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Leakage Current Optimization Techniques During Test Based on Don't Care Bits Assignment

Journal of Computer Science & technology 2007年第5期22卷 673-680页

作者：王伟胡瑜韩银和李晓维张佑生 School of Computer and Information Hefei University of Technology Hefei 230009China Key Laboratory of Computer System and ArchitectureInstitute of Computing TechnologyChinese Academy of Sciences Beijing 100080China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100080China School of Computer and Information Hefei University of Technology Hefei 230009China

It is a well-known fact that test power consumption may exceed that during functional operation. Leakage power dissipation caused by leakage current in Complementary Metal-Oxide-Semiconductor （CMOS） circuits during test has become a significant part of the total power dissipation. Hence, it is important to reduce leakage power to prolong battery life in portable systems which employ periodic self-test, to increase test reliability and to reduce test cost. This paper analyzes leakage current and presents a kind of leakage current simulator based on the transistor stacking effect. Using it, we propose techniques based on don＇t care bits （denoted by Xs） in test vectors to optimize leakage current in integrated circuit （IC） test by genetic algorithm. The techniques identify a set of don＇t care inputs in given test vectors and reassign specified logic values to the X inputs by the genetic algorithm to get minimum leakage vector （MLV）. Experimental results indicate that the techniques can effectually optimize leakage current of combinational circuits and sequential circuits during test while maintaining high fault coverage,

关键词： leakage current don＇t care bits minimum leakage vector leakage power

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Constructing prioritized interaction test suite with interaction relationship

Constructing prioritized interaction test suite with interac...

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2nd International Workshop on Education technology and Computer Science, ETCS 2010

作者： Yuan, Jianjun Jiang, Zuowen Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai China Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9780769539874

Interaction testing has addressed some issues on how to select a small subset of test cases. In many systems where interaction testing is needed, the entire test suite is not executed because of time or budget constraints. It is important to prioritize the test cases in these situations. On the other hand, there are not always interactions among any factors in real systems. Moreover, some factors may need N-way (N>2) testing since there is a closer relationship among them. We present a model for prioritized interaction testing with interaction relationship and propose a greedy algorithm for generating variable strength covering arrays with bias. © 2010 IEEE.

关键词： Testing

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Evolution of Cooperation in Public Goods Games

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Communications in Theoretical Physics 2011年第10期56卷 638-644页

作者：夏承遗张娟娟王祎玲王劲松 Key Laboratory of Computer Vision and System(Tianjin University of Technology) Ministry of Education Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology College of Life Science Shanxi Normal University

We investigate the evolution of cooperation with evolutionary public goods games based on finite populations, where four pure strategies： cooperators, defectors, punishers and loners who are unwilling to participate are considered. By adopting approximate best response dynamics, we show that the magnitude of rationality not only quantitatively explains the experiment results in [Nature （London） 425 （2003） 390], but also it will heavily influence the evolution of cooperation. Compared with previous results of infinite populations, which result in two equilibriums, we show that there merely exists a special equilibrium cooperation. In addition, we characterize that loner＇s and the relevant high value of bounded rationality will sustain payoff plays an active role in the maintenance of cooperation, which will only be warranted for the low and moderate values of loner＇s payoff. It thus indicates the effects of rationality and loner＇s payoff will influence the cooperation. Finally, we highlight the important result that the introduction of voluntary participation and punishment will facilitate cooperation greatly.

关键词： public goods games magnitude of rationality voluntary participation punishment

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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

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Journal of Computer Science & technology 2022年第4期37卷 797-813页

作者： Hui Yu Xin-Yu Jiang Jin Zhao Hao Qi Yu Zhang Xiao-Fei Liao Hai-Kun Liu Fu-Bing Mao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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A Physics-guided Multimodal Transformer Path to Weather and Climate Sciences

arXiv

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arXiv 2025年

作者： Han, Jing Chen, Hanting Han, Kai Huang, Xiaomeng Hu, Yongyun Xu, Wenjun Tao, Dacheng Zhang, Ping School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Huawei Noah’s Ark Lab Beijing China Department of Earth System Science Tsinghua University Beijing China Institute of Ocean Research Peking University Beijing China College of Computing & Data Science Nanyang Technological University Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

With the rapid development of machine learning in recent years, many problems in meteorology can now be addressed using AI models. In particular, data-driven algorithms have significantly improved accuracy compared to traditional methods. Meteorological data is often transformed into 2D images or 3D videos, which are then fed into AI models for learning. Additionally, these models often incorporate physical signals, such as temperature, pressure, and wind speed, to further enhance accuracy and interpretability. In this paper, we review several representative AI + Weather/Climate algorithms and propose a new paradigm where observational data from different perspectives, each with distinct physical meanings, are treated as multimodal data and integrated via transformers. Furthermore, key weather and climate knowledge can be incorporated through regularization techniques to further strengthen the model’s capabilities. This new paradigm is versatile and can address a variety of tasks, offering strong generalizability. We also discuss future directions for improving model accuracy and interpretability. Copyright © 2025, The Authors. All rights reserved.

关键词： Engineering education

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Balanced Multi-view Clustering

arXiv

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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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FMR-GNet: Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

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Chinese Journal of Electronics 2024年第6期33卷 1346-1359页

作者： Honghong YANG Hongxi LIU Yumei ZHANG Xiaojun WU Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University

Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatialtemporal dependencies among the body joints remains a challenging task. Current approaches are limited by performing graph convolutions solely on immediate neighbors, deploying separate spatial or temporal modules, and utilizing single-pass feedforward architectures. To solve these limitations, we propose a forward multi-scale residual graph convolutional network(FMR-GNet) for 3D pose estimation from monocular video. First, we introduce a mix-hop spatialtemporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields. The attention mechanism enables dynamically computing edge weights at each layer. Second,we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections, explicitly modeling interdependencies across spatial and temporal domains. Third, we integrate a forward dense connection block to propagate spatial-temporal representations across network layers, enabling high-level semantic skeleton information to enrich lower-level features. Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks, namely Human3.6M and MPI-INF-3DHP, demonstrate that the proposed FMR-GNet achieves superior performance, surpassing the most state-of-the-art methods.

关键词： Location awareness Three-dimensional displays Correlation Graph convolutional networks Fuses Aggregates Pose estimation Semantics Benchmark testing Skeleton

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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

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Journal of Computer Science & technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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ACDR-CRAFF Net: A Multi-Scale Network Based on Adaptive Channel and Coordinate Relational Attention Network for Remote Sensing Scene Classification

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IET Image Processing 2025年第1期19卷

作者： Wei Dai Haixia Xu Furong Shi Liming Yuan Xinyu Wang Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Accurate classification of remote sensing scene images is crucial for diverse applications, from environmental monitoring to urban planning. While convolutional neural networks (CNNs) have dramatically improved classification accuracy, challenges remain due to the complex distribution of small objects, varied spatial configurations, and intra-class multimodality in remote sensing images. In this work, we make three key contributions to address these challenges. (1) We propose the adaptive channel and coordinate relational attention network (ACDR-CRAFF), a novel multi-scale feature fusion framework designed to enhance feature representation across scales. (2) We introduce two innovative modules: the adaptive channel dimensionality reduction (ACDR) module, which dynamically adjusts channel representations to retain essential low-dimensional features, and the coordinate relational attention multi-scale feature fusion (CRAFF) module, which effectively captures and transfers spatial information between feature levels. (3) By integrating ACDR and CRAFF, our model achieves a progressive fusion of local to global features, ensuring robust feature expressiveness at multiple scales. Experimental results on four widely used benchmark datasets demonstrate that ACDR-CRAFF consistently outperforms several state-of-the-art methods, achieving significant improvements in classification accuracy and setting a new benchmark for complex remote sensing scene classification tasks. These results underscore the effectiveness of our approach in addressing the limitations of existing methods and advancing the state of the art in remote sensing image analysis.

关键词： image classification image processing remote sensing

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Efficient Distributed Skyline Queries for Mobile Applications

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计算机科学技术学报（英文版） 2010年第3期25卷 523-536页

作者： Ying-Yuan Xiao Yue-Guo Chen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191China Key Laboratory of Computer Vision and System Tianjin University of TechnologyTianjin 300191China School of Computing National University of Singapore117417Singapore Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

关键词： skyline distributed query processing mobile database system

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