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作者： Zhong, Wei Hu, Shuxiang Ma, Yuzhe Yang, Haoyu Ma, Xiuyuan Yu, Bei DUTRU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China

Combining multiple patterning lithography (MPL) and optical proximity correction (OPC) pushes the limit of 193-nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization (MO) process to select the best candidate for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and MO (LDMO) flow. In this article, we propose a convolutional neural network (CNN)-based prediction and integrate it into our new high-performance LDMO framework. The optimization process can be considerably improved as the decomposition quality has been inferred in the early phase. To facilitate the network training and ensure better estimation accuracy, we develop sampling strategies for both layout and decomposition. Moreover, we enhance the layout sampling approach by adopting autoencoder to distance evaluation that promises superior sampling results. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms. © 1982-2012 IEEE.

关键词： Manufacture

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TMAE: Entropy-Aware Masked Autoencoder for Low-Cost Traffic Flow Map Inference

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IEEE Internet of Things Journal 2025年

作者： Luo, Xucheng Wu, Qiong Wang, Ye Zhang, Kuan Dai, Hong-Ning Chen, Dajiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Pengcheng laboratory Shenzhen China University of Nebraska-Lincoln United States Hong Kong Baptist University Department of Computer Science Hong Kong

Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few deployed sensors to infer traffic flow across entire urban areas, incorporating prior knowledge from road distribution maps. Specifically, TMAE employs a shared encoder to process traffic flow context, using self-attention scores to identify the importance of each region and guide a masking policy that retains regions rich in traffic flow information. The road distribution map, reflecting inherent traffic flow patterns, is incorporated as prior knowledge by substituting masked tokens during training. A cross-attention mechanism in the decoder further refines inference, where embeddings from the road distribution map serve as queries, and retained visible patches act as keys and values. Additionally, regional traffic entropy is introduced to quantify the information richness of each region, enabling the selection of minimal measurement regions to optimize inference for other areas. Extensive experiments conducted on datasets from various cities demonstrate the effectiveness and efficiency of TMAE, highlighting its potential as a scalable solution for low-cost traffic flow inference in urban environments. The source code of this work is released at https://***/TextGraph/TMAE. © 2025 IEEE.

关键词： Costs

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Methodology,progress and challenges of geoscience knowledge graph in International Big Science Program of Deep-Time Digital Earth

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Journal of Geographical Sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China University of Chinese Academy of Sciences Beijing 100049China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing 210023China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Command Center of Natural Resources Comprehensive Survey China Geological SurveyBeijing 100055China School of Computer Science China University of GeosciencesWuhan 430074China

Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary *** advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in *** the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big *** DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience *** its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.

关键词： deep-time Earth geoscience knowledge graph Deep-time Digital Earth International Big Science Program

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Amplified locality-sensitive hashing-based recommender systems with privacy protection

Amplified locality-sensitive hashing-based recommender syste...

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作者： Chi, Xiaoxiao Yan, Chao Wang, Hao Rafique, Wajid Qi, Lianyong School of Information Science and Engineering Qufu Normal University Rizhao China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user-service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality-sensitive hashing (LSH)-based service recommendation method, that is, SRAmplified-LSH, is proposed in the article. SRAmplified-LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS-DREAM dataset validate the feasibility of our proposed method. © 2020 John Wiley & Sons, Ltd.

关键词： Efficiency

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Enhancing Multilabel ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Kashif Bashir, Ali Mondal, Tapas Hu, Xiping Jamal Deen, M. Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India McMaster University Faculty of Health Science Department of Pediatrics ON HamiltonL8S 4L8 Canada McMaster University McMaster School of Biomedical Engineering Electrical and Computer Engineering Department ON HamiltonL8S 4L8 Canada

With the rise of the Internet of Things, wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns interlead relationships to improve multilabel ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a temporal graph construction module to convert ecg signals into temporal graphs and uses a residual pyramid graph convolution module for multilevel graph embeddings, utilizing a graph convolutional network with independently learnable adjacency matrices. Combined with a temporal context convolution module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on seven tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Thermomechanical cloaking via compatibility-driven topology optimization

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International Journal of Mechanical Sciences 2025年 300卷

作者： Cheng, Xubing Du, Zongliang Meng, Wen Liu, Chang Zhang, Weisheng Guo, Xu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian116023 China Ningbo Institute of Dalian University of Technology Ningbo315016 China Beijing System Design Institute of Electro-Mechanic Engineering Beijing100854 China

