Novel word learning ability in chronic post-stroke aphasia : variability and modality effects

Novel word learning has been rarely studied in people with aphasia (PWA), although it can provide a relatively pure measure of their learning potential, and thereby contribute to the development of effective aphasia treatment methods. The main aim of the present thesis was to explore the capacity of PWA for associative learning of word–referent pairings and cognitive-linguistic factors related to it. More specifically, the thesis examined learning and long-term maintenance of the learned pairings, the role of lexical-semantic abilities in learning as well as acquisition of phonological versus semantic information in associative novel word learning. Furthermore, the effect of modality on associative novel word learning and the neural underpinnings of successful learning were explored. The learning experiments utilized the Ancient Farming Equipment (AFE) paradigm that employs drawings of unfamiliar referents and their unfamiliar names. Case studies of Finnishand English-speaking people with chronic aphasia (n = 6) were conducted in the investigation. The learning results of PWA were compared to those of healthy control participants, and active production of the novel words and their semantic definitions was used as learning outcome measures. PWA learned novel word–novel referent pairings, but the variation between individuals was very wide, from more modest outcomes (Studies I–II) up to levels on a par with healthy individuals (Studies III–IV). In incidental learning of semantic definitions, none of the PWA reached the performance level of the healthy control participants. Some PWA maintained part of the learning outcomes up to months post-training, and one individual showed full maintenance of the novel words at six months post-training (Study IV). Intact lexical-semantic processing skills promoted learning in PWA (Studies I–II) but poor phonological short-term memory capacities did not rule out novel word learning. In two PWA with successful learning and long-term maintenance of novel word–novel referent pairings, learning relied on orthographic input while auditory input led to significantly inferior learning outcomes (Studies III–IV). In one of these individuals, this previously undetected modalityspecific learning ability was successfully translated into training with familiar but inaccessible everyday words (Study IV). Functional magnetic resonance imaging revealed that this individual had a disconnected dorsal speech processing pathway in the left hemisphere, but a right-hemispheric neural network mediated successful novel word learning via reading. Finally, the results of Study III suggested that the cognitive-linguistic profile may not always predict the optimal learning channel for an individual with aphasia. Small-scale learning probes seem therefore useful in revealing functional learning channels in post-stroke aphasia.

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.

The influence of stress on fear memory processes

It is well recognized that stressful experiences promote robust emotional memories, which are well remembered. The amygdaloid complex, principally the basolateral complex (BLA), plays a pivotal role in fear memory and in the modulation of stress-induced emotional responses. A large number of reports have revealed that GABAergic interneurons provide a powerful inhibitory control of the activity of projecting glutamatergic neurons in the BLA. Indeed, a reduced GABAergic control in the BLA is essential for the stress-induced influence on the emergence of associative fear memory and on the generation of long-term potentiation (LTP) in BLA neurons. The extracellular signal-regulated kinase (ERK) subfamily of the mitogen-activated protein kinase (MAPK) signaling pathway in the BLA plays a central role in the consolidation process and synaptic plasticity. In support of the view that stress facilitates long-term fear memory, stressed animals exhibited a phospho-ERK2 (pERK2) increase in the BLA, suggesting the involvement of this mechanism in the promoting influence of threatening stimuli on the consolidation fear memory. Moreover, the occurrence of reactivation-induced lability is prevented when fear memory is encoded under intense stressful conditions since the memory trace remains immune to disruption after recall in previously stressed animals. Thus, the underlying mechanism in retrieval-induced instability seems not to be functional in memories formed under stress. All these findings are indicative that stress influences both the consolidation and reconsolidation fear memory processes. Thus, it seems reasonable to propose that the emotional state generated by an environmental challenge critically modulates the formation and maintenance of long-term fear memory.

Increased coherence among striatal regions in the theta range during attentive wakefulness

The striatum, the largest component of the basal ganglia, is usually subdivided into associative, motor and limbic components. However, the electrophysiological interactions between these three subsystems during behavior remain largely unknown. We hypothesized that the striatum might be particularly active during exploratory behavior, which is presumably associated with increased attention. We investigated the modulation of local field potentials (LFPs) in the striatum during attentive wakefulness in freely moving rats. To this end, we implanted microelectrodes into different parts of the striatum of Wistar rats, as well as into the motor, associative and limbic cortices. We then used electromyograms to identify motor activity and analyzed the instantaneous frequency, power spectra and partial directed coherence during exploratory behavior. We observed fine modulation in the theta frequency range of striatal LFPs in 92.5 ± 2.5% of all epochs of exploratory behavior. Concomitantly, the theta power spectrum increased in all striatal channels (P < 0.001), and coherence analysis revealed strong connectivity (coefficients >0.7) between the primary motor cortex and the rostral part of the caudatoputamen nucleus, as well as among all striatal channels (P < 0.001). Conclusively, we observed a pattern of strong theta band activation in the entire striatum during attentive wakefulness, as well as a strong coherence between the motor cortex and the entire striatum. We suggest that this activation reflects the integration of motor, cognitive and limbic systems during attentive wakefulness.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Behavioural and neural correlates of operant conditioning in Lymnaea stagnalis : role of previous experience during development /

