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arXiv 2021年

作者： Del Moral, Pierre Horton, Emma Jasra, Ajay Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 France Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science & Technology Building 1 Thuwal23955-6900 Saudi Arabia

The stability and contraction properties of positive integral semigroups on Polish spaces are investigated. Our novel analysis is based on the extension of V -norm contraction methods, associated to functionally weighted Banach spaces for Markov semigroups, to positive semigroups. This methodology is applied to a general class of positive and possibly time-inhomogeneous bounded integral semigroups and their normalised versions. The spectral theorems that we develop are an extension of Perron-Frobenius and Krein-Rutman theorems for positive operators to a class of time-varying positive semigroups. In the context of time-homogeneous models, the regularity conditions discussed in the present article appear to be necessary and sufficient condition for the existence of leading eigenvalues. We review and illustrate the impact of these results in the context of positive semigroups arising in transport theory, physics, mathematical biology and signal processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Banach spaces

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LoRaWAN Intelligent Outdoor Smart Street Lighting

LoRaWAN Intelligent Outdoor Smart Street Lighting

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Pandapotan Siagian Erick Fernando Faculty of Informatics and Electrical Engineering Computer Engineering Study Program Del Institute of Technology North Sumatera Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9780738125176

ISBN: (纸本)9781665418614

Nowadays, The roads have increased the number of streetlights for the roads vehicles/pedestrians, which raises investment and energy. Observations made to obtain most of the road lights are always active at night, even when there are no vehicles or pedestrians on the road. The problems that occur are the waste of energy sources that are used in streetlights. This research designs the concept of intelligent traffic flow based (LED) for energy optimization, maximum efficiency. This concept uses intelligent light architecture using the LoRaWAN Mesh network. The application of this concept offers system reliability, reduces costs, and makes user satisfaction. The results of this study are demonstrated by experimenting with comparing conventional LED lights. The proposed system, resulting in 33% to 62% energy savings depending on when the usage process in streetlights. Smart lighting LEDs with LoRaWAN provide a remote-control mechanism that can be dynamically adjusted based on environmental conditions, distance, and automatic motion.

关键词： Lighting Sensors Light emitting diodes Intelligent sensors Sensor systems Batteries Roads

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Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images

Semi-Supervised Variational Autoencoder for Cell Feature Ext...

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IEEE International Symposium on Biomedical Imaging

作者： Piumi Sandarenu Julia Chen Iveta Slapetova Lois Browne Peter H. Graham Alexander Swarbrick Ewan K.A. Millar Yang Song Erik Meijering School of Computer Science and Engineering University of New South Wales Sydney Australia Cancer Ecosystems Program Garvan Institute of Medical Research Darlinghurst Australia Cancer Care Centre St George Hospital Kogarah Australia St. George & Sutherland Clinical School UNSW Sydney Kensington Australia School of Clinical Medicine Faculty of Medicine UNSW Sydney Australia Dept. of Anatomical Pathology NSW Health Pathology St. George Hospital Kogarah Australia Faculty of Medicine & Health Sciences Sydney Western University Campbelltown Australia University of Technology Sydney Ultimo Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed immunofluorescence image analysis pipelines depend on cell feature representations characterised by morphological and stain intensity-based metrics generated using simple statistical and machine learning-based tools. However, these methods are not capable of generating complex representations of cells. We propose a deep learning-based cell feature extraction model using a variational autoencoder with supervision using a latent subspace to extract cell features in mIF images. We perform cell phenotype classification using a cohort of more than 44,000 multiplexed immunofluorescence cell image patches extracted across 1,093 tissue microarray cores of breast cancer patients, to demonstrate the success of our model against current and alternative methods.

关键词： Multiplexing Measurement Visualization Phenotypes Limiting Pipelines Feature extraction

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SEMI-SUPERVISED VARIATIONAL AUTOENCODER FOR CELL FEATURE EXTRACTION IN MULTIPLEXED IMMUNOFLUORESCENCE IMAGES

arXiv

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arXiv 2024年

作者： Sandarenu, Piumi Chen, Julia Slapetova, Iveta Browne, Lois Graham, Peter H. Swarbrick, Alexander Millar, Ewan K.A. Song, Yang Meijering, Erik School of Computer Science and Engineering University of New South Wales Sydney Australia Cancer Ecosystems Program Garvan Institute of Medical Research Darlinghurst Australia Cancer Care Centre St George Hospital Kogarah Australia St. George & Sutherland Clinical School UNSW Sydney Kensington Australia School of Clinical Medicine Faculty of Medicine UNSW Sydney Australia Dept. of Anatomical Pathology NSW Health Pathology St. George Hospital Kogarah Australia Faculty of Medicine & Health Sciences Sydney Western University Campbelltown Australia University of Technology Sydney Ultimo Australia

