arXiv subjects

Tong Li

Publications and source records attributed to Tong Li.

At least 109 records · Page 6Linked to original sources

arXiv2025

Machine learning accelerates fuel cell life testing

Accelerated life testing (ALT) can significantly reduce the economic, time, and labor costs of life testing in the process of equipment, device, and material research and development (R&D), and improve R&D efficiency. This paper proposes a performance characterization data prediction (PCDP) method and a life prediction-driven ALT (LP-ALT) method to accelerate the life test of polymer electrolyte membrane fuel cells (PEMFCs). The PCDP method can accurately predict different PCD using only four impedances (real and imaginary) corresponding to a high frequency and a medium frequency, greatly shortening the measurement time of offline PCD and reducing the difficulty of life testing. The test results on an open source life test dataset containing 42 PEMFCs show that compared with the determination coefficient (R^2) results of predicted aging indicators, including limiting current, total mass transport resistance, electrochemically active surface area, and crossover current, obtained based on the measured PCD, the R^2 results of predicted aging indicators based on the predicted PCD is only reduced by 0.04, 0.01, 0.05, and 0.06, respectively. The LP-ALT method can shorten the life test time through early life prediction. Test results on the same open-source life test dataset of PEMFCs show that the acceleration ratio of the LP-ALT method can reach 30 times under the premise of ensuring that the minimum R^2 of the prediction results of different aging indicators, including limiting current, total mass transport resistance, and electrochemically active surface area, is not less than 0.89. Combining the different performance characterization data predicted by the PCDP method and the life prediction of the LP-ALT method, the diagnosis and prognosis of PEMFCs and their components can be achieved.

stat.AP↗

arXiv2025

Baryon-number-violating nucleon decays in sterile neutrino effective field theories

The search for baryon-number-violating (BNV) nucleon decay provides an intriguing probe of new physics beyond the Standard Model (SM). Future neutrino experiments will improve the sensitivity to BNV nucleon decays and can serve to search for dark particles. In this work, we study the sterile neutrino effective field theories (EFTs) with baryon number violation and the impact of light sterile neutrino on BNV nucleon decays. We revisit the dimension-6 and dimension-7 EFT operator bases with $|Δ(B-L)|=2$ or $|Δ(B-L)|=0$. They are then matched to the baryon chiral perturbation theory. We obtain the effective chiral Lagrangian at low energies and the BNV interactions between the sterile neutrino and baryons and mesons. The rates of nucleon decay to SM neutrinos or a sterile neutrino are calculated. We then show the constraints on the ultraviolet scale from nucleon decay search at Super-K. The correlation of two EFT operators and the dependence on the sterile neutrino mass are also investigated.

hep-ph↗

arXiv2025

A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science

Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However, spatial intelligence spans a broader range of disciplines and scales, from navigation and urban planning to remote sensing and earth science. What are the differences and connections between spatial intelligence across these fields? In this paper, we first review human spatial cognition and its implications for spatial intelligence in LLMs. We then examine spatial memory, knowledge representations, and abstract reasoning in LLMs, highlighting their roles and connections. Finally, we analyze spatial intelligence across scales -- from embodied to urban and global levels -- following a framework that progresses from spatial memory and understanding to spatial reasoning and intelligence. Through this survey, we aim to provide insights into interdisciplinary spatial intelligence research and inspire future studies.

cs.AI↗

arXiv2025

Enabling Continuous THz Band Coverage via Precise Electron Beam Tailoring in Free-electron Lasers

High-power, continuously tunable narrowband terahertz (THz) sources are essential for advancing nonlinear optics, THz-driven material dynamics, and ultrafast spectroscopy. Conventional techniques typically impose a trade-off between pulse energy and frequency tunability. Here, we introduce a novel free-electron laser approach that overcomes these limitations by pre-modulating a relativistic electron beam with a frequency-beating laser pulse and leveraging bunch compression along with collective effects to enhance microbunching. Experimental results demonstrate that this technique generates narrowband THz emission with continuous frequency tunability from 7.8 to 30.8THz, achieving pulse energies up to 385μJ while maintaining spectral bandwidths between 7.7% and 14.7%. Moreover, the method exhibits exceptional robustness and scalability, highlighting its unique ability to bridge the long-standing THz gap and offering a promising solution for diverse cutting-edge scientific applications.

