Hybrid Intelligence for aDvanced collective perception and Decision- making in complex urban Environments

101202228 2025 - 2027

Ethics of Autonomous Vehicle Operation A project supported by the Technology Agency of the Czech Republic. The project primarily focused on collision dilemmas of autonomous vehicles and sought ethically acceptable solutions to these issues. One of the project's outputs was a publication in Oxford University Press. The project implementation is carried out in cooperation with the Ministry of Transport of the Czech Republic. Evaluation of the Behavior of Automated Vehicles from the Perspective of Adherence to Ethical and Legal Principles in Mixed Traffic A project supported by the Technology Agency of the Czech Republic. The project aims to find the optimal mix of automated vehicles adhering to different ethical principles to ensure optimal traffic flow. Using traffic microsimulations and a parametric model of ethical principles, the interactions among vehicles and their impact on traffic flow, throughput, and safety will be analyzed. This will identify suitable vehicle behavior distributions for various traffic situations and form a methodology for evaluating automated systems in vehicle testing and approval.

The power of objects: Materiality between past and future

2025 - 2029

Social-Cultural Determinants of Circulation of Representations: The Impact on Human Mind and Strategies of Remediation

0ducRt 2025 - 2028

Cross-sectoral collaboration for the development of new technologies in psychiatry - precision medicine and new therapies

CZ.02.01.01/00/23_020/0008560 2025 - 2028

AI: Artificial Intelligence for Science and Society

2025 - 2029

Development of generative AI technologies, AI as well as ML, has come to the forefront across a broad spectrum of human activities, including research. Applications based on LLMs have captured the attention of society in an unprecedentedly short time, sparking discussions on their potential uses. The AI phenomenon has dramatically infiltrated nearly all industries, significantly transforming how they function. AI is a great opportunity, as well as a major challenge, for nearly all scientific disciplines studied across the institutes of the CAS. In the context of research, the effective use of AI/ML methods can accelerate many aspects of the research process, while also providing new stimuli for the "creative component" of research activities –similar to what occurred with the development of formal and mathematical foundations in many scientific disciplines. AI plays a role in supporting research activities (e.g., project management in R&D etc.). AI can be viewed from many different perspectives, ranging from specific approaches for solving particular problems to more general considerations regarding AI’s impacts across different sectors. The program "AI: Artificial Intelligence for Science and Society" responds to these developments: its goal is to support not only research and development in the area of general AI and machine learning methods but also the application of AI methods in various institutes across all three scientific domains to address problems in a wide range of fields, as well as research into the societal aspects of AI. The program also encompasses a set of cross-cutting AI activities across the institutes, strengthening inter-institutional collaboration both within the Academy and with other research organizations outside of it, including international partnerships. It also creates the necessary infrastructure for collaboration with the commercial sector and public administration, as well as for knowledge transfer in the AI field. The program does not overlook opportunities related to education and training of the younger generation, nor issues of resilience and security within the Czech research space. This is reflected in the project’s structure of five research topics (plus one support topic for program coordination). The program will also contribute to enhancing the perception of the CAS as a key player in the AI field, both within the Czechia and beyond.

Interpolation, Amalgamation, and Computation

25-18306M 2025 - 2029

Interpolation is a fundamental metalogical property of broad importance to several fields, including software/hardware verification, databases, mathematics, and philosophy. This project aims to bridge the gap between the two dominant paradigms in the study of interpolation: the proof-theoretic approach and the algebraic approach. By reconciling these methodologies, the project will develop a systematic account of interpolation in varied environments, and, in particular, will produce a flexible and powerful toolkit for investigating interpolation in different logical contexts. Our investigation places a special emphasis on finding broadly applicable techniques for understanding computational aspects of interpolation, such as the decidability of various questions relating to interpolation and complexity of the associated algorithms.

