arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 217 records · Page 12Linked to original sources

arXiv2025

Study $Ξ_c^+ \to Λ{\bar{K}}^0 π^+$ and search for the low-lying baryons $Ξ(1/2^-)$ and $Σ(1/2^-)$

Since searches for the low-lying excited baryons $Ξ(1/2^-)$ and $Σ(1/2^-)$ are crucial to deepening our understanding of the light baryon spectrum, we have investigated the Cabibbo-favored process $Ξ_c^+ \to Λ{\bar{K}}^0 π^+$ by taking into account the $S$-wave pseudoscalar meson-octet baryon interactions within the chiral unitary approach, which could dynamically generate the resonances $Ξ(1/2^-)$ and $Σ(1/2^-)$. The contributions from the excited kaons are double Cabibbo-suppressed, and the contribution from the $Σ(1385)$ is also suppressed due to Korner-Pati-Woo theory, thus those states are expected to play negligible contributions in this process. We have predicted the $\bar{K}^0 Λ$ and $π^+Λ$ invariant mass distributions, which have the clear signals of the $Ξ(1/2^-)$ and $Σ(1/2^-)$. Thus, the $Ξ_c^+ \to Λ{\bar{K}}^0 π^+$ is an ideal process to search for the low-lying baryons $Ξ(1/2^-)$ and $Σ(1/2^-)$, and we make a call for a precise measurements of this process in experiments, such as Belle II, LHCb, and the proposed Super Tau-Charm Facility (STCF).

hep-ph↗

arXiv2025

Quantum ensemble learning with a programmable superconducting processor

Quantum machine learning is among the most exciting potential applications of quantum computing. However, the vulnerability of quantum information to environmental noises and the consequent high cost for realizing fault tolerance has impeded the quantum models from learning complex datasets. Here, we introduce AdaBoost.Q, a quantum adaptation of the classical adaptive boosting (AdaBoost) algorithm designed to enhance learning capabilities of quantum classifiers. Based on the probabilistic nature of quantum measurement, the algorithm improves the prediction accuracy by refining the attention mechanism during the adaptive training and combination of quantum classifiers. We experimentally demonstrate the versatility of our approach on a programmable superconducting processor, where we observe notable performance enhancements across various quantum machine learning models, including quantum neural networks and quantum convolutional neural networks. With AdaBoost.Q, we achieve an accuracy above 86% for a ten-class classification task over 10,000 test samples, and an accuracy of 100% for a quantum feature recognition task over 1,564 test samples. Our results demonstrate a foundational tool for advancing quantum machine learning towards practical applications, which has broad applicability to both the current noisy and the future fault-tolerant quantum devices.

quant-ph↗

arXiv2025

Time-Efficient Logical Operations on Quantum Low-Density Parity Check Codes

We propose schemes capable of measuring an arbitrary set of commutative logical Pauli operators in time independent of the number of operators. The only condition is commutativity, a fundamental requirement for simultaneous measurements in quantum mechanics. Quantum low-density parity check (qLDPC) codes show great promise for realizing fault-tolerant quantum computing. They are particularly significant for early fault-tolerant technologies as they can encode many logical qubits using relatively few physical qubits. By achieving simultaneous measurements of logical operators, our approaches enable fully parallelized quantum computing, thus minimizing computation time. Our schemes are applicable to any qLDPC codes and maintain the low density of parity checks while measuring multiple logical operators simultaneously. These results enhance the feasibility of applying early fault-tolerant technologies to practical problems.

quant-ph↗

arXiv2025

AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models

While diffusion models have achieved remarkable progress in style transfer tasks, existing methods typically rely on fine-tuning or optimizing pre-trained models during inference, leading to high computational costs and challenges in balancing content preservation with style integration. To address these limitations, we introduce AttenST, a training-free attention-driven style transfer framework. Specifically, we propose a style-guided self-attention mechanism that conditions self-attention on the reference style by retaining the query of the content image while substituting its key and value with those from the style image, enabling effective style feature integration. To mitigate style information loss during inversion, we introduce a style-preserving inversion strategy that refines inversion accuracy through multiple resampling steps. Additionally, we propose a content-aware adaptive instance normalization, which integrates content statistics into the normalization process to optimize style fusion while mitigating the content degradation. Furthermore, we introduce a dual-feature cross-attention mechanism to fuse content and style features, ensuring a harmonious synthesis of structural fidelity and stylistic expression. Extensive experiments demonstrate that AttenST outperforms existing methods, achieving state-of-the-art performance in style transfer dataset.

cs.CV↗

arXiv2025

Signal Processing Meets SGD: From Momentum to Filter

In deep learning, stochastic gradient descent (SGD) and its momentum-based variants are widely used for optimization. However, the internal dynamics of these methods remain underexplored. In this paper, we analyze gradient behavior through a signal processing lens, isolating key factors that influence gradient updates and revealing a critical limitation: momentum techniques lack the flexibility to adequately balance bias and variance components in gradients, resulting in gradient estimation inaccuracies. To address this issue, we introduce a novel method SGDF (SGD with Filter) based on Wiener Filter principles, which derives an optimal time-varying gain to refine gradient updates by minimizing the mean square error in gradient estimation. This method yields an optimal first-order gradient estimate, effectively balancing noise reduction and signal preservation. Furthermore, our approach could extend to adaptive optimizers, enhancing their generalization potential. Empirical results show that SGDF achieves superior convergence and generalization compared to traditional momentum methods, and performs competitively with state-of-the-art optimizers.

cs.LG↗

arXiv2025

Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History

Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defined keywords or entities, neglecting user attributes and preferences implicit in dialogue history, hindering the development of long-term user intimacy. To address these challenges, we take a radical step towards building a more human-like conversational agent by integrating proactive dialogue systems with long-term memory into a unified framework. Specifically, we define a novel task named Memory-aware Proactive Dialogue (MapDia). By decomposing the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive Dataset (ChMapData). Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, designed to steer dialogues towards relevant historical topics at the right time. The effectiveness of our dataset and models is validated through both automatic and human evaluations. We release the open-source framework and dataset at https://github.com/FrontierLabs/MapDia.

cs.CL↗

arXiv2025

Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients

Spiking neural networks (SNNs) have shown their competence in handling spatial-temporal event-based data with low energy consumption. Similar to conventional artificial neural networks (ANNs), SNNs are also vulnerable to gradient-based adversarial attacks, wherein gradients are calculated by spatial-temporal back-propagation (STBP) and surrogate gradients (SGs). However, the SGs may be invisible for an inference-only model as they do not influence the inference results, and current gradient-based attacks are ineffective for binary dynamic images captured by the dynamic vision sensor (DVS). While some approaches addressed the issue of invisible SGs through universal SGs, their SGs lack a correlation with the victim model, resulting in sub-optimal performance. Moreover, the imperceptibility of existing SNN-based binary attacks is still insufficient. In this paper, we introduce an innovative potential-dependent surrogate gradient (PDSG) method to establish a robust connection between the SG and the model, thereby enhancing the adaptability of adversarial attacks across various models with invisible SGs. Additionally, we propose the sparse dynamic attack (SDA) to effectively attack binary dynamic images. Utilizing a generation-reduction paradigm, SDA can fully optimize the sparsity of adversarial perturbations. Experimental results demonstrate that our PDSG and SDA outperform state-of-the-art SNN-based attacks across various models and datasets. Specifically, our PDSG achieves 100% attack success rate on ImageNet, and our SDA obtains 82% attack success rate by modifying only 0.24% of the pixels on CIFAR10DVS. The code is available at https://github.com/ryime/PDSG-SDA .

cs.CV↗

arXiv2025

An Empirical Study on Commit Message Generation using LLMs via In-Context Learning

Commit messages concisely describe code changes in natural language and are important for software maintenance. Several approaches have been proposed to automatically generate commit messages, but they still suffer from critical limitations, such as time-consuming training and poor generalization ability. To tackle these limitations, we propose to borrow the weapon of large language models (LLMs) and in-context learning (ICL). Our intuition is based on the fact that the training corpora of LLMs contain extensive code changes and their pairwise commit messages, which makes LLMs capture the knowledge about commits, while ICL can exploit the knowledge hidden in the LLMs and enable them to perform downstream tasks without model tuning. However, it remains unclear how well LLMs perform on commit message generation via ICL. In this paper, we conduct an empirical study to investigate the capability of LLMs to generate commit messages via ICL. Specifically, we first explore the impact of different settings on the performance of ICL-based commit message generation. We then compare ICL-based commit message generation with state-of-the-art approaches on a popular multilingual dataset and a new dataset we created to mitigate potential data leakage. The results show that ICL-based commit message generation significantly outperforms state-of-the-art approaches on subjective evaluation and achieves better generalization ability. We further analyze the root causes for LLM's underperformance and propose several implications, which shed light on future research directions for using LLMs to generate commit messages.

cs.SE↗

arXiv2025

Understanding infection risks of COVID-19 in the city: an investigation of infected neighborhoods in Wuhan

During the COVID-19 pandemic, built environments in dense urban settings become major sources of infection. This study tests the difference of demographics and surrounding built environments across high-, medium- and low-infection neighborhoods, to inform the high-risk areas in the city. We found that high-infection neighborhoods own a higher ratio of aged population than other neighborhoods on average. However, it shows no statistical difference in terms of population density. Additionally, high-infection neighborhoods are closer to high-risk built environments than the others. In a walking distance, they also can access more of the high-risk built environments except for the wholesale markets and shopping malls. These findings advise policy-makers to deploy social distancing measures in precision, regulating the access of high-risk facilities to mitigate the impacts of COVID-19.