physics.acc-ph↗

arXiv2025

Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising

Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especially a single noisy image. Existing self-supervised image denoising paradigms (Noise2Noise and Noise2Void) rely heavily on information-lossy operations, such as downsampling and masking, culminating in low quality denoising performance. In this paper, we propose a novel self-supervised single image denoising paradigm, Positive2Negative, to break the information-lossy barrier. Our paradigm involves two key steps: Renoised Data Construction (RDC) and Denoised Consistency Supervision (DCS). RDC renoises the predicted denoised image by the predicted noise to construct multiple noisy images, preserving all the information of the original image. DCS ensures consistency across the multiple denoised images, supervising the network to learn robust denoising. Our Positive2Negative paradigm achieves state-of-the-art performance in self-supervised single image denoising with significant speed improvements. The code is released to the public at https://github.com/Li-Tong-621/P2N.

cs.CV↗

arXiv2025

The pseudo-analytical density solution to parameterized Fokker-Planck equations via deep learning

Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density functions are only attainable in specific cases. To speed up the solving process of parameterized FPEs with several system parameters, we introduce a deep learning-based method to obtain the pseudo-analytical density (PAD). Unlike previous numerical methodologies that necessitate solving the FPE separately for each set of system parameters, the PAD simultaneously addresses all the FPEs within a predefined continuous range of system parameters during a single training phase. The approach utilizes a Gaussian mixture distribution (GMD) to represent the stationary probability density, the solution to the FPE. By leveraging a deep residual network, each system parameter configuration is mapped to the parameters of the GMD, ensuring that the weights, means, and variances of the Gaussian components adaptively align with the corresponding true density functions. A grid-free algorithm is further developed to effectively train the residual network, resulting in a feasible PAD obeying necessary normalization and boundary conditions. Extensive numerical studies validate the accuracy and efficiency of our method, promising significant acceleration in the response analysis of multi-parameter, multi-dimensional stochastic nonlinear systems.

physics.comp-ph↗

arXiv2025

Dark Photon Dark Matter in Quantum Electromagnetodynamics and Detection at Haloscope Experiments

The ultralight dark photon is one of intriguing dark matter candidates. The interaction between the visible photon and dark photon is introduced by the gauge kinetic mixing between the field strength tensors of the Abelian gauge groups in the Standard Model and dark sector. The relativistic electrodynamics was generalized to quantum electromagnetodynamics (QEMD) in the presence of both electric and magnetic charges. The photon is described by two four-potentials corresponding to two $U(1)$ gauge groups and satisfying non-trivial commutation relations. In this work, we construct the low-energy dark photon-photon interactions in the framework of QEMD and obtain new dark photon-photon kinetic mixings. The consequent field equations and the new Maxwell's equations are derived in this framework. We also investigate the detection strategies of dark photon as light dark matter as well as the generic kinetic mixings at haloscope experiments.

hep-ph↗

arXiv2025

Laser induced Compton Scattering to Dark Photon or Axion-like Particle

The laser of an intense electromagnetic field provides an important tool to study the strong-field particle physics. The nonlinear Compton scattering was observed in the collision of an ultra-relativistic electron beam with a laser pulse in 1990s. The precision measurement of the nonlinear Compton scattering shines the light on the studies of strong-field QED and nonlinear effects in QED. In this work, we propose to produce new massive particles through the nonlinear Compton scattering in the presence of a high intense laser field. We take dark photon or axion-like particle as illustrative new particle. Compared with other collider and beam dump experiments, the laser induced process provides a complementary and competitive search of new invisible particles lighter than 1 MeV.

hep-ph↗

arXiv2025

An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, items, aspects, and sentiment polarity by systematically constructing an aspect hypergraph based on user reviews. In addition, APH aggregates aspects representing users and items by employing an aspect performance-aware hypergraph aggregation method. It aggregates the sentiment polarities from multiple users by jointly considering user preferences and the semantics of their sentiments, determining the weights of sentiment polarities to infer the performance of items on various aspects. Such performances are then used as weights to aggregate neighboring aspects. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline. The source code and data are available at https://github.com/dianziliu/APH.

cs.IR↗

arXiv2025

VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework

Penetration testing is a vital practice for identifying and mitigating vulnerabilities in cybersecurity systems, but its manual execution is labor-intensive and time-consuming. Existing large language model (LLM)-assisted or automated penetration testing approaches often suffer from inefficiencies, such as a lack of contextual understanding and excessive, unstructured data generation. This paper presents VulnBot, an automated penetration testing framework that leverages LLMs to simulate the collaborative workflow of human penetration testing teams through a multi-agent system. To address the inefficiencies and reliance on manual intervention in traditional penetration testing methods, VulnBot decomposes complex tasks into three specialized phases: reconnaissance, scanning, and exploitation. These phases are guided by a penetration task graph (PTG) to ensure logical task execution. Key design features include role specialization, penetration path planning, inter-agent communication, and generative penetration behavior. Experimental results demonstrate that VulnBot outperforms baseline models such as GPT-4 and Llama3 in automated penetration testing tasks, particularly showcasing its potential in fully autonomous testing on real-world machines.

