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Peng Liu

Publications and source records attributed to Peng Liu.

At least 451 records · Page 25Linked to original sources
arXiv2017

Attack-resilient Estimation of Switched Nonlinear Stochastic Cyber-Physical Systems

This paper studies attack-resilient estimation of a class of switched nonlinear systems subject to stochastic noises. The systems are threatened by both of signal attacks and switching attacks. The problem is formulated as the joint estimation of states, attack vectors and modes of hidden-mode switched systems. We propose an estimation algorithm which is composed of a bank of state and attack vector estimators and a mode estimator. The mode estimator selects the most likely mode based on modes' posterior probabilities induced by the discrepancies between obtained outputs and predicted outputs. We formally analyze the stability of estimation errors in probability for the proposed estimator associated with the true mode when the hidden mode is time-invariant but remains unknown. For hidden-mode switched linear systems, we discuss a way to reduce computational complexity which originates from unknown signal attack locations. Lastly, we present numerical simulations on the IEEE 68-bus test system to show the estimator performance for time-varying modes with a regular mode set and a reduced mode set.

math.OC↗
arXiv2017

Y-system for $γ$-deformed ABJM Theory

We investigate the integrable aspects of the planar $γ$-deformed ABJM theory and propose the twisted asymptotic Bethe ansatz equations. A more general method through a twisted generating functional is discussed, based on which, the asymptotic large L solution of Y-system is modified in order to match the asymptotic Bethe ansatz equations. Several applications of our method were made in the $sl(2)$-like sector and some important examples in $β$-deformed ABJM are presented as well.

hep-th↗
arXiv2017

Note on the butterfly effect in holographic superconductor models

In this note we remark that the butterfly effect can be used to diagnose the phase transition of superconductivity in a holographic framework. Specifically, we compute the butterfly velocity in a charged black hole background as well as anisotropic backgrounds with Q-lattice structure. In both cases we find its derivative to the temperature is discontinuous at critical points. We also propose that the butterfly velocity can signalize the occurrence of thermal phase transition in general holographic models.

hep-th↗
arXiv2017

On the Unambiguous Distance of Multi-Carrier Phase Ranging with Random Hopped Frequencie

In a multi-carrier phase ranging (MPR) system, the distance that radio signal travels is estimated through phase shift of multiple carrier frequencies. Due to phase ambiguity, a unique estimation can only be obtained within the unambiguous distance (UD), which depends on the carrier frequencies used for ranging. Without external information, the maximum measurable distance of an MPR system is defined by its UD. The MPR system employing random frequency hopping (FH) waveform has a strong anti-jamming capability and sees promising potentials in many fields. However, it is challenging to depict its measurable distance. Different from current MPR system employing deterministic frequencies, the carrier frequency under the FH waveform hops randomly within the occupied bandwidth. Consequently, the UD is a random variable. In this paper, we try to find a deterministic value to depict the UD of an MPR system under the random spaced frequencies (RSF) configuration, serving as a metric of its measurable distance in engineering applications. It is safe to adopt the lower bound of the random UD as the metric, but the measurable distance may be drastically underestimated. Alternatively, we propose to adopt the upper bound of the random UD as the metric, because when the RSF set contains more than a dozen of carriers, i) we prove the probability that the random UD obtains its upper bound is very close to 1 if phase noise is not introduced; ii) simulations show that the upper bound can also be obtained reliably in the presence of phase noise. As a comparison, the practical measurable range of the MPR system under the traditional linearly spaced frequencies (LSF) configuration is only a fraction of its theoretical UD because the UD is more sensitive to phase error.

cs.NI↗
arXiv2017

Holographic Metal-Insulator Transition in Higher Derivative Gravity

We introduce a Weyl term into the Einstein-Maxwell-Axion theory in four dimensional spacetime. Up to the first order of the Weyl coupling parameter $γ$, we construct charged black brane solutions without translational invariance in a perturbative manner. Among all the holographic frameworks involving higher derivative gravity, we are the first to obtain metal-insulator transitions (MIT) when varying the system parameters at zero temperature. Furthermore, we study the holographic entanglement entropy (HEE) of strip geometry in this model and find that the second order derivative of HEE with respect to the axion parameter exhibits maximization behavior near quantum critical points (QCPs) of MIT. It testifies the conjecture in 1502.03661 and 1604.04857 that HEE itself or its derivatives can be used to diagnose quantum phase transition (QPT).

hep-th↗
arXiv2016

Extremes of Gaussian Random Fields with maximum variance attained over smooth curves

Let $X(s,t), (s,t)\in E$, with $E\subset \mathbb{R}^2$ a compact set, be a centered two dimensional Gaussian random field with continuous trajectories and variance function $σ(s,t)$. Denote by $\mathcal{L}=\{(s,t): σ(s,t)=\max_{(s',t')\in E}σ(s',t')\}$. In this contribution, we derive the exact asymptotics of $\mathbb{P}\left(\sup_{(s,t)\in E}X(s,t)>u\right)$, as $u\to\infty$, under condition that $\mathcal{L}$ is a smooth curve. We illustrate our findings by an application concerned with extremes of the aggregation of two independent fractional Brownian motions.

math.PR↗
arXiv2016

Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks

Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles. However, despite their superior performance in many applications, these models have been recently shown to be susceptible to a particular type of attack possible through the generation of particular synthetic examples referred to as adversarial samples. These samples are constructed by manipulating real examples from the training data distribution in order to "fool" the original neural model, resulting in misclassification (with high confidence) of previously correctly classified samples. Addressing this weakness is of utmost importance if deep neural architectures are to be applied to critical applications, such as those in the domain of cybersecurity. In this paper, we present an analysis of this fundamental flaw lurking in all neural architectures to uncover limitations of previously proposed defense mechanisms. More importantly, we present a unifying framework for protecting deep neural models using a non-invertible data transformation--developing two adversary-resilient architectures utilizing both linear and nonlinear dimensionality reduction. Empirical results indicate that our framework provides better robustness compared to state-of-art solutions while having negligible degradation in accuracy.

cs.LG↗
arXiv2016

Active Deep Learning for Classification of Hyperspectral Images

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

cs.LG↗
arXiv2016

Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation

Android Framework is a layer of software that exists in every Android system managing resources of all Android apps. A vulnerability in Android Framework can lead to severe hacks, such as destroying user data and leaking private information. With tens of millions of Android devices unpatched due to Android fragmentation, vulnerabilities in Android Framework certainly attract attackers to exploit them. So far, enormous manual effort is needed to craft such exploits. To our knowledge, no research has been done on automatic generation of exploits that take advantage of Android Framework vulnerabilities. We make a first step towards this goal by applying symbolic execution of Android Framework to finding bugs and generating exploits. Several challenges have been raised by the task. (1) The information of an app flows to Android Framework in multiple intricate steps, making it difficult to identify symbolic inputs. (2) Android Framework has a complex initialization phase, which exacerbates the state space explosion problem. (3) A straightforward design that builds the symbolic executor as a layer inside the Android system will not work well: not only does the implementation have to ensure the compatibility with the Android system, but it needs to be maintained whenever Android gets updated. We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework. It fundamentally changes the state of the art in exploit generation on the Android system, and has been applied to constructing new techniques for finding vulnerabilities.

cs.CR↗
arXiv2016

Implementation of A Nanosecond Time-resolved APD Detector System for NRS Experiment in HEPS-TF

A nanosecond time-resolved APD detector system is implemented for Nuclear Resonance Scattering (NRS) experiments in High Energy Photon Source-Test Facility (HEPS-TF) project of China. The detector system consists of three parts: the APD sensors, the fast preamplifiers and the TDC readout electronics. To improve the reception solid angle and the quantum efficiency, the C30703FH APDs (fabricated by Excelitas) are used as the sensors of the detectors. The C30703FH has an effective light-sensitive area of 10X10 mm2 and an absorption layer thickness of 110 μm. The fast preamplifier with gain of 59 dB and bandwidth of 2 GHz is designed to readout the weak signal outputted by the C30703FH APD. The detector system can work in single photon measurement mode because the preamplifier increases the signal-to-noise ratio. Moreover, the TDC is realized by FPGA multiphase method with a resolution bin of 1ns. The arrival time of all scattering events between two start triggers can be recorded by the FPGA TDC. In the X-ray energy of 14.4 keV, the time resolution (FWHM) of the developed detector (APD sensor + fast amplifier) is 0.86 ns, and the whole detector system (APD sensors + fast amplifiers + TDC readout electronics) achieves a time resolution of 1.4 ns.

physics.ins-det↗
arXiv2016

Revealing plasma oscillation in THz spectrum from laser plasma of molecular jet

Contribution of plasma oscillation to the broadband terahertz emission is revealed by interacting two-color laser pulses with a supersonic jet of nitrogen molecules. Temporal and spectral shifts of THz waves are observed as the plasma density varies. The former owes to the changing refractive index of the THz waves, and the latter correlates to the varying plasma frequency. Simulation of considering photocurrents, plasma oscillation and decaying plasma density explains the broadband THz spectrum and the varying THz spectrum. Plasma oscillation only contributes to THz waves at low plasma density owing to negligible plasma absorption. At the longer medium or higher density, the combining effects of plasma oscillation and absorption results in the observed low-frequency broadband THz spectra.

