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Peng Liu

Publications and source records attributed to Peng Liu.

At least 253 records · Page 14Linked to original sources
arXiv2023

Peculiar properties in quasi-normal spectra from loop quantum gravity effect

We investigate the quasi-normal mode (QNM) spectra for scalar and electromagnetic fields over a covairant loop quantum gravity black hole (LQG-BH). For the fundamental modes, the LQG effect reduces the oscillations in the scalar field, however it induces stronger oscillations in the electromagnetic field, comparing to the classical case. Under the scalar field perturbation, the system enjoys faster decaying modes with more oscillations than the electromagnetic field. Some peculiar phenomena emerge in the QNM spectra with higher overtones. A notable feature is the substantial divergence observed in the first several overtones from their Schwarzschild counterparts, with this discrepancy magnifying as the overtone number increases. Another remarkable phenomenon in higher overtones is that the QNFs of the scalar field with $l=0$ exhibit an oscillatory behavior as the quantum parameter $r_0$ increases significantly. These oscillations intensify with the rising overtone number. We hypothesize that this oscillatory pattern may be associated with the extremal effect.

gr-qc↗
arXiv2023

Diagnosing quantum phase transition via holographic entanglement entropy at finite temperature

We investigate the behavior of the holographic entanglement entropy (HEE) in proximity to the quantum critical points (QCPs) of the metal-insulator transition (MIT) in the Einstein-Maxwell-dilaton-axions (EMDA) model. Since both the metallic phase and the insulating phase are characterized by distinct IR geometries, we used to expect that the HEE itself characterizes the QCPs. This expectation is validated for certain cases, however, we make a noteworthy observation: for a specific scenario where $-1<γ\leq -1/3$, with $γ$ as a coupling parameter, it is not the HEE itself but rather the second-order derivative of HEE with respect to the lattice wave number that effectively characterizes quantum phase transitions (QPTs). This distinction arises due to the influence of thermal effects. These findings present novel insights into the interplay between HEE and QPTs in the context of the MIT, and have significant implications for studying QPTs at finite temperatures.

hep-th↗
arXiv2023

Mixed-State Entanglement and Transport in Einstein-Maxwell-Axion-Horndeski Theory

We present a comprehensive study exploring the relationship between transport properties and measures of quantum entanglement in the Einstein-Maxwell-Axion-Horndeski theory. By using holographic duality, we study the entanglement measures, holographic entanglement entropy (HEE) and entanglement wedge cross-section (EWCS), and transport coefficients, for this model and analyze their dependence on free parameters which we classify into action parameter, observable parameters and axion factor. We find contrasting behaviors between HEE and EWCS with respect to observable parameters (charge and temperature), and the axion factor, indicating that they capture different types of quantum correlations. We also find that HEE exhibits positive correlation with both charge and thermal excitations, whereas EWCS exhibits a negative correlation with charge-related conductivities and thermal fluctuations. Furthermore, we find that the Horndenski coupling term, as the modification to standard gravity theory, does not change the qualitative behaviors of the conductivities and the entanglement measures.

hep-th↗
arXiv2023

Robust Visual Imitation Learning with Inverse Dynamics Representations

Imitation learning (IL) has achieved considerable success in solving complex sequential decision-making problems. However, current IL methods mainly assume that the environment for learning policies is the same as the environment for collecting expert datasets. Therefore, these methods may fail to work when there are slight differences between the learning and expert environments, especially for challenging problems with high-dimensional image observations. However, in real-world scenarios, it is rare to have the chance to collect expert trajectories precisely in the target learning environment. To address this challenge, we propose a novel robust imitation learning approach, where we develop an inverse dynamics state representation learning objective to align the expert environment and the learning environment. With the abstract state representation, we design an effective reward function, which thoroughly measures the similarity between behavior data and expert data not only element-wise, but also from the trajectory level. We conduct extensive experiments to evaluate the proposed approach under various visual perturbations and in diverse visual control tasks. Our approach can achieve a near-expert performance in most environments, and significantly outperforms the state-of-the-art visual IL methods and robust IL methods.

cs.LG↗
arXiv2023

A Microgrid Trading Framework Based on PoC Consensus

In the field of energy Internet, blockchain-based distributed energy trading mode is a promising way to replace the traditional centralized trading mode. However, the current power blockchain platform based on public chain has problems such as low consensus efficiency and waste of computing resources. The energy trading platform based on the consortium chain has problems such as inability to attract users to join and collusion between alliances. We propose a microgrid trading framework based on proof of contribution (PoC). According to the contribution value, we randomly select nodes based on weights through verifiable random functions (VRF) in each round of consensus to form the next round of consensus committee. The proposed electricity trading framework not only improves the consensus efficiency of the blockchain, but also is suitable as an incentive mechanism to attract users to participate in the power blockchain. Experiments show that our framework is effective and the contribution value we designed is reasonable. Through our framework, we can motivate users to participate and make energy transactions more fair.

