arXiv subjects

Ling Zhang

Publications and source records attributed to Ling Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2025

HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges

Heuristic algorithms play a vital role in solving combinatorial optimization (CO) problems, yet traditional designs depend heavily on manual expertise and struggle to generalize across diverse instances. We introduce \textbf{HeurAgenix}, a two-stage hyper-heuristic framework powered by large language models (LLMs) that first evolves heuristics and then selects among them automatically. In the heuristic evolution phase, HeurAgenix leverages an LLM to compare seed heuristic solutions with higher-quality solutions and extract reusable evolution strategies. During problem solving, it dynamically picks the most promising heuristic for each problem state, guided by the LLM's perception ability. For flexibility, this selector can be either a state-of-the-art LLM or a fine-tuned lightweight model with lower inference cost. To mitigate the scarcity of reliable supervision caused by CO complexity, we fine-tune the lightweight heuristic selector with a dual-reward mechanism that jointly exploits singals from selection preferences and state perception, enabling robust selection under noisy annotations. Extensive experiments on canonical benchmarks show that HeurAgenix not only outperforms existing LLM-based hyper-heuristics but also matches or exceeds specialized solvers. Code is available at https://github.com/microsoft/HeurAgenix.

cs.AI↗

arXiv2025

Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation

Automated pathology report generation from Whole Slide Images (WSIs) faces two key challenges: (1) lack of semantic content in visual features and (2) inherent information redundancy in WSIs. To address these issues, we propose a novel Historical Report Guided \textbf{Bi}-modal Concurrent Learning Framework for Pathology Report \textbf{Gen}eration (BiGen) emulating pathologists' diagnostic reasoning, consisting of: (1) A knowledge retrieval mechanism to provide rich semantic content, which retrieves WSI-relevant knowledge from pre-built medical knowledge bank by matching high-attention patches and (2) A bi-modal concurrent learning strategy instantiated via a learnable visual token and a learnable textual token to dynamically extract key visual features and retrieved knowledge, where weight-shared layers enable cross-modal alignment between visual features and knowledge features. Our multi-modal decoder integrates both modals for comprehensive diagnostic reports generation. Experiments on the PathText (BRCA) dataset demonstrate our framework's superiority, achieving state-of-the-art performance with 7.4\% relative improvement in NLP metrics and 19.1\% enhancement in classification metrics for Her-2 prediction versus existing methods. Ablation studies validate the necessity of our proposed modules, highlighting our method's ability to provide WSI-relevant rich semantic content and suppress information redundancy in WSIs. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/BiGen.

cs.CV↗

arXiv2025

PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

The advent of Large Language Models (LLMs) offers potential solutions to address problems such as shortage of medical resources and low diagnostic consistency in psychiatric clinical practice. Despite this potential, a robust and comprehensive benchmarking framework to assess the efficacy of LLMs in authentic psychiatric clinical environments is absent. This has impeded the advancement of specialized LLMs tailored to psychiatric applications. In response to this gap, by incorporating clinical demands in psychiatry and clinical data, we proposed a benchmarking system, PsychBench, to evaluate the practical performance of LLMs in psychiatric clinical settings. We conducted a comprehensive quantitative evaluation of 16 LLMs using PsychBench, and investigated the impact of prompt design, chain-of-thought reasoning, input text length, and domain-specific knowledge fine-tuning on model performance. Through detailed error analysis, we identified strengths and potential limitations of the existing models and suggested directions for improvement. Subsequently, a clinical reader study involving 60 psychiatrists of varying seniority was conducted to further explore the practical benefits of existing LLMs as supportive tools for psychiatrists of varying seniority. Through the quantitative and reader evaluation, we show that while existing models demonstrate significant potential, they are not yet adequate as decision-making tools in psychiatric clinical practice. The reader study further indicates that, as an auxiliary tool, LLM could provide particularly notable support for junior psychiatrists, effectively enhancing their work efficiency and overall clinical quality. To promote research in this area, we will make the dataset and evaluation framework publicly available, with the hope of advancing the application of LLMs in psychiatric clinical settings.

cs.CL↗

arXiv2025

Putative excitonic insulating state in narrow-gap semiconductor La$_3$Cd$_2$As$_6$

