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arXiv 2025年

作者： Zhao, Haokun Han, Jinyi Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Shanghai Institute of Artificial Intelligence for Education East China Normal University China Shanghai Key Laboratory of Data Science School of Data Science Fudan University China

Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improvement. Some approaches utilize synthetic data generated by advanced LLMs based on evaluation results to train models. However, conventional evaluation methods fail to provide detailed, fine-grained profiles of LLMs, limiting their guidance for data synthesis. In this paper, we introduce the Cognitive Diagnostic Synthesis (CDS) method, which incorporates a diagnostic process inspired by Cognitive Diagnosis Theory (CDT) to refine evaluation results and characterize model profiles at the knowledge component level. Based on these diagnostics, we propose two diagnosis-synthesis strategies for weakness-targeted data synthesis. Additionally, we present an enhanced data augmentation and selection pipeline to improve the quality and diversity of synthesized data. Our experiments with several open-source models show significant improvements across multiple benchmarks, achieving up to 6.00% improvement in code generation, 13.10% in mathematical reasoning, and 5.43% in academic exams. Code and data are available on GitHub 1 Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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MoS_(2)Lubricate‑Toughened MXene/ANF Composites for Multifunctional Electromagnetic Interference Shielding

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Nano-Micro Letters 2025年第2期17卷 358-371页

作者： Jiaen Wang Wei Ming Longfu Chen Tianliang Song Moxi Yele Hao Zhang Long Yang Gegen Sarula Benliang Liang Luting Yan Guangsheng Wang School of Physical Science and Engineering Beijing Jiaotong UniversityBeijing 100044People’s Republic of China Key Laboratory of Bio‑Inspired Smart Interfacial Science and Technology of Ministry of Education School of ChemistryBeihang UniversityBeijing 100191People’s Republic of China

The design and fabrication of high toughness electromagnetic interference(EMI)shielding composite films with diminished reflection are an imperative task to solve electromagnetic pollution *** MXene/ANF(aramid nanofibers)–MoS_(2)composite films with nacre-like layered structure here are fabricated after the introduction of MoS_(2)into binary MXene/ANF composite *** introduction of MoS_(2)fulfills an impressive“kill three birds with one stone”improvement effect:lubrication toughening mechanical performance,reduction in secondary reflection pollution of electromagnetic wave,and improvement in the performance of photothermal *** the introduction of MoS_(2)into binary MXene/ANF(mass ratio of 50:50),the strain to failure and tensile strength increase from 22.1±1.7%and 105.7±6.4 MPa and to 25.8±0.7%and 167.3±9.1 MPa,*** toughness elevates from 13.0±4.1 to 26.3±0.8 MJ m^(−3)(~102.3%)*** the reflection shielding effectiveness(SE_(R))of MXene/ANF(mass ratio of 50:50)decreases~10.8%.EMI shielding effectiveness(EMI SE)elevates to 41.0 dB(8.2–12.4 GHz);After the introduction of MoS_(2)into binary MXene/ANF(mass ratio of 60:40),the strain to failure increases from 18.3±1.9%to 28.1±0.7%(~53.5%),the SE_(R)decreases~22.2%,and the corresponding EMI SE is 43.9 *** MoS_(2)also leads to a more efficient photothermal conversion performance(~45 to~55℃).Additionally,MXene/ANF–MoS_(2)composite films exhibit excellent electric heating performance,quick temperature elevation(15 s),excellent cycle stability(2,2.5,and 3 V),and long-term stability(2520 s).Combining with excellent mechanical performance with high MXene content,electric heating performance,and photothermal conversion performance,EMI shielding ternary MXene/ANF–MoS_(2)composite films could be applied in many industrial *** work broadens how to achieve a balance between mechanical properties and versatility of composites in the case of high-function fillers.

关键词： MXene-MoS_(2) Lubrication toughening EMI shielding Photothermal conversion Electric heating performance

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Analysis of the SEIR mean-field model in dynamic networks under intervention

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Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 79-92页

作者： Fang, Wenxuan Du, Wei Yu, Guo He, Renchu Tang, Yang Jin, Yaochu East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Nanjing Tech University Institute of Intelligent Manufacturing Nanjing211816 China China University of Petroleum Department of Automation College of Artificial Intelligence Beijing102249 China Westlake University School of Engineering Hangzhou310030 China

Gasoline blending scheduling is challenging, involving multiple conflicting objectives and a large decision space with many mixed integers. Due to these difficulties, one promising solution is to use preference-based multiobjective evolutionary algorithms (PBMOEAs). However, in practical applications, suitable preferences of decision makers are often difficult to generalize and summarize from their operational experience. This article proposes a novel framework called preference prediction-based evolutionary multiobjective optimization (PP-EMO). In PP-EMO, suitable preferences for a new environment can be automatically obtained from historical operational experience by a machine learning-based preference prediction model when we feed the model with the input of the optimization environment. We have found that the predicted preference is able to guide the optimization to efficiently obtain a set of promising scheduling scenarios. Finally, we conducted comparative tests across various environments, and the experimental results demonstrate that the proposed PP-EMO framework outperforms existing methods. Compared with no preference, PP-EMO reduces operating costs by about 25% and decreases blending errors by 50% under demanding operational conditions. © 2024 IEEE.

关键词： Job shop scheduling

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Stochastic SIRS models on networks: mean and variance of infection

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Infectious Disease Modelling 2025年第3期10卷 875-896页

作者： Chen, Tingting Liu, Guirong Jin, Zhen School of Mathematics and Statistics Shanxi University Shanxi Taiyuan 030006 China Complex Systems Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China

Due to the heterogeneity of contact structure, it is more reasonable to model on networks for epidemics. Because of the stochastic nature of events and the discrete number of individuals, the spread of epidemics is more appropriately viewed as a Markov chain. Therefore, we establish stochastic SIRS models with vaccination on networks to study the mean and variance of the number of susceptible and infected individuals for large-scale populations. Using van Kampen's system-size expansion, we derive a high-dimensional deterministic system which describes the mean behaviour and a Fokker-Planck equation which characterizes the variance around deterministic trajectories. Utilizing the qualitative analysis technique and Lyapunov function, we demonstrate that the disease-free equilibrium of the deterministic system is globally asymptotically stable if the basic reproduction number R₀ < 1;and the endemic equilibrium is globally asymptotically stable if R₀ > 1. Through the analysis of the Fokker-Planck equation, we obtain the asymptotic expression for the variance of the number of susceptible and infected individuals around the endemic equilibrium, which can be approximated by the elements of principal diagonal of the solution of the corresponding Lyapunov equation. Here, the solution of Lyapunov equation is expressed by vectorization operator of matrices and Kronecker product. Finally, numerical simulations illustrate that vaccination can reduce infections and increase fluctuations of the number of infected individuals and show that individuals with greater degree are more easily infected. © 2025 The Authors

关键词： Fokker-Planck equation Networks Stochastic SIRS models Van Kampen's system-size expansion Variance

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Lightweight Remote Sensing Target Detection Algorithm Based on Improved YOLOv5 16th

Lightweight Remote Sensing Target Detection Algorithm Based ...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Wang, Chaomin Dong, Fang Liang, Sheng Yu, Wanxin Wu, Kaixian Yu, Long Wenfeng, Wang School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Jiangxi Province Key Laboratory of Smart Water Conservancy Nanchang Institute of Technology Nanchang330099 China School of Statistics and Data Science Jiangxi University of Finance and Economics Nanchang330013 China

ISBN: (纸本)9789819615346

Aiming at the problems of large memory space occupied by remote sensing image detection models in complex scenes, high computational complexity, and detection speed that is difficult to meet the real-time target detection requirements, a model compression algorithm based on dynamic detection head and channel pruning is proposed. Based on YOLOv5s, the head of the original YOLOv5 is replaced by the DyHead detection head, which improves the detection performance of the model by combining and unifying the three attention mechanisms of scale, space and task. Secondly, the Focaler-IoU loss function is introduced to further improve the predictive regression ability of the model. Finally, the channel pruning algorithm is used to eliminate the redundant parameters in the network to further compress the parameters and computation of the model, and the accuracy of the pruned model is improved by model fine-tuning. The experimental results show that there is a large improvement on the DOTAV1.0 dataset, and the validity is further verified on the RSOD dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Jurassic

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Wen, Sian Li, Congyu Chen, Qing Li, Laquan College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG. © 2025 IEEE.