cs.SE↗

arXiv2025

SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs

Despite significant advancements in general-purpose AI agents, several challenges still hinder their practical application in real-world scenarios. First, the limited planning capabilities of Large Language Models (LLM) restrict AI agents from effectively solving complex tasks that require long-horizon planning. Second, general-purpose AI agents struggle to efficiently utilize domain-specific knowledge and human expertise. In this paper, we introduce the Standard Operational Procedure-guided Agent (SOP-agent), a novel framework for constructing domain-specific agents through pseudocode-style Standard Operational Procedures (SOPs) written in natural language. Formally, we represent a SOP as a decision graph, which is traversed to guide the agent in completing tasks specified by the SOP. We conduct extensive experiments across tasks in multiple domains, including decision-making, search and reasoning, code generation, data cleaning, and grounded customer service. The SOP-agent demonstrates excellent versatility, achieving performance superior to general-purpose agent frameworks and comparable to domain-specific agent systems. Additionally, we introduce the Grounded Customer Service Benchmark, the first benchmark designed to evaluate the grounded decision-making capabilities of AI agents in customer service scenarios based on SOPs.

cs.AI↗

arXiv2024

Sensitivity of two-mode SRF cavity to generic electromagnetism of sub-$μ$eV dark matter

The low-mass dark matter (DM) such as axion or wavelike scalar is a plausible DM candidate. Recently, the possible non-standard couplings of low-mass DM has drawn much attention. In this work we investigate the detection of electromagnetic couplings in a few benchmark models of low-mass DM. For illustration, we consider the generic axion electrodynamics including CP violating coupling as well as the newly proposed axion electromagnetodynamics. The superconducting radio frequency (SRF) cavity with two modes has more advantages than the traditional cavity approach with static background field. We utilize the two-mode SRF cavity to probe the generic couplings of low-mass DM with frequency lower than GHz. We show the sensitivity of the SRF cavity to the axion couplings in the above frameworks.

hep-ph↗

arXiv2024

AppGen: Mobility-aware App Usage Behavior Generation for Mobile Users

Mobile app usage behavior reveals human patterns and is crucial for stakeholders, but data collection is costly and raises privacy issues. Data synthesis can address this by generating artificial datasets that mirror real-world data. In this paper, we propose AppGen, an autoregressive generative model designed to generate app usage behavior based on users' mobility trajectories, improving dataset accessibility and quality. Specifically, AppGen employs a probabilistic diffusion model to simulate the stochastic nature of app usage behavior. By utilizing an autoregressive structure, AppGen effectively captures the intricate sequential relationships between different app usage events. Additionally, AppGen leverages latent encoding to extract semantic features from spatio-temporal points, guiding behavior generation. These key designs ensure the generated behaviors are contextually relevant and faithfully represent users' environments and past interactions. Experiments with two real-world datasets show that AppGen outperforms state-of-the-art baselines by over 12% in critical metrics and accurately reflects real-world spatio-temporal patterns. We also test the generated datasets in applications, demonstrating their suitability for downstream tasks by maintaining algorithm accuracy and order.

cs.HC↗

arXiv2024

GeoTP: Latency-aware Geo-Distributed Transaction Processing in Database Middlewares (Extended Version)

The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency between the middleware and data sources and the long lock contention span, where transactions may be blocked while waiting for the locks held by concurrent transactions. In this paper, we propose GeoTP, a latency-aware geo-distributed transaction processing approach in database middlewares. GeoTP incorporates three key techniques to enhance geo-distributed transaction performance. First, we propose a decentralized prepare mechanism, which diminishes the requirement of network round trips for distributed transactions. Second, we design a latency-aware scheduler to minimize the lock contention span by strategically postponing the lock acquisition time point. Third, heuristic optimizations are proposed for the scheduler to reduce the lock contention span further. We implemented GeoTP on Apache Shardingsphere, a state-of-the-art middleware, and extended it into Apache ScalarDB. Experimental results on YCSB and TPC-C demonstrate that GeoTP achieves up to 17.7x performance improvement over Shardingsphere.

cs.DB↗

arXiv2024

Towards the efficacy of federated prediction for epidemics on networks

Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning. However, privacy concerns often hinder the sharing of health data among institutions, limiting the development of accurate prediction models. In this paper, we develop a general privacy-preserving framework for node-level epidemic prediction on networks based on federated learning (FL). We frame the spatio-temporal spread of epidemics across multiple data-isolated subnetworks, where each node state represents the aggregate epidemic severity within a community. Then, both the pure temporal LSTM model and the spatio-temporal model i.e., Spatio-Temporal Graph Attention Network (STGAT) are proposed to address the federated epidemic prediction. Extensive experiments are conducted on various epidemic processes using a practical airline network, offering a comprehensive assessment of FL efficacy under diverse scenarios. By introducing the efficacy energy metric to measure system robustness under various client configurations, we systematically explore key factors influencing FL performance, including client numbers, aggregation strategies, graph partitioning, missing infectious reports. Numerical results manifest that STGAT excels in capturing spatio-temporal dependencies in dynamic processes whereas LSTM performs well in simpler pattern. Moreover, our findings highlight the importance of balancing feature consistency and volume uniformity among clients, as well as the prediction dilemma between information richness and intrinsic stochasticity of dynamic processes. This study offers practical insights into the efficacy of FL scenario in epidemic management, demonstrates the potential of FL to address broader collective dynamics.

cs.SI↗

arXiv2024

A Survey on Human-Centric LLMs

The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction. This survey provides a comprehensive examination of such human-centric LLM capabilities, focusing on their performance in both individual tasks (where an LLM acts as a stand-in for a single human) and collective tasks (where multiple LLMs coordinate to mimic group dynamics). We first evaluate LLM competencies across key areas including reasoning, perception, and social cognition, comparing their abilities to human-like skills. Then, we explore real-world applications of LLMs in human-centric domains such as behavioral science, political science, and sociology, assessing their effectiveness in replicating human behaviors and interactions. Finally, we identify challenges and future research directions, such as improving LLM adaptability, emotional intelligence, and cultural sensitivity, while addressing inherent biases and enhancing frameworks for human-AI collaboration. This survey aims to provide a foundational understanding of LLMs from a human-centric perspective, offering insights into their current capabilities and potential for future development.

cs.CL↗

arXiv2024

Probing ALP Lepton Flavour Violation at $μ$TRISTAN

Axion-like particles (ALPs) with lepton flavour violating (LFV) interactions are predicted within a wide range of flavoured ALP models. The proposed $μ$TRISTAN high-energy $e^-μ^+$ and $μ^+μ^+$ collider will provide a good opportunity to explore flavour physics in the charged lepton sector. In this work, based on a model-independent effective Lagrangian describing the ALP leptonic interactions, we investigate the potential of $μ$TRISTAN to probe ALP LFV couplings. We analyse the testability of selected ALP production channels with potential sensitivity at $μ$TRISTAN, considering different beams and collision energies, including $e^- μ^+ \to a γ$, $e^- μ^+ \to e^- τ^+ a$, $μ^+ μ^+ \to μ^+ τ^+ a$, and $e^- μ^+ \to τ^- μ^+ a$. The produced ALP $a$ is either long-lived or can promptly decay to flavour violating or conserving charged lepton final states. In particular, combining the above LFV ALP production modes with a suitable LFV decay mode, one can identify signatures that are virtually free of Standard Model background. We show the resulting sensitivity of $μ$TRISTAN to LFV ALP couplings and compare it with multiple low-energy leptonic constraints and the future improvements thereof. We find that $μ$TRISTAN can be generally complementary to searches for low-energy LFV processes and measurements of the leptonic magnetic dipole moments and has the capability to explore unconstrained parameter space for ALP masses in the $\mathcal{O}(1)$ to $\mathcal{O}(100)$~GeV range. In the light ALP regime, however, the parameter space that $μ$TRISTAN is sensitive to, has been already excluded by low-energy searches for LFV decays.

hep-ph↗

arXiv2024

Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with perceptual quality or suffer from significant distortion. Recently, the emerging diffusion model has achieved state-of-the-art performance in various tasks and demonstrates great potential for image denoising. However, stimulating diffusion models for image denoising is not straightforward and requires solving several critical problems. For one thing, the input inconsistency hinders the connection between diffusion models and image denoising. For another, the content inconsistency between the generated image and the desired denoised image introduces distortion. To tackle these problems, we present a novel strategy called the Diffusion Model for Image Denoising (DMID) by understanding and rethinking the diffusion model from a denoising perspective. Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model and an adaptive ensembling method that reduces distortion in the denoised image. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics, for both Gaussian and real-world image denoising.The code is available at https://github.com/Li-Tong-621/DMID.

cs.CV↗