arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 217 records · Page 12Linked to original sources

arXiv2025

Determination of Strong Coupling Constant from Inclusive Semileptonic Decays of Charmed Mesons

Employing the heavy quark expansion model with the kinetic scheme, we evaluate $α_S(m_c^2)$, the strong coupling constant at the charm quark mass $m_c$ with data on inclusive semileptonic decays of charmed mesons. Using the experimental values of semileptonic decay widths of the $D^0$ and the $D^+$, the value of $α_{s}(m_c^{2})$ is determined to be $0.445\pm0.009\pm0.114$, where the first uncertainty is experimental and the second systematic. This reported $α_{s}(m_c^{2})$ is in good agreement with the value of $α_{s}(m_c^{2})$ calculated by running $α_S(m_Z^2)$ at the $Z^0$ boson mass $m_Z$ with the renormalization group evolution equation. In addition, values of $α_{s}(m_c^{2})$ obtained individually from each of the $D^0$, $D^+$, and $D_s^+$ mesons are found to be consistent being of the same origin.

hep-ph↗

arXiv2025

FlowAgent: Achieving Compliance and Flexibility for Workflow Agents

The integration of workflows with large language models (LLMs) enables LLM-based agents to execute predefined procedures, enhancing automation in real-world applications. Traditional rule-based methods tend to limit the inherent flexibility of LLMs, as their predefined execution paths restrict the models' action space, particularly when the unexpected, out-of-workflow (OOW) queries are encountered. Conversely, prompt-based methods allow LLMs to fully control the flow, which can lead to diminished enforcement of procedural compliance. To address these challenges, we introduce FlowAgent, a novel agent framework designed to maintain both compliance and flexibility. We propose the Procedure Description Language (PDL), which combines the adaptability of natural language with the precision of code to formulate workflows. Building on PDL, we develop a comprehensive framework that empowers LLMs to manage OOW queries effectively, while keeping the execution path under the supervision of a set of controllers. Additionally, we present a new evaluation methodology to rigorously assess an LLM agent's ability to handle OOW scenarios, going beyond routine flow compliance tested in existing benchmarks. Experiments on three datasets demonstrate that FlowAgent not only adheres to workflows but also effectively manages OOW queries, highlighting its dual strengths in compliance and flexibility. The code is available at https://github.com/Lightblues/FlowAgent.

cs.AI↗

arXiv2025

Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC

The Circular Electron Positron Collider (CEPC) is a large-scale particle accelerator designed to collide electrons and positrons at high energies. One of the primary goals of the CEPC is to achieve high-precision measurements of the properties of the Higgs boson, facilitated by the large number of Higgs bosons that can be produced with significantly low contamination. The measurements of Higgs boson branching fractions into $b\overline{b} /c\overline{c} /gg$ and $τ\overlineτ /WW^{*} /ZZ^{*} $, where the $W$ or $Z$ bosons decay hadronically, are presented in the context of the CEPC experiment, assuming a scenario with 5600 fb$^{-1}$ of collision data at a center-of-mass energy of 240 GeV. In this study the Higgs bosons are produced in association with a $Z$ boson, with the $Z$ boson decaying into a pair of muons $(μ^{+}μ^{-})$, which have high efficiency and high resolution. In order to separate all decay channels simultaneously with high accuracy, the Particle Flow Network (PFN), a graph-based machine learning model, is considered. The precise classification provided by the PFN is employed in measuring the branching fractions using the migration matrix method, which accurately corrects for detector effects in each decay channel. The statistical uncertainty of the measured branching ratio is estimated to be 0.55% in $H\to b\overline{b}$ final state, and approximately 1.5%-16% in $H\to c\overline{c} /gg/τ\overlineτ/WW^{*} /ZZ^{*} $ final states. In addition, the main sources of systematic uncertainties to the measurement of the branching fractions are discussed.

hep-ex↗

arXiv2025

PAGNet: Pluggable Adaptive Generative Networks for Information Completion in Multi-Agent Communication

For partially observable cooperative tasks, multi-agent systems must develop effective communication and understand the interplay among agents in order to achieve cooperative goals. However, existing multi-agent reinforcement learning (MARL) with communication methods lack evaluation metrics for information weights and information-level communication modeling. This causes agents to neglect the aggregation of multiple messages, thereby significantly reducing policy learning efficiency. In this paper, we propose pluggable adaptive generative networks (PAGNet), a novel framework that integrates generative models into MARL to enhance communication and decision-making. PAGNet enables agents to synthesize global states representations from weighted local observations and use these representations alongside learned communication weights for coordinated decision-making. This pluggable approach reduces the computational demands typically associated with the joint training of communication and policy networks. Extensive experimental evaluations across diverse benchmarks and communication scenarios demonstrate the significant performance improvements achieved by PAGNet. Furthermore, we analyze the emergent communication patterns and the quality of generated global states, providing insights into operational mechanisms.

