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Slepian-Bangs-type formulas and the related Misspecified Cramér-Rao Bounds for Complex Elliptically Symmetric distributions
No full textInternational audienc
Asynchronous cellular automata
No full textThis text has been proposed for the Encyclopedia of Complexity and Systems Science edited by Springer Nature and should appear in 2018.International audienceThis text is intended as an introduction to the topic of asynchronous cellular automata. We start from the simple example of the Game of Life and examine what happens to this model when it is made asynchronous (Sec. 1). We then formulate our definitions and objectives to give a mathematical description of our topic (Sec. 2). Our journey starts with the examination of the shift rule with fully asynchronous updating and from this simple example, we will progressively explore more and more rules and gain insights on the behaviour of the simplest rules (Sec. 3). As we will meet some obstacles in having a full analytical description of the asynchronous behaviour of these rules, we will turn our attention to the descriptions offered by statistical physics, and more specifically to the phase transition phenomena that occur in a wide range of rules (Sec. 4). To finish this journey, we will discuss the various problems linked to the question of asynchrony (Sec. 5) and present some openings for the readers who wish to go further (Sec. 6)
kT-spokes: combining kT-points with spokes to ease ramp pulse design for TOF slab selection with parallel transmission at 7T
No full textInternational audienceTONE pulses counteract blood saturation through the imaged slab in TOF sequences, but their ramp profile is hampered by RF inhomogeneities at UHF. On the other hand, kz-spokes are known to compensate for in-plane B1 + heterogeneities in slice or slab selection. However, their design doesn't address thru-slab heterogeneities. To address them, a new pulse type called " kT-spokes " is introduced. As TONE pulses, kT-spokes efficacy is demonstrated with pTx at 7T in comparison with mere equivalent kz-spokes
Dynamic Lip Animation from a Limited number of Control Points: Towards an Effective Audiovisual Spoken Communication
No full textInternational audienceIn audiovisual speech communication, the lower part of the face (mainly lips and jaw) actively participates during speech production. Modeling well lip motion and deformation in audiovisual speech synthesis is important to achieve realism and effective communication. This is essential for challenged population as hard-of-hearing people or new language learners. In this scope, we propose a technique that allows for animation of a human face with realistic lips using a limited number of control points. We have used an articu-lograph that provides high temporal and spatial precision, allowing tracking the positions of small electromagnetic sensors, even when occluded, which is often the case when tracking the lip movement. In our work, the control point data are first acquired, then fitted to a 3D face model of a human speaker, i.e., each control point is associated with a region of the face by minimizing the distance between the control points and the surface of the face model. Finally, we apply an interpolation scheme of the displacement field between the control points. This displacement field describes the deformation of a surface. In the case of the face, this method is well adapted to animating the region of the face that is highly correlated with speech, specifically the lips and the lower part of the face, even with a very limited number of control points
Automatic summarization of scientific publications using a feature selection approach
No full textInternational audienceFeature Maximization is a feature selection method that deals efficiently with textual data: to design systems that are altogether language-agnostic, parameter-free and do not require additional corpora to function. We propose to evaluate its use in text summarization, in particular in cases where documents are structured. We first experiment this approach in a single-document summarization context. We evaluate it on the DUC AQUAINT corpus and show that despite the unstructured nature of the corpus, our system is above the baseline and produces encouraging results. We also observe that the produced summaries seem robust to redundancy. Next, we evaluate our method in the more appropriate context of SciSumm challenge, which is dedicated to research publications summarization. These publications are structured in sections and our class-based approach is thus relevant. We more specifically focus on the task that aims to summarize papers using those that refer to them. We consider and evaluate several systems using our approach dealing with specific bag of words. Furthermore, in these systems, we also evaluate cosine and graph-based distance for sentence weighting and comparison. We show that our Feature Maximization based approach performs very well in the SciSumm 2016 context for the considered task, providing better results than the known results so far, and obtaining high recall. We thus demonstrate the flexibility and the relevance of Feature Maximization in this context
DNN Uncertainty Propagation using GMM-Derived Uncertainty Features for Noise Robust ASR
No full textInternational audienceThe uncertainty decoding framework is known to improve deep neural network (DNN) based automatic speech recognition (ASR) performance in noisy environments. It operates by estimating the statistical uncertainty about the input features and propagating it to the output senone posteriors by sampling. Unfortunately, this approximate propagation scheme limits the performance improvement. In this work, we exploit the fact that uncertainty propagation can be achieved in closed form for Gaussian mixture acoustic models (GMMs). We introduce new GMM-derived (GMMD) uncertainty features for robust DNN-based acoustic model training and decoding. The GMMD features are computed as the difference between the GMM log-likelihoods obtained with vs. without uncertainty. They are concatenated with conventional acoustic features and used as inputs to the DNN. We evaluate the resulting ASR performance on the CHiME-2 and CHiME-3 datasets. The proposed features are shown to improve performance on both datasets, both for conventional decoding and for uncertainty decoding with different uncertainty estimation/propagation techniques
An improved extreme learning machine model for the prediction of human scenarios in smart homes
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Executable Schema Mappings for Statistical Data Processing
No full textInternational audienceData processing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models. We illustrate here a proposal where a high-level language, EXL, is used for the declarative specification of statistical programs, and a translation into executable form in various target systems is available. The language is based on the theory of schema mappings, in particular those defined by a specific class of tgds, which we actually use to optimize user programs and facilitate the translation towards several target systems. The characteristics of such class guarantee good tractability properties and the applicability in Big Data settings. A concrete implementation, EXLEngine, has been carried out and is currently used at the Bank of Italy
Pivotal decomposition schemes inducing clones of operations
No full textInternational audienceWe study pivotal decomposition schemes and investigate classes of pivotally decomposable operations. We provide sufficient conditions on pivotal operations that guarantee that the corresponding classes of pivotally decomposable operations are clones, and show that under certain assumptions these conditions are also necessary. In the latter case, the pivotal operation together with the constant operations generate the corresponding clone
Analysis of the Newsboy Problem subject to price dependent demand and multiple discounts
No full textInternational audienceExisting papers on the Newsboy Problem that deal with price dependent demand and multiple discounts often analyze those two problems separately. This paper considers a setting where price dependence and multiple discounts are observed simultaneously, as is the case of the apparel industry. Henceforth, we analyze the optimal order quantity, initial selling price and discount scheme in the News-Vendor Problem context. The term of discount scheme is often used to specify the number of discounts as well as the discount percentages. We present a solution procedure of the problem with general demand distributions and two types of price-dependent demand: additive case and multiplicative case. We provide interesting insights based on a numerical study. An approximation method is proposed which confirms our numerical results