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Flow-Based Sampling for Entanglement Entropy and the machine learning of Defects

Physical Review Letters 2025年第15期134卷 151601-151601页

作者： Andrea Bulgarelli Elia Cellini Karl Jansen Stefan Kühn Alessandro Nada Shinichi Nakajima Kim A. Nicoli Marco Panero Department of Physics University of Turin and INFN Turin unit Via Pietro Giuria 1 I-10125 Turin Italy Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Deutsches Elektronen-Synchrotron DESY Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany RIKEN Center for AIP Tokyo Japan Transdisciplinary Research Area (TRA) Matter University of Bonn Germany Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Bonn Germany Department of Physics and Helsinki Institute of Physics PL 64 FIN-00014 University of Helsinki Finland

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

关键词： Lattice theory

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Artificial Intelligence-Driven Protein Folding System Employing Alpha Fold

Artificial Intelligence-Driven Protein Folding System Employ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Sowmiya R M. Pravin Kumar T. M. Sathish Kumar Rasika P M. Poornima Devi S. Priya Dharshini Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Medical Electronics Velalar College of Engineering and Technology (Autonomous) Erode Tamil Nadu India Department of Electronics and Communication Engineering K.S.R. College of Engineering Namakkal Tamil Nadu India Department of Pharmaceutics SNS College of Pharmacy and Health Sciences Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning SNS College of Technology Coimbatore Tamil Nadu India Department of Pharmaceutics Periyar College of Pharmaceutical Sciences Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Artificial Intelligence (AI) is a main role to solve the lot of real-live issues. One of the significant challenges in the bioinformatics that researchers have been identified recently is understand the protein folding. First of all, people want to give a big hand off to the advancements because, day to day activities is performed in 3-dimensional basis that is formed based on a sequence of amino acids using protein folds. The folding of proteins is mostly related to three major issues. The first issue is the lack of a specific folding code. There is a folding mechanism in the second one. The ability to ascertain the 3-dimensional structure of proteins presents the last challenge. The structural components and microscopic machinery that make up human cells are called proteins. They provide vital tasks include storing oxygen, breaking down food, and creating scaffolding to support cells in maintaining their structure. Here, Amino acids is formed based on a single chain mechanism because amino acids folds safely and it performs a unique structure that is incorporated here. All living things, including viruses, are composed of proteins, including every element of human body and every other organism. The computational approach can be used to predict the structure of minuscule proteins. In this research work explained about how artificial intelligence is incorporated in predictions of protein structure and artificial intelligence give the solution for the problem of problem folding.

关键词： Proteins Hands Computer viruses Microscopy Amino acids Organisms Artificial intelligence machinery Collaborative intelligence Periodic structures

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VICE: variational interpretable concept embeddings 22

VICE: variational interpretable concept embeddings

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Lukas Muttenthaler Charles Y. Zheng Patrick McClure Robert A. Vandermeulen Martin N. Hebart Francisco Pereira Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Team FMRI Facility National Institute of Mental Health Bethesda MD Department of Computer Science Naval Postgraduate School Monterey CA Vision and Computational Cognition Group MPI for Human Cognitive and Brain Sciences Leipzig Germany

ISBN: (纸本)9781713871088

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

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A Review on the Developments in the Field of AI-Based Gait Analysis

A Review on the Developments in the Field of AI-Based Gait A...

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

作者： Vidhi Wankhede Prateek Verma Shailesh Gahane Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Faculty of Sciences and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. Artificial Intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation.

关键词： Patient monitoring Musculoskeletal system Reviews Parkinson's disease Medical treatment Motion capture Artificial intelligence Wearable devices Gait recognition Wearable sensors

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Autonomous Vehicles: Steering the Future of Transportation with Self-Driving Technology and Advanced AI Systems

Autonomous Vehicles: Steering the Future of Transportation w...

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Global Conference for Advancement in Technology (GCAT)

作者： Daniel Nareshkumar. M Desidi Narsimha Reddy A. Sowmiya V. Samuthira Pandi Rayala Sateesh N. Hindumathy Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of EEE Rajalakshmi Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Autonomous vehicles (AVs) are a game-changer in the transportation industry; they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governm

关键词： Ethics machine learning algorithms Urban planning Decision making Sensor fusion data processing Real-time systems Safety Computer security Autonomous vehicles

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Dynamic algorithms for online multiple testing

arXiv

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arXiv 2020年

作者： Xu, Ziyu Ramdas, Aaditya Machine Learning Department Carnegie Mellon University Department of Statistics and Data Science Carnegie Mellon University

We derive new algorithms for online multiple testing that provably control false discovery exceedance (FDX) while achieving orders of magnitude more power than previous methods. This statistical advance is enabled by the development of new algorithmic ideas: earlier algorithms are more "static" while our new ones allow for the dynamical adjustment of testing levels based on the amount of wealth the algorithm has accumulated. We demonstrate that our algorithms achieve higher power in a variety of synthetic experiments. We also prove that SupLORD can provide error control for both FDR and FDX, and controls FDR at stopping times. Stopping times are particularly important as they permit the experimenter to end the experiment arbitrarily early while maintaining desired control of the FDR. SupLORD is the first non-trivial algorithm, to our knowledge, that can control FDR at stopping times in the online setting. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

arXiv

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arXiv 2025年

作者： Liu, Zhiyu Han, Zhi Tang, Yandong Zhang, Hai Tang, Shaojie Wang, Yao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Statistics Northwest University Xi’an710000 China Department of Management Science and Systems State University of New York Buffalo United States Center for Intelligent Decision-making and Machine Learning School of Management Xi’an Jiaotong University Xi’an710049 China

We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank r is larger than the true rank r★. Specifically, our main objective is to recover a matrix X★ ∈ Rn1×n2 with rank r★ from noisy measurements using an over-parameterized factorized form LRT, where L ∈ n1×r, R ∈ n2×r and min{n1, n2} ≥ r > r★, with the true rank r★ being unknown. Recently, preconditioning methods have been proposed to accelerate the convergence of matrix sensing problem compared to vanilla gradient descent, incorporating preconditioning terms (LTL + λI)−1 and (RTR + λI)−1 into the original gradient. However, these methods require careful tuning of the damping parameter λ and are sensitive to initial points and step size. To address these limitations, we propose the alternating preconditioned gradient descent (APGD) algorithm, which alternately updates the two factor matrices, eliminating the need for the damping parameter and enabling faster convergence with larger step sizes. We theoretically prove that APGD achieves near-optimal error convergence at a linear rate, starting from arbitrary random initializations. Through extensive experiments, we validate our theoretical results and demonstrate that APGD outperforms other methods, achieving the fastest convergence rate. Notably, both our theoretical analysis and experimental results illustrate that APGD does not rely on the initialization procedure, making it more practical and versatile. Copyright © 2025, The Authors. All rights reserved.

关键词： Damping

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Innovative Healthcare Advancements: Harnessing Artificial and Human Intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Samruddhi Sapkal Sulaxan Jadhav P Mallikarjun Rejuwan Shamim Atowar Ul Islam Kalyan Bamane Artificial Intelligence and Machine Learning D Y Patil International University Akurdi Pune India D Y Patil International University Akurdi Pune India Department of Electrical and Electronic Engineering Malla Reddy Engineering College JNTUH Hyderabad School of data science Maharishi University of Information Technology Noida India Department of Computer Science and Electronics University of Science and Technology Meghalaya Ri-Bhoi Techno city Kiling Road Baridua Meghalaya Department of Information Technology D Y Patil College of Engineering Akurdi Pune

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as $F 1$ score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM.

关键词： Radio frequency Training Technological innovation Biological system modeling Artificial neural networks Receivers Predictive models

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Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Rakesh Kumar Joon Anitha R Saravana Selvan P. S. Latha Mageshwari S. Gayathri Priya K. Malathi Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Kablana Haryana India Department of Artificial Intelligence and Data Science S.A. Engineering College Chennai Tamil Nadu India School of Professional Engineering Manukau Institute of Technology Auckland New Zealand Department of Science & Humanities (Physics) R. M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications.

关键词： Wireless sensor networks Energy consumption Scalability Heuristic algorithms Load management Routing Energy efficiency Reliability Environmental monitoring Optimization

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