Hipertiroidismo felino : caso clínico

Tras efectuar una revisión bibliográfica centrada en el diagnóstico clínico y laboratorial del hipertiroidismo felino, así como en el tratamiento mediante antitiroideos orales, el presente artículo describe un caso de esta patología felina geriátrica todavía poco frecuente en España. Posteriormente se discuten aspectos importantes del diagnóstico y tratamiento.After a bibliographic update focused on clinical and laboratorial diagnosis of feline hiperthyroidism, as well on its oral treatment, this article describes one case of this geriatric unfrequent disease in Spain. Then, important diagnostic and treatment issues are discussed

TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn. readthedocs.io/en/latest

Solución de Problemas Matriciales de “Gran Escala” sobre Procesadores Multinúcleo y GPUs

Few realize that, for large matrices, many dense matrix computations achieve nearly the same performance when the matrices are stored on disk as when they are stored in a very large main memory. Similarly, few realize that, given the right programming abstractions, coding Out-of-Core (OOC) implementations of dense linear algebra operations (where data resides on disk and has to be explicitly moved in and out of main memory) is no more difficult than programming high-performance implementations for the case where the matrix is in memory. Finally, few realize that on a contemporary eight core architecture or a platform equiped with a graphics processor (GPU) one can solve a 100, 000 × 100, 000 symmetric positive definite linear system in about one hour. Thus, for problems that used to be considered large, it is not necessary to utilize distributed-memory architectures with massive memories if one is willing to wait longer for the solution to be computed on a fast multithreaded architecture like a multi-core computer or a GPU. This paper provides evidence in support of these claimsPocos son conscientes de que, para matrices grandes, muchos cálculos matriciales obtienen casi el mismo rendimiento cuando las matrices se encuentran almacenadas en disco que cuando residen en una memoria principal muy grande. De manera parecida, pocos son conscientes de que, si se usan las abstracciones de programacón correctas, codificar algoritmos Out-of-Core (OOC) para operaciones de Álgebra matricial densa (donde los datos residen en disco y tienen que moverse explícitamente entre memoria principal y disco) no resulta más difícil que codificar algoritmos de altas prestaciones para matrices que residen en memoria principal. Finalmente, pocos son conscientes de que en una arquictura actual con 8 núcleos o un equipo con un procesador gráfico (GPU) es posible resolver un sistema lineal simétrico positivo definido de dimensión 100,000 × 100,000 aproximadamente en una hora. Así, para problemas que solían considerarse grandes, no es necesario usar arquitecturas de memoria distribuida con grandes memorias si uno está dispuesto a esperar un cierto tiempo para que la solución se obtenga en una arquitectura multihebra como un procesador multinúcleo o una GPU. Este trabajo presenta evidencias que soportan tales afirmaciones

Accelerating multi-channel filtering of audio signal on ARM processors

The researchers from Universitat Jaume I are supported by the CICYT projects TIN2014-53495-R and TIN2011-23283 of the Ministerio de Economía y Competitividad and FEDER. The authors from the Universitat Politècnica de València are supported by projects TEC2015-67387-C4-1-R and PROMETEOII/2014/003. This work was also supported from the European Union FEDER (CAPAP-H5 network TIN2014-53522-REDT)

Programming matrix algorithms-by-blocks for thread-level parallelism

With the emergence of thread-level parallelism as the primary means for continued improvement of performance, the programmability issue has reemerged as an obstacle to the use of architectural advances. We argue that evolving legacy libraries for dense and banded linear algebra is not a viable solution due to constraints imposed by early design decisions. We propose a philosophy of abstraction and separation of concerns that provides a promising solution in this problem domain. The first abstraction, FLASH, allows algorithms to express computation with matrices consisting of blocks, facilitating algorithms-by-blocks. Transparent to the library implementor, operand descriptions are registered for a particular operation a priori. A runtime system, SuperMatrix, uses this information to identify data dependencies between suboperations, allowing them to be scheduled to threads out-of-order and executed in parallel. But not all classical algorithms in linear algebra lend themselves to conversion to algorithms-by-blocks. We show how our recently proposed LU factorization with incremental pivoting and closely related algorithm-by-blocks for the QR factorization, both originally designed for out-of-core computation, overcome this difficulty. Anecdotal evidence regarding the development of routines with a core functionality demonstrates how the methodology supports high productivity while experimental results suggest that high performance is abundantly achievabl

Energy-Aware High Performance Computing

High performance computing centres consume substantial amounts of energy to power large-scale supercomputers and the necessary building and cooling infrastructure. Recently, considerable performance gains resulted predominantly from developments in multi-core, many-core and accelerator technology. Computing centres rapidly adopted this hardware to serve the increasing demand for computational power. However, further performance increases in large-scale computing systems are limited by the aggregate energy budget required to operate them. Power consumption has become a major cost factor for computing centres. Furthermore, energy consumption results in carbon dioxide emissions, a hazard for the environment and public health; and heat, which reduces the reliability and lifetime of hardware components. Energy efficiency is therefore crucial in high performance computing

The Impact of the Multi-core Revolution on Signal Processing

This paper analyzes the influence of new multi- core and many-core architectures on Signal Processing. The article covers both the architectural design and the programming models of current general-purpose multi-core processors and graphics processors (GPU), with the goal of identifying their possibilities and impact on signal processing applications

Evolución de la innovación educativa en la Universidad Complutense: proyecto UNICOMEX

La evolución de la educación universitaria en España ha experimentado una enorme evolución en los últimos veinte años. Desde la enseñanza centrada en la clase magistral, con el consiguiente protagonismo del profesor, a lo que sucede actualmente, con el estudiante como eje principal, media todo un cambio en el planteamiento de nuestro sistema educativo universitario. La implantación del Espacio Europeo de Educación Superior (EEES) ha sido uno de los objetivos perseguidos por la Universidad Complutense de Madrid (UCM) en la última década. Con este fin existieron, hasta hace poco, las conocidas como Asignaturas Piloto (AP) e igualmente se convocan periódicamente, desde hace años, los Proyectos de Innovación Educativa (P.I.E.). Ambas herramientas han procurado facilitar dicha adaptación, así como promover un concepto transversal en la transmisión del conocimiento. Nuestro grupo de trabajo puede ser un ejemplo de lo que la UCM ha querido conseguir con ambas cosas. La asignatura ha participado en todas las convocatorias de AP, pasando por una evolución clara desde la clase magistral a los pequeños grupos de trabajo. Ha sido, además, el instrumento útil para ir desarrollando distintos P.I.E., hasta un total de nueve, tras formar un equipo interdisciplinar constituido por profesores universitarios y profesionales de diversos ámbitos. El último de estos PIE, concedido en el curso académico 2011-2012, consiste en la creación de un espacio virtual de referencia para el estudio de los animales exóticos, partiendo de los resultados obtenidos en proyectos anteriores. UNICOMEX (Universidad-Complutense-Exóticos) nace con el objetivo de poner al alcance de cualquiera una serie de recursos virtuales ordenados que faciliten el conocimiento biológico, anatómico y clínico de los animales exóticos, entendiendo como tales aquellos que, siendo de interés veterinario, no se consideran domésticos. La totalidad de su contenido está en español e inglés, con el fin de hacer de ella una herramienta virtual útil y abierta a la comunidad científica y didáctica internacional