arXiv subjects

Peng Liu

Publications and source records attributed to Peng Liu.

At least 271 records · Page 15Linked to original sources

arXiv2023

Charge transport properties in a novel holographic quantum phase transition model

We investigate the features of charge transport in a novel holographic quantum phase transition (QPT) model with two metallic phases: normal metallic and novel metallic. The scaling behaviors of direct current (DC) resistivity and thermal conductivity at low temperatures in both metallic phases are numerically computed. The numerical results and the analytical ones governed by the near horizon geometry agree perfectly. Then, the features of low-frequency alternating current (AC) electric conductivity are systematically investigated. A remarkable characteristic is that the normal metallic phase is a coherent system, whereas the novel metallic phase is an incoherent system with non-vanishing intrinsic conductivity. Especially, in the novel metallic phase, the incoherent behavior becomes stronger when the strength of the momentum dissipation enhances.

hep-th↗

arXiv2023

C$^3$PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation

Semi-supervised learning (SSL) methods, which can leverage a large amount of unlabeled data for improved performance, has attracted increasing attention recently. In this paper, we introduce a novel Context-aware Conditional Cross Pseudo Supervision method (referred as C$^3$PS) for semi-supervised medical image segmentation. Unlike previously published Cross Pseudo Supervision (CPS) works, this paper introduces a novel Conditional Cross Pseudo Supervision (CCPS) mechanism where the cross pseudo supervision is conditioned on a given class label. Context-awareness is further introduced in the CCPS to improve the quality of pseudo-labels for cross pseudo supervision. The proposed method has the additional advantage that in the later training stage, it can focus on the learning of hard organs. Validated on two typical yet challenging medical image segmentation tasks, our method demonstrates superior performance over the state-of-the-art methods.

cs.CV↗

arXiv2023

Traversable Thin-shell Wormhole in the 4D Einstein-Gauss-Bonnet Theory

This work investigates the spherically symmetric thin-shell wormhole solutions in four-dimensional Einstein-Gauss-Bonnet theory and explores their stabilities under radial, linear perturbations. These solutions are typically traversable and characterized by a thin-shell throat in accordance with Israel's junction conditions. In asymptotically flat and AdS spacetimes with a negative Gauss-Bonnet coupling constant, stable neutral wormholes are encountered when the magnitude of the coupling constant becomes significant. The throats of such wormholes are sustained by ordinary matter and possess finite radii. In asymptotically dS spacetimes, no stable neutral wormhole featuring ordinary matter is observed. On the other hand, for positive Gauss-Bonnet coupling constant, stable thin-shell wormhole solutions can be established when the throats are exclusively supported by exotic matter. Moreover, stable charged wormholes comprised of ordinary matter are found universally in the asymptotically flat, AdS, and dS spacetimes. Unlike their neutral counterparts, the throat radii of such charged wormholes can be arbitrarily small. However, as the charge becomes more significant, such solutions only remain stable when supported by exotic matter.

gr-qc↗

arXiv2023

Study on Extreme Precipitation Trends in Northeast China Based on Non-Stationary Generalized Extreme Value Distribution

Northeast China is the learding food productive base of China. The extreme precipitation (EP) event seriously impacts agricultural production and social life. Given the limited understanding of the EP in Northeast China, we investigate the trend and potential risk of the EP in Northeast China(107 stations) during 1959-2017, especially in early and later summer. For the first time, the non-stationary generalized extreme value (GEV) model is used to analyze the trend and potential risk of EP in Northeast China. Moreover, the mechanisms of EP trends over Northeast China in early and later summer were studied separately. Negative trends dominate EP in early summer but positive trends prevail in last summer. It is reasonable to discuss separately in the two periods. Meanwhile, all return levels are shown to increase trends in EP in early summer, corresponding to more frequent EP events. Nevertheless, in later summer, the 2-year return level decreases in location parameter diminish slightly, and the rare EP (20, 50, and 100-year return levels) slightly increase with scale parameter. Also, our results show that normal EP frequently occurs in Liaoning Province, and extreme EP is more likely to occur in Jilin Province and Heilongjiang Province. The increase of EP in early summer is mainly influenced by the northeast cold vortex. However, in later summer, the effect of cold air on EP is stronger in Northeast China, which gives a clear explanation that the EP does not increase. This study analyzed the trends and mechanism of return level and EP, which is beneficial for the development of policy strategies.

