Events: Matematiska vetenskaper events at Chalmers University of TechnologySun, 09 Aug 2020 19:58:37 +0200's thesis presentation<p>Online</p><p>Oskar Eklund: ​Computing Failure Probabilities for PDEs with Random Data</p><p>​<br />Abstract: The thesis deals with partial differential equations with random data and in particular Poisson's equation with random data. This equation has a unique solution. The failure probability is the probability that a functional of that solution is less (or greater) than a given value. Algorithms for approximating failure probabilities are studied and tested and a new iterative method of approximating the failure probability is presented and examined in numerical experiments. As the thesis involves both random variables and partial differential equations, both probabilistic problems and problems with partial differential equations are studied along the way. The results of the numerical experiments shows that the method performed well, with respect to computational cost, in comparison with a basic Monte Carlo simulation.</p> <p>Supervisor Axel Målqvist, examiner Annika Lang. </p> thesis presentation<p>Online</p><p>​Mattias Byléhn: Always Look on the Positive-Definite Side of Life</p><p>​<br />In this thesis we study relatively positive-definite distributions on Euclidean space R^n, more specifically distributions with the property of being positive-definite relative to a finite subgroup of the orthogonal group O(n). We construct examples of such distributions using a generalization of the Abel transform on both Euclidean space and the real/complex hyperbolic plane. The main theorem of the paper is due to Bopp, Gelfand-Vilenkin and Krein, expressing a relatively positive-definite distribution on R^n as the Fourier transform of a positive Radon measure on C^n. We present Bopp's proof of this theorem using a version of the Plancherel-Godement theorem for complex commutative *-algebras. </p> <p>A motivation for studying these types of distributions comes from the theory of spherical diffraction on homogeneous spaces, having connections to generalized Poisson summation and the Selberg trace formula.</p> <p>Supervisor: Michael Björklund<br />Examiner: Genkai Zhang </p> on Research with Marija Furdek<p>Zoom</p><p>​​</p><strong>​</strong><span style="background-color:initial"><strong>​​Welcome to Chalmers AI Research Center, CHAIR Spotlight on Research​. We are continuing this series of AI short talks with Assistant Professor at the Department of Electrical Engineering at Chalmers, Marija Furdek. On Friday August 21st she will handle the subject How can machine learning make optical communication networks more secure?</strong></span><br /><div><br /></div> <div><span style="background-color:initial">The first series of CHAIR Spotlight Research talks have the theme “New Chalmers researchers on the spot!”, meaning that researchers that came to Chalmers in the last three years are giving these talks. ​​​<br /></span><span style="background-color:initial"> ​</span></div> <div><span style="background-color:initial"><strong>Abstract: </strong>Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the diverse physical-layer effects of different attack techniques and the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect and their signatures are unknown. However, coherent receivers with digital signal processing capabilities can provide elaborate optical monitoring data collected at the destination node of each connection, and machine learning techniques can give an unprecedented insight into the intricate relationships among the various signal parameters. </span></div> <div><span style="background-color:initial"><br /></span></div> <div><span style="background-color:initial">This talk presents a machine learning framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field-deployed testbed subjected to in-band jamming, out-of-band jamming, and polarization modulation attacks with varying intensities. We evaluate different ML techniques in terms of their accuracy, robustness to missing features, and scalability in processing experimental data. We discuss the performance and suitability of supervised, unsupervised and semi-supervised learning techniques, as well as their embedding into the network service lifecycle. We also investigate challenges related to the integration of these techniques in real-life network management systems.</span></div>  ​<div><h2 class="chalmersElement-H2">Registration for this event will open in August</h2> <h3 class="chalmersElement-H3">About the speaker</h3> <div> </div> <div><br /></div> <div> <img src="/SiteCollectionImages/Centrum/CHAIR/events/Marija-Furdek-Prekratic_170x170px.jpg" class="chalmersPosition-FloatLeft" alt="" style="margin:5px" /><span style="background-color:initial">D</span><span style="background-color:initial">r. Furdek is an assistant professor at the Department of Electrical Engineering at Chalmers. She received her Docent degree in Optical Networking from KTH Royal Institute of</span><span style="background-color:initial"> Technology in 2017. Her research focuses on the design of high-performance networks supporting next generation services, encompassing issues related to the optical network architecture and control. Her ambition is to contribute to the development of optimized, cognitive and autonomous communication networks with a focus on physical-layer security and resilience to a wide range of faults. As (co)PI, work group/package leader and researcher, Marija has participated in several European and Swedish research projects with a wide network of collaborators