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Gang Li

Publications and source records attributed to Gang Li.

At least 217 records · Page 12Linked to original sources

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct doctor downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. Our model and dataset will be open-source for community development.

cs.CL↗

Detector integration at HEPS: a systematic, efficient and high-performance approach

At least 25 kinds of detector-like devices need to be integrated in Phase I of the High Energy Photon Source (HEPS), and the work needs to be carefully planned to maximise productivity with highly limited human resources. After a systematic analysis on the actual work involved in detector integration, a separation of concerns between collaborating groups of personnel is established to minimise the duplication of efforts. To facilitate software development for detector integration, the ADGenICam library, which abstracts repeated code in EPICS modules for cameras, is extended to support a much wider range of detectors. An increasingly considerable fraction of detectors, both inside and outside HEPS, offer performance that exceed capabilities of the areaDetector framework in EPICS. Given this background, areaDetector's limitations in performance and architecture are analysed, and a QueueIOC -based framework that overcomes these limitations is introduced. A simple, flexible ZeroMQ-based protocol is used for data transport in this framework, while RDMA transport and multi-node readout will be explored for higher data throughputs. By calling C/C++ libraries from within Python, the performance of the former and the expressiveness of the latter can coexist nicely; the expressiveness allows for much higher efficiency in the implementation and use of integration modules functionally comparable to their EPICS counterparts.

physics.ins-det↗

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare. However, the trustworthiness of Med-LVLMs remains unverified, posing significant risks for future model deployment. In this paper, we introduce CARES and aim to comprehensively evaluate the Trustworthiness of Med-LVLMs across the medical domain. We assess the trustworthiness of Med-LVLMs across five dimensions, including trustfulness, fairness, safety, privacy, and robustness. CARES comprises about 41K question-answer pairs in both closed and open-ended formats, covering 16 medical image modalities and 27 anatomical regions. Our analysis reveals that the models consistently exhibit concerns regarding trustworthiness, often displaying factual inaccuracies and failing to maintain fairness across different demographic groups. Furthermore, they are vulnerable to attacks and demonstrate a lack of privacy awareness. We publicly release our benchmark and code in https://cares-ai.github.io/.

cs.LG↗

UniAR: A Unified model for predicting human Attention and Responses on visual content

Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes. Yet most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual content. We propose UniAR -- a unified model of human attention and preference behavior across diverse visual content. UniAR leverages a multimodal transformer to predict subjective feedback, such as satisfaction or aesthetic quality, along with the underlying human attention or interaction heatmaps and viewing order. We train UniAR on diverse public datasets spanning natural images, webpages, and graphic designs, and achieve SOTA performance on multiple benchmarks across various image domains and behavior modeling tasks. Potential applications include providing instant feedback on the effectiveness of UIs/visual content, and enabling designers and content-creation models to optimize their creation for human-centric improvements.

cs.CV↗

3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation

Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded assessment (DccA)for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT). Our proposed 3D DccA accurately segments 3D vessel lumens and stents in IV-OCT images, using a spatial matching network and dual-layer training with style transfer. It quantifies and maps stent-lumen distances into a 3D color space, facilitating 3D visual assessment of PCI stent apposition. Achieving over 95% segmentation precision, our proposed DccA enhances clinical evaluation of PCI stent deployment and supports personalized treatment planning.

cs.CV↗

FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models

The rapid development of generative diffusion models has significantly advanced the field of style transfer. However, most current style transfer methods based on diffusion models typically involve a slow iterative optimization process, e.g., model fine-tuning and textual inversion of style concept. In this paper, we introduce FreeStyle, an innovative style transfer method built upon a pre-trained large diffusion model, requiring no further optimization. Besides, our method enables style transfer only through a text description of the desired style, eliminating the necessity of style images. Specifically, we propose a dual-stream encoder and single-stream decoder architecture, replacing the conventional U-Net in diffusion models. In the dual-stream encoder, two distinct branches take the content image and style text prompt as inputs, achieving content and style decoupling. In the decoder, we further modulate features from the dual streams based on a given content image and the corresponding style text prompt for precise style transfer. Our experimental results demonstrate high-quality synthesis and fidelity of our method across various content images and style text prompts. Compared with state-of-the-art methods that require training, our FreeStyle approach notably reduces the computational burden by thousands of iterations, while achieving comparable or superior performance across multiple evaluation metrics including CLIP Aesthetic Score, CLIP Score, and Preference. We have released the code at: https://github.com/FreeStyleFreeLunch/FreeStyle.

cs.CV↗