École Normale Supérieure

About: École Normale Supérieure is a other organization based out in Paris, Île-de-France, France. It is known for research contribution in the topics: Population & Catalysis. The organization has 68439 authors who have published 99414 publications receiving 3092008 citations.

Task-Driven Dictionary Learning

Abstract: Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

Dopamine depletion impairs precursor cell proliferation in Parkinson disease

Abstract: Cerebral dopamine depletion is the hallmark of Parkinson disease. Because dopamine modulates ontogenetic neurogenesis, depletion of dopamine might affect neural precursors in the subependymal zone and subgranular zone of the adult brain. Here we provide ultrastructural evidence showing that highly proliferative precursors in the adult subependymal zone express dopamine receptors and receive dopaminergic afferents. Experimental depletion of dopamine in rodents decreases precursor cell proliferation in both the subependymal zone and the subgranular zone. Proliferation is restored completely by a selective agonist of D2-like (D2L) receptors. Experiments with neural precursors from the adult subependymal zone grown as neurosphere cultures confirm that activation of D2L receptors directly increases the proliferation of these precursors. Consistently, the numbers of proliferating cells in the subependymal zone and neural precursor cells in the subgranular zone and olfactory bulb are reduced in postmortem brains of individuals with Parkinson disease. These observations suggest that the generation of neural precursor cells is impaired in Parkinson disease as a consequence of dopaminergic denervation.

Single-layer learning revisited: a stepwise procedure for building and training a neural network

Abstract: A stepwise procedure for building and training a neural network intended to perform classification tasks, based on single layer learning rules, is presented. This procedure breaks up the classification task into subtasks of increasing complexity in order to make learning easier. The network structure is not fixed in advance: it is subject to a growth process during learning. Therefore, after training, the architecture of the network is guaranteed to be well adapted for the classification problem.

A survey of various propagation models for mobile communication

Abstract: In order to estimate the signal parameters accurately for mobile systems, it is necessary to estimate a system's propagation characteristics through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. The ability to accurately predict radio-propagation behavior for wireless personal communication systems, such as cellular mobile radio, is becoming crucial to system design. Since site measurements are costly, propagation models have been developed as a suitable, low-cost, and convenient alternative. Channel modeling is required to predict path, loss and to characterize the impulse response of the propagating channel. The path loss is associated with the design of base stations, as this tells us how much a transmitter needs to radiate to service a given region. Channel characterization, on the other hand, deals with the fidelity of the received signals, and has to do with the nature of the waveform received at a receiver. The objective here is to design a suitable receiver that will receive the transmitted signal, distorted due to the multipath and dispersion effects of the channel, and that will decode the transmitted signal. An understanding of the various propagation models can actually address both problems. This paper begins with a review of the information available on the various propagation models for both indoor and outdoor environments. The existing models can be classified into two major classes: statistical models and site-specific models. The main characteristics of the radio channel - such as path loss, fading, and time-delay spread - are discussed. Currently, a third alternative, which includes many new numerical methods, is being introduced to propagation prediction. The advantages and disadvantages of some of these methods are summarized. In addition, an impulse-response characterization for the propagation path is also presented, including models for small-scale fading, Finally, it is shown that when two-way communication ports can be defined for a mobile system, it is possible to use reciprocity to focus the energy along the direction of an intended user without any explicit knowledge of the electromagnetic environment in which the system is operating, or knowledge of the spatial locations of the transmitter and the receiver.