arXiv subjects

Yun Song

Publications and source records attributed to Yun Song.

At least 19 recordsLinked to original sources

arXiv2026

Anomalous Orbital Reconstruction Controlled by Interorbital Correlations and Hund's Coupling

The microscopic origin of anomalous orbital polarization in a class of low-dimensional correlated oxides remains unresolved due to the competition among crystal-field effects, electronic correlations, and orbital-dependent dimensionality. Using dynamical mean-field theory, we investigate a two orbital Hubbard model with orbital-dependent dimensionality and reveal the mechanism for anomalous orbital polarization through orbital reconstruction beyond the bare crystal-field picture. We identify interorbital Coulomb interaction-induced charge competition as a key microscopic mechanism responsible for the orbital redistribution, which leads first to an orbital-polarized correlated metal and subsequently to an orbital-polarized Mott insulator. We further find that Hund's coupling acts as a filling-dependent regulator of orbital reconstruction. It weakens the correlation-induced orbital redistribution at quarter filling by competing with interorbital charge fluctuations, while at half filling it completely suppresses the orbital-polarized state by stabilizing high-spin orbital-balanced configurations. These results provide a unified picture of correlation driven orbital reconstruction and highlight the relevance of interorbital interactions and Hund's coupling for understanding orbital phenomena in low-dimensional transition-metal oxides.

cond-mat.str-el↗

arXiv2026

Local Spin Excitations Mediate Quasiparticle Breakdown in the Orbital-Selective Mott Phase

The orbital-selective Mott phase (OSMP) is commonly described as a coexistence of localized and itinerant electrons within effectively decoupled orbitals, but emerging evidence for quasiparticle breakdown points to physics beyond this picture, whose microscopic origin remains unknown. Using dynamical mean-field theory for the two-band Hubbard model, we show that the spin-flip and Ising-type components of Hund's coupling generate local spin excitations (LSEs). These LSEs couple electrons between different orbitals, renormalize quasiparticle lifetimes and binding energies, and thereby destroy well-defined quasiparticles in the OSMP. Removing these two components of Hund's coupling restores coherent quasiparticle behavior and fully decouples the charge dynamics of the two bands. Our results therefore identify electronic coupling to LSEs as the fundamental mechanism driving quasiparticle breakdown within the OSMP.

cond-mat.str-el↗

arXiv2026

LegalWorld: A Life-Cycle Interactive Environment for Legal Agents

Civil litigation is inherently a life-cycle process: what a lawyer drafts on day one constrains what unfolds at trial months later. Yet existing legal benchmarks evaluate isolated subtasks, and prior legal-agent simulators reinitialize each scenario from shared ground truth, leaving cross-stage causal dependencies unmodeled. We present LegalWorld, a life-cycle interactive environment that models Chinese civil litigation as a causally connected state chain of five stages (seven sub-scenarios), grounded in 75,309 paired Chinese civil judgments. We pair it with reusable infrastructure (local memory, global case memory, a Skill/Tool library) that keeps each dispute consistent across its full life cycle. Building on this environment, we construct LongJud-Bench to evaluate agent capability across all five connected stages. 18,992 ratings from 217 legal-background evaluators confirm that LegalWorld trajectories are procedurally faithful and role-consistent; and a capability-level cross-model evaluation reveals sharp divergences that aggregate scores cannot expose, with no single backbone leading across consultation, drafting, and courtroom advocacy. Detailed resources will be released publicly.

cs.CL↗

arXiv2026

Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation

With the rising prevalence of cardiovascular diseases, electrocardiograms (ECG) remain essential for the non-invasive detection of cardiac abnormalities. This study presents a comprehensive evaluation of deep neural network architectures for automated arrhythmia classification, integrating temporal modeling, attention mechanisms, and ensemble strategies. To address data scarcity in minority classes, the MIT-BIH Arrhythmia dataset was augmented using a Generative Adversarial Network (GAN). We developed and compared four distinct architectures, including Convolutional Neural Networks (CNN), CNN combined with Long Short-Term Memory (CNN-LSTM), CNN-LSTM with Attention, and 1D Residual Networks (ResNet-1D), to capture both local morphological features and long-term temporal dependencies. Performance was rigorously evaluated using accuracy, F1-score, and Area Under the Curve (AUC) with 95\% confidence intervals to ensure statistical robustness, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to validate model interpretability. Experimental results indicate that the CNN-LSTM model achieved the optimal stand-alone balance between sensitivity and specificity, yielding an F1-score of 0.951. Conversely, the CNN-LSTM-Attention and ResNet-1D models exhibited higher sensitivity to class imbalance. To mitigate this, a dynamic ensemble fusion strategy was introduced; specifically, the Top2-Weighted ensemble achieved the highest overall performance with an F1-score of 0.958. These findings demonstrate that leveraging complementary deep architectures significantly enhances classification reliability, providing a robust and interpretable foundation for intelligent arrhythmia detection systems.

eess.SP↗

arXiv2025

Multi-Scenario Highway Lane-Change Intention Prediction: A Physics-Informed AI Framework for Three-Class Classification

Lane-change maneuvers are a leading cause of highway accidents, underscoring the need for accurate intention prediction to improve the safety and decision-making of autonomous driving systems. While prior studies using machine learning and deep learning methods (e.g., SVM, CNN, LSTM, Transformers) have shown promise, most approaches remain limited by binary classification, lack of scenario diversity, and degraded performance under longer prediction horizons. In this study, we propose a physics-informed AI framework that explicitly integrates vehicle kinematics, interaction feasibility, and traffic-safety metrics (e.g., distance headway, time headway, time-to-collision, closing gap time) into the learning process. lane-change prediction is formulated as a three-class problem that distinguishes left change, right change, and no change, and is evaluated across both straight highway segments (highD) and complex ramp scenarios (exiD). By integrating vehicle kinematics with interaction features, our machine learning models, particularly LightGBM, achieve state-of-the-art accuracy and strong generalization. Results show up to 99.8% accuracy and 93.6% macro F1 on highD, and 96.1% accuracy and 88.7% macro F1 on exiD at a 1-second horizon, outperforming a two-layer stacked LSTM baseline. These findings demonstrate the practical advantages of a physics-informed and feature-rich machine learning framework for real-time lane-change intention prediction in autonomous driving systems.

cs.AI↗

arXiv2025

Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments

The gap between static benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices across three levels of environmental complexity. We further introduce J1-EVAL, a fine-grained evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that, while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model, GPT-4o, falls short of 60% overall performance. These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research.

cs.AI↗

arXiv2025

Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction

Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Leveraging real-legal case sources, MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants' characters and behaviors as well as addressing distractions. A Multi-stage Interactive Legal Evaluation (MILE) benchmark is further constructed to evaluate LLMs' performance in dynamic legal scenarios. Extensive experiments confirm the effectiveness of our framework.

cs.CL↗

arXiv2025

LDR-Net: A Novel Framework for AI-generated Image Detection via Localized Discrepancy Representation

With the rapid advancement of generative models, the visual quality of generated images has become nearly indistinguishable from the real ones, posing challenges to content authenticity verification. Existing methods for detecting AI-generated images primarily focus on specific forgery clues, which are often tailored to particular generative models like GANs or diffusion models. These approaches struggle to generalize across architectures. Building on the observation that generative images often exhibit local anomalies, such as excessive smoothness, blurred textures, and unnatural pixel variations in small regions, we propose the localized discrepancy representation network (LDR-Net), a novel approach for detecting AI-generated images. LDR-Net captures smoothing artifacts and texture irregularities, which are common but often overlooked. It integrates two complementary modules: local gradient autocorrelation (LGA) which models local smoothing anomalies to detect smoothing anomalies, and local variation pattern (LVP) which captures unnatural regularities by modeling the complexity of image patterns. By merging LGA and LVP features, a comprehensive representation of localized discrepancies can be provided. Extensive experiments demonstrate that our LDR-Net achieves state-of-the-art performance in detecting generated images and exhibits satisfactory generalization across unseen generative models. The code will be released upon acceptance of this paper.

cs.CV↗

arXiv2024

Overview of the CAIL 2023 Argument Mining Track

We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summarized judgment documents but can also refer to trial recordings. The track consists of two stages, and we introduce the tasks designed for each stage; we also extend the data from previous events into a new dataset -- CAIL2023-ArgMine -- with annotated new cases from various causes of action. We outline several submissions that achieve the best results, including their methods for different stages. While all submissions rely on language models, they have incorporated strategies that may benefit future work in this field.

cs.CL↗

arXiv2023

Quantitative determination of the orbital-selective Mott transition and quantum entanglement in the orbital-selective Mott phase

We examine the orbital-selective Mott transition in the non-hybridized two-band Hubbard model using the dynamical mean-field theory. We find that the orbital-selective Mott transition could be quantitatively depicted by the {local two-qubit fidelity}. Furthermore, within the orbital-selective Mott phase, the combined characteristics of the two orbitals lead to the presence of quantum entanglement, which is characterized by the non-semi-integer values of local two-qubit fidelity. It is demonstrated that the Hund's coupling results in the ground states of both wide and narrow bands exhibiting the specific superposition states, indicating the existence of quantum entanglement within orbital-selective Mott phase. Without Hund's coupling, there are no specific superposition states, nor does quantum entanglement occur within the orbital-selective Mott phase. The mechanisms underlying the orbital-selective Mott transition show prominent variations depending on the presence or absence of Hund's coupling and its transverse terms.

cond-mat.str-el↗

arXiv2021

Evolution between two orbital-selective Mott phases driven by interorbital hopping

The effect of interorbital hopping on the orbital selective Mottness in a two-band correlation system is investigated by using the dynamical mean-field theory with the Lanczos method as impurity solver. We construct the phase diagram of the two-orbital Hubbard model with interorbital hopping ($t_{12})$, where the orbital selective Mott phases (OSMP) show different evolution trends. We find that the negative interorbital hopping ($t_{12}<0$) can enhance the OSMP regime upon tuning the effective bandwidth ratio. On the contrary, for the cases with positive interorbital hopping ($t_{12}>0$), the OSMP region becomes narrow with the increase of orbital hybridization until it disappears. It is also shown that a new OSMP emerges for a large enough positive interorbital hopping, owing to the role exchange of wide and narrow effective orbitals caused by the large $t_{12}$. Our results are also applicable to the hole-overdoped Ba$_2$CuO$_{4-\delta}$ superconductor, which is an orbital-selective Mott compound at half-filling.

cond-mat.str-el↗

arXiv2021

Quantitative Determination of the Critical Points of Mott Metal-Insulator Transition in Strongly Correlated Systems

Mottness is at the heart of the essential physics in a strongly correlated system as many novel quantum phenomena occur in the metallic phase near the Mott metal-insulator transition. We investigate the Mott transition in a Hubbard model by using the dynamical mean-field theory and introduce the local quantum state fidelity to depict the Mott metal-insulator transition. The local quantum state fidelity provides a convenient approach for determining the critical point of the Mott transition. Additionally, it presents a consistent description of the two distinct forms of the Mott transition points.

cond-mat.str-el↗

arXiv2020

Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloud

With the prevalence of big-data-driven applications, such as face recognition on smartphones and tailored recommendations from Google Ads, we are on the road to a lifestyle with significantly more intelligence than ever before. Various neural network powered models are running at the back end of their intelligence to enable quick responses to users. Supporting those models requires lots of cloud-based computational resources, e.g., CPUs and GPUs. The cloud providers charge their clients by the amount of resources that they occupy. Clients have to balance the budget and quality of experiences (e.g., response time). The budget leans on individual business owners, and the required Quality of Experience (QoE) depends on usage scenarios of different applications. For instance, an autonomous vehicle requires an real-time response, but unlocking your smartphone can tolerate delays. However, cloud providers fail to offer a QoE-based option to their clients. In this paper, we propose DQoES, differentiated quality of experience scheduler for deep learning inferences. DQoES accepts clients' specifications on targeted QoEs, and dynamically adjusts resources to approach their targets. Through the extensive cloud-based experiments, DQoES demonstrates that it can schedule multiple concurrent jobs with respect to various QoEs and achieve up to 8x times more satisfied models when compared to the existing system

cs.DC↗

arXiv2019

Electronic correlation-driven orbital polarization transitions in the orbital-selective Mott compound Ba$_2$CuO$_{4-\delta}$

The electronic states near the Fermi level of recently discovered superconductor Ba$_2$CuO$_{4-\delta}$ consist primarily of the Cu $d_{x^2-y^2}$ and $d_{3z^2-r^2}$ orbitals. We investigate the electronic correlation effect and the orbital polarization of an effective two-orbital Hubbard model mimicking the low-energy physics of Ba$_2$CuO$_{4-\delta}$ in the hole-rich regime by utilizing the dynamical mean-field theory with the Lanczos method as the impurity solver. We find that the hole-overdoped Ba$_2$CuO$_{4-\delta}$ with $3d^8$ (Cu$^{3+}$) is in the orbital-selective Mott phase (OSMP) at half-filling, and the typical two-orbital feature remains in Ba$_2$CuO$_{4-\delta}$ when the electron filling approaches $n_e\sim 2.5$, which closely approximates to the experimental hole doping for the emergence of the high-$T_c$ superconductivity. We also obtain that the orbital polarization is very stable in the OSMP, and the multiorbital correlation can drive orbital polarization transitions. These results indicate that in hole-overdoped Ba$_2$CuO$_{4-\delta}$ the OSMP physics and orbital polarization, local magnetic moment, and spin or orbital fluctuations still exist. We propose that our present results are also applicable to Sr$_2$CuO$_{4-\delta}$ and other two-orbital cuprates, demanding an unconventional multiorbital superconducting scenario in hole-overdoped high-$T_c$ cuprates.

cond-mat.str-el↗

arXiv2019

A dynamical mean-field study of orbital-selective Mott phase enhanced by next-nearest neighbor hopping

The dynamical mean-field theory is employed to study the orbital-selective Mott transition (OSMT) of the two-orbital Hubbard model with nearest neighbor hopping and next-nearest neighbor (NNN) hopping. The NNN hopping breaks the particle-hole symmetry at half filling and gives rise to an asymmetric density of states (DOS). Our calculations show that the broken symmetry of DOS benefits the OSMT, where the region of the orbital-selective Mott phase significantly extends with the increasing NNN hopping integral. We also find that Hund's rule coupling promotes OSMT by blocking the orbital fluctuations, but the influence of NNN hopping is more remarkable.

cond-mat.str-el↗

arXiv2019

Doublon-holon excitations split by Hund's rule coupling within the orbital-selective Mott phase

Multiorbital interactions have the capacity to produce an interesting kind of doublon-holon bound state that consists of a single-hole state in one band and a doubly-occupied state in another band. Interband doublon-holon pair excitations in the two-orbital Hubbard model are studied by using dynamical mean-field theory with the Lanczos method as the impurity solver. We find that the interband bound states may provide several in-gap quasiparticle peaks in the density of states of the narrow band in the orbital-selective Mott phase with a small Hund's rule coupling ($J$). There exists a corresponding energy relation between the in-gap states of the narrow band and the peaks in the excitation spectrum of the doublon for the wide band. We also find that the spin flip and pair-hopping Hund interactions can divide one quasiparticle peak into two peaks, where the splitting energy increases linearly with increasing $J$. Strong Hund's rule coupling can move the interband doublon-holon pair excitations outside the Mott gap and restrict the bound states by suppressing the orbital selectivity of the doubly-occupied and single-hole states.

cond-mat.str-el↗

arXiv2016

Orbital-selective Mott phase of Cu-substituted iron-based Superconductors

We study the phase transition in Cu-substituted iron-based superconductors with a new developed real-space Green's function method. We find that Cu substitution has strong effect on the orbital-selective Mott transition introduced by the Hund's rule coupling. The redistribution of the orbital occupancy which is caused by the increase of the Hund's rule coupling, gives rise to the Mott-Hubbard metal-insulator transition in the half-filled $d_{xy}$ orbital. We also find that more and more electronic states appear inside that Mott gap of the $d_{xy}$ orbital with the increase of Cu substitution, and the in-gap states around the Fermi level are strongly localized at some specific lattice sites. Further, a distinctive phase diagram, obtained for the Cu-substituted Fe-based superconductors, displays an orbital-selective insulating phase, as a result of the cooperative effect of the Hund's rule coupling and the impurity-induced disorder.

cond-mat.str-el↗

arXiv2015

Localization and Orbital Selectivity in Iron-Based Superconductors with Cu Substitution

We study an inhomogeneous three-orbital Hubbard model for the Cu-substituted iron pnictides using an extended real-space Green's function method combined with density functional calculations. We find that the onsite interactions of the Cu ions are the principal determinant of whether an electron dopant or a hole dopant is caused by the Cu substitution. It is found that the Cu substitution could lead to a hole doping when its onsite interactions are smaller than a critical value, as opposed to an electron doping when the interactions of Cu ions are larger than the critical value, which may explain why the effects of Cu substitution on the carrier density are entirely different in NaFe$_{1-x}$Cu$_x$As and Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$. We also find that the effect of a doping-induced disorder is considerable in the Cu-substituted iron pnictides, and its cooperative effect with electron correlations contributes to the orbital-selective insulating phases in NaFe$_{1-x}$Cu$_x$As and Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$.

cond-mat.supr-con↗