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SSRN 2023年

作者： Juan, Yongfei Niu, Guoshuai Yang, Yang Dai, Yongbing Yang, Jian Zhang, Jiao Shanghai Key Lab of Advanced High-temperature Materials and Precision Forming State Key Lab of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China State Grid Shanghai Electric Power Research Institute Shanghai200437 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration Shanghai Jiao Tong University Shanghai200240 China

We proposed a strengthening element screening method through integrating a machine learning-based property optimization model with strength descriptors of Al-Zn-Mg-Cu alloy. By comparing the predicted properties of alloys with different elements added, we found that the trace element Hf, beyond the dataset, can effectively improve the strength and toughness of Al-Zn-Mg-Cu alloy. According to the alloy optimization result, we fabricated a high-strength Al-Zn-Mg-Cu-Zr-Hf alloy with superior mechanical properties with 816 MPa for tensile strength, 779 MPa for yield strength, and 9.1% for elongation. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Acceleration Slip Regulation for Electric Vehicles Based on Fuzzy PID Controller 11

Acceleration Slip Regulation for Electric Vehicles Based on ...

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11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

作者： Nguyen, An-Toan Nguyen, Binh-Minh Trovao, Joao Pedro F. Ta, Minh C. Université de Sherbrooke Dept. of Electrical and Computer Engineering SherbrookeJ1K 2R1 Canada Quy Nhon University Faculty of Engineering and Technology Binh Dinh Quy Nhon Viet Nam University of Tokyo Dept. of Advanced Energy Tokyo Japan Polytechnic of Coimbra IPC-ISEC Inesc Coimbra 3030-199 Portugal Hanoi University of Science and Technology Cti Lab. for EVs Hanoi Viet Nam

ISBN: (数字)9781665452489

ISBN: (纸本)9781665452489

This paper proposes an acceleration slip regulation (ASR) by the fuzzy PID (FPID) controller for electric vehicles with four-wheel independent motor drive. The ASR architecture is hierarchically presented by three layers. The upper layer determines the total traction force based on the control of vehicle velocity. The middle layer determines optimum tire longitudinal force distribution depending on the total tire workload. In case of wheel slip, the optimum longitudinal force will be adjusted by the FPID controller. In the lower layer, the traction forces are converted to reference torque for each powertrain. The modeling and control organization of the studied vehicle are presented by using energetic macroscopic representation (EMR). Simulation tests show that ASR with FPID controller can limit the slip ratio of the wheels and improve the vehicle stability. © 2022 IEEE.

关键词： Traction control

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Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models

arXiv

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arXiv 2024年

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518055 China Tongyi Lab Alibaba Group China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China Guangdong Guangzhou510555 China

Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely overlooked, especially without fine-tuning them. In this work, we show that frozen foundation models can be a versatile feature enhancer, even though they are not pre-trained for object detection. Specifically, we explore directly transferring the high-level image understanding of foundation models to detectors in the following two ways. First, the class token in foundation models provides an in-depth understanding of the complex scene, which facilitates decoding object queries in the detector’s decoder by providing a compact context. Additionally, the patch tokens in foundation models can enrich the features in the detector’s encoder by providing semantic details. Utilizing frozen foundation models as plug-and-play modules rather than the commonly used backbone can significantly enhance the detector’s performance while preventing the problems caused by the architecture discrepancy between the detector’s backbone and the foundation model. With such a novel paradigm, we boost the SOTA query-based detector DINO from 49.0% AP to 51.9% AP (+2.9% AP) and further to 53.8% AP (+4.8% AP) by integrating one or two foundation models respectively, on the COCO validation set after training for 12 epochs with R50 as the detector’s backbone. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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AI-based Consumers' Preference Prediction Using a Research-grade BCI and a Commercial-grade BCI for Neuromarketing: A Systematic Comparison

AI-based Consumers' Preference Prediction Using a Research-g...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Farhan Ishtiaque Fazla Rabbi Mashrur Mohammad Tohidul Islam Miya Khandoker Mahmudur Rahman Ravi Vaidyanathan Syed Ferhat Anwar Farhana Sarker Khondaker A. Mamun AIMS Lab Institute of Advanced Research United International University Dhaka Bangladesh Neuroengineering University of Rochester Rochester New York USA School of Business and Economics United International University Dhaka Bangladesh Department of Mechanical Engineering Imperial College London London United Kingdom Institute of Business Administration University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal ArtsBangladesh Dhaka Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh

In Neuromarketing, BCI technology is used to analyze how a consumer behaves in response to a marketing stimulus, to evaluate the stimuli itself. Traditionally it can be achieved by different marketing research techniques such as questioner-based surveys, interviews, field surveys, etc. But since these procedures are time-consuming and prone to human error, neuromarketing promises a more advanced, automated, and accurate solution. Most of the neuromarketing solutions use research-grade EEG devices to analyze consumer preferences, but their effectiveness using consumer-grade EEG devices is unknown. In this study, we designed an experiment to compare a research-grade EEG device with a consumer-grade EEG device for predicting consumer preference stated as affective attitude (AA) and purchase intention (PI). We determined what type of setup, processing, and algorithm brings out the best result using the two devices. EEG signals were collected while the participants were shown pictures of different products in two different setups After that several signal-processing techniques were applied to remove artifacts and multi-domain features were extracted. 50 features were selected using Recursing Feature Elimination techniques. SMOTE was used to balance out the data. After that SVM classifier was used to classify Positive and Negative consumer preferences. With the first setup, we managed to achieve 82.4 % and 85.23 % accuracy for predicting purchase intention and affective attitude respectively with the research-grade EEG device whereas we achieved 75.43% and 79.5% accuracy with the commercial-grade EEG device. With the second setup, it's 78.75% and 83.75% using the research-grade EEG device whereas it's 75% and 82.97% using the commercial-grade EEG device for purchase intention and affective attitude respectively.

关键词： Support vector machines Systematics Neuromarketing Feature extraction Prediction algorithms Electroencephalography Brain-computer interfaces

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Mako:A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth Graph mining Formation mechanism

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Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback 41

Position: Social Choice Should Guide AI Alignment in Dealing...

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41st International Conference on Machine Learning, ICML 2024

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI United States Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Stochastic Approximation and Brownian Repulsion based Evolutionary Search

arXiv

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arXiv 2024年

作者： Dutta, Rajdeep Varma, T. Venkatesh Sarkar, Saikat Mamajiwala, Mariya Awad, Noor Jayavelu, Senthilnath Roy, Debasish 138632 Singapore Faculty of Mechanical Engineering Technion–Israel Institute of Technology Haifa32000 Israel Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India Department of Computer Science University of Sheffield United Kingdom Machine Learning Lab University of Freiburg Germany Centre of Excellence in Advanced Mechanics of Materials Indian Institute of Science Bangalore India

Many global optimization algorithms of the memetic variety rely on some form of stochastic search, and yet they often lack a sound probabilistic basis. Without a recourse to the powerful tools of stochastic calculus, treading the fine balance between exploration and exploitation could be tricky. In this work, we propose an evolutionary algorithm (EA) comprising two types of additive updates. The primary update utilizes stochastic approximation to guide a population of randomly initialized particles towards an optimum. We incorporate derivative-free Robbins-Monro type gains in the first update so as to provide a directional guidance to the candidate solutions. The secondary update leverages stochastic conditioning to apply random perturbations for a controlled yet efficient exploration. Specifically, conceptualized on a change of measures, the perturbation strategy discourages two or more trajectories exploring the same region of the search space. Our optimization algorithm, dubbed as SABRES (Stochastic Approximation and Brownian Repulsion based Evolutionary Search), is applied to CEC-2022 benchmark functions on global optimization. Numerical results are indicative of the potentialities of SABRES in solving a variety of challenging multi-modal, non-separable, and asymmetrical benchmark functions. © 2024, CC BY-NC-ND.

关键词： Optimization algorithms

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A decrease in TDDB Lifetime of Commercial SiC power MOSFETs Under Repetitive Unclamped Inductive Switching Stresses

A decrease in TDDB Lifetime of Commercial SiC power MOSFETs ...

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China International Forum o Solid State Lighting: International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS)

作者： Sheng Yang Haonan Feng Xuefeng Yu Xinnan Lin The Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Key Laboratory of Functional Materials and Devices for Special Environments Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi

The degradation behavior under repetitive unclamped inductive switching stresses on the subsequent time-dependent dielectric breakdown (TDDB) of commercial SiC power MOSFETs are investigated. It is reported that repeated avalanche stresses can cause changes, including Vth (the threshold voltage) and Ron (on-state resistance) but not Igss (gate leakage current). However, the results show a significant decrease in the TDDB lifetime after 180k avalanche cycles, which suggests the degradation of the gate oxide. We attribute these results to defects at the SiC/SiO2 interface, which are caused by hot hole injection and trapping into the gate oxide above the channel and JFET region. The subthreshold curve analysis reveals that the density of the interface states changes during aging experiments, which further confirms our view. Meanwhile, interface states should be responsible for the increase in Ron, which was different from the previously research.

关键词： Degradation Resistance Silicon carbide Switches Logic gates Hot carriers Threshold voltage

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Distilling the Unknown to Unveil Certainty

arXiv

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arXiv 2023年

作者： Zhao, Zhilin Cao, Longbing Zhang, Yixuan Lin, Kun-Yu Zheng, Wei-Shi The Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia The DataX Research Centre Macquarie University SydneyNSW2109 Australia The China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Zhejiang Hangzhou310005 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510275 China

Out-of-distribution (OOD) detection is essential in identifying test samples that deviate from the in-distribution (ID) data upon which a standard network is trained, ensuring network robustness and reliability. This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network. This framework harnesses unknown OOD-sensitive knowledge from the standard network to craft a certain binary classifier adept at distinguishing between ID and OOD samples. To accomplish this, we introduce Confidence Amendment (CA), an innovative methodology that transforms an OOD sample into an ID one while progressively amending prediction confidence derived from the standard network. This approach enables the simultaneous synthesis of both ID and OOD samples, each accompanied by an adjusted prediction confidence, thereby facilitating the training of a binary classifier sensitive to OOD. Theoretical analysis provides bounds on the generalization error of the binary classifier, demonstrating the pivotal role of confidence amendment in enhancing OOD sensitivity. Extensive experiments spanning various datasets and network architectures confirm the efficacy of the proposed method in detecting OOD samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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