Excitonic insulators are electronically-driven phases of matter characterized by the spontaneous condensation of electron-hole pairs. Here we show that La$_3$Cd$_2$As$_6$ undergoes a transition at $T_{0}=278$ K to a highly insulating state with no accompanying structural transition. We observe quasi-two-dimensional electrical transport and charge fluctuations consistent with an electronic transition enabled by enhanced Coulomb interactions. Density functional theory calculations are unable to replicate the insulating ground state. Our results support the opening of a gap by excitonic effects at $T_{0}$, placing La$_3$Cd$_2$As$_6$ as a rare example of a bulk excitonic insulator.

cond-mat.str-el↗

arXiv2025

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data

Mixed methods research integrates quantitative and qualitative data but faces challenges in aligning their distinct structures, particularly in examining measurement characteristics and individual response patterns. Advances in large language models (LLMs) offer promising solutions by generating synthetic survey responses informed by qualitative data. This study investigates whether LLMs, guided by personal interviews, can reliably predict human survey responses, using the Behavioral Regulations in Exercise Questionnaire (BREQ) and interviews from after-school program staff as a case study. Results indicate that LLMs capture overall response patterns but exhibit lower variability than humans. Incorporating interview data improves response diversity for some models (e.g., Claude, GPT), while well-crafted prompts and low-temperature settings enhance alignment between LLM and human responses. Demographic information had less impact than interview content on alignment accuracy. These findings underscore the potential of interview-informed LLMs to bridge qualitative and quantitative methodologies while revealing limitations in response variability, emotional interpretation, and psychometric fidelity. Future research should refine prompt design, explore bias mitigation, and optimize model settings to enhance the validity of LLM-generated survey data in social science research.

cs.CL↗

arXiv2025

The Barzilai-Borwein Method for Distributed Optimization over Unbalanced Directed Networks

This paper studies optimization problems over multi-agent systems, in which all agents cooperatively minimize a global objective function expressed as a sum of local cost functions. Each agent in the systems uses only local computation and communication in the overall process without leaking their private information. Based on the Barzilai-Borwein (BB) method and multi-consensus inner loops, a distributed algorithm with the availability of larger stepsizes and accelerated convergence, namely ADBB, is proposed. Moreover, owing to employing only row-stochastic weight matrices, ADBB can resolve the optimization problems over unbalanced directed networks without requiring the knowledge of neighbors' out-degree for each agent. Via establishing contraction relationships between the consensus error, the optimality gap, and the gradient tracking error, ADBB is theoretically proved to converge linearly to the globally optimal solution. A real-world data set is used in simulations to validate the correctness of the theoretical analysis.

math.OC↗

arXiv2025

Increasing the density limit with ECRH-assisted Ohmic start-up on EAST

High plasma density operation is crucial for a tokamak to achieve energy breakeven and a burning plasma. However, there is often an empirical upper limit of electron density in tokamak operation, namely the Greenwald density limit $n_G$, above which tokamaks generally disrupt. Achieving high-density operations above the density limit has been a long-standing challenge in magnetic confinement fusion research. Here, we report experimental results on EAST tokamak achieving the line-averaged electron density in the range of 1.3 $n_G$ to 1.65 $n_G$,while the usual range in EAST is (0.8-1.0)$n_G$. This is performed with ECRH-assisted Ohmic start-up and a sufficiently high initial neutral density. This is motivated by and consistent with predictions of a recent plasma-wall self-organization (PWSO) theory, that increasing ECRH power or pre-filled gas pressure leads to lower plasma temperatures around divertor target and higher density limits. In addition, the experiments are shown to operate in the density-free regime predicted by the PWSO model. These results suggest a promising scheme for substantially increasing the density limit in tokamaks, a critical advancement toward achieving the burning plasma.

