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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Wenhao Wang, Jun Luo, Yong Yu, Lei Yu, Wei He, Zheng Shen, Jialie School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Electronics and Information Wuhan University China Department of Computer Science City University of London United Kingdom

ISBN: (纸本)157735897X

Lip-reading is to utilize the visual information of the speaker's lip movements to recognize words and sentences. Existing event-based lip-reading solutions integrate different frame rate branches to learn spatio-temporal features of varying granularities. However, aggregating events into event frames inevitably leads to the loss of fine-grained temporal information within frames. To remedy this drawback, we propose a novel framework termed Multi-view Temporal Granularity aligned Aggregation (MTGA). Specifically, we first present a novel event representation method, namely time-segmented voxel graph list, where the most significant local voxels are temporally connected into a graph list. Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated. Finally, we design a temporal aggregation module that incorporates positional encoding, which enables the capture of local absolute spatial and global temporal information. Experiments demonstrate that our method outperforms both the event-based and video-based lip-reading counterparts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An n-MOSFET Layout with Multi-Finger Z Gate for Radiation Tolerance 5

An n-MOSFET Layout with Multi-Finger Z Gate for Radiation To...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Shan, Chan Liu, Ying Chen, Huan Huo, Dong-Yang Xu, Tian-Rong Jimei University College of Ocean Information Engineering Xiamen China Xiamen Institute of Software Technology Department of Software Technology Xiamen China Minjiang University School of Journalism and Communication Fuzhou China Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Planning Faculty of Built Environment Department of Urban and Regional Kuala Lumpur Malaysia

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In this paper, a new n-MOSFET layout with multi-finger Z gate is proposed to reduce the total ionizing dose (TID) effect. In addition to the proposed layout, multi-finger single gate layout is also simulated using Sentaurus TCAD in 3D. Firstly, drain current - gate voltage curves of both layouts are compared in order to verify the radiation hardening ability of the proposed layout. Secondly, we compare TID responses by extracting threshold voltages and leakage currents from both layouts. Thirdly, the finger number is investigated for both layouts because it is an important factor for multi-finger layout. The results have indicated that the finger number significantly affects the multi-finger single gate layout, whereas the proposed multi-finger Z gate layout is relatively little affected by the finger number in radiation environment. © 2022 IEEE.

关键词： Drain current

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Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation

Continual Machine Reading Comprehension via Uncertainty-awar...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Wu, Zhijing Xu, Hua Fang, Jingliang Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China China School of Information Science and Engineering Hebei University of Science and Technology China

ISBN: (纸本)9781955917766

Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Continuous time systems

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Market Design for Capacity Sharing in Networks

arXiv

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arXiv 2023年

作者： Amin, Saurabh Jaillet, Patrick Pulyassary, Haripriya Wu, Manxi Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Sciences MIT United States Operations Research and Information Engineering Cornell University United States

We study a market mechanism that sets edge prices to incentivize strategic agents to efficiently share limited network capacity. In this market, agents form coalitions, with each coalition sharing a unit capacity of a selected route and making payments to cover edge prices. Our focus is on the existence and computation of market equilibrium, where challenges arise from the interdependence between coalition formation among strategic agents with heterogeneous preferences and route selection that induces a network flow under integral capacity constraints. To address this interplay between coalition formation and network capacity utilization, we introduce a novel approach based on combinatorial auction theory and network flow theory. We establish sufficient conditions on the network topology and agents’ preferences that guarantee both the existence and polynomial-time computation of a market equilibrium. Additionally, we identify a particular market equilibrium that maximizes utilities for all agents and is equivalent to the classical Vickrey-Clarke-Groves mechanism. Furthermore, we extend our results to multi-period settings and general networks, showing that when the sufficient conditions are not met, an equilibrium may still exist but requires more complex, path-based pricing mechanisms that set differentiated prices based on agents’ preference parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Commerce

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

关键词： Optical interconnects

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical interconnects

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Price Optimisation and Management 3

Price Optimisation and Management

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shafkhan, M.T.M Jayasundara, P.R.S.S Kariyapperuma, K.A.D.R.L Lakruwan, H.P.S Rupasinghe, Lakmal Sri Lanka Institute of Information Technology Dept. of Computer and Software Eng Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income;everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded;some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-To-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this. © 2021 IEEE.

关键词： Cost benefit analysis

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Early Experiences with Specifications Grading in Introductory CS Courses

Early Experiences with Specifications Grading in Introductor...

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Frontiers in Education (FIE) Conference

作者： Stephen H. Edwards Manuel A. Pérez-Quiñones Adrienne Decker Bob Edmison Audrey Rorrer Anmol Shukla Computer Science Virginia Tech Blacksburg VA USA Software and Information Systems UNC Charlotte Charlotte NC USA Engineering Education University at Buffalo Buffalo NY USA Computer Science UNC Charlotte Charlotte NC USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice paper describes our experiences with alternative grading practices in introductory computing courses and two large public universities in the United States. Computing classrooms often use traditional grading practices involving allocating points to assignments, deducting points for mistakes and tardiness, and combining assignment scores using a weighted average to determine grades. Recent research suggests that these practices may diminish achievement, discourage students, and suppress effort to such an extent that they are considered by some as detrimental. We approach our work as an exploratory case study, without predefined research questions or hypotheses. Our experiences began with the adoption of specifications grading. We outline the grading scheme applied to traditional programming assignments and exams/quizzes, and discuss the initial integration of these schemes with conventional auto-grading tools. We delve into student perceptions of alternative grading, their utilization of flexible deadlines, and resubmission opportunities. We conclude with a discussion of two challenges encountered during our exploration: student acceptance of a novel grading form, and the adaptation of tools designed for traditional grading to support alternative grading mechanisms. Our early exploration aims to inspire further research on the use of alternative grading in computing. It is clear from our observations that simply implementing the practices does not ensure the equitable and inclusive outcomes that can be achieved with these practices. If students are not prepared to use these practices, they find them difficult to understand and can feel that they are not being treated fairly. Additionally, we wish to foster a community of practice to assist faculty members exploring these changes, with the goal of creating more equitable and inclusive classrooms.

关键词： Programming profession

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Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection

Transformer-based Compound Correlation Miner for Smart Home ...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Andrew D’Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Information Warfare Division Air Force Research Laboratory Rome NY USA

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

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