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SEP-210669451 2021 - 2024
Modal logics are a family of formal systems based on classical logic which aim at improving the expressive power of the classical calculus allowing to reason about “modes of truth”. The aim of the present proposal is to put forward a systematic study of substructural modal logics, understood as those modal logics in which the modal operators are based upon the general ground of substructural logics, weaker deductive systems than classical logic. Our aim is also to explore the applications of substructural modal logics outside the bounds of mathematical logic and, in particular, in the areas of knowledge representation; legal reasoning; data privacy and security; logical analysis of natural language. This is a 4-year project in the framework H2020-MSCA-RISE-2020: Research and Innovation Staff Exchange.
NU21-08-00432 2021 - 2024
Schizophrenia is a chronic, severe and profoundly disabling disorder. For every 100 individuals with schizophrenia, only 1 or 2 individuals per year meet the recovery criteria, and approximately 14% recover over 10 years, with poor functional outcome for 27% of patients. There is an urgent need to develop predictive models of outcome to be applied in the initial stages of illness and thus optimize and intensify intervention programs to avoid an aversive outcome. Functional outcomes are difficult to predict solely on the basis of the clinical features, but Magnetic Resonance Imaging (MRI), particularly multi-modal, holds promise for improved stratification of patients. The aim of this project is to develop tools to predict the functional outcome of schizophrenia from neuroimaging, clinical and cognitive measurement taken early after the disease onset. To overcome the limitations due to high dimensionality of MRI data, we shall apply a combination of robust machine-learning tools, data-driven feature selection as well as theory-based constraint to key brain networks characteristics.
TL05000008 2021 - 2023
The aim of the proposed multidisciplinary project is to create a set of procedures and tools for a complex analysis of educational tests and to promote the implementation of proposed procedures and tools in school admission tests, school leaving examinations, and other educational assessments. The project will provide instant analysis of educational tests including a comparison of parallel test variants as well as a detailed analysis of between-group differences, e.g. in terms of gender or school type. The proposed set will facilitate the analysis of data from administered tests and support the development of new tests used to evaluate educational outcomes or to select applicants. It will also support evidence-based decision-making at the school level and inform national policy-making.
21-32608S 2021 - 2024
Current psychological theory provides complex description of mental functions and processes. It is generally accepted that mental functions have brain as their substrate, and that mental processes and states are reflected in brain activity dynamics. A rapidly developing area of brain research is the study of spontaneous brain activity with functional magnetic resonance imaging, allowing simultaneous measurement of activity dynamics of a plethora of brain networks. It has been suggested that during resting state condition, the brain explores its dynamic repertoire of possible states. However, the structure and dynamics of such exploration remains elusive. We propose to use a combination of data analysis techniques, simultaneous EEG/fMRI measurement of both spontaneous and richly stimulated mental activity and comparison with the ever-growing body of functional neuroanatomical knowledge to characterize the state repertoire and transition dynamics of spontaneous mental activity as observable by neuroimaging methods.
21-09458S 2021 - 2024
Decision procedures for predicate logical theories play an increasingly important role in computer science, especially in combination with Boolean satisfiability solvers, that is, in SAT modulo theory (SMT) solvers. While there is a vast amount of current research on decision procedures for integers, real numbers, arrays, and many other theories, there is almost no results for the case of real functions, although such functions play a fundamental role in many areas of computer science and mathematics. We conjecture that the reason for this situation is the difficulty of the problem which we propose to overcome by designing so-called quasi-decision procedures for real functions. A quasi-decision procedure relaxes the decision problem in such a way that it is not required to terminate in borderline cases where the satisfiability of the input formula changes under small perturbations of this formula. In many applications, such borderline cases are actively avoided, and hence quasi-decision procedures can solve precisely those cases that are important in such applications.
21-14727K 2021 - 2024
The concept of synchronization of nonlinear dynamical systems will serve as basis for development of mathematical methods and computer algorithms for detection and characterization of interactions and dependence in multivariate nonlinear time series. Directional links and causal relations will be quantified using the tools of information theory. The developed methods will be tailored to specific properties of scalp electroencephalogram (EEG). Overall structure of EEG synchronization, reflecting the functional integration within and across different spatial and temporal scales, will be classified in machine learning algorithms as a candidate method for description of brain states and their changes due to mental disorders. In particular, synchronization and its changes in EEG of depressive patients will be tested as predictors of antidepressant therapeutic efficacy. The developed methods will be applicable not only in analysis of electrophysiological signals in neurology and psychiatry, but generally in analysis of complex multivariate and multiscale signals.
TO01000219 2021 - 2023
The main aims of the project are to: - considerably improve spatial resolution and quality of the urban atmospheric environment assessment on the basis of state-of-the-art modeling, observation and data analysis technologies; - improve and validate advanced modelling tools with focus on modelling of the turbulent flow in complex urban environment; - improve methods for combination of observational and model data; - compare environmental effects of the selected impactful urban policy measures in Prague and Bergen; - equip public authorities with a set of tools to support urban governance: focus on air quality and thermal comfort. The specific results of the project are described in section Deliverables.
21-21762X 2021 - 2025
The theory of graph limits is one of the most important recently emerged tools of discrete mathematics. It has led to breakthrough solutions of many old problems in extremal graph theory, theory of random graphs and in particular in connecting discrete mathematics to fields such as probability, real anf functional analysis and group theory. In the project, we will study the foundations of the limit theory of graphs, graph norms, and connections to mathematical models of statistical physics.
21-03658S 2021 - 2023
Psychometrics, as a field concerning psychological, health-related, educational and other behavioral measurements, is the disciplinary home of number of statistical data science methods. This project studies theoretical and computational aspects of psychometrics with the aim to propose estimation and detection methods superior to traditional ones. Project focuses on two psychometric topics (estimation of reliability and detection of differential item functioning) and their extensions to more complex designs. Project also provides software implementations as well as simulated and real data examples demonstrating usefulness and superiority of the proposed methods. The theoretical and computational aspects and innovations improving psychometric algorithms covered in this project may have broader impact on statistical data science.
21-17211S 2021 - 2023
Development of methods for effective description of complex systems is a growing area of interdisciplinary research at the junction of cybernetics, informatics, mathematics and theoretical physics, with application to a range of scientific disciplines including neuroscience, sociology, economics, genetics, and ecology. One of the key problems is the robust characterization of the structure of interactions within a system based on the multivariate time series. A common method for system representation is using correlation matrix of the variables, treated as graph and studied using graph-theoretical metrics, in comparison with random graphs of matched size and density. Recent results show multiple issues of such approach: correlation matrices do not capture faithfully the interactions, neglect higher-order dependences, insufficiently capture the predictive power in multivariate nonlinear models and lead to a bias in key graph metrics. We shall move beyond the state-of-art and our recent results towards a more general and robust complex system characterization. The main aim of the project is to bring theoretical and methodological advances in complex network research by tackling four key challenges outlined in the abstract.
CZ.02.2.69/0.0/0.0/18_053/0017594 2020 - 2023
The Institute of Computer Science of the Czech Academy of Sciences will create a post-doctoral position for an excellent junior researcher from abroad. A research stay of a junior ICS researcher or a PhD student at a research institution abroad will take place. Two short-term visits of administrative or technical employees of the ICS at a research institution abroad will take place.
This project is co-funded by the EU.
TL04000282 2020 - 2021
The goal of our project is to create a software tool for simulation of social ties and anti-epidemic measures, which will enable to compare the efficiency of these measures with their impact on individuals as well as on society. The core of our tool is a network model of COVID-19 spread in the CR. Our model combines longitudinal sociological data from Life during pandemic survey (interactions, work and spare time activities, economic impact on households etc.) with demographic and epidemiological data. This way we estimate health, social, and economic impacts of various anti-epidemic measures (quarantine, contact tracing, local closures). The planned outcome will help municipalities, firms or hospitals to compare the effects of given measures on individuals and communities.
SS02030031 2020 - 2026
The main goal of the project is to develop methods of air quality control, methods of identification of air pollution sources and their share in air pollution concentrations with a focus on current main problems of air quality and difficult quantification of different types of pollution. Consequently, model tools need to be developed to identify dispersion of air pollution, both with regard to current concentrations but also with a view to future expansion. Part of the research is also the development of laboratory methods for air quality evaluation, both methods of manual, isotopic analysis of elements in aerosol particle samples and methods of elemental analysis of aerosol particles. With regard to the impact on the health of the population, the impact of ultrafine particles will be evaluated at 5 localities in the Czech Republic, also with regard to external influences such as meteorological conditions. The project also includes estimation of the fraction of fog and icing in the total atmospheric deposition and the outputs will be used for quantification of the ozone effect. An interesting result of the project will also be the maps of phytotoxic doses of ozone for various plants. The impact of transport is apparent across the whole project, both on the health of the population and on the pollutant and greenhouse gas emissions. An unforgettable task of this project is the development of methodologies and emission factors used in the preparation of emission balances in relation to the international requirements of the EU and the UN Conventions. Also, data standards for reporting obligations introduced by Act 25/2008 Coll. will be developed, which will be an essential element of the subsequently developed comprehensive information system on air quality.
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.
LM2018113 [Registered results] 2020 - 2022
Research infrastructure serves for Czech contribution to particle physics research on experiments at Fermilab consists of experiments on which Czech physicists collaborate in Fermilab and of infrastructures of the Czech collaborating institutions. We work on the Fermilab's experiments NOvA, D0 and we plan to join a new experiment in next years 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. Further service is support and implementation of novel statistical and non-statistical methods for data analysis based on artificial intelligence and machine learning, using specialized computing infrastructures located at FNSPE, Czech Technical University in Prague and Institute of Computer Science, CAS. It includes statistical pre-processing of neutrino data samples such as dimensionality reduction, nonparametric probability distribution estimates, statistical homogeneity testing, or signal reconstruction by means of artificial intelligence methods.
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 - 2021
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 - 2021
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.
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 - 2021
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 - 2021
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.
NV17-29622A [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.