physics.plasm-ph↗

arXiv2025

Long-range electron coherence in Kagome metals

The wave-like nature of electrons lies at the core of quantum mechanics, distinguishing them from classical particles. Landmark experiments have revealed phase coherence of mobile electrons within solids, such as Aharonov-Bohm interference in mesoscopic rings. However, this coherence is typically limited by numerous environmental interactions. Controlling and ideally mitigating such decoherence remains a central challenge in condensed matter physics. Here, we report magnetoresistance oscillations in mesoscopic pillars of the Kagome metal CsV$_3$Sb$_5$ for fields applied parallel to the Kagome planes. Their periodicity is independent of materials parameters, simply given by the number of flux quanta $h/e$ threading between adjacent Kagome layers akin to an atomic-scale Aharonov-Bohm interferometer. Intriguingly they occur under conditions not favorable for typical interference in solids, at temperatures above 20 K and in micrometer-scale devices well exceeding the single-particle mean free path. Further, the oscillations exhibit non-analytic field-angle dependence and scale consistently with a broad range of key electronic responses in CsV$_3$Sb$_5$, pointing to a cooperative mechanism that establishes intrinsic coherence. Our findings provide new insights into the debated origin of correlated order in CsV$_3$Sb$_5$ and establish Kagome metals as a promising platform for interaction-stabilized long-range electron coherence - crucial for both fundamental studies and technological advancements in quantum interference in metallic systems.

cond-mat.str-el↗

arXiv2025

MHD Modeling of the Near-Sun Evolution of Coronal Mass Ejection Initiated from a Sheared Arcade

Coronal mass ejections (CMEs) are phenomena in which the Sun suddenly releases a mass of energy and magnetized plasma, potentially leading to adverse space weather. Numerical simulation provides an important avenue for comprehensively understanding the structure and mechanism of CMEs. Here we present a global-corona MHD simulation of a CME originating from sheared magnetic arcade and its interaction with the near-Sun solar wind. Our simulation encompasses the pre-CME phase with gradual accumulation of free magnetic energy (and building up of a current sheet within the sheared arcade) as driven by the photospheric shearing motion, the initiation of CME as magnetic reconnection commences at the current sheet, and its subsequent evolution and propagation to around 0.1 AU. A twisted magnetic flux rope (MFR), as the main body of the CME, is created by the continuous reconnection during the eruption. By interacting with the ambient field, the MFR experiences both rotation and deflection during the evolution. The CME exhibits a typical three-part structure, namely a bright core, a dark cavity and a bright front. The bright core is mainly located at the lower part of the MFR, where plasma is rapidly pumped in by the high-speed reconnection outflow. The dark cavity contains both outer layer of the MFR and its overlying field that expands rapidly as the whole magnetic structure moves out. The bright front is formed due to compression of plasma ahead of the fast-moving magnetic structure. Future data-driven modeling of CME will be built upon this simulation with real observations used for the bottom boundary conditions.

astro-ph.SR↗

arXiv2025

Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding

Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versatile AI model requires large-scale data and comprehensive annotations, which are often impractical in medical settings. Recent studies leverage radiology reports as a naturally high-quality supervision for medical images, using contrastive language-image pre-training (CLIP) to develop language-informed models for radiological image interpretation. Nonetheless, these approaches typically contrast entire images with reports, neglecting the local associations between imaging regions and report sentences, which may undermine model performance and interoperability. In this paper, we propose a fine-grained vision-language model (fVLM) for anatomy-level CT image interpretation. Specifically, we explicitly match anatomical regions of CT images with corresponding descriptions in radiology reports and perform contrastive pre-training for each anatomy individually. Fine-grained alignment, however, faces considerable false-negative challenges, mainly from the abundance of anatomy-level healthy samples and similarly diseased abnormalities. To tackle this issue, we propose identifying false negatives of both normal and abnormal samples and calibrating contrastive learning from patient-level to disease-aware pairing. We curated the largest CT dataset to date, comprising imaging and report data from 69,086 patients, and conducted a comprehensive evaluation of 54 major and important disease diagnosis tasks across 15 main anatomies. Experimental results demonstrate the substantial potential of fVLM in versatile medical image interpretation. In the zero-shot classification task, we achieved an average AUC of 81.3% on 54 diagnosis tasks, surpassing CLIP and supervised methods by 12.9% and 8.0%, respectively.

cs.CV↗

arXiv2025

Doc-Guided Sent2Sent++: A Sent2Sent++ Agent with Doc-Guided memory for Document-level Machine Translation

