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arXiv 2021年

作者： David, Etienne Serouart, Mario Smith, Daniel Madec, Simon Velumani, Kaaviya Liu, Shouyang Wang, Xu Pinto, Francisco Shafiee, Shahameh Tahir, Izzat S.A. Tsujimoto, Hisashi Nasuda, Shuhei Zheng, Bangyou Kichgessner, Norbert Aasen, Helge Hund, Andreas Sadhegi-Tehran, Pouria Nagasawa, Koichi Ishikawa, Goro Dandrifosse, Sébastien Carlier, Alexis Dumont, Benjamin Mercatoris, Benoit Evers, Byron Kuroki, Ken Wang, Haozhou Ishii, Masanori Badhon, Minhajul A. Pozniak, Curtis LeBauer, David Shaner Lillemo, Morten Poland, Jesse Chapman, Scott de Solan, Benoit Baret, Frédéric Stavness, Ian Guo, Wei Arvalis Institut du végétal 3 Rue Joseph et Marie Hackin Paris75116 France UMR1114 EMMAH INRAE Centre PACA Bâtiment Climat Domaine Saint-Paul 228 Route de l’Aérodrome CS 40509 Avignon Cedex84914 France School of Food and Agricultural Sciences The University of Queensland GattonQLD4343 Australia Hiphen SAS Agroparc Bâtiment Technicité 120 rue Jean Dausset Avignon84140 France Plant Phenomics Research Center Nanjing Agricultural University Nanjing China Wheat Genetics Resource Center Dep. of Plant Pathology Kansas State Univ. 4024 Throckmorton Plant Sciences Center ManhattanKS United States Mexico D.F. Mexico Norwegian University of Life Sciences Faculty of Biosciences P.O. Box 5003 ÅsNO-1432 Norway Agricultural Research Corporation Wheat Research Program P.O. Box 126 Wad Medani Sudan Arid Land Research Center Tottori University Tottori680-0001 Japan Laboratories of Plant Genetics and Plant Breeding Graduate School of Agriculture Kyoto University Japan CSIRO Agriculture and Food Queensland Biosciences Precinct 306 Carmody Road St LuciaQLD4067 Australia Institute of Agricultural Sciences ETH Zurich Universitätstrasse 2 Zurich8092 Switzerland Plant Sciences Department Rothamsted Research Harpenden United Kingdom Institute of Crop Science National Agriculture and Food Research Organization Japan Hokkaido Agricultural Research Center National Agriculture and Food Research Organization Japan Biosystems Dynamics and Exchanges TERRA Teaching and Research Center Gembloux Agro-Bio Tech University of Liège Gembloux5030 Belgium Plant Sciences TERRA Teaching and Research Center Gembloux Agro-Bio Tech University of Liège Gembloux5030 Belgium Graduate School of Agricultural and Life Sciences The University of Tokyo 1-1-1 Midori-cho Nishitokyo City Tokyo Japan Department of Computer Science University of Saskatchewan Canada Department of Plant Sciences University of Saskatchewan Canada College of Agriculture and Life Sciences University o

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience in 2020, a few avenues for improvements have been identified, especially from the perspective of data size, head diversity and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and augmented by adding 1,722 images from 5 additional countries, allowing for 81,553 additional wheat heads to be added. We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version. The GWHD 2021 is now publicly available at http://***/ and a new data challenge has been organized on AIcrowd to make use of this updated dataset. © 2021, CC BY.

关键词： Object detection

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Rethinking meta-analysis: Addressing problems of non-transportability when combining treatment effects across patient populations

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Revue d'Épidémiologie et de Santé Publique 2019年 67卷 S121-S122页

作者： T. Vo R. Porcher S. Vansteelandt Université Paris Descartes Inserm UMR1153 Epidemiology and Statistics Sorbonne Paris Cité Research Center (CRESS) METHODS Team Paris France Ghent University Department of Applied Mathematics Computer Science and Statistics Gent Belgique

Introduction Standard methodology for meta-analysis (MA) only focuses on deriving a summary estimate but remains implicit about the patient population for which it describes the treatment effect. However, the evidence about treatment effect in one population cannot always be applied to another population that differs possibly in many aspects from that of the original trial, in which case we say there is lack of transportability. In practice, standard MAs do not always ensure homogeneity in the baseline characteristics of the different trial populations; thereby do not rule out a potential lack of transportability. Besides, standard MAs often overlook the under-representation of low- and middle-income countries in the available evidence for several medical interventions. As a consequence, simply meta-analyzing results of trials conducted in developed countries will be not informative for policymakers in low-income countries to implement the most efficient interventions for their populations. Method In this paper, we remedy these issues by proposing an alternative MA approach for randomized clinical trials, which uses individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on case-mix standardization. Denote Y(1 k ) and Y(0 k ) as the counterfactual outcome that would be observed for a given individual if (version of) treatment and control used in trial S = k were administered, respectively. Our focus is on estimating the probabilities P{Y(x k ) = 1|S = j} (x = 0,1), which are the chance of successfully treated if the patients in population S = j was given the version of treatment (x = 1) or control (x = 0) used in study S = k. Based on these, the effect of the treatment version in population can be measured–for instance, on the relative risk scale RR(j,k) = P{Y(1 k ) = 1|S = j}/P{Y(0 k ) = 1|S = j}. Under certain conditions, RR(j, k) can be estim

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Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

npj Imaging

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npj Imaging 2024年第1期2卷 1-12页

作者： Okyaz Eminaga Fred Saad Pierre I. Karakiewicz Zhe Tian Véronique Ouellet Feryel Azzi Dominique Trudel Ulrich Wolffgang Tilmann Spieker Burkhard M. Helmke Markus Graefen Sami-Ramzi Leyh-Bannurah Xiaoyi Jiang Lei Xing Jorn H. Witt AI Vobis Palo Alto CA USA Division of Urology Department of Surgery Centre Hospitalier de l’Université de Montréal University of Montreal Montréal QC H2X 0A9 Canada Cancer Prognostics and Health Outcomes Unit University of Montreal Health Center Montreal QC Canada Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM) 900 Saint-Denis Montréal QC H2X 0A9 Canada Institut du cancer de Montréal 900 Saint-Denis Montréal QC H2X 0A9 Canada Department of Pathology and Cellular Biology Université de Montréal 2900 Boulevard Édouard-Montpetit Montreal QC H3T 1J4 Canada Department of Pathology Centre Hospitalier de l’Université de Montréal (CHUM) 1051 Sanguinet Montreal QC H2X 0C1 Canada University of Münster Münster Germany Institute of Pathology St. Franziskus-Hospital Muenster Germany Department of Pathology University of Muenster Muenster Germany Institute of Pathology Elbe Klinikum Stade Academic Teaching Hospital of The University Medical Center Hamburg-Eppendorf (UKE) Hamburg Germany Martini-Klinik Prostate Cancer Center University Hospital Hamburg-Eppendorf Hamburg Germany Prostate Center Northwest Department of Urology Pediatric Urology and Uro-Oncology St. Antonius-Hospital Gronau Germany Department of Computer Science University of Muenster Muenster Germany Department of Radiation Oncology - Radiation Physics Stanford University School of Medicine Stanford CA USA

Malignancy grading of prostate cancer (PCa) is fundamental for risk stratification, patient counseling, and treatment decision-making. Deep learning has shown potential to improve the expert consensus for tumor grading, which relies on the Gleason score/grade grouping. However, the core problem of interobserver variability for the Gleason grading system remains unresolved. We developed a novel grading system for PCa and utilized artificial intelligence (AI) and multi-institutional international datasets from 2647 PCa patients treated with radical prostatectomy with a long follow-up of ≥10 years for biochemical recurrence and cancer-specific death. Through survival analyses, we evaluated the novel grading system and showed that AI could develop a tumor grading system with four risk groups independent from and superior to the current five grade groups. Moreover, AI could develop a scoring system that reflects the risk of castration resistant PCa in men who have experienced biochemical recurrence. Thus, AI has the potential to develop an effective grading system for PCa interpretable by human experts.

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Discovery of interesting effectiveness itemsets with several minimal thresholds using average constraint

Discovery of interesting effectiveness itemsets with several...

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International Conference on Systems and Informatics (ICSAI)

作者： Weimin Ouyang Department of Computer Teaching Shanghai University of Political Science and Law Shanghai China

Discovery of interesting effectiveness itemsets has been a hot topics in data mining and knowledge discovery. Most previous researches related to effectiveness mining in literature employ a individual minimal threshold of effectiveness to decide on if an item is a interesting effectiveness item. Nevertheless, a individual minimal threshold of effectiveness could not express the varied natures of diverse items. In this paper, the author put forward a algorithm to discover interesting effectiveness itemsets with several minimal thresholds using average constraint. The reports of tests demonstrated that our algorithm is better than other baseline algorithms in performance.

关键词： Itemsets Upper bound Data mining Knowledge discovery Informatics Education Portable computers

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Keep Your Friends Close and Your Colleagues Nearby: The Hidden Ties That Improve STEM Education

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Change 2019年第1期51卷 42页

作者： Mestre, Jose P. Herman, Geoffrey L. Tomkin, Jonathan H. West, Matthew Jose P. Mestre (mestre@illinois.edu). A theoretical nuclear physicist by training Mestre’s research focuses on the learning of physics contributing in areas such as the acquisition and use of knowledge by experts and novices transfer of learning problem solving and instructional design. He has offered Congressional testimony on The Science of Learning has published numerous articles and books and is a Fellow of the American Physical Society.http://***/0000-0003-2110-1954 Geoffrey L. Herman (glherman@illinois.edu). Herman has a Ph.D. in Electrical and Computer Engineering and is a Teaching Assistant Professor in Computer Science and a Research Assistant Professor in the Department of Curriculum & Instruction at the University of Illinois. His research focuses on how students learn engineering and computing concepts and studying processes for creating systemic instructional change in college settings. Previously he was a postdoctoral researcher in Engineering Education at Purdue University.http://***/0000-0002-9501-2295 Jonathan H. Tomkin (tomkin@illinois.edu). Tomkin is a mathematically based geoscientist with two research focus areas: first he uses models and large data sets to determine how climates glaciers tectonics and landscapes interact. Second he explores how new technological and organizational approaches can improve STEM education. He produced the first geoscience MOOC is the editor of an open-source textbook and has published widely in both geoscience and STEM education. Matthew West (mwest@illinois.edu). Matthew West is an Associate Professor of Mechanical Science and Engineering at the University of Illinois with research interests in scientific computing that include time integration of deterministic and stochastic systems distributed computation and particle methods for simulation and estimation. In education he researches computer-based assessment and the development of innovative teaching cultures. Professor West is a University of Illinois Distinguished Teacher-Scholar and Colleg

A science department on the campus of the University of Illinois has received numerous national awards for excellence in undergraduate teaching. The secret to their success? For nearly two decades, they have collaboratively used evidence-based instructional practices (EBIP) in their introductory level courses. Despite these successes and efforts to spread their EBIP, no other departments followed this department's lead. Then, suddenly six years ago, many instructors from other departments began adopting EBIP in their classrooms. This article asks what was the catalyst for this change? Why had previous attempts at institutional change failed? The authors answer these questions by analyzing their efforts to transform the teaching practices in large-enrollment science and engineering introductory courses on their campus.

关键词： STEM Education Evidence Based Practice teaching methods Undergraduate Study College Faculty Educational Change Communities of Practice Mentors School CultureIllinois

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Class switching ensembles for ordinal regression

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10305 LNCS卷 408-419页

作者： Gutiérrez, Pedro Antonio Pérez-Ortiz, María Suárez, Alberto Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Quantitative Methods Universidad Loyola Andalucía Córdoba Spain Computer Science Department Universidad Autónoma de Madrid Madrid Spain

ISBN: (纸本)9783319591520

The term ordinal regression refers to classification tasks in which the categories have a natural ordering. The main premise of this learning paradigm is that the ordering can be exploited to generate more accurate predictors. The goal of this work is to design class switching ensembles that take into account such ordering so that they are more accurate in ordinal regression problems. In standard (nominal) class switching ensembles, diversity among the members of the ensemble is induced by injecting noise in the class labels of the training instances. Assuming that the classes are interchangeable, the labels are modified at random. In ordinal class switching, the ordering between classes is taken into account by reducing the transition probabilities to classes that are further apart. In this manner smaller label perturbations in the ordinal scale are favoured. Two different specifications of these transition probabilities are considered;namely, an arithmetic and a geometric decrease with the absolute difference of the class ranks. These types of ordinal class switching ensembles are compared with an ensemble method that does not consider class-switching, a nominal class-switching ensemble, an ordinal variant of boosting, and two state-of-the-art ordinal classifiers based on support vector machines and Gaussian processes, respectively. These methods are evaluated and compared in a total of 15 datasets, using three different performance metrics. From the results of this evaluation one concludes that ordinal class-switching ensembles are more accurate than standard class-switching ones and than the ordinal ensemble method considered. Furthermore, their performance is comparable to the state-of-the-art ordinal regression methods considered in the analysis. Thus, class switching ensembles with specifically designed transition probabilities, which take into account the relationships between classes, are shown to provide very accurate predictions in ordinal regression prob

关键词： Regression analysis

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FCA-based -iceberg core decomposition in graphs

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Journal of Ambient Intelligence and Humanized Computing 2017年第2期15卷 1423-1428页

作者： Hao, Fei Xinchang, Khamphaphone Park, Doo-Soon Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an China Department of Computer Science and Engineering Soonchunhyang University Asan South Korea Department of Computer Software Engineering Soonchunhyang University Asan South Korea

Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, $$\theta$$ -Iceberg Core decomposition in graphs, which is able to incorporate a parameter $$\theta$$ ( $$0<\theta \le 1$$ ) used for relaxing the output of k-cores. Further, we propose a formal concept analysis based approach for $$\theta$$ -Iceberg Core decomposition. The proposed approach and its conclusions can provide theoretical basis and guidance for the potential applications of $$\theta$$ -Iceberg Core analysis in complex networks.

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Analysis on Real time Perception Technology of Wireless Sensor Network in CPS

Analysis on Real time Perception Technology of Wireless Sens...

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2016应用物理、光电子学和光学国际研讨会（APOP 2016）

作者： Zhou Benhai Teaching Department of Computer Science Shenyang Institute of Engineering

ISBN: (纸本)9781510825994

Cyber Physical Systems(CPS) combines physical and computing systems *** operating systems(OS) are fundamental units in *** are still many problems unsolved when designing CPS especially CPS node OS in aspects of predictability,reliability,robustness,*** at the problem,this paper proposes the effective shortest time priority algorithm and the adaptive shortest time priority *** results show that,compared with the traditional FIFO(advanced first in first out)and LSF(least slack first) algorithm,the algorithm proposed in this paper effectively reduce the deadline miss ratio,as a result,the real-time performance of the CPS are effectively improved.

关键词： LSF Analysis on Real time Perception Technology of Wireless Sensor Network in CPS Sensor

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Author Correction: Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

npj Imaging

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npj Imaging 2024年第1期2卷 1页

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

Summary Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood *** We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor *** The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy *** By co-analysing geospatial trends in d

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