cs.DC↗
arXiv2023

GPS Attack Detection and Mitigation for Safe Autonomous Driving using Image and Map based Lateral Direction Localization

The accuracy and robustness of vehicle localization are critical for achieving safe and reliable high-level autonomy. Recent results show that GPS is vulnerable to spoofing attacks, which is one major threat to autonomous driving. In this paper, a novel anomaly detection and mitigation method against GPS attacks that utilizes onboard camera and high-precision maps is proposed to ensure accurate vehicle localization. First, lateral direction localization in driving lanes is calculated by camera-based lane detection and map matching respectively. Then, a real-time detector for GPS spoofing attack is developed to evaluate the localization data. When the attack is detected, a multi-source fusion-based localization method using Unscented Kalman filter is derived to mitigate GPS attack and improve the localization accuracy. The proposed method is validated in various scenarios in Carla simulator and open-source public dataset to demonstrate its effectiveness in timely GPS attack detection and data recovery.

cs.RO↗
arXiv2023

A Trustworthy and Consistent Blockchain Oracle Scheme for Industrial Internet of Things

Blockchain provides decentralization and trustlessness features for the Industrial Internet of Things (IIoT), which expands the application scenarios of IIoT. To address the problem that the blockchain cannot actively obtain off-chain data, the blockchain oracle is proposed as a bridge between the blockchain and external data. However, the existing oracle schemes are difficult to solve the problem of low quality of service caused by frequent data changes and heterogeneous devices in IIoT, and the current oracle node selection schemes are difficult to balance security and quality of service. To tackle these problems, this paper proposes a secure and reliable oracle scheme that can obtain high-quality off-chain data. Specifically, we first design an oracle node selection algorithm based on Verifiable Random Function (VRF) and reputation mechanism to securely select high-quality nodes. Second, we propose a data filtering algorithm based on a sliding window to further improve the consistency of the collected data. We verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can effectively improve the service quality of the oracle.

cs.CR↗
arXiv2023

A Distributed Efficient Blockchain Oracle Scheme for Internet of Things

In recent years, blockchain has been widely applied in the Internet of Things (IoT). Blockchain oracle, as a bridge for data communication between blockchain and off-chain, has also received significant attention. However, the numerous and heterogeneous devices in the IoT pose great challenges to the efficiency and security of data acquisition for oracles. We find that the matching relationship between data sources and oracle nodes greatly affects the efficiency and service quality of the entire oracle system. To address these issues, this paper proposes a distributed and efficient oracle solution tailored for the IoT, enabling fast acquisition of real-time off-chain data. Specifically, we first design a distributed oracle architecture that combines both Trusted Execution Environment (TEE) devices and ordinary devices to improve system scalability, considering the heterogeneity of IoT devices. Secondly, based on the trusted node information provided by TEE, we determine the matching relationship between nodes and data sources, assigning appropriate nodes for tasks to enhance system efficiency. Through simulation experiments, our proposed solution has been shown to effectively improve the efficiency and service quality of the system, reducing the average response time by approximately 9.92\% compared to conventional approaches.

cs.NI↗
arXiv2023

Mixed-state Entanglement for AdS Born-Infeld Theory

We study the mixed-state entanglement for AdS Born-Infeld (BI) theory. We calculate the mixed-state entanglement and investigate the relationship between it and the system parameters. We find that the holographic entanglement entropy (HEE) and mutual information (MI) exhibit monotonically increasing and decreasing behavior with BI factor $b$. However, the entanglement wedge cross-section (EWCS) exhibits a very rich set of phenomena about system parameters. EWCS always increases with $b$ when $b$ is small and then monotonically decreases with $b$. These behaviors suggest that increasing the BI factor, which is essentially enhancing the coupling between the background geometry and the transport properties can always enhance the EWCS. The coupling between the entanglement and the transport behaviors has also been studied in condensed matter theories and is important to construct a stable quantum circuit. We also provide analytical understanding of the above phenomenon. Furthermore, we have tested two additional BI-like models and find the universality of these results, suggesting the crucial role of the BI term in governing the interplay between nonlinear electromagnetic effects and entanglement.

