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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： R. De Biasi O. Oztoprak F. Zanini S. Carmignato F. Berto S. Kollmannsberger M. Benedetti Department of Industrial Engineering Via Sommarive 9 University of Trento Trento Italy School of Engineering and Design Chair of Computational Modeling and Simulation Arcisstraße 21 Technical University of Munich Munich Germany Department of Management and Engineering Stradella San Nicola 3 University of Padova Vicenza Italy Department of Chemical Engineering Materials Environment Via Eudossiana 18 University of Rome Sapienza Roma Italy Faculty of Civil and Environmental Sciences Chair of Data Science in Engineering Coudraystraße 13b Bauhaus-Universität Weimar Weimar Germany

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

An accurate prediction of the fatigue life of metallic additively manufactured lattice structures is still a challenging task. The proposed work addresses this challenge of industrial relevance through the computation of an as-built geometry of a lattice structure directly from its X-ray computed tomography reconstruction. The computation is carried out by taking advantage of the Finite Cell Method and applies the average strain energy density method to predict fatigue failure. The proof of concept provided in this work shows an excellent agreement in the fatigue prediction, paving the way for a computationally accurate fatigue life prediction of other types of metamaterials.

关键词： Geometry Accuracy Lattices Fatigue Metamaterials Topology X-ray imaging Stress Strain Testing

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Weisfeiler and Leman go machine learning: the story so far

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 15865-15923页

作者： Christopher Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science and Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain View CA Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland and Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

关键词： machine learning for graphs graph neural networks Weisfeiler-Leman algorithm expressivity equivariance

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Taking the neural sampling code very seriously: a data-driven approach for evaluating generative models of the visual system 23

Taking the neural sampling code very seriously: a data-drive...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Suhas Shrinivasan Konstantin-Klemens Lurz Kelli Restivo George H. Denfield Andreas S. Tolias Edgar Y. Walker Fabian H. Sinz Institute for Computer Science and Campus Institute for Data Science University of Göttingen Göttingen Germany Center for Neuroscience and Artificial Intelligence Department of Neuroscience Baylor College of Medicine Houston Department of Psychiatry Columbia University New York City Center for Neuroscience and Artificial Intelligence Department of Neuroscience Baylor College of Medicine Houston and Department of Electrical and Computer Engineering Rice University Houston Department of Physiology and Biophysics and Computational Neuroscience Center University of Washington Seattle Institute for Computer Science and Campus Institute for Data Science University of Göttingen Göttingen Germany and Center for Neuroscience and Artificial Intelligence Department of Neuroscience Baylor College of Medicine Houston

Prevailing theories of perception hypothesize that the brain implements perception via Bayesian inference in a generative model of the world. One prominent theory, the Neural Sampling Code (NSC), posits that neuronal responses to a stimulus represent samples from the posterior distribution over latent world state variables that cause the stimulus. Although theoretically elegant, NSC does not specify the exact form of the generative model or prescribe how to link the theory to recorded neuronal activity. Previous works assume simple generative models and test their qualitative agreement with neurophysiological data. Currently, there is no precise alignment of the normative theory with neuronal recordings, especially in response to natural stimuli, and a quantitative, experimental evaluation of models under NSC has been lacking. Here, we propose a novel formalization of NSC, that (a) allows us to directly fit NSC generative models to recorded neuronal activity in response to natural images, (b) formulate richer and more flexible generative models, and (c) employ standard metrics to quantitatively evaluate different generative models under NSC. Furthermore, we derive a stimulus-conditioned predictive model of neuronal responses from the trained generative model using our formalization that we compare to neural system identification models. We demonstrate our approach by fitting and comparing classical- and flexible deep learning-based generative models on population recordings from the macaque primary visual cortex (V1) to natural images, and show that the flexible models outperform classical models in both their generative- and predictive-model performance. Overall, our work is an important step towards a quantitative evaluation of NSC. It provides a framework that lets us learn the generative model directly from neuronal population recordings, paving the way for an experimentally-informed understanding of probabilistic computational principles underlying perception a

