Research grants

From Biological Neural Networks to Next-Generation Whole-Brain Models

LQ100302301 2023 - 2027

Coalition and Epistemic Logic: An Intensional Approach to Groups

AA - Filosofie a náboženství [Registered results] 2022 - 2025

The project will lift an important and up to now largely overlooked idealization in logical models of group knowledge and action - the extensional view of groups where a group is reduced to the set of its members. Consequently, groups change identity when their membership changes, any uncertainty regarding who is in a given group is ruled out and the structure of groups is not reflected. However, firms, informal teams or even loose crowds typically remain even if they gain or lose members, and the identity of all members is rarely common knowledge in any group of moderate size. In order to lift this idealization the project will study an intensional view of groups, which loosens the relationship between group membership and group identity and takes into account its algebraic or relational structure, and apply this view to questions in multiagent epistemic logic (distributed and common knowledge, perfect recall) and coalition logic (groups’ agentive powers). It will allow for more natural applications of logical models to questions of collective memory, action and responsibility.

Proof analysis AND Automated deduction FOr REcursive STructures

22-06414L 2022 - 2025

Mathematical induction is one of the essential concepts in the mathematician's toolbox. Though, its use makes formal proof analysis difficult. In essence, induction compresses an infinite argument into a finite statement. This process obfuscates information essential for computational proof transformation and automated reasoning. Herbrand’s theorem covers classical predicate logic where this information can be finitely represented and used to analyze proofs and to provide a formal foundation for automated theorem proving. While there are interpretations of Herbrand’s theorem extending its scope to formal number theory, these results are at the expense of analyticity, the most desirable property of Herbrand’s theorem. Given the rising importance of formal mathematics and inductive theorem proving to many areas of computer science, developing our understanding of the analyticity boundary is essential.

Metamathematics of substructural modal logics

22-01137S 2022 - 2024

Classical logic models reasoning about Boolean combinations of atomic propositions. Modal logics extend it by adding propositional connectives (called `modalities') to allow reasoning about the modes of truth, such as `necessarily’, `is allowed', or `is known'. Conversely, substructural logics relax assumptions on logical atoms to allow reasoning about other interesting objects such as constructive proofs, resources, or the degrees of truth. There are deep mathematical theories available for both classes of logics, which both aid their applications in mathematics, computer science, economics, linguistics, etc., and are of independent mathematical interest. This is, however, not the case for their combination, which hinders their development and application potential. The goal of the project is to advance three underdeveloped areas of substructural modal logics by creating general theories of algebravalued frames and logics with layered syntax and establishing the foundations of quantified substructural modal logics.

AppNeCo: Approximate Neurocomputing

22-02067S 2022 - 2024

Nowadays, modern AI technologies based on deep neural networks, whose computation is demanding on energy consumption, are implemented in devices with limited resources (e.g. battery powered cellphones). In error-tolerant applications (e.g. image classification), the use of approximate computing methods can save enormous amount of energy at the cost of only a small loss in accuracy. AppNeCo is a basic research project of approximate neurocomputing, whose ambition is an original synergy of approximation and complexity theory of neural networks and empirical experience with the top design of high-performance approximate implementations of hardware circuits. Its goal is to develop complexity-theoretic foundations of approximate computation by convolutional neural networks (CNN) of bounded energy complexity for application domains specified by input space distributions. This knowledge will be used in designing new strategies for approximating components and learning algorithms of low-energy high-precision CNNs. The new methods will be tested on image processing tasks.

GRADLACT: Graded Logics of Action

22-16111S 2022 - 2024

Propositional Dynamic Logic, PDL, is a well-known tool used in the logical analysis of discourse about action. Being based on classical logic, it cannot provide adequate formalization of discourse involving graded, vague and imprecise concepts. This project will develop and study versions of PDL more suitable for this task, so-called graded dynamic logics. We will determine the basic properties of the most natural kinds of graded dynamic logic and we will develop versions of graded PDL aiming at formalizing various philosophically relevant kinds of discourse; in particular, we will develop graded dynamic logics suitable for formalizing reasoning about collective agency and deontic aspects of action in situations involving graded notions, and for analyzing reasoning about probabilistic aspects of action. The project will thus contribute to an elaboration of formal methods applicable in the theory of action and applied ethics.

Case studies design for a micro-climate modeling

2022 - 2025

During the last two decades, substantial progress in modelling urban microclimate processes has been associated with newly developed models. For the model validation, the precise and valid meteorological data represent the necessary information. This project represents a platform for open discussion about micro-scale measurement campaigns and its necessity for a correct interpretation of measured and modelled results.

MOSAIC Modalities in Substructural Logics: Theory, Methods and Applications

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.

Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data

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.

Advances in educational assessment: Analytical support for educational test development

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.

Characterizing state repertoire and dynamics of spontaneous brain activity by neuroimaging methods

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.

Quasi-decidability of the first-order theory of real functions

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.

Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants

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.

TURBAN Turbulent-resolving urban modeling of air quality and thermal comfort

TO01000219 2021 - 2023

The main aims of the international 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 project TURBAN benefits from Norway grants and Technology Agency of the Czech Republic

Graph limits and beyond

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.

Theoretical foundations of computational psychometrics

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.

Network modelling of complex systems: from correlation graphs to information hypergraphs

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.

L100302151 2021 - 2023

L100302001 2021 - 2023

Supporting the internationalization of the Institute of Computer Science of the Czech Academy of Sciences

CZ.02.2.69/0.0/0.0/18_053/0017594 2020 - 2023

The Institute of Computer Science of the Czech Academy of Sciences created a post-doctoral position for an excellent junior researcher from abroad. A research stay of a junior ICS researcher and 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.

Air quality Research, Assessment and Monitoring Integrated System

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.

Collaboration on Fermilab experiments 2

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.

Substructural Modal Logics for Knowledge Representation

CSIC-20-12 2020 - 2022

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.

Daily evolution of brain connectivity during the pre-ictal period in intracerebral recordings of epileptic patients

Mob_France 2020 - 2022

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.

Interactions and causality in complex systems

2020 - 2025

Street-level urban microclimate modelling using LES principle in north-american cities

MSM100302001 2020 - 2022

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.

Random discrete structures

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.

Research infrastructure Fermilab-CZ

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.

Structural properties of visibility in terrains and farthest color Voronoi diagrams

GJ19-06792Y [Registered results] 2019 - 2022

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.

National Competence Center - Cybernetics and Artificial Intelligence

TN01000024 [Registered results] 2019 - 2022

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.

Embedding, Packing, and Limits in Graphs

GA19-08740S [Registered results] 2019 - 2022

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.

Boolean Representation Languages Complete for Unit Propagation

GA19-19463S [Registered results] 2019 - 2022

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.

Timing of the spatial scene processing in the dorsal and ventral visual stream of the human brain

GA19-11753S [Registered results] 2019 - 2022

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.

Nonlinear interactions and information transfer in complex systems with extreme events

GA19-16066S [Registered results] 2019 - 2022

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.

Introduction of targeted protection of cereal crops against insect pests in precision farming

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.

CONNECT - Combinatorics of Networks and Computation

734922 (8. RP EU) 2017 - 2022

Networks are present in our lives in numerous different environments: to name just a few, networks can model social relationships, they can model the Internet and links between web pages, they might model the spread of a virus infection between people, and they might represent computer processors/sensors that have to exchange information. This project aims to obtain new insights into the behaviour of networks, which are studied from a geometric and computational perspective. Thereto, the project brings together researchers from different areas such as computational geometry, discrete mathematics, graph drawing, and probability. Among of the topics of research are enumerative problems on geometric networks, crossing numbers, random networks, imprecise models of data, restricted orientation geometry. Combinatorial approaches are combined with algorithms. Algorithmic applications of networks are also studied in the context of unmanned aerial vehicles (UAVs) and in the context of musical information retrieval (MIR). The project contains the work packages: “Geometric networks”, "Stochastic Geometry and Networks", “Restricted orientation geometry”, “Graph-based algorithms for UAVs and for MIR”, and “Dissemination and gender equality promotion”. The project connects researchers from 14 universities located in Austria, Belgium, Canada, Chile, Czech Republic, Italy, Mexico, and Spain, who will collaborate and share their different expertise in order to obtain new knowledge on the combinatorics of networks and applications.