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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Balčytis, Armandas Dinh, Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre Rmit University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonics

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Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation

arXiv

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arXiv 2024年

作者： Jurgas, Artur Wodzinski, Marek D’Amato, Marina van der Laak, Jeroen Atzori, Manfredo Müller, Henning AGH University of Krakow Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow30059 Poland Institute of Informatics Sierre3960 Switzerland Radboud University Medical Center Nijmegen Netherlands Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research. © 2024, CC BY.

关键词： Quality control

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Opinion Mining of Politics and Inflation using Roman Urdu Dataset

Opinion Mining of Politics and Inflation using Roman Urdu Da...

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Human-Centered Cognitive Systems (HCCS)

作者： Zunaira Shafqat Muddesar Iqbal Waqas Haider Bangyal Dhafer Almakhles Department of Computer Science University of Sialkot Sialkot Pakistan Division of Computer Science and Informatics School of Engineering London Southbank University United Kindgdom (UK) Department of Computer Science Kohsar University Murrree Murree Pakistan Communications and Networks Engineering Department Renewable Energy Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia

In today's world, data volume is immense and is expanding at an exponential rate. A leading factor of this is the data shared over social media by millions of people daily in the form of text, audio, video, and images. Opinion mining is a branch of text mining that seeks to extract public opinion about an event or particular topic on any online platform like review sites, Twitter, Facebook, etc. Most of the work in opinion mining is done in the English language and very little is done in Roman Urdu, which is a dominant social media language in Pakistan and India. Inflation and politics are amongst the most talked-about and discussed topics on social media in Pakistan. In this study, an Inflation and Politics-based Roman Urdu dataset is prepared that is extracted from the Roman Urdu dataset available at Kaggle. Various operations (with text processing, without text preprocessing, with attribute selection, without attribute selection) were performed on the data over machine learning algorithms of Naive Bayes, Bayes Net, KStar, Decision Tree, and Random Forest and determined which algorithm gave the best accuracy on the training data. WEKA (Version 3.8.4) was used to generate opinion mining regarding politics and inflation in Pakistan using the Roman Urdu dataset.

关键词： Text mining Sentiment analysis Machine learning algorithms Social networking (online) Blogs Training data Decision trees

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback Control for Linear Systems

LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Takuya Nakagawa Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example.

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Tele-Rheumatology-Advanced Neural Network Model for Remote Assessment and Management of Rheumatic Conditions

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

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International Conference on Communications and Signal Processing

作者： S. Aghalya R. Sarasu Nilamadhab Mishra G. Arunachalam D. Gobinath Suresh Babu Thandullu Naganathan Department of Electronics and Communication Engineering St. Joseph’s College of Engineering OMR Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority.

关键词： Ethics Technological innovation Biological system modeling Neural networks Medical treatment Signal processing Sensor fusion

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Using a Wearable Device and Patient Reported Outcome to Evaluate the Influence of Sleep on Quality of Life Among Breast and Prostate Cancer Patients 18

Using a Wearable Device and Patient Reported Outcome to Eval...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Dhar, Eshita Barsasella, Diana Srikanth, Sruthi Panja, Ajith Kumar Malwade, Shwetambara Syed-Abdul, Shabbir Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Taipei Medical University Taipei Taiwan Health Polytechnic of Health Ministry Tasikmalaya Tasikmalaya West Java Indonesia Department of Computer Science and Engineering College of Engineering Anna University Guindy Chennai India School of Gerontology Health Management College of Nursing Taipei Medical University Taiwan

ISBN: (纸本)9781643682648

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Surveys

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Decomposed and Distributed Modulation to Achieve Secure Transmission

Decomposed and Distributed Modulation to Achieve Secure Tran...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhao Li Siwei Le Jie Chen Kang G. Shin Riku Jäntti Zheng Yan Jia Liu School of Cyber Engineering Xidian University Xi'an China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Department of Communications and Networking Aalto University Espoo Finland Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan

Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). DDM-Sec realizes traditional QPSK modulation by using two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed received signal while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to encrypt data transmission, enrich the spatial signatures of the legitimate transmission by employing two cooperative Txs. Moreover, DDM-Sec distributes user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Our theoretical analysis, hardware experiment, and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

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Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration

arXiv

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arXiv 2024年

作者： Lu, Wanglong Wang, Jikai Wang, Tao Zhang, Kaihao Jiang, Xianta Zhao, Hanli Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou University China Department of Computer Science Memorial University of Newfoundland St. John’s Canada State Key Laboratory for Novel Software Technology Nanjing University China College of Engineering and Computer Science Australian National University Australia

Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. To address this, we introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts within the latent space of pre-trained generative models. These prompts are designed to guide the restoration process. To fully utilize the visual prompts and enhance the extraction of informative and rich patterns, we introduce a style-modulated aggregation transformation layer. Extensive experiments and applications demonstrate the superiority of our method in achieving high-quality blind face restoration. The source code is available at https://***/LonglongaaaGo/*** Codes 68U10 © 2024, CC BY.

关键词： Generative adversarial networks

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Current Progress of Digital Twin Construction Using Medical Imaging

arXiv

引用

arXiv 2024年

作者： Zhao, Feng Wu, Yizhou Hu, Mingzhe Chang, Chih-Wei Liu, Ruirui Qiu, Richard Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Radiation Oncology University of Nebraska Medical Center OmahaNE United States

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data—such as MRI, CT, PET, and ultrasound—with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Future of Cybersecurity

Future of Cybersecurity

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Recent Trends in Information Technology (CSCTIT), College of Science International Conference of

作者： Yasamin Alagrash Senior lecturer in the computer science department college of science Mustansiriyah University Baghdad Iraq Department of computer science Ph.D. in computer science and informatics college of engineering Oakland university MI USA Computer science department MSC in data security university of technology Baghdad Iraq

Cybersecurity is critical to the digital world's ongoing progress. Whether you are a company owner or a customer, you must be prepared to adapt to new technology, policies, and methods that will aid in the creation of a safer and more secure online environment. As a result, the frequency of exposed network surfaces compels us to invest in more advanced protection measures.

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