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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Boosting Anomaly Detection in Financial Transactions: Leveraging Deep Learning with Isolation Forest for Enhanced Accuracy

Boosting Anomaly Detection in Financial Transactions: Levera...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Prateek Kumar Bansal Divya Nimma Nripendra Narayan Das BVN Prasad Paruchuri Harishchander Anandaram M. Karthik Institute of Business Management GLA University Mathura India PhD in Computational Science Data Analyst in UMMC University of Southern Mississippi USA Department of Information Technology Manipal University Jaipur India Department of CSE KL University(Deemed to be) Vijayawada India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Anomaly detection in financial transactions is paramount for safeguarding against fraudulent activities that pose significant risks to financial institutions and customers alike. Traditional methods often struggle to accurately identify complex and evolving patterns of fraud, necessitating innovative approaches that leverage advanced techniques such as deep learning and isolation forest. In this study, we propose a novel framework for boosting anomaly detection in financial transactions by integrating deep learning with isolation forest to achieve enhanced accuracy. Firstly, it employ an autoencoder, a type of neural network, to learn complex representations of normal transaction patterns and reconstruct input data. The autoencoder's ability to capture subtle variations in transaction attributes enables it to effectively distinguish between normal and anomalous instances based on the reconstruction error. Furthermore, we augment the anomaly detection process by incorporating isolation forest, a tree-based algorithm that isolates anomalies in the feature space by recursively partitioning data subsets. By combining the representation learning capabilities of deep learning with the outlier detection prowess of isolation forest, our framework offers a comprehensive solution for detecting fraudulent activities in financial transactions. Through experimentation on real-world financial datasets, we demonstrate the superior performance of our proposed framework compared to existing methods. The proposed method is implemented in Python software and has an accuracy of about 99.12% which is 1.49% higher than other existing methods like Conv-LSTM, Convolutional Neural Network (CNN)-LSTM, and CNN-GRU (Gated Recurrent Unit). Moreover, the integration of deep learning with isolation forest enables our framework to adapt to evolving patterns of fraud, ensuring robust and reliable anomaly detection in dynamic financial environments. Overall, our study contributes to the advancement

关键词： Deep learning Accuracy Autoencoders Forestry Boosting Software Fraud Software reliability Convolutional neural networks Anomaly detection

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Transparent Correlated Metallic Perovskites with Conducive Chemical Disorder

arXiv

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arXiv 2025年

作者： Almishal, Saeed S.I. Kezer, Pat Iwabuchi, Yasuyuki Sivak, Jacob T. Ayyagari, Sai Venkata Gayathri Sarker, Saugata Furst, Matthew Bejger, Gerald Yang, Billy Gelin, Simon Alem, Nasim Dabo, Ismaila Rost, Christina M. Sinnott, Susan B. Crespi, Vincent Gopalan, Venkatraman Engel-Herbert, Roman Heron, John T. Maria, Jon-Paul Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Electrical Engineering University of Michigan Ann ArborMI48109 United States Department of Chemistry The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Paul-Drude Institute for Solid State Electronics Hausvogteiplatz 5-7 Berlin10117 Germany Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States

This manuscript presents a working model linking chemical disorder and transport properties in correlated-electron perovskites with high-entropy formulations and a framework to actively design them. We demonstrate this new learning in epitaxial Srx(Ti,Cr,Nb,Mo,W)O3 thin films that exhibit exceptional crystalline fidelity despite a diverse chemical formulation where most B-site species are highly misfit with respect to valence and radius. X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy confirm a unique combination of chemical disorder and structural perfection in thick epitaxial layers. This combination produces significant electron correlation, low electrical resistivity, and an optical transparency window that surpasses that of constituent end-members, with a flattened frequency- and temperature-dependent response. We address the computational challenges of modeling such systems and investigate short-range ordering using cluster expansion. These results showcase that unusual d-metal combinations access an expanded property design space that is predictable using end-member characteristics – though unavailable to them – thus offering performance advances in optical, spintronic, and quantum devices. © 2025, CC BY-NC-SA.

关键词： Perovskite

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Decomposing the Persistent Homology Transform of Star-Shaped Objects

arXiv

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arXiv 2024年

作者： Arya, Shreya Giunti, Barbara Hickok, Abigail Kanari, Lida McGuire, Sarah Turner, Katharine University of Pennsylvania Department of Mathematics PhiladelphiaPA United States SUNY University at Albany Department of Mathematics and Statistics AlbanyNY United States Yale University Wu Tsai Institute Department of Statistics and Data Science New HavenCT United States Blue Brain Project Campus Biotech Geneva1202 Switzerland Michigan State University Department of Computational Mathematics Science & Engineering East LansingMI United States Australian National University Mathematical Sciences Institute Canberra Australia

In this paper, we study the geometric decomposition of the degree-0 Persistent Homology Transform (PHT) as viewed as a persistence diagram bundle. We focus on star-shaped objects as they can be segmented into smaller, simpler regions known as "sectors". Algebraically, we demonstrate that the degree-0 persistence diagram of a star-shaped object in R2 can be derived from the degree-0 persistence diagrams of its sectors. Using this, we then establish sufficient conditions for star-shaped objects in R2 so that they have "trivial geometric monodromy". Consequently, the PHT of such a shape can be decomposed as a union of curves parameterized by S1, where the curves are given by the continuous movement of each point in the persistence diagrams that are parameterized by S1. Finally, we discuss the current challenges of generalizing these results to higher dimensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stars

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High-Accuracy Animal Species Classification Using Transfer Learning with ResNet50 on Wildlife Camera Trap Images for Enhanced Conservation Efforts

High-Accuracy Animal Species Classification Using Transfer L...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Divya Nimma Varalakshmi Dandu Ankur Singh Bisht Vuda Sreenivasa Rao P. Santosh Kumar S Muthuperumal PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of School of Management Presidency University Bangalore Karnataka India Department of CSE Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Mathematics K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Accurately identifying animal species from wildlife camera trap images is essential for conservation and biodiversity monitoring. However, manual processing is severely constrained by the massive volume of data collected by camera traps. Using transfer learning and the ResNet50 model, a high-accura cy animal species classification framework was offered as an approach to this challenge. Utilization of the iWildCam2020_256 dataset—a varied collection of wildlife photos taken in a range of ecosystems optimize ResNet50, a pre-trained neural network to improve its performance on this particular job. With the use of transfer learning approaches, this approach aims to maximize the model's feature extraction capabilities and make it capable of accurately differentiating between a large number of animal species. Compared with traditional techniques, the suggested framework significantly improved classification accuracy when trained and verified on the iWildCam2020_256 dataset. the application of data augmentation techniques and network hyperparameter tuning to reduce overfitting and enhance generalization in various contexts. The outcomes show how well the model can classify species in difficult situations, like changing lighting and partial occlusions. By offering a reliable instrument for automated species identification, our study advances the area of animal conservation by enabling more effective and scalable biodiversity assessments. With a 97% accuracy score, the suggested framework performs remarkably well and shows consistent species recognition across a wide range of animal images. It is essential to include the suggested framework into conservation initiatives, which would help with the precise and timely monitoring of wildlife populations—a crucial component in maintaining biodiversity.

关键词： Technological innovation Accuracy Biological system modeling Wildlife Transfer learning Cameras Biodiversity Monitoring Tuning Residual neural networks

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Embedding and trajectories of temporal networks

arXiv

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arXiv 2022年

作者： Thongprayoon, Chanon Livi, Lorenzo Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Computer Science University of Manitoba WinnipegMB Canada Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Shinjuku Tokyo Japan

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days. © 2022, CC BY.

关键词： Trajectories

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