arXiv subjects

Peng Liu

Publications and source records attributed to Peng Liu.

At least 343 records · Page 19Linked to original sources

arXiv2021

Informational properties of holographic Lifshitz field theory

In this paper, we explore the properties of holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP) in holographic Lifshitz theory. These informational quantities exhibit some universal properties of holographic dual field theory. For most configuration parameters and temperatures, these informational quantities change monotonously with the Lifshitz dynamical critical exponent $z$. However, we also observe some non-monotonic behaviors for these informational quantities in some specific spaces of configuration parameters and temperatures. A particularly interesting phenomenon is that a dome-shaped diagram emerges in the behavior of MI vs $z$, and correspondingly a trapezoid-shaped profile appears in that of EoP vs $z$. This means that for some specific configuration parameters and temperatures, the system measured in terms of MI and EoP is entangled only in a certain intermediate range of $z$.

hep-th↗

arXiv2021

An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients'life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions related to the disease from sMRI data. This approach is based on a 2D single model strategy and has the following differences from the current approaches: 1) Convolutional Neural Network (CNN) models of different structures and capacities are evaluated systemically and the most suitable model is adopted for AD diagnosis; 2) a data augmentation strategy named Two-stage Random RandAugment (TRRA) is proposed to alleviate the overfitting issue caused by limited training data and to improve the classification performance in AD diagnosis; 3) an explainable method of Grad-CAM++ is introduced to generate the visually explainable heatmaps that localize and highlight the brain regions that our model focuses on and to make our model more transparent. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI). The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and 3D CNN methods. The resultant localization heatmaps from our approach also highlight the lateral ventricle and some disease-relevant regions of cortex, coincident with the commonly affected regions during the development of AD.

eess.IV↗

arXiv2021

Automatic Firmware Emulation through Invalidity-guided Knowledge Inference (Extended Version)

Emulating firmware for microcontrollers is challenging due to the tight coupling between the hardware and firmware. This has greatly impeded the application of dynamic analysis tools to firmware analysis. The state-of-the-art work automatically models unknown peripherals by observing their access patterns, and then leverages heuristics to calculate the appropriate responses when unknown peripheral registers are accessed. However, we empirically found that this approach and the corresponding heuristics are frequently insufficient to emulate firmware. In this work, we propose a new approach called uEmu to emulate firmware with unknown peripherals. Unlike existing work that attempts to build a general model for each peripheral, our approach learns how to correctly emulate firmware execution at individual peripheral access points. It takes the image as input and symbolically executes it by representing unknown peripheral registers as symbols. During symbolic execution, it infers the rules to respond to unknown peripheral accesses. These rules are stored in a knowledge base, which is referred to during the dynamic firmware analysis. uEmu achieved a passing rate of 95% in a set of unit tests for peripheral drivers without any manual assistance. We also evaluated uEmu with real-world firmware samples and new bugs were discovered.

cs.CR↗

arXiv2021

Ordering and Inequalities for Mixtures on Risk Aggregation

Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation. In this paper, we investigate ordering relations between two aggregation sets for which the sets of marginals are related by two simple operations: distribution mixtures and quantile mixtures. Intuitively, these operations ``homogenize" marginal distributions by making them similar. As a general conclusion from our results, more ``homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures. We proceed to study inequalities on the worst-case values of risk measures in risk aggregation, which represent conservative calculation of regulatory capital. Among other results, we obtain an order relation on VaR under quantile mixture for marginal distributions with monotone densities. Numerical results are presented to visualize the theoretical results and further inspire some conjectures. Finally, we provide applications on portfolio diversification under dependence uncertainty and merging p-values in multiple hypothesis testing, and discuss the connection of our results to joint mixability.

