- Title: A contribution to distinguishing labellings of graphs
- When: December 15, 2020 — 09:00
- Where: online
- Cristina Bazgan, PR, Université Paris-Dauphine, Paris
- Frédéric Cazals, DR, Inria Sophia Antipolis – Méditerranée
- Louis Esperet CR HDR, CNRS, G-SCOP, Grenoble
- Mickaël Montassier (referee), PR, Université de Montpellier
- Éric Sopena PR, Université de Bordeaux
- Stéphan Thomassé, PR, ENS de Lyon
- Olivier Togni (referee), PR, Université de Bourgogne, Dijon
- Xuding Zhu (referee), PR, National Sun Yat-sen University (Taïwan)
Abstract: During the talk, I will present some of my contribution to distinguishing labellings of graphs, and the so-called 1-2-3 Conjecture that occupies an important place in this field. The general objective in this kind of problems is, given a (connected undirected) graph, to weight its edges in such a way that the adjacent vertices get distinguishable accordingly to some parameter computed from the edge-weighting. For instance, in the 1-2-3 Conjecture, raised by Karonski, Łuczak and Thomason in 2004, the aim is to weight the edges with 1,2,3 so that adjacent vertices get distinguished accordingly to their sums of incident weights.
Although the 1-2-3 Conjecture was raised as nothing but a toy problem when it was introduced, several results in the recent years have established its deeper nature. The conjecture, by its very definition, has undoubtedly an algebraic nature. Some results have also established that it has some decompositional flavour. Although the conjecture is rather artificial, it is also related to other classical notions of graph theory, such as proper vertex-colourings of graphs.
Through the results I will focus on during the talk, my main goal is to point out how deep this field is, and the many aspects of interest that are worth considering.
22e Journées Graphes et Algorithmes (JGA 2020)
- When: November 16-18, 2020
- Where: Online
- Link: conference website
Welcome to our new team member: Małgorzata Sulkowska.
Małgorzata Sulkowska got her M.Sc. in Computer Science in 2007 from Wrocław University of Science and Technology (Poland). The thesis, written jointly with Michał Przykucki, won second award in the Polish Mathematical Society contest for the best M.Sc. thesis in probability theory and applied mathematics. Małgorzata got her Ph.D. in Mathematics in 2013 from Wrocław University of Science and Technology under the supervision of prof. Michał Morayne. She got the assistant position at the same university already in 2012 and since 2014 she is an assistant professor in the Department of Fundamentals of Computer Science. In years 2015-2016 she spent 5-months as a postdoc at Federal University of Ceara (Fortaleza, Brazil) under the supervision of prof. Fabricio Siqueira Benevides. In years 2016-2020 she conducted a series of shorter research visits, among others, at University of London (Great Britain), Vienna University of Technology (Austria), University of Louisville (USA) and Inria Sophia-Antipolis (France). Since 2018 she has been a mentor of the Student Research Group Solvro at Wrocław University of Science and Technology. In 2019 Małgorzata was awarded a Miniatura3 grant from Polish National Science Center for research on stopping algorithms for graphs. In September 2020 she started a one-year postdoc position at Universite Cote d’Azur as a member of COATI team at INRIA Sophia-Antipolis. Her main research interests are optimal stopping problems, lying on the crossroads of graph theory, combinatorics and probability theory.
Welcome to our new team member: Ramon Aparicio-Pardo.
Ramon Aparicio-Pardo received a MEng in Telecom. and a Ph.D. from Universidad Politécnica de Cartagena (UPCT), Spain, in 2006 and 2011, respectively. His Ph.D. thesis, titled ‘Optimization and Planning of WDM Transparent Optical Networks,’ was distinguished with TELEFÓNICA Award for Best Thesis in Networking.
After two postdocs, at Orange Labs on optical switching networks (2012-2013) and at IRISA, in former IMT-Telecom Bretagne, on adaptive streaming (2013-2015), he joined Université Côte d’Azur (UCA) as an associate professor (“maître de conférences”) in Sept. 2015 and became a member of the SigNet group of the I3S laboratory. His main research interest is the optimal design and management of communication networks.
In September 2020, he joined obtained a “délégation Inria” to work with COATI on the design of AI-based methods for network optimization.
Several websites are currently collecting online talks. You can find a list of some of them below.
- Math Seminars, collection of pointers to seminars around the world.
- CS Theory Online Talks
- Combinatorics Lectures Online
- Princeton Discrete Mathematics Seminar
- University Guest Lectures by IBM Academic Ambassadors
You can also have a look to formerly recorded seminars
“Games on Web 2020” is the 2nd edition of a competition organized by CGI (IT systems and software consulting firm) in partnership with MIAGE, Polytech Sophia and IUT Nice Côte d’Azur. It consists in programming a retro Web game on an imposed theme with the Babylon.js library. This year’s theme is “The Place To Be”.
Also: presentation by Sebastien Vandenberghe, main developer of BabylonJS — amphi A1 Polytech site lucioles — February 12, 2020.
Members of COATI participates to the 2019 edition of Cérémonie des Lauréats de prix d’excellence d’Université Côte d’Azur.
- Christelle Caillouet for her Best paper award (more information here).
- Emanuele Natale for his nomination as the Best Italian Young Researcher in Theoretical Computer Science – Italian Chapter of EATCS (the European Association of Theoretical Computer Science) (see here).
- William Lochet for receiving the PhD prize Graphes “Charles Delorme” 2019 (see here).
More pictures here.
Emilio Cruciani and his co-authors (Breno Alexandro Ferreira de Miranda, Antonia Bertolino, and Roberto Verdecchia) won a 2019 Testing and Verification research award for their work on Static prediction of test flakiness.
Software companies invest a large amount of time in testing software. One of the problems they face during this process is that of “test flakiness”: some of the tests, instead of failing because of the presence of a bug in the software, have a nondeterministic behavior and could fail for external reasons (e.g., randomness in the software, concurrency, network latency, etc). When they are run multiple times on the same version of the software they could pass or fail and developer cannot rely on their outcome for the identification of bugs: When developers are asked to fix a potential bug found by a flaky test they could just lose their time since the bug is not there. The current approach to detect such flaky tests (and then treat them differently) is to rerun them: if they lead to different outcomes in different runs they are declared flaky. Of course this strategy is costly for large industries (e.g., Google runs millions of tests every day, out of which up to 16% are flaky).
Cruciani et al. proposed a fast, static approach to detect flaky tests based on machine learning techniques that proved to have high precision in our preliminary experimental results.
The full list of winning projects and some more details on the award can be found here.
See also here (in italian).