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arXiv 2022年

作者： Liu, Yuhan Helena Ghosh, Arna Richards, Blake A. Shea-Brown, Eric Lajoie, Guillaume Department of Applied Mathematics University of Washington SeattleWA United States Allen Institute for Brain Science 615 Westlake Ave N SeattleWA United States Computational Neuroscience Center University of Washington SeattleWA United States School of Computer Science McGill University MontrealQC Canada Mila - Quebec AI Institute MontrealQC Canada Department of Neurology and Neurosurgery Montreal Neurological Institute McGill University MontrealQC Canada Canada CIFAR AI Chair CIFAR TorontoON Canada Dept. de Mathématiques et Statistiques Université de Montréal MontrealQC Canada

To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutions that exhibit different levels of generalization than their non-biologically-plausible counterparts. Leveraging results from deep learning theory based on loss landscape curvature, we ask: how do biologically-plausible gradient approximations affect generalization? We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely. Next, we verify that such generalization performance is correlated significantly with loss landscape curvature, and we show that biologically-plausible learning rules tend to approach high-curvature regions in synaptic weight space. Using tools from dynamical systems, we derive theoretical arguments and present a theorem explaining this phenomenon. This predicts our numerical results, and explains why biologically-plausible rules lead to worse and more variable generalization properties. Finally, we suggest potential remedies that could be used by the brain to mitigate this effect. To our knowledge, our analysis is the first to identify the reason for this generalization gap between artificial and biologically-plausible learning rules, which can help guide future investigations into how the brain learns solutions that generalize. © 2022, CC BY-NC-SA.

关键词： Recurrent neural networks

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Performance evaluation of the modular J-PET detector in conventional PET imaging

Performance evaluation of the modular J-PET detector in conv...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： M. Das R. Bayerlein S. Parzych S. Sharma R. D. Badawi E. Y. Beyene E. Czerwiński A. Hubalewska-Dydejczyk T. Kaplanoglu G. Korcyl W. Krzemien W. Mryka S. Niedźwiecki M. Opalińska M. Rädler M. Skurzok B. A Spencer P. Tanty K. Tayefi Ardebili P. Moskal E.Ł. Stepien Doctoral School of Exact and Natural Sciences Jagiellonian University M. Smoluchowski Institute of Physics S. Łojasiewicza 11 30-348 Krakow Poland Department of Biomedical Engineering University of California Davis Sacramento California United States of America Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Centre for Theranostics Jagiellonian University Jagiellonian University Kopernika 40 31-501 Krakow Poland Department of Radiology University of California Davis Sacramento California United States of America Chair and Department of Endocrinology Jagiellonian University Medical College Krakow Poland High Energy Department National Centre for Nuclear Research Otwock Swierk Poland

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

The utilization of organic scintillator strips in the construction of J-PET represents a remarkable departure from the conventional PET methodology that relies on inorganic crystal scintillators [1, 2]. This innovative approach not only holds the potential for cost-effectiveness but also facilitates the development of stand-alone detection modules, making PET imaging accessible to non-specialized laboratories [3]. The J-PET scanner, recently commissioned for research imaging on clinical patients at the University Hospital in Krakow, has undergone rigorous evaluation. This work is based on the study in which a single-bed position scan was performed on patients injected with [$\left.{ }^{68} \mathrm{Ga}\right]$-DOTA-TATE. This study evaluates the performance of human imaging in J-PET with particular attention to incorporating various corrections, including attenuation, normalization, random and scatter corrections. Attenuation correction was applied using the kVp-dependent transformation [4]. Normalization corrections were calculated using the component-based approach [5]. Random coincidences were corrected using the delayed time window method [6]. A Monte Carlo-based scatter correction (SC) method was implemented using SimSET to simulate the scatter contribution and calculate the SC factors in between each OSEM iteration of the image reconstruction [7]. In this contribution, the reconstructed images of patients scanned with the JPET, the first scanner based on plastic scintillators, and supplemented with the necessary corrections, will be presented.

关键词： Performance evaluation Strips Microwave integrated circuits Scintillators Monte Carlo methods Semiconductor detectors Attenuation

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GWSkyNet-Multi II: an updated deep learning model for rapid classification of gravitational-wave events

arXiv

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arXiv 2025年

作者： Raza, Nayyer Chan, Man Leong Haggard, Daryl Mahabal, Ashish McIver, Jess Durand, Audrey Larouche, Alexandre Moazen, Hadi Department of Physics McGill University 3600 rue University MontréalQCH3A2T8 Canada Trottier Space Institute at McGill 3550 rue University MontréalQCH3A2A7 Canada Department of Physics and Astronomy University of British Columbia VancouverBCV6T1Z1 Canada Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Computer Science and Software Engineering Université Laval QuébecQCG1V0A6 Canada Canada CIFAR AI Chair Mila MontréalQCH2S3H1 Canada

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging;the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a deep learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values, making it more interpretable. For significant O4 event alerts issued between May 2023 and December 2024, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions. © 2025, CC BY.

