arXiv subjects

Yan Li

Publications and source records attributed to Yan Li.

At least 253 records · Page 14Linked to original sources

arXiv2025

Meridional Circulation. I. A Formation Channel for Lithium-rich and Super Lithium-rich Red Clump Stars

Current observations indicate that stars with higher rotation rates appear to maintain more surface lithium, and the majority of lithium-rich giants are indeed red clump stars. Hence, we investigate the mechanisms behind lithium enrichment in rotating red clump stars and the pathways to forming lithium-rich red clump stars. Meridional circulation is prevalent in the radiative zone of rotating giants. We model its radial mixing as a diffusion process and derive the corresponding diffusion coefficient based on its material transfer effect. Due to uncertainties in numerical calculations, we consider an average diffusion effect. Additionally, certain limiting conditions for the radial velocity of meridional circulation are incorporated. With varying input rotation velocities, we simulate the lithium evolution for red clump stars with this model. Our results indicate that the material transfer effect due to meridional circulation can efficiently transport beryllium, produced by H burning, into the convective envelope. This meridional circulation can lead to lithium enrichment, with a maximum lithium abundance increment approaching $3.0\,\rm dex$. Consequently, it is capable of forming both lithium-rich and super lithium-rich red clump stars. The degree of lithium enrichment exhibits a strong positive correlation with the rotation velocity, i.e., faster red clump stars show more surface lithium. Furthermore, our models indicate that lithium-rich red clump stars are relatively young ($\sim 10^6\,\rm yr$), which aligns with observation evidences.

astro-ph.SR↗

arXiv2025

Community Bank Establishment and Consumption Growth: Evidence from Panel Study of Income Dynamics in USA

Consumption is a primary source of economic growth and key indicator of poverty. The establishment of community banks can provide credit resources, unlocking household consumption potential and playing a crucial role in economic development. This study explores the role of community banks in promoting consumption by using data from the Panel Study of Income Dynamics (PSID) for 11 waves from 1980 to 1990, and constructing a fixed-effects model using the time-varying difference-in-differences (DID) method. The findings indicate that the establishment of community banks effectively stimulates growth in local household consumption, primarily by increasing household income and reducing precautionary savings. Therefore, both government and financial institutions should further promote the development of regional financial institutions and credit tools to promote economic growth.

econ.GN↗

arXiv2025

SOE's ESG Performance on Financial Flexibility: The Evidence from the Hong Kong Stock Market

As the global economic environment becomes increasingly unstable, enhancing financial flexibility to cope with risks has become the consensus of many companies. At the same time, environmental, social, and governance (ESG) performance may be one of the effective ways. We studied the impact of a firm's ESG performance on its financial flexibility with a sample of companies listed on the Hong Kong stock market from 2018 to 2022. The empirical results show that good environmental, social and governance performance can significantly improve a firm's financial flexibility. In addition, this paper also finds that the influence of ESG performance on financial flexibility is weak for state-owned enterprises due to the influence of governance structure and market characteristics. Finally, the further analysis shows that there is a mediating role played by financing constraints in this process. This study can provide background information for state-owned enterprises' governance, information disclosure, and corporate operations. It also has guiding significance for relevant investors, management and officials.

econ.GN↗

arXiv2025

Joint Transceiver Design for RIS Enhanced Dual-Functional Radar-Communication with Movable Antenna

Movable antennas (MAs) have shown significant potential in enhancing the performance of dual-functional radar-communication (DFRC) systems. In this paper, we investigate the MA-based transceiver design for DFRC systems, where a reconfigurable intelligent surface (RIS) is employed to enhance the communication quality in dead zones. To enhance the radar sensing performance, we formulate an optimization problem to maximize the radar signal-to-interference-plus-noise ratio (SINR) by jointly optimizing the beamforming vectors, receiving filter, antenna positions, and RIS reflecting coefficients. To tackle this challenging problem, we develop a fractional programming-based optimization framework, incorporating block coordinate descent (BCD), successive convex approximation (SCA), and penalty techniques. Simulation results demonstrate that the proposed method can significantly improve the radar SINR and achieve a satisfactory balance between the radar and communication performance compared with existing benchmark schemes.

eess.SP↗

arXiv2025

Power System Electromagnetic Transient Stability: an Analysis Based on Convergent Hamiltonian

