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arXiv 2021年

作者： Casamitjana Díaz, Adrià Lorenzi, Marco Ferraris, Sebastiano Peter, Loïc Modat, Marc Stevens, Allison Fischl, Bruce Vercauteren, Tom Iglesias, Juan Eugenio Center for Medical Image Computing University College London United Kingdom Université Côte dÁzur Inria Epione Team Sophia Antipolis06902 France School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Program in Health Sciences and Technology Massachusetts Institute of Technology United States

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (`2 norm, which can be minimised in closed form) and Laplacian (`1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely

关键词： Brain

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Multifunctionality in embodied agents: Three levels of neural reuse 40

Multifunctionality in embodied agents: Three levels of neura...

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40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018

作者： Candadai, Madhavun Izquierdo, Eduardo J. Cognitive Science Program Indiana University BloomingtonIN47406 United States Cognitive Science Program School of Informatics Computing and Engineering Indiana University BloomingtonIN47406 United States

ISBN: (纸本)9780991196784

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels. © 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.

关键词： Neural networks

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Experimental implementation of an efficient test of quantumness

arXiv

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arXiv 2022年

作者： Lewis, Laura Zhu, Daiwei Gheorghiu, Alexandru Noel, Crystal Katz, Or Harraz, Bahaa Wang, Qingfeng Risinger, Andrew Feng, Lei Biswas, Debopriyo Egan, Laird Vidick, Thomas Cetina, Marko Monroe, Christopher Institute for Quantum Information and Matter Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology CA91125 United States Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States IonQ Inc. College ParkMD20740 United States Departments of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Institute for Theoretical Studies ETH Zürich CH 8001 Switzerland Duke Quantum Center Department of Physics Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits non-classical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification, or non-interactive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient non-interactive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device’s success. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

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Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

arXiv

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arXiv 2020年

作者： Diaz, Keith Anshilo Dailisan, Damian Sharaf, Umang Santos, Carissa Gan, Qijian Uy, Francis Aldrine Lim, May T. Bayen, Alexandre M. The School of Civil Environmental and Geological Engineering Mapua University Manila Philippines The National Institute of Physics University of the Philippines Diliman Philippines The Analytics Computing and Complex Systems Laboratory Asian Institute of Management Philippines The Electrical Engineering and Computer Sciences department at University of California Berkeley United States The PATH program in the Institute of Transportation Studies University of California Berkeley United States The Electrical Engineering and Computer Sciences Department The Department of Civil and Environmental Engineering The Institute of Transportation Studies University of California Berkeley United States

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low-volume links for several cycles to optimize the network-level traffic flow. Here we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework improves arterial coordination while maintaining phase order and timing predictability. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. To evaluate the resulting policy, we compared its performance against the phase offsets deployed along a segment of Huntington Drive in the city of Arcadia. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed the baseline on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the Noon scenario, and 6.2% in the PM scenario when offsets are adjusted in 15-minute intervals. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges in off-peak hours and localized traffic incidents. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

arXiv

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation

arXiv

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arXiv 2025年

作者： Chen, Samuel Yen-Chi Liu, Chen-Yu Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Huang, Wei-Hao Wells Fargo New YorkNY United States Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Research Institute Taipei Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Jij inc. Tokyo Japan

The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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A Cluster-Based Method for Improving Water Depth Estimation from Satellite Derived Bathymetry

A Cluster-Based Method for Improving Water Depth Estimation ...

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Data Science and Its Applications (ICoDSA), International Conference on

作者： Litany Meliala Tabah Juliansah Didit Adytia Teguh Purnama Sidiq Wiwin Windupranata Poerbandono Geodesy and Geomatics Engineering Study Program Institut Teknologi Bandung Bandung Indonesia School of Computing Telkom University Bandung Indonesia Geodesy Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung Indonesia Hydrography Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9781728182353

ISBN: (纸本)9781728182360

In this presented paper, an effort to improve the accuracy of the bathymetric model derived from optical satellite imagery is discussed. According to the previous study, the accuracy approximately measures in 2 m for depth up to 10 m by means of the Global Bathymetric Inversion Methods. Therefore, the improvement is done based on the so-called Cluster-Based Method applied to the analytical and log-ratio methods. The Cluster-Based Method partitions the working area on the by classifying the errors between the known depths and the predicted ones. The data for the known and predicted depths are acquired from the in-situ survey and the Sentinel-2 imagery. Improvement by one-third to the accuracy of the resulting bathymetry is obtained.

关键词： Satellites Estimation Clustering algorithms Correlation Remote sensing Geodesy Optical imaging

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big Data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big Data Cost function Minimization

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What Do Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder

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Sage Open 2023年第2期13卷

作者： Lihuan Guo Wei Wang Yenchun Jim Wu Tan Siu Lin Business School Quanzhou Normal University Quanzhou Fujian ChinaCloud Computing IoT E-commerce Intelligence Engineering Research Center of Colleges and universities in Fujian Province Quanzhou Normal University Quanzhou Fujian China College of Business Administration Huaqiao University Quanzhou Fujian China MBA Program in Southeast Asia National Taipei University of Education Taipei TaiwanGraduate Institute of Global Business and Strategy National Taiwan Normal University Taipei Taiwan

To analyze the directions for future research suggested and to project future research plans, we extract relevant text from these publications with respect to COVID-19-related research based on 54,136 relevant academic journals published from the initial outbreak of COVID-19 in January 2020 until December 2020. First, we extract and preprocess the corpus and then determine that, according to the Elbow method, the optimal number of clusters is 7. Then, we construct a text clustering model based on an autoencoder, with the support of an artificial neural network. Distance measurements, such as correlation, cosine, Braycurtis, and Jaccard are compared, and the clustering results are evaluated with normal mutual information. The results show that cosine similarity has the best effect on clustering of COVID-19-related documents. A topic model analysis shows that the directions of future research can mainly be grouped into the following seven categories: infectivity testing, genome analysis, vaccine testing, diagnosis and infection characteristics, pandemic management, nursing care, and clinical testing. Among them, the topics of pandemic management, diagnosis and infection characteristics, and clinical testing trended upward in proportion to future directions. The topic of vaccine testing remains steady over the observation window, whereas other topics (infectivity testing, genome analysis, and nursing care) slowly trended downward. Among all the topics, medical research comprises 80%, and about 20% of the topics are related to public management, government functions, and economic development. This study enriches our scientific understanding of COVID-19 and helps us to effectively predict future scientific research output on COVID-19.

关键词： autoencoder COVID-19 deep learning text mining topic model

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Airr Community Curation and Standardised Representation for Immunoglobulin and T Cell Receptor Germline Sets

SSRN

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SSRN 2022年

作者： Lees, William D. Christley, Scott Peres, Ayelet Kos, Justin T. Corrie, Brian Ralph, Duncan Breden, Felix Cowell, Lindsay G. Yaari, Gur Corcoran, Martin Karlsson Hedestam, Gunilla B. Ohlin, Mats Collins, Andrew M. Watson, Corey T. Busse, Christian E. Institute of Structural and Molecular Biology Birkbeck College London United Kingdom Human-Centered Computing and Information Science Institute for Systems and Computer Engineering Technology and Science Porto Portugal Peter O’Donnell Jr School of Public Health Department of Immunology UT Southwestern Medical Center Texas United States Bioengineering Program Faculty of Engineering Bar-Ilan University Ramat Gan Israel Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Kentucky United States Department of Biological Sciences Simon Fraser University BurnabyBC Canada Fred Hutchinson Cancer Research Center SeattleWA United States Department of Microbiology Tumor and Cell Biology Karolinska Institute Stockholm Sweden Department of Immunotechnology Lund University Lund Sweden School of Biotechnology and Biomolecular Sciences University of New South Wales SydneyNSW Australia Division of B Cell Immunology German Cancer Research Center Heidelberg Germany

Analysis of an individual’s immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes and provides transparency based on principles of findability, accessibility, interoperability, and reusability. We describe results obtained by applying the data model and associated principles within the AIRR Community, including supporting software and databases. © 2022, The Authors. All rights reserved.

关键词： Genes

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