arXiv subjects

Jin Wang

Publications and source records attributed to Jin Wang.

At least 289 records · Page 16Linked to original sources

arXiv2023

Generalized free energy landscapes of the charged Gauss-Bonnet AdS black holes in diverse dimensions

The present study focuses on analyzing the generalized free energy function of the $D$-dimensional charged Gauss-Bonnet AdS black holes. We examine the fluctuating black holes that are in contact with thermal baths at an arbitrary ensemble temperature, resulting in the corresponding Euclidean geometry with a conical singularity at the event horizon. By properly regularizing the conical singularity, we have derived the generalized free energy of the fluctuating black holes in the canonical ensemble using the Euclidean gravitational path integral approach. We demonstrate that the derived generalized free energy is consistent with the definition from thermodynamic perspective. Then we explore the free energy landscapes of the charge Gauss-Bonnet black holes in diverse spacetime dimensions and examine the corresponding thermodynamics of the black hole phase transition. Finally, we discuss the generalized free energy landscape of the fluctuating black holes in grand canonical ensemble.

gr-qc↗

arXiv2023

Federated Ensemble Model-based Reinforcement Learning in Edge Computing

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data. Extending FL beyond the supervised learning models, federated reinforcement learning (FRL) was proposed to handle sequential decision-making problems in edge computing systems. However, the existing FRL algorithms directly combine model-free RL with FL, thus often leading to high sample complexity and lacking theoretical guarantees. To address the challenges, we propose a novel FRL algorithm that effectively incorporates model-based RL and ensemble knowledge distillation into FL for the first time. Specifically, we utilise FL and knowledge distillation to create an ensemble of dynamics models for clients, and then train the policy by solely using the ensemble model without interacting with the environment. Furthermore, we theoretically prove that the monotonic improvement of the proposed algorithm is guaranteed. The extensive experimental results demonstrate that our algorithm obtains much higher sample efficiency compared to classic model-free FRL algorithms in the challenging continuous control benchmark environments under edge computing settings. The results also highlight the significant impact of heterogeneous client data and local model update steps on the performance of FRL, validating the insights obtained from our theoretical analysis.

cs.LG↗

arXiv2023

Pulsar and Magnetar Navigation with Fermi/GBM and GECAM

The determination of the absolute and relative position of a spacecraft is critical for its operation, observations, data analysis, scientific studies, as well as deep space exploration in general. A spacecraft that can determine its own absolute position autonomously may perform more than that must rely on transmission solutions. In this work, we report an absolute navigation accuracy of $\sim$ 20 km using 16-day Crab pulsar data observed with $Fermi$ Gamma ray Burst Monitor (GBM). In addition, we propose a new method with the inverse process of the triangulation for joint navigation using repeated bursts like that from the magnetar SGR J1935+2154 observed by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) and GBM.

astro-ph.IM↗

arXiv2023

Discovery of the linear energy-dependence of the spectral lag of X-ray bursts from SGR J1935+2154

Spectral lag of the low-energy photons with respect to the high-energy ones is a common astrophysical phenomenon (such as Gamma-ray bursts and the Crab pulsar) and may serve as a key probe to the underlying radiation mechanism. However, spectral lag in keV range of the magnetar bursts has not been systematically studied yet. In this work, we perform a detailed spectral lag analysis with the Li-CCF method for SGR J1935+2154 bursts observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to Jan 2022. We discover that the spectral lags of about 61\% (non-zero significance >1$σ$) bursts from SGR J1935+2154 are linearly dependent on the photon energy ($E$) with $t_{\rm lag}(E)=α(E/{\rm keV})+C$, which may be explained by a linear change of the temperature of the blackbody-emitting plasma with time. The distribution of the slope ($α$) approximately follows a Gaussian function with mean and standard deviation of 0.02 ms/keV (i.e. high-energy photons arrive earlier) and 0.02 ms/keV, respectively. We also find that the distribution can be well fitted with three Gaussians with mean values of $\sim$ -0.009, 0.013 and 0.039 ms/keV, which may correspond to different origins of the bursts. These spectral lag features may have important implications on the magnetar bursts.

astro-ph.HE↗

arXiv2023

Fine-Grained Selective Similarity Integration for Drug-Target Interaction Prediction

The discovery of drug-target interactions (DTIs) is a pivotal process in pharmaceutical development. Computational approaches are a promising and efficient alternative to tedious and costly wet-lab experiments for predicting novel DTIs from numerous candidates. Recently, with the availability of abundant heterogeneous biological information from diverse data sources, computational methods have been able to leverage multiple drug and target similarities to boost the performance of DTI prediction. Similarity integration is an effective and flexible strategy to extract crucial information across complementary similarity views, providing a compressed input for any similarity-based DTI prediction model. However, existing similarity integration methods filter and fuse similarities from a global perspective, neglecting the utility of similarity views for each drug and target. In this study, we propose a Fine-Grained Selective similarity integration approach, called FGS, which employs a local interaction consistency-based weight matrix to capture and exploit the importance of similarities at a finer granularity in both similarity selection and combination steps. We evaluate FGS on five DTI prediction datasets under various prediction settings. Experimental results show that our method not only outperforms similarity integration competitors with comparable computational costs, but also achieves better prediction performance than state-of-the-art DTI prediction approaches by collaborating with conventional base models. Furthermore, case studies on the analysis of similarity weights and on the verification of novel predictions confirm the practical ability of FGS.