This study presents a novel topology optimization framework for thermomechanical cloaking by introducing the compatibility of cloak interface (CCI). The essence is to make the actual boundary conditions along the exterior of the cloak consistent with the target boundary conditions, including the Dirichlet and Neumann conditions (i.e., temperature and thermal loads for thermal cloaking, displacement and nodal forces for mechanical cloaking). Compared to conventional design strategies, the proposed method offers a unified design framework for the rational design of multifunctional cloaking without being restricted by the invariance requirement of transformation-based approaches. At the same time, this method only requires analysis of the cloak region during the optimization process, thereby reducing the computational cost. Furthermore, the explicit topology optimization based on Moving Morphable Voids (MMV) produces cloaks with geometric information, enabling its practical application. Several numerical examples demonstrate the method's effectiveness, robustness, and adaptability for thermal and multifunctional cloaking in lattice and continuum structures with various shaped openings, as well as multiple load cases. The current design strategy is extendable to other multiphysics cloaking applications, offering new insights into advanced functional material design. © 2025 Elsevier Ltd

关键词： Shape optimization

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Trusted Polarimetric Feature Fusion for Polsar Image Classification

Trusted Polarimetric Feature Fusion for Polsar Image Classif...

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SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA)

作者： Fangzhou Han Lamei Zhang Hongwei Dong Department of Information Engineering Harbin Institute of Technology Harbin China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

Benefiting from the rapid advancements in Deep Learning (DL), data-driven features exhibit remarkable separability in PolSAR image classification. However, data-driven features are challenging to interpret and susceptible to resolution and noise. On the contrary, polarimetric features based on the target decomposition not only have high robustness, but also has physical interpretability. Consequently, researchers have begun to explore ways to integrate both types of features to achieve enhanced performance, which typically concat the data-driven features and polarimetric features directly. However, polarimetric features are not stable in separability, which is because the inherent defects of the target decomposition algorithm. To mitigate the adverse effects of low-quality features, this paper proposes a trusted polarimetric feature fusion (TPFF). The new paradigm calculates the confidence of features and employs a novel feature fusion method. The proposed method has been tested on the EMISAR Foulum dataset, with experimental results demonstrating improved classification performance and robustness.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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A new transformation approach for complex systems modelling and simulation: Application to industrial control system

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International Journal of Simulation and Process Modelling 2021年第1期16卷 34-48页

作者： Seddari, Noureddine Boukelkoul, Sofiane Bouras, Abdelghani Belaoued, Mohamed Redjimi, Mohammed LICUS Laboratory Department of Computer Science University of 20 August 1955-Skikda Skikda21000 Algeria LIRE Laboratory Department of Software Technologies and Information Systems University of Abdelhamid Mehri-Constantine 2 Constantine25000 Algeria Department of Industrial Engineering Alfaisal University Riyadh12714 Saudi Arabia

Due to the increasing importance of complex systems, the problems of modelling and simulation (M&S) of these systems, their approaches and solutions have been studied extensively these last years. In this paper, we present a novel approach to modelling and simulation of discrete event dynamic systems (DEDS), based on the combination of discrete event systems specification (DEVS) and agent group role (AGR). In this study, the DEVS atomic models are transformed into agents and the DEVS coupled models into groups of agents. Thus, we provide a set of procedures and functions that allow the systematic transformation from DEVS into AGR models in order to be implemented in multi-agent platforms. The proposed approach was validated by a case study, an application in the water supply used in natural gas liquefaction process. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Water supply

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Enhance Training Objectives for Image Captioning with Decomposed Sequence-level Metric

Enhance Training Objectives for Image Captioning with Decomp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yehuan Wang Jiefeng Long Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image captioning aims to generate fluent and accurate descriptions for images. To evaluate the quality of captions, various metrics have been proposed. However, current metrics only assess captions at sequence-level, which overwhelms the distinctions between each token. Thus, existing objectives tend to treat each token equally, assigning them with identical weights in loss functions. Intuitively, key words in a caption carry the primary information and contribute more than other words to sequence-level metrics. They should be distinguished and weighted more during training. In this work, we propose to explicitly measure each word and guide the model to focus more on key words in captions. Firstly, we devise token-level CIDEr (CIDEr-T) as a new metric to quantify the importance of each word, by decomposing the sequence-level CIDEr into token-level granularity. CIDEr-T maintains consistency with CIDEr and shows the distinctions between tokens. Thus, we engage CIDEr-T scores of each token as their unique weights in the raw loss functions, which can bridge the gap between training and evaluation.

关键词： Measurement Training Accuracy Neural networks

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