检索结果-内蒙古大学图书馆

Scientific discovery in the age of artificial intelligence

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

来源：评论

学校读者我要写书评

暂无评论

Resilient to Byzantine Attacks Finite-Sum Optimization Over Networks

Resilient to Byzantine Attacks Finite-Sum Optimization Over ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhaoxian Wu Qing Ling Tianyi Chen Georgios B. Giannakis School of Data and Computer Science and Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the friendly workers. This motivates gradient noise reduction as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to achieve provable linear convergence to a neighborhood of the optimal solution, where the size of neighborhood is determined by the number of Byzantine workers. Numerical tests demonstrate the robustness of Byrd-SAGA to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine-resilient SGD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

引用

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Adaptive gradient methods with dynamic bound of learning rate 7

Adaptive gradient methods with dynamic bound of learning rat...

引用

7th International Conference on Learning Representations, ICLR 2019

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University China College of Information Science and Electronic Engineering Zhejiang University China Department of Computer Science University of Southern California United States Center for Data Science Beijing Institute of Big Data Research Peking University China

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

Accelerated evolution of an Lhx2 enhancer shapes mammalian social hierarchies

引用

细胞研究（英文版） 2020年第5期30卷 408-420页

作者： Yuting Wang Guangyi Dai Zhili Gu Guopeng Liu Ke Tang Yi-Hsuan Pan Yujie Chen Xin Lin Nan Wu Haoshan Chen Su Feng Shou Qiu Hongduo Sun Qian Li Chuan Xu Yanan Mao Yong Edward Zhang Philipp Khaitovich Yan-Ling Wang Qunxiu Liu Jing-Dong Jackie Han Zhen Shao Gang Wei Chun Xu Naihe Jing Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of SciencesYueyang Road 320 200031 Shanghai China University of Chinese Academy of Sciences Beijing China Precise Genome Engineering Center School of Life SciencesGuangzhou University Guangzhou 510405 Guangdong China Key Laboratory of Brain Functional Genomics of Ministry of Education School of Life Science East China Normal University 200062 Shanghai China Bio-Med Big Data Center CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China State Key Laboratory of Cell Biology Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences 650223 Kunming China State Key Laboratory of Stem cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Shanghai Zoological Park 200335 Shanghai China

Social hierarchies emerged during evolution,and social rank influences behavior and health of ***,the evolutionary mechanisms of social hierarchy are still unknown in *** we developed a new method and performed a genome-wide screening for identifying regions with accelerated evolution in the ancestral lineage of placental mammals,where mammalian social hierarchies might have initially *** functional analyses were conducted for the most accelerated region designated as placental-accelerated sequence 1 (PAS1,P=3.15 × 10-18).Multiple pieces of evidence show that PAS1 is an enhancer of the transcription factor gene Lhx2 involved in brain ***1s isolated from various amniotes showed different cis-regulatory activity in vitro,and affected the expression of Lhx2 differently in the nervous system of mouse ***1 knock-out mice lack social ***1 knock-in mouse models demonstrate that PAS1s determine the social dominance and subordinate of adult mice,and that social ranks could even be turned over by mutated *** homozygous mutant mice had normal huddled sleeping behavior,motor coordination and ***,PAS1-Lhx2 modulates social hierarchies and is essential for establishing social stratification in amniotes,and positive Darwinian selection on PAS1 plays pivotal roles in the occurrence of mammalian social hierarchies.

关键词： Bioinformatics Comparative genomics

来源：评论

学校读者我要写书评

暂无评论

DronePick: Object picking and delivery teleoperation with the drone controlled by a wearable tactile display

arXiv

引用

arXiv 2019年

作者： Ibrahimov, Roman Tsykunov, Evgeny Shirokun, Vladimir Somov, Andrey Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

We report on the teleoperation system DronePick which provides remote object picking and delivery by a human-controlled quadcopter. The main novelty of the proposed system is that the human user continuously gets the visual and haptic feedback for accurate teleoperation. DronePick consists of a quadcopter equipped with a magnetic grabber, a tactile glove with finger motion tracking sensor, hand tracking system, and the Virtual Reality (VR) application. The human operator teleoperates the quadcopter by changing the position of the hand. The proposed vibrotactile patterns representing the location of the remote object relative to the quadcopter are delivered to the glove. It helps the operator to determine when the quadcopter is right above the object. When the "pick" command is sent by clasping the hand in the glove, the quadcopter decreases its altitude and the magnetic grabber attaches the target object. The whole scenario is in parallel simulated in VR. The air flow from the quadcopter and the relative positions of VR objects help the operator to determine the exact position of the delivered object to be picked. The experiments showed that the vibrotactile patterns were recognized by the users at the high recognition rates: the average 99% recognition rate and the average 2.36s recognition time. The real-life implementation of DronePick featuring object picking and delivering to the human was developed and tested. Copyright © 2019, The Authors. All rights reserved.

关键词： Remote control

来源：评论

学校读者我要写书评

暂无评论

An Information-Theoretic Framework for Optimal Experimental Design in Magnetic Nanoparticle Hyperthermia

SSRN

引用

SSRN 2025年

作者： Nandyala, Mahesh Lanham, Andrew Jha, Prashant K. Wu, Chengyue Hazle, John D. Yankeelov, Thomas E. Stafford, R. Jason El-Gendy, Ahmed A. Fuentes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Imaging Physics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Applied Research Laboratories The University of Texas at Austin AustinTX78758 United States Department of Mechanical Engineering South Dakota School of Mines and Technology Rapid CitySD57701 United States Department of Breast Imaging The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biostatistics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States The Institute for Data Science in Oncology The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX78712 United States Department of Oncology The University of Texas at Austin AustinTX78712 United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at El Paso El PasoTX79968 United States

Magnetic nanoparticle hyperthermia is an emerging cancer therapy that utilizes magnetic nanoparticles subjected to alternating magnetic fields to generate localized heating and selectively target tumor tissues. Despite its potential, clinical implementation faces significant challenges due to uncertainties in the thermophysical properties, nanoparticle distribution, and magnetic particle parameters, which can compromise the precision and efficacy of the treatment. This article introduces an information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia to address these challenges. By accounting for uncertainties in key parameters, such as tissue thermal conductivity, blood perfusion rate, and nanoparticle anisotropy constant, the framework maximizes mutual information between the observed data and model parameters, enhancing the accuracy of parameter estimation. A surrogate 1D model is employed to reduce computational complexity, allowing the identification of optimal magnetic field amplitudes across diverse initial conditions and scenarios, including single and multiple uncertain parameters. The results highlight the robustness of the optimization approach, demonstrating consistent convergence to a stable solution that is expected to enable precise temperature measurement and effective parameter recovery. This study underscores the potential for mutual information-based optimization to advance the planning of magnetic nanoparticle hyperthermia treatment and provides a foundation for future experimental and clinical applications. © 2025, The Authors. All rights reserved.

关键词： Hyperthermia therapy

来源：评论

学校读者我要写书评

暂无评论

An adaptive algorithm for quantum circuit simulation

arXiv

引用

arXiv 2019年

作者： Schutski, Roman Oseledets, Ivan Lykov, Danil Center for Computational and Data-Intensive Science and Engineering Skoltech Skolkovo Innovation Center Moscow Region121205 Moscow Institute of Physics and Technology Dolgoprudny Moscow Region141701

Efficient simulation of quantum computers is essential for the development and validation of near-term quantum devices and the research on quantum algorithms. Up to date, two main approaches to simulation were in use, based on either full state or single amplitude evaluation. We propose an algorithm that efficiently interpolates between these two possibilities. Our approach elucidates the connection between quantum circuit simulation and partial evaluation of expressions in tensor algebra. Copyright © 2019, The Authors. All rights reserved.

关键词： Circuit simulation

来源：评论

学校读者我要写书评

暂无评论

The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

引用

arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Fast Volume Integral Equation Solver with Linear Basis Functions for the Accurate Computation of Electromagnetic Fields in MRI

arXiv

引用

arXiv 2019年

作者： Georgakis, Ioannis P. Giannakopoulos, Ilias I. Litsarev, Mikhail S. Polimeridis, Athanasios G. Skoltech Center for Computational Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia Q Bio RedwoodCA94063 United States

Objective: This paper proposes a stable volume integral equation (VIE) solver based on polarization/magnetization currents, for the accurate and efficient computation of the electromagnetic scattering from highly inhomogeneous and high contrast objects. Methods: We employ the Galerkin Method of Moments to discretize the formulation with discontinuous piecewise linear basis functions on uniform voxelized grids, allowing for the acceleration of the associated matrix-vector products in an iterative solver, with the help of FFT. Results: Numerical experiments are conducted to study the accuracy and convergence properties of the proposed framework. Their results are compared against standard low order (piecewise constant) discretization schemes, a more conventional VIE formulation based on electric flux densities, and a commercial software package that employs the finite difference time domain method. Conclusion: The results illustrate the superior accuracy and wellconditioned properties of the proposed scheme. Significance: The developed solver can be applied to accurately analyze complex geometries, including realistic human body models, typically used in modeling the interactions between electromagnetic waves and biological tissue, that arise in magnetic resonance scanners. Copyright © 2019, The Authors. All rights reserved.

关键词： Piecewise linear techniques

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：