physics.soc-ph↗

arXiv2025

Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image Classification

Data-hungry HSI classification methods require high-quality labeled HSIs, which are often costly to obtain. This characteristic limits the performance potential of data-driven methods when dealing with limited annotated samples. Bridging the domain gap between data acquired from different sensors allows us to utilize abundant labeled data across sensors to break this bottleneck. In this paper, we propose a novel Attention-Gated Tuning (AGT) strategy and a triplet-structured transformer model, Tri-Former, to address this issue. The AGT strategy serves as a bridge, allowing us to leverage existing labeled HSI datasets, even RGB datasets to enhance the performance on new HSI datasets with limited samples. Instead of inserting additional parameters inside the basic model, we train a lightweight auxiliary branch that takes intermediate features as input from the basic model and makes predictions. The proposed AGT resolves conflicts between heterogeneous and even cross-modal data by suppressing the disturbing information and enhances the useful information through a soft gate. Additionally, we introduce Tri-Former, a triplet-structured transformer with a spectral-spatial separation design that enhances parameter utilization and computational efficiency, enabling easier and flexible fine-tuning. Comparison experiments conducted on three representative HSI datasets captured by different sensors demonstrate the proposed Tri-Former achieves better performance compared to several state-of-the-art methods. Homologous, heterologous and cross-modal tuning experiments verified the effectiveness of the proposed AGT. Code has been released at: \href{https://github.com/Cecilia-xue/AGT}{https://github.com/Cecilia-xue/AGT}.

cs.CV↗

arXiv2025

Systematic Analysis of $B_s \to SP$ Decays in Perturbative QCD Approach

Within the perturbative QCD (PQCD) framework, we present a systematic investigation of charmless $B_s \to SP$ decays, where $S$ and $P$ denote scalar and pseudoscalar mesons, respectively. By employing two distinct structural scenarios for scalar mesons, we calculate the branching fractions and direct $CP$ asymmetries for these processes. Our results reveal branching fractions ranging from $10^{-7}$ to $10^{-5}$, values that are well within the measurable range of current experiments. A striking contrast emerges between penguin- and tree-dominated decays: while penguin-dominated processes yield larger branching fractions, tree-dominated decays exhibit significantly enhanced direct $CP$ asymmetries. In particular, the decays $B_s \to f_0(1370) η$ and $B_s \to a_0(1450) K$ demonstrate marked sensitivity to the choice of scalar meson scenario, offering critical constraints for identifying the optimal model once experimental data are available. Furthermore, we calculate the branching fractions of $B_s \to f_0(980)(σ)P$ decays in two distinct ranges of the mixing angle of $f_0(980)-σ$. The dependencies of both branching fractions and $CP$ asymmetries on this mixing angle are rigorously analyzed, establishing a framework essential for determining its value with future experimental results. These findings provide a robust theoretical foundation for advancing the understanding of nonleptonic $B_s$decays in QCD-based formalisms, as well as the nature of scalar mesons.

hep-ph↗

arXiv2025

Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction

Log parsing transforms log messages into structured formats, serving as a crucial step for log analysis. Despite a variety of log parsers that have been proposed, their performance on evolving log data remains unsatisfactory due to reliance on human-crafted rules or learning-based models with limited training data. The recent emergence of large language models (LLMs) has demonstrated strong abilities in understanding natural language and code, making it promising to apply LLMs for log parsing. Consequently, several studies have proposed LLM-based log parsers. However, LLMs may produce inaccurate templates, and existing LLM-based log parsers directly use the template generated by the LLM as the parsing result, hindering the accuracy of log parsing. Furthermore, these log parsers depend heavily on historical log data as demonstrations, which poses challenges in maintaining accuracy when dealing with scarce historical log data or evolving log data. To address these challenges, we propose AdaParser, an effective and adaptive log parsing framework using LLMs with self-generated in-context learning (SG-ICL) and self-correction. To facilitate accurate log parsing, AdaParser incorporates a novel component, a template corrector, which utilizes the LLM to correct potential parsing errors in the templates it generates. In addition, AdaParser maintains a dynamic candidate set composed of previously generated templates as demonstrations to adapt evolving log data. Extensive experiments on public large-scale datasets indicate that AdaParser outperforms state-of-the-art methods across all metrics, even in zero-shot scenarios. Moreover, when integrated with different LLMs, AdaParser consistently enhances the performance of the utilized LLMs by a large margin.

