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arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study

arXiv

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arXiv 2024年

作者： O’Gara, David Kerr, Cliff C. Klein, Daniel J. Binois, Mickaël Garnett, Roman Hammond, Ross A. Division of Computational and Data Sciences Washington University in St. Louis St. LouisMO United States Institute for Disease Modeling Bill & Melinda Gates Foundation SeattleWA United States Acumes Team Université Côte d’Azur Inria Sophia Antipolis France Department of Computer Science McKelvey School of Engineering Washington University in St. Louis St. LouisMO United States Public Health Brown School Washington University in St. Louis St. LouisMO United States Center on Social Dynamics and Policy Brookings Institution WashingtonDC United States The Santa Fe Institute Santa FeNM United States

Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for in-silico experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real-world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. computational constraints often form a significant hurdle to timely calibration and policy analysis in high-resolution agent-based models. In this paper, we present a technical solution to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model. © 2024, CC BY.

关键词： Digital elevation model

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Model Predictive Path Integral Control for Car Driving with Autogenerated Cost map Based on Prior Map and Camera Image

Model Predictive Path Integral Control for Car Driving with ...

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International Conference on Intelligent Transportation

作者： Alexander Buyval Aidar Gabdullin Konstantin Sozykin Alexandr Klimchik Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Skolkovo Institute of Science and Technology (Skoltech) Center for Computational and Data-Intensive Science and Engineering (CDISE) Russia

ISBN: (纸本)9781538670255

Model Predictive Path Integral (MPPI) algorithm is an efficient approach to control an autonomous car. However, it requires a cost map. Preparing a cost map in advance is a task that can be completed only for a static closed unchanging environment, but impossible for real city conditions where the environment is dynamic. In this work, we present a method for automatic creation of the cost map based on map data and image from a camera mounted on a self-driving car. The described approach was tested on the Kia Soul car.

关键词： Cameras Automobiles Fires Roads Image segmentation Prediction algorithms Heuristic algorithms

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Stability of stochastic gradient descent on nonsmooth convex losses 20

Stability of stochastic gradient descent on nonsmooth convex...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar Department of Computer Science & Engineering The Ohio State University Apple Pontificia Universidad Católica de Chile Institute for Mathematical and Computational Engineering ANID – Millennium Science Initiative Program Millennium Nucleus Center for the Discovery of Structures in Complex Data

ISBN: (纸本)9781713829546

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. [20] provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to differentially private convex optimization for smooth *** work is the first to address uniform stability of SGD on nonsmooth convex losses. Specifically, we provide sharp upper and lower bounds for several forms of SGD and full-batch GD on arbitrary Lipschitz nonsmooth convex losses. Our lower bounds show that, in the nonsmooth case, (S)GD can be inherently less stable than in the smooth case. On the other hand, our upper bounds show that (S)GD is sufficiently stable for deriving new and useful bounds on generalization error. Most notably, we obtain the first dimension-independent generalization bounds for multi-pass SGD in the nonsmooth case. In addition, our bounds allow us to derive a new algorithm for differentially private nonsmooth stochastic convex optimization with optimal excess population risk. Our algorithm is simpler and more efficient than the best known algorithm for the nonsmooth case [16].

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AA-ICP: Iterative closest point with anderson acceleration

AA-ICP: Iterative closest point with anderson acceleration

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2018 IEEE International Conference on Robotics and Automation, ICRA 2018

作者： Pavlov, Artem L. Ovchinnikov, Grigory W.V. Derbyshev, Dmitry Yu. Tsetserukou, Dzmitry Oseledets, Ivan V. Skolkovo Institute of Science and Technology Skolkovo Innovation Center Space Center Moscow143026 Russia Skolkovo Institute of Science and Technology Computational and Data-Intensive Science and Engineering Russia Moscow Institute of Physics and Technology Russia Skolkovo Institute of Science and Technology Space Center Russia Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Russia

ISBN: (纸本)9781538630815

Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust, this method is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose a novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into Point Cloud Library (PCL) and make it available online. Benchmarking on the real-world data fully supports our claims. © 2018 IEEE.

关键词： Iterative methods

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Teaching mechanics with individual exercise assignments and automated correction

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PAMM 2023年第3期23卷

作者： Michael H. Gfrerer Benjamin Marussig Katharina Maitz Mia M. Bangerl Institute of Applied Mechanics Graz Center of Computational Engineering (GCCE) Graz University of Technology Graz Austria Private University College of Teacher Education Augustinum (PPH Augustinum) Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Solving exercise problems by yourself is a vital part of developing a mechanical understanding. Yet, most mechanics lectures have more than 200 participants, so the workload for manually creating and correcting assignments limits the number of exercises. The resulting example pool is usually much smaller than the number of participants, making verifying whether students can solve problems themselves considerably harder. At the same time, unreflected copying of tasks already solved does not foster the understanding of the subject and leads to a false self-assessment. We address these issues by providing a scalable approach for creating, distributing, and correcting exercise assignments for problems related to statics, strength of materials, dynamics, and hydrostatics. The overall concept allows us to provide individual exercise assignments for each student. A quantitative survey among students of our recent statics lecture assesses the acceptance of our teaching tool. The feedback indicates a clear added value for the lecture, which fosters self-directed and reflective learning.

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Comparison of immune profiles between hepatocellular carcinoma subtypes

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Biophysics Reports 2020年第1期6卷 19-32页

作者： Xuemin Pan Ping Lin Fangyoumin Feng Jia Li Yuan-Yuan Li Wentao Dai Bo Hu Xin-Rong Yang Jia Fan Hong Li Yixue Li School of Life Sciences and Biotechnology Shanghai Jiao Tong University Bio-Med Big Data Center Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Shanghai Institutes for Biological Sciences University of Chinese Academy of Sciences Chinese Academy of Sciences University of the Chinese Academy of Sciences School of Life Science and Techonology Shanghai Tech University Shanghai Center for Bioinformation Technology Shanghai Academy of Science & Technology Shanghai Engineering Research Center of Pharmaceutical Translation Department of Liver Surgery Liver Cancer Institute Zhongshan Hospital Fudan University Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education Collaborative Innovation Center of Genetics and Development Fudan University

Immunotherapy, especially immune checkpoint inhibitors, is becoming a promising treatment for hepatocellular carcinoma(HCC). However, the response rate remains limited due to the heterogeneity of HCC samples. Molecular subtypes of HCC vary in genomic background, clinical features, and prognosis. This study aims to compare the immune profiles between HCC subtypes and find subtype-specific immune characteristics that might contribute to the prognosis and potential of immunotherapy. The immune profiles consist of immune-related genes, cytolytic activity, immune pathways, and tumorinfiltrating lymphocytes. HCC-c1 samples showed an overall higher activation level of immune genes and pathways, and this pattern was consistent in validation sets. We associated the difference in immune profiles with the activation level of cancer hallmarks and genomic mutations. There was a negative correlation between most of the metabolism pathway and immune-related pathways in HCC samples. CTNNB1/WNT signaling pathway mutation, one of the common mutations in HCC, appears to be associated with the expression of immune genes as well. These results reveal the difference of immune profiles between HCC subtypes and possible reasons and influence, which may also deepen our understanding of the carcinogenesis process.

关键词： Hepatocellular carcinoma Immune Tumor-infiltrating lymphocytes Metabolism CTNNB1 mutation

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Batch reverse osmosis:a new research direction in water desalination

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science Bulletin 2020年第20期65卷 1705-1708页

作者： Mingheng Li Yi Heng Jiu Luo Department of Chemical and Materials Engineering California State Polytechnic UniversityPomonaCA 91768USA School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Guangdong Province Key Laboratory of Computational Science Guangzhou 510006China National Supercomputing Center in Guangzhou(NSCC-GZ) Guangzhou 510006China School of Materials Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China

Water scarcity is one of the grand challenges across the world[1].Spiral wound reverse osmosis(RO)desalination is the most popular industrial technology to produce portable water from saline water *** terms of flow pattern,it falls into the category of cross-flow filtration where feed flows tangentially along the membrane surface while filtrate permeates across the membrane.

关键词： flow tangential category

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