arXiv subjects

Yan Li

Publications and source records attributed to Yan Li.

At least 163 records · Page 9Linked to original sources

arXiv2025

CogniEdit: Dense Gradient Flow Optimization for Fine-Grained Image Editing

Instruction-based image editing with diffusion models has achieved impressive results, yet existing methods struggle with fine-grained instructions specifying precise attributes such as colors, positions, and quantities. While recent approaches employ Group Relative Policy Optimization (GRPO) for alignment, they optimize only at individual sampling steps, providing sparse feedback that limits trajectory-level control. We propose a unified framework CogniEdit, combining multi-modal reasoning with dense reward optimization that propagates gradients across consecutive denoising steps, enabling trajectory-level gradient flow through the sampling process. Our method comprises three components: (1) Multi-modal Large Language Models for decomposing complex instructions into actionable directives, (2) Dynamic Token Focus Relocation that adaptively emphasizes fine-grained attributes, and (3) Dense GRPO-based optimization that propagates gradients across consecutive steps for trajectory-level supervision. Extensive experiments on benchmark datasets demonstrate that our CogniEdit achieves state-of-the-art performance in balancing fine-grained instruction following with visual quality and editability preservation

cs.CV↗

arXiv2025

COMPARE: Clinical Optimization with Modular Planning and Assessment via RAG-Enhanced AI-OCT: Superior Decision Support for Percutaneous Coronary Intervention Compared to ChatGPT-5 and Junior Operators

Background: While intravascular imaging, particularly optical coherence tomography (OCT), improves percutaneous coronary intervention (PCI) outcomes, its interpretation is operator-dependent. General-purpose artificial intelligence (AI) shows promise but lacks domain-specific reliability. We evaluated the performance of CA-GPT, a novel large model deployed on an AI-OCT system, against that of the general-purpose ChatGPT-5 and junior physicians for OCT-guided PCI planning and assessment. Methods: In this single-center analysis of 96 patients who underwent OCT-guided PCI, the procedural decisions generated by the CA-GPT, ChatGPT-5, and junior physicians were compared with an expert-derived procedural record. Agreement was assessed using ten pre-specified metrics across pre-PCI and post-PCI phases. Results: For pre-PCI planning, CA-GPT demonstrated significantly higher median agreement scores (5[IQR 3.75-5]) compared to both ChatGPT-5 (3[2-4], P<0.001) and junior physicians (4[3-4], P<0.001). CA-GPT significantly outperformed ChatGPT-5 across all individual pre-PCI metrics and showed superior performance to junior physicians in stent diameter (90.3% vs. 72.2%, P<0.05) and length selection (80.6% vs. 52.8%, P<0.01). In post-PCI assessment, CA-GPT maintained excellent overall agreement (5[4.75-5]), significantly higher than both ChatGPT-5 (4[4-5], P<0.001) and junior physicians (5[4-5], P<0.05). Subgroup analysis confirmed CA-GPT's robust performance advantage in complex scenarios. Conclusion: The CA-GPT-based AI-OCT system achieved superior decision-making agreement versus a general-purpose large language model and junior physicians across both PCI planning and assessment phases. This approach provides a standardized and reliable method for intravascular imaging interpretation, demonstrating significant potential to augment operator expertise and optimize OCT-guided PCI.

cs.AI↗

arXiv2025

Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging {sequences}, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen {modalities} and robustness to diverse undersampling patterns. We introduced the largest public multi-{modality} CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

eess.IV↗

arXiv2025

Orbital Current-Driven Magnetization Switching in a Magnetic Tunnel Junction

Spin-orbitronics, based on both spin and orbital angular momentum, presents a promising pathway for energy-efficient memory and logic devices. Recent studies have demonstrated the emergence of orbital currents in light transition metals such as Ti, Cr, and Zr, broadening the scope of spin-orbit torque (SOT). In particular, the orbital Hall effect, which arises independently of spin-obit coupling, has shown potential for enhancing torque efficiency in spintronic devices. However, the direct integration of orbital current into magnetic random-access memory (MRAM) remains unexplored. In this work, we design a light metal/heavy metal/ferromagnet multilayer structure and experimentally demonstrate magnetization switching by orbital current. Furthermore, we have realized a robust SOT-MRAM cell by incorporating a reference layer that is pinned by a synthetic antiferromagnetic structure. We observed a tunnel magnetoresistance of 66%, evident in both magnetic field and current-driven switching processes. Our findings underscore the potential for employing orbital current in designing next-generation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

A Comprehensive Survey of 3GPP Release 19 ISAC Channel Modeling: From Empirical Features to Unified Methodology and Standardized Simulator

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. Channel measurement and modeling is a prerequisite for ISAC system design and has attracted widespread attention from both academia and industry. 3GPP Release 19 initiated the ISAC channel study item in December 2023 and finalized its modeling specification in May 2025 after extensive technical discussions. However, a comprehensive survey that provides a systematic overview,from empirical channel features to modeling methodologies and standardized simulators,remains unavailable. In this paper, the key requirements and challenges in ISAC channel research are first analyzed, followed by a structured overview of the standardization workflow throughout the 3GPP Release 19 process. Then, critical aspects of ISAC channels, including physical objects, target channels, and background channels, are examined in depth, together with additional features such as spatial consistency, environment objects, Doppler characteristics, and shared clusters, supported by measurement-based analysis. To establish a unified ISAC channel modeling framework, an Extended Geometry-based Stochastic Model (E-GBSM) is proposed, incorporating all the aforementioned ISAC channel characteristics. Finally, a standardized simulator is developed based on E-GBSM, and a two-phase calibration procedure aligned with 3GPP Release 19 is conducted to validate both the model and the simulator, demonstrating close agreement with industrial reference results. Overall, this paper provides a systematic survey of 3GPP Release 19 ISAC channel standardization and offers insights into best practices for new feature characterization, unified modeling methodology, and standardized simulator implementation, which can effectively supporting ISAC technology evaluation and future 6G standardization.

eess.SP↗

arXiv2025

The First Scientific Flight and Observations of the 50-mm Balloon-Borne White-Light Coronagraph

A 50-mm balloon-borne white-light coronagraph (BBWLC) to observe whitelight solar corona over the altitude range from 1.08 to 1.50 solar radii has recently been indigenously developed by Yunnan Observatories in collaboration with Shangdong University (in Weihai) and Changchun Institute of Optics, Fine Mechanics and Physics, which will significantly improve the ability of China to detect and measure inner corona. On 2022 October 4, its first scientific flight took place at the Dachaidan area in Qinghai province of China. We describe briefly the BBWLC mission including its optical design, mechanical structure, pointing system, the first flight and results associated with the data processing approach. Preliminary analysis of the data shows that BBWLC imaged the Kcorona with three streamer structures on the west limb of the Sun. To further confirm the coronal signals obtained by BBWLC, comparisonswere made with observations of the Kcoronagraph of the High Altitude Observatory and the Atmospheric ImagingAssembly on board the Solar Dynamics Observatory. We conclude that BBWLC eventually observed the white-light corona in its first scientific flight.

astro-ph.SR↗

arXiv2025

ARPGNet: Appearance- and Relation-aware Parallel Graph Attention Fusion Network for Facial Expression Recognition

The key to facial expression recognition is to learn discriminative spatial-temporal representations that embed facial expression dynamics. Previous studies predominantly rely on pre-trained Convolutional Neural Networks (CNNs) to learn facial appearance representations, overlooking the relationships between facial regions. To address this issue, this paper presents an Appearance- and Relation-aware Parallel Graph attention fusion Network (ARPGNet) to learn mutually enhanced spatial-temporal representations of appearance and relation information. Specifically, we construct a facial region relation graph and leverage the graph attention mechanism to model the relationships between facial regions. The resulting relational representation sequences, along with CNN-based appearance representation sequences, are then fed into a parallel graph attention fusion module for mutual interaction and enhancement. This module simultaneously explores the complementarity between different representation sequences and the temporal dynamics within each sequence. Experimental results on three facial expression recognition datasets demonstrate that the proposed ARPGNet outperforms or is comparable to state-of-the-art methods.

cs.CV↗

arXiv2025

The Solar Close Observations and Proximity Experiments (SCOPE) mission

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of the current sheet between coronal mass ejections and their associated solar flares, and energetic particles produced by either reconnection or fast-mode shocks driven by coronal mass ejections. This will help to resolve the nature of reconnections in current sheets, and energetic particle acceleration regions. To investigate coronal heating, the mission will observe nano-flares on scales smaller than 70 km in the solar corona and regions smaller than 40 km in the photosphere, where magnetohydrodynamic waves originate. To study solar wind acceleration mechanisms, the mission will also track the process of ion charge-state freezing in the solar wind. A key achievement will be the observation of the coronal magnetic field at unprecedented proximity to the solar photosphere. The polar regions will also be observed at close range, and the inner edge of the solar system dust disk may be identified for the first time. This work presents the detailed background, science, and mission concept of SCOPE and discusses how we aim to address the questions mentioned above.

astro-ph.SR↗

arXiv2025

Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design

LLM-based search agents achieve strong performance but suffer from severe latency, as each step requires serialized LLM reasoning followed by action of tool execution. We revisit this bottleneck through the lens of speculation. While traditional predict-verify speculation paradigm can break serial execution, its benefit remains limited, as it retains the full original workload and adds extra inference overhead. We observe that early agent steps often involve simple evidence-gathering, where correct actions can often be predicted without full reasoning. Building on these observations, we present SPAgent, an algorithm-system co-design framework that expands the role of speculation in search agents to reduce latency. Algorithmically, SPAgent introduces a two-phase adaptive speculation mechanism that selectively omits verification when safe. System-wise, a two-level scheduler regulates speculative requests based on engine load to ensure speculation remains beneficial. We implement SPAgent in real-world systems. Across extensive experimental settings, SPAgent achieves up to $1.65\times$ end-to-end speedup while maintaining same or even achieving higher accuracy, enabling practical deployment of multi-step search agents.

cs.AI↗

arXiv2025

Active3D: Active High-Fidelity 3D Reconstruction via Hierarchical Uncertainty Quantification

In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit-explicit representation that fuses neural fields with Gaussian primitives to jointly capture global structural priors and locally observed details. Based on this hybrid state, we derive a hierarchical uncertainty volume that quantifies both implicit global structure quality and explicit local surface confidence. To focus optimization on the most informative regions, we propose an uncertainty-driven keyframe selection strategy that anchors high-entropy viewpoints as sparse attention nodes, coupled with a viewpoint-space sliding window for uncertainty-aware local refinement. The planning module formulates next-best-view selection as an Expected Hybrid Information Gain problem and incorporates a risk-sensitive path planner to ensure efficient and safe exploration. Extensive experiments on challenging benchmarks demonstrate that our approach consistently achieves state-of-the-art accuracy, completeness, and rendering quality, highlighting its effectiveness for real-world active reconstruction and robotic perception tasks.

cs.RO↗

arXiv2025

Canonical Quantum Mpemba Effect in a Dissipative Qubit

The Mpemba effect, where a hotter system cools faster than a colder one under otherwise identical conditions, has been extensively studied in classical systems. In this work, we present the quantum analogue of the Mpemba effect using a dissipative qubit, which is referred to as the canonical quantum Mpemba effect. We demonstrate that, under the identical conditions, the relaxation dynamics of a qubit initialized in a thermal state with a higher temperature can be exponentially faster than those of a colder thermal state. Strikingly, this acceleration is determined solely by the initial temperature of the system, independent of other parameters. The relaxation is confirmed to be a genuine cooling process via the effective steady state temperature, mirroring its classical counterpart. Last, we propose a practical classical quantum hybrid algorithmic quantum circuit to realize this effect using superconducting qubits experimentally.

quant-ph↗

arXiv2025

Asteroseismic Imprints of Mass Transfer in Binary Stars: Probing the Interiors of Donors and Accretors with Gravity and Acoustic Modes

Context. The synergy between close binary stars and asteroseismology enables constraints on mass-transfer episodes and their consequences for internal structure, rotation profiles, and oscillation modes. Aims. We investigate how mass accretion and donation in close binaries affects the internal structure and oscillation modes of main-sequence stars. Methods. Building on the established relation between the Brunt-Vaisala (buoyancy) glitch and the Fourier spectra of g-mode period spacings, we quantitatively explain the origins of the g-mode period-spacing differences between single-star and mass-accretion/donation models of intermediate-mass stars (M = 2.0, 3.0, and 4.5 Msun). In particular, the hydrogen mass fraction profiles X of the donor model show two chemical gradient regions, which results in a double-peaked Brunt-Vaisala profile. The presence of additional buoyancy glitches gives rise to further periodic modulations in the g-mode period spacings. Results. Mass-accretion induced changes in the chemical profile create sharp features in the buoyancy frequency, which modify both the amplitudes and frequencies of the g-mode period-spacing variations. This behavior resembles that produced by multiple chemical transition zones in compact pulsators such as white dwarfs and sub-dwarf B stars. Similarly, for acoustic modes in the M = 1 Msun solar-like models, we attribute the differences in frequency-separation ratios between single-star and mass-donor models to the variations in the internal sound-speed gradient (acoustic glitches). We discuss future prospects for using asteroseismology to discover the mass-transfer products and constrain the mass-transfer processes in binary star evolution.

astro-ph.SR↗

arXiv2025

Kerr-enhanced optomechanical entanglement generation via reservoir design

Quantum entanglement is a crucial resource in quantum technologies, enabling advancements in quantum computing, quantum communication, and quantum precision measurement. Here, we propose a method to enhance optomechanical entanglement by introducing an optical Kerr nonlinear medium and a squeezed vacuum reservoir of the optomechanical cavity. By performing the displacement and squeezing transformations, the system can be reduced to a standard linearized optomechanical system with normalized driving detuning and linearized-coupling strength, in which the optical and mechanical modes are, respectively, coupled to an optical vacuum bath and a mechanical heat bath. We focus on the entanglement generation in the single stable regime of the system. By evaluating the steady-state logarithm negativity, we find that the optomechanical entanglement can be enhanced within a wide range of the Kerr constant. In addition, the Kerr nonlinearity can extend the stable region, enabling considerable entanglement generation in the blue-sideband parameter region. We also investigate the dependence of the entanglement generation on the average thermal phonon occupation of the mechanical bath and the optical driving amplitude. It is found that the presence of the Kerr nonlinearity allows the generation of optomechanical entanglement even when the thermal phonon occupation of the mechanical bath is as high as 3000. Our findings will provide valuable insights into enhancing fragile quantum resources in quantum systems.

quant-ph↗

arXiv2025

Residual Symmetry Reductions and Painlevé Solitons

This letter introduces the novel concept of Painlevé solitons -- waves arising from the interaction between Painlevé waves and solitons in integrable systems. Painlevé solitons may also be viewed as solitons propagating against a Painlevé wave background, in analogy with the established notion of elliptic solitons, which refer to solitons on an elliptic wave background. By employing a novel symmetry decomposition method aided by nonlocal residual symmetries, we explicitly construct (extended) Painlevé II solitons for the Korteweg-de Vries (KdV) equation and (extended) Painlevé IV solitons for the Boussinesq equation.

nlin.SI↗

arXiv2025

An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones

The emergence of truck-drone collaborative systems in last-mile logistics has positioned the Traveling Salesman Problem with Drones (TSP-D) as a pivotal extension of classical routing optimization, where synchronized vehicle coordination promises substantial operational efficiency and reduced environmental impact, yet introduces NP-hard combinatorial complexity beyond the reach of conventional optimization paradigms. Deep reinforcement learning offers a theoretically grounded framework to address TSP-D's inherent challenges through self-supervised policy learning and adaptive decision-making. This study proposes a hierarchical Actor-Critic deep reinforcement learning framework for solving the TSP-D problem. The architecture consists of two primary components: a Transformer-inspired encoder and an efficient Minimal Gated Unit decoder. The encoder incorporates a novel, optimized k-nearest neighbors sparse attention mechanism specifically for focusing on relevant spatial relationships, further enhanced by the integration of global node features. The Minimal Gated Unit decoder processes these encoded representations to efficiently generate solution sequences. The entire framework operates within an asynchronous advantage actor-critic paradigm. Experimental results show that, on benchmark TSP-D instances of various scales (N=10 to 100), the proposed model can obtain competitive or even superior solutions in shorter average computation times compared to high-performance heuristic algorithms and existing reinforcement learning methods. Moreover, compared to advanced reinforcement learning algorithm benchmarks, the proposed framework significantly reduces the total training time required while achieving superior final performance, highlighting its notable advantage in training efficiency.

cs.LG↗

arXiv2025

A Novel Multi-Reference-Point Modeling Framework for Monostatic Background Channel: Toward 3GPP ISAC Standardization