from the industry and academia. </span></div> <div><br />She is the leader of the Vetenskapsrådet project ‘Safeguarding optical communication networks from cyber-security attacks’. She has co-authored more than 100 scientific publications in international journals and conferences, 5 of which received best paper awards. Marija is an editor of the IEEE/OSA Journal of Optical Communications and Networking and the Photonic Network Communications journal, and a Guest Editor of the IEEE/OSA Journal of Lightwave Technologies. She is a General Chair of the IEEE Optical Network Design and Modeling (ONDM2021). She is a Senior Member of IEEE and OSA. <div><br /></div> ​</div></div> on Research with Karinne Ramirez-Amaro<p>Zoom (online)</p><p></p><b>​Welcome to Chalmers AI Research Center, CHAIR Spotlight on Research . In this series of AI short talks Karinne Ramirez-Amaro Assistant Professor at the department of </b><b><span></span>Electrical Engineering at Chalmers, will handle the subjetct: AI meets Robotics - Robots that Reason about Human Activities, via Zoom on Friday August 28.</b><div><strong></strong><b><br /></b> The first series of CHAIR Spotlight on Research talks have the theme “New Chalmers researchers on the spot!”, meaning that researchers that came to Chalmers in the last three years are giving these talks. ​​​</div> <div><br /></div> <div> <strong>Abstract: </strong>Autonomous robots are expected to learn new skills and to re-use past experiences in different situations as efficient, intuitive and reliable as possible. Robots need to adapt to different sources of information, for example, videos, robot sensors, virtual reality, etc. Then, to advance the research in the understanding of human activities, in robotics, the development of learning methods that adapt to different sensors are needed. In this talk, I will introduce a novel learning method that generates compact and general semantic models to infer human activities. This learning method allows robots to obtain and determine a higher-level understanding of a demonstrator’s behavior via semantic representations. First, the low-level information is extracted from the sensory data, then a meaningful semantic description, the high-level, is obtained by reasoning about the intended human behaviors. The introduced method has been assessed on different robots, e.g. the iCub, REEM-C, PR2, and TOMM, with different kinematic chains and dynamics. Furthermore, the robots use different perceptual modalities, under different constraints and in several scenarios ranging from making a sandwich to driving a car assessed on different domains (home-service and industrial scenarios). One important aspect of our approach is its scalability and adaptability toward new activities, which can be learned on-demand. Overall, the presented compact and flexible solutions are suitable to tackle complex and challenging problems for autonomous robots.</div> <div><br /></div> <h2 class="chalmersElement-H2">Registration for this event will open in August</h2> <div><br /> <div> <h3 class="chalmersElement-H3"><span>About the speaker<br /></span></h3> <img src="/SiteCollectionImages/Centrum/CHAIR/events/karinne_ramires_170px.jpg" class="chalmersPosition-FloatLeft" alt="" style="margin:5px 15px" />Dr. Karinne Ramirez Amaro is an Assistant professor at Chalmers University of Technology since September 2019. Previously, she was a post-doctoral researcher at the Chair for Cognitive Systems (ICS) at the Technical University of Munich (TUM). She completed her Ph.D. (summa cum laude) at the Department of Electrical and Computer Engineering at the Technical University of Munich (TUM), Germany in 2015. From October 2009 until Dec 2012, she was a member of the Intelligent Autonomous Systems (IAS) group headed by Prof. Michael Beetz. She received a Master degree in Computer Science (with honours) at the Center for Computing Research of the National Polytechnic Institute (CIC-IPN) in Mexico City, Mexico in 2007. Dr. Ramirez-Amaro received the Laura Bassi award granted by TUM and the Bavarian government to conduct a one-year research project in December 2015. </div> <div><br /></div> <div>For her doctoral thesis, she was awarded the price of excellent Doctoral degree for female engineering students, granted by the state of Bavaria, Germany in September 2015. In addition, she was granted a scholarship for a Ph. D. research by DAAD – CONACYT and she received the Google Anita Borg scholarship in 2011. She was involved in the EU FP7 project Factory-in-a-day and in the DFG-SFB project EASE. Her research interests include Artificial Intelligence, Semantic Representations, Assistive Robotics, Expert Systems, and Human Activity Recognition and Understanding.<br /> <h3 class="chalmersElement-H3">About CHAIR Spotlight on Research</h3> Chalmers AI Research Center, CHAIR Spotlight on Research is a series of AI short talks hosting researchers from Chalmers. The seminars are targeted towards experts from the Chair Consortium core partners as well as other Chalmers researchers. </div> <div><br /></div> <div> Our aim is to increase awareness of AI at Chalmers between Chalmers researchers and AI experts in industry. In the seminars, speakers present an overview of their current research and thoughts for new research, ideas, challenges – anything they believe to be of interest for other researchers. </div> <div><br /></div> <div>The seminar is taking place online and is scheduled to contain 30 minutes of presentation and 15 minutes of discussion. The seminars are open to all and are free of charge. CHAIR Spotlight Research talks are taking place on Fridays 13:00-13:45.