This paper problematizes the current state of historical memory and cultural immunity as a cross-development trend in the field of education, noting that sociocultural thought has focused on understanding of historical memory and cultural immunity as parallel processes, and the transformation of the culture of education is described in relation to a new information reality, which gives rise to the dehistorization of the field of education. The author of the paper believes that the adoption of social and resource methodological schemes brings hope to get the answers for the two important aspects: first, cultural space of the educational society in the analysis of the effects of the wearable information technologies, and, in this regard, the degree of influence of historical memory and cultural immunity, second, the aspect that can be described as “specific” in the educational culture ...

This paper problematizes the current state of historical memory and cultural immunity as a cross-development trend in the field of education, noting that sociocultural thought has focused on understanding of historical memory and cultural immunity as parallel processes, and the transformation of the culture of education is described in relation to a new information reality, which gives rise to the dehistorization of the field of education. The author of the paper believes that the adoption of social and resource methodological schemes brings hope to get the answers for the two important aspects: first, cultural space of the educational society in the analysis of the effects of the wearable information technologies, and, in this regard, the degree of influence of historical memory and cultural immunity, second, the aspect that can be described as “specific” in the educational culture ...

Convolutional sparse representations and convolutional dictionary learning are mathematical models that consist in representing a whole signal or image as a sum of convolutions between dictionary filters and coefficient maps. Unlike the patch-based counterparts, these convolutional forms are receiving an increase attention in multiple image processing tasks, since they do not present the usual patchwise drawbacks such as redundancy, multi-evaluations and non-translational invariant. Particularly, the convolutional dictionary learning (CDL) problem is addressed as an alternating minimization between coefficient update and dictionary update stages. A wide number of different algorithms based on FISTA (Fast Iterative Shrinkage-Thresholding Algorithm), ADMM (Alternating Direction Method of Multipliers) and ADMM consensus frameworks have been proposed to efficiently solve the most expensive s...

Frecuentemente, cuando atendamos a un paciente en la especialidad de ortodoncia, se requiere un manejo multidisciplinario con el área de cirugía bucomaxilofacial para llegar a una completa aceptación de parte del paciente. Por ello la primera técnica en desarrollar es la tracción quirúrgica ortodóntica, la cual se realiza en casos de impactación dentaria, que es aquel estado en el que un diente parcial o totalmente desarrollado queda alojado en el interior de los maxilares, después que haya pasado la época normal de su erupción. Se realiza con mayor frecuencia la tracción en los caninos, en pacientes mujeres, cuya etiopatogenia es multifactorial tanto por un retraso fisiológico o patológico. Este se puede evidenciar haciendo un diagnostico tanto radiológico como clínico. Entonces podemos determinar que el tratamiento de elección para la reubicación del diente impactado ...

Convolutional sparse representations and convolutional dictionary learning are mathematical models that consist in representing a whole signal or image as a sum of convolutions between dictionary filters and coefficient maps. Unlike the patch-based counterparts, these convolutional forms are receiving an increase attention in multiple image processing tasks, since they do not present the usual patchwise drawbacks such as redundancy, multi-evaluations and non-translational invariant. Particularly, the convolutional dictionary learning (CDL) problem is addressed as an alternating minimization between coefficient update and dictionary update stages. A wide number of different algorithms based on FISTA (Fast Iterative Shrinkage-Thresholding Algorithm), ADMM (Alternating Direction Method of Multipliers) and ADMM consensus frameworks have been proposed to efficiently solve the most expensive s...