physics.soc-ph↗

arXiv2023

Diagnosis of Fast Electron Transport by Coherent Transition Radiation

Transport of fast electron in overdense plasmas is of key importance in high energy density physics. However, it is challenging to diagnose the fast electron transport in experiments. In this article, we study coherent transition radiation (CTR) generated by fast electrons on the back surface of the target by using 2D and 3D first-principle particle-in-cell (PIC) simulations. In our simulations, aluminium target of 2.7 g/cc is simulated in two different situations by using a newly developed high order implicit PIC code. Comparing realistic simulations containing collision and ionization effects, artificial simulations without taking collision and ionization effects into account significantly underestimate the energy loss of electron beam when transporting in the target, which fail to describe the complete characteristics of CTR produced by electron beam on the back surface of the target. Realistic simulations indicate the diameter of CTR increases when the thickness of the target is increased. This is attributed to synergetic energy losses of high flux fast electrons due to Ohm heatings and colliding drags, which appear quite significant even when the thickness of the solid target only differs by micrometers. Especially, when the diagnosing position is fixed, we find that the intensity distribution of the CTR is also a function of time, with the diameter increased with time. As the diameter of CTR is related to the speed of electrons passing through the back surface of the target, our finding may be used as a new tool to diagnose the electron energy spectra near the surface of solid density plasmas.

physics.plasm-ph↗

arXiv2023

Behavior Contrastive Learning for Unsupervised Skill Discovery

In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI objective tends to learn simple and static skills and may hinder exploration. In this paper, we propose a novel unsupervised skill discovery method through contrastive learning among behaviors, which makes the agent produce similar behaviors for the same skill and diverse behaviors for different skills. Under mild assumptions, our objective maximizes the MI between different behaviors based on the same skill, which serves as an upper bound of the previous MI objective. Meanwhile, our method implicitly increases the state entropy to obtain better state coverage. We evaluate our method on challenging mazes and continuous control tasks. The results show that our method generates diverse and far-reaching skills, and also obtains competitive performance in downstream tasks compared to the state-of-the-art methods.

cs.LG↗

arXiv2023

Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography

Purpose: Middle ear infection is the most prevalent inflammatory disease, especially among the pediatric population. Current diagnostic methods are subjective and depend on visual cues from an otoscope, which is limited for otologists to identify pathology. To address this shortcoming, endoscopic optical coherence tomography (OCT) provides both morphological and functional in-vivo measurements of the middle ear. However, due to the shadow of prior structures, interpretation of OCT images is challenging and time-consuming. To facilitate fast diagnosis and measurement, improvement in the readability of OCT data is achieved by merging morphological knowledge from ex-vivo middle ear models with OCT volumetric data, so that OCT applications can be further promoted in daily clinical settings. Methods: We propose C2P-Net: a two-staged non-rigid registration pipeline for complete to partial point clouds, which are sampled from ex-vivo and in-vivo OCT models, respectively. To overcome the lack of labeled training data, a fast and effective generation pipeline in Blender3D is designed to simulate middle ear shapes and extract in-vivo noisy and partial point clouds. Results: We evaluate the performance of C2P-Net through experiments on both synthetic and real OCT datasets. The results demonstrate that C2P-Net is generalized to unseen middle ear point clouds and capable of handling realistic noise and incompleteness in synthetic and real OCT data. Conclusion: In this work, we aim to enable diagnosis of middle ear structures with the assistance of OCT images. We propose C2P-Net: a two-staged non-rigid registration pipeline for point clouds to support the interpretation of in-vivo noisy and partial OCT images for the first time. Code is available at: https://gitlab.com/nct\_tso\_public/c2p-net.

cs.CV↗

arXiv2023

An Ethereum-compatible blockchain that explicates and ensures design-level safety properties for smart contracts

