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arXiv 2024年

作者： Zhang, Wenna Hu, Yutao Zhang, Hongyi Liu, Xiang Shen, Yuecheng Veronis, Georgios Alù, Andrea Huang, Yin Luo, Wenchen School of Physics Central South University Changsha410083 China Shenzhen Institute for Quantum Science and Engineering Southern university of science and technology Shenzhen518055 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200241 China 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 Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States

The skin effect, where bulk modes collapse into boundary modes, is a key phenomenon in topological non-Hermitian systems, has been predominantly studied in spinless systems. Recent studies illustrate the magnetic suppression of the first-order skin effect while ignoring spin. However, the physical significance of a magnetic field in non-Hermitian skin effect with spin remains elusive. Here, we systematically explore non-Hermitian spinful systems based on generalized Hatano-Nelson models with SU(2) gauge potential fields. In an open one-dimensional lattice, the spin-up and spin-down states can be uniquely separated and localized at the two boundaries without magnetic field. When an external magnetic field is applied, the skin effect exhibits a smooth transition from bidirectional to unidirectional. Remarkably, we demonstrate that the first-order skin effect can be anomalously induced by a magnetic field in a topologically trivial non-Hermitian spinful system without any skin effect at zero field. The direction of such magnetically induced skin modes can be controlled by simply changing the amplitude and polarity of the magnetic field. In addition, we demonstrate a transition between non-Bloch PT and anti-PT symmetries in the system, and uncover the spin-dependent mechanism of non-Bloch PT symmetry. Our results pave the way for the investigation of non-Hermitian skin effect with spin degrees of freedom. © 2024, CC BY.

关键词： Magnetic field effects

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Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook

arXiv

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arXiv 2023年

作者： Zhang, Huang Wang, Changshuo Tian, Shengwei Lu, Baoli Zhang, Liping Ning, Xin Bai, Xiao The School of Software Xinjiang University Xinjiang830000 China The Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China Cognitive Computing Technology Joint Laboratory Wave Group Beijing102208 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China The School of Computer Science and Engineering Beihang University Beijing100083 China

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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computational neuroscience:a frontier of the 21st century

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National science Review 2020年第9期7卷 1418-1422页

作者： Xiao-Jing Wang Hailan Hu Chengcheng Huang Henry Kennedy Chengyu Tony Li Nikos Logothetis Zhong-Lin Lu Qingming Luo Mu-ming Poo Doris Tsao Si Wu Zhaohui Wu Xu Zhang Douglas Zhou Center for Neural Science New York University Center for Neuroscience Key Laboratory of Medical Neurobiology of the Ministry of Health of China School of Medicine Zhejiang University Department of Neuroscience and Department of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh Université Claude Bernard Lyon 1 Inserm Stem Cell and Brain Research Institute U1208 Institute of Neuroscience State Key Laboratory of Neuroscience Chinese Academy of SciencesCAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Center for Brain Science and Brain-Inspired Technology Division of Arts and Sciences and NYU-ECNU Institute of Cognitive Neuroscience NYU Shanghai School of Biomedical Engineering Hainan University Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics Division of Biology and Biological Engineering California Institute of Technology Howard Hughes Medical Institute School of Electronics Engineering and Computer Science IDG/Mc Govern Institute for Brain Research PKU-Tsinghua Center for Life SciencesPeking University College of Computer Science and Technology Zhejiang University Institute of Brain-Intelligence Science and Technology Zhangjiang Lab School of Mathematical Sciences MOE-LSCand Institute of Natural Sciences Shanghai Jiao Tong University

THEORY IN NEUROscience The human brain is a biological organ,weighing about three pounds or 1.4 kg,that determines our behaviors, thoughts,emotions and consciousness. Although comprising only 2%of the total body weigh...