q-fin.RM↗

arXiv2021

Entanglement Wedge Minimum Cross-section in Holographic Massive Gravity Theory

We study the entanglement wedge cross-section (EWCS) in holographic massive gravity theory, in which a first and second-order phase transition can occur. We find that the mixed state entanglement measures, the EWCS and mutual information (MI) can characterize the phase transitions. The EWCS and MI show exactly the opposite behavior in the critical region, which suggests that the EWCS captures distinct degrees of freedom from that of the MI. More importantly, EWCS, MI and HEE all show the same scaling behavior in the critical region. We give an analytical understanding of this phenomenon. By comparing the quantum information behavior in the thermodynamic phase transition of holographic superconductors, we analyze the relationship and difference between them, and provide two mechanisms of quantum information scaling behavior in the thermodynamic phase transition.

hep-th↗

arXiv2021

Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection

Previous transfer methods for anomaly detection generally assume the availability of labeled data in source or target domains. However, such an assumption is not valid in most real applications where large-scale labeled data are too expensive. Therefore, this paper proposes an importance weighted adversarial autoencoder-based method to transfer anomaly detection knowledge in an unsupervised manner, particularly for a rarely studied scenario where a target domain has no labeled normal/abnormal data while only normal data from a related source domain exist. Specifically, the method learns to align the distributions of normal data in both source and target domains, but leave the distribution of abnormal data in the target domain unchanged. In this way, an obvious gap can be produced between the distributions of normal and abnormal data in the target domain, therefore enabling the anomaly detection in the domain. Extensive experiments on multiple synthetic datasets and the UCSD benchmark demonstrate the effectiveness of our approach.

cs.LG↗

arXiv2021

Development of a magnet power supply with a common-mode cancellation method based on automatic calibration of driving pulses

Based on conventional H-bridge topologies of magnet power supplies, a high-frequency common-mode equivalent model was established. It is pointed out that the two phase legs have complementary effect of common-mode noise under bipolar PWM modulation. Due to the phase difference of the driving pulses in the real product, however, the effect of common-mode cancellation is greatly disrupted. Based on the common-mode equivalent model, a frequency domain analysis was carried out to determine the theoretical relationship between the common-mode noise intensity and the phase difference, which can guide the adjustment of the driving pulses. It was also found that the increase of switching frequency will aggravate the common-mode noise caused by the phase difference. To reduce the common-mode noise, a method that automatic calibration of the driving pulses was evaluated by a simulation and experimentally. Finally, a prototype is built, and the results of the prototype show that the proposed method can effectively reduce the common-mode noise.

physics.ins-det↗

arXiv2021

Principled Exploration via Optimistic Bootstrapping and Backward Induction

One principled approach for provably efficient exploration is incorporating the upper confidence bound (UCB) into the value function as a bonus. However, UCB is specified to deal with linear and tabular settings and is incompatible with Deep Reinforcement Learning (DRL). In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I). OB2I constructs a general-purpose UCB-bonus through non-parametric bootstrap in DRL. The UCB-bonus estimates the epistemic uncertainty of state-action pairs for optimistic exploration. We build theoretical connections between the proposed UCB-bonus and the LSVI-UCB in a linear setting. We propagate future uncertainty in a time-consistent manner through episodic backward update, which exploits the theoretical advantage and empirically improves the sample-efficiency. Our experiments in the MNIST maze and Atari suite suggest that OB2I outperforms several state-of-the-art exploration approaches.

cs.LG↗

arXiv2021

Tackling Imbalanced Data in Cybersecurity with Transfer Learning: A Case with ROP Payload Detection

In recent years, deep learning gained proliferating popularity in the cybersecurity application domain, since when being compared to traditional machine learning, it usually involves less human effort, produces better results, and provides better generalizability. However, the imbalanced data issue is very common in cybersecurity, which can substantially deteriorate the performance of the deep learning models. This paper introduces a transfer learning based method to tackle the imbalanced data issue in cybersecurity using Return-Oriented Programming (ROP) payload detection as a case study. We achieved 0.033 average false positive rate, 0.9718 average F1 score and 0.9418 average detection rate on 3 different target domain programs using 2 different source domain programs, with 0 benign training data samples in the target domain. The performance improvement compared to the baseline is a trade-off between false positive rate and detection rate. Using our approach, the number of false positives is reduced by 23.20%, and as a trade-off, the number of detected malicious samples is reduced by 0.50%.

