Recovery and convergence rate of the Frank-Wolfe Algorithm for the m-EXACT-SPARSE Problem

We study the properties of the Frank-Wolfe algorithm to solve the m-EXACT-SPARSE reconstruction problem, where a signal y must be expressed as a sparse linear combination of a predefined set of atoms, called dictionary. We prove that when the signal is sparse enough with respect to the coherence of the dictionary, then the iterative process implemented by the Frank-Wolfe algorithm only recruits atoms from the support of the signal, that is the smallest set of atoms from the dictionary that allows for a perfect reconstruction of y. We also prove that under this same condition, there exists an iteration beyond which the algorithm converges exponentially

Regulation of Lysosomal Degradation by CA2+And CA2+-Binding Proteins

La macroautofagia y la endocitosis son dos procesos catabólicos conservados evolutivamente en los que, mediante un tráfico vesicular, se degrada el material secuestrado, cuyo origen es intra- y extracelular, respectivamente. Ambos procesos comienzan de manera diferente: mediante la formación de un nuevo orgánulo, el autofagosoma, que secuestra material citoplásmico (macroautofagia), o mediante la internalización de material extracelular y de algunos componentes de la membrana plasmática a través de vesículas endocíticas (endocitosis). Sin embargo, los dos terminan en el mismo compartimiento: el lisosoma. En un análisis proteómico de membranas lisosomales, purificadas a partir de fibroblastos de ratón, identificamos tres proteínas, que se unen a fosfolípidos de una manera dependiente de calcio, y cuyos niveles en la membrana lisosomal aumentaban en ausencia de aminoácidos, una condición que activa la macroautofagia. Basándonos en esos resultados iniciales, y teniendo en cuenta que el calcio es un segundo mensajero muy importante, decidimos: en primer lugar, abordar el papel del calcio en la activación de la autofagia producida por el ayuno de aminoácidos, y, en segundo lugar, investigar el papel de esas tres proteínas en el mecanismo autofágico. Como resultado de estos estudios, describimos en primer lugar una nueva vía de señalización dependiente de calcio que activa la formación de autofagosomas por los aminoácidos. Concretamente, hemos encontrado que el ayuno de aminoácidos esenciales produce un aumento en el calcio citosólico, procedente tanto del medio extracelular como de almacenes intracelulares. Como consecuencia de esto, la calmodulina quinasa quinasa- ß activa a AMPK y a mTORC1. En la última etapa de esta vía, ULK1, una quinasa responsable de la iniciación de la autofagia, se activa para contribuir a la formación de los autofagosomas. Las tres proteínas identificadas en el estudio proteómico y cuyos niveles en las membranas lisosomales aumentan en ausencia de aminoácidos son la anexina A1, la anexina A5 y la copina 1. Empleando métodos bioquímicos y de inmunofluorescencia observamos que el ayuno de aminoácidos causa la translocación de la anexina A5 desde el complejo de Golgi hasta las membranas lisosomales, donde también se acumulan la anexina A1 y la copina 1. Asimismo, demostramos por sobre-expresión y silenciamiento de esas tres proteínas, que las tres inducen la fusión de autofagosomas con lisosomas y que la copina 1, y en menor medida la anexina A1, aumentan el efecto individual de la anexina A5. Finalmente, la anexina A5 inhibe la endocitosis mientras que copina 1 la induce. En resumen, nuestros resultados ponen de manifiesto que la activación de la formación de autofagosomas por el ayuno de aminoácidos es debida, al menos en parte, a una vía de señalización dependiente de Ca2+ y que esta condición también conlleva la aceleración de la maduración de los autofagosomas a autolisosomas a través de proteínas que unen el Ca2+ como las anexinas A1 y A5 y la copina 1.Ghislat Cherfaoui, G. (2013). Regulation of Lysosomal Degradation by CA2+And CA2+-Binding Proteins [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/29690TESI

A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System

International audienceThe significant advance in bio-sensor technologies hold promise to monitor human physiologicalsignals in real time. In the context of public safety, such technology knows notable research investigations toobjectively detect early stage of driver drowsiness that impairs driver performance under various conditions.Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy stateproblem is challenging. While some enduring solutions have been available as prototypes for a while, many ofthese technologies are now in the development, validation testing, or even commercialization stages. Thecontribution of this paper is to assess current progress in the development of bio-sensors based driver drowsinessdetection technologies and study their fundamental specifications to achieve accuracy requirements. Existingmarket and research products are then ranked following the discussed specifications. The finding of this work isto provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcostdrowsiness detection system using existing market physiological based sensors

Efficient Möbius Transformations and their applications to D-S Theory

International audienceDempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Demp-ster's rule. The main approaches exploit either the structure of Boolean lattices or the information contained in belief sources. Each has its merits depending on the situation. In this paper, we propose sequences of graphs for the computation of the zeta and Möbius transformations that optimally exploit both the structure of distributive lattices and the information contained in belief sources. We call them the Efficient Möbius Transformations (EMT). We show that the complexity of the EMT is always inferior to the complexity of algorithms that consider the whole lattice, such as the Fast Möbius Transform (FMT) for all DST transformations. We then explain how to use them to fuse two belief sources. More generally, our EMTs apply to any function in any finite distributive lattice, focusing on a meet-closed or join-closed subset

Sensor Technology Trends in Industry

The development of digital and the Internet of Things (IoT) which are becoming widespread have a strong impact on the technological trends of conventional sensors and equipped with intelligence. This article gives the main axes of this trend: the first important axis concerns the monitoring and surveillance of equipment and industrial installations. The second axis concerns the coupling between the sensor and the Internet of Things. The 3rd axis deals with sensors dedicated to applications or to a family of machines or equipment. In axis 4 is interested in 2 very strong trends: predictive maintenance and digital twins

Trajectory planning for autonomous vehicle in uncertain environment using evidential grid

International audienc

Collaborative Grid Mapping for Moving Object Tracking Evaluation

Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadside units. In any case, the performance of perception systems has to be evaluated against ground-truth data, which is a particularly tedious task and requires numerous manual operations. In this article, we propose a novel semi-automatic method for pseudo ground-truth estimation. The principle consists in carrying out experiments with several vehicles equipped with LiDAR sensors and with fixed perception systems located at the roadside in order to collaboratively build reference dynamic data. The method is based on grid mapping and in particular on the elaboration of a background map that holds relevant information that remains valid during a whole dataset sequence. Data from all agents is converted in time-stamped observations grids. A data fusion method that manages uncertainties combines the background map with observations to produce dynamic reference information at each instant. Several datasets have been acquired with three experimental vehicles and a roadside unit. An evaluation of this method is finally provided in comparison to a handmade ground truth