arXiv subjects

Long Cheng

Publications and source records attributed to Long Cheng.

At least 109 records · Page 6Linked to original sources

arXiv2020

Why Two-Dimensional Semiconductors Generally Have Low Electron Mobility

Atomically thin (two-dimensional, 2D) semiconductors have shown great potential as the fundamental building blocks for next-generation electronics. However, all the 2D semiconductors that have been experimentally made so far have room-temperature electron mobility lower than that of bulk silicon, which is not understood. Here, by using first-principles calculations and reformulating the transport equations to isolate and quantify contributions of different mobility-determining factors, we show that the universally low mobility of 2D semiconductors originates from the high 'density of scatterings,' which is intrinsic to the 2D material with a parabolic electron band. The density of scatterings characterizes the density of phonons that can interact with the electrons and can be fully determined from the electron and phonon band structures without knowledge of electron-phonon coupling strength. Our work reveals the underlying physics limiting the electron mobility of 2D semiconductors and offers a descriptor to quickly assess the mobility.

cond-mat.mtrl-sci↗

arXiv2020

Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning is still a time-consuming and resource-intensive process. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

cs.DC↗

arXiv2020

3D Object Detection and Tracking Based on Streaming Data

Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning. However, previous researches are mostly based on individual frames, leading to limited exploitation of information between frames. In this paper, we attempt to leverage the temporal information in streaming data and explore 3D streaming based object detection as well as tracking. Toward this goal, we set up a dual-way network for 3D object detection based on keyframes, and then propagate predictions to non-key frames through a motion based interpolation algorithm guided by temporal information. Our framework is not only shown to have significant improvements on object detection compared with frame-by-frame paradigm, but also proven to produce competitive results on KITTI Object Tracking Benchmark, with 76.68% in MOTA and 81.65% in MOTP respectively.

cs.CV↗

arXiv2020

Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications

Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people's daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users' privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to the app store. Voice-app developers are required to provide privacy policies to disclose their apps' data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies. In this paper, we conduct the first large-scale data analytics to systematically measure the effectiveness of privacy policies provided by voice-app developers on two mainstream VA platforms. We seek to understand the quality and usability issues of privacy policies provided by developers in the current app stores. We analyzed 64,720 Amazon Alexa skills and 2,201 Google Assistant actions. Our work also includes a user study to understand users' perspectives on VA's privacy policies. Our findings reveal a worrisome reality of privacy policies in two mainstream voice-app stores, where there exists a substantial number of problematic privacy policies. Surprisingly, Google and Amazon even have official voice-apps violating their own requirements regarding the privacy policy.

cs.CR↗

arXiv2020

Rethinking Defeasible Reasoning: A Scalable Approach

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks and social media. Analytics in terms of defeasible reasoning - for example for decision making - could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

cs.LO↗

arXiv2019

Notes on the Causal Structure in a Tensor Network

In this paper we attempt to understand Lorentzian tensor networks, as a preparation for constructing tensor networks that can describe more exotic backgrounds such as black holes. To define notions of reference frames and switching of reference frames on a tensor network, we will borrow ideas from the algebraic quantum field theory literature. With these definitions, we construct simple examples of Lorentzian tensor networks and solve the spectrum for a choice of ``inertial frame'' based on Gaussian models of fermions and integrable models. In particular, the tensor network can be viewed as a periodically driven Floquet system, that by-pass the ``doubling problem'' and gives rise to fermions with exactly linear dispersion relations. We will find that a boost operator connecting different inertial frames, and notions of ``Rindler observers'' can be defined, and that important physics in Lorentz invariant QFT, such as the Unruh effect, can be captured by such skeleton of spacetime. We find interesting subtleties when the same approach is directly applied to bosons -- the operator algebra contains commutators that take the wrong sign -- resembling bosons behind horizons.

hep-th↗

arXiv2019

Checking is Believing: Event-Aware Program Anomaly Detection in Cyber-Physical Systems

Securing cyber-physical systems (CPS) against malicious attacks is of paramount importance because these attacks may cause irreparable damages to physical systems. Recent studies have revealed that control programs running on CPS devices suffer from both control-oriented attacks (e.g., code-injection or code-reuse attacks) and data-oriented attacks (e.g., non-control data attacks). Unfortunately, existing detection mechanisms are insufficient to detect runtime data-oriented exploits, due to the lack of runtime execution semantics checking. In this work, we propose Orpheus, a new security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics. We first present a general method for reasoning cyber-physical execution semantics of a control program (i.e., causal dependencies between the physical context and program control flows), including the event identification and dependence analysis. As an instantiation of Orpheus, we then present a new program behavior model, i.e., the event-aware finite-state automaton (eFSA). eFSA takes advantage of the event-driven nature of CPS control programs and incorporates event checking in anomaly detection. It detects data-oriented exploits if a specific physical event is missing along with the corresponding event dependent state transition. We evaluate our prototype's performance by conducting case studies under data-oriented attacks. Results show that eFSA can successfully detect different runtime attacks. Our prototype on Raspberry Pi incurs a low overhead, taking 0.0001s for each state transition integrity checking, and 0.063s~0.211s for the cyber-physical contextual consistency checking.

cs.CR↗

arXiv2019

Exploitation Techniques and Defenses for Data-Oriented Attacks

Data-oriented attacks manipulate non-control data to alter a program's benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this SoK paper, we first map data-oriented exploits, including Data-Oriented Programming (DOP) attacks, to their assumptions/requirements and attack capabilities. We also compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. Then, we experimentally assess the feasibility of a detection approach that is based on the Intel Processor Trace (PT) technology. PT only traces control flows, thus, is generally believed to be not useful for data-oriented security. However, our work reveals that data-oriented attacks (in particular the recent DOP attacks) may generate side-effects on control-flow behavior in multiple dimensions, which manifest in PT traces. Based on this evaluation, we discuss challenges for building deployable data-oriented defenses and open research questions.

cs.CR↗

arXiv2017

Evolution of Weyl orbit and quantum Hall effect in Dirac semimetal Cd3As2

Owing to the coupling between open Fermi arcs on opposite surfaces, topological Dirac semimetals exhibit a new type of cyclotron orbit in the surface states known as Weyl orbit. Here, by lowering the carrier density in Cd3As2 nanoplates, we observe a crossover from multiple- to single-frequency Shubnikov-de Haas (SdH) oscillations when subjected to out-of-plane magnetic field, indicating the dominant role of surface transport. With the increase of magnetic field, the SdH oscillations further develop into quantum Hall state with non-vanishing longitudinal resistance. By tracking the oscillation frequency and Hall plateau, we observe a Zeeman-related splitting and extract the Landau level index as well as sub-band number. Different from conventional two-dimensional systems, this unique quantum Hall effect may be related to the quantized version of Weyl orbits. Our results call for further investigations into the exotic quantum Hall states in the low-dimensional structure of topological semimetals.

cond-mat.mtrl-sci↗

arXiv2017

Effects of biaxial strain on the electronic structures and band topologies of group-V elemental monolayers

Using first-principles calculations, we systematically investigate the electronic structures and band topologies of four kinds of group-V elemental (P, As, Sb and Bi) monolayers with buckled honeycomb structure. It is found that all these monolayers can change from semiconducting to semimetallic under compressive strain. If a tensile strain is however applied, the P, As and Sb monolayers undergo phase transition from topologically trivial to non-trivial regime, whereas the topological insulating nature of Bi monolayer remains unchanged. With tunability of the band gaps and band topologies, it can be expected that these elemental monolayers could be promising candidates for future optoelectronic and spintronic applications.

cond-mat.mes-hall↗

arXiv2016

Relative Entropy, Mixed Gauge-Gravitational Anomaly and Causality

In this note we explored the holographic relative entropy in the presence of the 5d Chern-Simons term, which introduces a mixed gauge-gravity anomaly to the dual CFT. The theory trivially satisfies an entanglement first law. However, to quadratic order in perturbations of the stress tensor $T$ and current density $J$, there is a mixed contribution to the relative entropy bi-linear in $T$ and $J$, signalling a potential violation of the positivity of the relative entropy. Miraculously, the term vanishes up to linear order in a derivative expansion. This prompted a closer inspection on a different consistency check, that involves time-delay of a graviton propagating in a charged background, scattered via a coupling supplied by the Chern-Simons term. The analysis suggests that the time-delay can take either sign, potentially violating causality for any finite value of the CS coupling.

hep-th↗

arXiv2016

Maximizing the thermoelectric performance of topological insulator Bi2Te3 films in the few-quintuple layer regime

Using first-principles calculations and Boltzmann theory, we explore the feasibility to maximize the thermoelectric figure of merit (ZT) of topological insulator Bi2Te3 films in the few-quintuple layer regime. We discover that the delicate competitions between the surface and bulk contributions, coupled with the overall quantum size effects, lead to a novel and generic non-monotonous dependence of ZT on the film thickness. In particular, when the system crosses into the topologically non-trivial regime upon increasing the film thickness, the much longer surface relaxation time associated with the robust nature of the topological surface states results in a maximal ZT value, which can be further optimized to ~2.0 under physically realistic conditions. We also reveal the appealing potential of bridging the long-standing ZT asymmetry of p- and n-type Bi2Te3 systems.

