arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 163 records · Page 9Linked to original sources

arXiv2025

Emergent dynamical Kondo coherence and competing magnetic order in a correlated kagome flat-band metal CsCr6Sb6

Correlated kagome metals host unique electronic states that enable exotic quantum phenomena. In the recently emerged CsCr6Sb6, these manifest through Kondo behavior from localized Cr-3d electrons and unprecedented band flattening near the Fermi level. Yet the intricate interplay among Kondo screening, magnetic frustration, and electronic correlations remains poorly understood-a fundamental gap we address through multifaceted experimental and theoretical approaches. Our angle-resolved photoemission spectroscopy measurements reveal electronic correlation-renormalized flat bands and muon spin relaxation study detect short-range magnetic order at TN ~ 80 K. Complementing these findings, density-functional theory and dynamical mean-field theory calculations identify a coherent-incoherent crossover at TN, with a remarkable restoration of coherence accompanying local moment suppression-an anomalous hallmark of Kondo behavior. Intriguingly, despite strong interlayer antiferromagnetic coupling, the system evades long-range magnetic order due to competing magnetic configurations separated by sub-meV energy differences. These insights establish CsCr6Sb6 as a prototypical platform for investigating dynamical Kondo screening in correlated flat-band systems, opening new avenues to study flat band physics and frustrated magnetism in correlated kagome lattices.

cond-mat.str-el↗

arXiv2025

A Data-constrained Magnetohydrodynamic Simulation of Successive X-class Flares in Solar Active Region 13842. II. Dynamics of the Solar Eruption Associated with the X9.0 Solar Flare

Active region NOAA 13842 produced two successive solar flares: an X7.1-class flare on October 1, 2024, and an X9.0-class flare on October 3, 2024. This study continues our previous simulation work that successfully reproduced the X7.1-class solar flare (Matsumoto et al. 2025). In this study, we performed a data-constrained magnetohydrodynamic (MHD) simulation using the nonlinear force-free field (NLFFF) as the initial condition to investigate the X9.0-class solar flare. The NLFFF showed the sheared field lines, resulting in the tether-cutting reconnection, the magnetic flux ropes (MFRs), and eventually led to eruption. The magnetic reconnection during the pre-eruption phase plays a critical role in accelerating the subsequent eruption, which is driven by torus instability and magnetic reconnection. Furthermore, our simulation results are consistent with several observational features associated with the X9.0 flare. This simulation could reproduce diverse phenomena associated with the X9.0 flare, including the tether-cutting reconnection, the flare ribbons and the flare loops, the transverse field enhancement, and the remote brightening away from the flare ribbons. However, the initial trigger, magnetic flux emergence, was inferred from observations rather than explicitly modeled, and future comprehensive simulations should incorporate this mechanism directly.

astro-ph.SR↗

arXiv2025

Electrical control of quantum dots in GaAs-on-insulator waveguides for coherent single-photon generation

The integration of coherent quantum emitters with silicon photonic platforms essential for scalable quantum technologies. We demonstrate electrically controlled self-assembled quantum dots embedded in GaAs waveguides bonded onto a SiO2/Si substrate and coupled to low-loss SiN waveguides. Our approach uses a die-to-die adhesive bonding process to realize a GaAs-on-insulator platform incorporating a p-i-n junction for charge noise suppression and Stark tuning of excitonic transitions. Resonance fluorescence measurements reveal narrow optical linewidths below 2 μeV and high single-photon purity, matching the performance of unprocessed GaAs devices. These results establish a practical route to integrate high-coherence quantum light sources with mature silicon photonics, enabling scalable quantum photonic integrated circuits

physics.optics↗

arXiv2025

EvoVLMA: Evolutionary Vision-Language Model Adaptation

Pre-trained Vision-Language Models (VLMs) have been exploited in various Computer Vision tasks (e.g., few-shot recognition) via model adaptation, such as prompt tuning and adapters. However, existing adaptation methods are designed by human experts, requiring significant time cost and experience. Inspired by recent advances in Large Language Models (LLMs) based code generation, we propose an Evolutionary Vision-Language Model Adaptation (EvoVLMA) method to automatically search training-free efficient adaptation algorithms for VLMs. We recognize feature selection and logits computation as the key functions in training-free VLM adaptation, and propose a two-stage LLM-assisted evolutionary algorithm for optimizing these parts in a sequential manner, effectively addressing the challenge posed by the expansive search space through a divide-and-conquer strategy. Besides, to enhance the stability and efficiency of searching process, we propose low-precision code conversion, web based code execution and process monitoring, leading to a highly effective automatic algorithm design system. Extensive experiments demonstrate that the algorithms found by EvoVLMA can obtain promising results compared to previous manually-designed ones. More specifically, in the 8-shot image classification setting, the classical APE algorithm can be improved by 1.91 points in recognition accuracy. This research opens new possibilities for automating the optimization of adaptation algorithms of pre-trained multimodal models. Code is available at: https://github.com/kding1225/EvoVLMA

cs.CV↗

arXiv2025

Accelerometry-based Energy Expenditure Estimation During Activities of Daily Living: A Comparison Among Different Accelerometer Compositions

Physical activity energy expenditure (PAEE) can be measured from breath-by-breath respiratory data, which can serve as a reference. Alternatively, PAEE can be predicted from the body movements, which can be measured and estimated with accelerometers. The body center of mass (COM) acceleration reflects the movements of the whole body and thus serves as a good predictor for PAEE. However, the wrist has also become a popular location due to recent advancements in wrist-worn devices. Therefore, in this work, using the respiratory data measured by COSMED K5 as the reference, we evaluated and compared the performances of COM-based settings and wrist-based settings. The COM-based settings include two different accelerometer compositions, using only the pelvis accelerometer (pelvis-acc) and the pelvis accelerometer with two accelerometers from two thighs (3-acc). The wrist-based settings include using only the left wrist accelerometer (l-wrist-acc) and only the right wrist accelerometer (r-wrist-acc). We implemented two existing PAEE estimation methods on our collected dataset, where 9 participants performed activities of daily living while wearing 5 accelerometers (i.e., pelvis, two thighs, and two wrists). These two methods include a linear regression (LR) model and a CNN-LSTM model. Both models yielded the best results with the COM-based 3-acc setting (LR: $R^2$ = 0.41, CNN-LSTM: $R^2$ = 0.53). No significant difference was found between the 3-acc and pelvis-acc settings (p-value = 0.278). For both models, neither the l-wrist-acc nor the r-wrist-acc settings demonstrated predictive power on PAEE with $R^2$ values close to 0, significantly outperformed by the two COM-based settings (p-values $<$ 0.05). No significant difference was found between the two wrists (p-value = 0.329).

cs.LG↗

arXiv2025

Max-Min Rate Optimization for Multigroup Multicast MISO Systems Via Novel Transmissive RIS Transceiver

This paper investigates a novel transmissive reconfigurable intelligent surface (RIS) transceiver architectureenabled multigroup multicast downlink communication system. Under this setup, an optimization problem is formulated to maximize the minimum rate of users across all groups, subject to the maximum available power of each RIS unit. Due to the nondifferentiable nature of the objective function, the max-min rate problem is challenging to solve. To tackle this difficult problem, we develop an iterative solution by leveraging the successive convex approximation (SCA) and the penalty function method. However, the above approach has high computational complexity and may lead to compromised performance. To overcome these drawbacks, we design an efficient second-order cone programming (SOCP)-based method using the weighted minimum mean squared error (WMMSE) framework to reduce computational complexity. Furthermore, to further reduce the computational complexity, we also propose a low-complexity and solver-free algorithm that analytically updates all variables by combining the smooth approximation theory and the majorization-minimization (MM) method. Numerical results are provided to verify the convergence and effectiveness of our proposed three algorithms. It is also demonstrated that the SOCP-based method outperforms the penalty-based algorithm in terms of both the achieved min rate and the computational complexity. In contrast, the lowcomplexity design achieves significantly lower complexity with only slightly degraded performance.

eess.SP↗

arXiv2025

Platoon Coordination and Leader Selection in Mixed Transportation Systems via Dynamic Programming

With the growing penetration of electric trucks, freight transportation is transitioning toward a mixed system comprising both fuel-powered and electric trucks. Enhancing truck platoon formation in such a heterogeneous environment presents new challenges. This paper investigates the hub-based platoon coordination problem in a mixed truck fleet, where the focus is to optimize the trucks' waiting times, charging amounts for electric trucks, and platoon leader assignments. The objective is to maximize the overall platoon revenue of the fleet while accounting for the associated waiting and charging costs. We formulate the problem as a mixed-integer linear program and present a dynamic programming approach to compute its sub-optimal solution efficiently. The proposed method operates in polynomial time, ensuring scalable computational efficiency. Simulation studies involving 1,000 trucks traveling between two hubs in Sweden demonstrate the effectiveness and scalability of the proposed approach.

math.OC↗

arXiv2025

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains challenging due to the narrow scope of existing benchmarks. To bridge this gap, we introduce MMCircuitEval, the first multimodal benchmark specifically designed to assess MLLM performance comprehensively across diverse EDA tasks. MMCircuitEval comprises 3614 meticulously curated question-answer (QA) pairs spanning digital and analog circuits across critical EDA stages - ranging from general knowledge and specifications to front-end and back-end design. Derived from textbooks, technical question banks, datasheets, and real-world documentation, each QA pair undergoes rigorous expert review for accuracy and relevance. Our benchmark uniquely categorizes questions by design stage, circuit type, tested abilities (knowledge, comprehension, reasoning, computation), and difficulty level, enabling detailed analysis of model capabilities and limitations. Extensive evaluations reveal significant performance gaps among existing LLMs, particularly in back-end design and complex computations, highlighting the critical need for targeted training datasets and modeling approaches. MMCircuitEval provides a foundational resource for advancing MLLMs in EDA, facilitating their integration into real-world circuit design workflows. Our benchmark is available at https://github.com/cure-lab/MMCircuitEval.

cs.LG↗

arXiv2025

Detecting the most probable transition phenomenon of a nutrient-phytoplankton-zooplankton system

The population biology model holds a significant position within ecosystems. Introducing stochastic perturbations into the model can more accurately depict real biological processes. In this paper, we primarily investigate the most probable transition phenomenon in a three-dimensional nutrient-phytoplankton-zooplankton (NPZ) plankton model. With appropriate parameter values, the system coexists with a stable equilibrium point and a stable limit cycle. Under noise perturbations, transitions occur between these two steady states. Based on the Onsager-Machlup action functional and the neural shooting method, we have studied the most probable transition time, the most probable transition pathway and the most probable transition probability of the NPZ system. The transition between these metastable states plays a crucial role in stochastic ecosystems, providing guidance for a better understanding of complex biological processes.

q-bio.PE↗

arXiv2025

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials

The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (ML) models and a comprehensive materials database tailored for clean energy applications. A key feature of CEMP is the online calculation module, which enables fully automatic quantum and molecular dynamics simulations via structured table uploads. CEMP harmonizes heterogeneous data from experimental measurements, theoretical calculation and AI-based predictions for four material classes, including small molecules, polymers, ionic liquids, and crystals. The platform hosts ~ 376,000 entries, including ~6,000 experimental records, ~50,000 quantum-chemical calculations and ~320,000 AI-predicted properties. The database covers 12 critical properties and the corresponding ML models demonstrate robust predictive power with R2 ranging from 0.64 to 0.94, thus ensures rapid material screening, structure-property relationship analysis and multi-objective optimization for clean energy applications. CEMP aims to establish a digital ecosystem for clean energy materials, enabling a closed-loop workflow from data acquisition to material discovery and real-time online validation.

cond-mat.mtrl-sci↗

arXiv2025

SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networks

We propose a circuit-level attack, SQUASH, a SWAP-Based Quantum Attack to sabotage Hybrid Quantum Neural Networks (HQNNs) for classification tasks. SQUASH is executed by inserting SWAP gate(s) into the variational quantum circuit of the victim HQNN. Unlike conventional noise-based or adversarial input attacks, SQUASH directly manipulates the circuit structure, leading to qubit misalignment and disrupting quantum state evolution. This attack is highly stealthy, as it does not require access to training data or introduce detectable perturbations in input states. Our results demonstrate that SQUASH significantly degrades classification performance, with untargeted SWAP attacks reducing accuracy by up to 74.08\% and targeted SWAP attacks reducing target class accuracy by up to 79.78\%. These findings reveal a critical vulnerability in HQNN implementations, underscoring the need for more resilient architectures against circuit-level adversarial interventions.

quant-ph↗

arXiv2025

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution

LLMs demonstrate strong performance in auto-mated software engineering, particularly for code generation and issue resolution. While proprietary models like GPT-4o achieve high benchmarks scores on SWE-bench, their API dependence, cost, and privacy concerns limit adoption. Open-source alternatives offer transparency but underperform in complex tasks, especially sub-100B parameter models. Although quality Chain-of-Thought (CoT) data can enhance reasoning, current methods face two critical flaws: (1) weak rejection sampling reduces data quality, and (2) inadequate step validation causes error accumulation. These limitations lead to flawed reasoning chains that impair LLMs'ability to learn reliable issue resolution. The paper proposes MCTS-REFINE, an enhanced Monte Carlo Tree Search (MCTS)-based algorithm that dynamically validates and optimizes intermediate reasoning steps through a rigorous rejection sampling strategy, generating high-quality CoT data to improve LLM performance in issue resolution tasks. Key innovations include: (1) augmenting MCTS with a reflection mechanism that corrects errors via rejection sampling and refinement, (2) decomposing issue resolution into three subtasks-File Localization, Fault Localization, and Patch Generation-each with clear ground-truth criteria, and (3) enforcing a strict sampling protocol where intermediate outputs must exactly match verified developer patches, ensuring correctness across reasoning paths. Experiments on SWE-bench Lite and SWE-bench Verified demonstrate that LLMs fine-tuned with our CoT dataset achieve substantial improvements over baselines.Notably, Qwen2.5-72B- Instruct achieves 28.3%(Lite) and 35.0%(Verified) resolution rates, surpassing SOTA baseline SWE-Fixer-Qwen-72B with the same parameter scale, which only reached 24.7%(Lite) and 32.8%(Verified).

cs.SE↗

arXiv2025

Detecting transitions from steady states to chaos with gamma distribution

In this paper, we introduce a novel method to identify transitions from steady states to chaos in stochastic models, specifically focusing on the logistic and Ricker equations by leveraging the gamma distribution to describe the underlying population. We begin by showing that when the variance is sufficiently small, the stochastic equations converge to their deterministic counterparts. Our analysis reveals that the stochastic equations exhibit two distinct branches of the intrinsic growth rate, corresponding to alternative stable states characterized by higher and lower growth rates. Notably, while the logistic model does not show a transition from a steady state to chaos, the Ricker model undergoes such a transition when the shape parameter of the gamma distribution is small. These findings not only enhance our understanding of the dynamic behavior in biological populations but also provide a robust framework for detecting chaos in complex systems.

math.PR↗

arXiv2025

QiMeng: Fully Automated Hardware and Software Design for Processor Chip

Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conventional design paradigms face three major challenges: the physical constraints of fabrication technologies, the escalating demands for design resources, and the increasing diversity of ecosystems. Automated processor chip design has emerged as a transformative solution to address these challenges. While recent breakthroughs in Artificial Intelligence (AI), particularly Large Language Models (LLMs) techniques, have opened new possibilities for fully automated processor chip design, substantial challenges remain in establishing domain-specific LLMs for processor chip design. In this paper, we propose QiMeng, a novel system for fully automated hardware and software design of processor chips. QiMeng comprises three hierarchical layers. In the bottom-layer, we construct a domain-specific Large Processor Chip Model (LPCM) that introduces novel designs in architecture, training, and inference, to address key challenges such as knowledge representation gap, data scarcity, correctness assurance, and enormous solution space. In the middle-layer, leveraging the LPCM's knowledge representation and inference capabilities, we develop the Hardware Design Agent and the Software Design Agent to automate the design of hardware and software for processor chips. Currently, several components of QiMeng have been completed and successfully applied in various top-layer applications, demonstrating significant advantages and providing a feasible solution for efficient, fully automated hardware/software design of processor chips. Future research will focus on integrating all components and performing iterative top-down and bottom-up design processes to establish a comprehensive QiMeng system.

cs.AR↗

arXiv2025

Large Processor Chip Model

Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on manual expertise, fragmented optimization across software and hardware layers, and high costs associated with exploring expansive design spaces. While automated methods leveraging optimization algorithms and machine learning have improved efficiency, they remain constrained by a single-stage focus, limited data availability, and a lack of comprehensive human domain knowledge. The emergence of large language models offers transformative opportunities for the design of computer system architecture. By leveraging the capabilities of LLMs in areas such as code generation, data analysis, and performance modeling, the traditional manual design process can be transitioned to a machine-based automated design approach. To harness this potential, we present the Large Processor Chip Model (LPCM), an LLM-driven framework aimed at achieving end-to-end automated computer architecture design. The LPCM is structured into three levels: Human-Centric; Agent-Orchestrated; and Model-Governed. This paper utilizes 3D Gaussian Splatting as a representative workload and employs the concept of software-hardware collaborative design to examine the implementation of the LPCM at Level 1, demonstrating the effectiveness of the proposed approach. Furthermore, this paper provides an in-depth discussion on the pathway to implementing Level 2 and Level 3 of the LPCM, along with an analysis of the existing challenges.

