检索结果-内蒙古大学图书馆

HOW DOES THE BRAIN COMPUTE WITH PROBABILITIES?

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

machine learning to detect recent recreational drug use in intensive cardiac care units

学校读者我要写书评

暂无评论

Archives of Cardiovascular Diseases 2025年第5期118卷 277-286页

作者： El Bèze, Nathan Hamzi, Kenza Henry, Patrick Trimaille, Antonin El Ouahidi, Amine Zakine, Cyril Nallet, Olivier Delmas, Clément Aboyans, Victor Goralski, Marc Albert, Franck Bonnefoy-Cudraz, Eric Bochaton, Thomas Schurtz, Guillaume Lim, Pascal Lequipar, Antoine Gonçalves, Trecy Gall, Emmanuel Pommier, Thibaut Lemarchand, Léo Meune, Christophe Azzakani, Sonia Bouleti, Claire Amar, Jonas Dillinger, Jean-Guillaume Steg, P. Gabriel Vicaut, Eric Toupin, Solenn Pezel, Théo Inserm MASCOT – UMRS 942 Department of Cardiology University Hospital of Lariboisière Université Paris-Cité AP–HP Paris 75010 France Multimodality Imaging Research for Analysis Core Laboratory: Artificial Intelligence (MIRACL.ai) Department of Data Science Machine Learning and Artificial Intelligence in Health University Hospital of Lariboisière AP–HP Paris 75010 France Department of Cardiovascular Medicine Nouvel Hôpital Civil Strasbourg University Hospital Strasbourg 67000 France Department of Cardiology University Hospital of Brest Brest 29609 France NCT+ Saint-Cyr-sur-Loire 37540 France Department of Cardiology Hôpital Montfermeil Montfermeil 93370 France Department of Cardiology Rangueil University Hospital Toulouse 31000 France Department of Cardiology University Hospital of Limoges Limoges 87000 France Department of Cardiology Centre Hospitalier d'Orléans Orléans 45100 France Department of Cardiology Centre Hospitalier de Chartres Le Coudray 28630 France Intensive Cardiological Care Division Louis-Pradel Hospital Hospices Civils de Lyon Bron 69500 France Department of Cardiology University Hospital of Lille Lille 59000 France Intensive Cardiac Care Unit Henri-Mondor University Hospital Créteil 94000 France Department of Cardiology Dijon University Hospital Dijon 21000 France Department of Cardiology and Vascular Diseases CHU of Rennes Rennes 35000 France Department of Cardiology Hôpital Avicenne AP–HP Bobigny 93000 France Department of Cardiology Clinical Investigation Centre (Inserm 1204) University Hospital of Poitiers Poitiers 86000 France Inserm_U1148/LVTS hôpital Bichat université Paris-Cité AP–HP Paris 75877 France Unité de recherche clinique hôpital Fernand-Widal AP–HP Paris 75010 France

Background: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use. Aims: To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units. Methods: From 07 to 22 April 2021, systematic screening for all traditional recreational drugs (cannabis, opioids, cocaine, amphetamines, 3,4-methylenedioxymethamphetamine) was performed by urinary testing in all consecutive patients admitted to intensive cardiac care units in 39 French centres. The primary outcome was recreational drug detection by urinary testing. The framework involved automated variable selection by eXtreme Gradient Boosting (XGBoost) and model building with multiple algorithms, using 31 centres as the derivation cohort and eight other centres as the validation cohort. Results: Among the 1499 patients undergoing urinary testing for drugs (mean age 63 ± 15 years;70% male), 161 (11%) tested positive (cannabis: 9.1%;opioids: 2.1%;cocaine: 1.7%;amphetamines: 0.7%;3,4-methylenedioxymethamphetamine: 0.6%). Of these, only 57% had reported drug use. Using nine variables, the best machine learning model (random forest) showed good performance in the derivation cohort (area under the receiver operating characteristic curve = 0.82) and in the validation cohort (area under the receiver operating characteristic curve = 0.76). Conclusions: In a large intensive cardiac care unit cohort, a comprehensive machine learning model exhibited good performance in detecting recreational drug use, and provided valuable insights into the relationships between clinical variables and drug use through explainable machine learning techniques. ©

