International Professional University of Technology in Nagoya Repository
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Classification Aveugle de Modulation basée sur la Transformée non-linéaire de la mise à la Puissance M
No full textInternational audienc
DyKOSMap : from a prototype to a web application.
No full textInternational audienceDyKOSMap is a research prototype developed in the framework of the DynaMO project. It aims to maintain existing mappings established between knowledge organization systems (KOSs) by taking into account the dynamic nature of these KOSs. The aim of this work is to implement a web application, in order to make a more robust and easier-to-manipulate tool starting from this prototype
A formal analysis of the Neuchâtel e-voting protocol
No full textRemote electronic voting is used in several countries for legally binding elections. Unlike academic voting protocols, these systems are not always documented and their security is rarely analysed rigorously. In this paper, we study a voting system that has been used for electing political representatives and in citizen-driven referenda in the Swiss canton of Neuchâtel. We design a detailed model of the protocol in ProVerif for both privacy and veri-fiability properties. Our analysis mostly confirms the security of the underlying protocol: we show that the Neuchâtel protocol guarantees ballot privacy, even against a corrupted server; it also ensures cast-as-intended and recorded-as-cast verifiability, even if the voter's device is compromised. To our knowledge, this is the first time a full-fledged automatic symbolic analysis of an e-voting system used for politically-binding elections has been realized
Ultimate Boundedness Results for Noise-Corrupted Quaternion Output Feedback Attitude Tracking Controllers
No full textInternational audienc
Unbiasing Truncated Backpropagation Through Time
No full textTruncated Backpropagation Through Time (truncated BPTT) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce Anticipated Reweighted Truncated Backpropagation (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling, ARTBP slightly outperforms truncated BPTT
Market Integration VS Time Granularity: How to provide needed flexibility resources
No full textInternational audienc
Sequential Predictors under Time-Varying Delays: Effects of Delayed State Observations in Dynamic Controller
No full textInternational audienceIn a 2016 IEEE Conference on Decision and Control paper, our team designed sequential predictors for time-varying linear systems with time-varying delays, to prove global exponential stabilization properties using a feedback control that is computed in terms of the state of the last sequential predictor. This allowed feedback delays of arbitrarily large sup norm in the original system. Here we provide a significant generalization to more challenging cases with arbitrarily large feedback delay bounds, and where, in addition, current values of the plant state are not available to use in the sequential predictors. We illustrate our work in a pendulum example
New results on the transient analysis of some fundamental queuing systems - Keynote presentation
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Distanceless Label Propagation: an Efficient Direct Connected Component Labeling Algorithm for GPUs
No full textInternational audienceModern computer architectures are mainly composed of multi-core processors and GPUs. Consequently, solely providing a sequential implementation of algorithms or comparing algorithm performance without regard to architecture is no longer pertinent. Today, algorithms have to address parallelism, multithreading and memory topology (private/shared memory, cache or scratchpad, ...). Most Connected Component Labeling (CCL) algorithms are sequential, direct and optimized for processors. Few were designed specifically for GPU architectures and none were designed to be adapted to different architectures. The most efficient GPU implementations are iterative; in order to manage synchronizations between processing units, but the number of iterations depends on the image shape and density. This paper describes the DLP (Distanceless Label Propagation) algorithms, an adaptable set of algorithms usable both on GPU and multi-core architectures, and DLP-GPU, an efficient direct CCL algorithm for GPU based on DLP mechanisms
