arXiv subjects

Peng Cheng

Publications and source records attributed to Peng Cheng.

At least 271 records · Page 15Linked to original sources

arXiv2019

Stochastic gravitational waves from spin-3/2 fields -- Hunting SUSY in the sky

Stochastic gravitational waves can be produced during the preheating when out-of-equilibrium particles are produced with an anisotropic stress-tensor. We discuss the case where these particles carry spin 3/2. We compute the spectrum of the gravitational waves generated by the transverse and longitudinal components. We find a different scaling of the spectrum near the peak and the longitudinal components lead to an enhancement when compared to spin-1/2 fermions with Yukawa couplings. We note, as expected, that the corresponding typical frequency is too high for the current observation and calls for ultra-high frequency gravitational wave detectors in the future.

hep-ph↗

arXiv2019

Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation

With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML framework with two novel features: First, we remove the assumption commonly made in the literature that the users trust the server collecting their data. Second, the framework provides heterogeneous privacy for users depending on data's sensitive levels and servers' trust degrees. The challenging issue is to keep the accumulation of privacy losses over ADMM iterations minimal. In the proposed framework, a local randomization approach, which is differentially private, is adopted to provide users with self-controlled privacy guarantee for the most sensitive information. Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users' less sensitive information and strengthens the privacy protection of the most sensitive information. We provide detailed analyses on the performance of the trained model according to its generalization error. Finally, we conduct extensive experiments using real-world datasets to validate the theoretical results and evaluate the classification performance of the proposed framework.

cs.LG↗

arXiv2019

A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels

Multi-parameter cognition in a cognitive radio network (CRN) provides a more thorough understanding of the radio environments, and could potentially lead to far more intelligent and efficient spectrum usage for a secondary user. In this paper, we investigate the multi-parameter cognition problem for a CRN where the primary transmitter (PT) radiates multiple transmit power levels, and propose a learning-based two-stage spectrum sharing strategy. We first propose a data-driven/machine learning based multi-level spectrum sensing scheme, including the spectrum learning (Stage I) and prediction (the first part in Stage II). This fully blind sensing scheme does not require any prior knowledge of the PT power characteristics. Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation. The periodic structure features a fixed prediction interval, while the non-periodic one dynamically determines the interval with a proposed reinforcement learning algorithm to further improve the alignment metric. Finally, we extend the prediction-transmission structure to an online scenario, where the number of PT power levels might change as a consequence of PT adapting to the environment fluctuation or quality of service variation. The simulation results demonstrate the effectiveness of the proposed strategy in various scenarios.

eess.SP↗

arXiv2019

Finite volume effects on quarkonium dissociation temperature in an impenetrable QGP sphere

The system of a quarkonium confined by an impenetrable spherical cavity filled with a hot quantum chromodynamics (QCD) medium is studied by solving the Schrödinger equation. This is the first time this issue has been raised for discussion. The Schrödinger equation with an appropriate boundary condition of a quarkonium in an impenetrable cavity filled with a hot medium is derived. The numerical results are obtained with the help of Gaussian Expansion Method. Binding energies and radii of the ground and low-excited states are obtained as a function of the medium temperature and the cavity radius. We find the behaviour of quarkonium in this cavity is different from that in infinite space. Our results show that the quarkonium dissociation temperature decreases as the cavity radius decreases and the finite volume effects on the ground state are more obvious than on the excited states. We also find that the less mass of the constituents and the bigger radius of the quarkonium lead the finite volume effects to become more obvious.

nucl-th↗

arXiv2019

The effect of non-superconducting dopants (Mn, V, Cr and Cu) on the nematic fluctuations in iron-based superconductors

We have systematically studied the nematic susceptibility in non-superconducting Ba(Fe$_{1-x}$TM$_{x}$)$_2$As$_2$ (TM = Cr, Mn, V and Cu) by measuring the uniaxial pressure dependence of the resistivity along the Fe-As-Fe direction. The nematic susceptibilities in all samples show the Curie-Weiss-like behavior at high temperatures, where the nematic Curie constant $A_n$ can be derived, similar to the Curie constant in a paramagnetism. While all these dopants do not introduce superconductivity in BaFe$_2$As$_2$, their effects on nematic fluctuations are different. In Mn, Cr and V doped samples, $|A_n|$ decreases significantly with the increasing doping level. On the other hand, $|A_n|$ increases dramatically with Cu doping, similar to the superconducting Ni-doped BaFe$_2$As$_2$. However, the nematic susceptibility is suppressed at low temperatures for $x$ larger than $0.04$, which may be related to the short-range antiferromagnetic order that survives up to very high doping level. Doping Mn, Cr and Cu into the optimally-doped superconducting BaFe$_2$(As$_{0.69}$P$_{0.31}$)$_2$ also strongly reduces $|A_n|$. Compared with those systems that clearly exhibit superconductivity, such as Ni, K or P doped samples, our results suggest a strong connection between the nematic and spin degrees of freedom. Moreover, the reason of the suppression of superconductivity by dopants such as Cr, Mn, V and Cu may be correlated with the suppression of nematic fluctuations.

cond-mat.supr-con↗

arXiv2019

Domain wall pinning and hard magnetic phase in Co-doped bulk single crystalline Fe3GeTe2

We report the effects of cobalt doping on the magnetic properties of two-dimensional van der Waals ferromagnet Fe3GeTe2. Single crystals of (Fe{1-x}Cox)3GeTe2 with x=0-0.78 were successfully synthesized and characterized with x-ray diffraction, energy dispersive x-ray spectroscopy and magnetization measurements. Both the Curie-Weiss temperature and ferromagnetic (FM) ordered moment of Fe3GeTe2 are gradually suppressed upon Co doping. A kink in zero-field-cooling low field M(T) curve which is previously explained as an antiferromagnetic transition is observed for samples with x=0-0.58. Our detailed magnetization measurements and theoretical calculations strongly suggest that this kink is originated from the pinning of magnetic domain walls. The domain pinning effects are suddenly enhanced when the doping concentration of cobalt is around 50%, both the coercive field Hc and the magnetic remanence to saturated magnetization ratio MR/MS are largely improved and a hard magnetic phase emerges in bulk single crystal samples. The strong doping dependent magnetic properties suggest more spintronic applications of Fe3GeTe2.

cond-mat.mtrl-sci↗

arXiv2019

One Dimensional Nearly Free Electron States in Borophene

Two-dimensional boron (borophene) is featured by its structural polymorphs and distinct in-plane anisotropy, opening opportunities to achieve tailored electronic properties by intermixing different phases. Here, using scanning tunneling spectroscopy combined with first-principles calculations, delocalized one-dimensional nearly free electron states (NFE) in the (2,3) or \b{eta}12 borophene sheet on the Ag(111) surface were observed. The NFE states emerge from a line defect in the borophene, manifested as a structural unit of the (2,2) or \c{hi}3 sheet, which creates an in-plane potential well that shifts the states toward the Fermi level. The NFE states are held in the 2D plane of borophene, rather than in the vacuum region as observed in other nanostructures. Furthermore the borophene can provide a rare prototype to further study novel NFE behaviors, which may have potential applications on transport or field emission nanodevices based on boron.

cond-mat.mtrl-sci↗

arXiv2019

BotGraph: Web Bot Detection Based on Sitemap

The web bots have been blamed for consuming large amount of Internet traffic and undermining the interest of the scraped sites for years. Traditional bot detection studies focus mainly on signature-based solution, but advanced bots usually forge their identities to bypass such detection. With increasing cloud migration, cloud providers provide new opportunities for an effective bot detection based on big data to solve this issue. In this paper, we present a behavior-based bot detection scheme called BotGraph that combines sitemap and convolutional neural network (CNN) to detect inner behavior of bots. Experimental results show that BotGraph achieves ~95% recall and precision on 35-day production data traces from different customers including the Bing search engine and several sites.

cs.CR↗

arXiv2019

NetKernel: Making Network Stack Part of the Virtualized Infrastructure

This paper presents a system called NetKernel that decouples the network stack from the guest virtual machine and offers it as an independent module. NetKernel represents a new paradigm where network stack can be managed as part of the virtualized infrastructure. It provides important efficiency benefits: By gaining control and visibility of the network stack, operator can perform network management more directly and flexibly, such as multiplexing VMs running different applications to the same network stack module to save CPU cores, and enforcing fair bandwidth sharing with distributed congestion control. Users also benefit from the simplified stack deployment and better performance. For example mTCP can be deployed without API change to support nginx natively, and shared memory networking can be readily enabled to improve performance of colocated VMs. Testbed evaluation using 100G NICs shows that NetKernel preserves the performance and scalability of both kernel and userspace network stacks, and provides the same isolation as the current architecture.

cs.NI↗

arXiv2019

Effects of Vanadium doping on BaFe2As2

We report an investigation of the structural, magnetic and electronic properties of Ba(Fe(1-x)V(x))2As2 using x-ray, transport, magnetic susceptibility and neutron scattering measurements. The vanadium substitutions in Fe sites are possible up to 40\%. Hall effect measurements indicate strong hole-doping effect through V doping, while no superconductivity is observed in all samples down to 2K. The antiferromagnetic and structural transition temperature of BaFe2As2 is gradually suppressed to finite temperature then vanishes at x=0.245 with the emergence of spin glass behavior, suggesting an avoided quantum critical point (QCP). Our results demonstrate that the avoided QCP and spin glass state which were previously reported in the superconducting phase of Co/Ni-doped BaFe2As2 can also be realized in non-superconducting Ba(Fe(1-x)V(x))2As2.

cond-mat.supr-con↗

arXiv2019

WIDER Face and Pedestrian Challenge 2018: Methods and Results

This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.