Information-theoretic approaches to prediction of transitions in complex systems

25-18105S 2025 - 2027

This project aims at further development, original extensions and novel applications of stateoftheart methods that lie on the borderline between information theory, nonlinear dynamics and statistical physics to study and identify precursors and warning signals of transitions (critical or benign) in complex systems. Considering multiscale and non-Gaussian character of complex systems, state-of-the-art methods and techniques from information theory, nonlinear dynamical systems and (non-equilibrium) statistical physics will be used to develop measures of complexity and causal information flows and information geometry in different entropy frameworks (Shannon, Rényi and Tsallis). Primary application fields will be in neuroscience (epilepsy), geosciences (climate and space weather) and small-size complex systems.

Complex analysis of educational measurement data to understand cognitive demands of assessment tasks

25-16951S 2025 - 2027

Educational measurements yield complex data that are not sufficiently harnessed. The project uses available data from secondary school leaving exams, admission tests, and other educational assessments to gain a deeper understanding of cognitive demands of assessment tasks. It uses and develops methods that combine textual data extracted from item wording, numerical data on item difficulty, qualitative data on the test takers’ perceptions, and content experts’ judgments of task difficulty to investigate to what extent is an automated analysis of assessment data able to identify sources of task difficulty related to classroom teaching and learning. We expect that semantic-level features of item wording will reflect the cognitive demands of the subject matter closer than syntactic or lexical features, thus enabling a novel use of assessment data to improve student learning. We also expect that the features associated with task difficulty are dependent on the test takers’ ability and propose methods to equate item difficulty estimates under covariate and anchor test equating designs.

Modern Czech Logic within the Philosophy of Mathematics

25-16489S 2025 - 2027

Rieger, Vopěnka, and Hájek are pioneering figures in Czech formal logic. Their paradigmshifting works span roughly the second half of the twentieth century. Vopěnka and Hájek promoted new axiomatic theories with foundational ambitions. This project targets the examination of the body of their work from the present-day, pluralist position in the philosophy of mathematics, which we claim they instantiate. Vopěnka's contribution to foundations of mathematics is an opportunity to critically assess the possibility of combining the tradition represented by Frege, Dedekind and Cantor and the phenomenological tradition to which Vopěnka made claims under an indirect influence of Patočka. The project will focus on several key areas such as set-theoretic ontology or finitism and the notion of infinity. Individual works will be consistently explicated within regional and historical contexts.

LEDNeCo: Low Energy Deep Neurocomputing

25-15490S 2025 - 2027

Modern artificial intelligence technologies based on deep neural networks (DNNs) such as GPT are computationally extremely demanding. In addition to consuming an enormous amount of energy, this limits their deployment in battery-powered embedded (edge) devices (e.g. smart mobile apps). LEDNeCo is a project of basic research whose ambition is to develop a lowenergy deep neurocomputing paradigm based on machine-independent energy complexity theory for DNNs, which issues from practical experience in the design of diverse DNN hardware accelerators. Among other things, universal lower bounds on energy complexity of DNNs and estimates of inference error will be derived for identifying DNN components (e.g. weights, neurons, layers) whose approximation is provably the most energy efficient. The achieved theoretical knowledge will be used in new advanced approximation techniques (e.g. weight compression, Boolean optimization, robust aproximation of components) for low-power hardware implementations of DNN (incl. transformer), which will be tested on benchmark datasets.

Statistical procedures to detect between-group differences

L100302401 2025 - 2025

The main objective of this project is to research statistical advances for the detection of between-group differences in multi-item measurements to develop novel statistical and computational approaches in the analysis of DIF. I aim to incorporate novel statistical theory and link several topics of computational data science with psychometrics. Furthermore, I also plan to analyze the properties of the newly proposed methods in comparison with the existing ones, emphasising the applicability of methods in real-life scenarios. Additionally, I intend to demonstrate the proposed methods’ usefulness and superiority over traditional approaches using simulated and real data examples. Finally, contribution to developing software covering newly proposed methods is expected to enforce its use worldwide.

2025 - 2029

Does white Matter Matter?

CZ.02.01.01/00/22_010/0008697 2024 - 2026

Schizophrenia, a global mental disorder, often goes undetected until the first psychotic episode, despite earlier silent symptoms. Understanding its neural mechanisms is crucial for diagnosis and treatment efficacy. This project aims to uncover how white matter alterations impact brain function in schizophrenia, using innovative whole-brain modeling and advanced neuroimaging techniques. It promises groundbreaking insights into both computational neuroscience and psychiatry. This project is co-funded by the EU.

Optimizing the energy portfolio to increase the use of renewable resources

TS01020123 [Registered results] 2024 - 2026

The goal of this project is the development of a comprehensive Lancelot GDS (Generation Dispatch Systém) optimization tool for power flexibility aggregators to increase the use of renewable resources. The tool will provide an effective management of the portfolio of production and consumption equipment and optimization of the operational and business plan of production and consumption on flexible equipment with regard to market price development and predicted production of RES (solar and wind power plants), technical limits of storages (batteries) and production and consumption equipment operated within the aggregation block. The software solution will provide: - Flexible resource management - Commercial and technical optimization - Evaluation - Acceleration of the processes

Natural and Anthropogenic Georisks

0&h=EH22_008%2F0004605 [Registered results] 2024 - 2028

The main research objective of the project entitled Natural and Anthropogenic Georisks is to understand natural and man-made threats, hazards, and risks in the Earth's upper spheres, to explore their causes and to quantify their potential impacts on human society and infrastructures. This objective will be achieved by the planned end date of the project. Both natural and anthropogenic geohazards are inherently very complex processes and require a high degree of interdisciplinarity within the natural sciences. Thus, the project should result not only in a fundamental understanding of the mechanisms of these processes, but also in a proposal of tools for their better monitoring, possible minimization of their effects and prediction, including mitigation of their negative effects on human society. The project's specific aims therefore include the characterisation of geohazards through field observations and monitoring at model sites in the Czech Republic, elsewhere in Europe and on other continents (e.g., in sub-Saharan Africa), which represent suitable natural laboratories for the analysis of potentially hazardous disturbances. One of the key goals of the project is to understand and describe abiotic (physical, chemical) or biotic processes through suitably designed laboratory or in situ experiments using the state-of-the-art instrumental techniques in combination with modelling tools. Finally, an important aim of the project is the integration of data obtained in the field and in the laboratory into complex models and optimization of tools for prediction and implementation of warning systems and other application outputs useful for the society. The basic theme where the Institute of computer science cooperates is projection of the climate change and research on its impact on risk phenomena using the results of available climate models and measurement data, with analysis of their causes and assessment of their consequences in selected areas of human activity. This will be achieved primarily through the analysis of available model simulation data, supplemented by our own model results, especially through the analysis of extreme characteristics and indices, which are then easier to interpret in the research of the impacts in follow-up studies of selected areas.

This project is co-funded by the EU.

Potential replacement vegetation modelling for nature conservation

SS07020042 [Registered results] 2024 - 2026

Research of Excellence on Digital Technologies and Wellbeing

=0&h=LM2023061 [Registered results] 2024 - 2028

The DigiWELL project responds to research challenges in key areas of the rapidly evolving domain of digital technologies aimed at promoting physical, mental and social wellbeing. Due to the biopsychosocial perspective we take in the project, the project is interdisciplinary in nature. Specifically, the project consists of six research work packages (WPs) that are firmly anchored in the field of psychology as the primary expert domain. However, other disciplines are also applied within the individual WPs, which is reflected in the composition of the implementation team, which includes experts from the fields of psychology, media studies and communication, kinanthropology, sociology, social work, mental health, public health, statistics and computer science. Research Work Package in which the ICS is collaborating, will focus on the research, development, and application of statistical models of knowledge-based analytical methods for processing information burdened with uncertainty, missingness and unstructured text in the field of longitudinal intensive, biosensory, and social science data as well as other methods of processing time series, enabling the detection and prediction of changes in behavior and wellbeing. Several statistical and non-statistical approaches will be used, including cluster analysis and machine learning methods based on deep neural networks. Data will be modeled using state-of-the-art statistical approaches, including semiparametric, dynamic, and spatial models. Both frequentist and Bayesian methods will be used, enabling the fusion of empirical and expert information, using computationally efficient approximations suitable for big data. Analyses will serve both to explore and test new structural dependencies in data motivated by psychological theories and their extensions, and to develop models suitable for future use for predictively derived interventions. These will include state-space models, which will enable the sequential estimation of latent characteristics, on which it is then possible to build formalized intervention algorithms for behavior change.

This project is co-funded by the EU.

Brain dynamics

=0&h=EH22_008%2F0004643 [Registered results] 2024 - 2028

The project is focused on the development of computational models of brain activity that capture the dynamics of the brain at different time scales. The goal of the project is to develop research in the field of applied computational neuroscience in the Czech Republic, especially through the implementation of top research, strengthening ties to top foreign workplaces, developing the personnel capacities of the involved research teams and strengthening their experimental and computational capacities. The project has a distinctly interdisciplinary character both in terms of the methods used and the experts involved. It mainly uses neuropsychological experiments, including complex multimodal sensory stimulation, measurements using advanced neuroimaging methods, experimental or therapeutic intervention using non-invasive electrical/magnetic stimulation or drug administration. On the other hand, it develops and uses computational models of brain dynamics and advanced methods of data analysis, including machine learning. The application potential of the first research work package lies in the development of efficient and flexible computational models of brain dynamics, enabling adaptation to specific application tasks. The second research work package focuses on further development in the application area of monitoring and predicting brain states, for example monitoring tissue excitability in epilepsy and predicting seizures. The third research work package focuses on the use of computational models of intervention in brain dynamics with potential applications in the design and optimization of therapeutic brain stimulation and a personalised approach to pharmacotherapy. The team includes experts in neuropsychiatry, neuroscience, epileptology, cognitive neuroscience, mathematical and computational neuroscience, as well as modelling and analysis of complex systems. Research goals include the highly topical issue of the development of computational models of brain dynamics, which lies at the intersection of basic medical sciences, especially neuroscience, natural sciences, especially mathematics, and clinical medical sciences, especially applications in psychiatry and neurology. Research in this area has reached maturity: solid theoretical foundations and developed methodological procedures bring the first application results and thus open the field to further flourishing. At the same time, the project is designed to fulfil its application potential already during the project phase. This application potential is represented by the use of the results of modelling and data analyses in testing experimental therapies based on brain dynamics modulation in selected diseases. This project is co-funded by the EU.

This project is co-funded by the EU.

Climate-Resilient Development Pathways in Metropolitan Regions of Europe (CARMINE)

101137851 2024 - 2027

The frequency and intensity of climate and weather extremes associated with anthropogenic climate change are increasing and will challenge us in terms of adaptation strategies at the local level. The project “Climate Resilient Development Pathways in Metropolitan Regions of Europe (CARMINE)” bridges the local and regional scales by providing impact-based decision support services and multilevel climate governance supporting local adaptation, including both traditional and Nature-Based Solutions. CARMINE’s overarching goal is to help the metropolitan communities of Europe become more climate resilient, by co-producing knowledge-based tools, strategies, and plans for enhanced adaptation and mitigation actions in line with the Charter of the EU Mission on Adaptation to Climate Change by 2030. To achieve this goal, focusing on the 2030-2035 timeframe and with longer perspectives up to 2050, CARMINE proposes an interdisciplinary approach aiming at (1) co-creation and co-development of decision-support services and guidelines for enhanced resilience and adaptive capacity, including early warning and disaster risk management systems; (2) cooperating closely with local to regional communities (stakeholders and users), decision-, and policy-makers (local authorities) to co-develop cross-sectoral frameworks for adaptation and mitigation actions; (3) delivering science-based R&I roadmaps for multi-level climate governance supporting local adaptation assessments and plans. The CARMINE methodology will be implemented in eight selected Case Study Areas to demonstrate proof of concept and project methodology will be demonstrated through the digital replication of climate and socio-economic characteristics of each area. The co-created knowledge and transferable development pathways from CARMINE will be shared widely via project networks in order to drive adaptation in other metropolitan regions of Europe, and beyond.

Social and motivational factors of study and their influence on study results in tertiary education with a focus on technical fields

TQ01000538 [Registered results] 2023 - 2025

The project aims to create recommended procedures for effective work with social and motivational factors affecting study results. It is elicited from interdisciplinary research with a core in robust sociological analysis, which will be broadened by machine learning and AI methods. The unique connection of various sources and types of data (quant. and qual.) with the background in various disciplines will allow to address the problem in its complexity without omitting both hard indicators about students and the course of their studies, as well as their subjective evaluations of their studies over time and own lived experience. The intention is to provide higher education institutions with the means to work with the needs and potential of students, thus assisting to dropout prevention.

Neural networks realtime forecasting of variable baseline for the power equipment at HV, LV voltage levels

TK05020142 [Registered results] 2023 - 2025

The content of the project is the development of a software solution capable of meeting the requirements for predicting a variable baseline according to ČEPS standards for a wide range of technologies on the consumption side and on the production side. The software solution - thanks to the innovative baseline prediction procedure - will significantly increase the possibility of involving these technologies in the provision of support services. In the solution, we will use innovative deep learning methods of neural networks with regularization (Deep Learning - DL & Deep Neural Networks - DNN). The newly created and implemented DNN models will subsequently be practically validated for baseline prediction for specific entities from among flexibility providers.

Symmetries of brain connectivity

GA23-07074S [Registered results] 2023 - 2025

Symmetry of the human brain (and the lack thereof) has been a matter of prominent debate since the report of the left-hemispheric dominance of language by Broca in 1865. Functions including memory, perception, learning, spatial cognition, attention, emotion processing and motor skills show degree of hemispheric specialization, and disrupted brain anatomy and more recently connectivity asymmetry has been associated with neuropsychiatric disorders such as schizophrenia. However the origin, character and consequences of brain asymmetry are far from understood. Recently, the use of large datasets and graph theory has been identified among key future directions in brain symmetry research. The outstanding challenges include relating structural and functional symmetries, bridging the gap between global symmetries (such as bilateral symmetry) and local symmetries, providing normative description of brain symmetries, and relation of inter-individual differences in brain connectivity (a)symmetry to cognitive and clinical characteristics such as in aberrant lateralization in schizophrenia.

CK04000150 [Registered results] 2023 - 2025

Causal inference and interactions between Arctic sea ice and Eurasian winter climate

GF21-14727K [Registered results] 2023 - 2024

Discerning the cause from effect is the aim of many scientific disciplines. In this project we will further develop different methods for detection of causality from experimental time series and applied them to discover the causes of variability of Eurasian winter air temperature, with special attention to its extreme values. The causality methods will be based on information theory, dynamical system theory and compression complexity, combining methods from mathematics, statistical physics and computer science. Among the potential causes of Eurasian winter climate variability we will investigate the changing Arctic sea ice content as well as large-scale modes of circulation variability such as the North Atlantic Oscillation or Pacific Decadal Oscillation. The expected results will help to understand the causes of Eurasian winter air temperature variability and its extremes and serve as basis for their forecasting.