</div> ​</div> faculty assembly meeting<p>Join the meeting via Zoom (link will be added)</p><p></p>​Agenda: Vi får besök av Chalmers rektor som, precis som förra hösten, träffar alla institutionskollegier för att samtala om frågor som engagerar och/eller oroar rörande utbildning, forskning och nyttiggörande. Rektor inleder kort, men lämnar övrig tid för att frågor vi vill samtala om. evening<p>Online</p><p>​Lecturers Tobias Gebäck, Quanjiang Yu, Axel Flinth, Saga Helgadottìr, Marcus du Sautoy</p><p>​<br />Our researchers have Marcus du Sautoy as guest, and we learn how mathematics can be both concrete, abstract, useful and intelligent in a series of lectures. The Science Festival will be digital this year. Read more at <a href=""></a>.<br />Carl-Joar Karlsson, PhD Student,<br /><br /><strong>Modeller för transport i porösa material<br /></strong>18.00-18.20<br />Tobias Gebäck, Associate Professor</p> <p>Vi studerar matematiska modeller från mikroskala till makroskala för att förstå hur materialstrukturen på mikronivå styr transporten av molekyler och vätskor. Vi funderar också kring hur detta kan användas för att kontrollera transport i allt från blöjor till tabletter.<br /><br /><strong>How can we save money in the wind industry<br /></strong>18.20-18.40<br />Quanjiang Yu, PhD Student</p> <p>Global warming is a major issue now. Wind power is renewable and produces almost no greenhouse gas during operation.  However, the maintenance cost is quite huge. To reduce that, we have designed a methodology that maintains the equipment in a more beneficial way.<br /><br /><strong>Fantastiska oändligheter och hur man tämjer dem</strong><br />18.40-19.00<br />Axel Flinth, Guest Teacher</p> <p>Oändligheten kan ju verka omöjlig att föreställa sig, kanske till och med överväldigande eller skrämmande. Inom matematiken är det vardagsmat att färdas både till oändligheten, bortom den och tillbaka igen. Här lär vi känna några sätt matematiker använder för att inte gå vilse i dessa färder.<br /><br /><strong>Deep learning for object recognition</strong><br />19.00-19.20<br />Saga Helgadottìr, PhD Student</p> <p>Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. In this talk, I will show how Deep learning can be used to track objects, in particular microscopic particles.<br /><br /><strong>The creativity code decoded</strong><br />19.30-20.30<br />Marcus du Sautoy, Professor</p> <p>Abstract: TBA: TBA</p> Talks: Arthur Gretton<p>Zoom</p><p>Learning to generate realistic-looking images</p><p><br /><strong></strong></p> <strong>Critics for generative adversarial networks: results and conjectures<br /></strong> <p><strong>Abstract: </strong>Generative adversarial networks (GANs) use neural networks as generative models, creating realistic images that mimic real-life reference samples (for instance, images of faces, bedrooms, and more). These networks require an adaptive critic function while training, to teach the generator network how to improve its performance. To achieve this, the critic needs to measure how close generated samples are to true samples, and to provide a useful gradient signal the generator network.</p> <p>I will explore the design of critic functions for GANs, including f-divergences as used in the original GAN design, and integral probability metrics (such as the Maximum Mean Discrepancy) as used in later GANs. I will provide some observations and conjectures on critic design: in particular, a problem-specific critic seems to be helpful, and the critic needs to be deliberately weakened to ensure good GAN performance.</p> <p></p> <h2 class="chalmersElement-H2">About the speaker</h2> <p><img class="chalmersPosition-FloatRight" src="/SiteCollectionImages/Centrum/CHAIR/events/AI_Talks_ArthurGretton_180px_rev.jpg" alt="" style="margin:5px" /> </p> <p>Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University.</p> <p>Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods.</p> <p>He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, a member of the COLT Program Committee in 2013, and a member of Royal Statistical Society Research Section Committee since January 2020. Arthur was program co-chair for AISTATS in 2016, tutorials co-chair for ICML 2018, workshops co-chair for ICML 2019, program co-chair for the Dali workshop in 2019, and co-organsier of the Machine Learning Summer School 2019 in London.</p> in the North<p>Euler, Skeppsgränd 3</p><p>​New interactions between operator algebras and noncommutative geometry coming forth from recent developments in both areas</p><p>​​<br />The conference aims at bringing together experts and younger mathematicians working on operator algebras and noncommutative geometry in particular from Scandinavia. The focus of the conference is on fostering new interactions between operator algebras and noncommutative geometry coming forth from recent developments in both areas. <br /><br /><strong>Speakers:</strong></p> <ul><li>James Gabe (University of Southern Denmark) </li> <li>Jens Kaad (University of Southern Denmark) </li> <li>Matthew Kennedy (University of Waterloo)</li> <li>David Kyed (University of Southern Denmark) </li> <li>Franz Luef (NTNU, Trondheim)</li> <li>Mikael Rørdam (University of Copenhagen)</li> <li>Tatiana Shulman (IMPAN, Warzaw)</li> <li>Adam Skalski (IMPAN, Warzaw)</li> <li>Karen Strung (Radboud University Nijmegen)</li> <li>Hang Wang (East China Normal University, Shanghai) </li> <li>Michael Whittaker (University of Glasgow)</li> <li>Wilhelm Winter (University of Münster) <strong>TBC</strong></li> <li>Makoto Yamashita (University of Oslo) </li></ul>