Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Automatic Image Tagging

Las aplicaciones web como ImageShack, Flickr o Facebook que permiten a los usuarios compartir sus imágenes son cada vez mas populares. Cuando el usuario comparte una imagen, se le pide rellenar diferentes campos textuales como título, tags y comentarios entre otros. Esta tarea es aburrida y frustrante a largo plazo y como resultado, la información aportada es escasa y de mala calidad. Esto es perjudicial para los sistemas de búsqueda que explotan la información aportada por el usuario para realizar búsquedas textuales. Un sistema recomendador de tags mejoraría notablemente la calidad de los tags y, por lo tanto, se podrían implementar mejores sistemas de busqueda en estas páginas. Hemos desarrollado un recomendador de tags que, dada una imagen, propone diez tags. El sistema recomendador ha sido entrenado con un gran conjunto de imágenes obtenidas de Flickr. Proponemos nuevos algoritmos que combinan la utilizacion de características tanto globales como locales de la imagen (descriptores MPEG-7 y SURF), asi como técnicas de clustering para afrontar el problema de la recomendación de tags en un tiempo aceptable. [ABSTRACT] Web applications such as ImageShack, Flickr or Facebook that allow users to share their images have become extremely popular. When an image is uploaded, the user is asked to add title, tags, comments and other information. This task is annoying and frustrating for the user and, as a result, little and low quality data is provided. This is harmful for retrieval systems which exploit user annotation to support textual searchs. A tag recommender would certainly improve the quality of the tags, hence better retrieval systems could be implemented in these pages. We implemented a tag recommender that proposes ten tags for a given query image. The recommender is trained with a large dataset of over 100000 annotated images crawled from Flickr. We propose new algorithms that combine the use of both global and local features (MPEG-7 and SURF descriptors) and clustering techniques in order to address the tag recommendation problem in an acceptable time

Evolutionary approaches to solve the 3D thermal-aware floorplanning problem using heterogeneous processors

La integración en 3D es una técnica prometedora para llevar a cabo el proceso de fabricación de futuras arquitecturas multiprocesador. Esta técnica mejora el rendimiento y reduce el cableado obteniendo así un menor consumo global. Sin embargo, la integración en 3D provoca problemas térmicos de gran importancia debidos a la mayor proximidad de elementos que irradian calor, acentuando el impacto de los puntos calientes. Los algoritmos de floorplanning juegan un papel importante en la reducción del impacto térmico, pero no tienen en cuenta el perfil dinámico de las aplicaciones. Este trabajo propone un innovador floorplanner guiado por los pérfiles de consumo de potencia de un conjunto de aplicaciones representativas del ámbito de ejecución. Los resultados muestran que tener en cuenta el perl dinámico de las aplicaciones en vez del los valores en el caso peor lleva a mejorar la respuesta térmica del chip. [ABSTRACT] 3D integration has become one of the most promising techniques for the integration future multi-core processors, since it improves performance and reduces power consumption by decreasing global wire length. However, 3D integration causes serious thermal problems since the closer proximity of heat generating dies makes existing thermal hotspots more severe. Thermal-aware oorplanners can play an important role to improve the thermal prole, but they have failed in considering the dynamic power proles of the applications. This work proposes a novel thermal-aware oorplanner guided by the power proling of a set of benchmarks that are representative of the application scope. The results show how our approach outperforms the thermal metrics as compared with the worst-case scenario usually considered in traditional thermal-aware oorplanners

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)