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Aligning enhanced feature representation for generalized zero-shot learning

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Science China(Information Sciences) 2025年

作者： Zhiyu FANG Xiaobin ZHU Chun YANG Hongyang ZHOU Jingyan QIN Xucheng YIN school of Computer&communication engineering University of Science and Technology Beijing

Abstract Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders （VAEs） is a popular strategy for generalized zero-shot learning （GZSL）.However,due to the lack of fine-grained instance-wise annotations,existing VAE methods can easily suffer from the posterior collapse *** this paper,we propose an innovative asymmetric VAE network by aligning enhanced feature representation （AEFR） for *** from general VAE structures,we designed two asymmetric encoders for visual and semantic observations and one decoder for visual ***,we propose a simple yet effective gated attention mechanism （GAM） in the visual encoder for enhancing the information interaction between observations and latent variables,alleviating the possible posterior collapse problem *** addition,we propose a novel distributional decoupling-based contrastive learning （D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation *** experiments on publicly available datasets demonstrate the state-of-the-art performance of our *** source code is available at https://***/seeyourmind/AEFR.

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN school of Information and communication Technology Griffith University school of Computer and communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology school of Electrical engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

Enhanced rate capability and cycle stability of Ti2C MXene for sodium storage through an aniline molecules welding strategy

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Tungsten 2025年第1期7卷 161-171页

作者： Hao Chen Ya-Qin Wang Dong-Ting Zhang Bei Zhao Min-Peng Li Chen-Yang Li Tian-Peng Xu Mao-Cheng Liu State Key Laboratory of Advanced Processing and Recycling of Non-Ferrous Metals Lanzhou University of Technology school of Materials Science and engineering Lanzhou University of Technology school of Computer and communication Technology Lanzhou University of Technology

MXenes obtained significant attention in the field of energy storage devices due to their characteristic layered structure,modifiable surface functional groups,large electrochemically active surface,and regulable interlayer ***,the self-restacking and sluggish ions diffusion kinetics performance of MXenes during the alkali metal ions insertion/extraction process severely impedes their cycle stability and rate *** paper proposes an aniline molecule welding strategy for welding p-phenylenediamine（PPDA） into the interlayers of Ti2C through a dehydration condensation *** welded PPDA molecules can contribute pillar effect to the layered structure of *** pillar effect effectively maintains the structural stability during the sodium ions insertion/extraction process and effectively expands the interlayer spacing of Ti2C from 1.16 to 1.38 nm,thereby enhancing ions diffusion kinetics performance and improving the long-term cycle *** Ti2C-PPDA demonstrates outstanding Na+storage capability,exhibiting a specific capacity of 100.2 mAh·g-1at a current density of 0.1 A·g-1over 960 cycles and delivering a remarkable rate capability 81.2 mAh·g-1at a current density of 5 A·*** study demonstrates that expanding interlayer spacing is a promising strategy to enhance the Na+storage capacity and improve long-term cycling stability,which provides significant guidance for the design of two-dimensional Na+storage materials with high-rate capability and cycle stability.

关键词： Diffusion kinetics Expanding interlayer spacing Rate capability Sodium ions storage Ti2C

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho school of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the school of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

Optimization of CZTSSe thin film solar cell performance using PbS back surface field layer

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Optoelectronics Letters 2025年

作者： WENJING LUO ZHIJIAN BAI YUMING XUE LUOXIN WANG YANHONG DING HONGLI DAI Tianjin Key Laboratory of Film Electronic & communication Devices， school of electronics Information engineering， Tianjin University of Technology school of Materials Science and engineering ，Jilin University

CZTSSe is an Earth-abundant， environmentally friendly， and low-cost material， with a current record power conversion efficiency of 14.9%， This result is well below the prediction of the Shockley-Queisser limit. To improve the performance of solar cells， a layer of PbS (BSF) was added to the CZTSSe， which was simulated using the SCAPS-1D software. The optimal photovoltaic performance of AZO/ZnO/CdS/CZTSSe/PbS/Mo was studied by changing the parameters of the CZTSSe absorption layer and PbS layer. Studies have shown that the efficiency can be increased to 20.86% after using PbS.

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Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑Generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： Kiran A.Nirmal Tukaram D.Dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae Geun Kim school of Electrical engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(Rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

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A reduction scheme of PAPR in CO-OFDM system using time-delay neural networks

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Optoelectronics Letters 2025年

作者： TONG Zhengrong LIU Zhihong WANG Hao ZHANG Weihua ZHANG Tianhao JIA Qiqi Tianjin Key Laboratory of Thin Film electronics and communication Devices， engineering Research Center for Optoelectronic Devices and communication Technology of the Ministry of Education， school of Integrated Circuit Science and engineering， Tianjin University of Technology

An iterative partial transmission sequence （IPTS） cascading time-delay neural network （TDNN） scheme is proposed to reduce peak-to-average power ratio （PAPR） in the coherent optical orthogonal frequency division multiplexing （CO-OFDM） systems. The IPTS algorithm further reduces computational complexity based on the partial transmission sequence （PTS） algorithm. TDNN is trained by the iterative clipping filtering （ICF） method. Simulation results show that the proposed scheme achieves a PAPR reduction of 5.84 dB when complementary cumulative distribution function （CCDF） is 10-4and an extra transmission distance of 200 km for 10-3bit error rate （BER） compared to the original signals.

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The optimal control theory——a scientific approach to fundamentally solving control problems

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Science China(Information Sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG school of Electrical and Automation engineering Shandong University of Science and Technology school of Control Science and engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and communication Sun Yat-sen University school of Software Technology Zhejiang University school of Software engineering Sun Yat-sen University school of Software Dalian University of Technology school of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University school of Computer Science and engineering Beihang University Institute of Information engineering Chinese Academy of Sciences school of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University school of Computer and Information Technology Beijing Jiaotong University school of Computer Science and Technology Harbin Institute of Technology school of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey