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CZ.02.1.01/0.0/0.0/18_046/0015954 2020 - 2022
The project follows the OP VVV project CZ.02.1.01/0.0/0.0/16_013/0001787 of similar name and abbreviation. Its objective is the continuation of reconditioning and increasing the computing and storage capacity for processing data from experiments in Fermilab. The aim is to improve the infrastructure to gain new scientific knowledge in collaborative experiments in Fermilab. This project is co-funded by the EU.
This project is co-funded by the EU.
CSIC-20-12 2020 - 2021
Modal logics are a well-known tool in Artificial Intelligence and Computer Science in general, used for representing and reasoning with information about possibility and necessity, time, actions, knowledge and abilities of agents and other “modes of truth”. However, their versions based on classical logic have limitations, for example when it comes to modal reasoning in contexts involving graded predicates, resource-sensitivity, cognitively limited agents etcetera. This project will contribute to a systematic development of the theory of modal logics based on a large class of logics weaker than classical logic, namely, substructural logics. Teams from IIIA-CSIC, Barcelona, and ICS-CAS, Prague, will build on their expertise in modal and substructural logics and their previous collaborations, and achieve the project goals through a two-year period of intensive collaboration, reinforced by mutual research visits and workshops.
Mob_France 2020 - 2021
Epilepsy is a chronic neurological disorder affecting around 50 million people worldwide, characterized by repeated brain seizures, the neurophysiological mechanisms of which are still largely unknown. Recent studies show the existence of an altered brain state before seizure. We aim to identify changes in brain connectivity that correspond to these differences in brain states during pre-ictal period. We recorded high-quality intracranial EEG as well as scalp EEG data from 74 patients, each monitored for more than a week. The aim is to understand the changes in interactions between brain areas as the brain is approaching a seizure. Based on such interactions we strive to identify markers of ictogenesis that can help seizure prediction and treatment. The proposed project will deepen the current cooperation between both institutes.
2020 - 2025
MSM100302001 2020 - 2021
Human thermal environment is in researchers’ interest for several decades. At the same time, with increasing computation power is possible to solve more complex problems. One of them, urban climate, demands on resolution raises the question, whether turbulence should be treated as a (partly) resolved or as a (complete) sub-grid scale process. Atmospheric modelling of urban areas usually considers a limited number of relevant processes, alternatively it is performed with coarser resolution which cannot properly describe the conditions in street canyons. PALM-4U is the first open-source meteorological model based on large-eddy simulation (LES) principle with implementation of the most of urban canopy related processes. The recent version of PALM-4U include Urban Surface Model (USM) and Radiative Transfer Model (RTM), necessary modules for calculation of MRT for biometeorology module. Those features open new challenges in street-level scale research of biometeorology.
20-27757Y 2020 - 2022
The project focuses on problems in the overlap of discrete mathematics and probability theory. We consider basic discrete structures: graphs, digraphs, trees, uniform hypergraphs, which in applied areas are used as abstract models for networks, population dynamics, etc. We will study randomly generated discrete structures from a theoretical perspective. Focusing on random variables which count the number of certain substructures (e.g., copies of a given graph), we seek answers to the following questions: what are their asymptotic distributions, how likely are events that these random variables deviate significantly from their expected values. Among the objectives of the project is to study the similarity between the random regular graph and the binomial graph by resolving the Sandwiching Conjecture of Kim and Vu; estimating the upper tail probability for small subgraph counts in sparse random graphs; determining the limit distribution of functionals (e.g., number of large matchings, maximum independent set) in random graphs and Galton-Watson trees.
CZ.02.2.69/0.0/0.0/19_074/0016209 2019 - 2021
Memory consolidation, a prominent example of higher cognitive processes, relies on two important neural phenomena: slow- wave sleep with a wealth of distinct rhythms, and cross-frequency coupling between these rhythms. Neither of these processes is, to this day, fully understood. However, according to the two-stage model of memory consolidation, the interplay between slow oscillations and sleep spindles by means of phase-amplitude coupling, as well as the interplay between sleep spindles and hippocampal sharp-wave ripples, seem to promote neural plasticity and initiate a cortical- hippocampal dialogue that leads to experience replay and, ultimately, migration of newly encoded memories to longer-lasting storage. The overall aim of this project is to shed light on the slow oscillation-spindle interplay using a biologically realistic neural mass model and, additionally, reproduce the cross-frequency phenomena.
This project is co-funded by the EU.
