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Cryogenic magnetocaloric effect in the Fe17 molecular nanomagnet

Trabajo presentado al "The 23rd IIR International Congress of Refrigeration" celebrado del 21 al 26 de Agosto del 2011 en Praga.-- Dedicated to Alfred Werner on the 100th Anniversary of his Nobel prize in Chemistry in 1913.We study the magnetothermal properties of magnetically isotropic high-spin molecular nanomagnets containing 17 Fe3+ ions per molecule linked via oxide and hydroxide ions, packed in a crystallographic cubic symmetry. Low-temperature magnetization and heat capacity experiments reveal that each molecular unit carries a net spin ground state as large as S = 35/2 and a magnetic anisotropy as small as D = −0.023 K, while no magnetic order, purely driven by dipolar interactions, is to be expected down to very-low temperatures. These characteristics suggest that the Fe17 molecular nanomagnet can potentially be employed as a sub-Kelvin magnetic refrigerant.This work has been partially supported by Spanish MINECO through grants MAT2009-13977-C03 and PIE201060I012, the EPSRC and The Leverhulme Trust (UK).Peer Reviewe

Inter-Judge Agreement: An Analysis of the 1990 NFA and AFA-NIET National Individual Events Tournaments

Given the increasing concern about the judge\u27s role in individual events tournaments, and given the paucity of literature specifically pertaining to inter-judge agreement, we sought to analyze the degree of inter-judge agreement at two national level tournaments which employ multiple judge panels in preliminary rounds. The results of the 1990 National Forensics Association Tournament and the 1990 American Forensic Association - National Individual Events Tournament serve as a basis for the analysis

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark

Tunable dipolar magnetism in high-spin molecular clusters

We report on the Fe17 high-spin molecular cluster and show that this system is an exemplification of nanostructured dipolar magnetism. Each Fe17 molecule, with spin S=35/2 and axial anisotropy as small as D=-0.02K, is the magnetic unit that can be chemically arranged in different packing crystals whilst preserving both spin ground-state and anisotropy. For every configuration, molecular spins are correlated only by dipolar interactions. The ensuing interplay between dipolar energy and anisotropy gives rise to macroscopic behaviors ranging from superparamagnetism to long-range magnetic order at temperatures below 1K.Comment: Replaced with version accepted for publication in Physical Review Letter