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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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Time-dependent global sensitivity analysis of the Doyle-Fuller-Newman model

arXiv

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arXiv 2025年

作者： Zonta, Elia Buha, Ivana Jovanovic Spinola, Michele Weißinger, Christoph Bungartz, Hans-Joachim Jossen, Andreas Technical University of Munich School of Engineering and Design Department of Energy and Process Engineering Arcisstraße 21 Munich80333 Germany Technical University of Munich School of Computation Information and Technology Department of Computer Science Boltzmannstraße 3 Garching85748 Germany Capgemini Engineering Center of Excellence Battery Frankfurter Ring 81 Munich80807 Germany

The Doyle-Fuller-Newman model is arguably the most ubiquitous electrochemical model in lithium-ion battery research. Since it is a highly nonlinear model, its input-output relations are still poorly understood. Researchers therefore often employ sensitivity analyses to elucidate relative parametric importance for certain use cases. However, some methods are ill-suited for the complexity of the model and appropriate methods often face the downside of only being applicable to scalar quantities of interest. We implement a novel framework for global sensitivity analysis of time-dependent model outputs and apply it to a drive cycle simulation. We conduct a full and a subgroup sensitivity analysis to resolve lowly sensitive parameters and explore the model error when unimportant parameters are set to arbitrary values. Our findings suggest that the method identifies insensitive parameters whose variations cause only small deviations in the voltage response of the model. By providing the methodology, we hope research questions related to parametric sensitivity for time-dependent quantities of interest, such as voltage responses, can be addressed more easily and adequately in simulative battery research and beyond. © 2025, CC BY.

关键词： Sensitivity analysis

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network science and engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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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 Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #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

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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Brain organoids in motor cortex involved in motor output 24

Brain organoids in motor cortex involved in motor output

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9th International Conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Fan, Xiaoman Xu, Yuchen Xu, Shibo Yang, Yuqi Shi, Lufeng Liu, Yan Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies School of Engineering Westlake University China State Key Laboratory of Reproductive Medicine Institute for Stem Cell and Neural Regeneration Nanjing Medical University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China Department of Neurosurgery Sir Run Run Shaw Hospital Medical College Zhejiang University China

ISBN: (纸本)9798400717970

Motor function is essential in daily life while brain damage in motor system such as stroke would lead to long term motor function deficits. Brain organoids, a novel in vitro stem cell culture technology, offer a new idea for motor function recovery. In this study, we pumped out the motor cortex of forelimbs to mimic brain injury in mice and implanted human brain organoids into the cavity combined with microwire electrodes to simultaneously record neural signals from both the organoids and the host animals. Our findings indicate that the brain organoids can be involved in motor output and the involvement can be improved through behavioral training. © 2024 Copyright held by the owner/author(s).

关键词： Stem cells

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Shared-Space Autoencoders with Randomized Skip Connections for Building Footprint Detection with Missing Views 25th

Shared-Space Autoencoders with Randomized Skip Connections f...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Ashiotis, Giannis Oldfield, James Chrysostomou, Charalambos Christoudias, Theodoros Nicolaou, Mihalis A. Computation-Based Science and Technology Research Center The Cyprus Institute Lefkosia Cyprus Climate and Atmosphere Research Center The Cyprus Institute Lefkosia Cyprus School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9783030687861

Recently, a vast amount of satellite data has become available, going beyond standard optical (EO) data to other forms such as synthetic aperture radars (SAR). While more robust, SAR data are often more difficult to interpret, can be of lower resolution, and require intense pre-processing compared to EO data. On the other hand, while more interpretable, EO data often fail under unfavourable lighting, weather, or cloud-cover conditions. To leverage the advantages of both domains, we present a novel autoencoder-based architecture that is able to both (i) fuse multi-spectral optical and radar data in a common shared-space, and (ii) perform image segmentation for building footprint detection under the assumption that one of the data modalities is missing–resembling a situation often encountered under real-world settings. To do so, a novel randomized skip-connection architecture that utilizes autoencoder weight-sharing is designed. We compare the proposed method to baseline approaches relying on network fine-tuning, and established architectures such as UNet. Qualitative and quantitative results show the merits of the proposed method, that outperforms all compared techniques for the task-at-hand. © 2021, Springer Nature Switzerland AG.

关键词： Synthetic aperture radar

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Multilayer broadband switchable absorbers based on phase-change materials

Multilayer broadband switchable absorbers based on phase-cha...

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2023 Active Photonic Platforms, APP 2023

作者： Emrose, Md Tanvir Payne, Emily L. You, Chenglong Veronis, Georgios School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Physics Brigham Young University - Idaho RexburgID83460 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States

ISBN: (纸本)9781510665088

We introduce multilayer structures with the phase-change material germanium-antimony-tellurium (GST) for use as broadband switchable absorbers in the infrared wavelength range. We use a memetic optimization algorithm coupled with the transfer-matrix method to optimize both the material composition and the layer thicknesses of the multilayer structures. We show that in the optimized structures near perfect absorption can be switched to very low absorption in a broad wavelength range by switching GST from its crystalline to its amorphous phase. Our results could pave the way to a new class of broadband switchable absorbers and thermal sources in the infrared wavelength range. © 2023 SPIE.

关键词： Phase change materials

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On Implementation of Brownian Circuits by Swarm Agents

On Implementation of Brownian Circuits by Swarm Agents

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Masahiro Kimura Teijiro Isokawa Ferdinand Peper Sho Nakade Jia Lee Naotake Kamiura Graduate School of Engineering University of Hyogo Himeji Japan Center for Information and Neural Networks National Institute of Information and Communications Technology Kobe Japan College of Computer Science Chongqing University Chongqing China

Brownian circuit is a type of delay-insensitive asynchronous circuits in which circuit modules are connected by bi-directional signal lines and computational signals can be fluctuated on signal lines. It is expected to achieve low power dissipation for conducting computation, and there are several types of implementations such as by Cellular Automata and Swarm Networks. In this paper, we propose an implementation of Brownian circuit based on swarm agents for possible applications on biomolecular computers. Swarm agents consist of several types of agents each of which has only a few functions with a fixed internal states. Operations on signals for the circuit elements are conducted by mechanical interactions between groups of agents, rather than by changing states in cells or agents through their communication. We show how to construct each of elements in Brownian circuit, such as signal, signal line, Hub, C-Join, and Ratchet, by collections of agents, thus computational universality is established by swarm agents.

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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models 39

Expensive Multi-Objective Bayesian Optimization Based on Dif...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Bingdong Di, Zixiang Lu, Yongfan Qian, Hong Wang, Feng Yang, Peng Tang, Ke Zhou, Aimin Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Computer Science Wuhan University Wuhan430072 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)157735897X

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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Possible contribution to data-driven primate research: Comment on "Kinematic coding: Measuring information in naturalistic behaviour" by Becchio, Pullar, Scaliti, and Panzeri

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Physics of life reviews 2025年 54卷 80-82页

作者： Koki Mimura Ken Nakae Kenji Doya Department of Mental Disorder Research National Institute of Neuroscience National Center of Neurology and Psychiatry Tokyo 187-8502 Japan. Graduate School of Engineering University of Fukui Fukui 910-8507 Japan. Neural Computation Unit Okinawa Institute of Science and Technology Graduate University Okinawa 904-0495 Japan. Electronic address: doya@oist.jp.

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