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. A realistic, standard-compatible channel model is essential for ISAC system design. To characterize the impact of Sensing Targets (STs), 3GPP defines ISAC channel as a combination of target and background channels, comprising multipath components related to STs and those originating solely from the environment, respectively. Although the background channel does not carry direct ST information, its accurate modeling is critical for evaluating sensing performance, especially in complex environments. Existing communication standards characterize propagation between separated transmitter (Tx) and receiver (Rx). However, modeling background channels in the ISAC monostatic mode, where the Tx and Rx are co-located, remains a pressing challenge. In this paper, we firstly conduct ISAC monostatic background channel measurements for an indoor scenario at 28 GHz. Realistic channel parameters are extracted, revealing pronounced single-hop propagation and discrete multipath distribution. Inspired by these properties, a novel stochastic model is proposed to characterizing the ISAC monostatic background channel as the superposition of sub-channels between the monostatic Tx&Rx and multiple communication Rx-like Reference Points (RPs). This model is compatible with standardizations, and a 3GPP-extended implementation framework is introduced. Finally, a genetic algorithm-based method is proposed to extract the optimal number and placement of multi-RPs. The optimization approach and modeling framework are validated by comparing measured and simulated channel parameters. Results demonstrate that the proposed model effectively captures monostatic background channel characteristics, addresses a critical gap in ISAC channel modeling, and supports 6G standardization.

eess.SP↗

arXiv2025

Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions

Gaussian-Process-based Bayesian optimization (GP-BO), is a prevailing model-based framework for DBMS auto-tuning. However, recent work shows GP-BO-based DBMS auto-tuners significantly outperformed auto-tuners based on SMAC, which features random forest surrogate models; such results motivate us to rethink and investigate the limitations of GP-BO in auto-tuner design. We find the fundamental assumptions of GP-BO are widely violated when modeling and optimizing DBMS performance, while tree-ensemble-BOs (e.g., SMAC) can avoid the assumption pitfalls and deliver improved tuning efficiency and effectiveness. Moreover, we argue that existing tree-ensemble-BOs restrict further advancement in DBMS auto-tuning. First, existing tree-ensemble-BOs can only achieve distribution-free point estimates, but still impose unrealistic distributional assumptions on uncertainty estimates, compromising surrogate modeling and distort the acquisition function. Second, recent advances in gradient boosting, which can further enhance surrogate modeling against vanilla GP and random forest counterparts, have rarely been applied in optimizing DBMS auto-tuners. To address these issues, we propose a novel model-based DBMS auto-tuner, Centrum. Centrum improves distribution-free point and interval estimation in surrogate modeling with a two-phase learning procedure of stochastic gradient boosting ensembles. Moreover, Centrum adopts a generalized SGBE-estimated locally-adaptive conformal prediction to facilitate a distribution-free uncertainty estimation and acquisition function. To our knowledge, Centrum is the first auto-tuner to realize distribution-freeness, enhancing BO's practicality in DBMS auto-tuning, and the first to seamlessly fuse gradient boosting ensembles and conformal inference in BO. Extensive physical and simulation experiments on two DBMSs and three workloads show Centrum outperforms 21 SOTA methods.

cs.LG↗

arXiv2025

The oscillation properties of the Blue Large Amplitude Pulsators (BLAPs): relative change rate of periods, excitations, and period relations

\textbf{B}lue \textbf{L}arge \textbf{A}mplitude \textbf{P}ulsators (BLAPs) are a type of variable star that has been identified relatively recently. They are characterized by their large amplitude, high gravity, and long periods (2-75 minutes). Some BLAPs exhibit a rich helium abundance on their surfaces, while some of the others show rich hydrogen atmospheres. Up to date, both the mode identification and formation pathways of BLAPs remain subjects of ongoing debate. In this work, we gave an non-adiabatic pulsation analyses for low-order radial and non-radial dipole modes on a theoretical model grid to explore the characterist ics of BLAPs. The effects of element diffusion and radiative levitation are not taken into account our theoretical models. This because that these processes could lead to discrepancies in surface element abundance of the sdB or BLAP stars between theoretical models and observations. Based on the theoretical model grid, our analyses revealed that the radial fundamental modes align well with various observed properties of BLAPs, including the pulsation instability, the position of the excitable models on the HR diagram, surface element compositions, period ranges, and period changes. Our theoretical models also predicted a period-gravity ($\log g$) relation with a narrow distribution, while the observations exhibit a broader range. Furthermore, our findings indicate that the excitation of pulsation in BLAPs is primarily influenced by metal abundance. The thickness of the outer rich hydrogen envelope and the ratio of helium to hydrogen on the surface almost do not affect the pulsation instability.

astro-ph.SR↗