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A quixotic view of spatial bias in modelling the distribution of species and their diversity

NPJ Biodiversity 2023年第1期2卷 1-11页

作者： Rocchini, Duccio Tordoni, Enrico Marchetto, Elisa Marcantonio, Matteo Barbosa, A. Márcia Bazzichetto, Manuele Beierkuhnlein, Carl Castelnuovo, Elisa Gatti, Roberto Cazzolla Chiarucci, Alessandro Chieffallo, Ludovico Da Re, Daniele Di Musciano, Michele Foody, Giles M. Gabor, Lukas Garzon-Lopez, Carol X. Guisan, Antoine Hattab, Tarek Hortal, Joaquin Kunin, William E. Jordán, Ferenc Lenoir, Jonathan Mirri, Silvia Moudrý, Vítězslav Naimi, Babak Nowosad, Jakub Sabatini, Francesco Maria Schweiger, Andreas H. Šímová, Petra Tessarolo, Geiziane Zannini, Piero Malavasi, Marco BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Czech University of Life Sciences Prague Faculty of Environmental Sciences Department of Spatial Sciences Kamýcka 129 Praha - Suchdol 16500 Czech Republic Department of Botany Institute of Ecology and Earth Science University of Tartu J. Liivi 2 Tartu 50409 Estonia Evolutionary Ecology and Genetics Group Earth and Life Institute UCLouvain Louvain-la-Neuve 1348 Belgium CICGE (Centro de Investigação em Ciências Geo-Espaciais) Universidade do Porto Porto Portugal Biogeography BayCEER University of Bayreuth Universitaetsstraße 30 Bayreuth 95440 Germany Georges Lemaître Center for Earth and Climate Research Earth and Life Institute UCLouvain Louvain-la-Neuve Belgium Department of Life Health and Environmental Sciences University of L’Aquila Piazzale Salvatore Tommasi 1 L’Aquila 67100 Italy School of Geography University of Nottingham Nottingham United Kingdom Dept of Ecology and Evolutionary Biology Yale University New Haven CT United States Center for Biodiversity and Global Change Yale University New Haven CT United States Knowledge Infrastructures Campus Fryslan University of Groningen Leeuwarden Netherlands Department of Ecology and Evolution University of Lausanne Lausanne 1015 Switzerland Institute of Earth Surface Dynamics University of Lausanne Lausanne 1015 Switzerland MARBEC Univ Montpellier CNRS Ifremer IRD Sète France Department of Biogeography and Global Change Museo Nacional de Ciencias Naturales (MNCN-CSIC) Madrid Spain University of Leeds Leeds United Kingdom University of Parma Parma Italy UMR CNRS 7058 “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN) Université de Picardie Jules Verne 1 Rue des Louvels Amiens 80000 France Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Rui Nabeiro

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias;this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias;(ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses;and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling. © The Author(s) 2023.

关键词： Biodiversity Biogeography Community ecology Ecological modelling

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Deformation Detection and Classification system for Car parts Products Using Image Processing Algorithms

引用

IOP Conference Series: Materials science and engineering 2019年第4期518卷

作者： Murthad AL-Yoonus Aqeel Adel Yaseen Mustafa Zuhaer Nayef Al-Dabagh Department of Technical Computer Engineering Faculty of Engineering AL-Kunooze University College Basrah Iraq. Department of Computer Science Faculty of Science Knowledge University Erbil Iraq.

Sensitive products deformation marking, and detection projects are becoming more interests in quality control management and widely used in automated industrial sensitive products production. The manual inspection by the workers for the sensitive industrial products is still taking long time and is not accurate and supportive enough for high quality control, automated deformations marking and detection by using image processing algorithms can increase highly the accuracy of detection for the defects in the products. In this paper, automated deformation detection and classification system based on some steps of image processing. Decision-making is made based on the percentage of deformations detection that aids for accept or reject the final car part product shape before deliver to users. In this work, the results were accurate to detecting the mismatching in industrial products for many cases. Automated deformations detections aid for better decision making to accepting or rejecting the products.

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Accumulated gradient normalization 9

Accumulated gradient normalization

引用

9th Asian Conference on Machine Learning, ACML 2017

作者： Hermans, Joeri R. Spanakis, Gerasimos Möckel, Rico Department of Electrical Engineering and Computer Science Liège University Belgium Department of Data Science and Knowledge Engineering Maastricht University Netherlands

This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a parameter server. This implies that the magnitude of a worker delta is smaller compared to an accumulated gradient, and provides a better direction towards a minimum compared to first-order gradients, which in turn also forces possible implicit momentum fluctuations to be more aligned since we make the assumption that all workers contribute towards a single minima. As a result, our approach mitigates the parameter staleness problem more effectively since staleness in asynchrony induces (implicit) momentum, and achieves a better convergence rate compared to other optimizers such as asynchronous easgd and dynsgd, which we show empirically. © 2017 J.R. Hermans, G. Spanakis & R. Möckel.

关键词： Optimization

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Correction to: Compositional and genetic alterations in Graves' disease gut microbiome reveal specific diagnostic biomarkers

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The ISME journal 2022年第4期16卷 1201页

作者： Qiyun Zhu Qiangchuan Hou Shi Huang Qianying Ou Dongxue Huo Yoshiki Vázquez-Baeza Chaoping Cen Victor Cantu Mehrbod Estaki Haibo Chang Pedro Belda-Ferre Ho-Cheol Kim Kaining Chen Rob Knight Jiachao Zhang Department of Pediatrics University of California San Diego La Jolla CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. School of Food Science and Engineering Hainan University Haikou China. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. Hubei Provincial Engineering and Technology Research Center for Food Ingredients Hubei University of Arts and Science Xiangyang Hubei province China. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. Department of Bioengineering University of California San Diego La Jolla CA USA. Scalable Knowledge Intelligence IBM Research-Almaden San Jose CA USA. School of Food Science and Engineering Hainan University Haikou China. kainch@***. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. kainch@***. Department of Pediatrics University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Bioengineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. zhjch321123@***. School of Food Science and Engineering Hainan University Haikou China. zhjch321123@***. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. zhjch321123@***.

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Expert finding in community question answering: A review

arXiv

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arXiv 2018年

作者： Yuan, Sha Zhang, Yu Tang, Jie Cabotà, Juan Bautista Knowledge Engineering Lab Department of Computer Science and Technology Tsinghua University Institute of Medical Information Peking Union Medical College Chinese Academy of Medical Sciences Computer Science Department University of Valencia

The rapid development recently of Community Question Answering (CQA) satisfies users quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. Sparse data and new features violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify all the existing solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the field of expert finding in CQA. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results can help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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A hybrid intrusion detection system: Integrating hybrid feature selection approach with heterogeneous ensemble of intelligent classifiers

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International Journal of Network Security 2018年第1期20卷 41-55页

作者： Amrita Ravulakollu, Kiran Kumar Department of Computer Science and Engineering Sharda University SET Plot No. 32-34 Knowledge Park III Greater Noida Uttar Pradesh201306 India

This paper proposes Hybrid Feature Selection Approach - Heterogeneous Ensemble of Intelligent Classifiers (HyFSA-HEIC) for intelligent lightweight network intrusion detection system (NIDS). The purpose is to classify for anomaly from the incoming traffic. This system hierarchically integrates HyFSA and HEIC. The HyFSA will obtain the optimal number of features and then HEIC is built using these optimal features. HyFSA helps to decrease the computation time of the system and make it lightweight to work in real time. The aim of HEIC is to obtain accurate and robust classifier and enhance overall performance of the system. The results demonstrate that proposed system outperforms other ensemble and single classifier methods used in this paper. It has true positive rate (99.9%), accuracy (99.91%), precision (99.9%), receiver operating characteristics (99.9%), low false positive rate (0.1%) and lower root mean square error rate (3.06%) with a minimum number of selected 6 features. It also reduces time to build and time to test the model by 50.79% and 55.30% respectively on reduced features set. The results evince that detection rate, accuracy and precision of the system is increased by incorporating feature selection approach with heterogeneous ensemble of intelligent classifiers and significantly reduce the computation time. © The Japan Society for Analytical Chemistry.

