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Tifratene K.,University of Nice Sophia Antipolis | Robert P.,University of Nice Sophia Antipolis | Metelkina A.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Pradier C.,Nice University Hospital Center | Dartigues J.F.,French Institute of Health and Medical Research
Neurology | Year: 2015

Objectives: To describe the positive predictive value of mild cognitive impairment (MCI) and the factors associated with progression in routine practice. Methods: A retrospective cohort study was conducted from the French National Alzheimer Database. Among 446,439 patients cared for in the participating centers between January 2009 and January 2014, 45,386 (10.2%) were classified as having MCI and 23,676 had at least one follow-up visit. Annual progression rate was used to describe the progression of patients with MCI to dementia due to Alzheimer disease. Hazard ratios of dementia due to Alzheimer disease were estimated using Cox regression model. Results: Annual progression rate (95% confidence interval) was 13.7% person-years (py) (13.5%-13.9%) with higher rate for amnestic MCI (aMCI) (18.2% py [17.9%-18.5%]) than for nonamnestic MCI (naMCI) (9.5% py [9.3%-9.6%]). Separate regression models were performed for each MCI subtype. Higher education, older age, female sex, and lower Mini-Mental State Examination score were associated with an increased risk of progression for both subtypes. Use of anxiolytics (adjusted hazard ratio [95% confidence interval]: 0.77 [0.66-0.91]) was a protective factor for aMCI whereas antidepressant drugs (1.16 [1.04-1.29]) were associated with an increased risk. For naMCI, prescriptions of antidepressants (0.85 [0.74-0.98]) and antipsychotics (0.55 [0.32-0.93]) were protective for progression. Conclusions: Under circumstances emulating routine clinical practice, the positive predictive value of an MCI diagnosis is in line with previous clinical studies and the external validity of the concept is strengthened. Distinguishing between aMCI and naMCI is particularly relevant. © 2015 American Academy of Neurology.


Regin J.-C.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Rezgui M.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Malapert A.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

We propose the Embarrassingly Parallel Search, a simple and efficient method for solving constraint programming problems in parallel. We split the initial problem into a huge number of independent subproblems and solve them with available workers, for instance cores of machines. The decomposition into subproblems is computed by selecting a subset of variables and by enumerating the combinations of values of these variables that are not detected inconsistent by the propagation mechanism of a CP Solver. The experiments on satisfaction problems and optimization problems suggest that generating between thirty and one hundred subproblems per worker leads to a good scalability. We show that our method is quite competitive with the work stealing approach and able to solve some classical problems at the maximum capacity of the multi-core machines. Thanks to it, a user can parallelize the resolution of its problem without modifying the solver or writing any parallel source code and can easily replay the resolution of a problem. © 2013 Springer-Verlag.


Allibert G.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Courtial E.,Institute Pluridisciplinaire Of Recherche En Ingenierie Des Systemes | Chaumette F.,CNRS Research on Informatics and Random Systems
IEEE Transactions on Robotics | Year: 2010

This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-control strategy, the IBVS task is written into a nonlinear optimization problem in the image plane, where the constraints can be easily and explicitly taken into account. Second, the contribution of the image prediction and influence of the prediction horizon are pointed out. The image prediction is obtained due to a model. The latter can be a local model based on the interaction matrix or a nonlinear global model based on 3-D data. Its choice is discussed with respect to the constraints to be handled. Finally, simulations that were obtained with a 6-degree-of-freedom (DOF) free-flying camera highlight the potential advantages of the proposed approach with respect to the image prediction and the constraint handling. © 2010 IEEE.


Regin J.-C.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Constraint Programming (CP) is a general technique for solving combinatorial optimization problems. Real world problems are quite complex and solving them requires to divide work into different parts. Mainly, there are: the abstraction of interesting and relevant subparts, the definition of benchmarks and design of a global model and the application of a particular search strategy. We propose to identify for each of these parts some common pitfalls and to discuss them. We will successively consider undivided model, rigid search, biased benchmarking and wrong abstraction. © 2011 Springer-Verlag.


Meo M.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2011

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice. Radiofrequency catheter ablation (CA) is becoming one of the most widely employed therapies. Yet selection of patients who will benefit from this treatment remains a challenging task. Previous works have examined several electrocardiogram (ECG) parameters as potential predictors of CA success, such as fibrillatory wave (f-wave) amplitude. However, they require a manual computation and consider only a subset of electrodes, so inter-lead spatial variability of the 12-lead ECG is not fully exploited. The present study puts forward an automatic procedure for f-wave amplitude computation to non-invasively predict CA outcome. An extension of this quantitative measure to the whole set of leads is also proposed, based on Principal Component Analysis (PCA). We show that exploiting the spatial diversity present in the surface ECG not only improves the robustness to electrode selection but also increases the predictive power of the amplitude parameter.


Gorisse D.,Yakaz Laboratory | Cord M.,University Pierre and Marie Curie | Precioso F.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2012

In the past 10 years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm, which provides approximate nearest neighbors in a euclidean space with sublinear complexity, is probably the most popular, the euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the Earth-Mover Distance and distances. In this paper, we present a new LSH scheme adapted to distance for approximate nearest neighbors search in high-dimensional spaces. We define the specific hashing functions, we prove their local-sensitivity, and compare, through experiments, our method with the Euclidean Locality Sensitive Hashing algorithm in the context of image retrieval on real image databases. The results prove the relevance of such a new LSH scheme either providing far better accuracy in the context of image retrieval than euclidean scheme for an equivalent speed, or providing an equivalent accuracy but with a high gain in terms of processing speed. © 2012 IEEE.


Da Costa Pereira C.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Dragoni M.,Fondazione Bruno Kessler | Pasi G.,University of Milan Bicocca
Information Processing and Management | Year: 2012

A new model for aggregating multiple criteria evaluations for relevance assessment is proposed. An Information Retrieval context is considered, where relevance is modeled as a multidimensional property of documents. The usefulness and effectiveness of such a model are demonstrated by means of a case study on personalized Information Retrieval with multi-criteria relevance. The following criteria are considered to estimate document relevance: aboutness, coverage, appropriateness, and reliability. The originality of this approach lies in the aggregation of the considered criteria in a prioritized way, by considering the existence of a prioritization relationship over the criteria. Such a prioritization is modeled by making the weights associated to a criterion dependent upon the satisfaction of the higher-priority criteria. This way, it is possible to take into account the fact that the weight of a less important criterion should be proportional to the satisfaction degree of the more important criterion. Experimental evaluations are also reported. © 2011 Elsevier Ltd. All rights reserved.


Acher M.,University of Namur | Acher M.,University of Rennes 1 | Collet P.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Lahire P.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | France R.B.,Colorado State University
Science of Computer Programming | Year: 2013

The feature model formalism has become the de facto standard for managing variability in software product lines (SPLs). In practice, developing an SPL can involve modeling a large number of features representing different viewpoints, sub-systems or concerns of the software system. This activity is generally tedious and error-prone. In this article, we present FAMILIAR a Domain-Specific Language (DSL) that is dedicated to the large scale management of feature models and that complements existing tool support. The language provides a powerful support for separating concerns in feature modeling, through the provision of composition and decomposition operators, reasoning facilities and scripting capabilities with modularization mechanisms. We illustrate how an SPL consisting of medical imaging services can be practically managed using reusable FAMILIAR scripts that implement reasoning mechanisms. We also report on various usages and applications of FAMILIAR and its operators, to demonstrate their applicability to different domains and use for different purposes. © 2012 Elsevier B.V. All rights reserved.


Richard A.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis | Comet J.-P.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
Journal of Mathematical Biology | Year: 2011

We provide a counter-example to a conjecture of René Thomas on the relationship between negative feedback circuits and stable periodicity in ordinary differential equation systems (Kaufman et al. in J Theor Biol 248:675-685, 2007). We also prove a weak version of this conjecture by using a theorem of Snoussi. © 2010 Springer-Verlag.


Bucci M.,University of Turku | De Luca A.,University of Naples Federico II | Fici G.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis
Theoretical Computer Science | Year: 2013

Trapezoidal words are words having at most n+1 distinct factors of length n for every n≥0. They therefore encompass finite Sturmian words. We give combinatorial characterizations of trapezoidal words and exhibit a formula for their enumeration. We then separate trapezoidal words into two disjoint classes: open and closed. A trapezoidal word is closed if it has a factor that occurs only as a prefix and as a suffix; otherwise it is open. We investigate open and closed trapezoidal words, in relation with their special factors. We prove that Sturmian palindromes are closed trapezoidal words and that a closed trapezoidal word is a Sturmian palindrome if and only if its longest repeated prefix is a palindrome. We also define a new class of words, semicentral words, and show that they are characterized by the property that they can be written as uxyu, for a central word u and two different letters x,y. Finally, we investigate the prefixes of the Fibonacci word with respect to the property of being open or closed trapezoidal words, and show that the sequence of open and closed prefixes of the Fibonacci word follows the Fibonacci sequence. © 2012 Elsevier B.V. All rights reserved.

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