Transient stability is crucial to the reliable operation of power systems. Existing theories rely on the simplified electromechanical models, substituting the detailed electromagnetic dynamics of inductor and capacitor with their impedance representations. However, this simplification is inadequate for the growing penetration of fast-switching power electronic devices. Attempts to extend the existing theories to include electromagnetic dynamics lead to overly conservative stability conditions. To tackle this problem more directly, we study the condition under which the power source and dissipation in the electromagnetic dynamics tend to balance each other asymptotically. This is equivalent to the convergence of the Hamiltonian (total stored energy) and can be shown to imply transient stability. Using contraction analysis, we prove that this property holds for a large class of time-varying port-Hamiltonian systems with (i) constant damping matrix and (ii) strictly convex Hamiltonian. Then through port-Hamiltonian modeling of the electromagnetic dynamics, we obtain that the synchronized steady state of the power system is globally stable if it exists. This result provides new insights into the reliable operation of power systems. The proposed theory is illustrated in the simulation results of a two-machine system.

eess.SY↗

arXiv2025

NSPG-Miner: Mining Repetitive Negative Sequential Patterns

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/NSPG-Miner.

cs.DB↗

arXiv2025

Filling-dependent intertwined electronic and atomic orders in the flat-band state of 1T-TaS2

The delicate interplay among the complex intra-/inter-layer electron-electron and electron-lattice interactions is the fundamental prerequisite of these exotic quantum states, such as superconductivity, nematic order, and checkerboard charge order. Here we explore the filling-dependent multiple stable intertwined electronic and atomic orders of flat-band state of 1T-TaS2 encompassing hole order, phase orders, coexisting left- and right-chiral orders and mixed phase/chiral orders via scanning tunneling microscopy (STM). Combining first-principles calculations, the emergent electronic/atomic orders can be attributed to the weakening of electron-electron correlations and stacking-dependent interlayer interactions. Moreover, achiral intermediate ring-like clusters and nematic charge density wave (CDW) states are successfully realized in intralayer chiral domain wall and interlayer heterochiral stacking regions through chiral overlap configurations. Our study not only deepens the understanding of filling-dependent electronic/atomic orders in flat-band systems, but also offers perspectives for exploring exotic quantum states in correlated electronic systems.

cond-mat.str-el↗

arXiv2025

Nucleon axial, tensor, and scalar charges and $σ$-terms in lattice QCD

We determine the nucleon axial, scalar and tensor charges at the continuum limit by analyzing three $N_f=2+1+1$ twisted mass fermion ensembles with all quark masses tuned to approximately their physical values. We include all contributions from valence and sea quarks. We use the Akaike Information Criterion to evaluate systematic errors due to excited states and the continuum extrapolation. For the nucleon isovector axial charge we find $g_A^{u-d}=1.250(24)$, in agreement with the experimental value. We compute the axial, tensor and scalar charges for each quark flavor. The axial charge provides crucial information on the intrinsic spin carried by quark in the nucleon and the the latter two provide input for experimental searches of physics beyond the standard model. Moreover, we extract the nucleon $σ$-terms and find $σ_{πN}=41.9(8.1)$ MeV, for the strange $σ_{s}=30(17)$ MeV and for the charm $σ_{c}=82(29)$ MeV. We also present preliminary results on the isovector quantities using a fourth ensemble at smaller lattice spacing.

hep-lat↗

arXiv2025

Trade Dynamics of the Global Dry Bulk Shipping Network

This study investigates the inherently random structures of dry bulk shipping networks, often likened to a taxi service, and identifies the underlying trade dynamics that contribute to this randomness within individual cargo sub-networks. By analysing micro-level trade flow data from 2015 to 2023, we explore the evolution of dry commodity networks, including grain, coal, and iron ore, and suggest that the Giant Strongly Connected Components exhibit small-world phenomena, indicative of efficient bilateral trade. The significant heterogeneity of in-degree and out-degree within these sub-networks, primarily driven by importing ports, underscores the complexity of their dynamics. Our temporal analysis shows that while the Covid-19 pandemic profoundly impacted the coal network, the Ukraine conflict significantly altered the grain network, resulting in changes in community structures. Notably, grain sub-networks display periodic changes, suggesting distinct life cycles absent in coal and iron ore networks. These findings illustrate that the randomness in dry bulk shipping networks is a reflection of real-world trade dynamics, providing valuable insights for stakeholders in navigating and predicting network behaviours.

q-fin.MF↗

arXiv2025

CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

Cardiac magnetic resonance imaging (MRI) has emerged as a clinically gold-standard technique for diagnosing cardiac diseases, thanks to its ability to provide diverse information with multiple modalities and anatomical views. Accelerated cardiac MRI is highly expected to achieve time-efficient and patient-friendly imaging, and then advanced image reconstruction approaches are required to recover high-quality, clinically interpretable images from undersampled measurements. However, the lack of publicly available cardiac MRI k-space dataset in terms of both quantity and diversity has severely hindered substantial technological progress, particularly for data-driven artificial intelligence. Here, we provide a standardized, diverse, and high-quality CMRxRecon2024 dataset to facilitate the technical development, fair evaluation, and clinical transfer of cardiac MRI reconstruction approaches, towards promoting the universal frameworks that enable fast and robust reconstructions across different cardiac MRI protocols in clinical practice. To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset. It is acquired from 330 healthy volunteers, covering commonly used modalities, anatomical views, and acquisition trajectories in clinical cardiac MRI workflows. Besides, an open platform with tutorials, benchmarks, and data processing tools is provided to facilitate data usage, advanced method development, and fair performance evaluation.

eess.IV↗

arXiv2025

Fast Searching of Extreme Operating Conditions for Relay Protection Setting Calculation Based on Graph Neural Network and Reinforcement Learning

Searching for the Extreme Operating Conditions (EOCs) is one of the core problems of power system relay protection setting calculation. The current methods based on brute-force search, heuristic algorithms, and mathematical programming can hardly meet the requirements of today's power systems in terms of computation speed due to the drastic changes in operating conditions induced by renewables and power electronics. This paper proposes an EOC fast search method, named Graph Dueling Double Deep Q Network (Graph D3QN), which combines graph neural network and deep reinforcement learning to address this challenge. First, the EOC search problem is modeled as a Markov decision process, where the information of the underlying power system is extracted using graph neural networks, so that the EOC of the system can be found via deep reinforcement learning. Then, a two-stage Guided Learning and Free Exploration (GLFE) training framework is constructed to accelerate the convergence speed of reinforcement learning. Finally, the proposed Graph D3QN method is validated through case studies of searching maximum fault current for relay protection setting calculation on the IEEE 39-bus and 118-bus systems. The experimental results demonstrate that Graph D3QN can reduce the computation time by 10 to 1000 times while guaranteeing the accuracy of the selected EOCs.

cs.LG↗

arXiv2025

Coastal wave refraction in variable currents over a varying bathymetry

Refraction is the predominant mechanism causing spatially inhomogeneous surface gravity wave fields. However, the complex interplay between depth- and current-induced wave refraction remains poorly understood. Assuming weak currents and slowly varying bathymetry, we derive an analytical approximation to the wave ray curvature, which is validated by an open-source ray tracing framework. The approximation has the form of linear superposition of a current- and depth-induced component, each depending on the gradients in the ambient fields. This separation enables quantification of their individual and combined contributions to refraction. Through analysis of a few limiting cases, we demonstrate how the sign and magnitude of these components influence the wave refraction, and identify conditions where they either amplify or counteract each other. We also identify which of the two plays a dominant role. These findings provide physically resolved insights into the influence of current- and depth-gradients on wave propagation, and are relevant for applications related to remote sensing and coastal wave forecasting services.

physics.ao-ph↗

arXiv2025

RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-lineof-sight scenarios, significantly outperforming state-of-the-art methods.

eess.SP↗

arXiv2025

COMET:Combined Matrix for Elucidating Targets

Identifying the interaction targets of bioactive compounds is a foundational element for deciphering their pharmacological effects. Target prediction algorithms equip researchers with an effective tool to rapidly scope and explore potential targets. Here, we introduce the COMET, a multi-technological modular target prediction tool that provides comprehensive predictive insights, including similar active compounds, three-dimensional predicted binding modes, and probability scores, all within an average processing time of less than 10 minutes per task. With meticulously curated data, the COMET database encompasses 990,944 drug-target interaction pairs and 45,035 binding pockets, enabling predictions for 2,685 targets, which span confirmed and exploratory therapeutic targets for human diseases. In comparative testing using datasets from ChEMBL and BindingDB, COMET outperformed five other well-known algorithms, offering nearly an 80% probability of accurately identifying at least one true target within the top 15 predictions for a given compound. COMET also features a user-friendly web server, accessible freely at https://www.pdbbind-plus.org.cn/comet.

cs.LG↗

arXiv2024

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance and poor generalizations. Leveraging the capability of large language models to comprehend and reason about textual content presents a promising avenue for advancing recommendation systems. To achieve this, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through experiments on the real large-scale industrial dataset and online A/B tests, we demonstrate the efficacy of our approach in industry application. We also achieve state-of-the-art performance on six Amazon Review datasets to verify the superiority of our method.

cs.IR↗

arXiv2024

Investigation of $πN$ contributions to nucleon matrix elements

We investigate an improved method to extract nucleon matrix elements from lattice 3-point functions using a generalized eigenvalue problem (GEVP) with nucleon and pion-nucleon interpolating fields. Our method avoids the computation of the costly three-point functions that have pion-nucleon interpolators at both source and sink. We demonstrate that excited state contamination from $Nπ$ is minimized in nucleon matrix elements of the scalar, vector, pseudoscalar, axial, and tensor currents and discuss our results based on a physical-point ensemble with a pion mass value of 131 MeV. We find that the GEVP is most significant for the isovector pseudoscalar and axial currents.

hep-lat↗

arXiv2024

Self-Paced Learning Strategy with Easy Sample Prior Based on Confidence for the Flying Bird Object Detection Model Training

In order to avoid the impact of hard samples on the training process of the Flying Bird Object Detection model (FBOD model, in our previous work, we designed the FBOD model according to the characteristics of flying bird objects in surveillance video), the Self-Paced Learning strategy with Easy Sample Prior Based on Confidence (SPL-ESP-BC), a new model training strategy, is proposed. Firstly, the loss-based Minimizer Function in Self-Paced Learning (SPL) is improved, and the confidence-based Minimizer Function is proposed, which makes it more suitable for one-class object detection tasks. Secondly, to give the model the ability to judge easy and hard samples at the early stage of training by using the SPL strategy, an SPL strategy with Easy Sample Prior (ESP) is proposed. The FBOD model is trained using the standard training strategy with easy samples first, then the SPL strategy with all samples is used to train it. Combining the strategy of the ESP and the Minimizer Function based on confidence, the SPL-ESP-BC model training strategy is proposed. Using this strategy to train the FBOD model can make it to learn the characteristics of the flying bird object in the surveillance video better, from easy to hard. The experimental results show that compared with the standard training strategy that does not distinguish between easy and hard samples, the AP50 of the FBOD model trained by the SPL-ESP-BC is increased by 2.1%, and compared with other loss-based SPL strategies, the FBOD model trained with SPL-ESP-BC strategy has the best comprehensive detection performance.

cs.CV↗

arXiv2024

SINGER: Vivid Audio-driven Singing Video Generation with Multi-scale Spectral Diffusion Model

Recent advancements in generative models have significantly enhanced talking face video generation, yet singing video generation remains underexplored. The differences between human talking and singing limit the performance of existing talking face video generation models when applied to singing. The fundamental differences between talking and singing-specifically in audio characteristics and behavioral expressions-limit the effectiveness of existing models. We observe that the differences between singing and talking audios manifest in terms of frequency and amplitude. To address this, we have designed a multi-scale spectral module to help the model learn singing patterns in the spectral domain. Additionally, we develop a spectral-filtering module that aids the model in learning the human behaviors associated with singing audio. These two modules are integrated into the diffusion model to enhance singing video generation performance, resulting in our proposed model, SINGER. Furthermore, the lack of high-quality real-world singing face videos has hindered the development of the singing video generation community. To address this gap, we have collected an in-the-wild audio-visual singing dataset to facilitate research in this area. Our experiments demonstrate that SINGER is capable of generating vivid singing videos and outperforms state-of-the-art methods in both objective and subjective evaluations.

cs.CV↗