack six kinds of real-world networks and found that it was able to achieve the effect as well as edge betweenness-based(EB) and better than edge degree-based(ED) and random attack strategies. In simulation, we found that the greater the network asymmetry the better the EA-based attack strategy performed. By definition, the computational complexity of EA was much lower than that of EB. Therefore, EA-based attack strategies were superior in efficiency. We verified the effect of the EA-based attack strategy with four groups of large-scale networks.

physics.soc-ph↗

arXiv2018

Finite groups with coprime fixed-point-free automorphisms and applications

We study a class of finite groups $G$ which behave similarly to elementary abelian $p$-groups with $p$ prime, that is, there exists a subgroup $N$ such that all elements of $G\setminus N$ are conjugate or inverse-conjugate under $\Aut(G)$. In this paper, we show that such groups are solvable. As an application, we characterise a class of normal edge-transitive Cayley graphs of $G$, which are complete multipartite graphs.

math.GR↗

arXiv2018

Femtosecond Optical Superregular Breathers

Superregular (SR) breathers are nonlinear wave structures formed by a unique nonlinear superposition of pairs of quasi-Akhmediev breathers. They describe a complete scenario of modulation instability that develops from localized small perturbations as well as an unusual quasiannihilation of breather collision. Here, we demonstrate that femtosecond optical SR breathers in optical fibers exhibit intriguing half-transition and full-suppression states, which are absent in the picosecond regime governed by the standard nonlinear Schrödinger equation. In particular, the full-suppression mode, which is strictly associated with the regime of vanishing growth rate of modulation instability, reveals a crucial \textit{non-amplifying} nonlinear dynamics of localized small perturbations. We numerically confirm the robustness of such different SR modes excited from ideal and nonideal initial states in both integrable and nonintegrable cases.

nlin.PS↗

arXiv2018

A light scalar dark matter extension of the type-II two-Higgs-doublet model

We examine the type-II two-Higgs-doublet model with a light scalar dark matter ($S$) after imposing the constraints from the Higgs searches at the LHC and dark matter experiments. We first assume that both two CP-even Higgses ($h$ and $H$) are portals between the DM and SM sectors, and the CP-odd Higgs ($A$) and $H$ are heavier than 130 GeV. We find that the DM with a mass of $10\sim 50$ GeV is disfavored by the joint constraints of the 125 GeV Higgs signal data, the relic density, XENON1T (2017), PandaX-II (2017) and the Fermi-LAT. Next, we consider a special scenario in which the heavy CP-even Higgs is taken as the 125 GeV Higgs. The light CP-even Higgs is the only portal between the DM and SM sectors, and the DM mass is slightly below Higgs resonance. We find that the signal data of the 125 GeV Higgs restrict $\tanβ$ to be in the range of $1\sim 1.5$ for $m_h<$ 62 GeV. The $gg\to A\to hZ$ and $b\bar{b}\to h \to τ^+τ^-$ channels at the LHC can impose lower limits and upper limits on $\tanβ$, respectively. For appropriate values of $\tanβ$, $λ_h$ and $m_h$, the DM with a mass of $10\sim 50$ GeV is allowed by the constraints of the Higgs searches at the LHC and dark matter experiments. For example, $\tanβ$ is restricted to be in the range of $1.0\sim1.5$ for 10 GeV $ $ 1.125 is excluded for 30 GeV $<m_S<$ 50 GeV.

hep-ph↗

arXiv2018

Anatomy of an online misinformation network

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.

cs.SI↗

arXiv2018

Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition

In this paper, we proposed a effective but extensible residual one-dimensional convolution neural network as base network, based on the this network, we proposed four subnets to explore the features of skeleton sequences from each aspect. Given a skeleton sequences, the spatial information are encoded into the skeleton joints coordinate in a frame and the temporal information are present by multiple frames. Limited by the skeleton sequence representations, two-dimensional convolution neural network cannot be used directly, we chose one-dimensional convolution layer as the basic layer. Each sub network could extract discriminative features from different aspects. Our first subnet is a two-stream network which could explore both temporal and spatial information. The second is a body-parted network, which could gain micro spatial features and macro temporal features. The third one is an attention network, the main contribution of which is to focus the key frames and feature channels which high related with the action classes in a skeleton sequence. One frame-difference network, as the last subnet, mainly processes the joints changes between the consecutive frames. Four subnets ensemble together by late fusion, the key problem of ensemble method is each subnet should have a certain performance and between the subnets, there are diversity existing. Each subnet shares a wellperformance basenet and differences between subnets guaranteed the diversity. Experimental results show that the ensemble network gets a state-of-the-art performance on three widely used datasets.

cs.CV↗

arXiv2018

AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience

We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.

cs.CL↗

arXiv2018

Phononic Weyl Nodal Straight Lines in High-Temperature Superconductor MgB$_2$

Based on first-principles calculations, we predict that the superconducting MgB$_2$ with a AlB$_2$-type centrosymmetric lattice host the so-called phononic topological Weyl nodal lines (PTWNLs) on its bulk phonon spectrum. These PTWNLs can be viewed as countless Weyl points (WPs) closely aligned along the straight lines in the $-$H-K-H direction within the three-dimensional Brillouin zone (BZ). Their topological non-trivial natures are confirmed by the calculated Berry curvature distributions on the planes perpendicular to these lines. These lines are highly unique, because they exactly locate at the high-symmetry boundary of the BZ protected by the mirror symmetry and, simultaneously, straightly transverse the whole BZ, in different from known classifications including nodal rings, nodal chains or nets, and nodal loops. On the (10$\bar{1}$0) crystal surface, the PTWNLs-induced drumhead-like non-trivial surface states appear within the rectangular area confined by the projected lines of the PTWNLs with opposite chirality. Moreover, when the mirror symmetry is broken, the double-degenerate PTWNLs are further lifted to form a pair WPs with opposite chirality. Our results pave the ways for future experimental study on topological phonons on MgB$_2$ and highlights similar results in a series of isostructural AlB$_2$-type metallic diborides.

cond-mat.mtrl-sci↗

arXiv2017

Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems

The increasing popularity of server usage has brought a plenty of anomaly log events, which have threatened a vast collection of machines. Recognizing and categorizing the anomalous events thereby is a much salient work for our systems, especially the ones generate the massive amount of data and harness it for technology value creation and business development. To assist in focusing on the classification and the prediction of anomaly events, and gaining critical insights from system event records, we propose a novel log preprocessing method which is very effective to filter abundant information and retain critical characteristics. Additionally, a competitive approach for automated classification of anomalous events detected from the distributed system logs with the state-of-the-art deep (Convolutional Neural Network) architectures is proposed in this paper. We measure a series of deep CNN algorithms with varied hyper-parameter combinations by using standard evaluation metrics, the results of our study reveals the advantages and potential capabilities of the proposed deep CNN models for anomaly event classification tasks on real-world systems. The optimal classification precision of our approach is 98.14%, which surpasses the popular traditional machine learning methods.

cs.DC↗

arXiv2017

BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework

Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System benchmarking is the classic way of meeting the above demands. However, existent big data benchmarks either fail to represent the variety of big data processing requirements, or target only one specific platform, e.g. Hadoop. In this paper, with our industrial partners, we present BigOP, an end-to-end system benchmarking framework, featuring the abstraction of representative Operation sets, workload Patterns, and prescribed tests. BigOP is part of an open-source big data benchmarking project, BigDataBench. BigOP's abstraction model not only guides the development of BigDataBench, but also enables automatic generation of tests with comprehensive workloads. We illustrate the feasibility of BigOP by implementing an automatic test generation tool and benchmarking against three widely used big data processing systems, i.e. Hadoop, Spark and MySQL Cluster. Three tests targeting three different application scenarios are prescribed. The tests involve relational data, text data and graph data, as well as all operations and workload patterns. We report results following test specifications.

cs.DC↗

arXiv2017

Galaxy-galaxy weak-lensing measurement from SDSS: II. host halo properties of galaxy groups

As the second paper of a series on studying galaxy-galaxy lensing signals using the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we present our measurement and modelling of the lensing signals around groups of galaxies. We divide the groups into four halo mass bins, and measure the signals around four different halo-center tracers: brightest central galaxy (BCG), luminosity-weighted center, number-weighted center and X-ray peak position. For X-ray and SDSS DR7 cross identified groups, we further split the groups into low and high X-ray emission subsamples, both of which are assigned with two halo-center tracers, BCGs and X-ray peak positions. The galaxy-galaxy lensing signals show that BCGs, among the four candidates, are the best halo-center tracers. We model the lensing signals using a combination of four contributions: off-centered NFW host halo profile, sub-halo contribution, stellar contribution, and projected 2-halo term. We sample the posterior of 5 parameters i.e., halo mass, concentration, off-centering distance, sub halo mass, and fraction of subhalos via a MCMC package using the galaxy-galaxy lensing signals. After taking into account the sampling effects (e.g. Eddington bias), we found the best fit halo masses obtained from lensing signals are quite consistent with those obtained in the group catalog based on an abundance matching method, except in the lowest mass bin. Subject headings: (cosmology:) gravitational lensing, galaxies: clusters: general

astro-ph.CO↗

arXiv2017

3D Printing of Flexible Room-Temperature Liquid Metal Battery

The entirely soft and transformable room-temperature liquid metal battery based on 3D hybrid printing was proposed and experimentally demonstrated. Liquid metal gallium and conductive gel were employed as the negative and positive electrode material, respectively. The discharge process of batteries with different structure, single-cell and multi-cell, were clarified. The results indicated that the obtained battery possessed rather stable and persisted discharge property, but the multi-cell battery presented stair-step decrease overtime. SEM and EDS images were obtained to evaluate the morphological change of the electrode during the discharge process, and the liquid metal was found to be easily exposed and active according to the solubility of the alkaline electrolyte to oxidized gallium. The successful 3D hybrid printing allowed different polymers, including PLA, ABS and TPU, to be precisely patterned in any required battery body structures, with liquid electrodes in appropriate specified positions. The flexibility and practicability of this new generation battery was illustrated through working devices. The present soft battery offers important power supply for future wearable and epidermal electronics.

physics.app-ph↗

arXiv2017

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states respectively.Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the restricted Boltzmann machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches. We verify our reasonings by comparing the performance of RBMs of various architectures on the standard MNIST datasets.

physics.data-an↗

arXiv2017

Measurement of $\mathrm{H} \to μ^+ μ^-$ production in association with a Z boson at the CEPC

The Circular Electron-Positron Collider (CEPC) is a future Higgs factory proposed by the Chinese high energy physics community. It will operate at a center-of-mass energy of 240-250 GeV and is expected to accumulate an integrated luminosity of 5 ab$^{\rm{-1}}$ with ten years of operation. At CEPC, Higgs bosons are dominantly produced from $ZH$ associated process. Vast amount of Higgs events collected will enable precise studies of its properties including Yukawa couplings to massive particles. With {\sc GEANT4}-based simulation of detector effects, we study CEPC feasibility on measuring Higgs boson decaying into a pair of muons. The results with or without information from Z boson decay products are provided, which show a signal significance of over 10 standard deviations can be achieved and the H-$\mathrmμ$-$\mathrmμ$ coupling can be measured within $10\%$ accuracy.

hep-ex↗

arXiv2017

$H \rightarrow e^+ e^- $ at CEPC: ISR effect with MadGraph

The Circular Electron Positron Collider (CEPC) is a future Higgs factory proposed by the Chinese high energy physics community. It will operate at a center-of-mass energy of 240-250 GeV. The CEPC will accumulate an integrated luminosity of 5 ab$^{\rm{-1}}$ in ten years' operation. With GEANT4-based full simulation samples for CEPC, Higgs boson decaying into electron pair is studied at the CEPC. The upper limit of ${\cal B}(H \rightarrow e^+ e^-)$ could reach 0.024\% at 95\% confidence level. The signal process is generated by MadGraph, with Initial State Radiation (ISR) implemented, as a first step to adjust MadGraph for a electron positron Collider.

hep-ph↗

arXiv2017

Wrong sign Yukawa coupling of the 2HDM plus a singlet scalar dark matter confronted with dark matter and Higgs data

In the framework of type-II two-Higgs-doublet model with a scalar dark matter ($S$), we examine the wrong sign Yukawa coupling of the 125 GeV Higgs which is the only portal between the dark matter and SM sectors. After imposing the constraints from the Higgs searches at the LHC and dark matter experiments, we obtain some interesting observables: (i) The theory, oblique parameters, and the Higgs searches at the LHC can impose stringent constraints on $\tanβ$ in the case of the wrong sign Yukawa coupling of the 125 GeV Higgs. For example, for $m_H=600$ GeV and 140 GeV $ -0.82$. (iv) The Fermi-LAT limits exclude most of samples in the range of 62.5 GeV $<m_S <65$ GeV, including the samples with $f^n/f^p\sim -0.7$.

hep-ph↗

arXiv2017

HybridTune: Spatio-temporal Data and Model Driven Performance Diagnosis for Big Data Systems

With tremendous growing interests in Big Data systems, analyzing and facilitating their performance improvement become increasingly important. Although there have much research efforts for improving Big Data systems performance, efficiently analysing and diagnosing performance bottlenecks over these massively distributed systems remain a major challenge. In this paper, we propose a spatio-temporal correlation analysis approach based on stage characteristic and distribution characteristic of Big Data applications, which can associate the multi-level performance data fine-grained. On the basis of correlation data, we define some priori rules, select features and vectorize the corresponding datasets for different performance bottlenecks, such as, workload imbalance, data skew, abnormal node and outlier metrics. And then, we utilize the data and model driven algorithms for bottlenecks detection and diagnosis. In addition, we design and develop a lightweight, extensible tool HybridTune, and validate the diagnosis effectiveness of our tool with BigDataBench on several benchmark experiments in which the outperform state-of-the-art methods. Our experiments show that the accuracy of abnormal/outlier detection we obtained reaches about 80%. At last, we report several Spark and Hadoop use cases, which are demonstrated how HybridTune supports users to carry out the performance analysis and diagnosis efficiently on the Spark and Hadoop applications, and our experiences demonstrate HybridTune can help users find the performance bottlenecks and provide optimization recommendations.

cs.DC↗

arXiv2017

DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition

Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition. Recent study shows that kernel matrix can give considerably better representation by modelling the nonlinearity in the local descriptor set. Nevertheless, neither the descriptors nor the kernel matrix is deeply learned. Worse, they are considered separately, hindering the pursuit of an optimal SPD representation. This work proposes a deep network that jointly learns local descriptors, kernel-matrix-based SPD representation, and the classifier via an end-to-end training process. We derive the derivatives for the mapping from a local descriptor set to the SPD representation to carry out backpropagation. Also, we exploit the Daleckii-Krein formula in operator theory to give a concise and unified result on differentiating SPD matrix functions, including the matrix logarithm to handle the Riemannian geometry of kernel matrix. Experiments not only show the superiority of kernel-matrix-based SPD representation with deep local descriptors, but also verify the advantage of the proposed deep network in pursuing better SPD representations for fine-grained image recognition tasks.

cs.CV↗