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arXiv 2019年

作者： Fluri, Janis Kacprzak, Tomasz Lucchi, Aurelien Refregier, Alexandre Amara, Adam Hofmann, Thomas Schneider, Aurel Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density Ωm, the fluctuation amplitude σ8, and the intrinsic alignment amplitude AIA. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, different value of H0, or the lightcone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of σ = 2.34 arcmin. The uncertainties on shear calibration and n(z) error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints of S8= σ8(Ωm/0.3)0.5= 0.777+0.038-0.036with our CNN analysis, with AIA= 1.398+0.779-0.724. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Cosmology

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On the computational Bayesian survival spatial DHF modelling with CAR frailty

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AIP Conference Proceedings 2021年第1期2329卷

作者： Dwi Rantini Ni Luh Putu Ika Candrawengi Nur Iriawan Irhamah Musofa Rusli 1Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia 2Department of Internal Medicine Faculty of Medicine Universitas Airlangga Surabaya Indonesia

Dengue Hemorrhagic Fever (DHF) is one of the most common diseases experienced in Indonesia, especially in the rainy season. DHF is caused by the dengue virus which is transmitted by female Aedes aegypti mosquitoes. The recovery rate of DHF patients is influenced by several factors. In this study, we are modeling how these factors can influence the recovery rate for DHF patients. Thus, we can provide advice to the hospital. Survival analysis is a method used to model survival data. In this case, the survival model will be used to find the relationship between the recovery rate of DHF patients and factors that can affect it. The environment of patient residence is one of the factors that need to be considered. This case required analysis by considering the spatial effect of the proximity between one sub-district and another. The addition of a random effect can be done to avoid bias on estimation by using conditionally autoregressive (CAR) prior. The result showed that DHF in eastern Surabaya mostly occurred in Tambaksari and Gubeng sub-districts. The mean of survival time of patients is 4 days. In this study, the Weibull and Generalized Gamma distributions were used to modeling the patient recovery rate. In this study, there are spatial autocorrelations cases which are measured with the Moran's I index. The Weibull distribution with addition frailty using Normal CAR is the best model that can be used to modeling the recovery rate of DHF patients.

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Correction to “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices” [Feb 20 1817-1829]

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IEEE Internet of Things Journal 2022年第1期10卷 973-973页

作者： Yang Zhao Jun Zhao Linshan Jiang Rui Tan Dusit Niyato Zengxiang Li Lingjuan Lyu Yingbo Liu School of Computer Science and Engineering Nanyang Technological University Jurong West Singapore Institute of High Performance Computing A*STAR Fusionopolis Singapore Department of Computer Science National University of Singapore Queenstown Singapore Big Data Research Institute of Yunnan Economy and Society Yunnan University of Finance and Economics Kunming China

In [1] , on page 1824, Fig. 3 should be as follows:

关键词： Federated learning Internet of Things Bockchains Differential learning Crowdsourcing

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GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis 16th

GAN-based multiple adjacent brain MRI slice reconstruction f...

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16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019

作者： Han, Changhee Rundo, Leonardo Murao, Kohei Milacski, Zoltán Ádám Umemoto, Kazuki Sala, Evis Nakayama, Hideki Satoh, Shin’ichi LPIXEL Inc. Tokyo Japan Research Center for Medical Big Data National Institute of Informatics Tokyo Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Radiology University of Cambridge Cambridge United Kingdom Cancer Research UK Cambridge Centre Cambridge United Kingdom Department of Artificial Intelligence ELTE Eötvös Loránd University Budapest Hungary Department of Rehabilitation Medicine Juntendo University School of Medicine Tokyo Japan The University of Tokyo Institutes for Advanced Study The University of Tokyo Tokyo Japan

ISBN: (纸本)9783030630607

Unsupervised learning can discover various diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + 1 loss—trained on 3 healthy slices to reconstruct the next 3 ones—reconstructs unseen healthy/AD cases;(Diagnosis) Average/Maximum loss (e.g., 2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) 0.780 while also detecting AD at a late stage much more accurately with ROC-AUC 0.917;since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease. © Springer Nature Switzerland AG 2020.

