Miguel Gutierrez

This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem, and the second is based on a new mathematical model. By solving VSP, we can find nodes that cause the rupture of the giant component. On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months. This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior.

Inception module is one of the most used variants in convolutional neural networks. It has a large portfolio of success cases in computer vision. In the past years, diverse inception flavours, differing in the number of branches, the size and the number of the kernels, have appeared in the scientific literature. They are proposed based on the expertise of the practitioners without any optimization process. In this work, an implementation of population-based incremental learning is proposed for automatic optimization of the hyperparameters of the inception module. This hyperparameters optimization undertakes classification of the MNIST database of handwritten digit images. This problem is widely used as a benchmark in classification, and therefore, the learned best configurations for the Inception module will be of wide use in the deep learning community. In order to reduce the carbon footprint of the optimization process, policies for reducing the redundant evaluations have been undertaken. As a consequence of this work, an evaluation of configurations of the inception module and a mechanism for optimizing hyperparameters in deep learning architectures are stated.

Lévinas is one of the most important thinkers of the 20th century and is perhaps the philosopher who most seriously has taken the intent to think the difference, meeting the limits of language itself and the difficulties found in it for thinking the other, the difference, outside of the concept which closes the universe of sense in a totality. Lévinas centers his ethical ideas in the figure of the other and is extremely cautious, and maybe the (permanently open) totality of his work is no more than the intent to give that twirl to overcome the impossibility he faces, in affirming the place of that other not like the other of me, but the other as other. The outline of this essay pursues the comprehension of that radical other preserving its outside, its “beyond”, without enclosing it in the totalizing frontiers of the concept.