Student IT&Math Conferences in Visegrad - 2016
(Standard Visegrad Project No. 21520043)
The subject of the project is to improve the methods for data processing of Fermilab experiments and design of new analyzing procedures of these data based on machine learning algorithms and artificial intelligence. The goal is to continually improve the potential of acquiring new scientific knowledge on collaborative experiments in Fermilab, the development of new innovative data processing methods in high energy physics and the improvements of computing infrastructures of participating experimental institutions in Fermilab. The target group of the project results are the researchers of universities and research and development experts in general. From project outputs benefit experts from all research institutions cooperating experiments in Fermilab.
01. 04. 2017 - 31. 03. 2020
Theoretical computer science uses mathematical methods in order to clarify some of the most important notions used in computer science and, therefore, it is a key research area within computer science. Logic is an important part of theoretical computer science, focusing on modelling bodies of information and ways how agents use them in reasoning. So called non-classical logics, alternatives to classical logic, aim at a better representation of various aspects of information and reasoning than the representations based on classical logic (these aspects include vagueness, incompleteness and inconsistency of information or limitations of reasoning agents). Within this project, the Institute of Computer Science hosts three researchers working on non-classical logics and their applications to specific problems in theoretical computer science. The overall goal of the project is to study (i) applications of non-classical logics in program verification, (ii) applications of non-classical logics in the study of weighted structures (with focus on the Valued Constraint Satisfaction Problem) and (iii) non-classical logics with so-called generalized quantifiers (with focus on their computational properties).This project is funded by the European structural and investment funds within the Operational Programme Research, Development, Education.
01. 05. 2018 - 30. 04. 2020
This project will develop statistical tools for prediction of crop pests in the age of precision agriculture. The developed methodologz will be based on modern semiparametric and dznamical modeling in the GAM framework. The models will be developed in several variants and the most suitable model will be selected by formalized statistical procedures. Based on the validated model, we will construct both routine predictions and derive recommendations for crop management timing.
01. 01. 2019 - 30. 12. 2022
One of the key directions of data mining, particularly important if human-comprehensibility plays a role, is the extraction of rules from data. The project aims at rules with consequents corresponding to numerical variables. In spite of the ubiquity of such variables, rules extraction is not yet as mature for them as for classification and association rules. The main objective of the project is to develop a framework to enable assessing different algorithms for the extraction of rules with numerical consequents from a given dataset. Traditional algorithms view consequent variables as responses and antecedent variables as regressors of regression models. They are complemented by emerging algorithms of computational topology. The framework will be based on metalearning, i.e., learning from metadata about the past performance of the algorithms on datasets with similar values of metafeatures. Metalearning has been for several decades successfully used in classification and some other areas of data mining, but its application to the extraction of this kind of rules is novel. Targets: development of a metalearning framework for the extraction of rules with numerice consequents, search for metafeatures in two selected application domains, research into robust metalearning, validation of the metalearning framework on metadata from both application domains.
01. 01. 2017 - 31. 12. 2019
Classical mathematical logic, built on the conceptually simple core of propositional Boolean calculus, plays a crucial role in modern computer science. A critical limit to its applicability is the underlying bivalent principle that forces all propositions to be either true or false. Propositional logics of graded notions (such as tall, rich, etc.) have been deeply studied for over two decades but their predicate extensions (accommodating, among others, modalities and quantifiers) are still only very partially developed and scarcely applied to particular komputer science problems. The overall goal of the proposed project is to develop predicate graded logics in two complementary directions: (1) studying logical systems in full generality in order to provide a solid mathematical framework and (2) applying achieved results to three particular problems in computer science which heavily involve graded notions: representation of vague and uncertain knowledge, valued constraint satisfaction problems, and modelling of coalition games. We plan to develop predicate graded logics by giving them solid mathematical foundations and applying the achieved results to three particular computer science problems involving graded notions: management of uncertainty, valued constraint satisfaction problems, and modelling of coalition games.
01. 01. 2017 - 31. 12. 2019
RI serves for Czech contribution to particle physics research on experiments at Fermilab. It consists of experiments on which Czech physicists collaborate in Fermilab and of infrastructures of the Czech collaborating institutions.Members of RI work on the Fermilab's experiments NOvA, D0 and plan to join a new experiment in two years to contribute to its design and construction. In the Czech Republic it is a RCCPP computing farm and physics laboratory in FZU, cluster for artificial intelligence and neural networks algorithms in ICS and numerical and statistical computing servers at CTU. The whole infrastructure serves for particle physics experiments and for researchers for many years. The RI as top world research environment serves also for education of undergraduate and postgraduate students.
01. 01. 2016 - 31. 12. 2019
Ductile or brittle behavior of cracks is one of the key phenomena which may have a crucial influence on static and dynamic strength of mechanical structures utilizing bcc iron based materials, e.g. ferritic steels. Continuum predictions on ductile/brittle behavior of a central crack under biaxial tension show that the change of called T-stress can change ductile crack behavior to brittle crack extension. We utilize 3D atomistic molecular dynamic (MD) simulations in bcc iron at various temperatures to verify predictions on ductile-brittle transition caused by T-stress. It will be done for central cracked specimens under biaxial tension and as well for edge cracked samples under uniaxial tension, available for experiments. The topic is important for reactor pressure vessels and interpretation of fracture experiments. Another important aim is interconnecting with first-principles calculations for model clusters of restricted size, pointed at cohesive energy, tension and shear strength, atomic configurations and forces at defects,determining interatomistic potential parameters for MD.
01. 01. 2017 - 31. 12. 2019
The project Urbi Pragensi addresses improvements and implementation of the weather prediction and air quality prediction for region of Prague together with more detailed assessment of impacts of climate change in the city. The prediction improvements are achieved by utilization of the modern methods based on incorporation of parameterization of urban level processes into the high resolution atmospheric models. Complementary approach are microscale simulations which can expose in detail situation in given parts of the town with substantial burden of heat island and air pollution. This can contribute to estimation of the health risks as well as to efficiency assessment of proposed mitigation measures. ICS is involved in works on concept KK1 (meteorological prediction), KK2 (air quality prediction) and it is the coordinator of the concept KK4 (microscale simulations). The main partner of the project is Charles University in Prague, another partner is Czech Hydrometeorological Institute in Prague. In the scope of the project, we tightly collaborate with other Czech and foreign academic institutions (from Germany and Finland). We also tightly cooperate with principal intended users of the project outputs (e.g. Prague municipality and Prague Institute of Planning and Development).
01. 01. 2018 - 30. 06. 2020
