arXiv subjects

Yue Sun

Publications and source records attributed to Yue Sun.

At least 73 records · Page 4Linked to original sources

arXiv2026

Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction

Radiotherapy treatment planning often relies on time-consuming, trial-and-error adjustments that heavily depend on the expertise of specialists, while existing deep learning methods face limitations in generalization, prediction accuracy, and clinical applicability. To tackle these challenges, we propose ADDiff-Dose, an Anatomical-Dose Dual Constraints Conditional Diffusion Model for end-to-end multi-tumor dose prediction. The model employs LightweightVAE3D to compress high-dimensional CT data and integrates multimodal inputs, including target and organ-at-risk (OAR) masks and beam parameters, within a progressive noise addition and denoising framework. It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence to ensure both dosimetric accuracy and compliance with clinical constraints. Evaluation on a large-scale public dataset (2,877 cases) and three external institutional cohorts (450 cases in total) demonstrates that ADDiff-Dose significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154 (compared to 0.316 for UNet and 0.169 for GAN models), a DICE coefficient of 0.927 (a 6.8% improvement), and limiting spinal cord maximum dose error to within 0.1 Gy. The average plan generation time per case is reduced to 22 seconds. Ablation studies confirm that the structural encoder enhances compliance with clinical dose constraints by 28.5%. To our knowledge, this is the first study to introduce a conditional diffusion model framework for radiotherapy dose prediction, offering a generalizable and efficient solution for automated treatment planning across diverse tumor sites, with the potential to substantially reduce planning time and improve clinical workflow efficiency.

cs.CV↗

arXiv2025

Distinct Suppression Mechanisms of Superconductivity by Magnetic Domains and Spin Fluctuations in EuFe2(As1-xPx)2 superconductors

Using ac composite magnetoelectric technique, we map the phase diagrams of EuFe(As1-xPx)2 to resolve the interplay between superconductivity and ferromagnetism. For samples with Tc TFM, strong short-range spin correlations and phase boundaries within a multiphase coexistence regime near the triple point act as potent pair-breaking centers, leading to pronounced Hc2 suppression.

cond-mat.supr-con↗

arXiv2025

Anomalous Point-Gap Interactions Unveil the Mirage Bath

Non-Hermitian topology has revolutionized our understanding of energy gaps and band topology, unveiling phases that do not exist within the Hermitian framework. Nonetheless, its fundamental implications for quantum interactions in open quantum systems remain largely unexplored. Here, we uncover a novel interaction mechanism by examining a quantum-optical system where quantum emitters interact through the photonic band-gap of a dissipative photon bath with periodic boundaries, described by a nonreciprocal Su-Schrieffer-Heeger model. Although localized photons within the gap should inhibit interactions between emitters in certain regimes, we find that long-range interactions emerge, defying conventional expectations. These anomalous interactions are mediated by a ``mirage bath" - a virtual bath that unfolds onto a distinct layer of the Riemann surface. This mirage bath generates emitter dynamics identical to those produced by the physical bath but possesses distinct band topology. Crucially, the interactions inherit the topology of the mirage bath, not the physical one. This bath duality is inherent to any dissipative bath with spectral topology, leading to a fundamentally new mechanism for long-range interactions and correlations across the multi-layered Riemann surface, unseen in traditional settings. Our findings open new avenues in quantum optics, many-body quantum simulations, and offer fresh insights into non-Hermitian topology.

quant-ph↗

arXiv2025

Boost of critical current density near quantum critical points in FeSe-Based superconductors with two superconducting domes

Recent studies have identified two superconducting domes in FeSe-based superconductors. It was discovered that each dome is accompanied by a distinct nematic quantum critical point (QCP): one associated with a pure nematic QCP, and the other with a nematic QCP entangled with antiferromagnetism (AFM). In this study, we delve into the evolution of the critical current density ($J_{\rm{c}}$) with doping in FeSe${_{1-x}}$(Te/S)${_{x}}$ single crystals, focusing on the behavior within the two superconducting domes. Surprisingly, three maxima of $J_{\rm{c}}$ were found in the two superconducting domes, with two sharp peaks in $J_{\rm{c}}$ observed precisely at the endpoints of the nematic phases, at $x$(Te) $\sim$ 0.5 for Te-doped and $x$(S) $\sim$ 0.17 for S-doped FeSe. The mechanisms of vortex pinning and the influence of quantum critical fluctuations have been extensively explored, emphasizing the contribution of quantum critical fluctuations in modulating $J_{\rm{c}}$. Additionally, an increase in $J_{\rm{c}}$ was also noted near FeSe$_{0.1}$Te$_{0.9}$, where its origin has been explored and discussed. This finding provides crucial clues about the existence of an ordered phase endpoint beneath the superconducting dome, offering an initial basis for further investigation into the potential presence of a QCP beneath it.

