Differences in Wealth and Altruistic Behaviour: A Model

In an economy with N individuals each individual is concerned, by assumption, with his own felicity as well as that of all or some others. If the society is composed of very few individuals with a high level of wealth and many with a low one and if altruistic behaviour of any two individuals toward each other decreases with their wealth difference, the largest contribution to the felicity of other individuals is made by those whose wealth is neither too high nor too low (middleclass).

How deep convolutional neural networks lose spatial information with training

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image datasets, this view is supported by the observation that after (and not before) training, the neural representation becomes less and less sensitive to diffeomorphisms acting on images as the signal propagates through the net. This loss of sensitivity correlates with performance, and surprisingly correlates with a gain of sensitivity to white noise acquired during training. These facts are unexplained, and as we demonstrate still hold when white noise is added to the images of the training set. Here, we (i) show empirically for various architectures that stability to image diffeomorphisms is achieved by spatial pooling in the first half of the net, and by channel pooling in the second half, (ii) introduce a scale-detection task for a simple model of data where pooling is learned during training, which captures all empirical observations above and (iii) compute in this model how stability to diffeomorphisms and noise scale with depth. The scalings are found to depend on the presence of strides in the net architecture. We find that the increased sensitivity to noise is due to the perturbing noise piling up during pooling, after being rectified by ReLU units

Rentabilidade fisica e econômica de inseticidas para pulgões safra de trigo de 1977.

bitstream/item/84146/1/CNPT-COM.-TEC.-1-77.pd

Fluorine Effect in the Gelation Ability of Low Molecular Weight Gelators

The three gelators presented in this work (Boc-D-Phe-L-Oxd-OH F0, Boc-D-F1Phe-L-Oxd-OH F1 and Boc-D-F2Phe-L-Oxd-OH F2) share the same scaffold and differ in the number of fluorine atoms linked to the aromatic ring of phenylalanine. They have been applied to the preparation of gels in 0.5% or 1.0% w/v concentration, using three methodologies: solvent switch, pH change and calcium ions addition. The general trend is an increased tendency to form structured materials from F0 to F1 and F2. This property ends up in the formation of stronger materials when fluorine atoms are present. Some samples, generally formed by F1 or F2 in 0.5% w/v concentration, show high transparency but low mechanical properties. Two gels, both containing fluorine atoms, show increased stiffness coupled with high transparency. The biocompatibility of the gelators was assessed exposing them to fibroblast cells and demonstrated that F1 and F2 are not toxic to cells even in high concentration, while F0 is not toxic to cells only in a low concentration. In conclusion, the presence of even only one fluorine atom improves all the gelators properties: the gelation ability of the compound, the rheological properties and the transparency of the final materials and the gelator biocompatibility