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Power generation and pollution control from unwanted noise

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AIP Conference Proceedings 2022年第1期2464卷

作者： M. Ananthi J. Harinarayanan M. Muhibullah 1)Department of Computer Science and Business Systems Sri Sairam Engineering College West Tambaram Chennai India 2)Department of Electrical Engineering Sri Sairam Engineering College West Tambaram Chennai India 3)Department of Information Technology Sri Sairam Engineering College West Tambaram Chennai India

Air pollution is the major level of environmental pollution which affects all the living beings health and atmospheric condition. In which it is a major challenging issue today's scenario. Air pollution leads to adverse effects on climate, ecosystem and living things including human, animals and plants etc,. The Atmospheric air is getting polluted by the industry, power plants, vehicle emission and many sources releasing the unwanted toxic, harmful gases and particulate matter into the atmosphere. This Particulate matter is one of the major sources for the contribution of increase in air pollution in the atmosphere. Such particulate matters create a serious requirement for monitoring, measuring and analysis of real-time air quality condition in order that appropriate resolutions are often taken during a period of time. This paper presents a real-time standalone solution for monitoring the air pollution level which includes various parameters: PM (Particulate Matters) 2.5, CO (Carbon Monoxide), CO2 (Carbon dioxide), air pressure, humidity and atmospheric temperature. The Internet of Things (IoT) is plays vital role in monitoring the pollution level in the atmosphere. IoT mingle with cloud computing gives a completely unique way for management of data from different sensors were collected and transmitted to the low power, a low-cost ARM Processor which act as a minicomputer based on Raspberry pi all the input from the various sensors are combined and compared to the standard level, when the variation is more than the standard it will inform the individual and also alert the user. Pollution in India has been increasing year by year and most of the states are suffering severely from it. So this system monitors pollution and updates pollution levels from all over the areas within the city in a real-time manner.

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Analysing the direction of emotional influence in nonverbal dyadic communication: A facial-expression study

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arXiv 2020年

作者： Shadaydeh, Maha Müller, Lea Schneider, Dana Thümmel, Martin Kessler, Thomas Denzler, Joachim Computer Vision Group Department of Mathematics and Computer Science Friedrich Schiller University of Jena Ernst-Abbe-Platz 2 Jena07743 Germany Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany Department of Social Psychology Friedrich Schiller University of Jena Humboldtstrasse 26 Jena07743 Germany

Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to overtly influence. As such, they are a perfect measure for a better understanding of unintentional behavior cues about social-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogue based on facial expressions only. We exploit computer vision capabilities along with causal inference theory for quantitative verification of hypotheses on the direction of emotional influence, i.e., causal effect relationships, in dyadic dialogues. We address two main issues. First, in a dyadic dialogue, emotional influence occurs over transient time intervals and with intensity and direction that are variant over time. To this end, we propose a relevant interval selection approach that we use prior to causal inference to identify those transient intervals where causal inference should be applied. Second, we propose to use fine-grained facial expressions that are present when strong distinct facial emotions are not visible. To specify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions over selected relevant intervals. We tested our approach on newly, experimentally obtained data. Based on quantitative verification of hypotheses on the direction of emotional influence, we were able to show that the proposed approach is most promising to reveal the causal effect pattern in various instructed interaction conditions. © 2020, CC BY.

关键词： Statistical tests

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

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arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： computer aided diagnosis

Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing

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arXiv 2020年

作者： Liu, Xiaolan Yu, Jiadong Gao, Yue Department of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Institute for Communication Systems University of Surrey GU2 7XH United Kingdom

To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency. However, it’s challenging for end users to offload computation due to their massive requirements on spectrum and computation resources and frequent requests on Radio Access Technology (RAT). In this paper, we investigate computation offloading mechanism with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Here, each end user is a learning agent observing its local environment to learn optimal decisions on either local computing or edge computing with the goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other end users. Therefore, a multiagent reinforcement learning framework is developed to solve the stochastic game with a proposed independent learners based multi-agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway compared to the other two benchmark algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

