Gotowa bibliografia na temat „Données fonctionelles”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Données fonctionelles”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Données fonctionelles"
Chan, Vincy, Brandon Zagorski, Daria Parsons i Angela Colantonio. "Older Adults with Acquired Brain Injury: Outcomes After Inpatient Rehabilitation". Canadian Journal on Aging / La Revue canadienne du vieillissement 32, nr 3 (6.08.2013): 278–86. http://dx.doi.org/10.1017/s0714980813000317.
Pełny tekst źródłaLehéricy, Stéphane, i Emmanuel Gerardin. "Normal functional imaging of the basal ganglia". Epileptic Disorders 4, S3 (grudzień 2002). http://dx.doi.org/10.1684/j.1950-6945.2002.tb00543.x.
Pełny tekst źródłaBeaton, Nicholas R., Filippo Disanto, Anthony J. Guttmann i Simone Rinaldi. "On the enumeration of column-convex permutominoes". Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AO,..., Proceedings (1.01.2011). http://dx.doi.org/10.46298/dmtcs.2895.
Pełny tekst źródłaRozprawy doktorskie na temat "Données fonctionelles"
Staerman, Guillaume. "Functional anomaly detection and robust estimation". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.
Pełny tekst źródłaEnthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) together with the availability of massive data samples has put pressure on the scientific community to develop new reliable Machine-Learning methods and algorithms. The work presented in this thesis focuses around two axes: unsupervised functional anomaly detection and robust learning, both from practical and theoretical perspectives.The first part of this dissertation is dedicated to the development of efficient functional anomaly detection approaches. More precisely, we introduce Functional Isolation Forest (FIF), an algorithm based on randomly splitting the functional space in a flexible manner in order to progressively isolate specific function types. Also, we propose the novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. Estimation and computational issues are addressed and various numerical experiments provide empirical evidence of the relevance of the approaches proposed. In order to provide recommendation guidance for practitioners, the performance of recent functional anomaly detection techniques is evaluated using two real-world data sets related to the monitoring of helicopters in flight and to the spectrometry of construction materials.The second part describes the design and analysis of several robust statistical approaches relying on robust mean estimation and statistical data depth. The Wasserstein distance is a popular metric between probability distributions based on optimal transport. Although the latter has shown promising results in many Machine Learning applications, it suffers from a high sensitivity to outliers. To that end, we investigate how to leverage Medians-of-Means (MoM) estimators to robustify the estimation of Wasserstein distance with provable guarantees. Thereafter, a new statistical depth function, the Affine-Invariant Integrated Rank-Weighted (AI-IRW) depth is introduced. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical confirmation of the relevance of the depth function proposed. The upper-level sets of statistical depths—the depth-trimmed regions—give rise to a definition of multivariate quantiles. We propose a new discrepancy measure between probability distributions that relies on the average of the Hausdorff distance between the depth-based quantile regions w.r.t. each distribution and demonstrate that it benefits from attractive properties of data depths such as robustness or interpretability. All algorithms developed in this thesis are open-sourced and available online
VIDIER, SYLVIE. "Asthme du sujet age : donnees cliniques et fonctionelles : etude comparative avec differentes situations d'obstruction bronchique". Limoges, 1988. http://www.theses.fr/1988LIMO0185.
Pełny tekst źródłaSchwartz, Yannick. "Large-scale functional MRI analysis to accumulate knowledge on brain functions". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112056/document.
Pełny tekst źródłaHow can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process. Conversely, [Poldrack 2006] describes reverse inference as the probability of a cognitive process given an activation, but warns of a logical fallacy in concluding on such inference from evoked activity. Avoiding this issue requires to perform reverse inference with a large coverage of the cognitive space. We present a framework that uses a "meta-design" to describe many different tasks with a common vocabulary, and use forward and reverse inference in conjunction to outline functional networks that are consistently represented across the studies. We use a predictive model for reverse inference, and perform prediction on unseen studies to guarantee that we do not learn studies' idiosyncrasies. This final contribution permits to learn functional atlases, i.e. functional networks associated with a cognitive concept. We explored different possibilities to jointly analyse multiple fMRI experiments. We have found that one of the main challenges is to be able to relate the experiments with one another. As a solution, we propose a common vocabulary to describe the tasks. [Henson 2006] advocates the use of forward and reverse inference in conjunction to associate cognitive functions to brain regions, which is only possible in the context of a large scale analysis to overcome the limitations of reverse inference. This framing of the problem therefore makes it possible to establish a large statistical model of the brain, and accumulate knowledge across functional neuroimaging studies
Chaumont, Marc. "Représentation en objets vidéo pour un codage progressif et concurrentiel des séquences d'images". Phd thesis, Université Rennes 1, 2003. http://tel.archives-ouvertes.fr/tel-00004146.
Pełny tekst źródła