GJ19-06792Y [Registered results] 2019 - 2021
This research project deals with two popular topics in Combinatorial and computational geometry: visibility and Voronoi diagrams. The first concrete topic is visibility in terrains in the presence of multiple observers. This variant has received much less attention than the case of a single guard and presents a great number of applications. Given a terrain and a set of observers, the most fundamental question is being able to describe which parts of the terrain are visible by at least one of the observers; we will try to improve on the current fastest algorithms to solve this problem. We also plan to study approximate versions of the visibility maps, and realistic settings where the observers or the terrain satisfy some natural assumptions. The second topic concerns the farthest color Voronoi diagram, which has not been as studied as other types of Voronoi diagrams. We intend to get new insights on the structure of this diagram, and explore their algorithmic consequences.
TN01000024 [Registered results] 2019 - 2020
The NCK KUI project aims to create a national platform for cybernetics and artificial intelligence which interlinks research and application oriented centers of robotics and cybernetics for Industry 4.0, Smart Cities, intelligent transport systems and cybersecurity. The connection of innovation leaders will raise effectivity of applied research in key areas, as advanced technology for globally competitive industry, ICT and transportation for the 21st century. NCK KUI is closely related to application sector and enables cross-domain collaboration, innovation development and technology transfer.
GA19-08740S [Registered results] 2019 - 2021
Graphs are among the simplest mathematical structures. They forms the foundation of much of Computer Science and their importance grew enormously with the development of computer networks. In this project, we focus on central problems from extremal graph theory, as well as the recent related area of limits of graphs. We shall exploit classical methods from extremal graph theory, as well as probabilistic and analytical methods. Our main topics are embedding problems, packing problems, and the study of graph limits via a weak* topology approach.
GA19-05704S [Registered results] 2019 - 2021
The computational paradigm has recently been shifted towards intelligent information processing which can be demonstrated by tremendous success of (deep) neural networks (NNs) producing state-of-the-art results in artificial intelligence. The majority of techniques used in NNs are of heuristic nature and therefore only partially theoretically justified. FoNeCo is a basic research project whose ambition is to contribute to the development of analytical foundations of selected practical neurocomputing models. Its aim is to characterize the computational power of subrecursive NNs between integer and rational weights using quasiperiodicity, and bio-inspired NNs based on synfire rings, to study approximation properties (e.g. model complexity) and robust fitting of various regression NNs, and to analyze the complexity of loading deep NNs. The analysis of respective NN models will be the basis of new architectures and more efficient, reliable, and theoretically-founded learning algorithms which will be implemented as open-source software and experimentally tested on benchmark problems.
GA19-19463S [Registered results] 2019 - 2021
This is a basic research project in which we plan to work on problems in the area of knowledge compilation. We consider a particular case of knowledge compilation where the knowledge base is represented by a boolean function, usually in a conjunctive normal form (CNF) formula. We plan to study a compilation of such a formula into another CNF formula which is complete for unit propagation. More precisely, we will consider compilation into the target language of unit refutation complete formulas (URC), the language of propagation complete formulas (PC), and the corresponding variants of encodings with auxiliary variables, namely URC and PC encodings. The goal of this project is to solve theoretical questions related to compilation into URC or PC representation or encoding and develop a compiler for this task. To this end we shall develop and test algorithms and heuristic for automatic compilation. The outputs of this project will be published in journal and conference papers.
GA19-11753S [Registered results] 2019 - 2021
The project’s focus is the dynamics of brain processes during spatial scene processing in the dorsal and ventral visual streams. While the dorsal stream, going from visual cortex to parietal lobe, processes mainly the spatial information, the ventral stream, going to the temporal lobe, is focused on object recognition. The level of separation of the two streams and their interconnection is not fully established. Both streams converge in the temporal lobe, enabling the emergence of the spatial representation containing orientation marks, but the precise localization and dynamics of this connection is unclear. The project will examine several models of cooperation between these visual streams and the information flow between occipital, parietal and temporal lobe and retrosplenial cortex. Using intracranial recordings in epileptic patients, we will determine the time windows of activation and synchronization of these brain areas with high time resolution. The project will bring results concerning the dynamics of brain processes fundamental for visual scene processing theories.
GA19-16066S [Registered results] 2019 - 2021
Theory of synchronization of nonlinear dynamical systems and information theory meet in effort to understand cooperative behaviour in complex systems. Yet features such as multiscale dynamics and fat-tailed probability distributions have not been adequately addressed in development of tools for uncovering interactions and causal information flow from experimental time series. In this project we will employ and further develop methods of information theory and superstatistics in order to detect and quantify internal dynamics and information flow in real-world complex systems in which extreme events occur. In particular, we will study Shannon and Renyi information transfer in multivariate and multiscale time series and advance the application of the superstatistics paradigm in complex dynamics with non-Gaussian fluctuations. The primary application ground will be the multivariate meteorological data reflecting the changing Earth climate, evolving on multiple time and space scales.