Neuroscience needs Network science

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arXiv 2023年

作者： Barabási, Dániel L. Bianconi, Ginestra Bullmore, Ed Burgess, Mark Chung, SueYeon Eliassi-Rad, Tina George, Dileep Kovács, István A. Makse, Hernán Papadimitriou, Christos Nichols, Thomas E. Sporns, Olaf Stachenfeld, Kim Toroczkai, Zoltán Towlson, Emma K. Zador, Anthony M. Zeng, Hongkui Barabási, Albert-László Bernard, Amy Buzsáki, György Biophysics Program Harvard University CambridgeMA United States Department of Molecular and Cellular Biology Center for Brain Science Harvard University CambridgeMA United States School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom The Alan Turing Institute The British Library LondonNW1 2DB United Kingdom Department of Psychiatry Wolfson Brain Imaging Centre University of Cambridge Cambridge United Kingdom ChiTek-i AS Oslo Norway Center for Neural Science New York University New YorkNY United States Center for Computational Neuroscience Flatiron Institute Simons Foundation New YorkNY United States Network Science Institute Northeastern University BostonMA United States Khoury College of Computer Sciences Northeastern University BostonMA United States Santa Fe Institute Santa FeNM United States DeepMind United Kingdom Department of Physics and Astronomy Northwestern University 633 Clark Street EvanstonIL60208 United States Northwestern Institute on Complex Systems Chambers Hall Northwestern University 600 Foster St EvanstonIL60208 United States Levich Institute and Physics Department City College of New York New YorkNY10031 United States Columbia University New YorkNY10027 United States Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population Health University of Oxford OxfordOX3 7LF United Kingdom Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford OxfordOX3 9DU United Kingdom Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States Department of Physics University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Department of Computer Science Department of Physics and Astronomy Hotchkiss Brain Institute Children’s Research Hospital University of Calgary CalgaryAB Canada Cold Spring Harbor Laboratory

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions. © 2023, CC BY-NC-SA.

关键词： Complex networks

GardnerNet: An Interpretable Deep Learning Model for Quantitative Human Blastocyst Evaluation - A Retrospective Model Development and Validation Study

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SSRN

SSRN 2024年

作者： Liu, Hang Chen, Longbin Shan, Guanqiao Sun, Chen Liao, Hongqing Lu, Changfu Zhang, Shuoping Dong, Shaonan Xu, Xinxin Yan, Qiuyun Gong, Fei Zhang, Zhuoran Dai, Changsheng Chen, Wenyuan Song, Haocong Chen, Lei Sun, Haixiang Wang, Shanshan Lin, Ge Sun, Yu Gu, Yifan Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada Institute of Reproductive and Stem Cell Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Hengyang Nanhua-Xinghui Reproductive Health Hospital 30 Jiefang Road Hunan Province Hengyang China The second Affiliated Hospital Hengyang Medical School University of South China 30 Jiefang Road Hunan Province Hengyang City China Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China NHC Key Laboratory of Human Stem Cell and Reproductive Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Clinical Research Center for Reproduction and Genetics in Hunan Province Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen 2001 Longxiang Boulevard Longgang District Shenzhen China Institute of Robotics and Intelligent Systems Dalian University of Technolgy 2 Linggong Road Liaoning Province Dalian China Department of Computer Science University of Toronto 40 St George Street TorontoONM5S 2E4 Canada Center for Reproductive Medicine and Obstetrics and Gynecology Nanjing Drum Tower Hospital Nanjing University Medical School 321 Zhongshan Road Jiangsu Province Nanjing China National Engineering and Research Center of Human Stem Cell 8 Luyun Road Jiangsu Province Changsha China Department of Electrical and Computer Engineering 10 King’s College Road TorontoONM5S 3G8 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Background: Selecting the blastocyst with the highest potential for live birth from a cohort is crucial for the success of in vitro fertilization (IVF). While the Gardner and Schoolcraft morphological grading system, commonly known as the Gardner grading system, is widely used by embryologists worldwide, blastocysts having similar or the same inner cell mass (ICM) and trophectoderm (TE) grades cause challenges for embryologists in decision-making. Furthermore, human assessment is subjective and inconsistent in predicting which blastocysts have higher potential to result in live birth. This study aims to develop an interpretable, quantitative method aligned with the Gardner grading system, providing continuous scores that correlate with live birth outcomes, thereby assisting embryologists in improving blastocyst selection. Methods: In this retrospective study, we developed GardnerNet, an interpretable deep learning model that outputs expansion degree grade and continuous scores quantifying a blastocyst's ICM morphology and TE morphology, based on the Gardner grading system. The continuous ICM and TE scores were calculated by weighting each base grade's predicted probability and summing the predicted probabilities. To represent each blastocyst's overall potential for live birth, we combined the ICM and TE scores using their odds ratios (ORs) for live birth. We further assessed the correlation between the ICM score, TE score, and the OR-combined score (adjusted for expansion degree) with live birth rates by applying GardnerNet to blastocysts with known live birth outcomes. To test its generalizability, we also applied GardnerNet to an external IVF institution, accounting for variations in imaging conditions, live birth rates, and embryologists' experience levels. Findings: GardnerNet was developed using data from 2760 blastocysts with majority-voted grades for expansion degree, ICM, and TE. The model achieved mean area under the receiver operating characteristic curve

关键词： Risk assessment

Post-2000 Nonlinear Optical Materials and Measurements: Data Tables and Best Practices

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arXiv 2023年

作者： Vermeulen, Nathalie Espinosa, Daniel Ball, Adam Ballato, John Boucaud, Philippe Boudebs, Georges Campos, Cecília L.A.V. Dragic, Peter Gomes, Anderson S.L. Huttunen, Mikko J. Kinsey, Nathaniel Mildren, Rich Neshev, Dragomir Padilha, Lázaro A. Pu, Minhao Secondo, Ray Tokunaga, Eiji Turchinovich, Dmitry Yan, Jingshi Yvind, Kresten Dolgaleva, Ksenia Van Stryland, Eric W. Brussels Photonics B-PHOT Department of Applied Physics and Photonics IR-TONA Vrije Universiteit Brussel Pleinlaan 2 Brussel1050 Belgium School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Dept. Of Electrical & Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Dept of Materials Science and Engineering Clemson University ClemsonSC29634 United States Université Côte d’Azur CNRS CRHEA Rue Bernard Grégory Sophia-Antipolis06905 France Univ Angers LPHIA SFR MATRIX AngersF-49000 France Departamento de Física Universidade Federal de Pernambuco PE Recife50670-901 Brazil Dept. Of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33014 Finland MQ Photonics Research Centre Macquarie University NSW2109 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems TMOS Department of Electronic Materials Engineering Research School of Physics Australian National University CanberraACT2601 Australia Instituto de Fisica "Gleb Wataghin" Universidade Estadual de Campinas Campinas Sao Paulo13083-970 Brazil Department of Electrical and Photonics engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Faculty of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States

In its 60 years of existence, the field of nonlinear optics has gained momentum especially over the past two decades thanks to major breakthroughs in material science and technology. In this article, we present a new set of data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000. The papers included in the data tables are representative experimental works on bulk materials, solvents, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, hybrid waveguiding systems, and materials suitable for nonlinear optics at THz frequencies. In addition to the data tables, we also provide best practices for performing and reporting nonlinear-optical experiments. These best practices underpin the selection process that was used for including papers in the tables. While the tables indeed show strong advancements in the field over the past two decades, we encourage the nonlinear-optics community to implement the identified best practices in future works. This will allow a more adequate comparison, interpretation and use of the published parameters, and as such further stimulate the overall progress in nonlinear-optical science and applications. © 2023, CC BY.

关键词： Nonlinear optics

Lesion size of early cerebral infarction on a Non-Contrast CT influences detection ability in Cascade Mask Region-Convolutional neural networks

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Biomedical Signal Processing and Control 2023年第PartA期86卷

作者： Chou, Chi-Hsiang Chai, Jyh-Wen Wang, Li-Chih Fu, Jachih Lin, Yu-Shian Chang, Pei-Jou Chen, Wen-Hsien Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University No. 145 Xingda Rd. South Dist. Taichung40227 Taiwan Department of Neurology Taichung Veterans General Hospital No. 1650 Taiwan Boulevard Sect. 4 Xitun District Taichung407219 Taiwan Department of Neurology National Yang-Ming University School of Medicine No. 155 Sec. 2 Linong St. Beitou Dist. Taipei112304 Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Xitun District Taichung407224 Taiwan Computer-Aided Measurement and Diagnostic Systems Laboratory Department of Industrial Engineering and Management National Yunlin University of Science and Technology No. 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Radiology Taichung Veterans General Hospital No. 1650 Taiwan Boulevard Sect. 4 Xitun District Taichung407219 Taiwan College of Medicine China Medical University No. 100 Section 1 Economic and Trade Road Beitun District Taichung406040 Taiwan