Research Infrastructure for Fermilab Experiments

LM2023061 [Registered results] 2023 - 2026

The FERMILAB-CZ is devoted to Czech collaboration with the U.S. national laboratory Fermilab, the primary concern of which is the research of elementary particles. The core of the present Fermilab research programme is the neutrino experiments, including NOvA and DUNE experiments. FERMILAB-CZ's main knowledge expertise is the detector laboratory, which is engaged in the design and construction of detectors, and the mathematical expert group, which is involved in the development and application of advanced statistical and deep machine-learning artificial intelligence algorithms for data analysis. FERMILAB-CZ contributes to research activities with its significant computing resources in distributed processing of experimental data and simulations and participates in the development of software for data acquisition. The FERMILAB-CZ group of experts is part of a community of around 12,000 researchers who work together to develop detectors, electronics and data analysis methods that are used across many areas of human activity. Through deep involvement in the construction and operation of experiments, FERMILAB-CZ enables Czech research institutions to participate in unique physics research. Both Czech researchers and all individual members and research groups of supported experiments use and benefit from all FERMILAB-CZ open access services free of charge. The main objectives are the delivery of these four services: contribution to the design, construction, operation, and upgrade of our detectors at Fermilab; delivery of needed computing and storage capacities for data simulation, processing and analysis by our computing centre; theoretic, algorithmic, and technical support and final software implementation of novel statistical and non-statistical methods for advanced data analysis based on artificial intelligence and machine learning; design, proof of concept, construction, and upgrade of our experiment’s detectors in the Detector development and electronics laboratory.

Models of non-invasive brain stimulation

DAAD-23-01 2023 - 2024

Transcranial magnetic stimulation (TMS) is a tool that is used regularly in experimental and clinical research, as well as for therapeutic and diagnostic purposes. Yet, little is known about the detailed processes that lead to the nervous and muscular responses. This causes considerable uncertainty in the interpretation of results, and large interindividual variability in treatment effectiveness. The goal of this interdisciplinary project is to create a computational model of nerve and muscle activity in response to TMS, which synthesises current models for the conversion of electromagnetic fields to nervous activity (expertise of Leipzig), and the propagation of activity in the central and peripheral nervous system (expertise of Prague). This will be achieved by simulating the complete communication pathway from neuronal activity in cortex, to motor evoked potentials (MEP) in the muscular periphery. The resulting model will be calibrated with existing data (Leipzig), and be subjected to detailed parameter studies with the aim to improve the use of TMS as a diagnostic tool.

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

LQ100302301 2023 - 2027

Coalition and Epistemic Logic: An Intensional Approach to Groups

GF22-23022L [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

GF22-06414L [Registered results] 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.

Case studies design for a micro-climate modeling

CA20108 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.

Metamathematics of substructural modal logics

GA21-32608S [Registered results] 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

GA22-02067S [Registered results] 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

GA22-16111S [Registered results] 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.

Data Visualization Under Uncertainity

L100302301 2022 - 2024

The topic of the proposal lies in computational geometry which is a branch of theoretical computer science. Considering the objects as points is a common modeling assumption in many fields. Let D = {d1, d2, . . . , dn} be a set of imprecise points, where each imprecise point is a finite or infinite set of possible points instead of a definite point. We say that the set P = {p1, p2, . . . , pn} realizes D if pi belongs to di for all values of i. The assumption of representing an imprecision point by a disk, for instance, is common, since the measuring devices (for instance GPS devices) usually have a predetermined value of the error of estimation. In this proposal, we study several computations on some theoretical problems in computer science and mathematics under the assumption that the input is imprecise. We mainly focus on preprocessing input regions to speed up further computations. In particular, we study some computations on the networks constructed on imprecise inputs and also basic measurements of a point set such as convex hull or separability of imprecise points (which we assume we also assigned some colors).

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 [Registered results] 2021 - 2025

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.

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

GA21-32608S [Registered results] 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

GA21-09458S [Registered results] 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

GF21-14727K [Registered results] 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.

Turbulent-resolving urban modeling of air quality and thermal comfort

TO01000219 [Registered results] 2021 - 2024

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

GX21-21762X [Registered results] 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.

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

GA21-17211S [Registered results] 2021 - 2024

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.

Air quality Research, Assessment and Monitoring Integrated System

SS02030031 [Registered results] 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.

Interactions and causality in complex systems

Akademická prémie 2020 - 2025