The Virtual Image in Streaming Video Indexing

Multimedia technology has been applied to many types of applications and the great amount of multimedia data need to be indexed. Especially the usage of digital video data is very popular today. In particular video browsing is a necessary activity in many kinds of knowledge. For effective and interactive exploration of large digital video archives there is a need to index the videos using their visual, audio and textual data. In this paper, we focus on the visual and textual content of video for indexing. In the former approach we use the Virtual Image and in the latter one we use the Dublin Core Metadata, opportunely extended and multilayered for the video browsing and indexing. Before to concentrate our attemption on the visual content we will explain main methods to video segmentation and annotation, in order to introduce the steps for video keyfeature extraction and video description generation

Sistema Financiero Nacional : Análisis del riesgo crediticio que enfrento el Banco La Fise Bancentro en el periodo 2012-2013

El presente documento abordo los principales riesgos financieros que afecta el sistema bancario nicaragüense, enfocado en el análisis detallado del riesgo crediticio que enfrento el Banco BANCENTRO LAFISE en el periodo 2012-2013, debido a que este tipo de riesgo puede afectar directamente la cartera de las instituciones financieras cuando se realiza un mal análisis en el otorgamiento de crédito, entre otros factores que puedan incidir en el incremento de dicho riesgo. En esta investigación se procedió a recopilar la información sustancial sobre los tipos de riesgos, incluyendo la clasificación y el análisis de la cartera de crédito del Banco BANCENTRO LAFISE en el periodo 2012-2013, logrando documentar el nivel de riesgo al que estuvo expuesto dicho banco en el periodo antes mencionado, resultando con un porcentaje de riesgo para la cartera de crédito que no excede el porcentaje máximo establecido en las normas reguladas por la Superintendencia de Bancos y Otras Instituciones Financieras.LEY 316, Ley General de Bancos y Otras Instituciones Financieras. Con los resultados obtenidos de la investigación lograremos valorar el riesgo crediticio y evaluar la estabilidad financiera de Bancentro Lafise en el periodo 2012-2013. Concluyendo que las instituciones financieras del Sistema bancario nicaragüense están altamente expuestas a enfrentar los riesgos abordados en este documento, por lo que se debe realizar un análisis profundo para el otorgamiento del crédito, así mismo deben tener claramente establecidas sus Políticas de Crédito, ejecutando revisiones periódicas a las mismas para mejorar los procedimientos utilizados, logrando con esto minimizar el riesgo al que se encontraron expuestos en periodos anteriores

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Chronic Obstructive Pulmonary Disease and Lung Cancer: Underlying Pathophysiology and New Therapeutic Modalities

Chronic obstructive pulmonary disease (COPD) and lung cancer are major lung diseases affecting millions worldwide. Both diseases have links to cigarette smoking and exert a considerable societal burden. People suffering from COPD are at higher risk of developing lung cancer than those without, and are more susceptible to poor outcomes after diagnosis and treatment. Lung cancer and COPD are closely associated, possibly sharing common traits such as an underlying genetic predisposition, epithelial and endothelial cell plasticity, dysfunctional inflammatory mechanisms including the deposition of excessive extracellular matrix, angiogenesis, susceptibility to DNA damage and cellular mutagenesis. In fact, COPD could be the driving factor for lung cancer, providing a conducive environment that propagates its evolution. In the early stages of smoking, body defences provide a combative immune/oxidative response and DNA repair mechanisms are likely to subdue these changes to a certain extent; however, in patients with COPD with lung cancer the consequences could be devastating, potentially contributing to slower postoperative recovery after lung resection and increased resistance to radiotherapy and chemotherapy. Vital to the development of new-targeted therapies is an in-depth understanding of various molecular mechanisms that are associated with both pathologies. In this comprehensive review, we provide a detailed overview of possible underlying factors that link COPD and lung cancer, and current therapeutic advances from both human and preclinical animal models that can effectively mitigate this unholy relationship