arXiv subjects

Peng Liu

Publications and source records attributed to Peng Liu.

At least 397 records · Page 22Linked to original sources

arXiv2019

Recalibration of the binding energy of hypernuclei measured in emulsion experiments and its implications

The $Λ$ separation energy for $Λ$-hypernuclei, denoted $B_Λ$, measured in 1967, 1968, and 1973 are recalibrated using the current best mass estimates for particles and nuclei. The recalibrated $B_Λ$ are systematically larger (except in the case of $^6_Λ$He) than the original published values by about 100 keV. The effect of this level of recalibration is very important for light hypernuclei, especially for the hypertriton. The early $B_Λ$ values measured in 1967, 1968, and 1973 are widely used in theoretical research, and the new results provide better constraints on the conclusions from such studies.

nucl-ex↗

arXiv2019

Entanglement of Purification in Holographic Systems

The holographic entanglement of purification (EoP) in AdS$_{4}$ and AdS-RN black hole backgrounds is studied. We develop an algorithm to compute the EoP for bipartite configuration with infinitely long strips. The temperature behavior of EoP is revealed for small, intermediate and large configurations: EoP monotonically increases with the temperature for small configurations; while for intermediate configurations, EoP is configuration-dependent; EoP vanishes for large configurations. Our numerical results verify some important inequalities of EoP, which we also prove geometrically in Poincaré coordinate.

hep-th↗

arXiv2019

The Effect of Anisotropy on Holographic Entanglement Entropy and Mutual Information

We study the effect of anisotropy on holographic entanglement entropy (HEE) and holographic mutual information (MI) in the Q-lattice model, by exploring the HEE and MI for infinite strips along arbitrary directions. We find that the lattice always enhances the HEE. The MI, however, is enhanced by lattice for large sub-regions; while for small sub-regions, the MI is suppressed by the lattice. We also discuss how these phenomena result from the deformation of geometry caused by Q-lattices.

hep-th↗

arXiv2019

A speculation about a puzzled result in energy spectrum of cosmic-ray electrons around TeV energies

Nuclear Coulomb potential at completely ionized and extremely thin atmosphere can leaks to a macroscopic spatial scale. This effect is used to explain the difference between the energy spectra of cosmic-ray electrons around $1 TeV$ measured by different experimental groups. The result inspires us to review the traditional electromagnetic shower theory at the extreme conditions. It is also reminds us that the energy spectrum of cosmic-ray electrons, which are measured by Fermi-LAT and DAMPE at a higher altitude is more closer to a true signature.

astro-ph.HE↗

arXiv2019

Bias induced spin state transition mediated by electron excitations

Recent experiments reported that spin-state transitions were realized by applying bias voltages. But these bias-induced spin state transitions (BISSTs) are not fully understood, especially the mechanism. It is well known that the metal-to-ligand charge transfer excitation (MLCT) and the metal-centered excitation (MC) activated by light radiation can induce the transition from low spin (LS) to high spin (HS) and that from HS to LS. Moreover, electronic excitations are accessible by inelastic cotunneling in molecular junctions with bias voltages applied. Based on these two experimental facts, we propose the MLCT basically leads to the BISST from LS to HS, and the MC results in the BISST from HS to LS. The rationality of the mechanism is demonstrated by comparing first-principles results and experimental observations. The calculated voltage threshold for activating the MLCT (MC) is close to the experimental voltage for observing the BISST from LS to HS (from HS to LS). The activation of MLCT (MC) depends on the bias polarity, which can explain the bias-polarity dependence of BISST in the experiment. Our study is important for further design of molecular spintronic devices working on spin state transition.

cond-mat.mes-hall↗

arXiv2019

DurIAN: Duration Informed Attention Network For Multimodal Synthesis

In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously. The key component of this system is the Duration Informed Attention Network (DurIAN), an autoregressive model in which the alignments between the input text and the output acoustic features are inferred from a duration model. This is different from the end-to-end attention mechanism used, and accounts for various unavoidable artifacts, in existing end-to-end speech synthesis systems such as Tacotron. Furthermore, DurIAN can be used to generate high quality facial expression which can be synchronized with generated speech with/without parallel speech and face data. To improve the efficiency of speech generation, we also propose a multi-band parallel generation strategy on top of the WaveRNN model. The proposed Multi-band WaveRNN effectively reduces the total computational complexity from 9.8 to 5.5 GFLOPS, and is able to generate audio that is 6 times faster than real time on a single CPU core. We show that DurIAN could generate highly natural speech that is on par with current state of the art end-to-end systems, while at the same time avoid word skipping/repeating errors in those systems. Finally, a simple yet effective approach for fine-grained control of expressiveness of speech and facial expression is introduced.