cs.MA↗

arXiv2025

Constraints on neutrino non-standard interactions from COHERENT, PandaX-4T and XENONnT

We investigate constraints on neutrino non-standard interactions (NSIs) in the effective field theory framework, using data from the first measurement of solar $^8$B neutrinos via coherent elastic neutrino-nucleus scattering (CE$ν$NS) in the PandaX-4T and XENONnT experiments and data from the COHERENT experiment. The impacts of neutrino NSIs on the CE$ν$NS cross section and the matter effect in the propagation of solar neutrinos are included, while we obtain that the expected number of CE$ν$NS events is more sensitive to neutrino NSIs appearing in the cross section. Due to relatively large statistical uncertainties, the sensitivities of the PandaX-4T and XENONnT experiments to the neutrino NSIs are currently limited, compared to the COHERENT experiment. Besides, we find that since the central value of the measured CE$ν$NS counts significantly differs from the Standard Model prediction, the sensitivity of PandaX-4T experiment is even more restricted compared to XENONnT. However, the measurements of PandaX-4T and XENONnT are uniquely sensitive to the neutrino NSIs for the $τ$ flavor due to oscillation feature of the solar $^8$B neutrinos. We also assess how the experimental central value, exposure, and systematic uncertainties will affect the constraints on neutrino NSIs from various CE$ν$NS measurements in the future.

hep-ph↗

arXiv2025

Fusion of Millimeter-wave Radar and Pulse Oximeter Data for Low-burden Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome

Objective: The aim of the study is to develop a novel method for improved diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical or home settings, with the focus on achieving diagnostic performance comparable to the gold-standard polysomnography (PSG) with significantly reduced monitoring burden. Methods: We propose a method using millimeter-wave radar and pulse oximeter for OSAHS diagnosis (ROSA). It contains a sleep apnea-hypopnea events (SAE) detection network, which directly predicts the temporal localization of SAE, and a sleep staging network, which predicts the sleep stages throughout the night, based on radar signals. It also fuses oxygen saturation (SpO2) information from the pulse oximeter to adjust the score of SAE detected by radar. Results: Experimental results on a real-world dataset (>800 hours of overnight recordings, 100 subjects) demonstrated high agreement (ICC=0.9870) on apnea-hypopnea index (AHI) between ROSA and PSG. ROSA also exhibited excellent diagnostic performance, exceeding 90% in accuracy across AHI diagnostic thresholds of 5, 15 and 30 events/h. Conclusion: ROSA improves diagnostic accuracy by fusing millimeter-wave radar and pulse oximeter data. It provides a reliable and low-burden solution for OSAHS diagnosis. Significance: ROSA addresses the limitations of high complexity and monitoring burden associated with traditional PSG. The high accuracy and low burden of ROSA show its potential to improve the accessibility of OSAHS diagnosis among population.

eess.SP↗

arXiv2025

Production and decay of anticharmed pentaquark state with quark content $\bar{c}sudd$

We used an effective Lagrangian approach to investigate the production of the anticharmed pentaquark state $P_{\bar{c}s}^{(*)-}$ with the minimal quark content $\bar{c}sudd$ in the $Λ_b^0$ decay and its decay into the $ΛD^{(\ast)-}$ and $ΣD^{(\ast)-}$. In our calculation, the $P_{\bar{c}s}^{(*)-}$ is considered as the molecule state of the $nD_{s0(s1)}^{-}$ in an $S$ wave, and its production and decay occur via triangle loops at the hadron level. The predicted branching fractions for the processes $Λ_b^0\to P_{\bar{c}s}^{(*)-} D^+ $ are around $10^{-4}\sim 10^{-3}$. The partial decay widths of the $ P_{\bar{c}s}^{(*)-} \to ΛD^{(\ast)-}$ are between $0.1$ and $\sim 10~\mathrm{MeV}$, whereas the decay widths of the $ P_{\bar{c}s}^{(*)-} \to ΣD^{(\ast)-}$ are about one order of magnitude smaller. It is found that the width ratios for the $P_{\bar{c}s}^{(*)-}$ decays are nearly independent of the model parameter $α$. It is hoped that these predictions could be helpful in searching for the anticharmed-strange pentaquark candidates in future LHCb experiments.

hep-ph↗

arXiv2025

A Semiparametric Bayesian Method for Instrumental Variable Analysis with Partly Interval-Censored Time-to-Event Outcome

This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confounders and measurement errors with partly interval-censored time-to-event data, where event times are observed exactly for some subjects but left-censored, right-censored, or interval-censored for others. Our method is based on a two-stage Dirichlet process mixture instrumental variable (DPMIV) model which simultaneously models the first-stage random error term for the exposure variable and the second-stage random error term for the time-to-event outcome using a bivariate Gaussian mixture of the Dirichlet process (DPM) model. The DPM model can be broadly understood as a mixture model with an unspecified number of Gaussian components, which relaxes the normal error assumptions and allows the number of mixture components to be determined by the data. We develop an MCMC algorithm for the DPMIV model tailored for partly interval-censored data and conduct extensive simulations to assess the performance of our DPMIV method in comparison with some competing methods. Our simulations revealed that our proposed method is robust under different error distributions and can have superior performance over its parametric counterpart under various scenarios. We further demonstrate the effectiveness of our approach on an UK Biobank data to investigate the causal effect of systolic blood pressure on time-to-development of cardiovascular disease from the onset of diabetes mellitus.

stat.ME↗

arXiv2025

Towards A Hybrid Quantum Differential Privacy

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights teh need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect teh multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation.

quant-ph↗

arXiv2025

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation

Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety. The prevailing approach to address these issues involves collecting human feedback on generated images, training reward models to estimate human feedback, and then fine-tuning T2I models based on the reward models to align them with human preferences. However, while existing reward fine-tuning methods can produce images with higher rewards, they may change model behavior in unexpected ways. For example, fine-tuning for one quality aspect (e.g., safety) may degrade other aspects (e.g., prompt alignment), or may lead to reward hacking (e.g., finding a way to increase rewards without having the intended effect). In this paper, we propose Focus-N-Fix, a region-aware fine-tuning method that trains models to correct only previously problematic image regions. The resulting fine-tuned model generates images with the same high-level structure as the original model but shows significant improvements in regions where the original model was deficient in safety (over-sexualization and violence), plausibility, or other criteria. Our experiments demonstrate that Focus-N-Fix improves these localized quality aspects with little or no degradation to others and typically imperceptible changes in the rest of the image. Disclaimer: This paper contains images that may be overly sexual, violent, offensive, or harmful.

cs.CV↗

arXiv2025

Large Language Models for Bioinformatics

With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

q-bio.QM↗

arXiv2024

Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models

Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pinpoint the most beneficial datapoints, data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. However, under the context of instruction tuning, there still exists a gap in knowledge on what kind of data evaluation metrics can be employed and how they can be integrated into the selection mechanism. To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs. We systematically categorize all applicable methods into quality-based, diversity-based, and importance-based ones where a unified, fine-grained taxonomy is structured. For each category, representative methods are elaborated to describe the landscape of relevant research. In addition, comparison between the latest methods is conducted on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize the open challenges and propose the promosing avenues for future studies. All related contents are available at https://github.com/yuleiqin/fantastic-data-engineering.

cs.CV↗

arXiv2024

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline

Enterprises possess a vast array of API assets scattered across various functions, forming the backbone of existing business processes. By leveraging these APIs as functional tools, enterprises can design diverse, scenario-specific agent applications, driven by on-premise function-calling models as the core engine. However, generic models often fail to meet enterprise requirements in terms of computational efficiency, output accuracy, and stability, necessitating scenario-specific adaptation. In this paper, we propose a training pipeline for function-calling capabilities tailored to real-world business scenarios. This pipeline includes the synthesis and augmentation of scenario-specific function-calling data, model fine-tuning, and performance evaluation and analysis. Using this pipeline, we generated 1,260 fully AI-generated samples and 1,035 augmented manually-labeled samples in digital HR agent scenario. The Qwen2.5-Coder-7B-Instruct model was employed as the base model and fine-tuned using the LoRA method on four GPUs with 24GB VRAM. Our fine-tuned model demonstrated outstanding performance in evaluations and practical applications, surpassing GPT-4 and GPT-4o in accuracy on the test set. These results validate the reliability of the proposed pipeline for training scenario-specific function-calling models.

cs.AI↗

arXiv2024

Fast-rotating A- and F-type stars with Hα emissions in NGC 3532, candidate UV-dim stars?

