TOWARDS AN ENVIRONMENTALLY SUSTAINABLE AGRICULTURE: MATHEMATICS AND DEEP LEARNING IN THE FIELD

Alessandro Benfenati, Paola Causin, Roberto Oberti Università degli Studi di Milano Crop protection from diseases through applications of plant protection products is crucial to secure worldwide food production. Nevertheless, sustainable management of plant diseases is an open challenge with a major role in the economic and environmental impact of agricultural […]

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Big Data challenges for Mathematics: state of the art and future perspectives – online workshop

February 25, 2022 Aims of the workshop: Nowadays the data deluge is the hallmark of a new kind of ‘Law of Large Numbers’ that builds intelligence from large, heterogeneous, noisy and, in general, complex data sets collected from mobile devices, the Internet of Things, software logs, automated medical devices, social […]

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GENEO and Explainable Machine Learning applied to protein pocket detection

This post deals with some new geometrical techniques for explainable machine learning, called GENEOs [1], that we are applying in a research group working at University of Milan and University of Bologna, to problems of drug design and molecular docking. The research is developed in collaboration with Dompé, an Italian […]

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ECMI Webinar “Maths for Industry 4.0”, Dec. 2-3, 2020

This is the 2nd announcement for the ECMI WEBINAR “MATHS FOR INDUSTRY 4.0 – MODELS, METHODS AND BIG DATA” jointly organised by the Special Interest Groups Mathematics for Big Data and Math for the Digital Factory . The workshop brings together data scientists, mathematicians, and engineers from academia and industry to discuss recent developments […]

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ECMI Webinar “Math for Industry 4.0 – Models, Methods and Big Data”, December 2 – 3, 2020

In a joint activity of the Special Interest Groups Mathematics for Big Data and Math for the Digital Factory of the European Consortium for Mathematics in Industry (ECMI) this workshop strives to bring together data scientists, mathematicians, and engineers from academia and industry to discuss recent developments in digital manufacturing. […]

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PRecision crop protECtion: deep learnIng and data fuSION

Current farming practices require a uniform application of pesticides in order to protect crop plants from pest and disease. These treatments are typically repeated at regular time intervals. However, it is well known that several pests and diseases exhibit an uneven spatial distribution, with typical patch structures evolving around localized […]

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ITWM Researcher Stefanie Schwaar Establishes New Research Group for Artificial Intelligence

Dr. Stefanie Schwaar from the Fraunhofer Institute for Industrial Mathematics ITWM won the BMBF’s call for tenders for funding among young female AI researchers. She will establish and head her own research group at the mathematical institute from August 2020. Under the title “EP-KI: Decision Support for Business Management Processes […]

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Improving tax collection with big data analytics

Tax evasion is one of the major obstacles to increasing the competitiveness of an economy. It directly and negatively affects the conditions for business activities in the market for the companies that legally declare and pay taxes, making their production costs, and, consequently, the price of their products and services […]

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Bigmath Advanced Course 4: Large scale and distributed optimization

The goal of the training course, realized within the H2020 Marie Skłodowska-Curie project Big Data Challenges for Mathematics, Grant Agreement No 812912,  is to provide an overview of tools and algorithms in the area of large scale and distributed optimization. An illustrative examples which help in understanding how optimization-based modelling can […]

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The Experts Method for the prediction of big data streams of energy flow

We have developed a method, called Experts Method, to forecast the evolution of a multivariate set of time series, of big dimension, and with partially censored data. It has been applied to the data provided by the H2020 Big Data Horizon Prize 2017 (http://ec.europa.eu/research/horizonprize/index.cfm?prize=bigdata). The data subject to the forecast […]

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