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Patent
French Institute for Research in Computer Science and Automation | Date: 2016-11-07

The invention relates to a device for monitoring physical objects that comprises one or more short-range remote readers, memory elements to be attached to physical objects, and a controller adapted for executing a reading function capable of interaction with the one or more remote readers in order to acquire data contained in adjacent memory elements, and for executing an integrity validation function capable of distinguishing, from the acquired data, individual identifiers particular to each of the memory elements as well as group description data stored in at least some of said memory elements, and of checking the sufficiency of group description data while checking the compliance of individual identifiers with corresponding group description data.


Patent
Thomson Licensing, French Institute for Research in Computer Science and Automation | Date: 2017-01-11

A technique for efficient multimedia content indexing, search and retrieval is proposed based on Product Quantization (PQ) codes, by using the processor more efficiently to make the scanning faster. The technique indexes a multimedia content database according to a tree structure based on index groups, each group represented by at least one bit per sub-quantizer, for less than all sub-quantizer. In addition, sub-quantizers centroids are grouped and each group, named minimum group, is represented by their minimum distance. The search is performed by storing in a fast access memory distance table segments for at least one index group and at least one minimum group at a time.


Patent
Thomson Licensing, French Institute for Research in Computer Science and Automation | Date: 2017-01-18

A method and a device for encoding and decoding High Dynamic Range (HDR) images and videos whose pixels are represented by high bit depth integers. In particular, a scalable compression scheme involving numerical representation and prediction of color information for the HDR enhancement layer given and LDR layer by generating color prediction information in response to a reconstructed LDR layer and a reconstructed HDR layer.


Patent
Thomson Licensing, French Institute for Research in Computer Science and Automation | Date: 2017-04-05

The disclosure relates to a method for predicting a current block of pixels in a current frame, comprising:- obtaining (11) a first prediction of the current block, delivering a first predicted block,- updating said first predicted block by implementing at least one iteration (12) of the following steps: obtaining (121) a current patch formed by a current template of the current block and an updated predicted block obtained at a previous iteration;o searching (122) for K nearest neighbor patches of said current patch in a decoded part of the current frame or in at least one reference frame;o determining (123) a linear combination of the K nearest neighbor patches which approximates the current patch, or of the K templates of the nearest neighbor patches which approximates the current template, according to a predetermined criterion, obtaining (124) an updated predicted block by applying said linear combination to the corresponding K blocks of the nearest neighbor patches.


Patent
Thomson Licensing, French Institute for Research in Computer Science and Automation | Date: 2017-04-05

A decoding method is disclosed that comprises:- decoding (S110, S120) a plurality of picture blocks;- clustering (S130) the plurality of decoded picture blocks into clusters;- decoding (S140) at least one projection function for each cluster;- applying (S150), on at least one decoded picture block, the at least one projection function associated with the cluster to which said at least one decoded picture block belongs.


Patent
Thomson Licensing, French Institute for Research in Computer Science and Automation | Date: 2017-01-11

A technique for efficient multimedia content indexing, search and retrieval is proposed based on Product Quantization (PQ) codes, by using the processor more efficiently to make the scanning faster. The technique indexes a multimedia content database according to a depth-first tree structure based on index groups, each group represented by at least one bit per sub-quantizer, and performs the search by storing in a fast access memory distance table segments for at least one index group at a time. The m b PQ code may be transformed from an original (2 m) (b/2) PQ code.


Leverrier A.,French Institute for Research in Computer Science and Automation
Physical Review Letters | Year: 2015

We give the first composable security proof for continuous-variable quantum key distribution with coherent states against collective attacks. Crucially, in the limit of large blocks the secret key rate converges to the usual value computed from the Holevo bound. Combining our proof with either the de Finetti theorem or the postselection technique then shows the security of the protocol against general attacks, thereby confirming the long-standing conjecture that Gaussian attacks are optimal asymptotically in the composable security framework. We expect that our parameter estimation procedure, which does not rely on any assumption about the quantum state being measured, will find applications elsewhere, for instance, for the reliable quantification of continuous-variable entanglement in finite-size settings. © 2015 American Physical Society.


