On the trade-off between electrical power consumption and flight performance in fixed-wing UAV autopilots

This paper sets out a study of the autopilot design for fixed wing Unmanned Aerial Vehicles (UAVs) taking into account the aircraft stability, as well as the power consumption as a function of the selected control strategy. To provide some generality to the outcomes of this study, construction of a reference small-UAV model, based on averaging the main aircraft defining parameters, is proposed. Using such a reference model of small, fixed-wing UAVs, different control strategies are assessed, especially with a view towards enlarging the controllers' sampling time. A beneficial consequence of this sample time enlargement is that the clock rate of the UAV autopilots may be proportionally reduced. This reduction in turn leads directly to decreased electrical power consumption. Such energy saving becomes proportionally relevant as the size and power of the UAV decrease, with benefits of lengthening battery life and, therefore, the flight endurance. Additionally, through the averaged model, which is derived from both published data and computations made from actual data captured from real UAVs, it is shown that behavior predictions beyond that of any particular UAV model may be extrapolated.Peer ReviewedPostprint (author's final draft

Distributional analyses in the picture-word interference paradigm: Exploring the semantic interference and the distractor frequency effects.

he present study explores the distributional features of two important effects within the picture-word interference paradigm: the semantic interference and the distractor frequency effects. These two effects display different and specific distributional profiles. Semantic interference appears greatly reduced in faster response times, while it reaches its full magnitude only in slower responses. This can be interpreted as a sign of fluctuant attentional efficiency in resolving response conflict. In contrast, the distractor frequency effect is mediated mainly by a distributional shift, with low frequency distractors uniformly shifting reaction times distribution towards a slower range of latencies. This finding fits with the idea that distractor frequency exerts its effect by modulating the point in time in which operations required to discard the distractor can start. Taken together, these results are congruent with current theoretical accounts of both the semantic interference and distractor frequency effects. Critically, distributional analyses highlight and further describe the different cognitive dynamics underlying these two effects, suggesting that this analytical tool is able to offer important insights about lexical access during speech productio

The Manipulability Effect in Object Naming

Seeing objects triggers activation of motor areas. The implications of this motor activation in tasks that do not require object-use is still a matter of debate in cognitive sciences. Here we test whether motor activation percolates into the linguistic system by exploring the effect of object manipulability in a speech production task. Italian native speakers name the set of photographs provided by Gu\ue9rard, Lagac\ue8 and Brodeur (Beh Res Meth, 2015). Photographs varied on four motor dimensions concerning on how easily the represented objects can be grasped, moved, or pantomimed, and the number of actions that can be performed with them. The results show classical psycholinguistic phenomena such as the effect of age of acquisition and name agreement in naming latencies. Critically, linear mixed-effects models show an effect of three motor predictors over and above the psycholinguistic effects (replicating, in part, previous findings, Gu\ue9rard et al., 2015). Further research is needed to address how, and at which level, the manipulability effect emerges in the course of word production

Local Government Actions to Prevent Childhood Obesity

Offers guidance on policy and programmatic actions local governments can take, with community input, to promote healthy eating and physical activity and to ensure equal opportunities for healthy living in low-income neighborhoods. Profiles best practices

A Policy Switching Approach to Consolidating Load Shedding and Islanding Protection Schemes

In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements, the power systems industry has seen relatively small advances in this regard. For instance, power quality deficiencies, a high number of localized contingencies, and large cascading outages are still too widespread. Though progress has been made in improving generation, transmission, and distribution infrastructure, remedial action schemes (RAS) remain non-standardized and are often not uniformly implemented across different utilities, ISOs, and RTOs. Traditionally, load shedding and islanding have been successful protection measures in restraining propagation of contingencies and large cascading outages. This paper proposes a novel, algorithmic approach to selecting RAS policies to optimize the operation of the power network during and after a contingency. Specifically, we use policy-switching to consolidate traditional load shedding and islanding schemes. In order to model and simulate the functionality of the proposed power systems protection algorithm, we conduct Monte-Carlo, time-domain simulations using Siemens PSS/E. The algorithm is tested via experiments on the IEEE-39 topology to demonstrate that the proposed approach achieves optimal power system performance during emergency situations, given a specific set of RAS policies.Comment: Full Paper Accepted to PSCC 2014 - IEEE Co-Sponsored Conference. 7 Pages, 2 Figures, 2 Table

Working memory cross-modal binding and decoding ability in children in the first and second grades

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The iconicity advantage in sign production: The case of bimodal bilinguals