cs.SE↗

arXiv2025

Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing their underlying promotion campaigns. Particularly, we observe that PIPs are prevalent on Twitter, along with extensive visibility on other three popular OSNs including YouTube, Facebook, and TikTok. For instance, applying our PIP hunter to the Twitter platform for 6 months has led to the discovery of 12 million distinct PIPs which are widely distributed in 5 major natural languages and 10 illicit categories, e.g., drugs, data leakage, gambling, and weapon sales. Along the discovery of PIPs are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging accounts that are embedded in PIPs and serve as next hops of communication with prospective customers. Also, an arms race between Twitter and illicit promotion operators is also observed. Especially, 90% PIPs can survice the first two months since getting published on Twitter, which is likely due to the diverse evasion tactics adopted by miscreants to masquerade PIPs.

cs.CR↗

arXiv2025

SoK: Towards Effective Automated Vulnerability Repair

The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques. This Systematization of Knowledge (SoK) provides a comprehensive overview of the AVR landscape, encompassing both synthetic and real-world vulnerabilities. Through a systematic literature review and quantitative benchmarking across diverse datasets, methods, and strategies, we establish a taxonomy of existing AVR methodologies, categorizing them into template-guided, search-based, constraint-based, and learning-driven approaches. We evaluate the strengths and limitations of these approaches, highlighting common challenges and practical implications. Our comprehensive analysis of existing AVR methods reveals a diverse landscape with no single ``best'' approach. Learning-based methods excel in specific scenarios but lack complete program understanding, and both learning and non-learning methods face challenges with complex vulnerabilities. Additionally, we identify emerging trends and propose future research directions to advance the field of AVR. This SoK serves as a valuable resource for researchers and practitioners, offering a structured understanding of the current state-of-the-art and guiding future research and development in this critical domain.

cs.CR↗

arXiv2025

Quasi-Two-Body $B\to P (K^+K^-, π^+π^-)$ Decays with $f_0(980)$ Resonance

In this study, we investigate the $CP$-averaged branching fractions and direct $CP$ asymmetries in the quasi-two-body decays $B\to P (f_0(980) \to )(K^+K^-, π^+π^-)$ within the framework of perturbative QCD approach, where $P$ denotes light pseudoscalar mesons ($π$,$K$). The timelike form factor $F_{S}(ω)$, characterizing final-state interactions between collinear particles in the resonant region, is modeled through the revised relativistic Breit-Wigner formalism for the $S$-wave $f_0(980)$ resonance. Within the Gegenbauer moments of the $S$-wave two-meson distribution amplitudes constrained by current data, we predict the branching fractions of $B\to P (f_0(980) \to )(K^+K^-, π^+π^-)$ to lie in the range of $10^{-8}$ to $10^{-6}$. Our calculations reveal that our theoretical results for some decay modes exhibit satisfactory agreement with existing measurements from the BaBar and Belle collaborations. Furthermore, we extract the corresponding branching fractions of $B \to P f_0(980)$ decays from the quasi-two-body counterparts $B\to P (f_0(980) \to )π^+π^-$ rather than $B\to P (f_0(980) \to )K^+K^-$. All predictions are awaiting experimental verification in current and upcoming collider experiments.

hep-ph↗

arXiv2025

Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.

cs.SE↗

arXiv2025

Convection-modulated topological edge mode and extended-localized criticality in thermal metamaterials

Convection offers a dynamic and flexible approach to achieving a variety of novel physical phenomena beyond pure conduction. Here, we demonstrate that thermal metamaterials with convection modulation enable the realization of non-Hermitian topological edge modes and bulk mode criticality. We illustrate that a periodic modulation can induce localized edge modes within the band gap. The temperature field of the topological state is localized at the edge rings, decaying exponentially at a fixed rate. Additionally, we introduce an extended-localized criticality through the quasiperiodic convection modulation in thermotics. The convections have an advantage of fantastic tunability in the application. Our work proposes a scheme for implementing topological modes and bulk mode criticality through modulating convection in diffusion systems, paving the way for the design of reconfigurable thermal devices.

physics.app-ph↗

arXiv2025

Device-aware Optical Adversarial Attack for a Portable Projector-camera System

Deep-learning-based face recognition (FR) systems are susceptible to adversarial examples in both digital and physical domains. Physical attacks present a greater threat to deployed systems as adversaries can easily access the input channel, allowing them to provide malicious inputs to impersonate a victim. This paper addresses the limitations of existing projector-camera-based adversarial light attacks in practical FR setups. By incorporating device-aware adaptations into the digital attack algorithm, such as resolution-aware and color-aware adjustments, we mitigate the degradation from digital to physical domains. Experimental validation showcases the efficacy of our proposed algorithm against real and spoof adversaries, achieving high physical similarity scores in FR models and state-of-the-art commercial systems. On average, there is only a 14% reduction in scores from digital to physical attacks, with high attack success rate in both white- and black-box scenarios.

cs.CV↗