cs.CR↗

arXiv2021

Evolution of Anti-de Sitter black holes in Einstein-Maxwell-dilaton theory

We study the nonlinear evolution of the spherical symmetric black holes under a small neutral scalar field perturbation in Einstein-Maxwell-dilaton theory with coupling function $f(ϕ)=e^{-bϕ}$ in asymptotic anti-de Sitter spacetime. The non-minimal coupling between scalar and Maxwell fields allows the transmission of the energy from the Maxwell field to the scalar field, but also behaves as a repulsive force for the scalar. The scalar field oscillates with damping amplitude and converges to a final value by a power law. The irreducible mass of the black hole increases abruptly at initial times and then saturates to the final value exponentially. The saturating rate is twice the decaying rate of the dominant mode of the scalar. The effects of the black hole charge, the cosmological constant and the coupling parameter on the evolution are studied in detail. When the initial configuration is a naked singularity spacetime with a large charge to mass ratio, a horizon will form soon and hide the singularity.

gr-qc↗

arXiv2021

Dynamical charged black hole spontaneous scalarization in Anti-de Sitter spacetimes

We study the fully nonlinear dynamics of black hole spontaneous scalarizations in Einstein-Maxwell scalar theory with coupling function $f(ϕ)=e^{-bϕ^{2}}$, which can transform usual Reissner-Nordström Anti-de Sitter (RN-AdS) black holes into hairy black holes. Fixing the Arnowitt-Deser-Misner mass of the system, the initial scalar perturbation will destroy the original RN-AdS black hole and turn it into a hairy black hole provided that the constant $-b$ in the coupling function and the charge of the original black hole are sufficiently large, while the cosmological constant is small enough. In the scalarization process, we observe that the black hole irreducible mass initially increases exponentially, then it approaches to and finally saturates at a finite value. Choosing stronger coupling and larger black hole charge, we find that the black hole mass exponentially grows earlier and it takes a longer time for a hairy black hole to be developed and stabilized. We further examine phase structure properties in the scalarization process and confirm the observations in the non-linear dynamical study.

gr-qc↗

arXiv2021

Positive mass theorems of ALF and ALG manifolds

In this paper, we want to prove positive mass theorems for ALF and ALG manifolds with model spaces $\mathbb R^{n-1}\times \mathbb S^1$ and $\mathbb R^{n-2}\times \mathbb T^2$ respectively in dimensions no greater than $7$ (Theorem \ref{ALFPMT0}). { Different from the compatibility condition for spin structure in \cite[Theorem 2]{minerbe2008a}, we show that some type of incompressible condition for $\mathbb S^1$ and $\mathbb T^2$ is enough to guarantee the nonnegativity of the mass.} As in the asymptotically flat case, we reduce the desired positive mass theorems to those ones concerning non-existence of positive scalar curvature metrics on closed manifolds coming from generalize surgery to $n$-torus. { Finally, we investigate certain fill-in problems and obtain an optimal bound for total mean curvature of admissible fill-ins for flat product $2$-torus $\mathbb S^1(l_1)\times \mathbb S^1(l_2)$.}

math.DG↗

arXiv2021

Ion-beam-assisted characterization of quinoline-insoluble particles in nuclear graphite

The irradiation behavior of graphite is essential for its applications in nuclear industry. However, the differences between graphite's behaviors are not well understood because of the very limited knowledge of microstructural differences between graphites. One typical structure, quinoline insoluble (QI) particle, was investigated using IG-110 and NBG-18 graphite. After irradiation, the QI particles on the polished surface were proved to become hillocks and can be easily identified by using a scanning electron microscope (SEM). Thus a method combining ion irradiation and SEM characterization was proposed to study the distribution and concentration of QI particles in graphite. During irradiation, the QI particles were found to evolve into densified spheres which have weak bonding with the surrounding graphite structures, indicating that the densification of QI particles does not contribute obviously to graphite dimensional shrinkage. A much higher concentration of QI particles in NBG-18 than IG-110 was characterized and is suggested to be responsible for the smaller maximum dimensional shrinkage of NBG-18 than IG-110 during irradiation.

