"Centre-Ville," Montreal, Quebec, Canada H3C 3P8. Electronic mail may be sent via the Internet to vallerand.robertj@uqam.ca. a motivational model of high school dropout on the basis of theory and research in the field of intrinsic and extrinsic motivation. We believe that such an undertaking

, with specific reference to the Jeffreys prior and other objective prior choices. This presentation aims to generalize the chosen objective prior distribution to derive estimators for the parameter under each loss function considered, all in the Bayesian paradigm. Shrinkage estimators and reliability estimators

Lossless compression refers to compression methods for which the original uncompressed data set can be recovered exactly from the compressed stream. The need for lossless compression arises from the fact that many applications, such as the compression of digitized medical data, require that no loss

(1999) recently suggested that "the ability to read or extract information from phonological memory quickly" might be involved; PROBLEMS WITH DECAY The second arm of the standard model is decay, defined as the loss of trace information exclusively as a function of time. Activation

-off in complexity and performance that is possible when using sparse recovery. To do so, we propose a sequential sparsity-aware compressive sampling (C-SA) acquisition scheme, where a compressive multi-channel sampling (CMS) front-end is followed by a sparsity regularized likelihood ratio test (SR-LRT) module

Abstract — We consider the problem of distributed target tracking of a linear dynamical system via networked sensors. Our setup consists of a set of sensors connected to a fusion center by means of communication links. Unreliable communication channels leads to communication delays and loss

to be stored and compressed without significant loss of signal quality;- this requires reliable and adequate methods;- the P wave in the ECG signal is a result of the contraction of the atria;- the contraction of the ventricles produces the QRS complex;- its repolarization manifests itself in the T wave

Abstract not found

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order

as well as a variety of compression techniques. However, none of the protocols scale well to the parallel streaming model of large scale graphic applications, and the existing parallel streaming protocols have limited synchronization mechanisms to synchronize the streams efficiently, and are prone