Polyunsaturated fatty acid-derived lipid mediators and T cell function

Copyright © 2014 Nicolaou, Mauro, Urquhart and Marelli-Berg . This is an open- access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

Polyunsaturated fatty acid-derived lipid mediators and T cell function

Copyright © 2014 Nicolaou, Mauro, Urquhart and Marelli-Berg . This is an open- access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

"Where is My Parcel?" Fast and Efficient Classifiers to Detect User Intent in Natural Language

We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order?”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with twowell established classification models: logistic regression via n-grams to account for sequences in the dataand recurrent neural networks that perform the extraction of these sequential patterns automatically. Maintaining the embedding layer fixed to GloVe coordinates, a Mann-Whitney U test indicated that the F1 score on aheld out set of messages was lower for recurrent neural network classifiers than for linear n-grams classifiers (M1=0.828, M2=0.815; U=1,196, P=1.46e-20), unless all layers were jointly trained with all other network parameters (M1=0.831, M2=0.828, U=4,280, P=8.24e-4). This plain neural network produced top performance on a denoised set of labels (0.887 F1) matching with Human annotators (0.889 F1) and superior to linear classifiers (0.865 F1). Calibrating these models to achieveprecision levels above Human performance (0.93 Precision), our results indicate a small difference in Recall of 0.05 for the plain neural networks (training under 1hr), and 0.07 for the linear n-grams (training under 10min), revealing the latter as a judicious choice of model architecture in modern AI production systems

Determining the Surface-To-Bulk Progression in the Normal-State Electronic Structure of Sr2RuO4 by Angle-Resolved Photoemission and Density Functional Theory

In search of the potential realization of novel normal-state phases on the surface of Sr2RuO4 - those stemming from either topological bulk properties or the interplay between spin-orbit coupling (SO) and the broken symmetry of the surface - we revisit the electronic structure of the top-most layers by ARPES with improved data quality as well as ab-initio LDA slab calculations. We find that the current model of a single surface layer (\surd2x\surd2)R45{\deg} reconstruction does not explain all detected features. The observed depth-dependent signal degradation, together with the close quantitative agreement with LDA+SO slab calculations based on the LEED-determined surface crystal structure, reveal that (at a minimum) the sub-surface layer also undergoes a similar although weaker reconstruction. This points to a surface-to-bulk progression of the electronic states driven by structural instabilities, with no evidence for Dirac and Rashba-type states or surface magnetism.Comment: 4 pages, 4 figures, 1 table. Further information and PDF available at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/articles.htm

New electronic orderings observed in cobaltates under the influence of misfit periodicities

We study with ARPES the electronic structure of CoO2 slabs, stacked with rock-salt (RS) layers exhibiting a different (misfit) periodicity. Fermi Surfaces (FS) in phases with different doping and/or periodicities reveal the influence of the RS potential on the electronic structure. We show that these RS potentials are well ordered, even in incommensurate phases, where STM images reveal broad stripes with width as large as 80\AA. The anomalous evolution of the FS area at low dopings is consistent with the localization of a fraction of the electrons. We propose that this is a new form of electronic ordering, induced by the potential of the stacked layers (RS or Na in NaxCoO2) when the FS becomes smaller than the Brillouin Zone of the stacked structure

PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control