Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution. Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convol...

A microstructure consisting of ultrafine grained (UFG) ferrite with average grain size of ~ 0.7 µm and dispersed nano-sized carbides was produced by cold-rolling and tempering of the martensite starting microstructure in a low carbon steel. Subsequently, fine grained dual phase (DP) steel consisting of equiaxed ferrite grains with average size of ~ 5 µm and martensite islands with average size ...

An important task in information access methods is distinguishing factual information from speculative or negated information. Fine-grained certainty levels of diagnostic statements in Swedish clinical text are annotated in a corpus from a medical university hospital. The annotation model has two polarities (positive and negative) and three certainty levels. However, there are many e-health sce...

Gay-Berne anisotropic potential has been widely used to evaluate the non-bonded interactions between coarse-grained particles being described as elliptical rigid bodies. In this paper, we are presenting a coarse-grained model for twenty kinds of amino acids and proteins, based on the anisotropic Gay-Berne and point electric multipole (EMP) potentials. We demonstrate that the anisotropic coarse-...

the microphase separation of hydrated perfluorinated sulfonic acid membrane nafion was investigated using dissipative particle dynamics (dpd). the nafion as a polymer was modelled by connecting coarse grained beads which corresponds to the hydrophobic backbone of polytetrafluoroethylene and perfluorinated side chains terminated by hydrophilic end particles of sulfonic acid groups [1, 2]. each f...

We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.