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neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping

arXiv

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arXiv 2022年

作者： Bryan, Matthew J. Jiang, Linxing Preston Rao, Rajesh P.N. Neural Systems Laboratory Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Center for Neurotechnology University of Washington SeattleWA United States Computational Neuroscience Center University of Washington SeattleWA United States

Objective A major challenge in designing closed-loop brain-computer interfaces (BCIs) is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and different objectives. Traditional approaches, such as those currently used for deep brain stimulation (DBS), have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation. Approach To achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence (AI) to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a "neural co-processor" which uses artificial neural networks (ANNs) and deep learning to learn optimal closed-loop stimulation policies. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for future in vivo tests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity in preparation for future in vivo tests. Main results Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function in the range 75-90%. Significance Our results provide the first proofof-concept demonstration, usi

关键词： Brain

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Contrastive Fusion of Generative and Discriminative Lesion Maps for Weakly Supervised Medical Image Segmentation

SSRN

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SSRN 2023年

作者： Du, Wei Huo, Yongkang Tang, Shiyi Chen, Liang Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Engineering Shantou University Shantou515063 China Translational Medical Center for Stem Cell Therapy Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

Medical image segmentation is a crucial computer-aided diagnosis (CAD) procedure in various clinical applications. However, most existing methods rely on pixel-level annotations, which are costly and time-consuming. We propose a novel contrastive fusion model of generative and discriminative lesion maps for weakly supervised medical image segmentation to address this limitation. The generative lesion map is generated by a lesion region-guided cycle-consistent adversarial network (CCANet), and the discriminative lesion map is produced by a class-aware-based discriminative lesion localization network (DLLNet). CCANet and DLLNet share weights at the encoder layer, which contains skip connections and channel attention modules to enhance feature embedding. In order to fully utilize and integrate the generative and discriminative lesion maps, and improve the segmentation accuracy, we design a fusion method based on contrastive loss to jointly train CCANet and DLLNet. The final lesion segmentation result is obtained by applying dense conditional random fields (Dense CRF) to the fused lesion map. Extensive experiments on three benchmark medical image datasets show that our model outperforms the state-of-the-art methods on weakly supervised medical image segmentation, especially in datasets with few samples, validating the proposed model's good generalization ability and robustness in various medical image segmentation scenarios. The source code and experiment datasets are available on GitHub: https://***/mlcb-jlu/GDC4MedSeg. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv

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arXiv 2024年

作者： Lopez-Ramos, Luis M. Leiser, Florian Rastogi, Aditya Hicks, Steven Strümke, Inga Madai, Vince I. Budig, Tobias Sunyaev, Ali Hilbert, Adam Holistic Systems department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Economics and Management Karlsruhe Institute of Technology Germany Department of Neuroradiology University Hospital Bonn Germany Department of Computer Science Faculty of Informatics Norwegian University of Science and Technology Trondheim Norway QUEST Center for Responsible Research Charité - Universitätsmedizin Berlin Berlin Germany School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom ETH Zurich Switzerland School of Computation Information and Technology Technical University of Munich Germany Charité - Universitätsmedizin Berlin Germany

The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens. © 2024, CC BY.

关键词： Federated learning

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Quantum Optics Applications of Hexagonal Boron Nitride Defects

arXiv

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arXiv 2024年

作者： Çakan, Aslı Cholsuk, Chanaprom Gale, Angus Kianinia, Mehran Paçal, Serkan Ateş, Serkan Aharonovich, Igor Toth, Milos Vogl, Tobias Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Munich80799 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney UltimoNSW2007 Australia University of Technology Sydney UltimoNSW2007 Australia Department of Physics Izmir Institute of Technology Izmir35430 Turkey

Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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DR-GAN: Distribution Regularization for Text-to-Image Generation

arXiv

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arXiv 2022年

作者： Tan, Hongchen Liu, Xiuping Yin, Baocai Li, Xin Artificial Intelligence Research Institute Beijing University of Technology Beijing100124 China School of Electrical Engineering & Computer Science Center for Computation & Technology Louisiana State University Baton RougeLA70808 United States School of Mathematical Sciences Dalian University of Technology Dalian116024 China

This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning. In DR-GAN, we introduce two novel modules: a Semantic Disentangling Module (SDM) and a Distribution Normalization Module (DNM). SDM combines the spatial self-attention mechanism and a new Semantic Disentangling Loss (SDL) to help the generator distill key semantic information for the image generation. DNM uses a Variational Auto-Encoder (VAE) to normalize and denoise the image latent distribution, which can help the discriminator better distinguish synthesized images from real images. DNM also adopts a Distribution Adversarial Loss (DAL) to guide the generator to align with normalized real image distributions in the latent space. Extensive experiments on two public datasets demonstrated that our DR-GAN achieved a competitive performance in the Text-to-Image task. The code link: https://***/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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