关键词： Intensive cardiac care machine learning Recreational drug use

Analysis of Multiscale Reinforcement Q-learning Algorithms for Mean Field Control Games

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Angiuli, Andrea Fouque, Jean-Pierre Laurière, Mathieu Zhang, Mengrui Prime Machine Learning Team Amazon. 320 Westlake Ave N SEA83 SeattleWA98109 United States Department of Statistics and Applied Probability South Hall University of California Santa BarbaraCA93106 United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai NYU Shanghai 567 West Yangsi Road Shanghai200126 China

Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a], represent competitive games between a large number of large collaborative groups of agents in the infinite limit of number and size of groups. In this paper, we prove the convergence of a three-timescale Reinforcement Q-learning (RL) algorithm to solve MFCG in a model-free approach from the point of view of representative agents. Our analysis uses a Q-table for finite state and action spaces updated at each discrete time-step over an infinite horizon. In [Angiuli et al., 2023], we proved convergence of two-timescale algorithms for MFG and MFC separately highlighting the need to follow multiple population distributions in the MFC case. Here, we integrate this feature for MFCG as well as three rates of update decreasing to zero in the proper ratios. Our technique of proof uses a generalization to three timescales of the two-timescale analysis in [Borkar, 1997]. We give a simple example satisfying the various hypothesis made in the proof of convergence and illustrating the performance of the algorithm. © 2024, CC BY.

关键词： Reinforcement learning

Intrusion Detection in Cloud Architecture Using machine learning

学校读者我要写书评

暂无评论

Intrusion Detection in Cloud Architecture Using Machine Lear...

Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Govinda Rajulu. G R. Santhoshkumar D. Venkatesan Kalvikkarasi. S P. Santosh Kumar Patra Department of Computer Science and Design St. Martin’s Engineering College Department of Artificial Intelligence and Data Science St. Martin’s Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE St. Martin’s Engineering College Professor in ECE Department Podhagai College of Engineering & Technology St. Martin’s Engineering College

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.

关键词： databases Cloud computing security Neural networks Intrusion detection Computer architecture Passwords Decision trees

Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xiong, Jiaqi Yin, Nan Liang, Shiyang Li, Haoyang Wang, Yingxu Ai, Duo Pan, Fang Wang, Jingjie Department of Gastroenterology Tangdu Hospital Fourth Military Medical University Shaanxi 710038 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou528225 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Internal Medicine The No. 944 Hospital of Joint Logistic Support Force of PLA Xiongguan Road Jiu Quan735000 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Dermatology Xijing Hospital Fourth Military Medical University No 127 of West Changle Road Shaanxi Xi’an710032 China

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://***/kikixiong/XATGRN. © 2024, CC BY.

关键词： Graph embeddings

PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Chen, Zifan Nan, Xinyu Li, Jiazheng Zhao, Jie Li, Haifeng Lin, Ziling Li, Haoshen Chen, Heyun Liu, Yiting Tang, Lei Zhang, Li Dong, Bin Center for Data Science Peking University Beijing China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, limiting their transferability across different tasks or modalities. While general segmentation models offer some versatility in natural image processing, they struggle with the unique characteristics of medical images. There is an urgent need in clinical practice for a new segmentation approach that can effectively handle medical imagery features while maintaining adaptability across various three-dimensional objects and imaging modalities. Methods: We introduce PAM (Propagating Anything Model), a propagation-based segmentation approach that operates on 3D medical image volumes using a 2D prompt (bounding box or sketch mask). PAM extrapolates this initial input to generate a complete 3D segmentation by modeling inter-slice structural relationships, establishing a continuous information flow within 3D medical structures. This approach enhances segmentation effectiveness across various imaging modalities by focusing on structural and semantic continuities rather than isolating specific objects. The model combines a CNN-based UNet architecture for intra-slice information processing with a Transformer-based attention module to facilitate inter-slice propagation. This innovative framework results in a method with unique generalizability, capable of segmenting diverse 3D objects across different medical imaging modalities. Results: PAM demonstrated superior performance on 44 diverse medical datasets, notably improving the dice similarity coefficient (DSC) for hundreds of segmentation object types and various medical imaging modalities. Compared to modern models like MedSAM and SegVol, PAM achieved an average DSC improvement of over 18.1%, while maintaining stable predictions despite prompt deviation (one-way ANOVA test, P ≥ 0.5985) and varying propagation confi