Extended main-sequence stars that are dim in the ultraviolet passbands of Hubble Space Telescope (UV-dim stars) are found in several young and intermediate-age Magellanic Cloud star clusters. The obscuring of the dust in the discs of stars expelled due to fast rotation have been suggested to be responsible for the appearance of UV-dim stars, and play an important role in the formation of extended main-sequences. In this paper, we report a population of A- and F-type stars who show Hα emission features in their spectra in a young (~ 340 Myr-old) Galactic neighboring star cluster NGC 3532. By fitting the observed absorption profiles, we found that most Hα emitters are fast rotating stars, indicating that they form decretion discs by fast rotation like Be stars. As A- and F-type stars dominate the extended main-sequence turn-off regions of intermediate-age clusters, their appearance provides observational evidence to support the dust extinction scenario for these clusters, and might be the counterparts of UV-dim stars that are detected in remote Magellanic Cloud star clusters like NGC 1783.

astro-ph.SR↗

arXiv2024

Separate, Dynamic and Differentiable (SMART) Pruner for Block/Output Channel Pruning on Computer Vision Tasks

Block pruning, which eliminates contiguous blocks of weights, is a structural pruning method that can significantly enhance the performance of neural processing units (NPUs). In industrial applications, an ideal block pruning algorithm should meet three key requirements: (1) maintain high accuracy across diverse models and tasks, as machine learning deployments on edge devices are typically accuracy-critical; (2) offer precise control over resource constraints to facilitate user adoption; and (3) provide convergence guarantees to prevent performance instability. However, to the best of our knowledge, no existing block pruning algorithm satisfies all three requirements simultaneously. In this paper, we introduce SMART (Separate, Dynamic, and Differentiable) pruning, a novel algorithm designed to address this gap. SMART leverages both weight and activation information to enhance accuracy, employs a differentiable top-k operator for precise control of resource constraints, and offers convergence guarantees under mild conditions. Extensive experiments involving seven models, four datasets, three different block types, and three computer vision tasks demonstrate that SMART pruning achieves state-of-the-art performance in block pruning.

cs.CV↗

arXiv2024

Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response can enable innovative applications across various industries. Currently, no efforts have achieved the inverse design of FRPCs that target the entire stress-strain curve. Here, we develop PC3D_Diffusion, a 3D spatial diffusion model designed for the inverse design of FRPCs. We generate 1.35 million FRPCs and calculate their stress-strain curves for training. Although the vanilla PC3D_Diffusion can generate visually appealing results, less than 10% of FRPCs generated by the vanilla model are collision-free, in which fibers do not intersect with each other. We then propose a loss-guided, learning-free approach to apply physical constraints during generation. As a result, PC3D_Diffusion can generate high-quality designs with tailored mechanical behaviors while guaranteeing to satisfy the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.

cond-mat.soft↗

arXiv2024

KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern Asteroseismic modelling of the g-mode period-spacing pattern

In this work, we aim to estimate the stellar parameters of the primary (Aa) by performing asteroseismic analysis on its period-spacing pattern. We use the C-3PO neural network to perform asteroseismic modelling of the g-mode period-spacing pattern of Aa, discussing the interplay of this information with external constraints from spectroscopy ($T_{\rm eff}$ and $\log(g)$) and eclipse modelling ($R$). To estimate the level of uncertainty due to different frequency extraction and pattern identification processes, we consider four different variations on the period-spacing patterns. To better understand the correlations between and the uncertainty structure of our parameter estimates, we also employed a classical, parameter-based MCMC grid search on four different stellar grids. The best-fitting, externally constrained model to the period-spacing pattern arrives at estimates of the stellar properties for Aa of: $M=1.51 \pm 0.05 M_\odot$, $X_c =0.43 \pm 0.04$, $R=1.66 \pm 0.1 R_\odot$, $f_{\rm ov}=0.010$, $Ω_c=1.58 \pm 0.01$ d$^{-1}$ with rigid rotation to within the measurement errors, $\log(T_{\rm eff})=3.856 \pm 0.008$ dex, $\log(g)=4.18 \pm 0.04$ dex, and $\log(L)=0.809 \pm 0.005$ dex, which agree well with previous measurements from eclipse modelling, spectroscopy, and the Gaia DR3 luminosity. We find that the near-core properties of the best-fitting asteroseismic models are consistent with external constraints from eclipse modelling and spectroscopy. Aa appears to be a typical example of a $γ$ Dor star, fitting well within existing populations. We find that Aa is quasi-rigidly rotating to within the uncertainties, and note that the asteroseismic age estimate for Aa (1100 $\pm$ 100 Myr) is considerably older than the young (35 Myr) age implied by previous isochrone fits to the B binary in the literature. Our MCMC parameter-based grid-search agrees well with our pattern-modelling approach.

astro-ph.SR↗

arXiv2024

Artificial General Intelligence for Medical Imaging Analysis

Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.

cs.AI↗