关键词： Generative adversarial networks

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Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network

Very-Short-Term Solar Forecasting with Long Short-Term Memor...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Yanbin Lin Dongliang Duan Xueming Hong Xiang Cheng Liuqing Yang Shuguang Cui School of Information Science and Engineering Xiamen University Xiamen China Shenzhen Research Institute of Big Data (SRIBD) Shenzhen China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University Fort Collins CO United States

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

To address the serious challenges to traditional power systems caused by high variability of solar production, especially the ramp events, this paper is aimed at predicting solar radiation accurately at a very-short-term scale, i.e. several minutes ahead. Quite different from traditional methods, our designed system conducts time series prediction based upon meteorological data and sky images' features with the Long Short-Term Memory (LSTM) network to learn the long-term dependency. Experiments have been conducted based on sky images' feature extracted by Convolutional Neural Network, meteorological data and solar geometric data with a large number of features collected over a seven-summer period. Experimental results show that the proposed forecasting system outperforms other methods in the literature, such as solar forecasting based upon optical flow tracking, Feedforward Neural Network (FNN) and Support Vector Regression (SVR), in terms of accuracy and robustness.

关键词： photovoltaic power forecasting solar irradiance time series prediction Long Short-Term Memory (LSTM) Solar Irradiance Short-Term Memory Sun Time series meteorological data design system power systems sky

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Comparative analysis of SIMILAR to RCD ONE(SRO) family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第4期 438页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture Faisalabad Sub-Campus Burewala Department of Chemistry and Biochemistry University of AgricultureFaisalabad Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad Department of Plant Breeding and Genetics University of AgricultureFaisalabad Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud Computing Centre for Advanced Studies in Agriculture and Food Security University of Agriculture Faisalabad Department of Plant Pathology University of California State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Sciences Federal Seed Certification and Registration Department Ministry of National Food Security and Research School of Agriculture Sciences Zhengzhou University

Background SRO(Similar to RCD1) genes family is largely recognized for their importance in the growth,development,and in responding to environmental ***,genome-wide identification and functional characterization of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribution of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes during ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

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Dynamics of cascades on burstiness-controlled temporal networks

arXiv

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arXiv 2020年

作者： Unicomb, Samuel Iñiguez, Gerardo Gleeson, James P. Karsai, Márton Université de Lyon ENS de Lyon INRIA CNRS IXXI UMR 5668 Lyon69364 France Department of Network and Data Science Central European University ViennaA-1100 Austria Department of Computer Science Aalto University School of Science AaltoFI-00076 Finland IIMAS Universidad Nacional Autonóma de México Ciudad de México01000 Mexico MACSI and Insight Centre for Data Analytics University of Limerick LimerickV94 T9PX Ireland

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. We develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts on a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

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Application of 3D U-Net Neural Networks in Extracting the Epoch of Reionization Signal from SKA-Low Observations Based on Real Observations of NCP Field from LOFAR

arXiv

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arXiv 2024年

作者： Gao, Li-Yang Koopmans, Léon V.E. Mertens, Florent G. Munshi, Satyapan Li, Yichao Brackenhoff, Stefanie A. Ceccotti, Emilio Chege, J. Kariuki Acharya, Anshuman Ghara, Raghunath Giri, Sambit K. Iliev, Ilian T. Mellema, Garrelt Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang110819 China Kapteyn Astronomical Institute University of Groningen P.O. Box 800 Groningen9700 AV Netherlands LERMA Observatoire de Paris Université PSL CNRS Sorbonne Université ParisF-75014 France INAF - Istituto di Radioastronomia Via P. Gobetti 101 Bologna40129 Italy Max-Planck-Institut für Astrophysik Garching85748 Germany Department of Physical Sciences Indian Institute of Science Education and Research Mohanpur WB Kolkata741246 India Van Swinderen Institute for Particle Physics and Gravity University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Department of Physics & Astronomy University of Sussex BrightonBN1 9QH United Kingdom Dept. of Astronomy & Oskar Klein Centre Stockholm University AlbaNova Stockholm University StockholmSE-106 91 Sweden MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