cs.AI↗

arXiv2023

Positivity Preserving non-Markovian Master Equation for Open Quantum System Dynamics: Stochastic Schrödinger Equation Approach

Positivity preservation is naturally guaranteed in exact non-Markovian master equations for open quantum system dynamics. However, in many approximated non-Markovian master equations, the positivity of the reduced density matrix is not guaranteed. In this paper, we provide a general class of time-local, perturbative and positivity-preserving non-Markovian master equations generated from stochastic Schrodinger equations, particularly quantum-state-diffusion equations. Our method has an expanded range of applicability for accommodating a variety of non-Markovian environments. We show the positivity-preserving master equation for a three-level system coupled to a dissipative bosonic environment as a particular example to exemplify our general approach. We illustrate the numerical simulations with an analysis explaining why the previous approximated non-Markovian master equations cannot guarantee positivity. Our work provides a consistent master equation for studying the non-Markovian dynamics in ultrafast quantum processes and strong-coupling systems.

quant-ph↗

arXiv2023

Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization

In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM.

cs.CV↗

arXiv2023

The performance of SiPM-based gamma-ray detector (GRD) of GECAM-C

As a new member of GECAM mission, the GECAM-C (also called High Energy Burst Searcher, HEBS) is a gamma-ray all-sky monitor onboard SATech-01 satellite, which was launched on July 27th, 2022 to detect gamma-ray transients from 6 keV to 6 MeV, such as Gamma-Ray Bursts (GRBs), high energy counterpart of Gravitational Waves (GWs) and Fast Radio Bursts (FRBs), and Soft Gamma-ray Repeaters (SGRs). Together with GECAM-A and GECAM-B launched in December 2020, GECAM-C will greatly improve the monitoring coverage, localization, as well as temporal and spectral measurements of gamma-ray transients. GECAM-C employs 12 SiPM-based Gamma-Ray Detectors (GRDs) to detect gamma-ray transients . In this paper, we firstly give a brief description of the design of GECAM-C GRDs, and then focus on the on-ground tests and in-flight performance of GRDs. We also did the comparison study of the SiPM in-flight performance between GECAM-C and GECAM-B. The results show GECAM-C GRD works as expected and is ready to make scientific observations.

astro-ph.IM↗

arXiv2023

Self-Calibrated Atom-Interferometer Gyroscope by Modulating Atomic Velocities

Atom-interferometer gyroscopes have attracted much attention for their potential superior long-term stability and extremely low drift. For such high precision instrument, a self-calibration to achieve an absolute rotation measurement is highly demanded. Here we propose and demonstrate a self-calibration of the atomic gyroscope. The calibration is realized by using the detuning of laser frequency to control the atomic velocity thus to modulate the scale factor of the gyroscope. The modulation determines the order and the initial phase of the interference stripe, thus eliminates the ambiguity caused by the periodicity of the interferometric signal. The calibration method is verified by measuring the Earth's rotation. Long-term stable and self-calibrated atom-interferometer gyros can find important applications in the fields of fundamental physics and long-time navigation.

physics.atom-ph↗

arXiv2023

Compact multi-channel radio frequency pulse sequence generator with fast switching capability for cold atom interferometers

Cold atom interferometers have matured to a powerful tool in fundamental physics research, and they are currently on their way from realizations in the laboratory to applications in the real world. The radio frequency (RF) generator is an indispensable component to control lasers and then manipulate atoms. We developed a highly compact RF generator for fast switching and sweeping frequencies/amplitudes of atomic interference pulse sequences. Multi-channel RF signals are generated by using a field-programmable gate array (FPGA) to control eight direct digital synthesizers (DDSs). We further proposed and demonstrated a method of preloading the parameters of all RF pulse sequences to the DDS registers before the execution of the pulse sequences, which eliminates the data transfer between the FPGA and DDSs to change RF signals and thus sharply shortens the delay of frequency switching when the pulse sequences are running. The characterized performance shows the generated RF signals achieve 119 ns frequency switching delay, and 40 dB harmonic rejection ratio. The generated RF pulse sequences are applied to a cold atom-interferometer gyroscope, and the contrast of atomic interference fringes reaches 38%. This compact multi-channel generator with the fast frequency/amplitude switching or/and sweeping capability has beneficial applications for the real-world atom interferometers.

physics.atom-ph↗

arXiv2023

Cross calibration of gamma-ray detectors (GRD) of GECAM-C

The gamma-ray detectors (GRDs) of GECAM-C onborad SATech-01 satellite is designed to monitor gamma-ray transients all over the sky from 6 keV to 6 MeV. The energy response matrix is the key to do spectral measurements of bursts, which is usually generated from GEANT4 simulation and partially verified by the ground calibration. In this work, energy response matrix of GECAM-C GRD is cross-calibrated with Fermi/GBM and Swift/BAT using a sample of Gamma-Ray Bursts (GRBs) and Soft Gamma-Ray Repeaters (SGRs). The calibration results show there is a good agreement between GECAM-C and other reasonably well calibrated instrument (i.e. Fermi/GBM and Swift/BAT). We also find that different GRD detectors of GECAM-C also show consistency with each other. All these results indicate that GECAM-C GRD can provide reliable spectral measurements.

astro-ph.IM↗

arXiv2023

Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues

Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is available at https://github.com/Chenrj233/LMEDR.

cs.CL↗

arXiv2023

Text-Guided Mask-free Local Image Retouching

In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but it also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. In order to perform image retouching without mask supervision, our technique can construct plausible and edge-sharp masks based on the text for each object in the image. Extensive experiments have shown that our method can produce high-quality, accurate images based on spoken language. The source code will be released soon.

cs.CV↗

arXiv2023

A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this counts distribution localization: $χ^{2}$ minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the $χ^{2}$ method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than $χ^{2}$ method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapid follow-up observations.

astro-ph.HE↗

arXiv2023

The topological natures of the Gauss-Bonnet black hole in AdS space

In the recent proposal [Phys. Rev. Lett. 129, 191101 (2022)], the black holes were viewed as topological thermodynamic defects by using the generalized off-shell free energy. In this paper, we follow such proposal to study the local and global topological natures of the Gauss-Bonnet black holes in AdS space. The local topological natures are reflected by the winding numbers, where the positive and negative winding numbers correspond to the stable and unstable black hole branches. The global topological natures are reflected by the topological numbers, which are defined as the sum of the winding numbers for all black hole branches and can be used to classify the black holes into different classes. When the charge is present, we find that the topological number is independent on the values of the parameters, and the charged Gauss-Bonnet AdS black holes can be divided into the same class of the RNAdS black holes with the same topological number 1. However, when the charge is absent, we find that the topological number has certain dimensional dependence. This is different from the previous studies, where the topological number is found to be a universal number independent of the black hole parameters. Furthermore, the asymptotic behaviors of curve τ(r_h) in small and large radii limit can be a simple criterion to distinguish the different topological number. We find a new asymptotic behavior as τ(r_h \to 0) = 0 and τ(r_h \to \infty) = 0 in the black hole system, which shows topological equivalency with the asymptotic behaviors τ(r_h \to 0)=\infty and τ(r_h \to \infty)=\infty. We also give an intuitional proof of why there are only three topological classes in the black hole system under the condition (\partial_{r_h} S)P > 0.

gr-qc↗

arXiv2023

Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE). Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid the error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model.

cs.CL↗

arXiv2023

Topology of the landscape and dominant kinetic path for the thermodynamic phase transition of the charged Gauss-Bonnet AdS black holes

We study the generalized free energy of the five dimensional charged Gauss-Bonnet AdS black holes in the grand canonical ensemble by treating the black hole radius and the charge as the order parameters. On the two dimensional free energy landscape, the lowest points in the basins represent the local stable black holes and the saddle point represents the unstable black hole. We show that black hole is the topological defect of gradient field of the landscape. The black hole stability is determined by the topography of the free energy landscape in terms of the basin depths and the barrier height between the basins and is not by the topology of the gradient field. In addition, we study the stochastic dynamics of the black hole phase transition and obtain the dominant kinetic path for the transition on the free energy landscape. Unlike the one dimensional landscape, the dominant kinetic path between the small and the large black hole state does not necessarily pass through the intermediate black hole state. Furthermore, the inhomogeneity in diffusions can lead to the switching from the coupled cooperative process of black hole phase transition to the decoupled sequential process, giving different kinetic mechanisms.

gr-qc↗

arXiv2023

Quantum geometrical current and coherence of the open gravitation system: loop quantum gravity coupled with a thermal scalar field

Open quantum systems interacting with the environments often show interesting behaviors, such as decoherence, non-unitary evolution, dissipation, etc. It is interesting but still challenging to study the open quantum gravitation system interacting with the environments. In this work, we develop a general parameterized theoretical framework for the open quantum gravitation system. Under the Born-Markov approximation, we derived the quantum master equation with a new method which determines the evolution for certain types of open quantum gravitation system. Finally, we studied a specific model where the real scalar field plays the role of the environment and the spacetime is assumed to be homogeneous and isotropic. We quantize the spacetime through the loop quantum gravity. We show that the scalar field can induce the quantum geometry in the equilibrium state when the scalar field is under the thermal equilibrium. For the non-steady state, the quantum geometry flux emerges. We point out that the quantum geometry flux and the coherence can drive the evolution of the spacetime geometry. This provides us a new view on the evolution of the spacetime geometry. Our results shown that the coherence of the spacetime monotonically decreases as the temperature of the bath decreases. This indicates that a classical cold universe can emerge from an initial hot quantum universe. We found in this model that the entanglement entropy of the gravitation system is related to the average volume of the space. We take the continuous limit for studying the loop quantum cosmology and approximately obtain a tunneling probability of the spacetime from the zero volume state to the finite volume state.

gr-qc↗