"Towards clinically useful biomarkers of mental disorders based on brain activity patterns"
Nicolaus Copernicus University, Toruń, Poland
Link to CV: http://www.is.umk.pl/~duch/cv/cv.html
The costs of mental disorders are a great burden on health systems.
Despite the concerted efforts of the machine learning community and the application of advanced experimental techniques such as functional magnetic resonance (fMRI), near-infrared spectroscopy (NIRS), and high-density EEG/MEG, we have yet to identify objective biomarkers that can be utilized in clinical settings. Ideally, we should be able to employ cost-effective methods, like basic EEG systems in a resting state, for the early and precise diagnosis of disorders such as autism, PTSD, and schizophrenia, among others.
This presentation will summarize the state-of-the-art in this field, delve into the challenges faced, issues with generalization, and the shortcomings of deep learning to provide effective features for the characterization of brain neurodynamics. A promising alternative proposed by our group combines EEG microstates with recurrence quantification and topological data analysis for a practical and potentially game-changing solution.
Duch W, Tołpa K, Ratajczak E, Hajnowski M, Furman Ł, Alexandre L.
(2023) Asymptotic spatiotemporal averaging of the power of EEG signals for schizophrenia diagnostics. Communications in Computer and Information Science (Springer Nature Singapore) 1963, pp. 428–439, 2024.
Furman Ł, Tołpa K, Minati L, Duch W. (2022) Short-Time Fourier Transform and Embedding Method for Recurrence Quantification Analysis of EEG Time Series. European Physical Journal Special Topics, 1-15
"Random Neural Networks (RNN) for Accurate CyberAttack Detection and Mitigation at the Edge"
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences
University Côte d'Azur I3S CNRS, 06100 Nice, France
Erol Gelenbe FIEEE FACM FIFIP FRSS received his MS and PhD degrees at the NYU Tandon School that awarded him its Distinguished Alumnus Award in 2010. He has held named personal chairs at NJIT (USA), Duke University (USA), University of Central Florida (USA), Imperial College (UK), and full professorships at the University of Liege, University Paris Saclay, and University Paris Descartes. He served as Department Head at University Paris Descartes (1986-92), Duke University (1993-1998), and Director of the School of EECS at UCF (1993-1998). His research focuses on QoS, Security and Energy, and was funded by Industry, DoD and NSF in the USA, EPSRC and MoD in the UK, and numerous EU FP5, FP6, FP7, and Horizon 2020 projects since 2003; the IEEE Spectrum credits him with some of the inventions regarding fast many-to-many video connectivity that have led to systems such as Zoom (https://spectrum.ieee.org/erol-gelenbe-profile). Currently Professor at the Institute of Theoretical & Applied Informatics, Polish Academy of Sciences, since 2017, he also cooperates with the CNRS I3S Laboratory of University Côte d'Azur (Nice, France), and Yasar University (Izmir, Turkey), and his work is now supported by grants from H2020 Horizon and UKRI. He is ranked among the top 25 PhD advisors in mathematical sciences by the American Mathematical Society's Math. Genealogy Project, and he has graduated 24 women PhDs. Winner of the Grand Prix France Telecom 1996 (French Academy of Sciences), the ACM SIGMETRICS 2008 Life-Time Award, and the Mustafa Prize 2017, he was awarded the high honours of Commander of the Order of the Crown, Belgium (2022), Commander of the Order of Merit, France (2019), Knight of the Legion of Honour, France (2014), Commander of the Order of Merit, Italy (2005), Grand Officer of the Order of the the Star, Italy (2007). Fellow of the French National Academy of Engineering, and of the Science Academies of Belgium, Poland and Turkey, he is also Honorary Fellow of the Hungarian and Islamic Academy of Sciences. He currently chairs the Informatics Section of Academia Europaea.
Even simple cyberattacks can impair the operation and performance of network systems substantially for many hours and sometimes days, and also increase the system's energy consumption. Their impact on data security, and the effects of the malware that they convey and install, are also well known. Thus there is a widespread need for accurate cyberattack detection, and rapid reaction and mitigation when attacks occur. On the other hand, the detection must avoid false alarms, to avoid impairing the smooth operation of a system which is not under attack. Thus considerable research has been conducted in this important field. Our presentation will briefly introduce the subject, and then focus on some recent results from the last 7-8 years, that are based on the Random Neural Network (RNN). The mathematical model will be described, and its extensions and deep learning algorithms will be discussed in the context of cyberattack detection and mitigation. The presentation will then focus on practical applications illustrated with different cyberattack datasets, showing the high accuracy and low false alarm rates that can be achieved. Measurements of active control schemes for attack mitigation will also be shown. Finally we will also show how the RNN can be used with Reinforcement Learning and SDN (Software Defined Networks), to dynamically control an Edge System that optimises Security, QoS and Energy Consumption. Note: The talk is based on our publications in the following journals and conferences: Proceedings of the IEEE (2020), Sensors (2021, 2023), ACM SIGCOMM Flexnets (2021), ICC (2016, 2022), IEEE Access (2022, 2023), Performance Evaluation (2022), IEEE Trustcom (2023).
"Toward Better Accuracy and Transferability in Self-Supervised Monocular Depth Estimation"
East China University of Science and Technology, Shanghai, China
Qiyu Sun received her B.S. degree in automation from the East China University of Science and Technology, Shanghai, China, in 2019. She is currently pursuing her Ph.D. degree in control science and engineering in the same university. From 2022 to 2023, she was a visiting Ph.D. student at Linkoping University, Sweden. Her research interests concern computer vision and deep learning, mainly focusing on depth estimation and semantic segmentation. She has authored or co-authored more than 20 papers in international journals and conferences, including TNNLS, TIE, TITS, ICCV, etc.
Monocular depth estimation is a classical task in computer vision, which aims to estimate the distances between the objects in the environment and the agent itself. Recently, deep-learning-based depth estimation methods have obtained significant advancements. The self-supervised framework uses geometric constraints between images as the main supervisory signal and hence the training data is much more available. This talk will introduce the main accomplishments and recent developments in self-supervised monocular depth estimation. Then, the recent work performed by the speaker’s group will be presented, which focuses on improving the accuracy and transferability of the self-supervised monocular depth estimation model. Finally, the prospects for future development will be discussed.
"Distributed Optimization, Decision-making and Games in Multi-agent Systems"
East China University of Science and Technology, Shanghai, China
Yang Tang received the B.S. and Ph.D. degrees in electrical engineering from Donghua University, Shanghai, China, in 2006 and 2010, respectively. From 2008 to 2010, he was a Research Associate with The Hong Kong Polytechnic University, Hong Kong. From 2011 to 2015, he was a Post-Doctoral Researcher with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. He is now a Professor with the East China University of Science and Technology, Shanghai. His current research interests include distributed estimation/control/optimization, cyber-physical systems, hybrid dynamical systems, computer vision, reinforcement learning and their applications.
Prof. Tang is an IEEE Fellow. He was a recipient of the Alexander von Humboldt Fellowship. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Industrial Informatics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Systems Journal, Engineering Applications of Artificial Intelligence (IFAC Journal) and Science China Information Sciences, etc. He has published more than 200 papers in international journals and conferences, including more than 100 papers in IEEE Transactions and 20 papers in IFAC journals. He has been awarded as best/outstanding Associate Editor in IEEE journals for four times. He is a (leading) guest editor for several special issues focusing on autonomous systems, robotics, and industrial intelligence in IEEE Transactions.
In the current era, multi-agent systems have witnessed rapid development, showcasing immense potential in domains such as energy-economic dispatch, machine learning, and collaborative operations of complex systems. This talk introduces the optimization and games in multi-agent systems. In the context of privacy protection and asynchronous computation, the talk explores reducing the communication load in distributed optimization through various event-triggered mechanisms. Considering the system's inherent uncertainties and the model’s interpretability in decision-making, the talk also delves into data-driven decision-making methods. Swarm game theory contains two primary categories: analytical games and reinforcement learning (RL)-based games. Analytical games focus on constructing mathematical models among swarm members, utilizing these models to devise effective strategies through thorough analysis. On the other hand, RL-based games prioritize the application of reinforcement learning techniques, emphasizing the role of trial and error in the strategic decision-making process to refine and perfect game strategies.