Author: - | Post date: 11.07.2016
Director of Institute of Theoretical and Applied Informatics,
Polish Academy of Sciences announces open competition for
research assistant position in Quantum Systems of Informatics Group
- Author: Zbigniew Puchała | Post date: 25.05.2016 | Last modified: 18.06.2018
Date:
13/07/2016 - 13:15Speaker:
Agata Mendys, Muzeum Narodowe w KrakowieDue to their non-invasive nature analytical techniques based on reflectance spectroscopy are almost ideal for the analysis of such sensitive and valuable objects as works of art. Point reflection spectroscopy can be extended in the direction of the analysis performed on the entire surface to perform hyperspectral imaging. Large amounts of data, obtained by this method, in conjunction with statistical analysis become a source of new information about studied objects and open up new possibilities in the field of non-destructive testing of historic buildings.
- Author: Zbigniew Puchała | Post date: 25.05.2016 | Last modified: 25.05.2016
Date:
30/05/2016 - 12:30Speaker:
Renata Onety, Manaus University, Brazil - Author: Zbigniew Puchała | Post date: 18.05.2016 | Last modified: 18.06.2018
Date:
15/06/2016 - 13:15Speaker:
Daniel Burgarth, Aberystwyth UniversityWhich algorithms could you run on a given quantum device? I give an introduction into the concept of "reachable operations" in controlled quantum systems. In practice, quantum devices will be noisy, so it is important to characterise the reachable set of open quantum systems. This, however, brings many mathematical challenges, which I will briefly discuss. I then provide a case study of a qubit in a bath of spin 1/2 particles, for which the reachability question can be solved analytically, and provide numerical results using an open-source software library developed by our group.
- Author: Jarosław Miszczak | Post date: 05.05.2016 | Last modified: 18.06.2018
Date:
11/05/2016 - 13:15Speaker:
Jarosław Duda, Jagiellonian UniversityThe standard way of choosing stochastic models (transition probabilities) in many cases turns out to be in disagreement with experiment, correctly described by quantum mechanics. For example it would allow electrons to freely travel through defected lattice of semiconductor, while we know that it is not a conductor - these electrons are statistically imprisoned (Anderson localization).
- Author: - | Post date: 13.04.2016 | Last modified: 18.06.2018
Date:
13/04/2016 - 13:15Speaker:
Waldemar Kłobus, Uniwersytet Adama Mickiewicza w PoznaniuWe develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. The formalism is built around a mathematical relation that we call conditional majorization. We define and characterize conditional majorization, and use it to develop tools for the construction of measures of the conditional uncertainty of individual measurements, and also of the joint conditional uncertainty of sets of measurements.
- Author: - | Post date: 04.02.2016 | Last modified: 18.06.2018
Date:
09/03/2016 - 13:15Speaker:
Jarosław Duda, Uniwersytet JagiellońskiEntropy coding is the heart of most of data compressors. Standard methods are Huffman coding - fast but inaccurate (suboptimal), and arithmetic/range coding - accurate but an order of magnitude slower (costly). I will tell about new approach: Asymmetric Numeral Systems, which is accurate while having cost similar to Huffman coding. It is for example used in Apple LZFSE (default compressor in iOS9 and OS X 10.11) or CRAM 3.0 DNA compressor of European Bioinformatics Institute. - Author: - | Post date: 02.02.2016 | Last modified: 18.06.2018
Date:
04/02/2016 - 12:00Speaker:
Krisztian Buza, Budapest University of Technology and EconomicsPrediction on a numeric scale, i.e., regression, is one of the most prominent machine learning tasks with various applications in finance, medicine, social and natural sciences. Due to its simplicity, theoretical performance guarantees and successful real-world applications, one of the most popular regression techniques is the k nearest neighbor regression.
