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Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks

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arXiv 2021年

作者： Jönemo, Johan Abramian, David Eklund, Anders Division of Medical Informatics Department of Biomedical Engineering Division of Statistics & Machine Learning Department of Computer and Information Science Linköping University Sweden

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniques affect the test accuracy. Specifically, we use resting state derivatives from 1,112 subjects in ABIDE preprocessed to train a 3D convolutional neural network (CNN) to perform the classification. Our results show that augmentation only provide minor improvements to the test accuracy. © 2021, CC BY.

关键词： Convolutional neural networks

Sensor Allocation and Online-learning-based Path Planning for Maritime Situational Awareness Enhancement: A Multi-Agent Approach

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arXiv 2023年

作者： Nguyen, Bach Long Doan, Anh-Dzung Chin, Tat-Jun Guettier, Christophe Gupta, Surabhi Parra, Estelle Reid, Ian Wagner, Markus The Department of Data Science and AI Monash Univeristy ClaytonVIC3800 Australia Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia Safran Electronics and Defense Massy91300 France Safran Electronics and Defense Australasia BotanyNSW2019 Australia

Countries with access to large bodies of water often aim to protect their maritime transport by employing maritime surveillance systems. However, the number of available sensors (e.g., cameras) is typically small compared to the to-be-monitored targets, and their Field of View (FOV) and range are often limited. This makes improving the situational awareness of maritime transports challenging. To this end, we propose a method that not only distributes multiple sensors but also plans paths for them to observe multiple targets, while minimizing the time needed to achieve situational awareness. In particular, we provide a formulation of this sensor allocation and path planning problem which considers the partial awareness of the targets’ state, as well as the unawareness of the targets’ trajectories. To solve the problem we present two algorithms: 1) a greedy algorithm for assigning sensors to targets, and 2) a distributed multi-agent path planning algorithm based on regret-matching learning. Because a quick convergence is a requirement for algorithms developed for high mobility environments, we employ a forgetting factor to quickly converge to correlated equilibrium solutions. Experimental results show that our combined approach achieves situational awareness more quickly than related work. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

Sentiment Analysis Using machine learning Methods

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Sentiment Analysis Using Machine Learning Methods

Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Abhishek Badholia Tarun Dhar Diwan Preeti Narooka Pravin B Khatkale Ankit Vishnoi Keshav Kaushik Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur Atal Bihari Vajpayee University Bilaspur India Artificial Intelligence and Machine Learning School of Computer and Engineering Manipal University Jaipur India Sanjivani University Kopargaon Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations.

关键词： Support vector machines Sentiment analysis Recurrent neural networks Transforms Market research Robustness Naive Bayes methods Noise measurement Random forests Resilience

Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment

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arXiv 2024年

作者： Sun, Qiyang Li, Yupei Alturki, Emran Murthy, Sunil Munthumoduku Krishna Schuller, Björn W. GLAM Department of Computing Imperial College London United Kingdom MRI Technical University of Munich Germany Technical University of Munich Germany relAI Konrad Zuse School of Excellence in Reliable AI Munich Germany MDSI – Munich Data Science Institute Munich Germany MCML – Munich Center for Machine Learning Munich Germany

As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development. © 2024, CC BY-NC-SA.

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A Review on Soil Moisture Monitoring Methods using Satellite Images

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A Review on Soil Moisture Monitoring Methods using Satellite...

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Prateek Verma Aahash Kamble Aditya Barhate Abhay Tale Amit Gudadhe Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Basic Sciences and Humanities Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Soil moisture is an important parameter in the hydrological cycle and an important component for agricultural production and prediction. The regular monitoring of soil moisture plays a big role in the prediction and management of crops and droughts in a region. Climate change has led to irregular patterns in rainfall and extreme weather conditions worldwide. This has led to the farmers taking extreme steps. The traditional method is the best method of measuring soil moisture, but it requires a lot of time and is labor-intensive. Therefore, satellite remote sensing provides an alternative approach to measuring soil moisture covering a large area within less time. The application of satellite remote sensing provides comparative results to traditional methods. This study provides a discussion on spectral indices obtained from satellite data for measuring soil moisture. The current study also provides insights about the applications, challenges, and advantages of using satellite remote sensing for monitoring of soil moisture using remote sensing datasets. The study may be helpful to farmers and government agencies for planning and management of crops and water preservation over the drought prone regions of India.

关键词： Soil measurements Moisture measurement Soil moisture Droughts Crops Satellite images Monitoring Remote sensing Farming Vegetation mapping Indexes Climate change Normalized difference vegetation index

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions

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arXiv 2022年

作者： Jesson, Andrew Douglas, Alyson Manshausen, Peter Solal, Maëlys Meinshausen, Nicolai Stier, Philip Gal, Yarin Shalit, Uri OATML Department of Computer Science University of Oxford United Kingdom AOPP Department of Physics University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom Seminar for Statistics Department of Mathematics ETH Zurich Switzerland Machine Learning and Causal Inference in Healthcare Lab Technion - Israel Institute of Technology

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

Heat flux for semilocal machine-learning potentials

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Physical Review B 2023年第10期108卷 L100302-L100302页

作者： Marcel F. Langer Florian Knoop Christian Carbogno Matthias Scheffler Matthias Rupp Machine Learning Group Technische Universität Berlin 10587 Berlin Germany Berlin Institute for the Foundations of Learning and Data 10623 Berlin Germany The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS Adlershof of the Humboldt Universität zu Berlin 14195 Berlin Germany Theoretical Physics Division Department of Physics Chemistry and Biology (IFM) Linköping University 581 83 Linköping Sweden Department of Computer and Information Science University of Konstanz 78464 Konstanz Germany Materials Research and Technology Department Luxembourg Institute of Science and Technology Belvaux Luxembourg

The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this Letter, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semilocal interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures.

关键词： Lattice thermal conductivity machine learning Molecular dynamics Neural network simulations

Human-in-the-loop: Provably Efficient Preference-based Reinforcement learning with General Function Approximation

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arXiv 2022年

作者： Chen, Xiaoyu Zhong, Han Yang, Zhuoran Wang, Zhaoran Wang, Liwei Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China Center for Data Science Peking University China Peng Cheng Laboratory China Department of Statistics and Data Science Yale University United States Department of Industrial Engineering and Management Sciences Northwestern University United States

We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of preference-based RL (PbRL) is only limited to the tabular case. In this paper, we propose the first optimistic model-based algorithm for PbRL with general function approximation, which estimates the model using value-targeted regression and calculates the exploratory policies by solving an optimistic planning problem. Our_ algorithm achieves the regret of Õ(poly(dH)√K), where d is the complexity measure of the transition and preference model depending on the Eluder dimension and log-covering numbers, H is the planning horizon, K is the number of episodes, and Õ(·) omits logarithmic terms. Our lower bound indicates that our algorithm is near-optimal when specialized to the linear setting. Furthermore, we extend the PbRL problem by formulating a novel problem called RL with n-wise comparisons, and provide the first sample-efficient algorithm for this new setting. To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms