Online representation learning with single and multi-layer Hebbian networks for image classification

Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks

Video summarization based on local features

Keyframe extraction process consists on presenting an abstract of the entire video with the most representative frames. It is one of the basic procedures relating to video retrieval and summary. This paper present a novel method for keyframe extraction based on SURF local features. First, we select a group of candidate frames from a video shot using a leap extraction technique. Then, SURF is used to detect and describe local features on the candidate frames. After that, we analyzed those features to eliminate near duplicate keyframes, helping to keep a compact set, using FLANN method. We developed a comparative study to evaluate our method with three state of the art approaches based on local features. The results show that our method overcomes those approaches

Online representation learning with single and multi-layer Hebbian networks for image classification

Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks

Rôles des polymorphismes génétiques dans la détermination de la dose individuelle de la warfarine chez les patients traités avec de l’amiodarone

Introduction : Bien que la pratique de l’usage de la warfarine se soit améliorée au cours de la dernière décennie, aucune recommandation claire basée sur le dosage de l’amiodarone n’a été jusqu’à maintenant standardisée, ce qui représente un grand obstacle pour les cliniciens. La warfarine a un index thérapeutique étroit nécessitant un suivi régulier et un ajustement individuel de la posologie, ceci afin de déterminer la dose thérapeutique, tout en prévenant les effets secondaires qui pourraient être fatals dans certains cas. La variabilité interindividuelle de la réponse à la warfarine dépend de plusieurs facteurs, dont l’âge, le sexe, le poids, l’alimentation et l’interaction médicamenteuse, mais ceux-ci n’expliquent que partiellement les différences de sensibilité à la warfarine. Les polymorphismes des gènes CYP2C9 et VKORC1 jouent un rôle important dans la réponse à la warfarine et expliquent jusqu’à 50% de la variabilité des doses. L’utilisation d’antiarythmiques telle l’amiodarone peut accentuer considérablement l’effet de la warfarine et nécessite généralement une diminution de 30 à 50% de la dose de la warfarine. Aucune étude à ce jour n’a tenté de déterminer l’utilité du génotypage des polymorphismes des gènes CYP2C9 et VKORC1 chez les patients sous traitement combiné de warfarine et amiodarone. Objectif : Notre étude a pour objectif tout d’abord de déterminer si des facteurs génétiques influencent la première dose de stabilisation de la warfarine chez les patients en FA après l’introduction de l’amiodarone. Nous allons également tenter de confirmer l’association préalablement rapportée entre les facteurs génétiques et la première dose de stabilisation de warfarine dans notre population à l’étude. Méthodes : Un devis de cohorte rétrospective de patients qui fréquentaient la clinique d'anticoagulothérapie de l’Institut de cardiologie de Montréal entre le 1er janvier 2007 et le 29 février 2008 pour l’ajustement de leur dose selon les mesures d'INR. Au total, 1615 patients ont été recrutés pour participer à cette étude de recherche. Les critères de sélection des patients étaient les patients avec fibrillation auriculaire ou flutter, ayant un ECG documenté avec l'un de ces deux diagnostics et âgé de moins de 67 ans, en raison d’une moindre comorbidité. Les patients souffrant d’insuffisance hépatique chronique ont été écartés de l’étude. Tous les patients devaient signer un consentement éclairé pour leur participation au projet et échantillon de sang a été pri pour les tests génétiques. La collecte des données a été effectuée à partir du dossier médical du patient de l’Institut de cardiologie de Montréal. Un formulaire de collecte de données a été conçu à cet effet et les données ont ensuite été saisies dans une base de données SQL programmée par un informaticien expert dans ce domaine. La validation des données a été effectuée en plusieurs étapes pour minimiser les erreurs. Les analyses statistiques utilisant des tests de régression ont été effectuées pour déterminer l’association des variants génétiques avec la première dose de warfarine. Résultats : Nous avons identifié une association entre les polymorphismes des gènes CYP2C9 et VKORC1 et la dose de la warfarine. Les polymorphismes génétiques expliquent jusqu’à 42% de la variabilité de dose de la warfarine. Nous avons également démontré que certains polymorphismes génétiques expliquent la réduction de la dose de warfarine lorsque l’amiodarone est ajoutée à la warfarine. Conclusion : Les travaux effectués dans le cadre de ce mémoire ont permis de démontrer l’implication des gènes CYP2C9 et VKORC1 dans la réponse au traitement avec la warfarine et l’amiodarone. Les résultats obtenus permettent d’établir un profil personnalisé pour réduire les risques de toxicité, en permettant un dosage plus précis de la warfarine pour assurer un meilleur suivi des patients. Dans le futur, d’autres polymorphismes génétiques dans ces gènes pourraient être évalués pour optimiser davantage la personnalisation du traitement.Background: Although the practice of the use of warfarin has improved during the last decade, no clear recommendation based on the determination of Amiodarone has been standardized until now, which is a major obstacle for clinicians. Warfarin has a narrow therapeutic index requiring regular monitoring and an individual dose ajustement, to this determines the therapeutic dose, while avoiding the side effects that could be fatal in some cases. The interindividual variability to the Warfarin depends on several factoring age, sex, weight, food and drug interactions but they only partially explain the differences in sensitivity to Warfarin. The polymorphisms of the genes CYP2C9 and VKORC1 play an important role in the response to the Warfarine and explain 50% of the variability of doses.The use of antiarrhythmic Amiodarone can greatly enhancethe effect of Warfain and generally requires a reduction of 30-50% of the dose of Warfarin. No study to date has attempted to determine the utility of genotyping polymorphisms of CYP2C9 and VKORC1 in patients on combination therapy of Warfarin and Amiodarone. Objectives: Our study aims to first determine if genetic factors influence the first dose stabilization of Warfarin in patients with AF after the introduction of Amiodarone. We will also attempt to confirm the previously reported between genetic association and the first dose of Warfarin stabilization in our study population. Methods: A retrospective cohort of all patients who frequent the clinic Warfarin of Montreal Heart Institute between 01/01/2007 and 02/30/2008 for the adjustment of their INR. The total of 1615 patients were recruited. The criteria for selection were patients with atrial fibrillation or flutter, with ECG documented with one of these tow diagnostic and younger than 67 years because of reduced morbidity. Patients with chronic liver disease were excluded from the study. All patients had to sign an informed consent for their participation in the project to which they contributed 15 ml of blood for genetic testing. Data collection was conducted from the patient's medical record of the Montreal Heart Institute. A data collection form was designed for this purpose and the data were then entered into a SQL database programmed by a computer expert in this field. Data validation was performed in several steps to minimize errors. Statistical analysis using regression tests were conducted to determine the association of genetic variants with the first dose of Warfarin. Results: We identified an association between polymorphisms of the genes CYP2C9 and VKORC1 and warfarin dose. Genetic polymorphisms to explain 42% of the variability in dose of Warfarin. We also demonstrated that genetic polymorphisms explain the reduction in the dose of Warfarin when Amiodarone is added to Warfarin. Conclusion: Our Work in the context of this thesis have shown the involvement of CYP2C9 and VKORC1 genes in response to treatment with Warfarin and Amiodarone. The results are used to create a personalized profile to reduce the risk of toxicity, enabling a more accurate dosing of warfarin for better monitoring of patients. In the future, other genetic polymorphisms in these genes could be evaluated to optimize the value of personalised therapy

