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Toward a generative modeling analysis of CLAS exclusive 2π photoproduction

Physical Review D 2023年第9期108卷 094030-094030页

作者： T. Alghamdi Y. Alanazi M. Battaglieri Ł. Bibrzycki A. V. Golda A. N. Hiller Blin E. L. Isupov Y. Li L. Marsicano W. Melnitchouk V. I. Mokeev G. Montaña A. Pilloni N. Sato A. P. Szczepaniak T. Vittorini Department of Computer Science Old Dominion University Norfolk Virginia 23529 USA Department of Computer Science College of Computer Science and Information Technology Al-Baha University Al-Baha Alaqiq 65779 Saudi Arabia Thomas Jefferson National Accelerator Facility Newport News Virginia 23606 USA INFN sezione di Genova I-16146 Genova Italy AGH University of Krakow Faculty of Physics and Applied Computer Science PL-30-059 Kraków Poland Faculty of Physics Lomonosov Moscow State University 119991 Moscow Russia Institute for Theoretical Physics Tübingen University D-72076 Tübingen Germany Skobeltsyn Institute of Nuclear Physics Lomonosov Moscow State University 119234 Moscow Russia CSSM and CDMPP Department of Physics University of Adelaide 5005 Australia Dipartimento di Scienze Matematiche e Informatiche Scienze Fisiche e Scienze della Terra Università degli Studi di Messina I-98166 Messina Italy INFN Sezione di Catania I-95123 Catania Italy Department of Physics Indiana University Bloomington Indiana 47405 USA Center for Exploration of Energy and Matter Indiana University Bloomington Indiana 47403 USA Università di Genova I-16146 Genova Italy

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. This work employs a generative modeling approach to unfold detector effects specifically tailored for exclusive reactions that involve multiparticle final states. Our study demonstrates the preservation of correlations between kinematic variables in a multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector’s nontrivial effects represents an ideal test case for AI-supported analysis. Uncertainty quantification performed via bootstrap provides an estimate of the systematic uncertainty associated with the procedure. The test demonstrates that GANs can reproduce highly correlated multidifferential cross sections even in the presence of detector-induced distortions in the training datasets, and provides a solid basis for applying the framework to real experimental data.

关键词： Hadrons Artificial neural networks

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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Strain-induced formation of self-assembled InGaN/GaN superlattices in nominal InGaN films grown by plasma-assisted molecular beam epitaxy

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Physical Review Materials 2021年第12期5卷 124606-124606页

作者： Kamruzzaman Khan Kai Sun Christian Wurm Kanak Datta Parag B. Deotare Elaheh Ahmadi Department of Materials Science and Engineering University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA

In this work, we report the spontaneous formation of superlattice structures in nominal InGaN films grown by plasma-assisted molecular beam epitaxy. A 700-nm-thick self-assembled In0.2Ga0.8N/GaN superlattice with excellent structural quality was achieved. Strain was studied as a possible driving force for the formation of self-assembled superlattice (SASL) structure by growth of InGaN on ZnO substrate using similar growth conditions. The SASL structures were optically characterized using photoluminescence spectroscopy. Structural characterization was conducted via transmission electron microscopy and atom probe tomography. High-resolution x-ray diffraction (XRD) and XRD reciprocal space map were utilized to determine the average composition and the degree of relaxation of InGaN films. We propose that the vertical phase separation observed in the SASL structure is caused by high-temperature growth and intensified by strain. This work provides a method for engineering strain and growth of thick InGaN films for a variety of applications including solar cells and photodetectors.

关键词： Nitrides Superlattices Molecular beam epitaxy Photoluminescence Time-resolved photoluminescence X-ray diffraction

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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Unifying (Machine) Vision via Counterfactual World Modeling

arXiv

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arXiv 2023年

作者： Bear, Daniel M. Feigelis, Kevin Chen, Honglin Lee, Wanhee Venkatesh, Rahul Kotar, Klemen Durango, Alex Yamins, Daniel L.K. Department of Psychology Stanford University StanfordCA94305 United States Department of Physics Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Department of Applied Physics Stanford University StanfordCA94305 United States Neurosciences Graduate Program Stanford University StanfordCA94305 United States Wu Tsai Neurosciences Institute Stanford University StanfordCA94305 United States

Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets. This complexity has held back progress in areas, such as robotics, where robust task-general perception remains a bottleneck. In contrast, "foundation models" of natural language have shown how large pre-trained neural networks can provide zero-shot solutions to a broad spectrum of apparently distinct tasks. Here we introduce Counterfactual World Modeling (CWM), a framework for constructing a visual foundation model: a unified, unsupervised network that can be prompted to perform a wide variety of visual computations. CWM has two key components, which resolve the core issues that have hindered application of the foundation model concept to vision. The first is structured masking, a generalization of masked prediction methods that encourages a prediction model to capture the low-dimensional structure in visual data. The model thereby factors the key physical components of a scene and exposes an interface to them via small sets of visual tokens. This in turn enables CWM’s second main idea – counterfactual prompting – the observation that many apparently distinct visual representations can be computed, in a zero-shot manner, by comparing the prediction model’s output on real inputs versus slightly modified ("counterfactual") inputs. We show that CWM generates high-quality readouts on real-world images and videos for a diversity of tasks, including estimation of keypoints, optical flow, occlusions, object segments, and relative depth. Taken together, our results show that CWM is a promising path to unifying the manifold strands of machine vision in a conceptually simple foundation. © 2023, CC BY.

关键词： computer vision

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physics-Regulated Digital Backpropagation for Optical Fiber Systems with Imprecise Parameters

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IEEE Transactions on Communications 2024年

作者： Zhang, Jiahao Zhang, Lu Yu, Xianbin Wang, Zhanhong Ozolins, Oskars Pang, Xiaodan Brandt-Pearce, Maite Zhang, Qun Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Shandong Zhike Intelligence Computing Co. Ltd Jinan250101 China KTH Royal Institute of Technology Applied Physics Department Stockholm106 91 Sweden RISE Research Institutes of Sweden Networks Unit Kista164 40 Sweden Riga Technical University Institute of Telecommunications Riga1048 Latvia University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

Current signal processing algorithms excel at impairment compensation when the parameters of optical fiber systems are precisely defined. However, their effectiveness diminishes considerably in the presence of imprecise parameters. By integrating 'knowledge application from physics to neural networks (NN)' with 'information feedback from NNs to physics', this paper proposes a physics-regulated digital backpropagation (PR-DBP) algorithm, which shows great promise for impairment compensation with imprecise fiber parameters. The PR-DBP employs an optimization-estimation-initialization loop structure. The optimization process provides the network's adaptability by minimizing the loss value as in conventional NNs. The estimation process dynamically tracks physical parameters by extracting information from NNs to the physical domain. The initialization process offers a physics-based global control over the neural network, thereby mitigating the risk of overfitting by preventing an excessive focus on loss minimization. Moreover, a phase-shift weight function is applied to further improve algorithmic efficiency. Numerical analyses indicate that the PR-DBP significantly outperforms conventional methods in optical fiber systems with imprecise parameters, achieving a bit error rate reduction by an order of magnitude. As a mutually reinforcing part of impairment compensation, a high-accuracy and adaptive fiber parameter estimation is also demonstrated. © 1972-2012 IEEE.

关键词： Structural optimization

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String-Breaking Dynamics in Quantum Adiabatic and Diabatic Processes

arXiv

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arXiv 2024年

作者： Surace, Federica Maria Lerose, Alessio Katz, Or Bennewitz, Elizabeth R. Schuckert, Alexander Luo, De De, Arinjoy Ware, Brayden Morong, William Collins, Kate Monroe, Christopher Davoudi, Zohreh Gorshkov, Alexey V. Department of Physics Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Rudolf Peierls Centre for Theoretical Physics Clarendon Laboratory OxfordOX1 3PU United Kingdom Institute for Theoretical Physics KU Leuven Celestijnenlaan 200D Leuven3001 Belgium Duke Quantum Center Departments of Physics and Electrical and Computer Engineering Duke University DurhamNC27708 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking: the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

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Riemann-Encircling Exceptional Points for Efficient Asymmetric Polarization-Locked Devices

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Physical Review Letters 2022年第12期129卷 127401-127401页

作者： Aodong Li Weijin Chen Heng Wei Guowei Lu Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing 100871 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) have unveiled intriguing chiral dynamics in photonics. However, the traditional approach based on an open manifold of Hamiltonian parameter space fails to explore trajectories that pass through an infinite boundary. Here, by mapping the full parameter space onto a closed manifold of the Riemann sphere, we introduce a framework to describe encircling-EP loops. We demonstrate that an encircling trajectory crossing the north vertex can realize near-unity asymmetric transmission. An efficient gain-free, broadband asymmetric polarization-locked device is realized by mapping the encircling path onto L-shaped silicon waveguides.

关键词： PT-symmetric quantum mechanics Photonics

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Deep-Strong Coupling between Cavity Photons and Terahertz TO Phonons in PbTe

Deep-Strong Coupling between Cavity Photons and Terahertz TO...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Andrey Baydin Manukumara Manjappa Sobhan Subhra Mishra Hongjing Xu Fuyang Tay Dasom Kim Felix G. G. Hernandez Paulo H. O. Rappl Eduardo Abramof Ranjan Singh Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas USA Smalley-Curl Institute Rice University Houston Texas USA Instrumentation and Applied Physics Department Indian Institute of Science Bangalore India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore Department of Physics and Astronomy Rice University Houston Texas USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas USA Instituto de Física Universidade de São Paulo São Paulo SP Brazil Laboratório Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais São José dos Campos SP Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Material Science and NanoEngineering Rice University Houston Texas USA

We have investigated the resonant coupling of photons with TO phonons in lead telluride in small-mode-volume terahertz cavities, observing a giant vacuum Rabi splitting on the order of the bare cavity–phonon frequency.

ISBN: (纸本)9781957171258

关键词： Electric fields Numerical simulation Optical constants Photons Physical vapor deposition Terahertz spectroscopy

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