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Physical Review Research 2022年第4期4卷 043153-043153页

作者： Yu-Hsueh Chen Ke Hsu Wei-Lin Tu Hyun-Yong Lee Ying-Jer Kao Center for Theoretical Physics and Department of Physics National Taiwan University Taipei 10607 Taiwan Division of Display and Semiconductor Physics Korea University Sejong 30019 Korea Department of Applied Physics Graduate School Korea University Sejong 30019 Korea Interdisciplinary Program in E·ICT-Culture-Sports Convergence Korea University Sejong 30019 Korea Center for Quantum Science and Technology National Taiwan University Taipei 10607 Taiwan National Center for High-Performance Computing Hsinchu City 30076 Taiwan

We propose a simple and generic construction of the variational tensor network operators to study the quantum spin systems by the synergy of ideas from the imaginary-time evolution and the variational optimization of trial wave functions. By applying these operators to simple initial states, accurate variational ground-state wave functions with extremely few parameters can be obtained. Furthermore, the framework can be applied to spontaneously study symmetry-breaking, symmetry-protected topological, and intrinsic topologically ordered phases, and we show that symmetries of the local tensors associated with these phases can emerge directly after the optimization without any gauge fixing. This provides a universal way to identify quantum phase transitions without prior knowledge of the system.

关键词： Magnetism Topological order Quantum many-body systems Tensor network methods

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Combined electric and photocontrol of selective light reflection at an oblique helicoidal cholesteric liquid crystal doped with azoxybenzene derivative

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Physical Review E 2021年第4期104卷 044702-044702页

作者： Kamal Thapa Olena S. Iadlovska Hari Krishna Bisoyi Daniel A. Paterson John M. D. Storey Corrie T. Imrie Quan Li Sergij V. Shiyanovskii Oleg D. Lavrentovich Advanced Materials and Liquid Crystal Institute Kent State University Kent Ohio 44242 USA Department of Physics Kent State University Kent Ohio 44242 USA Department of Chemistry School of Natural and Computing Sciences University of Aberdeen AB24 3UE Scotland United Kingdom Materials Science Graduate Program Kent State University Kent Ohio 44242 USA

An oblique helicoidal cholesteric liquid crystal ChOH represents a unique optical material with a single-harmonic periodic modulation of the refractive index and a pitch that can be tuned by an electric or magnetic field in a broad range from submicrometers to micrometers. In this work, we demonstrate that the oblique helicoidal cholesteric doped with azoxybenzene molecules can be tuned by both the electric field and light irradiation. The tuning mechanism is explained by the kinetics of trans-cis photoisomerization of the azoxybenzene molecules. At a fixed voltage, UV irradiation causes a redshift of the reflection peak by more than 200 nm. The effect is caused by an increase of the bend elastic constant of ChOH under irradiation. The demonstrated principle has the potential for applications such as smart windows, sensors, tunable lasers, and filters.

关键词： Chirality

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Specialized reporting of article review – SRAR Method

Specialized reporting of article review – SRAR Method

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IEEE World Engineering Education Conference (EDUNINE)

作者： Julio Ernesto Quispe Rojas Julio Enrique Quispe Tuesta Computing and Electronics Departament Universidad Nacional Pedro Ruiz Gallo Lambayeque Perú Systems Engineering Program Universidad Nacional Tecnologica de Lima Sur Lima Perú

ISBN: (数字)9781728166384

ISBN: (纸本)9781728166391

The professional training of the engineering student must be innovated in order to meet the accreditation criterion and with the requirement of the university law that requires presenting an investigation to obtain the bachelor's degree and another to obtain the professional degree of engineer. In order to develop in students, the skills to search for scientific and technological articles, and the critical skills to evaluate documents and extract the main ideas from them in relation to a particular topic, the SRAR method or specialized report review method has been proposed. It has been applied during five academic semesters in the electronic engineering telephone systems course, with satisfactory results. The SRAR method has six stages:*** of the SRAR method and proposed topic, 2. Initial bibliographic search, 3. Autonomous training, 4. Selection of articles for review, 5. Study and evaluation of articles 6. Writing of specialized report for review of articles.

关键词： Training Telephone sets Accreditation Writing Search problems ISDN Engineering students

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Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

arXiv

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arXiv 2024年

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Orsmaa, Lotta Kangas, Jari Rasouli, Nastaran Raisamo, Roope Hirvonen, Jussi Mehtonen, Helena Järnstedt, Jorma Mäkitie, Antti Naser, Mohamed Fuller, Clifton Kann, Benjamin Kaski, Kimmo Department of Computer Science Aalto University School of Science Espoo Finland Faculty of Information Technology and Communication Sciences Computing Sciences University of Tampere Tampere Finland Department of Radiology Tampere University Faculty of Medicine and Health Technology Tampere University Hospital Tampere Finland Department of Otorhinolaryngology-Head and Neck Surgery Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki University Hospital Helsinki Finland Department of Radiation Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School BostonMA United States The Alan Turing Institute British Library 96 Euston Rd LondonNW1 2DB United Kingdom

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 ± 0.152 without user interaction and 0.824 ± 0.099 after five interactions, outperforming existing methods in both cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Using Machine Learning to Study the Effects of Genetic Predisposition on Brain Aging in the UK Biobank

Using Machine Learning to Study the Effects of Genetic Predi...

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IEEE International Symposium on Biomedical Imaging

作者： Karen Ardila Emily Munro Fernando Vega Aashka Mohite Charlotte Curtis Amanda V. Tyndall M. Ethan MacDonald Department of Electronic and Automation Biomedical Engineering Universidad Autónoma de Manizales Manizales Colombia Department of Biomedical Engineering University of Calgary Calgary Canada Department of Electrical & Software Engineering University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Mitacs Globalink Research Internship Program Canada Department of Chemical Engineering University of Calgary Calgary Canada Department of Mathematics and Computing Mount Royal University Calgary Canada Department of Medical Genetics University of Calgary Calgary Canada

The influence of genetic predisposition on changes in brain morphology during aging remains largely unknown. This study explores the effects of genetic predisposition on three key brain regions: total brain volume (TBV), lateral ventricular volume (LVV), and total hippocampal volume (THV). The brain age gap estimate (BrainAGE) biomarker is used as an input to a genome-wide association study to determine which single nucleotide polymorphisms (SNPs) and genes are associated with accelerated brain aging. Six independent significant SNPs were found to contribute to accelerated morphological changes: TBV had associations on chromosome 17 linked with brain aging, and the total THV had independent significant associations in the APOC1 and TOMM40 gene regions related to neurodegeneration. Lastly, LVV presented a possible novel discovery in the gene NUAK1, known to play a role in cellular senescence. This study provides a framework to uncover complex associations between brain aging physiology and genetics.

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Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

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Journal of Cerebral Blood Flow and Metabolism 2024年第9期44卷 1480-1514页

作者： Kostoglou, Kyriaki Bello-Robles, Felipe Brassard, Patrice Chacon, Max Claassen, Jurgen AHR Czosnyka, Marek Elting, Jan-Willem Hu, Kun Labrecque, Lawrence Liu, Jia Marmarelis, Vasilis Z Payne, Stephen J Shin, Dae Cheol Simpson, David Smirl, Jonathan Panerai, Ronney B Mitsis, Georgios D Department of Electrical and Computer Engineering McGill University Montreal QC Canada Institute of Neural Engineering Graz University of Technology Graz Austria Departamento de Ingeniería Informática Universidad de Santiago de Chile Santiago Chile Department of Kinesiology Faculty of Medicine Université Laval QC Canada Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Quebec QC Canada Department of Geriatrics Radboud University Medical Center Research Institute for Medical Innovation and Donders Institute Nijmegen Netherlands Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Clinical Neurosciences Neurosurgery Department University of Cambridge Cambridge United Kingdom Department of Neurology and Clinical Neurophysiology University Medical Center Groningen Groningen Netherlands Medical Biodynamics Program Division of Sleep and Circadian Disorders Brigham and Women's Hospital Harvard Medical School Boston MA United States Division of Sleep Medicine Harvard Medical School Boston MA United States Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States Institute of Applied Mechanics National Taiwan University Taipei Taiwan Institute of Sound and Vibration Research University of Southampton Southampton United Kingdom Cerebrovascular Concussion Laboratory Faculty of Kinesiology University of Calgary Calgary AB Canada Sport Injury Prevention Research Centre Faculty of Kinesiology University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada NIHR Leicester Biomedical Research Centre Bri

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA. © The Author(s) 2024.

关键词： CARNet cerebral autoregulation cerebral blood flow time-domain methods white paper

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Generative Data Augmentation for Diabetic Retinopathy Classification

Generative Data Augmentation for Diabetic Retinopathy Classi...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Gilbert Lim Pranav Thombre Mong Li Lee Wynne Hsu Ophthalmology and Visual Science Academic Clinical Program Duke-NUS Medical School School of Computer Science Carnegie Mellon University School of Computing National University of Singapore

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

A fundamental factor limiting the effectiveness of classification algorithms, especially in the medical imaging domain, has been an insufficient quantity of relevant class-specific data. In particular, positive examples of disease conditions tend to be rare, and represent a common bottleneck in improving model performance. In this paper, we introduce GAN-based generative data augmentation methods with dynamic input sampling, and compare their performance against an image feature transfer technique, towards improving the performance of real-world diabetic retinopathy classification tasks. Results suggest that generative data augmentation has the potential to significantly improve classification performance over the baseline.

关键词： Training Retinopathy Tools Diabetes Task analysis Gallium nitride Biomedical imaging

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Development and Validation of a Computable Radiation Therapy Phenotype

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International Journal of Radiation Oncology Biology Physics 2025年

作者： Madison, Cecelia J. Lynch, Julie A. Duvall, Scott L. Alba, Patrick R. Hanchrow, Elizabeth Agiri, Fatai Y. Pridgen, Kathryn M. Burri, Ryan J. Thompson, Reid F. Kelly, Maria Katsoulakis, Evangelia VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Salt Lake City Utah United States Division of Epidemiology Department of Internal Medicine University of Utah School of Medicine Salt Lake City Utah United States Radiation Bay Pines VA Healthcare System Bay Pines FL United States VA National Radiation Oncology Program Richmond Virginia United States Division of Hospital and Specialty Medicine VA Portland Healthcare System Portland Oregon United States Department of Radiation Medicine Oregon Health & Science University Portland Oregon United States

Purpose: This study aims to develop a robust methodology using structured and semistructured health data to identify patients who have undergone radiation therapy, thereby facilitating future research on treatment outcomes. Methods and Materials: In this retrospective cohort study, we identified Veterans receiving radiation oncology care through documentation of referrals, encounters, and billing codes from 2014 to 2023. We classified administrative codes based on the process of care and type of radiation received and then analyzed utilization patterns. Unstructured data analysis was performed using keyword search of relevant clinical notes in the metadata fields and categorizing those notes by radiation oncology processes. To validate our algorithm, we compared the cohort we developed using existing data sources to a cohort that was chart reviewed. Results: The final cohort included 589,318 Veterans with radiation oncology care. Among these, 355,276 Veterans had codes indicating radiation therapy delivery. The most common treatments were image guided radiation therapy, 3-dimensional conformal radiation therapy, intensity modulated radiation therapy, and stereotactic radiosurgery/stereotactic body radiation therapy. Anatomy-specific billing codes were underutilized. Clinical note analysis identified 1341 unique labels for radiation oncology content, with 947,928 notes found for 204,064 patients. Validation against chart-reviewed data sets showed strong concordance and confirmed the accuracy of our algorithm in identifying radiation oncology care. Conclusions: Automated extraction of medical records can be used to identify cohorts of patients who have undergone radiation therapy. Employing this algorithm may facilitate more precise phenotyping of radiation therapy cases and thus significantly enhance our understanding of these cohorts. © 2025

关键词： Radiotherapy

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Statistics Pooling Time Delay Neural Network Based on X-Vector for Speaker Verification

Statistics Pooling Time Delay Neural Network Based on X-Vect...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qian-Bei Hong Chung-Hsien Wu Hsin-Min Wang Chien-Lin Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan PingAn AI Lab Palo Alto CA USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

This paper aims to improve speaker embedding representation based on x-vector for extracting more detailed information for speaker verification. We propose a statistics pooling time delay neural network (TDNN), in which the TDNN structure integrates statistics pooling for each layer, to consider the variation of temporal context in frame-level transformation. The proposed feature vector, named as statsvector, are compared with the baseline x-vector features on the VoxCeleb dataset and the Speakers in the Wild (SITW) dataset for speaker verification. The experimental results showed that the proposed stats-vector with score fusion achieved the best performance on VoxCeleb1 dataset. Furthermore, considering the interference from other speakers in the recordings, we found that the proposed statsvector efficiently reduced the interference and improved the speaker verification performance on the SITW dataset.

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms

Integration of Artificial Intelligence in Software Defined N...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Lamhot Naibaho Pulkit Raj Saxena Kuldeep Sharma Badria Sulaiman Alfurhood L. Pallavi Bhasker Pant English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Department of Computer Sciences College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE B V Raju Institute of Technology Narsapur Telangana India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.

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