cs.AR↗

arXiv2025

Towards Real-time Structural Dynamics Simulation with Graph-based Digital Twin Modelling

Precise and timely simulation of a structure's dynamic behavior is crucial for evaluating its performance and assessing its health status. Traditional numerical methods are often limited by high computational costs and low efficiency, while deep learning approaches offer a promising alternative. However, these data-driven methods still face challenges, such as limited physical interpretability and difficulty in adapting to diverse structural configurations. To address these issues, this study proposes a graph-based digital twin modelling (GDTM) framework to simulate structural dynamic responses across various spatial topologies. In this framework, the adjacency matrix explicitly represents the spatial relationships between structural vertices, enhancing the model's physical interpretability. The effectiveness of the proposed framework was validated through comprehensive numerical and experimental studies. The results demonstrate that the framework accurately simulated structural dynamics across different topological configurations, with Normalized Mean-Squared Error (NMSE) values consistently below 0.005 in numerical simulations and 0.0015 in experimental validations. Furthermore, the framework achieved over 80-fold improvements in computational efficiency compared to traditional finite element methods (FEM). This research promotes the practical application of graph-based structural dynamics modelling, which has the potential to significantly advance structural performance evaluation and health monitoring.

cs.CE↗

arXiv2025

Adaptive Physics-Informed System Modeling with Control for Nonlinear Structural System Estimation

Accurately capturing the nonlinear dynamic behavior of structures remains a significant challenge in mechanics and engineering. Traditional physics-based models and data-driven approaches often struggle to simultaneously ensure model interpretability, noise robustness, and estimation optimality. To address this issue, this paper proposes an Adaptive Physics-Informed System Modeling with Control (APSMC) framework. By integrating Kalman filter-based state estimation with physics-constrained proximal gradient optimization, the framework adaptively updates time-varying state-space model parameters while processing real-time input-output data under white noise disturbances. Theoretically, this process is equivalent to real-time tracking of the Jacobian matrix of a nonlinear dynamical system. Within this framework, we leverage the theoretical foundation of stochastic subspace identification to demonstrate that, as observational data accumulates, the APSMC algorithm yields state-space model estimates that converge to the theoretically optimal solution. The effectiveness of the proposed framework is validated through numerical simulations of a Duffing oscillator and the seismic response of a frame structure, as well as experimental tests on a scaled bridge model. Experimental results show that, under noisy conditions, APSMC successfully predicts 19 consecutive 10-second time series using only a single initial 10-second segment for model updating, achieving a minimum normalized mean square error (NMSE) of 0.398%. These findings demonstrate that the APSMC framework not only offers superior online identification and denoising performance but also provides a reliable foundation for downstream applications such as structural health monitoring, real-time control, adaptive filtering, and system identification.

nlin.AO↗

arXiv2025

Engineering Graphene Nanoribbons via Periodically Embedding Oxygen Atoms

Heteroatom doping is an important method for engineering graphene nanoribbons (GNRs) because of its ability to modify electronic properties by introducing extra electrons or vacancies. However, precisely integrating oxygen atoms into the lattice of GNRs is unexplored, and the resulting electronic properties remain elusive. Here, we achieve the precise embedding of oxygen atoms into the lattice of GNRs via in situ formation of pyrans, synthesizing two types of oxygen-doped GNRs (O-doped chevron-GNR and O-doped chiral (2,1)-GNR). Using scanning tunneling microscopy, non-contact atomic force microscopy, and density functional theory calculations, the atomic structures and electronic properties of O-doped GNRs are determined, demonstrating that both GNRs are direct bandgap semiconductors with different sensitivities to oxygen dopants. Oxygen dopants have a minor impact on the bandgap of chevron-GNR but a significant effect on the bandgap of chiral (2,1)-GNR, which is attributed to the difference in density of states near the Fermi level between substituted intrinsic carbon atoms and their pristine counterparts. Compared with the pristine chiral (2,1)-GNR, the band structure of O-doped chiral (2,1)-GNR exhibits unexpected band edges transition, which is ascribed to sp2-hybridized oxygen atoms which introduces additional electrons to the conduction band of chiral (2,1)-GNR, leading to the upward shift of Fermi surface.

cond-mat.mtrl-sci↗