Neutral hydrogen (HI) serves as a crucial probe for the Cosmic Dawn and the Epoch of Reionization (EoR). Actual observations of the 21-cm signal often encounter challenges such as thermal noise and various systematic effects. To overcome these challenges, we simulate SKA-Low-depth images and process them with a deep learning method. We utilized foreground residuals acquired by LOFAR during actual North Celestial Pole (NCP) field observations, thermal and excess variances calculated via Gaussian process regression (GPR), and 21-cm signals generated with 21cmFAST for signal extraction tests. Our approach to overcome these foreground, thermal noise, and excess variance components employs a 3D U-Net neural network architecture for image analysis. When considering thermal noise corresponding to 1400 hours of integration, U-Net provides reliable 2D power spectrum predictions, and robustness tests ensure that we get realistic EoR signals. Adding foreground residuals, however, causes inconsistencies below the horizon delay-line. Lastly, evaluating both thermal and excess variance with observations up to 3700 and 14000 hours ensures reliable power spectrum estimations within the EoR window and across nearly all scales, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Thermal noise

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Plant trait retrieval from hyperspectral data: Collective efforts in scientific data curation outperform simulated data derived from the PROSAIL model

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2025年 15卷

作者： Mederer, Daniel Feilhauer, Hannes Cherif, Eya Berger, Katja Hank, Tobias B. Kovach, Kyle R. Dao, Phuong D. Lu, Bing Townsend, Philip A. Kattenborn, Teja Institute for Earth System Science and Remote Sensing Leipzig University Talstr. 35 Leipzig 04103 Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig University - TU Dresden Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Germany Helmholtz-Centre for Environmental Research (UFZ) Permoserstraße 15 Leipzig 04318 Germany GFZ Helmholtz Centre for Geosciences Telegrafenberg Potsdam 14473 Germany Department of Geography Faculty of Geosciences Ludwig-Maximilians-Universität München (LMU) Luisenstr. 37 Munich 80333 Germany Department of Forest and Wildlife Ecology University of Wisconsin-Madison 1630 Linden Drive Madison 53706 WI United States Department of Agricultural Biology Colorado State University 307 University Avenue Fort Collins 80523 CO United States Department of Geography Simon Fraser University 8888 University Drive Burnaby V5A 1S6 BC Canada Sensor-based Geoinformatics (geosense) University of Freiburg Tennenbacherstr. 4 Freiburg 79106 Germany

Plant traits play a pivotal role in steering ecosystem dynamics. As plant canopies have evolved to interact with light, spectral data convey information on a variety of plant traits. Machine learning techniques have been used successfully to retrieve diverse traits from hyperspectral data. Nonetheless, the efficacy of machine learning is restricted by limited access to high-quality reference data for training. Previous studies showed that aggregating data across domains, sensors, or growth forms provided by collaborative efforts of the scientific community enables the creation of transferable models. However, even such curated databases are still sparse for several traits. To address these challenges, we investigated the potential of filling such data gaps with simulated hyperspectral data generated through the most widely-used radiative transfer model (RTM) PROSAIL. We coupled trait information from the TRY plant trait database with information on plant communities from the sPlot database, to build a realistic input trait dataset for the RTM-based simulation of canopy spectra. Our findings indicate that simulated data can alleviate the effects of data scarcity for highly underrepresented traits. In most other cases, however, the effects of including simulated data from RTMs are negligible or even negative. While more complex RTM models promise further improvements, their parameterization remains challenging. This highlights two key observations: firstly, RTM models, such as PROSAIL, exhibit limitations in producing realistic spectra across diverse ecosystems;secondly, real-world data repurposed from various sources exhibit superior retrieval success compared to simulated data. As a result, we advocate to emphasize the importance of active data sharing over secrecy and overreliance on modeling to address data limitations. © 2024 The Authors

关键词： Biophysical properties Canopy properties Deep learning Hyperspectral remote sensing Imaging spectroscopy Plant trait retrieval Radiative transfer modeling

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