cs.CL↗

arXiv2019

Sojourn times of Gaussian processes with trend

We derive exact tail asymptotics of sojourn time above the level $u\geq 0$ $$ \mathbb{P}\left(v(u)\int_0^T \mathbb{I}(X(t)-ct>u)d t>x\right), \quad x\geq 0 $$ as $u\to\infty$, where $X$ is a Gaussian process with continuous sample paths, $c>0$, $v(u)$ is a positive function of $u$ and $T\in (0,\infty]$. Additionally, we analyze asymptotic distributional properties of $$τ_u(x):=\inf\left\{t\geq 0: v(u) \int_0^t \mathbb{I}(X(s)-cs>u)d s>x\right\}, $$ as $u\to\infty$, $x\geq 0$, where $\inf\emptyset=\infty$. The findings of this contribution are illustrated by a detailed analysis of a class of Gaussian processes with stationary increments and a family of self-similar processes.

math.PR↗

arXiv2019

Good Motive but Bad Design: Why ARM MPU Has Become an Outcast in Embedded Systems

As more and more embedded devices are connected to the Internet, leading to the emergence of Internet-of-Things (IoT), previously less tested (and insecure) devices are exposed to miscreants. To prevent them from being compromised, the memory protection unit (MPU), which is readily available on many devices, has the potential to become a free lunch for the defenders. To our surprise, the MPU is seldom used by real-world products. The reasons are multi-fold. While there are non-technical reasons such as compatibility issues, more importantly, we found that MPU brings virtually no security enhancement at the expense of decreased performance and responsiveness. In this work, we investigate the MPU adoption in major real-time operating systems (RTOSs), in particular, the FreeRTOS, and try to pinpoint the fundamental reasons to explain why MPU is not favored. We hope our findings can inspire new remedial solutions to change the situation. We also review the latest MPU design and provide technical suggestions to build more secure embedded systems.

cs.CR↗

arXiv2019

Learning Privately over Distributed Features: An ADMM Sharing Approach

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters among parties is prohibited due to privacy concerns. We propose an ADMM sharing framework to approach risk minimization over distributed features, where each party only needs to share a single value for each sample in the training process, thus minimizing the data leakage risk. We establish convergence and iteration complexity results for the proposed parallel ADMM algorithm under non-convex loss. We further introduce a novel differentially private ADMM sharing algorithm and bound the privacy guarantee with carefully designed noise perturbation. The experiments based on a prototype system shows that the proposed ADMM algorithms converge efficiently in a robust fashion, demonstrating advantage over gradient based methods especially for data set with high dimensional feature spaces.

cs.LG↗

arXiv2019

Tail Asymptotic Behavior of the supremum of a class of chi-square processes

In this paper, we analyze a multivariate counterpart of the generalized weighted Kolmogorov-Smirnov statistic, which is the supremum of weighted locally stationary chi-square process over non-compact interval. The boundedness and the exact tail asymptotic behavior of the statistics are derived. We illustrate our findings by two examples where the statistic is defined by Brownian bridge and fractional Brownian motion respectively.

math.PR↗

arXiv2019

Discovering and Understanding the Security Hazards in the Interactions between IoT Devices, Mobile Apps, and Clouds on Smart Home Platforms

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and blackbox testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms.

cs.CR↗

arXiv2019

SvTPM: A Secure and Efficient vTPM in the Cloud

Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to provide virtual root-of-trust for virtual machines. Unfortunately, current state-of-the-art vTPM implementations are suffering from confidential data leakage and high performance overhead. In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud. SvTPM offers strong isolation protection, so that cloud tenants or even cloud administrators cannot get vTPM's private keys or any other sensitive data. In SvTPM, we identify and solve a couple of critical security challenges for vTPM protection with SGX, such as NVRAM replacement attack, rollback attacks, trust establishment, and a fine-grained trusted clock. We implement a prototype of SvTPM on both QEMU and KVM. Performance evaluation results show that SvTPM achieves orders of magnitude of performance gains comparing to the vTPMs protected with physical TPM. The launch time of SvTPM is 2600$\times$ faster than vTPMs built upon hardware TPM. In the micro-benchmarks evaluation, we find that the command execution latency of SvTPM is smaller than or equal to the existing schemes.

cs.CR↗

arXiv2019

Extremes of Spherical Fractional Brownian Motion

Let $\{B_β(x), x \in \mathbb{S}^N\}$ be a fractional Brownian motion on the $N$-dimensional unit sphere $\mathbb{S}^N$ with Hurst index $β$. We study the excursion probability $\mathbb{P}\{\sup_{x\in T} B_β(x) > u \}$ and obtain the asymptotics as $u\to \infty$, where $T$ can be the entire sphere $\mathbb{S}^N$ or a geodesic disc on $\mathbb{S}^N$.

math.PR↗

arXiv2018

AME Blockchain: An Architecture Design for Closed-Loop Fluid Economy Token System

In this white paper, we propose a blockchain-based system, named AME, which is a decentralized infrastructure and application platform with enhanced security and self-management properties. The AME blockchain technology aims to increase the transaction throughput by adopting various optimizations in network transport and storage layers, and to enhance smart contracts with AI algorithm support. We introduce all major technologies adopted in our system, including blockchain, distributed storage, P2P network, service application framework, and data encryption. To properly provide a cohesive, concise, yet comprehensive introduction to the AME system, we mainly focus on describing the unique definitions and features that guide the system implementation.

cs.DC↗

arXiv2018

Towards Scalable Subscription Aggregation and Real Time Event Matching in a Large-Scale Content-Based Network

Although many scalable event matching algorithms have been proposed to achieve scalability for large-scale content-based networks, content-based publish/subscribe networks (especially for large-scale real time systems) still suffer performance deterioration when subscription scale increases. While subscription aggregation techniques can be useful to reduce the amount of subscription dissemination traffic and the subscription table size by exploiting the similarity among subscriptions, efficient subscription aggregation is not a trivial task to accomplish. Previous research works have proved that it is either a NP-Complete or a co-NP complete problem. In this paper, we propose DLS (Discrete Label Set), a novel subscription representation model, and design algorithms to achieve the mapping from traditional Boolean predicate model to the DLS model. Based on the DLS model, we propose a subscription aggregation algorithm with O(1) time complexity in most cases, and an event matching algorithm with O(1) time complexity. The significant performance improvement is at the cost of memory consumption and controllable false positive rate. Our theoretical analysis shows that these algorithms are inherently scalable and can achieve real time event matching in a large-scale content-based publish/subscribe network. We discuss the tradeoff between memory, false positive rate and partition granules of content space. Experimental results show that proposed algorithms achieve expected performance. With the increasing of computer memory capacity and the dropping of memory price, more and more large-scale real time applications can benefit from our proposed DLS model, such as stock quote distribution, earthquake monitoring, and severe weather alert.

cs.DC↗

arXiv2018

Model predictive trajectory optimization and tracking for on-road autonomous vehicles

Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking control process which faithfully tracks planned trajectories. In this paper, a control scheme is presented which first optimizes a planned trajectory and then tracks the optimized trajectory using a feedback-feedforward controller. The feedforward element is calculated in a model predictive manner with a cost function focusing on driving performance. Stability of the error dynamic is then guaranteed by the design of the feedback-feedforward controller. The tracking performance of the control system is tested in a realistic simulated scenario where the control system must track an evasive lateral maneuver. The proposed controller performs well in simulation and can be easily adapted to different dynamic vehicle models. The uniqueness of the solution to the control synthesis eliminates any nondeterminism that could arise with switching between numerical solvers for the underlying mathematical program.

cs.RO↗

arXiv2018

Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications

Linear temporal logic and automaton-based run-time verification provide a powerful framework for designing task and motion planning algorithms for autonomous agents. The drawback to this approach is the computational cost of operating on high resolution discrete abstractions of continuous dynamical systems. In particular, the computational bottleneck that arises is converting perceived environment variables into a labeling function on the states of a Kripke structure or analogously the transitions of a labeled transition system. This paper presents the design and empirical evaluation of an approach to constructing the labeling function that exposes a large degree of parallelism in the operation as well as efficient memory access patterns. The approach is implemented on a commodity GPU and empirical results demonstrate the efficacy of the labeling technique for real-time planning and decision-making.

cs.AI↗

arXiv2018

Protecting User Privacy Based on Secret Sharing with Error Tolerance for Big Data in Smart Grid

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications usually is sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with error tolerance in smart grid, which ensures that the control center gets the integrated data without revealing users' privacy. Meanwhile, we also consider the differential privacy and error tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

cs.CR↗