Se alla kommande seminarier i LäsIT samt seminariesidor hos doktorandseminariets hemsida, TDB, Vi2, Theory and Applications Seminars (TAS) @ UpMARC, Matematiska institutionen och The Stockholm Logic Seminar.
TDB Seminar Imorgon (22 nov)  Carl Nettelblad: Hidden Markov Models for Genotype Phasing and Handwritten Text Alignment Plats: ITC 2446, Tid: 13:1514:00 Abstract: While there are more recent forms of statistical models for complex stochastic processes, hidden Markov models are still useful in many fields. I will discuss two applications, for analyzing the transmission of genetic material between parents and offspring, and a nascent collaboration with Vi2 regarding using a hidden Markov model as a "proofreading" stage when identifying words in written text. In addition, I will mention why you would want to solve a small ODE in a modification of the expectationmaximization (EM) parameter estimation algorithm. 
CSD Seminar Torsdag 23 nov  Ghafour Ahani: CostOptimal Caching for D2D Networks with Presence of User Mobility Plats: ITC 1245, Tid: 13:0014:00 Abstract: Nowadays, there is a heavy burden on the backhaul networks due to exponential data traffic growth. A promising approach is caching files at the user equipments (e.g., mobile phones) due to massive device connectivity. Users can download their files from each other through devicetodevice communications. In this seminar, an optimal caching problem with respect to user mobility will be discussed. 
DoCSSeminar Fredag 24 nov  Bengt Jonsson: Model Learning: Generating Automata Models from Tests Plats: ITC 1245, Tid: 14:15 Abstract: Modelbased approaches to development, verification, and testing are becoming increasingly important for efficient development of reliable software Its application is hampered by a lack of adequate specifications for software components, libraries, and services. This problem is addressed by the area of Model Learning, also known under names such as "Specification Mining" or "TestBased Modeling". More technically, and in this context, Model Learning consists of techniques for generating automata models from outcomes of tests on a blackbox component. This presentation will review some basic principles of model learning, and present an overview of recent results and work in progress by the Uppsala team (also in collaboration with the groups of Bernhard Steffen (TU Dortmund) and Frits Vaandrager (U Nijmegen)). We present how results on learning finitestate models can be extended to the learning of infinitestate models, e.g., to capture the influence of data value on the dynamic behavior of a component, or the influence of timers. We also survey some recent applications of this generalization to learning models of, communication protocols and library components. 
Seminar Måndag 27 nov  JeanNoël Monette, (Tacton Systems AB): CP for Product Configuration at Tacton Plats: ITC 1311, Tid: 10:15 Abstract: Tacton is a world leader in product configuration, where a configuration engine is used to configure a complex product (e.g., a truck) in order to meet the users requirements (e.g., the truck should be used mostly on highways) and respect the established rules (e.g., compatibility between wheels and suspensions). A configuration engine helps the user by guiding him through the potentially huge space of feasible configurations (e.g, by presenting a default solution, by marking which values can be selected, or by resolving potential conflicts). To do so, the Tacton configuration engine maps configuration problems to constraint satisfaction problems (CSP) and uses an offtheshelf constraint programming solver. In this presentation, I will first present Tacton, what product configuration is, and how we help businesses be more effective. I will then describe and contrast existing approaches to product configuration, before focusing on Tacton's constraintbased solution. 
Vi2 Seminar Måndag 27 nov  Axel Ringh: Optimal Mass Transport as a Distance Measure between Images Plats: ITC 4307, Tid: 14:15 Abstract: The optimal mass transport problem is a geometric framework for how to transport masses in an optimal way. Historically it has had large impact in economic theory and operations research, and recently it has also gained significant interest in application areas such as signal processing, image processing, and machine learning. The optimal mass transport problem can be formulated as a linear programming problem, however when computing the distance between two images the size of this linear program becomes prohibitively large. A recently development to address this builds on using an entropic barrier term and solving the resulting optimization problem using so called Sinkhorn iterations. This allows for an approximate solution of large optimal mass transport problems. In this work we show how these results can be used and extended in order to use optimal mass transport for solving inverse problems in, e.g., computerized tomography. Vi2 Seminars Group

Vi2 Seminar Måndag 27 nov  Olle Terenius: Disseminate your research with Wikipedia Plats: ITC 4307, Tid: 15:15 Abstract: TBA Vi2 Seminars Group

Gender Equality Group  Monthly Meeting 29 november  Plats: ITC 1345, Tid: 13:3014:30 ON THE AGENDA:

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