关键词： Neutrons

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Chinese News Text Classification and Its Application Based on Combined-Convolutional Neural Network

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Journal of computers (Taiwan) 2022年第4期33卷 1-14页

作者： Liu, Kai-Feng Zhang, Yu Zhang, Quan-Xin Wang, Yan-Ge Gao, Kai-Long Jiangsu Vocational College of Finance and Economics Huaian223003 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China State Key Laboratory in China for GeoMechanics and Deep Underground Engineering China University of Mining & Technology Beijing100083 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

A method based on combined-convolutional neural network (Combined-CNN) for Chinese news text classification is proposed. First of all, in order to solve the problem of a lack of special term set for Chinese news classification, a vocabulary suitable for Chinese long text classification is made by constructing a data index method. The Word2Vec pre-trained model was used to embed the text features word vectors. Second, by optimizing the structure of the classical convolutional neural network (CNN) model, a new idea of Combined-CNN model is proposed, which solves the problem of incomplete feature extraction of local text blocks and improves the accuracy rate of Chinese news text classification. Effective model regularization and RAdam optimization algorithm are designed in the model to enhance the model training effect. The experimental results show that the precision of the Combined-CNN model for Chinese news text classification reaches 93.69%. Compared with traditional machine learning methods and deep learning algorithms, the accuracy rate is improved by a maximum of 11.82% and 1.9%, respectively, and it is better than the comparison model in Recall and F-Measure. Finally, the Chinese news classification algorithm of the Combined-CNN is applied to realize a personalized recommendation system. © 2022 computer Society of the Republic of China. All rights reserved.

关键词： Recommender systems

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Adaptive architecture: regulating human building interaction ^*

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International Review of Law, computers and Technology 2019年第1期33卷 3-33页

作者： Urquhart, Lachlan Schnädelbach, Holger Jäger, Nils School of Law University of Edinburgh Edinburgh United Kingdom Horizon Digital Economy Research Institute School of Computer Science University of Nottingham Nottingham United Kingdom School of Computer Science University of Nottingham Nottingham United Kingdom School of Architecture Building & Civil Engineering Loughborough University Loughborough United Kingdom

In this paper, we explore the regulatory, technical and interactional implications of Adaptive architecture (AA) and how it will recalibrate the nature of human-building interaction. We comprehensively unpack the emergence and history of this novel concept, reflecting on the current state of the art and policy foundations supporting it. As AA is underpinned by the Internet of Things (IoT), we consider how regulatory and surveillance issues posed by the IoT are manifesting in the built environment. In our analysis, we utilise a prominent architectural model, Stuart Brand’s Shearing Layers, to understand temporal change and informational flows across different physical layers of a building. We use three AA applications to situate our analysis, namely a smart IoT security camera;an AA research prototype;and an AA commercial deployment. Focusing on emerging information privacy and security regulations, particularly the EU General Data Protection Regulation 2016, we examine AA from 5 perspectives: physical & information security risks;challenges of establishing responsibility;enabling occupant rights over flows, collection, use & control of personal data;addressing increased visibility of emotions and bodies;understanding surveillance of everyday routine activities. We conclude with key challenges for AA regulation and the future of human–building interaction. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adaptive architecture built environment human–building interaction internet of things law and ethics smart cities ubiquitous computing

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Tractably modelling dependence in networks beyond exchangeability

arXiv

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arXiv 2020年

作者： Wu, Weichi Olhede, Sofia Wolfe, Patrick Center for Statistical Sciences Department of Industrial Engineering Tsinghua University China Institute of Mathematics Chair of Statistical Data Science EPFL Swaziland Departments of Statistics and Computer Science Purdue University United States

We propose a general framework for modelling network data that is designed to describe aspects of non-exchangeable networks. Conditional on latent (unobserved) variables, the edges of the network are generated by their finite growth history (with latent orders) while the marginal probabilities of the adjacency matrix are modeled by a generalization of a graph limit function (or a graphon). In particular, we study the estimation, clustering and degree behavior of the network in our setting. We determine (i) the minimax estimator of a composite graphon with respect to squared error loss;(ii) that spectral clustering is able to consistently detect the latent membership when the block-wise constant composite graphon is considered under additional conditions;and (iii) we are able to construct models with heavy-tailed empirical degrees under specific scenarios and parameter choices. This explores why and under which general conditions non-exchangeable network data can be described by a stochastic block model. The new modelling framework is able to capture empirically important characteristics of network data such as sparsity combined with heavy tailed degree distribution, and add understanding as to what generative mechanisms will make them *** Codes 62G05, 62R07, 62E20, 62G20, secondary 53C20 Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems

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IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

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Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization

arXiv

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arXiv 2020年

作者： Chu, Yi Luo, Chuan Hoos, Holger H. Lin, Qingwei You, Haihang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Microsoft Research China Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm which gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state-of-the-art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence. Copyright © 2020, The Authors. All rights reserved.

关键词： Local search (optimization)

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Comparison of Feedback Approaches to Improve Training in Partial Weight-Bearing

Comparison of Feedback Approaches to Improve Training in Par...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ian Smith Satinder Gill Scott Bateman Erik Scheme Institute of Biomedical Engineering (IBME) University of New Brunswick (UNB) Fredericton Canada PDF with the IBME at UNB Faculty of Computer Science at UNB Department of Electrical and Computer Engineering at UNB New Brunswick Innovation Research Chair in Medical Devices and Technologies at the IBME

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Assistive devices, including canes or crutches, are used in partial weight-bearing (PWB) to offload weight from limbs weakened by disease or injury, promote recovery, and prevent reinjury. While weight must be offloaded accurately to target loads prescribed by healthcare providers for maximum benefit, current training methods result in poor adherence. It is, however, currently unknown how best to provide feedback during training so that users can build an accurate internal model for PWB. In this work, we investigate seven feedback schemes using an instrumented cane, which vary the modality, timing, and the level of detail provided. We find that auditory schemes and a retrospective visual scheme outperform current clinical practices for PWB training. These findings provide results that can be applied directly to improve current clinical practices and provide valuable new insight into the design of feedback for training internal models in force-based motor control tasks. Clinically, this work presents a simple modification to clinical PWB training practices that can improve compliance by up to 75%, positively influencing rehabilitation outcomes and reducing the risk of complications.

关键词： Training Visualization Bandwidth Haptic interfaces Loading Instruments Task analysis

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