¡No tengo [papeles], no me los habéis dado!. Implicaciones de la arbitrariedad e itinerarios

Treballs Finals de Grau Antropologia Social i Cultural, Facultat de Geografia i Història, Universitat de Barcelona, Curs: 2012-2013, Tutor: Mikel AramburuEste artículo sintetiza el ejercicio de aproximación etnográfica al fenómeno de la inmigración irregular a partir de tres ejes interconectados de análisis: por un lado, las prácticas y discursos emitidos a través de los procedimientos burocráticos de instituciones estatales dedicadas a la inmigración (street-level bureaucracy). Por otro, la mediación ejercida por entidades no gubernamentales entre las exigencias del Estado y una población inmigrada carente de recursos materiales y simbólicos. Finalmente, la experiencia de liminalidad que atraviesa la vida y el día a día de esas personas marcadas por el estatus de la “ilegalidad” civil. A partir de la observación participante en el proyecto de mediación de una entidad sin ánimo de lucro de Barcelona y del acompañamiento al recorrido por lo que yo llamaré “itinerarios burocráticos” de seis casos específicos, se intentará describir la complejidad resultante de esta relación a tres: una relación marcada por la percepción de la arbitrariedad y en la que entran en juego discursos y relaciones de poder que inciden directa e indirectamente en la experiencia del mundo social de estos migrantes

Monotonicity Properties of Musielak–Orlicz Spaces and Dominated Best Approximation in Banach Lattices

AbstractCriteria for strict monotonicity, lower local uniform monotonicity, upper local uniform monotonicity and uniform monotonicity of a Musielak–Orlicz space endowed with the Amemiya norm and its subspace of order continuous elements are given in the cases of nonatomic and the counting measure space. To complete the results of Kurc (J. Approx. Theory69(1992), 173–187), criteria for upper local uniform monotonicity of these spaces equipped with the Luxemburg norm are also given. Some applications to dominated best approximation are presented

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

In this paper, we address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.Comment: 37 pages, 16 figure

Research on the determination of heat and water vapor emissions of Anatolian water buffaloes under indoor environmental conditions

This study was carried out to determine the amount of total heat, sensible heat, latent heat, and water vapor emitted by Anatolian water buffaloes in a closed barn located in the Thrace region of Turkey. At the research farm, a group was formed by randomly selecting water buffaloes based on their genetic similarities and lactation numbers. The buffalo group formed was housed in a closed-type barn. The inside and outside air temperatures, relative humidity, and milk yield values of the buffaloes were recorded for 1 year. According to the results of this research, the Anatolian water buffaloes emitted 653 kcal/h AU sensible heat, 351 kcal/h AU latent heat, and 518 g/h AU water vapor during the winter, while they emitted 421 kcal/h AU sensible heat, 536 kcal/h AU latent heat, and 916 g/h AU water vapor during the summer in the barn environment under optimum design conditions. These values can be used to control indoor climatic environmental conditions, design ventilation systems, select structural properties, and determine insulation requirement in terms of animal welfare.Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TOVAG 115O602]This work contains a part of the TOVAG 115O602 project supported by the Scientific and Technological Research Council of Turkey (TUBITAK). We are grateful to the Presidency of TUBITAK for supporting this project

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN

A thousand words about microparticles in cardiology

Microparticles (MPs) are membrane vesicles of 0.1-1 \mum in diameter produced mainly by platelets, vascular endothelium and blood cells in response to cell activation and stress factors. MPs can be also released during malignant transformation or apoptosis. The essential step in MP formation is the loss of the cell membrane asymmetric phospholipid distribution as response to the increased intracellular calcium levels. MPs contain, proteins and genetic material (DNA, miRNA, mRNA) which enables them to interact and influence target cell. MPs are considered to be markers of ongoing pathophysiological processes in cardiovascular system, due to their role in inflammation and coagulation