cond-mat.supr-con↗

arXiv2025

Increase of critical current density in FeSe superconductor by strain effect

Conventional $J_c$-enhancement methods like doping and irradiation often introduce extrinsic elements or defects, altering intrinsic properties. Here, we report a significant $J_c$ enhancement in FeSe single crystals through compressive strain applied using a glass-fiber-reinforced plastic substrate with anisotropic thermal contraction during cooling. Under zero field at 2 K, $J_{\text{c}}$ increases by a factor of $\sim$4 from $\sim 2.3 \times 10^{4}$ to $\sim 8.7 \times 10^{4}$ A cm$^{-2}$; at 5 T, it achieves an order-of-magnitude enhancement, rising from $\sim 1.0 \times 10^{3}$ to $\sim 1.0 \times 10^{4}$ A cm$^{-2}$. Analysis based on the Dew-Hughes model of the $f_{\text{p}}$(h) relationship shows that strain strengthens vortex pinning, and shifts the pinning mechanism from point-like pinning to combined point and surface pinnings. This work offers an effective method to enhance FeSe's current-carrying limitation, deepens understanding of iron-based superconductors' pinning mechanisms, and highlights strain engineering's potential for optimizing superconducting performance.

cond-mat.supr-con↗

arXiv2025

Depairing critical current density and the vortex-free state in FeSe nanobridges

The depairing limit and the vortex-free state in a superconductor is crucial for both the study of supercurrent related physics and the application eliminating noise linked to vortex motion. In this work, we report the evidence of depairing limit and the vortex-free state achieved by geometric constraint in FeSe superconductors. A series of narrow bridges with varying widths at the same location of a single crystal were prepared by the \textquotedblleft pickup\textquotedblright method using successive focused ion beam millings. By simply reducing the width of bridge, the magnitude of critical current density ($J_{\rm{c}}$) is enhanced more than one order, evidence the achievement of depairing limit. Moreover, in the bridge with a width smaller than the penetration depth ($λ$), $J_{\rm{c}}$ is found to be robust against magnetic field up to 1 kOe. The field-robust $J_{\rm{c}}$ is a strong piece of evidence for vortex-free state, which is created by the enhancement of lower critical fields due to geometric constraint.

cond-mat.supr-con↗

arXiv2025

SUGAR: Learning Skeleton Representation with Visual-Motion Knowledge for Action Recognition

Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs understand skeleton? 2) How can LLMs distinguish among actions? To address these problems, we introduce a novel paradigm named learning Skeleton representation with visUal-motion knowledGe for Action Recognition (SUGAR). In our pipeline, we first utilize off-the-shelf large-scale video models as a knowledge base to generate visual, motion information related to actions. Then, we propose to supervise skeleton learning through this prior knowledge to yield discrete representations. Finally, we use the LLM with untouched pre-training weights to understand these representations and generate the desired action targets and descriptions. Notably, we present a Temporal Query Projection (TQP) module to continuously model the skeleton signals with long sequences. Experiments on several skeleton-based action classification benchmarks demonstrate the efficacy of our SUGAR. Moreover, experiments on zero-shot scenarios show that SUGAR is more versatile than linear-based methods.

cs.CV↗

arXiv2025

EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion

Endoscopic images often suffer from diverse and co-occurring degradations such as low lighting, smoke, and bleeding, which obscure critical clinical details. Existing restoration methods are typically task-specific and often require prior knowledge of the degradation type, limiting their robustness in real-world clinical use. We propose EndoIR, an all-in-one, degradation-agnostic diffusion-based framework that restores multiple degradation types using a single model. EndoIR introduces a Dual-Domain Prompter that extracts joint spatial-frequency features, coupled with an adaptive embedding that encodes both shared and task-specific cues as conditioning for denoising. To mitigate feature confusion in conventional concatenation-based conditioning, we design a Dual-Stream Diffusion architecture that processes clean and degraded inputs separately, with a Rectified Fusion Block integrating them in a structured, degradation-aware manner. Furthermore, Noise-Aware Routing Block improves efficiency by dynamically selecting only noise-relevant features during denoising. Experiments on SegSTRONG-C and CEC datasets demonstrate that EndoIR achieves state-of-the-art performance across multiple degradation scenarios while using fewer parameters than strong baselines, and downstream segmentation experiments confirm its clinical utility.

eess.IV↗

arXiv2025

Imaginarity measures induced by real part states and the complementarity relations

