Dogmatism and Theoretical Pluralism in Modern Cosmology

This work discusses the presence of a dogmatic tendency within modern cosmology, and some ideas capable of neutralizing its negative influence. It is verified that warnings about the dangers of dogmatic thinking in cosmology can be found as early as the 1930's, and we discuss the modern appearance of "scientific dogmatism". The solution proposed to counteract such an influence, which is capable of neutralizing this dogmatic tendency, has its origins in the philosophical thinking of the Austrian physicist Ludwig Boltzmann (1844-1906). In particular we use his two main epistemological theses, scientific theories as representations of nature and theoretical pluralism, to show that once they are embodied in the research practice of modern cosmology, there is no longer any reason for dogmatic behaviours.Comment: 14 pages; LaTeX sourc

Boltzmann's Concept of Reality

In this article we describe and analyze the concept of reality developed by the Austrian theoretical physicist Ludwig Boltzmann. It is our thesis that Boltzmann was fully aware that reality could, and actually was, described by different points of view. In spite of this, Boltzmann did not renounce the idea that reality is real. We also discuss his main motivations to be strongly involved with philosophy of science, as well as further developments made by Boltzmann himself of his main philosophical ideas, namely scientific theories as images of Nature and its consequences. We end the paper with a discussion about the modernity of Boltzmann's philosophy of science.Comment: 13 pages, pdf only. To appear in the book on Ludwig Boltzmann scientific philosophy, published by Nova Science. Edited by A. Eftekhar

Convolutional Neural Networks Via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs. The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. A second contribution is to replace the node-varying GF with a hybrid node-varying GF, which is a new type of GF introduced in this paper. While the alternative architecture can still be run locally without requiring a pooling stage, the number of trainable parameters is smaller and can be rendered independent of the data dimension. Tests are run on a synthetic source localization problem and on the 20NEWS dataset.Comment: Submitted to DSW 2018 (IEEE Data Science Workshop