关键词： Feature Selection

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An Efficient Jaya Algorithm for Resource Allocation in the Cognitive-Radio-Networks-Aided Internet of Things

An Efficient Jaya Algorithm for Resource Allocation in the C...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Xiong Luo Zhijie He Long Wang Weiping Wang Huansheng Ning Jenq-Haur Wang Wenbing Zhao Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) Beijing China Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering and Computer Science Cleveland State University Cleveland Ohio USA

More recently, there has been an ever-increasing demand for communication network bandwidth in Internet of Things (IoT), while requiring more and more powerful technologies in using scarce spectrum resources. Given the success of big data and artificial intelligence methods in a variety of industrial applications, it is expected that they can be also employed successfully to address the above issue. Cognitive radio networks (CRNs) identified as one of the potential solutions to improve the utilization of scarce radio spectrum resources, enable IoT to be achieved with high-performance. While in CRNs aided IoT systems, dynamic resource allocation is the main task. Then, orthogonal frequency division multiplexing (OFDM) as a multi-carrier parallel radio transmission technology, has been identified as one of the main approaches well-matched for CRNs aided IoT systems. In this paper, motivated by swarm intelligence paradigm, a solution method is proposed by applying an enhanced Jaya algorithm, named S-Jaya, to address the power allocation problem in cognitive OFDM radio networks for IoT. Due to the algorithm-specific parameter-free feature of the proposed Jaya algorithm with fast convergence speed, a satisfactory computational performance would be achieved in handling this problem. The simulation results show that, for the optimization problem with some constraints, the efficiency of spectrum utilization could be further improved through the use of S-Jaya algorithm, while maximizing the total transmission rate with faster convergence speed, compared with some popular algorithms.

关键词： OFDM Resource management Optimization Interference Internet of Things Convergence Bandwidth

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Ontology Driven Semantic Profiling and Retrieval in Medical Information Systems

SSRN

引用

SSRN 2018年

作者： Bhatt, Mehul Rahayu, Wenny Soni, Sury Prakash Wouters, Carlo Cognitive Systems Lab Universität Bremen Bremen28359 Germany Data Engineering and Knowledge Management Group Department of Computer Science and Computer Engineering La Trobe University Australia

We propose the application of a novel sub-ontology extractionmethodology for achieving interoperability and improving the semantic validity of information retrieval in the medical information systems (MIS) domain. The system offers advanced profiling of a user’s field of specialization by exploiting the concept of sub-ontology extraction, i.e., each sub-ontology may subsequently represent a particular user profile. Semantic profiling of a user’s field of specialization or interest is necessary functionality in any medical domain information retrieval system;this is because the (structural and semantic) extent of information sources is massive and individual users are only likely to be interested in specific parts of the overall knowledge documents on the basis of their area of specialization. The prototypical system, OntoMOVE, has been specifically designed for application in themedical information systems domain. OntoMOVE utilizes semantic web standards like RDF(S) and OWL in addition to medical domain standards and vocabularies encompassed by the UMLS knowledge sources. © 2018, The Authors. All rights reserved.

关键词： Medical information systems

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Remotely accessible, automatic file downloading system with interactive smartphone application using embedded system

Remotely accessible, automatic file downloading system with ...

引用

2017 International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017

作者： Faheem, Mohammed Sajid, Mohammed Yusra, K Nasif, Muhammed Ali, Akbar Sanal Kumar, T.S. Department of Computer Science Engineering Eranad Knowledge City Technical Campus ManjeriKerala India

ISBN: (纸本)9781509056873;9781509056866

Internet plays a significant role that it has helped the mankind to be advanced. One of the remarkable applications of Internet is the feature that enables to download any form of data. In order to accomplish it, devices are required that has specific functions. When a PC works for downloading purpose, it uses all its resources, though they are not wholly required and leads to large power consumption. An embedded system is being proposed to be designed so as to perform uninterrupted or continuous download with very low energy consumption. The system is fabricated using an embedded circuit which isa small single board computer stamped with chips and I/O connectors. The employing of cloud Server as an intermediate online database has made the system more efficient becausecloud Server system hasbeen emerged as a novel trend in the research fields. Cloud helps in processing the exchange of data between android application and the embedded system. © 2017 IEEE.

关键词： Embedded systems

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