关键词： Feature extraction

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Assessing Water Quality Characteristics of pH and Biochemical Oxygen Demand (CBOD) of Public Utility using Statistical Quality Control (SQC) 23

Assessing Water Quality Characteristics of pH and Biochemica...

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23rd World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2019

作者： Blevins, Nathaniel Bryce Segall, Richard S. Choppari, Sharanya Engineering Program College of Engineering and Computer Science Arkansas State University State University AR7247 United States Department of Computer and Information Technology Neil Griffin College of Business Arkansas State University State University AR72467 United States Engineering Management Program College of Engineering and Computer Science Arkansas State University State University AR72467 United States

ISBN: (纸本)9781950492091

This paper is a continuation of previous research supervised by co-author Segall that appeared in Akarapu et al. (2016a, 2016b) at WMSCI 2016. This research studies in depth only two of the characteristics of water quality for actual 2016 data collected for a water treatment plant located in United States where the previous studies Akarapu et al. (2016a, 2016) studied five characteristics of water quality for 2014 data for the same water treatment plant. The two water quality characteristics studied in-depth with this paper with a more recent data set for the same water treatment plant are pH and Carbonaceous Biochemical Oxygen Demand (CBOD). Discussions of the analyses are presented as obtained using Minitab 17 and Minitab 18. © WMSCI 2019 - 23rd World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. All rights reserved.

关键词： Water treatment plants

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A Big Data-Based Target Cost Management System

A Big Data-Based Target Cost Management System

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International Symposium on computer, Consumer and Control (IS3C)

作者： Yin-Chen Lin Jyun-Jie Wang Chi-Yuan Lin Sheng-Chih Yang Ph. D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9781728193625

ISBN: (纸本)9781728193632

This study proposed a novel big data-based target cost management (TCM) system that automatically calculates the cost table. MATLAB, Python, and Excel were used to realize the system. TCM can be performed using the content of the calculated cost table. The system is suitable for managing catering inventory. This study presented the theoretical model of the proposed system and revealed that the system outperformed the traditional cost table implementation.

关键词： Procurement Databases programming Synchronization Mathematical model Python Matlab

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Visualizing Field-free Deterministic Magnetic Switching of all–van der Waals Spin-Orbit Torque System Using Spin Ensembles in Hexagonal Boron Nitride

arXiv

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arXiv 2025年

作者： Zhang, Xi Zhou, Jingcheng Hu, Chaowei Deng, Kuangyin Wang, Chuangtang Agarwal, Nishkarsh Jin, Hanshang Al-Matouq, Faris A. Xu, Stelo Trivedi, Roshan S. Li, Senlei Rathi, Sumedh Lu, Hanyi Jiang, Zhigang Taufour, Valentin Hovden, Robert Zhao, Liuyan Cheng, Ran Xu, Xiaodong Chu, Jiun-Haw Du, Chunhui Rita Wang, Hailong School of Physics Georgia Institute of Technology AtlantaGA30332 United States Department of Physics University of Washington SeattleWA98105 United States Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California DavisCA95616 United States Department of Physics University of California San Diego La Jolla CA92093 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of Washington SeattleWA98105 United States