The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Hydrosilylation and hydroboration catalyzed by imido-hydride complexes of molybdenum (IV)

This thesis describes the synthesis, structural studies, stoichiometric and catalytic reactivity of novel Mo(IV) imido hydride complexes (Cp)(ArN)Mo(H)(PMe3) (1) and (Tp )(ArN)Mo(H)(PMe3) (2). Both 1 and 2 catalyze hydrosilylation of a variety of carbonyls. Detailed kinetic and DFT studies found that 1 reacts by an unexpected associative mechanism, which does not involve Si-H addition either to the imido group or the metal. Despite 1 being a d2 complex, its reaction with PhSiH3 proceeds via a a-bond metathesis mechanism giving the silyl derivative (Cp )(ArN)Mo(SiH2Ph)(PMe3). In the presence of BPh3 reaction of 1 with PhSiH3 results in formation of (Cp)(ArN)Mo(SiH2Ph)(H)2 and (Cp)(ArN)Mo(SiH2Ph)2(H), the first examples ofMo(VI) silyl hydrides. AI: 1 : 1 reaction between 2, PhSiD3 and carbonyl substrate established that hydrosilylation is not accompanied by deuterium incorporation into the hydride position of the catalyst, thus ruling out the conventional mechanism based on carbonyl insertion carbonyl. As 2 is nomeactive to both the silane and ketone, the only mechanistic alternative we are left with is that the metal center activates the carbonyl as a Lewis acid. The analogous nonhydride mechanism was observed for the catalysis by (ArN)Mo(H)(CI)(PMe3), (Ph3P)2(I)(O)Re(H)(OSiMe2Ph) and (PPh3CuH)6. Complex 2 also catalyzes hydroboration of carbonyls and nitriles. We report the first case of metal-catalyzed hydroboration of nitriles as well as hydroboration of carbonyls at very mild conditions. Conversion of carbonyl functions can be performed with high selectivities in the presence of nitrile groups. This thesis also reports the first case of the HlH exchange between H2 and Si-H of silanes mediated by Lewis acids such as Mo(IV) , Re(V) , Cu(I) , Zn(II) complexes, B(C6Fs)3 and BPh3.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Au-delà de l'instrumentalisation: une nouvelle identité pour le tiers secteur en France?

Ce mémoire porte sur la constitution du tiers secteur français en tant qu’acteur social et politique. Dans de nombreux pays, les relations entre l’État et les organismes mutualistes, coopératifs et associatifs de la société civile (un ensemble hétérogène qu’on appelle ici le « tiers secteur ») ont été récemment formalisées par des partenariats. En France, cette institutionnalisation s’est concrétisée en 2001 par la signature d’une Charte (CPCA). Nous explorons l’hypothèse qu’à travers l’institutionnalisation, le tiers secteur français se construit en tant qu’acteur –ayant une (ou des) identités propres de même qu’un projet de société relativement bien défini. La perspective dominante présente dans la littérature internationale traitant de l’institutionnalisation des rapports entre l’État et le tiers secteur est celle d’une instrumentalisation des organisations du tiers secteur au détriment de leurs spécificités et de leur autonomie. Cette perspective nous semble limitative, car elle semble être aveugle à la capacité d’action des organisations. Par conséquent, dans ce mémoire, nous cherchons à comprendre si une transformation identitaire a eu lieu ou est en cours, au sein du tiers secteur français, et donc s’il se transforme en acteur collectif. Pour apporter certains éléments de réponse à nos hypothèses et questions de recherche, nous avons effectué une analyse des discours via deux sources de données; des textes de réflexion rédigés par des acteurs clés du tiers secteur français et des entretiens effectués avec certains d’entre eux au printemps 2003 et à l’automne 2005. Sur la base de deux inspirations théoriques (Hobson et Lindholm, 1997 et Melucci, 1991), notre analyse a été effectuée en deux étapes. Une première phase nous a permis d’identifier deux cadres cognitifs à partir desquels se définissent les acteurs du tiers secteur français, les cadres « association » et « économie solidaire ». Une deuxième phase d’analyse consistait à déterminer si les deux cadres cognitifs pouvaient être considérés comme étant des tensions existant au sein d’un seul et même acteur collectif. Nos résultats nous permettent de conclure que les organisations du tiers secteur français ne se perçoivent pas globalement comme un ensemble unifié. Néanmoins, nous avons pu dégager certains éléments qui démontrent que les cadres sont partiellement conciliables. Cette conciliation est grandement subordonnée aux contextes sociopolitiques et économiques français, européen et international et est également conditionnelle à la découverte d’un mode de fonctionnement convenant à tous les acteurs.

Mécanismes de plasticité synaptique dans l’amygdale lors de la réactivation de la mémoire de peur auditive chez le rat : interaction dynamique des récepteurs NMDA et AMPA

La plasticité synaptique est une propriété indispensable à l’acquisition de la mémoire chez toutes les espèces étudiées, des invertébrés aux primates. La formation d’une mémoire débute par une phase de plasticité qui inclut une restructuration synaptique ; ensuite elle se poursuit par la consolidation de ces modifications, contribuant à la mémoire à long terme. Certaines mémoires redeviennent malléables lorsqu’elles sont rappelées. La trace mnésique entre alors dans une nouvelle de phase de plasticité, au cours de laquelle certaines composantes de la mémoire peuvent être mises à jour, puis reconsolidées. L’objectif de la présente thèse est d’étudier les mécanismes cellulaires et moléculaires qui sont activés lors du rappel d’une mémoire. Nous avons utilisé un modèle de conditionnement Pavlovien, combiné à l’administration d’agents pharmacologiques et à l’analyse quantitative de marqueurs de plasticité synaptique, afin d’étudier la dynamique de la mémoire de peur auditive chez des rats Sprague Dawley. La circuiterie neuronale et les mécanismes associatifs impliqués dans la neurobiologie de cette mémoire sont bien caractérisés, en particulier le rôle des récepteurs glutamatergiques de type NMDA et AMPA dans la plasticité synaptique et la consolidation. Nos résultats démontrent que le retour de la trace mnésique à un état de labilité nécessite l’activation des récepteurs NMDA dans l’amygdale baso-latérale à l’instant même du rappel, alors que les récepteurs AMPA sont requis pour l’expression comportementale de la réponse de peur conditionnée. D’autre part, les résultats identifient le rappel comme une phase bien plus dynamique que présumée, et suggèrent que l’expression de la peur conditionnée mette en jeu la régulation du trafic des récepteurs AMPA par les récepteurs NMDA. Le présent travail espère contribuer à la compréhension de la neurobiologie fondamentale de la mémoire. De plus, il propose une intégration des résultats aux modèles animaux d’étude des troubles psychologiques conséquents aux mémoires traumatiques chez l’humain, tels que les phobies et les syndromes de stress post-traumatiques.

L'exception de non-subrogation: l'influence de ses principes justificatifs sur sa mise en oeuvre

À la lecture de l'article 2365 c.c.Q., le créancier et la caution ne peuvent pas percevoir les droits et les libertés que ce texte concrétise à leur encontre ou à leur profit. Pour pallier ce problème, les auteurs et la jurisprudence ont alors laissé place à leur imagination afin de tenter de classifier cette disposition à l'intérieur d'institutions juridiques éprouvées, le tout en vue de démythifier le contenu de la règle de droit. Pour notre part, nous considérons que l'exception de non-subrogation est une notion originale en soi, qui trouve sa source à l'intérieur même de son institution. La thèse que nous soutenons est que l'exception de non-subrogation, mode de libération qui a pour mission de combattre le comportement opportuniste, cristallise l'obligation de bonne foi en imposant implicitement au créancier une obligation de bonne subrogation. Tout manquement du créancier à cette obligation a comme conséquence de rendre le droit de créance du créancier irrecevable à l'égard de la caution devant les tribunaux. Ce précepte éclaircit le contexte de l'article 2365 C.c.Q. et, par le fait même, il permet de délimiter le contour de son domaine et de préciser ses conditions d'application. L'exception de non-subrogation est un mécanisme juridique qui date de l'époque romaine. Elle est maintenant intégrée dans presque tous les systèmes juridiques du monde, tant en droit civil qu'en common law. Dans la législation québécoise, elle s'est cristallisée à l'article 2365 C.c.Q. Il s'agit d'une disposition d'ordre public qui ne peut être invoquée que par la caution. Son application dépend du cumul de quatre conditions: 1) le fait du créancier; 2) la perte d'un droit subrogatoire; 3) le préjudice de la caution; 4) le lien causal entre les trois derniers éléments. Lorsque ces quatre conditions sont remplies, la caution est libérée de son engagement dans la mesure du préjudice qu'elle subit.