Any scientific discipline strives to explain causes of observed phenomena. Quantitative, mathematical description of causality is possible when studying phenomena that evolve in time and provide measurable quantities which can be registered in consecutive instants of time and stored in datasets called time series.

Any scientific discipline strives to explain causes of observed phenomena. Quantitative, mathematical description of causality is possible when studying phenomena that evolve in time and provide measurable quantities which can be registered in consecutive instants of time and stored in datasets called time series. As examples we can mention long-term recordings of air temperature, or recordings of the electrical activity of the human brain, known as the electroencephalogram.

In this talk we will follow ideas of the father of cybernetics, Norbert Wiener, and Nobel prize winner Sir C.W.J. Granger. We will explain how to detect causality using probability distribution functionals from information theory and the interpretation of causal relations as information transfer. We will study the information transfer in chaotic systems on the route to synchronization. The time and the arrow of time play a natural role in the definition of causality: the cause precedes the effect. We will investigate whether this principle is obeyed by chaotic dynamical systems. Another role of time can be seen in complex systems evolving on multiple time scales. We will show how to measure the information transfer across time scales. As an application we will demonstrate a causal influence of climate oscillations with a period about 7-8 years on the amplitude of the annual temperature cycle and the inter-annual variability of the mean winter temperature in central Europe.

Milan Paluš studied mathematical physics at the Faculty of Mathematics and Physics of the Charles University in Prague. At the Prague Psychiatric Centre he worked on applications of deterministic chaos in the analysis of brain waves and was awarded the CSc. degree (PhD equivalent) at the Czech Academy of Sciences. Supported by the Fogarty research fellowship he worked as a postdoctoral fellow at the University of Illinois and the Santa Fe Institute. He was a visiting scholar at the School of Mathematical Sciences, Queensland University of Technology, Brisbane, and participated in research programs at the Cambridge University and the Max Planck Institute for the Physics of Complex Systems in Dresden. At the Institute of Computer Science he studies complex systems and their cooperative behaviour with focus on the detection of nonlinearity, synchronization and causality in time series.

Its **program** consists of a **one-hour lecture** followed by a **discussion**. The lecture is based on an (internationally) exceptional or remarkable achievement of the lecturer, presented in a way which is comprehensible and interesting to a broad computer science community. The lectures are in English.

**The seminar** is organized by the organizational committee consisting of Roman Barták (Charles University, Faculty of Mathematics and Physics), Jaroslav Hlinka (Czech Academy of Sciences, Computer Science Institute), Michal Chytil, Pavel Kordík (CTU in Prague, Faculty of Information Technologies), Michal Koucký (Charles University, Faculty of Mathematics and Physics), Jan Kybic (CTU in Prague, Faculty of Electrical Engineering), Michal Pěchouček (CTU in Prague, Faculty of Electrical Engineering), Jiří Sgall (Charles University, Faculty of Mathematics and Physics), Vojtěch Svátek (University of Economics, Faculty of Informatics and Statistics), Michal Šorel (Czech Academy of Sciences, Institute of Information Theory and Automation), Tomáš Werner (CTU in Prague, Faculty of Electrical Engineering), and Filip Železný (CTU in Prague, Faculty of Electrical Engineering)

**The idea to organize this seminar** emerged in discussions of the representatives of several research institutes on how to avoid the undesired fragmentation of the Czech computer science community.