Recently, optically active spin defects embedded in van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science and quantum physics. Taking advantage of excellent solid-state integrability, this new class of spin defects can be arranged in controllable nanoscale proximity of target materials in vdW heterostructures, showing great promise for improving spatial resolution and field sensitivity of current sensing technologies. Building on this state-of-the-art measurement platform, here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching of room-temperature two-dimensional magnet Fe3GaTe2 in an all-vdW spin-orbit torque (SOT) system. By visualizing SOT-driven variations of nanoscale Fe3GaTe2 magnetic stray field profile under different conditions, we have revealed how the observed magnetic switching evolves from deterministic to indeterministic behavior due to the interplay between out-of-plane spins, in-plane spins and Joule heating. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights on material design, testing, and evaluation of next-generation vdW spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Feasibility of Inferring Spatial Transcriptomics from Single...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Fatemi, Michael Y. Lu, Yunrui Azher, Zarif L. Sharma, Cyril Feng, Eric Diallo, Alos B. Srinivasan, Gokul Rosner, Grace M. Pointer, Kelli B. Christensen, Brock C. Salas, Lucas A. Tsongalis, Gregory J. Palisoul, Scott M. Perreard, Laurent Kolling, Fred W. Vaickus, Louis J. Levy, Joshua J. University of Virginia CharlottesvilleVA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health LebanonNH United States Department of Computer Science Purdue University West LafayetteIN United States Thomas Jefferson High School for Science and Technology AlexandriaVA United States Department of Epidemiology Dartmouth College Geisel School of Medicine HanoverNH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine HanoverNH United States Department of Medicine Section of Radiology Oncology Dartmouth Health LebanonNH United States Department of Molecular and Cell Biology Dartmouth College Geisel School of Medicine HanoverNH United States Genomics Shared Resource Dartmouth Cancer Center LebanonNH United States Department of Dermatology Dartmouth Health LebanonNH United States Department of Pathology and Laboratory Medicine Cedars Sinai Medical Center Los AngelesCA United States Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Biology and Biological Engineering California Institute of Technology PasadenaCA United States

Spatial transcriptomics (ST) enables studying spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. Recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. Elucidating such information from histology alone presents a significant challenge, but if solved can enable spatial molecular analysis at cellular resolution even where ST data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in colorectal cancer whole slide images. A cell-graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA, facilitating the analysis of cellular groupings and gene relationships. We demonstrate that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within it. Our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional computer vision methods which did not incorporate single cell expression during the model training. This innovative approach augments the resolution of spatial molecular assays utilizing histology as sole input through co-mapping of histological and transcriptomic datasets at the single-cell level. © 2025 by SCITEPRESS– Science and technology Publications, Lda.

关键词： Tumors

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Multi-class classification of COVID-19 and other infections using machine learning model with wavelet and laws features

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AIP Conference Proceedings 2024年第1期2919卷

作者： Shaik Mahaboob Basha Aloísio Vieira Lira Neto Mohamed Abd Elaziz Shaik Hashmitha Mohisin Victor Hugo C. e Albuquerque Department of Electronics& Communication Engg N.B.K.R. Institute of Science &Technology Vidyanagar India Graduation Program in Telecommunication Engineering Federal Institute of Ceará Fortaleza-CE Brazil Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza-CE Brazil

Corona virus disease (COVID-19) was responsible for inevitable chaos in the human life due to its inherent nature of high contagious across the globe. This very reason also hindered the physicians and healthcare support systems to provide in-time medical aid to the suffering *** diagnosis of COVID-19 using clinical information is the prime source for detection and treatment. However, computerised tomography (CT) and Chest X-ray (CXR) images have found useful for screening purposes due to the successful usage of image based analysis by physicians which was aided by image processing based *** means of screening, diagnosis and prognostic studies using CT and CXR with the help of comprehensive image processing pipelines could invariably augment the efficiency in the respective tasks. The main objective of this study is to implement multi-class classification of COVID-19, viral-pneumonia (VP) and lung opacity (LO) using threshold-based segmented CXR images further transformed using wavelet transform (WT)and Laws based texture representation. Machine learning (ML) based implementation and comparison was carried using Random Forest (RF) and Logistic regression (LR) techniques with of the WT and Laws features individually as well as with combined feature vectors. The ML based implementation was aimed to classify the CXR image based on the individual or combined features into COVID-19, VP or LO pathology. The validation of the ML techniques was further carried using confusion matrix and other validation measures. The performance of the RF classifier was superior with WT features than Laws texture features on individual category of features than LR technique. Amarginal improvement was observed in the classifier performance when WT and Laws based features were combined.

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Meta-control of social learning strategies

arXiv

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arXiv 2021年

作者： Yaman, Anil Bredeche, Nicolas Çaylak, Onur Leibo, Joel Z. Lee, Sang Wan Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands Institut des Systèmes Intelligents et de Robotique Sorbonne Université CNRS Paris France Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands DeepMind London United Kingdom Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Center for Neuroscience-Inspired AI Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Health Science and Technology Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Artificial Intelligence Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands

Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

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