关键词： Semantic Segmentation

Large language models:game-changers in the healthcare industry

学校读者我要写书评

暂无评论

science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

Lightweight starshade position sensing with convolutional neural networks and simulation-based inference

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Chen, Andrew Harness, Anthony Melchior, Peter Princeton University Department of Computer Science Princeton NJ United States Princeton University Department of Mechanical & Aerospace Engineering Princeton NJ United States Princeton University Department of Astrophysical Sciences Princeton NJ United States Princeton University Center for Statistics and Machine Learning Princeton NJ United States

Starshades are a leading technology to enable the direct detection and spectroscopic characterization of Earth-like exoplanets. To keep the starshade and telescope aligned over large separations, reliable sensing of the peak of the diffracted light of the occluded star is required. Current techniques rely on image matching or model fitting, both of which put substantial computational burdens on resource-limited spacecraft computers. We present a lightweight image processing method based on a convolutional neural network paired with a simulation-based inference technique to estimate the position of the spot of Arago and its uncertainty. The method achieves an accuracy of a few centimeters across the entire pupil plane, while only requiring 1.6 MB in stored data structures and 5.3 MFLOPs (million floating point operations) per image at test time. By deploying our method at the Princeton Starshade Testbed, we demonstrate that the neural network can be trained on simulated images and used on real images, and that it can successfully be integrated in the control system for closed-loop formation flying. © 2022, CC BY.

关键词： Digital arithmetic

Ultrasound-Based Kidney Disease Detection: Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Network

学校读者我要写书评

暂无评论

Ultrasound-Based Kidney Disease Detection: Resampling Cubatu...

International Conference on Inventive Computation Technologies (ICICT)

作者： Padmanayana N.S.L. Kumar Kurumeti Veena S Ch. Raja Vijay Jagdish Upadhye Ramya Maranan Department of Artificial Intelligence and Data Science Engineering AJ Institute of Engineering and Technology Mangaluru Karnataka India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Andhra Pradesh India Department of Electronics and Communication Engineering SJC Institute of Technology Chikkaballapur Karnataka India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Hyderabad Telangana India Department of Microbiology Dept of Microbiology Research and Development Cell (RDC) Parul Institute of Applied Sciences (PIAS) Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Limitations of ultrasound-based identification of kidney disease include poor image resolution, operator reliance, and lack of detection of small tissue anomalies. Diagnostic accuracy may be affected by artefacts or noise, or changes in patient anatomy. It may also have difficulty detecting microscopic structural changes or renal disease in its incipiency. The limitations are overcome by the analysis of the Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Networks (RCKF-PG-VGG-CCNet) for kidney disease detection from ultrasound images based on the Kidney Ultrasound Image dataset. The initial use of the Resampling Cubature Kalman Filter and the first use of Physics-Guided VGG Cross-Contextual Networks is to enhance image quality. Through identifying regions through deviations, the Scale-Aware Meet Transformer (SAMT) performs segmentation. While the Synergistic Fibroblast Optimization (SFO) enhances classification performance through optimization of the search for diseased regions, the Physics-Guided VGG Cross-Contextual Networks (PG-VGG-CCNet) classify kidney disease and identify features. Experimental output from a Python-based experiment demonstrates that RCKF-PG-VGG-CCNet is more efficient than other methods with 99.9% efficiency and 99.8% sensitivity and may enhance the accuracy of kidney disease detection.

关键词： Ultrasonic imaging Accuracy Sensitivity Noise Fibroblasts Transformers Kalman filters Kidney Optimization Diseases