España y el espacio ex soviético: un largo camino por recorrer

En los últimos años crece el interés de España por el espacio ex soviético. Por una parte, se realizan esfuerzos por profundizar en las relaciones con Rusia, algo lógico, por razones económicas, políticas y de seguridad energética. Por otra, poco a poco aumentan los contactos políticos y económicos con los demás países de esta región. No obstante, en ambos casos el balance sigue siendo pobre y aún queda mucho por hacer para potenciar la presencia española en esta región vecina de la UE. La presidencia de la OSCE asumida por España a principios de este año es, indudablemente, una buena oportunidad para hacerlo

España: ¿un nuevo socio estratégico de Rusia en la Unión Europea?

En la reciente cumbre de la UE y Rusia se ha inaugurado la Asociación para la Modernización. España –que desde hace algunos años aboga por la profundización de relaciones entre la UE y Rusia– fue uno de los principales defensoras de esta nueva fórmula de relaciones entre la UE y su principal vecino oriental. Para España, Rusia es actualmente el socio político y económico más importante del espacio postsoviético. En los últimos años se ha producido un importante acercamiento político entre ambos países, llegándose a establecer una asociación estratégica. Lo que queda por ver es si esta multiplicación de encuentros y declaraciones se traducirá realmente en resultados económicos visibles y un diálogo político de carácter sólido, duradero y no sujeto a cambios coyunturales en Rusia

Polonia en busca de su lugar en la Unión Europea

La política europea de Polonia viene siendo objeto de controversia. Este análisis proporciona algunas claves para entender cuáles son sus fines y objetivos. Durante muchos años la integración en la Unión Europea fue el objetivo prioritario de la política exterior polaca. Tras haberlo alcanzado en mayo de 2004, Polonia intenta ajustarse a la situación de miembro de pleno derecho y definir su lugar en una Europa ampliada, pero no es una tarea fácil. Los grandes cambios en la escena política de Polonia y la crisis en la que se encuentra la UE dificultan enormemente la definición de una estrategia coherente y clara de la política europea de este nuevo país miembro

Extracting structured information from 2D images

Convolutional neural networks can handle an impressive array of supervised learning tasks while relying on a single backbone architecture, suggesting that one solution fits all vision problems. But for many tasks, we can directly make use of the problem structure within neural networks to deliver more accurate predictions. In this thesis, we propose novel deep learning components that exploit the structured output space of an increasingly complex set of problems. We start from Optical Character Recognition (OCR) in natural scenes and leverage the constraints imposed by a spatial outline of letters and language requirements. Conventional OCR systems do not work well in natural scenes due to distortions, blur, or letter variability. We introduce a new attention-based model, equipped with extra information about the neuron positions to guide its focus across characters sequentially. It beats the previous state-of-the-art benchmark by a significant margin. We then turn to dense labeling tasks employing encoder-decoder architectures. We start with an experimental study that documents the drastic impact that decoder design can have on task performance. Rather than optimizing one decoder per task separately, we propose new robust layers for the upsampling of high-dimensional encodings. We show that these better suit the structured per pixel output across the board of all tasks. Finally, we turn to the problem of urban scene understanding. There is an elaborate structure in both the input space (multi-view recordings, aerial and street-view scenes) and the output space (multiple fine-grained attributes for holistic building understanding). We design new models that benefit from a relatively simple cuboidal-like geometry of buildings to create a single unified representation from multiple views. To benchmark our model, we build a new multi-view large-scale dataset of buildings images and fine-grained attributes and show systematic improvements when compared to a broad range of strong CNN-based baselines

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error on the validation set (3.6% error on the test set) and 17.3% top-1 error on the validation set

The multivariate models of the reserves control and their applications

The multidimensional stock control that functions in a random Markov environment is considered. The mathematical formalization of this model was considered with the use of sums of the random variables defined on the Markov chains. The authors introduce a definition of risk function of the type of downside risk measures and find the explicit formulas for its determinations. The example of the application of these formulas is provided: the tasks of the reliability and optimal configuration for the queueing problem are regarded. The formulas defining the function by the system parameters were obtainedstock control, multidimensional model, risk function, Markov chain, queueing system

Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays

Recent advancements in computer vision promise to automate medical image analysis. Rheumatoid arthritis is an autoimmune disease that would profit from computer-based diagnosis, as there are no direct markers known, and doctors have to rely on manual inspection of X-ray images. In this work, we present a multi-task deep learning model that simultaneously learns to localize joints on X-ray images and diagnose two kinds of joint damage: narrowing and erosion. Additionally, we propose a modification of label smoothing, which combines classification and regression cues into a single loss and achieves 5% relative error reduction compared to standard loss functions. Our final model obtained 4th place in joint space narrowing and 5th place in joint erosion in the global RA2 DREAM challenge.Comment: Presented at the Workshop on AI for Public Health at ICLR 202

The Devil is in the Decoder: Classification, Regression and GANs

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consist of encoders which decrease spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. This paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise tasks ranging from classification, regression to synthesis. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce new residual-like connections for decoders. (3) We introduce a novel decoder: bilinear additive upsampling. (4) We explore prediction artifacts