ASSESSMENT AND PERFORMANCE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GAS SENSING E-NOSE SYSTEMS

E-noses that combine machine learning and gas sensor arrays (GSAs) are widely used for the detection and identification of various gases. GSAs produce signals that provide vital information about the exposed gases for the machine learning algorithms, rendering them indispensable within the smart-gas sensing arena. In this work, we present a detailed assessment of several machine learning techniques employed for the detection of gases and estimation of their concentrations. The modeling and predictive analysis conducted in this paper are based on kNN, ANN, Decision Trees, Random Forests, SVM and other ensembling-based techniques. Predictive models are implemented and tested on three different MoX gas sensor-based experimental datasets as reported in the literature. The assessment includes a delineated analysis of the different models’ performance followed by a detailed comparison against results found in the literature. It highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets

Domestiquer la visibilité: l'internet chinois entre travail et praxis

The less we question the technical and semiotic properties of the so-called social media, the more oblivious we remain to their political agency. The media transformations, which occurred in China at incredible speed and depth, must lead us to question the ways in which ‘new media’ affect the sites and forms of power. This article offers a reading into the economic and theoretical dynamics of what is commonly referred to as the ‘Chinese Internet’. Its history is marked by strategic attempts to shape, constrain and leverage social visibility. Thus, the author uses the notion of computerized media and invokes Hannah Arendt’s conception of visibility to describe what appears in China to be a new form of globalized media capitalism

Neural networks for efficient nonlinear online clustering

Unsupervised learning techniques, such as clustering and sparse coding, have been adapted for use with data sets exhibiting nonlinear relationships through the use of kernel machines. These techniques often require an explicit computation of the kernel matrix, which becomes expensive as the number of inputs grows, making it unsuitable for efficient online learning. This paper proposes an algorithm and a neural architecture for online approximated nonlinear kernel clustering using any shift-invariant kernel. The novel model outperforms traditional low-rank kernel approximation based clustering methods, it also requires significantly lower memory requirements than those of popular kernel k-means while showing competitive performance on large data sets

Building efficient deep Hebbian networks for image classification tasks

Multi-layer models of sparse coding (deep dictionary learning) and dimensionality reduction (PCANet) have shown promise as unsupervised learning models for image classification tasks. However, the pure implementations of these models have limited generalisation capabilities and high computational cost. This work introduces the Deep Hebbian Network (DHN), which combines the advantages of sparse coding, dimensionality reduction, and convolutional neural networks for learning features from images. Unlike in other deep neural networks, in this model, both the learning rules and neural architectures are derived from cost-function minimizations. Moreover, the DHN model can be trained online due to its Hebbian components. Different configurations of the DHN have been tested on scene and image classification tasks. Experiments show that the DHN model can automatically discover highly discriminative features directly from image pixels without using any data augmentation or semi-labeling