cond-mat.mes-hall↗

arXiv2015

First Law of Entanglement Entropy vs Black Hole Entropy in an Anomalous Theory

In this note we explore the validity of the first law of entanglement entropy in the context of the topologically massive gravity (TMG). We found that the variation of the holographic entanglement entropy under perturbation from the pure AdS background satisfies the first law upon imposing the bulk equations of motion in a given time slice, despite the appearance of instabilities in the bulk for generic gravitationl Chern-Simons coupling $μ$. The black hole entropy is different from the holographic entanglement entropy in a general boosted frame. This discrepancy however do not affect the entanglement first law.

hep-th↗

arXiv2015

Drag force of Anisotropic plasma at finite $U(1)$ chemical potential

We perform the calculation of drag force acting on a massive quark moving through an anisotropic ${\cal N}=4$ SU(N)= 4 SU(N) Super Yang-Mills plasma in the presence of a U(1) chemical potential. We present the numerical results for any value of anisotropy and arbitrary direction of the quark velocity with respect to the direction of anisotropy. We find the effect of chemical potential or charge density will enhance the drag force for the our charged solution.

hep-th↗

arXiv2015

Containment Control of Multi-Agent Systems with Dynamic Leaders Based on a $PI^n$-Type Approach

This paper studies the containment control problem of multi-agent systems with multiple dynamic leaders in both the discrete-time domain and the continuous-time domain. The leaders' motions are described by $(n-1)$-order polynomial trajectories. This setting makes practical sense because given some critical points, the leaders' trajectories are usually planned by the polynomial interpolations. In order to drive all followers into the convex hull spanned by the leaders, a $PI^n$-type ($P$ and $I$ are short for {\it Proportion} and {\it Integration}, respectively; $I^n$ implies that the algorithm includes high-order integral terms) containment algorithm is proposed. It is theoretically proved that the $PI^n$-type containment algorithm is able to solve the containment problem of multi-agent systems where the followers are described by any order integral dynamics. Compared with the previous results on the multi-agent systems with dynamic leaders, the distinguished features of this paper are that: (1) the containment problem is studied not only in the continuous-time domain but also in the discrete-time domain while most existing results only work in the continuous-time domain; (2) to deal with the leaders with the $(n-1)$-order polynomial trajectories, existing results require the follower's dynamics to be $n$-order integral while the followers considered in this paper can be described by any-order integral; and (3) the "sign" function is not employed in the proposed algorithm, which avoids the chattering phenomenon. Furthermore, in order to illustrate the practical value of the proposed approach, an application, the containment control of multiple mobile robots is studied. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2015

On Convergence Rate of Leader-Following Consensus of Linear Multi-Agent Systems with Communication Noises

This note further studies the previously proposed consensus protocol for linear multi-agent systems with communication noises in [15], [16]. Each agent is allowed to have its own time-varying gain to attenuate the effect of communication noises. Therefore, the common assumption in most references that all agents have the same noise-attenuation gain is not necessary. It has been proved that if all noise-attenuation gains are infinitesimal of the same order, then the mean square leader-following consensus can be reached. Furthermore, the convergence rate of the multi-agent system has been investigated. If the noise-attenuation gains belong to a class of functions which are bounded above and below by $t^{-β}$ $(β\in(0,1))$ asymptotically, then the states of all follower agents are convergent in mean square to the leader's state with the rate characterized by a function bounded above by $t^{-β}$ asymptotically.

eess.SY↗

arXiv2015

Thermoelectric DC conductivities with momentum dissipation from higher derivative gravity

We present a mechanism of momentum relaxation in higher derivative gravity by adding linear scalar fields to the Gauss-Bonnet theory. We analytically computed all of the DC thermoelectric conductivities in this theory by adopting the method given by Donos and Gauntlett in [arXiv:1406.4742]. The results show that the DC electric conductivity is not a monotonic function of the effective impurity parameter $β$: in the small $β$ limit, the DC conductivity is dominated by the coherent phase, while for larger $β$, pair creation contribution to the conductivity becomes dominant, signaling an incoherent phase. In addition, the DC heat conductivity is found independent of the Gauss-Bonnet coupling constant.

hep-th↗

arXiv2014

Anisotropic plasma at finite $U(1)$ chemical potential

We present a type IIB supergravity solution dual to a spatially anisotropic $\mathcal{N}=4$ super Yang-Mills plasma at finite $U(1)$ chemical potential and finite temperature. The effective five-dimensional gravitational theory is a consistent Einstein-Maxwell-dilaton-Axion truncation of the gauged supergravity. We obtain the solutions both numerically and analytically. We study the phase structure and thermodynamic instabilities of the solution, and find new instabilities independent of the renormalization scheme.

hep-th↗