关键词： federated learning hybrid-supervised learning medical image segmentation

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A universal approach to high-performance thermoelectric module design for power generation (vol 9, 101818, 2025)

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JOULE 2025年第4期9卷

作者： Cheng, Jinxuan Xue, Wenhua Zhang, Tianyu Li, Xiaofang Zhu, Yichen Yin, Li Yao, Honghao Fu, Zixuan Wu, Longzhi Chen, Chen Zhao, Peng Ma, Xiaojing Jiang, Feng Wang, Xiaodong Li, Mingyu Mao, Jun Wang, Yumei Cao, Feng Zhang, Qian School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen 518055 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Science Beijing 100190 China School of Science Harbin Institute of Technology Shenzhen 518055 China Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen 518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 China School of Physical Sciences Great Bay University Dongguan 523000 China Institute of Special Environments Physical Sciences Harbin Institute of Technology Shenzhen 518055 China Beijing Branch of Songshan Lake Materials Laboratory Dongguan 523808 China

Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion, and residual thermal stress at the interface. Transient liquid phase bonding enables the realization of joints with high-melting-point compounds at low bonding temperatures, providing a promising solution for achieving "low-temperature bonding and high-temperature service." Owing to the low eutectic point of the solder and high melting points of compounds, we optimize the design of a germanium-telluride-based module at 533 K, which is successfully applied at the hot-side temperature of 773 K. Attributed to high-performance materials and reliable joints, the module realizes a high conversion efficiency of ∼15.1% and remains stable throughout 150 h of service. By adopting the same approach, the low- (Bi2Te3) and high-temperature (half-Heusler) modules are assembled. We provide a promising and general route for the assembly of full-temperature-range thermoelectric devices.

关键词： transient liquid phase bonding thermoelectric module intermetallic compounds GeTe

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Multi-attention key-factor-aware convolutional neural network developed for quality prediction of batch processes tackling data sampled at various frequencies

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Canadian Journal of Chemical Engineering 2025年第0期

作者： Dong, Yufeng Yan, Xuefeng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China State Key Laboratory of Bioreactor Engineering East China University of Science and Technology Shanghai China

Quality prediction is a critical issue in batch processes, where it encounters numerous challenges. Actual batch processes exhibit characteristics of multiple sampling frequencies and multiple stages. The former influences the efficient utilization of data, while the latter typically corresponds to sequential microbial growth stages or operational steps, manifesting as complex process dynamics that affect the effective extraction of process features. This paper presents a multi-attention key-factor-aware convolutional neural network (MKCNN) designed to address both aspects. MKCNN is a multi-branch model, with each branch receiving data sampled at a different frequency as input. Two types of branches are designed: Main Branch and Auxiliary Branch. The former tackles data containing process stage characteristics and local dynamics. In this branch, spatial attention enhances stage-specific features, while channel attention emphasizes the overall local dynamics. The latter handles data covering local dynamics or overall static features. In this branch, either spatial attention enhances local dynamics, or channel attention emphasizes overall static features. Subsequently, features from each branch are fused by a feature decomposition and fusion module (FDFM). FDFM employs cross-attention to capture the correlation among the features from different branches. The proposed MKCNN was evaluated on a real-world ethanol fermentation process (EFP) against support vector regression (SVR), multi-branch convolutional neural network (MCNN), and multi-branch long short-term memory (MLSTM), and so forth. MKCNN demonstrated an average improvement of 11.7% in R2 compared to SVR and a 5.7% improvement compared to MLSTM. These results underscore its superior performance in quality prediction. © 2025 Canadian Society for Chemical Engineering.

关键词： Convolutional neural networks

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