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Optimal impurity distribution model and experimental verification of variation of lateral doping termination

Chinese Physics B 2023年第4期32卷 724-729页

作者：任敏叶昶宇周建宇张新郑芳马荣耀李泽宏张波 State key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaChengdu 610054China Guangdong Institute of Electronic Information Engineering University of Electronic Science and Technology of ChinaDongguan 523808China Wuxi China Resources Huajing Microelectronics Co.LTD Wuxi 214061China

Based on the charge balance principle,an optimal impurity distribution variation of lateral doping termination(OIDVLD)and its ion-injection mask design method are proposed and *** comparative simulations and experiments show that OID-VLD can achieve better blocking ability and reliability than the traditional VLD(T-VLD).Vertical double diffusion MOSFET(VDMOS)with OID-VLD achieved breakdown voltage(BV)of 1684 V and passed the 168 hours 100℃-110℃-120℃-125℃high-temperature reverse bias(HTRB)test,while VDMOS with T-VLD obtained BV of 1636 V and failed in the 20 hours 120℃HTRB test.

关键词： variation of lateral doping(VLD) junction termination breakdown voltage reliability

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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

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Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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Digital Twin-Based Service System Platform

Digital Twin-Based Service System Platform

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ma, Zeyu Wang, Hongjun Zhang, Mingliang School of Mechanical Electrical Engineering Beijing University of Information Science and Technology Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China Beijing Institute of Radio Measurement Beijing100854 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9783031702341

This paper discusses the integration and application of digital twin technology within the manufacturing sector, underscoring its pivotal role in advancing the Industry 4.0 revolution. Digital twin technology, which creates dynamic virtual replicas of physical systems, facilitates enhanced simulation, analysis, and optimization of real-world processes. The implementation of this technology, illustrated through examples from NASA and the U.S. Air Force Research laboratory, demonstrates its significant contributions across the entire product lifecycle, including design, manufacturing, service operation, and maintenance. The paper introduces a comprehensive framework for a digital twin-based production line service system, leveraging the Internet of Things (IoT) technology and flexible simulation to enable real-time user-application interaction, front-end operation responsiveness, and efficient data management. This system enhances operational efficiency, reduces time-to-market, and improves issue prediction and mitigation through virtual-real mapping and debugging. An application case using MongoDB highlights the technology's adaptability and effectiveness in managing complex data structures. The study concludes that digital twin technology is instrumental in driving smart manufacturing, innovation, and maintaining a competitive advantage in the evolving industrial landscape. Key references provide a foundation for understanding the technology's development and application, reinforcing the digital twin's role as a transformative element in manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： NASA

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Fictitious Play in Markov Games with Single Controller 22

Fictitious Play in Markov Games with Single Controller

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23rd ACM Conference on Economics and Computation, EC 2022

作者： Sayin, Muhammed O. Zhang, Kaiqing Ozdaglar, Asuman Electrical and Electronics Engineering Bilkent University Turkey Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States

ISBN: (纸本)9781450391504

Certain but important classes of strategic-form games, including zero-sum and identical-interest games, have thefictitious-play-property (FPP), i.e., beliefs formed in fictitious play dynamics always converge to a Nash equilibrium (NE) in the repeated play of these games. Such convergence results are seen as a (behavioral) justification for the game-theoretical equilibrium analysis. Markov games (MGs), also known as stochastic games, generalize the repeated play of strategic-form games to dynamic multi-state settings with Markovian state transitions. In particular, MGs are standard models for multi-agent reinforcement learning - a reviving research area in learning and games, and their game-theoretical equilibrium analyses have also been conducted extensively. However, whether certain classes of MGs have the FPP or not (i.e., whether there is a behavioral justification for equilibrium analysis or not) remains largely elusive. In this paper, we study a new variant of fictitious play dynamics for MGs and show its convergence to an NE in n-player identical-interest MGs in which a single player controls the state transitions. Such games are of interest in communications, control, and economics applications. Our result together with the recent results in [42] establishes the FPP of two-player zero-sum MGs and n-player identical-interest MGs with a single controller (standing at two different ends of the MG spectrum from fully competitive to fully cooperative). © 2022 Owner/Author.

关键词： Reinforcement learning

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Identifying Influential Spreaders in Complex Networks Using Neighborhood Network Structure 35

Identifying Influential Spreaders in Complex Networks Using ...

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Zhang, Ziyi Yan, Rong Yuan, Wei College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Identifying influential spreaders is a hot topic in complex network research. While centrality-based algorithms are easy to implement, they often have lower accuracy. Topology-based algorithms are effective for identifying network center influential spreaders but may not perform well in identifying peripheral influential spreaders. To address these problems, we propose a Neighborhood Structure Centrality (NSC) algorithm, which utilizes structural embedding and clustering to collect various network structural information and calculates node influence based on both node and neighborhood structural information. We compare the NSC algorithm with twelve baseline algorithms on six public datasets and four synthetic network datasets and demonstrate its higher accuracy. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Complex networks

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Joint Optimization of Transmission and Computation for Multi-source MEC System Based on Deep Reinforcement Learning 20th

Joint Optimization of Transmission and Computation for Mu...

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

作者： Zhang, Qi Zhu, Jianhang Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangzhou51006 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Guangdong Key Laboratory of Information Security and Technology Sun Yat-sen University Guangzhou51006 China

ISBN: (纸本)9789819628636

Due to the limitation of computing resources in end nodes, computation tasks in Internet of Things (IoT) can be offloaded to mobile edge servers. To measure the effectiveness and timeliness of data, Age of information (AoI) is proposed as a timeliness metric. In this paper, we consider the AoI minimization problem for computation-intensive status update. Our former work has designed optimal policy for Peak AoI (PAoI) optimization in single source case. This paper further studies the average AoI optimization in multi-source case, which is challenging. We focus on a multi-source Mobile Edge Computing (MEC) system and study when to generate a new packet. The system operates in two modes: preemptive and non-preemptive computing server. Firstly, we formulate this problem as a Markov Decision Process (MDP). Then, we adopt Deep Reinforcement Learning (DRL) algorithm to optimize average AoI and PAoI respectively, considering both random transmission and computation time. Simulation results demonstrate that preemptive server gets better performance than non-preemptive server. We further extend the PAoI-optimal policy in single source case to the multi-source case, which is shown to be optimal for minimizing average PAoI with the DRL algorithm approaching its performance. When minimizing average AoI, the DRL algorithm achieves better performance than the PAoI-optimal policy. © IFIP International Federation for information Processing 2025.

关键词： Deep reinforcement learning

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Assessing Susceptibility Factors of Confirmation Bias in News Feed Reading 25

Assessing Susceptibility Factors of Confirmation Bias in New...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Boonprakong, Nattapat Pareek, Saumya Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne Parkville VIC Australia School of Computing and Information Systems University of Melbourne Melbourne VIC Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases. © 2025 Copyright held by the owner/author(s).

关键词： cognitive bias confirmation bias individual difference recall social media

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FairCoRe: Fairness-Aware Recommendation Through Counterfactual Representation Learning

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4049-4062页

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Jinan University College of Cyber Security Engineering Research Center of Trustworthy AI Ministry of Education Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. First, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Second, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users’ interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accuracy Mutual information Correlation Data Visualization Medical Services Training Measurement Disentangled Representation Learning Semantics Recommender systems Recommender System Recommendation Fairness Representation Learning Counterfactual Representations Potential Bias Mutual information User Preferences Sensitive Attributes Counterfactual Scenario User Representation Time Complexity Generative Adversarial Networks Learning Objectives Demographic Groups Baseline Methods Recommender systems Minimum Method Prediction Loss Preference information Graph Convolution User Side Recommendation Model Counterfactual Thinking Maximum Mutual information Representation Learning Methods Maximization Method Recommendation Accuracy Recommendation Method Recommendation Task Notions Of Fairness Negative Samples Mutual information Estimation

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Pixel+ and Pixel++: Compact and Efficient Forward-Secure Multi-Signatures for PoS Blockchain Consensus 33

Pixel+ and Pixel++: Compact and Efficient Forward-Secure Mul...

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33rd USENIX Security Symposium, USENIX Security 2024

作者： Wei, Jianghong Tian, Guohua Wang, Ding Guo, Fuchun Susilo, Willy Chen, Xiaofeng Xidian University China State Key Laboratory of Mathematical Engineering and Advanced Computing China College of Cyber Science Nankai University China School of Computing and Information Technology University of Wollongong Australia

ISBN: (纸本)9781939133441

Multi-signature schemes have attracted considerable attention in recent years due to their popular applications in PoS blockchains. However, the use of general multi-signature schemes poses a critical threat to the security of PoS blockchains once signing keys get corrupted. That is, after an adversary obtains enough signing keys, it can break the immutable nature of PoS blockchains by forking the chain and modifying the history from some point in the past. Forward-secure multi-signature (FS-MS) schemes can overcome this issue by periodically updating signing keys. The only FS-MS construction currently available is Drijvers et al's Pixel, which builds on pairing groups and only achieves forward security at the time period level. In this work, we present new FS-MS constructions that either are free from pairing or capture forward security at the individual message level (i.e., fine-grained forward security). Our first construction Pixel+ works for a maximum number of time periods T. Pixel+ signatures consist of only one group element, and can be verified using two exponentiations. It is the first FS-MS from RSA assumption, and has 3.5x and 22.8x faster signing and verification than Pixel, respectively. Our second FS-MS construction Pixel++ is a pairing-based one. It immediately revokes the signing key's capacity of re-signing the message after creating a signature on this message, rather than at the end of the current time period. Thus, it provides more practical forward security than Pixel. On the other hand, Pixel++ is almost as efficient as Pixel in terms of signing and verification. Both Pixel+ and Pixel++ allow for non-interactive aggregation of signatures from independent signers and are proven to be secure in the random oracle model. In addition, they also support the aggregation of public keys, significantly reducing the storage overhead on PoS blockchains. We demonstrate how to integrate Pixel+ and Pixel++ into PoS blockchains. As a proof-of-concept, we prov

关键词： Blockchain

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract]

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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International Conference on Data engineering

作者： Weixin Zeng Xiang Zhao Zhen Tan Jiuyang Tang Xueqi Cheng Laboratory for Big Data and Decision National University of Defense Technology China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China Institute of Computing Technology CAS China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches $KGs$ in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.

关键词： Surveys Representation learning Knowledge graphs Benchmark testing Data engineering Mirrors

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