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Anomalous heat transfer in nonequilibrium quantum systems

Physical Review Research 2025年第1期7卷 L012044-L012044页

作者： Teng Ma Jing-Ning Zhang Yuan-Sheng Wang Hong-Yi Xie Man-Hong Yung Beijing Academy of Quantum Information Sciences Beijing 100193 China BGI Research Shenzhen 518083 China BGI Research Wuhan 430074 China School of Systems Science Beijing Normal University Beijing 100875 China Department of Physics and Astronomy Center for Quantum Research and Technology University of Oklahoma Norman Oklahoma 73069 USA Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China International Quantum Academy Shenzhen 518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

Anomalous heat transfer (AHT), a process by which heat spontaneously flows from a cold system into a hot one, superficially contradicts the Clausius statement of the second law of thermodynamics. Here we provide a full classification of mechanisms of AHT in nonequilibrium quantum systems from a quantum-information perspective. For initial states in local equilibrium, we find that AHT can arise from three resources: initial correlation, intrasystem interaction, and intrasystem temperature inhomogeneity. In particular, for qubit systems, we prove that initial quantum coherence is necessary for AHT if the intersystem interactions are limited to the two-body type. We explicitly show AHT dominated by each of the mechanisms in a three-qubit system. Our classification scheme may offer a guideline for developing high-efficiency quantum autonomous thermal machines.

关键词： Qubits

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Multi-scale CNN-Swin transformer network with boundary supervision for multiclass biomarker segmentation in retinal OCT images

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Biocybernetics and Biomedical engineering 2025年第3期45卷 340-356页

作者： Fan, Zhanpeng Liu, Xiaoming Zhang, Ying Zhang, Jia School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430070 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430070 China Wuhan Aier Eye Hospital of Wuhan University Wuhan 430064 China

Retinal biomarker morphology is closely associated with a variety of chronic ophthalmic diseases, in which biomarker localization and segmentation in optical coherence tomography (OCT) play a key role in the diagnosis of retina-related diseases. Although great progress has been made in deep learning based OCT biomarker segmentation, several challenges still exist. Due to issues such as image noise or class imbalance, retinal biomarkers affect the model's recognition of other biomarkers. Moreover, small biomarkers are prone to lose accuracy during downsampling. And most existing methods rely on convolutional neural networks, which make it challenging to obtain the global context due to locality of convolution. Benefiting from the Swin Transformer with powerful modeling capabilities, we propose MSCS-Net (Multi-scale CNN-Swin Network), a network for OCT biomarker segmentation, which effectively combines CNN and Swin Transformer and integrates them in parallel into a dual-encoder structure. Specifically, an edge detection path is added alongside to enhance the localization of biomarkers at the edges. For the Swin Transformer branch, considering the irregular distribution of most OCT biomarkers, a new windowing partition is performed in the Swin Transformer to capture the features more efficiently. Meanwhile, we design a Feature Dimensionality Reduction Module to extensively collect the information of small-scale biomarkers. To effectively integrate information from two scales, we design a Transformer Cross Fusion Module to finely fuse the global and local feature information from the two-branch encoders. We validate the proposed approach on local and public datasets, and the experimental results demonstrate the effectiveness of the proposed framework. © 2025 Nalecz Institute of Biocybernetics and Biomedical engineering of the Polish Academy of sciences

关键词： Convolutional Neural Network Multi-class Segmentation Optical Coherence Tomography (OCT) Retinal Biomarker Segmentation

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SUCP Analysis for Region-Centric UAV-Assisted MEC Networks 20th

SUCP Analysis for Region-Centric UAV-Assisted MEC Networks

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Li, Yan Yi, Zhaozhi Long, Qingmin Luo, Lailong Guo, Deke School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410004 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628636

With the increase of computing-intensive and delay-sensitive applications, mobile edge computing (MEC) technology has sprung up. It effectively satisfies the needs of user equipment (UE) for real-time computing resources by placing servers at the edge of the network. However, traditional MEC infrastructures are constrained by their fixed locations and emergency mobility needs. Unmanned aerial vehicles (UAVs) offer an effective solution with low cost, high mobility, and flexible deployment capabilities. In this paper, we propose a region-centric UAV-assisted MEC model, where the whole network space is divided into a set of hexagonal cells of equal area, and all UAVs in the same cell jointly process UE offloading data. Assume all UAVs are assumed to be equipped with independent MEC servers, and both UAVs and terrestrial UE follow independent homogeneous Poisson point process (PPP) distributions. Using the stochastic geometry analysis framework, we derive the successful uplink communication probability (SUCP) of uplink transmission for users. Finally, we compare the simulation results with the theoretical values to verify the model’s accuracy and evaluate the influence of key performance parameters on the network performance. © IFIP International Federation for information Processing 2025.

关键词： Stochastic systems

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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Twofold symmetric superconductivity in NbSe2 monolayers with vacancy defects

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Physical Review B 2025年第6期111卷 L060503-L060503页

作者： Jizheng Wu Wenyu He Yong Xu Chen Si School of Materials Science and Engineering Beihang University Beijing 100191 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Frontier Science Center for Quantum Information Beijing 100084 China

Monolayer NbSe2, as an Ising superconductor, has attracted considerable attention. Experimentally, its samples inevitably contain defects, yet the effects of these defects on superconductivity remain largely unexplored. Here, we demonstrate that selenium (Se) monovacancies, the most common defects in monolayer NbSe2, critically affect superconductivity. Specifically, Se monovacancies disrupt both the out-of-plane mirror symmetry and the C3z rotational symmetry in pristine monolayer NbSe2, resulting in an asymmetric spin-orbit coupling (SOC). While pristine monolayer NbSe2, which possesses Ising SOC, exhibits an isotropic in-plane upper critical field (Bc2), the asymmetric SOC induced by Se monovacancies leads the in-plane Bc2 to display an anisotropic twofold symmetry in response to the direction of the in-plane magnetic field. Furthermore, Se monovacancies significantly reduce the in-plane spin susceptibility of the superconducting state, causing Bc2 to exceed the Pauli limit only moderately, consistent with experimental observations. These findings provide valuable insights into the role of defects in tuning superconductivity of two-dimensional materials.

关键词： Spin orbit coupling

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Adaptive Closed-Loop Functional Electrical Stimulation system With Visual Feedback for Enhanced Grasping in Neurological Impairments

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 678-686页

作者： Lin, Chengyu Cheng, Kong Hoi Pan, Wei Sun, Jinxin Gou, Guotao Fu, Junyun Leng, Yuquan Fu, Chenglong Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities Department of Mechanical and Energy Engineering Shenzhen518055 China Guilin University of Electronic Technology Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin541004 China

Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object properties. This paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system's potential to improve daily task performance for individuals with neural impairments. © 2018 IEEE.

关键词： Visual servoing

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A Novel Transfer Learning Method for Sub/Super-Synchronous Oscillation Mode Identification in Power systems

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IEEE Internet of Things Journal 2025年第11期12卷 18385-18396页

作者： Fang, Jiashu Kong, Lingran Li, Aobing Hu, Weihao University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu610054 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China

With the increasing integration of renewable energy in power systems, accurate and rapid mode identification of the sub/super-synchronous oscillation (sub/super-SO) is essential for ensuring grid security. This paper presents a deep learning (DL)-based method for early prediction of the dominant frequency in sub/super-SO events. To address the challenge of limited sub/super-SO samples for DL training, we propose a domain-adversarial neural network-based transfer learning framework, which leverages easily obtainable forced oscillation data to capture sub/super-SO features. Our method introduces several enhancements over previous approaches in both model structure and optimization. First, an encoder-decoder module with a mask component is designed to reconstruct missing data from field sub/super-SO events, utilizing a specialized reconstruction loss function. Second, the proposed dual-predictor configuration applies supervised learning in both source and target domains, imposing stronger optimization constraints. Furthermore, the proposed method exhibits generality and robustness when the topologies of target and source wind farms differ, highlighting its potential for practical applications in power systems. © 2014 IEEE.

关键词： Structural optimization

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MHAN: Bottleneck Fusion Model Based on Hybrid Attention Network for Multimodal Emotion Recognition

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Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Li, Kai Huang, Ya Zhong, Gang Nurmemet, Yolwas Wushouer, Silamu School of Software Xinjiang University Urumqi830091 China College of Information Science and Engineering Xinjiang University Urumqi830017 China Xinjiang Multilingual Information Technology Laboratory Xinjiang University Urumqi830017 China

Multimodal emotion recognition (MER) offers a more comprehensive and accurate understanding of human emotions by analyzing and integrating information from various modalities. However, previous work may have yet to fully consider the emotion feature information within and between modalities, which is the most significant challenge in this domain. Therefore, a novel multimodal hybrid attention network (MHAN) is proposed to achieve more effective information aggregation between text and audio. MHAN includes three main modules, i.e., hybrid encoder block (HEB), multi-head cross-attention (MCA) block, and bottleneck fusion (BF) block. Specifically, the HEB can extract advanced feature representation from text and audio sequences. Moreover, the MCA block can capture common features between text and audio to support emotion recognition. To filter out some redundant information, the BF block is designed to better extract the complementary and most relevant information between text and audio modalities. To validate the effectiveness of MHAN, we have conducted extensive experiments on the IEMOCAP and MELD datasets, and the experimental results demonstrate that MHAN outperforms existing state-of-the-art methods and better interprets subjective human emotions in the applications. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A bottleneck fusion (BF) multi-head cross-attention (MCA) multimodal emotion recognition (MER) multimodal hybrid attention network (MHAN) TP183

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Remote sensing of liquid cloud profiles based on an analytical cloud profiling model

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science China Earth sciences 2025年第4期68卷 998-1012页

作者： Huazhe SHANG Husi LETU Lesi WEI Run MA Yutong WANG Zhaoxin CAI Shuai YIN Chong SHI State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing100101China University of Chinese Academy of Sciences Beijing100049China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science&EngineeringYibin644005China Meteorological Disasters Defense Technology Center Meteorological Bureau of Shanxi ProvinceTaiyuan030032China

The characteristics of microphysical profiles of liquid clouds(i.e.,the cloud effective radius(ER)and liquid water content(LWC))are important factors in understanding the aerosol-cloud-precipitation process and improving the evaluation of cloud radiative effects on a global ***,the profiling of clouds is limited to measurements such as radars or lidars,and retrievals from active sensors are influenced by the attenuation of signals and surface *** when vast amounts of cloud property retrievals that express column or cloud-top/base characteristics from passive sensors are generated,these two-dimensional cloud products are scarcely ever used to further produce three-dimensional cloud profiles because of the lack of a reasonable analytical model to bridge the ***,this study developed a cloud profile reconstruction model(CPRM)based on our previous analytical cloud profile model and evaluated the sensitivities of visible-to-infrared bands of passive cloud observation sensors to each parameter of the *** results indicated that the optical thickness,cloud-top ER,and geometrical thickness can be derived from multiwavelength measurements,whereas the slope of the cloud droplet number concentration(CDNC)and the cloud-base ER could hardly be directly ***,the analytical model was utilized to rebuild cloud profiles seen by CloudSat and *** analytical model could capture the shape and the vertical variations of the cloud ER and LWC.

关键词： Analytical cloud profiling model Cloud effective radius Cloud liquid water content CloudSat Airborne mea-surements

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