Background: Currently, convolutional neural network (CNN) methods for early non-contrast brain CT (NCCT) infarction detection have limited accuracy. We suspect this may be due to the significant scale variance of lesion size, which would result in small infarctions not being properly detected. Materials and Methods: Patients’ early NCCT infarction signs were labeled and divided into training/validation and test datasets. The 1st Cascade Mask Region-Convolutional Neural Network (Cascade Mask R-CNN) model (1st model) was train/validated. The test dataset, with added normal controls, was used to evaluate the performances. We further divided the lesions into large and small ones by statistics. We filtered the test dataset's ‘small’ lesions and re-examined the 1st model. Finally, we filtered the ‘small’ lesions of the training/validation datasets to train/validate the 2nd Cascade Mask R-CNN (2nd model), and the performances were examined by the filtered test dataset. Results: In total, 266 ischemic stroke patients (Male/Female 163/103, age 69.9 ± 14.1 years) were included, and the duration from last known well to NCCT was 28.5 ± 39.3 h. Two hundred twenty patients were placed in training/validation datasets and 46 were assigned to the test dataset. Twenty normal control cases (Male/Female 14/6, age 68.7 ± 10.8 years) were added into the test dataset. The results were Sensitivity: 74.0%;Specificity: 91.8%;Accuracy: 86.1%;Precision: 80.9%;and F1-score: 77.3%. Based on the histogram, large lesions were > 2,012 pixels (4.8 cm2), and small lesions were ≤ 2,012 pixels. The 1st model's performances by the filtered test dataset were Sensitivity: 96.9%;Specificity: 92.9%;Accuracy: 93.6%;Precision: 74.1%;and F1-score: 84.0%. The 2nd model's performances were Sensitivity: 97.1%;Specificity: 96.4%;Accuracy: 96.5%;Precision: 85.7%;and F1-score: 91.0%. Conclusions: The target objects in most benchmark databases do not have the property of significant scale variance, which means common

关键词： Convolutional neural networks

Cerberus: Minimalistic multi-shard byzantine-resilient transaction processing

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arXiv 2020年

作者： Hellings, Jelle Hughes, Daniel P. Primero, Joshua Sadoghi, Mohammad Exploratory Systems Lab Department of Computer Science University of California DavisCA95616-8562 United States Radix DLT Ltd Argyle Works 29-31 Euston Road LondonNW1 2SD United Kingdom

To enable high-performance and scalable blockchains, we need to step away from traditional consensus-based fully-replicated designs. One direction is to explore the usage of sharding in which we partition the managed dataset over many shards that—independently—operate as blockchains. Sharding requires an efficient fault-tolerant primitive for the ordering and execution of multi-shard transactions, however. In this work, we seek to design such a primitive suitable for distributed ledger networks with high transaction throughput. To do so, we propose Cerberus, a set of minimalistic primitives for processing single-shard and multi-shard UTXO-like transactions. Cerberus aims at maximizing parallel processing at shards while minimizing coordination within and between shards. First, we propose Core-Cerberus, that uses strict environmental requirements to enable simple yet powerful multi-shard transaction processing. In our intended UTXO-environment, Core-Cerberus will operate perfectly with respect to all transactions proposed and approved by well-behaved clients, but does not provide any guarantees for other transactions. To also support more general-purpose environments, we propose two generalizations of Core-Cerberus: we propose Optimistic-Cerberus, a protocol that does not require any additional coordination phases in the well-behaved optimistic case, while requiring intricate coordination when recovering from attacks;and we propose Pessimistic-Cerberus, a protocol that adds sufficient coordination to the well-behaved case of Core-Cerberus, allowing it to operate in a general-purpose fault-tolerant environments without significant costs to recover from attacks. Finally, we compare the three protocols, showing their potential scalability and high transaction throughput in practical environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain