arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 1,189 records · Page 66Linked to original sources

arXiv2016

Stationary nonlinear waves, superposition modes and modulational instability characteristics in the AB system

We study the AB system describing marginally unstable baroclinic wave packets in geophysical fluids and also ultra-short pulses in nonlinear optics. We show that the breather can be converted into different types of stationary nonlinear waves on constant backgrounds, including the multi-peak soliton, M-shaped soliton, W-shaped soliton and periodic wave. We also investigate the nonlinear interactions between these waves, which display some novel patterns due to the non-propagating characteristics of the solitons: (1) Two antidark solitons can produce a W-shaped soliton instead of a higher-order antidark one, (2) The interaction between an antidark soliton and a W-shaped soliton can not only generate a higher-order antidark soliton, but also form a W-shaped solion pair, (3) The interactions between an oscillation W-shaped soliton and an oscillation M-shaped soliton show the multipeak structures. We find that the transition occurs at a modulational stability region in a low perturbation frequency region.

nlin.PS↗

arXiv2016

Toward a fractal spectrum approach for neutron and gamma pulse shape discrimination

There is a key research issue to accurately select out neutron signals and discriminate gamma signals from a mixed radiation field in the neutron detection. This paper proposes a fractal spectrum discrimination approach by means of different spectrum characteristics of neutron and gamma. Figure of merit and average discriminant error ratio are adopted together to evaluate the discriminant effects. Different neutron and gamma signals with various noises and pulse pile-ups are simulated according to real data in the literature. The proposed approach is compared with the digital charge integration and pulse gradient methods. It is found that the fractal approach exhibits the best discriminant performance among three methods. The fractal spectrum approach is not sensitive to the high frequency noises and pulse pile-ups. It means that the proposed approach takes the advantages of anti-noises and high discriminant ability, and can be used to better discriminate neutron and gamma in neutron detection.

physics.ins-det↗

arXiv2016

An extension of two-Higgs-doublet model and the excesses of 750 GeV diphoton, muon g-2 and $h\toμτ$

In this paper we simultaneously explain the excesses of the 750 GeV diphoton, muon g-2 and $h\to μτ$ in an extension of the two-Higgs-doublet model (2HDM) with additional vector-like fermions and a CP-odd scalar singlet ($P$) which is identified as the 750 GeV resonance. This 750 GeV resonance has a mixing with the CP-odd scalar ($A$) in 2HDM, which leads to a coupling between $P$ and the SM particles as well as a coupling between $A$ and the vector-like fermions. Such a mixing and couplings are strongly constrained by $τ\toμγ$, muon g-2 and the 750 GeV diphoton data. We scan over the parameter space and find that such an extension can simultaneously account for the observed excesses of 750 GeV diphoton, muon g-2 and $h\to μτ$. The 750 GeV resonance decays in exotic modes, such as $P\to hA$, $P\to HZ$, $P\to HA$ and $P\to W^\pm H^\mp$, and its width can be dozens of GeV and is sensitive to the mixing angle.

hep-ph↗

arXiv2016

A Cylindrical Basis Function for Solving Partial Differential Equations on Manifolds

Numerical solutions of partial differential equations (PDEs) on manifolds continues to generate a lot of interest among scientists in the natural and applied sciences. On the other hand, recent developments of 3D scanning and computer vision technologies have produced a large number of 3D surface models represented as point clouds. Herein, we develop a simple and efficient method for solving PDEs on closed surfaces represented as point clouds. By projecting the radial vector of standard radial basis function(RBF) kernels onto the local tangent plane, we are able to produce a representation of functions that permits the replacement of surface differential operators with their Cartesian equivalent. We demonstrate, numerically, the efficiency of the method in discretizing the Laplace Beltrami operator.

math.NA↗

arXiv2016

Directional Generation of Graphene Plasmons by Near Field Interference

The highly unidirectional excitation of graphene plasmons (GPs) through near-field interference of orthogonally polarized dipoles is investigated. The preferred excitation direction of GPs by a single circularly polarized dipole can be simply understood with the angular momentum conservation law. Moreover, the propagation direction of GPs can be switched not only by changing the phase difference between dipoles, but also by placing the z-polarized dipole to its image position, whereas the handedness of the background field remains the same. The unidirectional excitation of GPs can be extended directly into arc graphene surface as well. Furthermore, our proposal on directional generation of GPs can be realized in a semiconductor nanowire/graphene system, where a semiconductor nanowire can mimic the circularly polarized dipole when illuminated by two orthogonally polarized plane waves.

cond-mat.mes-hall↗

arXiv2015

BigDataBench-MT: A Benchmark Tool for Generating Realistic Mixed Data Center Workloads

Long-running service workloads (e.g. web search engine) and short-term data analysis workloads (e.g. Hadoop MapReduce jobs) co-locate in today's data centers. Developing realistic benchmarks to reflect such practical scenario of mixed workload is a key problem to produce trustworthy results when evaluating and comparing data center systems. This requires using actual workloads as well as guaranteeing their submissions to follow patterns hidden in real-world traces. However, existing benchmarks either generate actual workloads based on probability models, or replay real-world workload traces using basic I/O operations. To fill this gap, we propose a benchmark tool that is a first step towards generating a mix of actual service and data analysis workloads on the basis of real workload traces. Our tool includes a combiner that enables the replaying of actual workloads according to the workload traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users' requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant version of our comprehensive benchmark suite BigDataBench and it is publicly available from http://prof.ict.ac.cn/BigDataBench/multi-tenancyversion/.

cs.DC↗

arXiv2015

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. We construct a Freestyle Multilingual Image Question Answering (FM-IQA) dataset to train and evaluate our mQA model. It contains over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations. The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test. Specifically, we mix the answers provided by humans and our model. The human judges need to distinguish our model from the human. They will also provide a score (i.e. 0, 1, 2, the larger the better) indicating the quality of the answer. We propose strategies to monitor the quality of this evaluation process. The experiments show that in 64.7% of cases, the human judges cannot distinguish our model from humans. The average score is 1.454 (1.918 for human). The details of this work, including the FM-IQA dataset, can be found on the project page: http://idl.baidu.com/FM-IQA.html

cs.CV↗

arXiv2015

Split orthogonal group: A guiding principle for sign-problem-free fermionic simulations

We present a guiding principle for designing fermionic Hamiltonians and quantum Monte Carlo (QMC) methods that are free from the infamous sign problem by exploiting the Lie groups and Lie algebras that appear naturally in the Monte Carlo weight of fermionic QMC simulations. Specifically, rigorous mathematical constraints on the determinants involving matrices that lie in the split orthogonal group provide a guideline for sign-free simulations of fermionic models on bipartite lattices. This guiding principle not only unifies the recent solutions of the sign problem based on the continuous-time quantum Monte Carlo methods and the Majorana representation, but also suggests new efficient algorithms to simulate physical systems that were previously prohibitive because of the sign problem.

cond-mat.str-el↗

arXiv2015

Quantum Monte Carlo study of mass-imbalanced Hubbard models

Building on recent solutions of the fermion sign problem for specific models we present two continuous-time quantum Monte Carlo methods for efficient simulation of mass-imbalanced Hubbard models on bipartite lattices at half-filling. For both methods we present the solutions to the fermion sign problem and the algorithms to achieve efficient simulations. As applications, we calculate the dependence of the spin correlation on the mass imbalance in a one-dimensional lattice and study the thermal and quantum phase transitions to an antiferromagnetic Ising long-range ordered state in two dimensions. These results offer unbiased predictions for experiments on ultracold atoms and bridge known exact solutions of Falicov-Kimball model and previous studies of the SU(2)-symmetric Hubbard model.

cond-mat.str-el↗

arXiv2015

Joint Relay and Jammer Selection Improves the Physical Layer Security in the Face of CSI Feedback Delays

We enhance the physical-layer security (PLS) of amplify-and-forward relaying networks with the aid of joint relay and jammer selection (JRJS), despite the deliterious effect of channel state information (CSI) feedback delays. Furthermore, we conceive a new outage-based characterization approach for the JRJS scheme. The traditional best relay selection (TBRS) is also considered as a benchmark. We first derive closed-form expressions of both the connection outage probability (COP) and of the secrecy outage probability (SOP) for both the TBRS and JRJS schemes. Then, a reliable-and-secure connection probability (RSCP) is defined and analyzed for characterizing the effect of the correlation between the COP and SOP introduced by the corporate source-relay link. The reliability-security ratio (RSR) is introduced for characterizing the relationship between the reliability and security through the asymptotic analysis. Moreover, the concept of effective secrecy throughput is defined as the product of the secrecy rate and of the RSCP for the sake of characterizing the overall efficiency of the system, as determined by the transmit SNR, secrecy codeword rate and the power sharing ratio between the relay and jammer. The impact of the direct source-eavesdropper link and additional performance comparisons with respect to other related selection schemes are further included. Our numerical results show that the JRJS scheme outperforms the TBRS method both in terms of the RSCP as well as in terms of its effective secrecy throughput, but it is more sensitive to the feedback delays. Increasing the transmit SNR will not always improve the overall throughput. Moreover, the RSR results demonstrate that upon reducing the CSI feedback delays, the reliability improves more substantially than the security degrades, implying an overall improvement in terms of the security-reliability tradeoff.

cs.IT↗

arXiv2015

Higgs pair signal enhanced in the 2HDM with two degenerate 125 GeV Higgs bosons

We discuss a scenario of the type-II 2HDM in which the $b\bar{b}γγ$ rate of the Higgs pair production is enhanced due to the two nearly degenerate 125 GeV Higgs bosons ($h$, $H$). Considering various theoretical and experimental constraints, we figure out the allowed ranges of the trilinear couplings of these two Higgs bosons and calculate the signal rate of $b\bar{b}γγ$ from the productions of Higgs pairs ($hh$, $hH$, $HH$) at the LHC. We find that in the allowed parameter space some trilinear Higgs couplings can be larger than the SM value by an order and the production rate of $b\bar{b}γγ$ can be greatly enhanced. We also consider a "decoupling" benchmark point where the light CP-even Higgs has a SM-like cubic self-coupling while other trilinear couplings are very small. With a detailed simulation on the $b\bar{b}γγ$ signal and backgrounds, we find that in such a "decoupling" scenario the $hh$ and $hH$ channels can jointly enhance the statistical significance to 5$σ$ at 14 TeV LHC with an integrated luminosity of 3000 fb$^{-1}$.

hep-ph↗

arXiv2015

A Practical O(R\log\log n+n) time Algorithm for Computing the Longest Common Subsequence

In this paper, we revisit the much studied LCS problem for two given sequences. Based on the algorithm of Iliopoulos and Rahman for solving the LCS problem, we have suggested 3 new improved algorithms. We first reformulate the problem in a very succinct form. The problem LCS is abstracted to an abstract data type DS on an ordered positive integer set with a special operation Update(S,x). For the two input sequences X and Y of equal length n, the first improved algorithm uses a van Emde Boas tree for DS and its time and space complexities are O(R\log\log n+n) and O(R), where R is the number of matched pairs of the two input sequences. The second algorithm uses a balanced binary search tree for DS and its time and space complexities are O(R\log L+n) and O(R), where L is the length of the longest common subsequence of X and Y. The third algorithm uses an ordered vector for DS and its time and space complexities are O(nL) and O(R).

cs.DS↗

arXiv2015

Immersion and Invariance Stabilization of Nonlinear Systems: A Horizontal Contraction Approach

The main objective of this paper is to propose an alternative procedure to carry out one of the key steps of immersion and invariance stabilising controller design. Namely, the one that ensures attractivity of the manifold whose internal dynamics contains a copy of the desired system behaviour. Towards this end we invoke contraction theory principles and ensure the attractivity of the manifold rendering it horizontally contractive. The main advantage of adopting this alternative approach is to make more systematic the last step of the design with more explicit degrees of freedom to accomplish the task. The classical case of systems in feedback form is used to illustrate the proposed controller design.

eess.SY↗

arXiv2015

Fate of the Kondo Effect and Impurity Quantum Phase Transitions Through the Lens of Fidelity Susceptibility

The Kondo effect is an ubiquitous phenomenon appearing at low temperature in quantum confined systems coupled to a continuous bath. Efforts in understanding and controlling it have triggered important developments across several disciplines of condensed matter physics. A recurring pattern in these studies is that the suppression of the Kondo effect often results in intriguing physical phenomena such as impurity quantum phase transitions or non-Fermi-liquid behavior. We show that the fidelity susceptibility is a sensitive indicator for such phenomena because it quantifies the sensitivity of the system's state with respect to its coupling to the bath. We demonstrate the power of fidelity susceptibility approach by using it to identify the crossover and quantum phase transitions in the one and two impurity Anderson models.

cond-mat.str-el↗

arXiv2015

Characterization and Architectural Implications of Big Data Workloads

Big data areas are expanding in a fast way in terms of increasing workloads and runtime systems, and this situation imposes a serious challenge to workload characterization, which is the foundation of innovative system and architecture design. The previous major efforts on big data benchmarking either propose a comprehensive but a large amount of workloads, or only select a few workloads according to so-called popularity, which may lead to partial or even biased observations. In this paper, on the basis of a comprehensive big data benchmark suite---BigDataBench, we reduced 77 workloads to 17 representative workloads from a micro-architectural perspective. On a typical state-of-practice platform---Intel Xeon E5645, we compare the representative big data workloads with SPECINT, SPECCFP, PARSEC, CloudSuite and HPCC. After a comprehensive workload characterization, we have the following observations. First, the big data workloads are data movement dominated computing with more branch operations, taking up to 92% percentage in terms of instruction mix, which places them in a different class from Desktop (SPEC CPU2006), CMP (PARSEC), HPC (HPCC) workloads. Second, corroborating the previous work, Hadoop and Spark based big data workloads have higher front-end stalls. Comparing with the traditional workloads i. e. PARSEC, the big data workloads have larger instructions footprint. But we also note that, in addition to varied instruction-level parallelism, there are significant disparities of front-end efficiencies among different big data workloads. Third, we found complex software stacks that fail to use state-of-practise processors efficiently are one of the main factors leading to high front-end stalls. For the same workloads, the L1I cache miss rates have one order of magnitude differences among diverse implementations with different software stacks.

cs.DC↗

arXiv2015

Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

cs.CV↗

arXiv2015

Benchmarking Big Data Systems: State-of-the-Art and Future Directions

The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.

cs.PF↗

arXiv2015

Parallel Spectral Clustering Algorithm Based on Hadoop

Spectral clustering and cloud computing is emerging branch of computer science or related discipline. It overcome the shortcomings of some traditional clustering algorithm and guarantee the convergence to the optimal solution, thus have to the widespread attention. This article first introduced the parallel spectral clustering algorithm research background and significance, and then to Hadoop the cloud computing Framework has carried on the detailed introduction, then has carried on the related to spectral clustering is introduced, then introduces the spectral clustering arithmetic Method of parallel and relevant steps, finally made the related experiments, and the experiment are summarized.

cs.DC↗