hep-th↗
arXiv2023

Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems

The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success in the field of Natural Language Processing (NLP) by learning universal representations on large corpora in a self-supervised manner. The pre-trained models and the learned representations can be beneficial to a series of downstream NLP tasks. This training paradigm has recently been adapted to the recommendation domain and is considered a promising approach by both academia and industry. In this paper, we systematically investigate how to extract and transfer knowledge from pre-trained models learned by different PLM-related training paradigms to improve recommendation performance from various perspectives, such as generality, sparsity, efficiency and effectiveness. Specifically, we propose a comprehensive taxonomy to divide existing PLM-based recommender systems w.r.t. their training strategies and objectives. Then, we analyze and summarize the connection between PLM-based training paradigms and different input data types for recommender systems. Finally, we elaborate on open issues and future research directions in this vibrant field.

cs.IR↗
arXiv2023

Sojourns of fractional Brownian motion queues: transient asymptotics

We study the asymptotics of sojourn time of the stationary queueing process $Q(t),t\ge0$ fed by a fractional Brownian motion with Hurst parameter $H\in(0,1)$ above a high threshold $u$. For the Brownian motion case $H=1/2$, we derive the exact asymptotics of \[ P\left(\int_{T_1}^{T_2} 1(Q(t)>u+h(u))d t>x \Big{|}Q(0) >u \right) \] as $u\to\infty$, {where $T_1,T_2, x\geq 0$ and $T_2-T_1>x$}, whereas for all $H\in(0,1)$, we obtain sharp asymptotic approximations of \[ P\left( \frac 1 {v(u)} \int_{[T_2(u),T_3(u)]}1(Q(t)>u+h(u))dt>y \Bigl \lvert \frac 1 {v(u)} \int_{[0,T_1(u)]}1(Q(t)>u)dt>x\right), \quad x,y >0 \] as $u\to\infty$, for appropriately chosen $T_i$'s and $v$. Two regimes of the ratio between $u$ and $h(u)$, that lead to qualitatively different approximations, are considered.

math.PR↗
arXiv2023

IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse

Humans have the ability to reuse previously learned policies to solve new tasks quickly, and reinforcement learning (RL) agents can do the same by transferring knowledge from source policies to a related target task. Transfer RL methods can reshape the policy optimization objective (optimization transfer) or influence the behavior policy (behavior transfer) using source policies. However, selecting the appropriate source policy with limited samples to guide target policy learning has been a challenge. Previous methods introduce additional components, such as hierarchical policies or estimations of source policies' value functions, which can lead to non-stationary policy optimization or heavy sampling costs, diminishing transfer effectiveness. To address this challenge, we propose a novel transfer RL method that selects the source policy without training extra components. Our method utilizes the Q function in the actor-critic framework to guide policy selection, choosing the source policy with the largest one-step improvement over the current target policy. We integrate optimization transfer and behavior transfer (IOB) by regularizing the learned policy to mimic the guidance policy and combining them as the behavior policy. This integration significantly enhances transfer effectiveness, surpasses state-of-the-art transfer RL baselines in benchmark tasks, and improves final performance and knowledge transferability in continual learning scenarios. Additionally, we show that our optimization transfer technique is guaranteed to improve target policy learning.

cs.LG↗
arXiv2023

Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning

The Bidirectional Encoder Representations from Transformers (BERT) were proposed in the natural language process (NLP) and shows promising results. Recently researchers applied the BERT to source-code representation learning and reported some good news on several downstream tasks. However, in this paper, we illustrated that current methods cannot effectively understand the logic of source codes. The representation of source code heavily relies on the programmer-defined variable and function names. We design and implement a set of experiments to demonstrate our conjecture and provide some insights for future works.

cs.CL↗
arXiv2023

Double-chain Constraints for 3D Human Pose Estimation in Images and Videos

Reconstructing 3D poses from 2D poses lacking depth information is particularly challenging due to the complexity and diversity of human motion. The key is to effectively model the spatial constraints between joints to leverage their inherent dependencies. Thus, we propose a novel model, called Double-chain Graph Convolutional Transformer (DC-GCT), to constrain the pose through a double-chain design consisting of local-to-global and global-to-local chains to obtain a complex representation more suitable for the current human pose. Specifically, we combine the advantages of GCN and Transformer and design a Local Constraint Module (LCM) based on GCN and a Global Constraint Module (GCM) based on self-attention mechanism as well as a Feature Interaction Module (FIM). The proposed method fully captures the multi-level dependencies between human body joints to optimize the modeling capability of the model. Moreover, we propose a method to use temporal information into the single-frame model by guiding the video sequence embedding through the joint embedding of the target frame, with negligible increase in computational cost. Experimental results demonstrate that DC-GCT achieves state-of-the-art performance on two challenging datasets (Human3.6M and MPI-INF-3DHP). Notably, our model achieves state-of-the-art performance on all action categories in the Human3.6M dataset using detected 2D poses from CPN, and our code is available at: https://github.com/KHB1698/DC-GCT.