Complex numbers are indispensable in quantum mechanics and the resource theory of imaginarity has been developed recently. In this paper, we propose a method to construct imaginary measures by real part states. Specifically, we propose an imaginarity measure in terms of fidelity and explore its properties. The analytical expression of the imaginarity measure is presented in qubit systems. The relations between the proposed imaginarity measure and some other imaginarity measures (such as geometric imaginarity, Tsallis relative entropy imaginarity and trace norm imaginarity) are derived. The complementarity relations of the imaginarity measure under a complete set of mutually unbiased bases are provided in low-dimensional systems. This work not only highlights the prominent role of the real part state in the imaginarity resource theory, but also reveals the constraint of imaginarity on a complete set of mutually unbiased bases physically.

quant-ph↗

arXiv2025

Impact of Disorder on the Superconducting Properties and BCS-BEC Crossover in FeSe Single Crystals

We investigate the crystal structure, transport properties and specific heat in five selected FeSe single crystals containing different amounts of disorder. Transport measurements show that disorder significantly suppresses superconducting transition temperature, $T_\mathrm{c}$, and upper critical field, $H_\mathrm{c2}$. Specific heat results confirm a robust multi-gap nature, a larger isotropic gap ($Δ_\mathrm{s}$) and a smaller anisotropic gap ($Δ_\mathrm{es}$). The smaller gap $Δ_\mathrm{es}$ becomes more isotropic with increasing disorder. Additionally, FeSe is regarded as a superconductor in the crossover regime from Bardeen-Cooper-Schrieffer (BCS) to Bose-Einstein condensation (BEC) because of its comparable $Δ$ and Fermi energy $E_\mathrm{F}$. By introducing disorder, the BCS-BEC crossover in FeSe can be tuned closer to BCS limit, reducing $Δ/E_\mathrm{F}$ from 1.3 to 0.4.

cond-mat.supr-con↗

arXiv2025

The complementarity relations in a multi-path interferometer with quantum memory

The complementarity relations impose the constraints on different aspects of quantum states. We study the complementarity relation within a multi-path interferometer that includes detectors and quantum memory. Here we consider the mixed states as the input states. We establish a duality relation between the visibility and the path distinguishability. Based on this duality, two triality relations, one is related with visibility, path distinguishability, and mixedness, the other is related with visibility, path distinguishability, and entanglement, were derived respectively. Therefore, the role of entanglement in multi-path interferometer is characterized quantitatively. These complementarity relations are all complete for the two-path interferometer.

quant-ph↗

arXiv2025

Spin light-emitting devices in a 2D magnet

Emerging two-dimensional (2D) magnetic semiconductors represent transformative platforms to explore magneto-optics and opto-spintronic applications. Though 2D opto-spintronics has attracted tremendous research efforts in spin-dependent photodetectors and non-volatile memory components, the realization of one core application - spin-modulated light-emitting device (spin-LED) - remains elusive so far. Here we successfully realize prototype spin-LED integrated with a 2D semiconducting magnet CrSBr, demonstrating considerable electroluminescence (EL) down to bilayers. Intriguingly, the EL of the spin-LED is discovered to be directly manipulated by spin-flip and spin-canting transitions. Notably, spin-flip transitions enable unprecedented hysteretic behaviors of EL characteristics, while spin-canting transitions induce EL continuous modulation with robust anisotropy. This versatile manipulation is originated from the synergy of magnetic-order mediated excitonic transitions and spintronic transport. The prototype demonstration of spin-LED establishes an indispensable scheme of opto-spintronic devices leveraging 2D spin transitions and strong excitonic effects, presenting a critical step towards integrated 2D opto-spintronics.

physics.app-ph↗

arXiv2025

MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment

Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical IQA methods, however, struggle to generalize across diverse modalities and clinical scenarios. In response, we introduce MedIQA, the first comprehensive foundation model for medical IQA, designed to handle variability in image dimensions, modalities, anatomical regions, and types. We developed a large-scale multi-modality dataset with plentiful manually annotated quality scores to support this. Our model integrates a salient slice assessment module to focus on diagnostically relevant regions feature retrieval and employs an automatic prompt strategy that aligns upstream physical parameter pre-training with downstream expert annotation fine-tuning. Extensive experiments demonstrate that MedIQA significantly outperforms baselines in multiple downstream tasks, establishing a scalable framework for medical IQA and advancing diagnostic workflows and clinical decision-making.

cs.CV↗

arXiv2025

Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems

Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper addresses the critical challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs). We define two categories of anomalies: cyber anomalies, which propagate through interconnected variables, and measurement anomalies, which remain localized to individual variables. To address these challenges, we propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework. ICODE leverages Neural ODEs for anomaly detection while employing causality inference through an explanation channel to perform root cause analysis (RCA), elucidating why specific time periods are flagged as anomalous. ICODE is designed to simultaneously perform anomaly detection, RCA, and anomaly type classification within a single, interpretable framework. Our approach is grounded in the hypothesis that anomalies alter the underlying ODEs of the system, manifesting as changes in causal relationships between variables. We provide a theoretical analysis of how perturbations in learned model parameters can be utilized to identify anomalies and their root causes in time series data. Comprehensive experimental evaluations demonstrate the efficacy of ICODE across various dynamical systems, showcasing its ability to accurately detect anomalies, classify their types, and pinpoint their origins.

cs.LG↗

arXiv2025

SLIF-MR: Self-loop Iterative Fusion of Heterogeneous Auxiliary Information for Multimodal Recommendation

Knowledge graphs (KGs) and multimodal item information, which respectively capture relational and attribute features, play a crucial role in improving recommender system accuracy. Recent studies have attempted to integrate them via multimodal knowledge graphs (MKGs) to further enhance recommendation performance. However, existing methods typically freeze the MKG structure during training, which limits the full integration of structural information from heterogeneous graphs (e.g., KG and user-item interaction graph), and results in sub-optimal performance. To address this challenge, we propose a novel framework, termed Self-loop Iterative Fusion of Heterogeneous Auxiliary Information for Multimodal Recommendation (SLIF-MR), which leverages item representations from previous training epoch as feedback signals to dynamically optimize the heterogeneous graph structures composed of KG, multimodal item feature graph, and user-item interaction graph. Through this iterative fusion mechanism, both user and item representations are refined, thus improving the final recommendation performance. Specifically, based on the feedback item representations, SLIF-MR constructs an item-item correlation graph, then integrated into the establishment process of heterogeneous graphs as additional new structural information in a self-loop manner. Consequently, the internal structures of heterogeneous graphs are updated with the feedback item representations during training. Moreover, a semantic consistency learning strategy is proposed to align heterogeneous item representations across modalities. The experimental results show that SLIF-MR significantly outperforms existing methods, particularly in terms of accuracy and robustness.

cs.IR↗

arXiv2025

Pure geometric $f(R)$ branes

In this paper, we investigate pure geometric $f(R)$ cosmology branes embedded in five-dimensional spacetime. The form of $f(R)$ is chosen as a polynomial. The Five-dimensional scalar curvature $R$ is assumed to be constant. Based on the value of the four-dimensional cosmological constant $λ_4$, the branes can be classified into Minkowski, de Sitter, and de anti-de Sitter cases. Solutions for each case can be calculated. These solutions are stable against linear tensor perturbations in all cases. In the Minkowski brane case, the zero mode of gravity can be localized on the brane. In the de Sitter brane case, the zero mode and one massive Kaluza-Klein mode can be localized on the brane. In the anti-de Sitter brane case, all massive Kaluza-Klein modes can be localized on the brane. The results of the analysis of tensor fluctuations are the same in both the Einstein frame and the Jordan frame.

hep-th↗

arXiv2025

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the need for robust task generalization. Prompt-based methodologies have emerged as a pivotal strategy to guide deep learning models, providing flexible, domain-specific adaptations that significantly enhance model performance and adaptability without extensive retraining. This systematic review critically examines the burgeoning landscape of prompt engineering in medical imaging. We dissect diverse prompt modalities, including textual instructions, visual prompts, and learnable embeddings, and analyze their integration for core tasks such as image generation, segmentation, and classification. Our synthesis reveals how these mechanisms improve task-specific outcomes by enhancing accuracy, robustness, and data efficiency and reducing reliance on manual feature engineering while fostering greater model interpretability by making the model's guidance explicit. Despite substantial advancements, we identify persistent challenges, particularly in prompt design optimization, data heterogeneity, and ensuring scalability for clinical deployment. Finally, this review outlines promising future trajectories, including advanced multimodal prompting and robust clinical integration, underscoring the critical role of prompt-driven AI in accelerating the revolution of diagnostics and personalized treatment planning in medicine.

eess.IV↗

arXiv2025

Electron-magnon coupling at the interface of a "twin-twisted" antiferromagnet

We identify a "twin-twist" angle in orthorhombic two-dimensional magnets that maximizes interlayer orbital overlap and enables strong interfacial coupling. Focusing on the van der Waals antiferromagnet CrSBr, we show that this twist angle, near 72 deg, aligns diagonal lattice vectors across the layers, enhancing the interlayer hopping that is spin-forbidden in pristine systems and orbital-forbidden in 90-deg-twisted samples. The enhanced hopping modifies the electronic structure and activates a novel mechanism for excitation of interfacial magnons. Using optical probes we discover that excitons on one side of the interface selectively excite magnons localized on the opposite side. We show that this cross-coupling phenomenon can be understood as a consequence of the spin-transfer torque as that arises as electrons tunnel across the twin-twisted interface. Our findings demonstrate that large-angle twisting in anisotropic 2D materials offers a powerful tool for engineering spin and charge transport through controlled interlayer hybridization, opening new avenues for twisted magnetism and strongly correlated moiré physics.

cond-mat.mes-hall↗