physics.optics↗
arXiv2016

Full Duplex Joint Sensing for Opportunistic Access in Spectrum-Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRN), secondary users (SUs) can share spectrum with licensed primary users (PUs). Because an SU receiver (SU-Rx) does not always share the same view of spectrum availability as the corresponding SU transmitter (SU-Tx), spectrum sensing conducted only by an SU transmitter tends to be overly sensitive to guarantee safe spectrum access at the price of SU inefficiency. In this letter, we propose a joint spectrum sensing mechanism, named Full-Duplex Joint Sensing (FJDS), to relax sensitivity of SU detection and improve SU throughput. FDJS employs instantaneous feedback enabled by in-band full duplex communication to facilitate the sharing of spectral information from SU-Rx to SU-Tx. The joint detection problem in FDJS is modeled as non-linear optimization and solved by a binary searching algorithm. Simulation results show that FDJS could improve SU throughput as well as meeting PU interruption constraints in a wide range of parameter settings.

cs.NI↗
arXiv2016

Extremes of locally stationary chi-square processes with trend

Chi-square processes with trend appear naturally as limiting processes in various statistical models. In this paper we are concerned with the exact tail asymptotics of the supremum taken over (0; 1) of a class of locally stationary chi-square processes with particular admissible trends. An important tool for establishing our results is a weak version of Slepian's lemma for chi-square processes. Some special cases including squared Brownian bridge and Bessel process are discussed.

math.PR↗
arXiv2016

Characterization of Quantum Phase Transition using Holographic Entanglement Entropy

The entanglement exhibits extremal or singular behavior near quantum critical points (QCPs) in many condensed matter models. These intriguing phenomena, however, still call for a widely accepted understanding. In this letter we study this issue in holographic framework. We investigate the connection between the holographic entanglement entropy (HEE) and the quantum phase transition (QPT) in a lattice-deformed Einstein-Maxwell-Dilaton theory. Novel backgrounds exhibiting metal-insulator transitions (MIT) have been constructed in which both metallic phase and insulating phase have vanishing entropy density in zero temperature limit. We find that the first order derivative of HEE with respect to lattice parameters exhibits extremal behavior near QCPs. We propose that it would be a universal feature that HEE or its derivatives with respect to system parameters can characterize QPT in a generic holographic system. Our work opens a window for understanding the relation between entanglement and the QPT from holographic perspective.

hep-th↗
arXiv2016

High-speed real-time single-pixel microscopy based on Fourier sampling

Single-pixel cameras based on the concepts of compressed sensing (CS) leverage the inherent structure of images to retrieve them with far fewer measurements and operate efficiently over a significantly broader spectral range than conventional silicon-based cameras. Recently, photonic time-stretch (PTS) technique facilitates the emergence of high-speed single-pixel cameras. A significant breakthrough in imaging speed of single-pixel cameras enables observation of fast dynamic phenomena. However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time. To address this challenge, we propose a novel single-pixel imaging technique that can produce high-quality images through rapid acquisition of their effective spatial Fourier spectrum. We employ phase-shifting sinusoidal structured illumination instead of random illumination for spectrum acquisition and apply inverse Fourier transform to the obtained spectrum for image restoration. We evaluate the performance of our prototype system by recognizing quick response (QR) codes and flow cytometric screening of cells. A frame rate of 625 kHz and a compression ratio of 10% are experimentally demonstrated in accordance with the recognition rate of the QR code. An imaging flow cytometer enabling high-content screening with an unprecedented throughput of 100,000 cells/s is also demonstrated. For real-time imaging applications, the proposed single-pixel microscope can significantly reduce the time required for image reconstruction by two orders of magnitude, which can be widely applied in industrial quality control and label-free biomedical imaging.

cs.CV↗
arXiv2016

Electron motion enhanced high harmonic generation in xenon clusters

Atomic clusters presents an isolated system that models the bulk materials whose mechanism of HHG remains uncertain, and a promising medium to produce HHG beyond the limited conversion efficiency for gaseous atoms. Here we reveal that the oscillation of collective electron motion within clusters develops after the interaction of intense laser fields, and it significantly enhances the harmonic dipole and increases the quantum phase of the harmonics. Experimentally, the phase matching conditions of HHG from nanometer xenon clusters and atoms are distinguished, which confirms the enhanced internal field that was proposed theoretically a decade ago. The separation of HHG from atoms and clusters allows the determination of the amplitude of the HHG for clusters to be 5 orders higher, corresponding to 4 times higher conversion efficiency for atomic response. The finding provides an insight on the HHG mechanism of bulk materials and a means by which an efficient coherent X-ray source can be developed.

physics.optics↗
arXiv2016

Lévy-driven GPS queues with heavy-tailed input

In this paper we derive exact large-buffer asymptotics for a two-class Generalized Processor Sharing (GPS) model, under the assumption that the input traffic streams generated by both classes correspond to heavy-tailed Lévy processes. Four scenarios need to be distinguished, which differ in terms of (i)~the level of heavy-tailedness of the driving Lévy processes as well as (ii)~the values of the corresponding mean rates relative to the GPS weights. The derived results are illustrated by two important special cases, in which the queues' inputs are modeled by heavy-tailed compound Poisson processes and by $α$-stable Lévy motions.

math.PR↗