Smart contracts are crucial elements of decentralized technologies, but they face significant obstacles to trustworthiness due to security bugs and trapdoors. To address the core issue, we propose a technology that enables programmers to focus on design-level properties rather than specific low-level attack patterns. Our proposed technology, called Theorem-Carrying-Transaction (TCT), combines the benefits of runtime checking and symbolic proof. Under the TCT protocol, every transaction must carry a theorem that proves its adherence to the safety properties in the invoked contracts, and the blockchain checks the proof before executing the transaction. The unique design of TCT ensures that the theorems are provable and checkable in an efficient manner. We believe that TCT holds a great promise for enabling provably secure smart contracts in the future. As such, we call for collaboration toward this vision.

cs.CR↗

arXiv2023

Learning the black hole metric from holographic conductivity

We construct a neural network to learn the RN-AdS black hole metric based on the data of optical conductivity by holography. The linear perturbative equation for the Maxwell field is rewritten in terms of the optical conductivity such that the neural network is constructed based on the discretization of this differential equation. In contrast to all previous models in AdS/DL (deep learning) duality, the derivative of the metric function appears in the equation of motion and we propose distinct finite difference methods to discretize this function. The notion of the reduced conductivity is also proposed to avoid the divergence of the optical conductivity near the horizon.The dependence of the training outcomes on the location of the cutoff, the temperature as well as the frequency range is investigated in detail. This work provides a concrete example for the reconstruction of the bulk geometry with the given data on the boundary by deep learning.

hep-th↗

arXiv2023

Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification

Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias or domain shift. To counter this, a phased progressive learning schedule has been proposed that gradually shifts the emphasis from representation learning to training the upper classifier. This approach is particularly beneficial for datasets with larger imbalances or fewer samples. Another new method a coupling-regulation-imbalance loss function is proposed, which combines three parts: a correction term, Focal loss, and LDAM loss. This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on samples with varying classification difficulties. These approaches have yielded satisfactory results on several benchmark datasets, including Imbalanced CIFAR10, Imbalanced CIFAR100, ImageNet-LT, and iNaturalist 2018, and can be easily generalized to other imbalanced classification models.

cs.LG↗

arXiv2023

Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.

cs.IR↗

arXiv2023

Approaching Shannon's One-Time Pad: Metrics, Architectures, and Enabling Technologies

The rapid development of advanced computing technologies such as quantum computing imposes new challenges to current wireless security mechanism which is based on cryptographic approaches. To deal with various attacks and realize long-lasting security, we are in urgent need of disruptive security solutions. In this article, novel security transmission paradigms are proposed to approach Shannon's one-time pad perfect secrecy. First, two metrics, termed as Degree-of-Approaching (DoA) and Degree-of-Synchronous-Approaching (DoSA), are developed to characterize the closeness between the achieved security strength and perfect secrecy. These two metrics also serve as a guideline for secure transmission protocol design. After that, we present two paths towards Shannon's one-time pad, i.e., an explicit-encryption based approach and an implicit-encryption based approach. For both of them, we discuss the architecture design, enabling technologies, as well as preliminary performance evaluation results. The techniques presented in this article provide promising security-enhancing solutions for future wireless networks.

cs.CR↗

arXiv2023

In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks

Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This paper proposes an in-situ calibration framework with a user terminal transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in-situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for 1-$σ$ DOA estimation error can be achieved by in-situ calibration using the proposed method.

eess.SP↗

arXiv2023

Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving stochastic games. In most MARL studies, agents in a game are defined as teammates or enemies beforehand, and the relationships among the agents remain fixed throughout the game. However, in real-world problems, the agent relationships are commonly unknown in advance or dynamically changing. Many multi-party interactions start off by asking: who is on my team? This question arises whether it is the first day at the stock exchange or the kindergarten. Therefore, training policies for such situations in the face of imperfect information and ambiguous identities is an important problem that needs to be addressed. In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task. In the IDRL framework, a relation network is constructed to deduce the identities of other agents by observing the behaviors of the agents. A danger network is optimized to estimate the risk of false-positive identifications. Beyond that, we propose an intrinsic reward that balances the need to maximize external rewards and accurate identification. After identifying the cooperation-competition pattern among the agents, IDRL applies one of the off-the-shelf MARL methods to learn the policy. To evaluate the proposed method, we conduct experiments on Red-10 card-shedding game, and the results show that IDRL achieves superior performance over other state-of-the-art MARL methods. Impressively, the relation network has the par performance to identify the identities of agents with top human players; the danger network reasonably avoids the risk of imperfect identification. The code to reproduce all the reported results is available online at https://github.com/MR-BENjie/IDRL.

cs.AI↗

arXiv2023

Quasinormal modes and Hawking radiation of a charged Weyl black hole

We investigate the scalar field system over a charged Weyl black hole, depicted by a parameter $λ$. It is found that the imaginary part of the quasinormal mode spectra is always negative and the perturbation does not increase with the time, indicating that the system is stable under scalar field perturbation. Furthermore, the quasinormal mode spectra and Hawking radiation exhibit a qualitatively similar characteristic in that they both rise with rising $λ$ and approach a constant when $λ$ is large enough. Especially, we would like to emphasize that an exponential decay obviously emerges in the phase of the ringing tail as $λ$ increases. It indicates that the characteristic parameter $λ$ has an obvious imprint on the ringing tail, which is expected to be detected by future observations.

gr-qc↗

arXiv2023

The mixed-state entanglement in holographic p-wave superconductor model

In this paper, we investigate the mixed-state entanglement in a model of p-wave superconductivity phase transition using holographic methods. We calculate several entanglement measures, including holographic entanglement entropy (HEE), mutual information (MI), and entanglement wedge cross-section (EWCS). Our results show that these measures display critical behavior at the phase transition points, with the EWCS exhibiting opposite temperature behavior compared to the HEE. Additionally, we find that the critical exponents of all entanglement measures are twice those of the condensate. Moreover, we find that the EWCS is a more sensitive indicator of the critical behavior of phase transitions than the HEE. Furthermore, we uncover a universal inequality in the growth rates of EWCS and MI near critical points in thermal phase transitions, such as p-wave and s-wave superconductivity, suggesting that MI captures more information than EWCS when a phase transition first occurs.

hep-th↗

arXiv2023

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain

Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant successes across a wide range of domains, including game AI, autonomous vehicles, robotics, and so on. However, DRL and deep MARL agents are widely known to be sample inefficient that millions of interactions are usually needed even for relatively simple problem settings, thus preventing the wide application and deployment in real-industry scenarios. One bottleneck challenge behind is the well-known exploration problem, i.e., how efficiently exploring the environment and collecting informative experiences that could benefit policy learning towards the optimal ones. This problem becomes more challenging in complex environments with sparse rewards, noisy distractions, long horizons, and non-stationary co-learners. In this paper, we conduct a comprehensive survey on existing exploration methods for both single-agent and multi-agent RL. We start the survey by identifying several key challenges to efficient exploration. Beyond the above two main branches, we also include other notable exploration methods with different ideas and techniques. In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. According to our algorithmic and empirical investigation, we finally summarize the open problems of exploration in DRL and deep MARL and point out a few future directions.

cs.AI↗

arXiv2023

Broadband high-resolution integrated spectrometer architecture & data processing method

Up-to-date network telemetry is the key enabler for resource optimization by capacity scaling, fault recovery, and network reconfiguration among other means. Reliable optical performance monitoring in general and, specifically, the monitoring of the spectral profile of WDM signals in fixed- and flex- grid architectures across the entire C-band, remains challenging. This article describes a two-stage spectrometer architecture amenable to integration on a single chip that can measure quantitatively the spectrum across the entire C-band with a resolution of 1 GHz approximately. The first stage consists of a ring resonator with intra-ring phase shifter to provide a tuneable fine filter. The second stage makes use of an AWG subsystem and novel processing algorithm to synthesize a tuneable coarse filter with a flat passband which isolates individual resonances of a multiplicity of ring resonances. Due to its maturity and low loss, CMOS compatible Si$_3$N$_4$ is chosen for integration. A fabricated ring resonator functioning over the entire C-band with 1.3 GHz FWHM bandwidth resonances tunable over a complete free spectral range of 50 GHz is experimentally demonstrated. The complete system operation is demonstrated using an industry standard simulation tool and AWG constructor data. The operation of the circuit is invariant to the optical path length between individual components substantially improving robustness to fabrication process variations.

physics.optics↗