physics.app-ph↗

arXiv2021

Using Deep Learning to Solve Computer Security Challenges: A Survey

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.

cs.CR↗

arXiv2021

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage, the general problem of analyzing silent yet harmful executions is still an open problem. In this work, we propose a graph neural network (GNN) assisted data flow analysis method for spotting silent buffer overflows in execution traces. The new method combines a novel graph structure (denoted DFG+) beyond data-flow graphs, a tool to extract {\tt DFG+} from execution traces, and a modified Relational Graph Convolutional Network as the GNN model to be trained. The evaluation results show that a well-trained model can be used to analyze vulnerabilities in execution traces (of previously-unseen programs) without support of any source code. Our model achieves 94.39\% accuracy on the test data and successfully locates 29 out of 30 real-world silent buffer overflow vulnerabilities. Leveraging deep learning, the proposed method is, to our best knowledge, the first general-purpose analysis method for silent buffer overflows. It is also the first method to spot silent buffer overflows in global variables, stack variables, or heap variables without crossing the boundary of allocated chunks.

cs.CR↗

arXiv2021

VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention

This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which refines the textual-to-acoustic alignment layer-wisely. Hierarchical latent variables with different temporal resolutions from the VDVAE are used as queries for residual attention module. By leveraging the coarse global alignment from previous attention layer as an extra input, the following attention layer can produce a refined version of alignment. This amortizes the burden of learning the textual-to-acoustic alignment among multiple attention layers and outperforms the use of only a single attention layer in robustness. An utterance-level speaking speed factor is computed by a jointly-trained speaking speed predictor, which takes the mean-pooled latent variables of the coarsest layer as input, to determine number of acoustic frames at inference. Experimental results show that VARA-TTS achieves slightly inferior speech quality to an AR counterpart Tacotron 2 but an order-of-magnitude speed-up at inference; and outperforms an analogous non-AR model, BVAE-TTS, in terms of speech quality.

cs.SD↗

arXiv2021

Security and Privacy for Artificial Intelligence: Opportunities and Challenges

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversarial attacks against AI applications, including aspects such as adversarial knowledge and capabilities, as well as existing methods for generating adversarial examples and existing cyber defence models. We explain mathematical AI models, especially new variants of reinforcement and federated learning, to demonstrate how attack vectors would exploit vulnerabilities of AI models. We also propose a systematic framework for demonstrating attack techniques against AI applications and reviewed several cyber defences that would protect AI applications against those attacks. We also highlight the importance of understanding the adversarial goals and their capabilities, especially the recent attacks against industry applications, to develop adaptive defences that assess to secure AI applications. Finally, we describe the main challenges and future research directions in the domain of security and privacy of AI technologies.

cs.CR↗

arXiv2021

Sojourn times of Gaussian related random fields

This paper is concerned with the asymptotic analysis of sojourn times of random fields with continuous sample paths. Under a very general framework we show that there is an interesting relationship between tail asymptotics of sojourn times and that of supremum. Moreover, we establish the uniform double-sum method to derive the tail asymptotics of sojourn times. In the literature, based on the pioneering research of S. Berman the sojourn times have been utilised to derive the tail asymptotics of supremum of Gaussian processes. In this paper we show that the opposite direction is even more fruitful, namely knowing the asymptotics of supremum o f random processes and fields (in particular Gaussian) it is possible to establish the asymptotics of their sojourn times. We illustrate our findings considering i) two dimensional Gaussian random fields, ii) chi-process generated by stationary Gaussian processes and iii) stationary Gaussian queueing processes.

math.PR↗