Color Constancy Using CNNs

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR 2015 workshop

Traditional meditation, mindfulness and psychodynamic approach: An integrative perspective

The purpose of this article is to consolidate the inter-theoretical bridge between psychodynamic approach and TM, beyond the apparent incompatibility. Our impression is that even if some authors have already worked in order to fill the gap between TM and psychodynamic psychotherapy at theoretical level, this integration could be underrated and these efforts remain isolated. This could be due mainly to ambiguities in the translation of those terms with respect to the fundaments of core concepts of both perspectives, and a lack of empirical research on psychodynamic and meditation. Psychodynamic approach could embrace those aspects of TM that have been less developed in MBIs\u2019 theory and practice. Moreover, an integration of modern mindfulness practices into a psychodynamic framework should be explored. Further empirical studies and theoretical considerations are needed to corroborate testable hypotheses and comparing classical and combine

A fluctuating environment as a source of periodic modulation

We study the intermittent fluorescence of a single molecule, jumping from the "light on" to the "light off" state, as a Poisson process modulated by a fluctuating environment. We show that the quasi-periodic and quasi-deterministic environmental fluctuations make the distribution of the times of sojourn in the "light off" state depart from the exponential form, and that their succession in time mirrors environmental dynamics. As an illustration, we discuss some recent experimental results, where the environmental fluctuations depend on enzymatic activity.Comment: 13 pages, 4 figures. Accepted for publication on Chem. Phys. Let

Benchmark Analysis of Representative Deep Neural Network Architectures

This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online.Comment: Will appear in IEEE Acces