Polyakov A.,French Institute for Research in Computer Science and Automation
IEEE Transactions on Automatic Control | Year: 2012

Two types of nonlinear control algorithms are presented for uncertain linear plants. Controllers of the first type are stabilizing polynomial feedbacks that allow to adjust a guaranteed convergence time of system trajectories into a prespecified neighborhood of the origin independently on initial conditions. The control design procedure uses block control principles and finite-time attractivity properties of polynomial feedbacks. Controllers of the second type are modifications of the second order sliding mode control algorithms. They provide global finite-time stability of the closed-loop system and allow to adjust a guaranteed settling time independently on initial conditions. Control algorithms are presented for both single-input and multi-input systems. Theoretical results are supported by numerical simulations. © 2012 IEEE.


Munos R.,French Institute for Research in Computer Science and Automation
Foundations and Trends in Machine Learning | Year: 2014

This work covers several aspects of the optimism in the face of uncertainty principle applied to large scale optimization problems under finite numerical budget. The initial motivation for the research reported here originated from the empirical success of the so-called Monte-Carlo Tree Search method popularized in Computer Go and further extended to many other games as well as optimization and planning problems. Our objective is to contribute to the development of theoretical foundations of the field by characterizing the complexity of the underlying optimization problems and designing efficient algorithms with performance guarantees. The main idea presented here is that it is possible to decompose a complex decision making problem (such as an optimization problem in a large search space) into a sequence of elementary decisions, where each decision of the sequence is solved using a (stochastic) multi-armed bandit (simple mathematical model for decision making in stochastic environments). This so-called hierarchical bandit approach (where the reward observed by a bandit in the hierarchy is itself the return of another bandit at a deeper level) possesses the nice feature of starting the exploration by a quasi-uniform sampling of the space and then focusing progressively on the most promising area, at different scales, according to the evaluations observed so far, until eventually performing a local search around the global optima of the function. The performance of the method is assessed in terms of the optimality of the returned solution as a function of the number of function evaluations. Our main contribution to the field of function optimization is a class of hierarchical optimistic algorithms designed for general search spaces (such as metric spaces, trees, graphs, Euclidean spaces) with different algorithmic instantiations depending on whether the evaluations are noisy or noiseless and whether some measure of the "smoothness" of the function is known or unknown. The performance of the algorithms depends on the "local" behavior of the function around its global optima expressed in terms of the quantity of near-optimal states measured with some metric. If this local smoothness of the function is known then one can design very efficient optimization algorithms (with convergence rate independent of the space dimension). When this information is unknown, one can build adaptive techniques which, in some cases, perform almost as well as when it is known. In order to be self-contained, we start with a brief introduction to the stochastic multi-armed bandit problem in Chapter 1 and describe the UCB (Upper Confidence Bound) strategy and several extensions. In Chapter 2 we present the Monte-Carlo Tree Search method applied to Computer Go and show the limitations of previous algorithms such as UCT (UCB applied to Trees). This provides motivation for designing theoretically well-founded optimistic optimization algorithms. The main contributions on hierarchical optimistic optimization are described in Chapters 3 and 4 where the general setting of a semimetric space is introduced and algorithms designed for optimizing a function assumed to be locally smooth (around its maxima) with respect to a semi-metric are presented and analyzed. Chapter 3 considers the case when the semi-metric is known and can be used by the algorithm, whereas Chapter 4 considers the case when it is not known and describes an adaptive technique that does almost as well as when it is known. Finally in Chapter 5 we describe optimistic strategies for a specific structured problem, namely the planning problem in Markov decision processes with infinite horizon discounted rewards. © 2014 R. Munos.


Martinelli A.,French Institute for Research in Computer Science and Automation
International Journal of Computer Vision | Year: 2014

This paper investigates the visual-inertial structure from motion problem. A simple closed form solution to this problem is introduced. Special attention is devoted to identify the conditions under which the problem has a finite number of solutions. Specifically, it is shown that the problem can have a unique solution, two distinct solutions and infinite solutions depending on the trajectory, on the number of point-features and on their layout and on the number of camera images. The investigation is also performed in the case when the inertial data are biased, showing that, in this latter case, more images and more restrictive conditions on the trajectory are required for the problem resolvability. © 2013 Springer Science+Business Media New York.

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