QK1910281 [Registered results] 2019 - 2022
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.
TL01000238 [Registered results] 2018 - 2022
The main objective of the proposed multidisciplinary project is to conduct vulnerability analysis and to create a methodology of integrated vulnerability assessment of cities and their inhabitants to temperature extremes, using the classification of urban surfaces (delivered 02/2022), aiming on supporting the adaptation planning and adaptation measures in cities. Detailed spatial vulnerability assessment will be performed for three pilot cities (Prague, Brno, Ostrava), using climatological analysis and participatory approaches, considering projections of future developments (climatological scenarios, land cover changes, socio-demographic trends), with an output of specialized vulnerability maps. The integrated assessment aims to contribute to the sustainable urban planning in Czechia.
GA18-23827S [Registered results] 2018 - 2020
The goal of the project is to contribute to development of neurocomputing by combining theoretical and experimental research investigating capabilities and limitations of shallow and deep artificial neural networks from the point of view of their model complexity and robustness of learning.
UH0383 [Registered results] 2018 - 2020
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).
This project is co-funded by the EU.
GA18-00113S [Registered results] 2018 - 2020
The goal of the grant is to create logical framework to model reasoning with graded predicates. We distinguish several types of such predicates, discuss their ubiquity in rational interaction and the logical challenges they may pose. We plan to utilize mathematical fuzzy logic as a set of logical tools that allow to model reasoning with graded predicates and concentrate on a philosophical account of the vagueness problem that makes use of these tools. Other aim is to generalize this approach to other kinds of graded predicates a lay foundation for a general research program towards a logic-based account of reasoning with graded predicates.
GA18-18080S [Registered results] 2018 - 2020
The proposed project of basic research in the area of multimedia data mining attempts to contribute to further development of methods for knowledge discovery in video data recording human activities. It concentrates on the one hand on combining knowledge about the detected human bodies, their parts and their motion with other knowledge obtainable from the available data, such as knowledge about scene, context, or person-object interactions, on the other hand on research into different ways of fusion of multiple classification or regression models during such knowledge discovery, including fusion based on deep artificial neural networks. These objectives are supported by experimental equipment and by the composition of the project team. At the Faculty of Information Technology of the Czech Technical University, a modern Image Processing Laboratory has been recently inaugurated, and the team includes, apart from the head of the laboratory and several PhD students performing in it their research, also two internationally recognized researchers in the area of data mining.
GJ18-19162Y [Registered results] 2018 - 2020
Reasoning about modifications of information available to cognitive agents (about information dynamics) is an essential part of many everyday activities. A better understanding of mechanisms of such reasoning is important both from the practical viewpoint and in the context of epistemology. The project studies logical models of information dynamics based on non-classical logics. These models are mode adequate that the standard model based on classical logic. Their advantages include a better representations of information modifications resulting from reasoning of logically imperfect agents, a better representation of graded acceptance of information and the fact that they are able to model dynamics of questions in the context of logically imperfect agents. The project will provide a complete picture of some of these models. We will provide new results concerning their properties and study their applications in epistemology. The project will therefore lay a technically sound foundation of future research in the intersection of formal epistemology and philosophical logic.
NV17-28427A [Registered results] 2017 - 2021
Cognitive impairment and epilepsy are the most common and serious post-stroke comorbidities. They have far-reaching impacts on patient health status and decrease the efficacy of rehabilitation and delay the recovery. Severe attention deficits, post-stroke dementia or epilepsy are comorbidities which have failed to be explained in terms of being a direct conseguence of localized damage to specific brain structures. Converging evidence suggests that understanding the emergence of these comorbidities requires radically new research paradigms that consider the impact of stroke on whole-brain connectivity. In this multidisciplinary project we seek to brain areas with high connectivity and information transfer - network hubs. Application of such a fundamental and innovative framework has potential to advance our understanding of stroke consequences and to develop early, network-targeting. therapeutic interventions to prevent or ameliorate these detrimental comorbiditiens.
NV17-29622A [Registered results] 2017 - 2021
Retrospective analysis of the consecutive data on prenatally and postnatally diagnosed cases of birth defects (BD) diagnosed in the Czech Republic between 1994 and 2015. We will perform 1) an analysis of incidences of prenatally diagnosed cases of BDs in the Czech Republic; 2) an analysis of incidences of postnatally diagnosed cases of BDs in the Czech Republic (incidence in births); 3) an analysis of effectiveness of prenatal diagnosis according to the individual diagnoses of BDs; 4) an analysis of stillbirth rate, perinatal neonatal and infant mortality in children with BDs.