December 5, 2019
Keynote: Flandrin
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December 5, 2019
MMonday | TTuesday | WWednesday | TThursday | FFriday | SSaturday | SSunday |
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25November 25, 2019
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26November 26, 2019
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27November 27, 2019
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Back from Supercomputing by Bruno Raffin (Datamove) – Bruno will present his report from the new trends he saw this year at supercomputing as well as explain some technical talks he liked presented at the conference. Bâtiment IMAG (442) |
29November 29, 2019
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30November 30, 2019
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December1December 1, 2019 |
2December 2, 2019
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3December 3, 2019
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4December 4, 2019
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Keynote: Flandrin – |
6December 6, 2019
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7December 7, 2019
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8December 8, 2019
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9December 9, 2019
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10December 10, 2019
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Phd Defense: Autonomic Resilience of Distributed IoT Applications in the Fog, by Umar Ozeer (Polaris) – Abstract: The Fog, however, is unstable because it is constituted of billions of heterogeneous devices in a dynamic ecosystem. IoT devices may regularly fail because of bulk production and cheap design. Moreover, the Fog-IoT ecosystem is cyber-physical and thus devices are subjected to external physical world conditions which increase the occurrence of failures. When failures occur in such an ecosystem, the resulting inconsistencies in the application affect the physical world by inducing hazardous and costly situations. In this Thesis, we propose an end-to-end autonomic failure management approach for IoT applications deployed in the Fog. The proposed approach recovers from failures in a cyber-physical consistent way. Cyber-physical consistency aims at maintaining a consistent behavior of the application with respect to the physical world, as well as avoiding dangerous and costly circumstances. The approach was validated using model checking techniques to verify important correctness properties. It was then implemented as a framework called F3ARIoT. This framework was evaluated on a smart home application. The results showed the feasibility of deploying F3ARIoT on real Fog-IoT applications as well as its good performances in regards to end user experience. Bâtiment IMAG Saint-Martin-d'Hères, 38400 France |
12December 12, 2019
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13December 13, 2019
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14December 14, 2019
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15December 15, 2019
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16December 16, 2019
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17December 17, 2019
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18December 18, 2019
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Tropical approach to semidefinite programming and mean payoff games, by Mateusz Skomra (ENS Lyon) – Semidefinite programming (SDP) is a fundamental tool in convex and polynomial optimization. It consists in minimizing linear functions over spectrahedra (sets defined by linear matrix inequalities). In particular, SDP is a generalization of linear programming. In this talk, we discuss the nonarchimedean analogue of SDP, replacing the field of real numbers by the field of Puiseux series. Our methods rely on tropical geometry and, in particular, on the study of tropicalization of spectrahedra. We show that, under genericity conditions, tropical spectrahedra encode Shapley operators associated with stochastic mean payoff games. As a result, a large class of semidefinite feasibility problems defined over Puiseux series can be solved efficiently using combinatorial algorithms designed for stochastic games. Conversely, we use tropical spectrahedra to introduce a condition number for stochastic mean payoff games. We show that this conditioning controls the number of value iterations needed to decide whether a mean payoff game is winning. In particular, we obtain a pseudopolynomial bound for the complexity of value iteration provided that the number of random positions is fixed. Bâtiment IMAG (442) |
HDR defense of Panayotis Mertikopoulos (Polaris) – Online optimization and learning in games: Theory and applications HDR Jury: The traditional "pot de soutenance" will take place right after the defense at the ground floor of the IMAG building. Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France |
21December 21, 2019
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22December 22, 2019
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23December 23, 2019
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24December 24, 2019
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25December 25, 2019
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26December 26, 2019
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27December 27, 2019
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28December 28, 2019
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29December 29, 2019
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30December 30, 2019
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31December 31, 2019
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January1January 1, 2020 |
2January 2, 2020
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3January 3, 2020
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4January 4, 2020
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5January 5, 2020
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6January 6, 2020
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7January 7, 2020
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8January 8, 2020
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Keynote: Radu Horaud – |
10January 10, 2020
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11January 11, 2020
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12January 12, 2020
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13January 13, 2020
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14January 14, 2020
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15January 15, 2020
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Capacity of a LoRaWAN cell, by Martin Heusse (Drakkar) – We propose a model to estimate the packet delivery rate in a LoRaWAN cell, when all nodes have the same traffic generation process and may use repetitions. The model predicts the transmission success rate for any cell range and node density, with similar traffic from all nodes. We find that the transmission success depends on striking a balance between the adverse effects of attenuation and collisions; in small cells, it is highly dependent on the suitable allocation of spreading factors, whereas using packet repetitions is more effective in large cells. Bâtiment IMAG (442) |
17January 17, 2020
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18January 18, 2020
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19January 19, 2020
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20January 20, 2020
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21January 21, 2020
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22January 22, 2020
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Can random matrices change the future of machine learning? by Romain Couillet (Gipsa) – Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes) |
24January 24, 2020
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25January 25, 2020
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26January 26, 2020
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27January 27, 2020
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28January 28, 2020
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29January 29, 2020
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HDR Nicolas Gast – Abstract: The design of efficient algorithms is closely linked to the evaluation of their performances. My work focuses on the use of stochastic models for the performance evaluation of large distributed systems. I am interested in developing tools that can characterize the emergent behavior of such systems and improve their performance. This leads me to solve stochastic control and optimization problems, notably through operations research methods. These problems suffer from combinatorial explosion: the complexity of a problem grows exponentially with the number of objects that compose it. It is therefore necessary to design models but also algorithmic processes whose complexity does not increase too rapidly with the size of the system.In this presentation, I will summarize a few of my contributions on the use and the refinements of mean field approxiamtion to study the performance of distributed systems and algorithms. I will introduce the key concepts behind mean field approximation, by giving some examples of where it can be applied. I will review some of the classical models and try to answer a very natural question: how large should a system be for mean field to apply? Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France |
31January 31, 2020
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February1February 1, 2020 |
2February 2, 2020
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