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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

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Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

IEEE International Conference on Big Data

作者： Michael O. Chen Julian Hodge Peter L. Jin Ella Protz Elizabeth Wong Ruth Obe Ehsan Shakeri Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Vijay R. Sharma Sina Mossahebi Lei Ren Stephen W. Peterson Jerimy C. Polf Departments of Mathematics Dartmouth College USA Dept. of Mathematics and Statistics U. of Maryland USA James M. Bennett High School Salisbury MD USA Department of Mathematics and Sciences Florida Atlantic University USA Department of Mathematics Brookdale Community College USA Dept. of Computer Science and of Software Engineering U. of Houston Clear Lake USA Dept. of Information Systems U. of Maryland USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa M3D Inc. USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel ‘Big-data REU Integrated Development and Experimentation’ (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research.

关键词： Particle beams Recurrent neural networks Accuracy Pipelines Phantoms Scattering Cameras Data models software Radiation therapy

Extraction of image features based on the HOG method in the HSV color space

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Extraction of image features based on the HOG method in the ...

International Conference on sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Fayçal Hamdaoui Sana Bougharriou Manel Gueddari Anis Sakly Electrical Department Laboratory of Control Electrical Systems and Environment (LAESE) National Engineering School of Monastir University of Monastir Monastir Tunisia Laboratory of EμE Faculty of Sciences of Monastir University of Monastir Monastir Tunisia Higher Institute of Computer Science and Mathematics University of Monastir Monastir Tunisia

ISBN: (纸本)9781665482622

In this paper, a fast, effective and robust features extraction system has been developed. To successfully design this hardware architecture, a conversion from RGB to HSV was applied. Likewise, hardware architecture of Histogram of Oriented Gradient (HOG) method was proposed. Vivado System Generator (VSG) tool was used to design architectures. To validate the performance of the proposed system, Benchmarks was applied. Moreover, execution time and resources utilization demonstrated a promising quality of such proposed system.

关键词： Histograms Automatic frequency control Time-frequency analysis Image color analysis computer architecture Feature extraction Hardware

Atomistic Behavior of Deformed Au Thin Film during the Indentation Process

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Atomistic Behavior of Deformed Au Thin Film during the Inden...

Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Abdelkarim Ou-khouya Hassane Mes-adi Meryem Taoufiki Imane Ait Ibrahim Amin El Harfouf Rachid Herbazi Mohamed Tahiri Process Engineering Computer Science and Mathematics Laboratory Khouribga National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco Faculty of Sciences and Techniques Sustainable Development Laboratory Sultan Moulay Slimane University Beni-Mellal Morocco Departement of Physics Laboratory of Condensed Matter and interdisciplinary Sciences CNRST Labeled Research Unit Faculty of Sciences Mohamed V University Rabat Morocco Department of Physics Polydisciplinary Faculty of Khouribga University of Sultan Moulay Slimane Khouribga Morocco Intelligent Systems and Applications Laboratory (LSIA) EMSI Tangier Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In this study, we investigate the mechanical properties of gold (Au) materials using molecular dynamics simulations. In this simulation, the nanoindentation process was performed to analyze the deformation defects and hardness properties of Au under different indentation velocities. The results suggest that the deformation behavior of the Au specimen was affected by different indentation velocities. These findings were confirmed by examining the load-displacement curve, which indicated elastic deformation through the appearance of a linear load-displacement relationship at low indentation velocities. In contrast, plastic deformation was identified by the presence of intensified serrations in the load-displacement curve at higher indentation velocities. Under such conditions, the hardness exhibited minimal variation when the indenter velocity increased from 5 mls to 10 mls. However, a significant increase in hardness was noted when the indentation velocity was further raised to 15 mls.

关键词： Deformable models Gold Analytical models Deformation Dynamics Plastics Load modeling

Quantum Linear System Solvers: A Survey of Algorithms and Applications

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances

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arXiv 2025年

作者： Dinitz, Michael Fan, Chenglin Liu, Jingcheng Upadhyay, Jalaj Zou, Zongrui Department of Computer Science Johns Hopkins University United States Department of Computer Science and Engineering Seoul National University Korea Republic of State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University China Management Science & Information Systems Department Rutgers University United States

We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of Õ(h(nW)1/3), where W represents the maximum edge weight;For grid graphs, the same algorithmic scheme achieve additive error of Õ(n1/4√W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. Copyright © 2025, The Authors. All rights reserved.

关键词： Binary trees

EXPLAINABLE GRAPH PYRAMID AUTOFORMER FOR LONG-TERM TRAFFIC FORECASTING

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arXiv 2022年

作者： Zhong, Weiheng Mallick, Tanwi Meidani, Hadi Macfarlane, Jane Balaprakash, Prasanna University of Illinois at Urbana-Champaign ChampaignIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign ChampaignIL United States Sustainable Energy Systems Group Lawrence Berkeley National Laboratory BerkeleyCA United States Mathematics and Computer Science Division Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35% better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic. © 2022, CC BY.

关键词： Graph neural networks

Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations

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arXiv 2025年

作者： Zhang, Jiancheng Li, Xiangting Guo, Xiaolu You, Zhaoyi Böttcher, Lucas Mogilner, Alex Hoffmann, Alexander Chou, Tom Xia, Mingtao Department of Electrical and Computer Engineering University of California RiversideCA United States Department of Computational Medicine University of California Los AngelesCA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles Los AngelesCA United States Ray and Stephanie Lane Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Computational Science and Philosophy Frankfurt School of Finance and Management Frankfurt am Main Germany Laboratory for Systems Medicine Department of Medicine University of Florida FL32610 United States Courant Institute of Mathematical Sciences New York University NY10012 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Mathematics University of Houston HoustonTX77004 United States

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions ("intrinsic noise") and (ii) heterogeneity of cellular states across different cells that are influenced by external factors ("extrinsic noise"). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NFκB signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical. © 2025, CC BY.

关键词： Stochastic systems

A Guide to Image and Video based Small Object Detection using Deep Learning: Case Study of Maritime Surveillance

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arXiv 2022年

作者： Rekavandi, Aref Miri Xu, Lian Boussaid, Farid Seghouane, Abd-Krim Hoefs, Stephen Bennamoun, Mohammed The Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The Department of Electrical Electronics and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The School of Mathematics and Statistics The University of Melbourne Melbourne Australia submarine optronics undersea combat systems and undersea command and control maritime division Defence Science and Technology Group Australia

Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. © 2022, CC BY-NC-SA.

关键词： Object recognition

Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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