cs.CV↗

arXiv2019

Topological proximity effects in a Haldane-graphene bilayer system

We reveal a proximity effect between a topological band (Chern) insulator described by a Haldane model and spin-polarized Dirac particles of a graphene layer. Coupling weakly the two systems through a tunneling term in the bulk, the topological Chern insulator induces a gap and an opposite Chern number on the Dirac particles at half-filling resulting in a sign flip of the Berry curvature at one Dirac point. We study different aspects of the bulk-edge correspondence and present protocols to observe the evolution of the Berry curvature as well as two counter-propagating (protected) edge modes with different velocities. In the strong-coupling limit, the energy spectrum shows flat bands. Therefore we build a perturbation theory and address further the bulk-edge correspondence. We also show the occurrence of a topological insulating phase with Chern number one when only the lowest band is filled. We generalize the effect to Haldane bilayer systems with asymmetric Semenoff masses. We propose an alternative definition of the topological invariant on the Bloch sphere.

cond-mat.mes-hall↗

arXiv2019

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities. UAV equipped with vision techniques, could be leveraged to establish navigation autonomous control for UAV itself. Also, object detection from UAV could be used to broaden the utilization of drone to provide ubiquitous surveillance and monitoring services towards military operation, urban administration and agriculture management. As the data-driven technologies evolved, machine learning algorithm, especially the deep learning approach has been intensively utilized to solve different traditional computer vision research problems. Modern Convolutional Neural Networks based object detectors could be divided into two major categories: one-stage object detector and two-stage object detector. In this study, we utilize some representative CNN based object detectors to execute the computer vision task over Stanford Drone Dataset (SDD). State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.

cs.CV↗

arXiv2018

Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach

Spectrum sensing in a large-scale heterogeneous network is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. To tackle this problem, in this paper, we propose a new framework based on Bayesian machine learning. We exploit the mobility of multiple SUs to simultaneously collect spectrum sensing data, and cooperatively derive the global spectrum states. We first develop a novel non-parametric Bayesian learning model, referred to as beta process sticky hidden Markov model (BP-SHMM), to capture the spatial-temporal correlation in the collected spectrum data, where SHMM models the latent statistical correlation within each mobile SU's time series data, while BP realizes the cooperation among multiple SUs. Bayesian inference is then carried out to automatically infer the heterogeneous spectrum states. Based on the inference results, we also develop a new algorithm with a refinement mechanism to predict the spectrum availability, which enables a newly joining SU to immediately access the unoccupied frequency band without sensing. Simulation results show that the proposed framework can significantly improve spectrum sensing performance compared with the existing spectrum sensing techniques.

cs.IT↗

arXiv2018

Planar Hall effect in the Dirac semimetal PdTe2

We report the synthesis and magneto-transport measurements on the single crystal of Dirac semimetal PdTe$_2$. The de Haas-van Alphen oscillations with multiple frequencies have been clearly observed, from which the small effective masses and nontrivial Berry phase are extracted, implying the possible existence of the Dirac fermions in PdTe$_2$. The planar Hall effect and anisotropic longitudinal resistivity originating from the chiral anomaly and nontrivial Berry phase are observed, providing strong evidence for the nontrivial properties in PdTe$_2$. With the increase of temperature up to 150 K, planar Hall effect still remains. The possible origin of mismatch between experimental results and theoretical predictions is also discussed.

cond-mat.mtrl-sci↗

arXiv2018

Task Assignment on Spatial Crowdsourcing [Experiments and Analyses] (Technical Report)

Recently, with the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions, and workers need to physically move to specified locations to conduct tasks. Many works have studied task assignment problems in spatial crowdsourcing, however, their problem settings are different from each other. Thus, it is hard to compare the performances of existing algorithms on task assignment in spatial crowdsourcing. In this paper, we present a comprehensive experimental comparison of most existing algorithms on task assignment in spatial crowdsourcing. Specifically, we first give general definitions about spatial workers and spatial tasks based on definitions in the existing works such that the existing algorithms can be applied on the same synthetic and real data sets. Then, we provide an uniform implementation for all the tested algorithms of task assignment problems in spatial crowdsourcing (open sourced). Finally, based on the results on both synthetic and real data sets, we discuss the strengths and weaknesses of tested algorithms, which can guide future research on the same area and practical implementations of spatial crowdsourcing systems.

cs.DB↗

arXiv2018

SonarSnoop: Active Acoustic Side-Channel Attacks

We report the first active acoustic side-channel attack. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smart phone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim's finger movements can be inferred to steal Android phone unlock patterns. In our empirical study, the number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 Android phone can be reduced by up to 70% using this novel acoustic side-channel. Our approach can be easily applied to other application scenarios and device types. Overall, our work highlights a new family of security threats.

cs.CR↗

arXiv2018

Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches

The fifth-generation cellular mobile networks are expected to support mission critical ultra-reliable low latency communication (URLLC) services in addition to the enhanced mobile broadband applications. This article first introduces three emerging mission critical applications of URLLC and identifies their requirements on end-to-end latency and reliability. We then investigate the various sources of end-to-end delay of current wireless networks by taking the 4G Long Term Evolution (LTE) as an example. Subsequently, we propose and evaluate several techniques to reduce the end-to-end latency from the perspectives of error control coding, signal processing, and radio resource management. We also briefly discuss other network design approaches with the potential for further latency reduction.

cs.IT↗