The field of artificial intelligence has witnessed significant advancements in natural language processing, largely attributed to the capabilities of Large Language Models (LLMs). These models form the backbone of Agents designed to address long-context dependencies, particularly in Document-level Machine Translation (DocMT). DocMT presents unique challenges, with quality, consistency, and fluency being the key metrics for evaluation. Existing approaches, such as Doc2Doc and Doc2Sent, either omit sentences or compromise fluency. This paper introduces Doc-Guided Sent2Sent++, an Agent that employs an incremental sentence-level forced decoding strategy \textbf{to ensure every sentence is translated while enhancing the fluency of adjacent sentences.} Our Agent leverages a Doc-Guided Memory, focusing solely on the summary and its translation, which we find to be an efficient approach to maintaining consistency. Through extensive testing across multiple languages and domains, we demonstrate that Sent2Sent++ outperforms other methods in terms of quality, consistency, and fluency. The results indicate that, our approach has achieved significant improvements in metrics such as s-COMET, d-COMET, LTCR-$1_f$, and document-level perplexity (d-ppl). The contributions of this paper include a detailed analysis of current DocMT research, the introduction of the Sent2Sent++ decoding method, the Doc-Guided Memory mechanism, and validation of its effectiveness across languages and domains.

cs.CL↗

arXiv2025

Evaluation of tungsten influx rate using line emissions from W$^{5+}$ ions in EAST Tokamak

The S/XB ratios (ionization per emitted photon) allow one to relate spectroscopic emissivity measurements to the impurity influx from a localized source. In this work, we determine the tungsten influx by examining two dominant EUV (Extreme Ultraviolet) line emissions at 382.13 Åand 394.07 Å, corresponding to the $4f 14 5f \rightarrow 4f 14 5d$ radiative transitions of the W$^{5+}$ ion. The ground configuration of W$^{5+}$ consists of the ground level and a metastable level, with the latter having a higher population than the ground state. Therefore, a simple approach assuming that the transitions are independent, i.e., only populated by a unique level source, requires correction. To address this, we have developed a fully collisional-radiative modeling in which 430 levels contribute to the ionization. We have utilized three advanced computational codes -- HULLAC (Hebrew University - Lawrence Livermore Atomic Code), AS (AutoStructure), and FAC (Flexible Atomic Code) -- for the atomic structure calculations. These codes provide the necessary information such as wavelengths, collisional and radiative transition rate coefficients. The FAC code was also used to calculate the direct electron-impact ionization under the distorted-wave approximation. We also included contributions to total ionization from excitation-autoionization processes up to $n = 15$ manifolds from the distorted-wave calculations. Subsequently, we used these results to ascertain the tungsten impurity influx in a dedicated discharge of the EAST tokamak, which operates with full tungsten divertors. In our findings, we observed that for the density range relevant to the edge region of a tokamak reactor, the S/XB ratios are almost independent of electron density but exhibit significant variation with electron temperature.

physics.plasm-ph↗

arXiv2024

Post-hoc Interpretability Illumination for Scientific Interaction Discovery

Model interpretability and explainability have garnered substantial attention in recent years, particularly in decision-making applications. However, existing interpretability tools often fall short in delivering satisfactory performance due to limited capabilities or efficiency issues. To address these challenges, we propose a novel post-hoc method: Iterative Kings' Forests (iKF), designed to uncover complex multi-order interactions among variables. iKF iteratively selects the next most important variable, the "King", and constructs King's Forests by placing it at the root node of each tree to identify variables that interact with the "King". It then generates ranked short lists of important variables and interactions of varying orders. Additionally, iKF provides inference metrics to analyze the patterns of the selected interactions and classify them into one of three interaction types: Accompanied Interaction, Synergistic Interaction, and Hierarchical Interaction. Extensive experiments demonstrate the strong interpretive power of our proposed iKF, highlighting its great potential for explainable modeling and scientific discovery across diverse scientific fields.

cs.LG↗

arXiv2024

A Multicast Scheme for Live Streaming Courses in Large-Scale, Geographically Dense Campus Networks

Video courses have become a significant component of modern education. However, the increasing demand for live streaming video courses places considerable strain on the service capabilities of campus networks. The challenges associated with live streaming course videos in campus network environments exhibit distinct spatial distribution characteristics. The audience for specific video courses may be highly concentrated in certain areas, leading to a large number of users attempting to access the same live stream simultaneously. Utilizing a Content Delivery Network (CDN) to distribute videos in these campus scenarios creates substantial unicast pressure on edge CDN servers. This paper proposes a two-layer dynamic partitioning Recursive Bit String (RBS) virtual domain network layer multicast architecture specifically designed for large-scale, geographically dense multicast scenarios within campus networks. This approach reduces redundant multicast messages by approximately 10-30\% compared to the two-layer fixed partitioning method. Additionally, it establishes multicast source authentication capabilities based on Source Address Validation Improvement (SAVI) and facilitates secure multicast group key exchange using a concise exchange protocol within the WebRTC framework. In the next-generation data plane of programmable software-defined networks, the RBS stateless multicast technology can be integrated with the unique characteristics of large-scale, geographically dense campus network scenarios to dynamically and efficiently extend multicast coverage to every dormitory.

cs.NI↗

arXiv2024

An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees

In this paper, we consider the scenario-based two-stage stochastic DC optimal power flow (OPF) problem for optimal and reliable dispatch when the load is facing uncertainty. Although this problem is a linear program, it remains computationally challenging to solve due to the large number of scenarios needed to accurately represent the uncertainties. To mitigate the computational issues, many techniques have been proposed to approximate the second-stage decisions so they can be dealt more efficiently. The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies. To address these challenges, this paper proposes a learning method to solve the two-stage problem in a more efficient and optimal way. A technique called the gauge map is incorporated into the learning architecture design to guarantee the learned solutions' feasibility to the network constraints. Namely, we can design policies that are feed forward functions and only output feasible solutions. Simulation results on standard IEEE systems show that, compared to iterative solvers and the widely used affine policy, our proposed method not only learns solutions of good quality but also accelerates the computation by orders of magnitude.

eess.SY↗

arXiv2024

Improved Esophageal Varices Assessment from Non-Contrast CT Scans

Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures. Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-invasive imaging modality, it has yet to gain full acceptance as a primary clinical diagnostic tool for EV evaluation. To overcome existing diagnostic challenges, we present the Multi-Organ-cOhesion-Network (MOON), a novel framework enhancing the analysis of critical organ features in NC-CT scans for effective assessment of EV. Drawing inspiration from the thorough assessment practices of radiologists, MOON establishes a cohesive multiorgan analysis model that unifies the imaging features of the related organs of EV, namely esophagus, liver, and spleen. This integration significantly increases the diagnostic accuracy for EV. We have compiled an extensive NC-CT dataset of 1,255 patients diagnosed with EV, spanning three grades of severity. Each case is corroborated by endoscopic diagnostic results. The efficacy of MOON has been substantiated through a validation process involving multi-fold cross-validation on 1,010 cases and an independent test on 245 cases, exhibiting superior diagnostic performance compared to methods focusing solely on the esophagus (for classifying severe grade: AUC of 0.864 versus 0.803, and for moderate to severe grades: AUC of 0.832 versus 0.793). To our knowledge, MOON is the first work to incorporate a synchronized multi-organ NC-CT analysis for EV assessment, providing a more acceptable and minimally invasive alternative for patients compared to traditional endoscopy.

eess.IV↗

arXiv2024

LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning

The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors. In this work, a precise LIver tumor DIAgnosis network on multi-phase contrast-enhance CT, named LIDIA, is proposed for real-world scenario. To fully utilize all available phases in contrast-enhanced CT, LIDIA first employs the iterative fusion module to aggregate variable numbers of image phases, thereby capturing the features of lesions at different phases for better tumor diagnosis. To effectively mitigate the high heterogeneity problem of liver tumors, LIDIA incorporates asymmetric contrastive learning to enhance the discriminability between different classes. To evaluate our method, we constructed a large-scale dataset comprising 1,921 patients and 8,138 lesions. LIDIA has achieved an average AUC of 93.6% across eight different types of lesions, demonstrating its effectiveness. Besides, LIDIA also demonstrated strong generalizability with an average AUC of 89.3% when tested on an external cohort of 828 patients.

cs.CV↗

arXiv2024

Prompting Whole Slide Image Based Genetic Biomarker Prediction

Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio.

eess.IV↗