cs.CV↗
arXiv2023

Topological Insulator VxBi1.08-xSn0.02Sb0.9Te2S as a Promising n-type Thermoelectric Material

As one of the most important n-type thermoelectric (TE) materials, Bi2Te3 has been studied for decades, with efforts to enhance the thermoelectric performance based on element doping, band engineering, etc. In this study, we report a novel bulk-insulating topological material system as a replacement for n-type Bi2Te3 materials: V doped Bi1.08Sn0.02Sb0.9Te2S (V:BSSTS) . The V:BSSTS is a bulk insulator with robust metallic topological surface states. Furthermore, the bulk band gap can be tuned by the doping level of V, which is verified by magnetotransport measurements. Large linear magnetoresistance is observed in all samples. Excellent thermoelectric performance is obtained in the V:BSSTS samples, e.g., the highest figure of merit ZT of ~ 0.8 is achieved in the 2% V doped sample (denoted as V0.02) at 550 K. The high thermoelectric performance of V:BSSTS can be attributed to two synergistic effects: (1) the low conductive secondary phases Sb2S3, and V2S3 are believed to be important scattering centers for phonons, leading to lower lattice thermal conductivity; and (2) the electrical conductivity is increased due to the high-mobility topological surface states at the boundaries. In addition, by replacing one third of costly tellurium with abundant, low-cost, and less-toxic sulfur element, the newly produced BSSTS material is inexpensive but still has comparable TE performance to the traditional Bi2Te3-based materials, which offers a cheaper plan for the electronics and thermoelectric industries. Our results demonstrate that topological materials with unique band structures can provide a new platform in the search for new high performance TE materials.

cond-mat.mtrl-sci↗
arXiv2023

Fast Current Regulation and Persistent Current Maintenance of High-Temperature Superconducting Magnets with Contact Power Supply and Flux Pump

Due to the properties of high temperature superconducting (HTS) materials, current attenuation is inevitable during the closed-loop operation of HTS magnets. When a contact DC power supply is used to supplement this attenuation, it inevitably creates a huge thermal burden on the cryogenic system. The flux pump is a revolutionary new power source that can charge closed-loop HTS magnet wirelessly. However, for HTS magnets with a large inductance, such as particle accelerator magnets and magnetic confinement magnet in Tokamak devices, the flux pump cannot fast adjust the DC current of the magnet, due to its small DC output voltage. Here, we present a method to fast regulate the current in a closed-loop HTS magnet using a contact DC power supply and persistent current switch (PCS). After current regulation, the HTS magnet is operated in the persistent current mode (PCM) with a flux pump. By applying the "four-quadrant" control theory of the flux pump allows, the current in HTS magnet is controlled with high stability. This study provide a power strategy for the fast current regulation and maintenance of persistent current in the HTS magnet, enabling the industrial applications of flux pumps for HTS magnets with large inductance.

physics.app-ph↗
arXiv2023

SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark

Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. However, it has been increasingly recognized that the "comparing different MI attacks" methodology used in the existing works has serious limitations. Due to these limitations, we found (through the experiments in this work) that some comparison results reported in the literature are quite misleading. In this paper, we seek to develop a comprehensive benchmark for comparing different MI attacks, called MIBench, which consists not only the evaluation metrics, but also the evaluation scenarios. And we design the evaluation scenarios from four perspectives: the distance distribution of data samples in the target dataset, the distance between data samples of the target dataset, the differential distance between two datasets (i.e., the target dataset and a generated dataset with only nonmembers), and the ratio of the samples that are made no inferences by an MI attack. The evaluation metrics consist of ten typical evaluation metrics. We have identified three principles for the proposed "comparing different MI attacks" methodology, and we have designed and implemented the MIBench benchmark with 84 evaluation scenarios for each dataset. In total, we have used our benchmark to fairly and systematically compare 15 state-of-the-art MI attack algorithms across 588 evaluation scenarios, and these evaluation scenarios cover 7 widely used datasets and 7 representative types of models. All codes and evaluations of MIBench are publicly available at https://github.com/MIBench/MIBench.github.io/blob/main/README.md.

cs.CR↗