Recent evidence demonstrates that pictures corresponding to iconic signs are named faster than pictures corresponding to non-iconic signs. The present study investigates the locus of the iconicity advantage in hearing bimodal bilinguals. A naming experiment with iconic and noniconic pictures in Italian Sign Language (LIS) was conducted. Bimodal bilinguals named the pictures either using a noun construction that involved the production of the sign corresponding to the picture or using a marked demonstrative pronoun construction replacing the picture sign. In this last condition, the pictures were colored and participants were instructed to name the pronoun together with the color. The iconicity advantage was reliable in the noun utterance but not in the marked demonstrative pronoun utterance. In a third condition, the colored pictures were presented as distractor stimuli and participants required to name the color. In this last condition, distractor pictures with iconic signs elicited faster naming latencies than non-iconic signs. The results suggest that the advantage of iconic signs in production arises at the level of semantic-tophonological links. In addition, we conclude that bimodal bilinguals and native signers do not differ in terms of the activation flow within the sign production system

Learning disentangled representations of satellite image time series in a weakly supervised manner

Cette thèse se focalise sur l'apprentissage de représentations de séries temporelles d'images satellites via des méthodes d'apprentissage non supervisé. Le but principal est de créer une représentation qui capture l'information la plus pertinente de la série temporelle afin d'effectuer d'autres applications d'imagerie satellite. Cependant, l'extraction d'information à partir de la donnée satellite implique de nombreux défis. D'un côté, les modèles doivent traiter d'énormes volumes d'images fournis par les satellites. D'un autre côté, il est impossible pour les opérateurs humains d'étiqueter manuellement un tel volume d'images pour chaque tâche (par exemple, la classification, la segmentation, la détection de changement, etc.). Par conséquent, les méthodes d'apprentissage supervisé qui ont besoin des étiquettes ne peuvent pas être appliquées pour analyser la donnée satellite. Pour résoudre ce problème, des algorithmes d'apprentissage non supervisé ont été proposés pour apprendre la structure de la donnée au lieu d'apprendre une tâche particulière. L'apprentissage non supervisé est une approche puissante, car aucune étiquette n'est nécessaire et la connaissance acquise sur la donnée peut être transférée vers d'autres tâches permettant un apprentissage plus rapide avec moins d'étiquettes. Dans ce travail, on étudie le problème de l'apprentissage de représentations démêlées de séries temporelles d'images satellites. Le but consiste à créer une représentation partagée qui capture l'information spatiale de la série temporelle et une représentation exclusive qui capture l'information temporelle spécifique à chaque image. On présente les avantages de créer des représentations spatio-temporelles. Par exemple, l'information spatiale est utile pour effectuer la classification ou la segmentation d'images de manière invariante dans le temps tandis que l'information temporelle est utile pour la détection de changement. Pour ce faire, on analyse plusieurs modèles d'apprentissage non supervisé tels que l'auto-encodeur variationnel (VAE) et les réseaux antagonistes génératifs (GANs) ainsi que les extensions de ces modèles pour effectuer le démêlage des représentations. Considérant les résultats impressionnants qui ont été obtenus par les modèles génératifs et reconstructifs, on propose un nouveau modèle qui crée une représentation spatiale et une représentation temporelle de la donnée satellite. On montre que les représentations démêlées peuvent être utilisées pour effectuer plusieurs tâches de vision par ordinateur surpassant d'autres modèles de l'état de l'art. Cependant, nos expériences suggèrent que les modèles génératifs et reconstructifs présentent des inconvénients liés à la dimensionnalité de la représentation, à la complexité de l'architecture et au manque de garanties sur le démêlage. Pour surmonter ces limitations, on étudie une méthode récente basée sur l'estimation et la maximisation de l'informations mutuelle sans compter sur la reconstruction ou la génération d'image. On propose un nouveau modèle qui étend le principe de maximisation de l'information mutuelle pour démêler le domaine de représentation. En plus des expériences réalisées sur la donnée satellite, on montre que notre modèle est capable de traiter différents types de données en étant plus performant que les méthodes basées sur les GANs et les VAEs. De plus, on prouve que notre modèle demande moins de puissance de calcul et pourtant est plus efficace. Enfin, on montre que notre modèle est utile pour créer une représentation qui capture uniquement l'information de classe entre deux images appartenant à la même catégorie. Démêler la classe ou la catégorie d'une image des autres facteurs de variation permet de calculer la similarité entre pixels et effectuer la segmentation d'image d'une manière faiblement supervisée.This work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner