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arXiv 2023年

作者： Bao, Peng Wang, Gong Yang, Ruijie Dong, Bin The Center for Data Science Peking University the Department of Radiation Oncology Peking University Third Hospital Beijing China The Department of Radiation Oncology Peking University Third Hospital Beijing China Beijing International Center for Mathematical Research Center for Machine Learning Research National Biomedical Imaging Center Peking University Beijing China

Objective: Intensity-modulated radiation therapy (IMRT) beam angle optimization (BAO) is a challenging combinatorial optimization problem that is NP-hard. In this study, we aim to develop a personalized BAO algorithm for IMRT that improves the quality of the final treatment. Methods: To improve the quality of IMRT treatment planning, we propose a deep reinforcement learning (DRL)based approach for IMRT BAO. We consider the task as a sequential decision-making problem and formulate it as a Markov Decision Process. To facilitate the training process, a 3D-Unet is designed to predict the dose distribution for the different number of beam angles, ranging from 1 to 9, to simulate the IMRT environment. By leveraging the simulation model, double deep-Q network (DDQN) and proximal policy optimization (PPO) are used to train agents to select the personalized beam angle sequentially within a few seconds. Results: The treatment plans with beam angles selected by DRL outperform those with clinically used evenly distributed beam angles. For DDQN, the overall average improvement of the CIs is 0.027, 0.032, and 0.03 for 5, 7, and 9 beam angles respectively. For PPO, the overall average improvement of CIs is 0.045, 0.051, and 0.025 for 5, 7, and 9 beam angles respectively. Conclusion: The proposed DRL-based beam angle selection strategy can generate personalized beam angles within a few seconds, and the resulting treatment plan is superior to that obtained using evenly distributed angles. Significance: A fast and automated personalized beam angle selection approach is been proposed for IMRT BAO. © 2023, CC BY.

关键词： Reinforcement learning

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression machine learning algorithms Decision making Transportation Receivers data models Classification algorithms Random forests

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On the Atypical Solutions of the Symmetric Binary Perceptron

arXiv

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arXiv 2023年

作者： Barbier, Damien Alaoui, Ahmed El Krzakala, Florent Zdeborová, Lenka Information Learning & Physics laboratory École Polytechnique Fédérale de Lausanne Switzerland Department of Statistics and Data Science Cornell University IthacaNY United States Statistical Physics of Computation Laboratory École Polytechnique Fédérale de Lausanne Switzerland

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters via a local entropy, defined as a Franz-Parisi potential, which we rigorously evaluate using the first and second moment methods in the limit of a small constraint density α (corresponding to vanishing margin κ) under a certain assumption on the concentration of the entropy. This examination unveils several intriguing phenomena: i) We demonstrate that these clusters have an entropic barrier in the sense that the entropy as a function of the distance from the reference high-margin solution is non-monotone when κ ≤ 1.429 √−α/log α, while it is monotone otherwise, and that they have an energetic barrier in the sense that there are no solutions at an intermediate distance from the reference solution when κ ≤ 1.239 √−α/log α. The critical scaling of the margin κ in √−α/log α corresponds to the one obtained from the earlier work of Gamarnik et al. [62] for the overlap-gap property, a phenomenon known to present a barrier to certain efficient algorithms. ii) We establish using the replica method that the complexity (the logarithm of the number of clusters of such solutions) versus entropy (the logarithm of the number of solutions in the clusters) curves are partly non-concave and correspond to very large values of the Parisi parameter, with the equilibrium being reached when the Parisi parameter diverges. © 2023, CC BY.

关键词： Entropy

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Optical Character Recognition (OCR) in Handwritten Character...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： P L Lekshmy S. Velmurugan Indra Kumari S. Kayalvili B. Teja Sree P. Karthik Kumar Department of Computer Science and Engineering LBS Institute of Technology for Women Kerala India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Machine Learning Data Research Applied AI Korea National University of Science and Technology (UST) Daejeon South Korea Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamilnadu India Department of Information Technology S.R.K.R. Engineering College Chinaamiram Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate.

关键词： Training Image segmentation Image recognition Codes Optical character recognition Neural networks Streaming media

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Alchemical insights into approximately quadratic energies of iso-electronic atoms

arXiv

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arXiv 2024年

作者： Krug, Simon León Anatole von Lilienfeld, O. Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada

Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use the Alchemical Integral Transform (AIT) to study multi-electron atoms, and to gain a better understanding of the approximately quadratic behavior of energy differences between iso-electronic atoms in their nuclear charges. Based on this, we arrive at the following simple analytical estimate of energy differences between any two iso-electronic atoms, ∆E ≈ −(1 + 2γ √Ne − 1) ∆Z Z¯. Here, γ ≈ 0.3766 ± 0.0020 Ha corresponds to an empirical constant, and Ne, ∆Z, and Z¯ respectively to electron number, and nuclear charge difference and average. We compare the formula’s predictive accuracy using experimental numbers and non-relativistic, numerical results obtained via DFT (pbe0) for the entire periodic table up to Radon. A detailed discussion of the atomic Helium-series is included. © 2024, CC BY.

关键词： Atoms

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RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

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Explainability and transparency in the realm of digital humanities: toward a historian XAI

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International Journal of Digital Humanities 2023年第2期5卷 299-331页

作者： El-Hajj, Hassan Eberle, Oliver Merklein, Anika Siebold, Anna Shlomi, Noga Büttner, Jochen Martinetz, Julius Müller, Klaus-Robert Montavon, Grégoire Valleriani, Matteo Max Planck Institute for the History of Science Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany German Center for Art History Paris (DFK Paris) Paris France The Cohn Institute for the History and Philosophy of Science and Ideas Faculty of Humanities Tel-Aviv University Tel-Aviv Israel Department of Artificial Intelligence Korea University Seoul South Korea Max Planck Institute for Informatics Saarbrüken Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.

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Weisfeiler and Leman go machine learning: the story so far

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 15865-15923页

作者： Christopher Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science and Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain View CA Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland and Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

关键词： machine learning for graphs graph neural networks Weisfeiler-Leman algorithm expressivity equivariance

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

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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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