检索结果-内蒙古大学图书馆

Physion: Evaluating Physical Prediction from Vision in Humans and Machines

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Bear, Daniel M. Wang, Elias Mrowca, Damian Binder, Felix Tung, Hsiao-Yu Fish Pramod, R.T. Holdaway, Cameron Tao, Sirui Smith, Kevin Sun, Fan-Yun Fei-Fei, Li Kanwisher, Nancy Tenenbaum, Joshua B. Yamins, Daniel L.K. Fan, Judith Department of Psychology StanfordCA94305 United States Department of Electrical Engineering StanfordCA94305 United States Department of Computer Science StanfordCA94305 United States Wu Tsai Neurosciences Institute StanfordCA94305 United States Department of Cognitive Science UC San Diego CA92093 United States Department of Psychology UC San Diego CA92093 United States Department of Brain and Cognitive Sciences and CBMM MIT CambridgeMA02139 United States

While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time. Our dataset features realistic simulations of a wide range of physical phenomena, including rigid and soft-body collisions, stable multi-object configurations, rolling, sliding, and projectile motion, thus providing a more comprehensive challenge than previous benchmarks. We used Physion to benchmark a suite of models varying in their architecture, learning objective, input-output structure, and training data. In parallel, we obtained precise measurements of human prediction behavior on the same set of scenarios, allowing us to directly evaluate how well any model could approximate human behavior. We found that vision algorithms that learn object-centric representations generally outperform those that do not, yet still fall far short of human performance. On the other hand, graph neural networks with direct access to physical state information both perform substantially better and make predictions that are more similar to those made by humans. These results suggest that extracting physical representations of scenes is the main bottleneck to achieving human-level and human-like physical understanding in vision algorithms. We have publicly released all data and code to facilitate the use of Physion to benchmark additional models in a fully reproducible manner, enabling systematic evaluation of progress towards vision algorithms that understand physical environments as robustly as people do. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Artificial intelligence for modelling infectious disease epidemics

学校读者我要写书评

暂无评论

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

Wireless Power Transfer to Randomly Distributed Implants via Homogeneous Magnetic Fields

学校读者我要写书评

暂无评论

Wireless Power Transfer to Randomly Distributed Implants via...

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： George Alexandrov Jan M. Rabaey Department of Electrical Engineering and Computer Science UC Berkeley California

Complex gesture recognition and multi-degree-of-freedom prostheses necessitate the ability to chronically record electromyography (EMG) signals from multiple physically distant muscles. This paper presents an inductive wireless power transfer (WPT) system for a distributed EMG recording platform that can power multiple implants via a homogeneous magnetic field. The proposed coil array is designed to optimally power randomly distributed implants across a large area on the forearm. Efficiency is maximized by carefully optimizing the geometry of the hexagonal sub-coil of the array and the frequency of the WPT, while field homogeneity is maximized by stacking three horizontally offset layers of coils in order to minimize magnetic field variations. The array achieves best-case, worst-case, and average efficiencies of 70.3%, 65.5%, and 68.5%, respectively, enabling efficient power delivery to multiple randomly distributed implants.

关键词： Implants Electromyography Muscles Magnetic recording Skin Fats Gesture recognition

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ramli, Albara Ah Liu, Xin Berndt, Kelly Goude, Erica Hou, Jiahui Kaethler, Lynea B. Liu, Rex Lopez, Amanda Nicorici, Alina Owens, Corey Rodriguez, David Wang, Jane Zhang, Huanle Aranki, Daniel McDonald, Craig M. Henricson, Erik K. Department of Computer Science School of Engineering University of California DavisCA United States Department of Physical Medicine and Rehabilitation School of Medicine University of California DavisCA United States Department of Electrical and Computer Engineering School of Engineering University of Waterloo WaterlooON Canada UC Davis Center for Health and Technology University of California DavisCA United States Berkeley School of Information University of California Berkeley BerkeleyCA United States University of California DavisCA United States

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically-developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens. Copyright © 2021, The Authors. All rights reserved.

关键词： Accelerometers

Margin-based generalization lower bounds for boosted classifiers

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Grønlund, Allan Kamma, Lior Larsen, Kasper Green Mathiasen, Alexander Nelson, Jelani Computer Science Department Aarhus University Department of Electronic Engineering and Computer Science. UC Berkeley

Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem from margin theory. The study of margins in the context of boosting algorithms was initiated by Schapire, Freund, Bartlett and Lee (1998) and has inspired numerous boosting algorithms and generalization bounds. To date, the strongest known generalization (upper bound) is the kth margin bound of Gao and Zhou (2013). Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds. In this paper, we give the first margin-based lower bounds on the generalization error of boosted classifiers. Our lower bounds nearly match the kth margin bound and thus almost settle the generalization performance of boosted classifiers in terms of margins. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive boosting

Testing for Outliers with Conformal p-values

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Bates, Stephen Candès, Emmanuel Lei, Lihua Romano, Yaniv Sesia, Matteo Departments of Statistics and of EECS UC Berkeley United States Departments of Statistics and of Mathematics Stanford University United States Department of Statistics Stanford University United States Departments of Electrical Engineering and of Computer Science Technion-Israel Institute of Technology Israel Department of Data Sciences and Operations University of Southern California United States

This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belong to the same distribution as a reference data set or are outliers. We propose a solution based on conformal inference, a broadly applicable framework which yields p-values that are marginally valid but mutually dependent for different test points. We prove these p-values are positively dependent and enable exact false discovery rate control, although in a relatively weak marginal sense. We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points;this paves the way to stronger type-I error guarantees. Our results depart from classical conformal inference as we leverage concentration inequalities rather than combinatorial arguments to establish our finitesample guarantees. Furthermore, our techniques also yield a uniform confidence bound for the false positive rate of any outlier detection algorithm, as a function of the threshold applied to its raw statistics. Finally, the relevance of our results is demonstrated by numerical experiments on real and simulated data. © 2021, CC BY.

关键词： Statistics

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Rampal, Nakul Wang, Kaiyu Burigana, Matthew Hou, Lingxiang Al-Johani, Juri Sackmann, Anna Murayshid, Hanan S. Al-Sumari, Walaa Abdullah Al-Abdulkarim, Arwa M. Al-Hazmi, Nahla Eid Al-Awad, Majed O. Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Data Services Librarian University of California Berkeley Library United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States Riyadh12354 Saudi Arabia Hydrogen Technologies Institute King Abdulaziz City for Science and Technology P.O. Box 6086 Riyadh11442 Saudi Arabia KACST−UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce 'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 Q&As for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks. The dataset is available at the following link: https://***/nakulrampal/RetChemQA. © 2024, CC BY.

关键词： Machine learning

Privacy and utility perceptions of social robots in healthcare

学校读者我要写书评

暂无评论

Computers in Human Behavior: Artificial Humans

computers in Human Behavior: Artificial Humans 2024年第1期2卷

作者： Sandhya Jayaraman Elizabeth K. Phillips Daisy Church Laurel D. Riek Department of Computer Science and Engineering UC San Diego 9500 Gilman Dr La Jolla 92093 California USA Department of Psychology George Mason University 4400 University Dr Fairfax 22030 Virginia USA Academy of Art University 180 New Montgomery St San Francisco 94105 California USA

Many Human-Robot Interaction (HRI) researchers are exploring the use of healthcare robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility and adoption. While HRI research has explored some dimensions of privacy for robots in general, to our knowledge, no prior work has empirically studied how human-like robot design affects people's privacy and utility perceptions of robots across different healthcare contexts and tasks. We conducted a 3 × 3 × 3 study (n = 239) to understand these relationships, varying robot Human Likeness (HL) (low, medium, and high) and scenario/task type (hospital waiting room/robot check-in support, hospital patient room/robot mobility support, home care/robot neurorehabilitation support) via a mixed between-within subjects design. To our knowledge, this is one of the first studies that operationalizes complex constructs of privacy, healthcare, and HL across multiple realistic healthcare contexts, with a high degree of cognitive fidelity. Our results suggest the tasks and contexts in which privacy is considered in healthcare contexts with robots is more impactful than other factors like robot HL appearance. In particular, some settings include more complex tradeoffs between privacy and utility for robots than others. For example, HRI researchers and practitioners who want to build healthcare robots intended for the home may encounter the greatest challenges for balancing privacy risks. Finally, for the community, we demonstrate that design fiction animations can be a useful way to facilitate cognitive fidelity for supporting studies in HRI and serving as a bridge between narrative methods and the use of real-world robots.

关键词： Human-robot interaction Privacy Utility Healthcare robots

METB-08. CIRCULAR EXTRACHROMOSOMAL DNA IS ASSOCIATED WITH POOR SURVIVAL IN CHILDHOOD CANCER TYPES

学校读者我要写书评

暂无评论

Neuro-Oncology 2024年第SUPPLEMENT_4期26卷 0–0页

作者： Sunita Sridhar Owen Chapman Aditi Dutta Shanqing Wang Rishaan Kenkre Jens Luebeck Vineet Bafna Jill P Mesirov Lukas Chavez Department of Medicine University of California San Diego San Diego CA USA Department of Pediatrics UC San Diego and Rady Children’s Hospital San Diego CA USA Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA Department of Computer Science and Engineering University of California San Diego San Diego CA USA Department of Computer Science and Engineering San Diego CA USA Moores Cancer Center University of California San Diego San Diego CA USA

BACKGROUND Circular extrachromosomal DNA (ecDNA) is a common form of oncogene amplification in aggressive cancers and a promising target for improved cancer therapy. The frequency and diversity of ecDNA has been described in different types of adult cancers and in selected childhood cancer types such as neuroblastoma and medulloblastoma. However, the frequency of ecDNA and its association with survival has not been comprehensively analyzed across the spectrum of pediatric cancers.

关键词：