A Bottom Up Procedure for Text Line Segmentation of Latin Script

In this paper we present a bottom up procedure for segmentation of text lines written or printed in the Latin script. The proposed method uses a combination of image morphology, feature extraction and Gaussian mixture model to perform this task. The experimental results show the validity of the procedure.Comment: Accepted and presented at the IEEE conference "International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2017

Electric field induced softening of glass: What can it tell about the mechanism of flash sintering?

Electric field induced softening (EFIS) of glass is a recently discovered phenomenon, which was inspired by dramatic effects of electric field on sintering of ceramic powders. It represents the effect of DC or AC electric field on the softening of glass that is heated at a constant rate under fixed compressive load. As shown in Fig. 1(a), the application of applied voltage reduces the softening temperature, and the softening transition becomes significantly sharper when voltage is above a critical value.[1] Remarkably, this behavior is similar to that reported for flash sintering, as seen in Fig. 1(b).[2] In both types of experiments, emission of white light is observed when the sample is in the vicinity of sharp transition. The power density at the onset of EFIS and flash sintering is comparable at ~1W/cm2. Neither phenomena can be explained as Joule heating of a homogeneous solid. Notwithstanding these empirical similarities, we note that flash sintering of powders of varying properties is a far more complex phenomenon than heating of a clear, homogeneous, ion conducting silicate glass. As a result, EFIS is relatively well understood, while there are several diverging explanations of flash sintering that has been investigated much more extensively. Please click Additional Files below to see the full abstract

Locally Non-linear Embeddings for Extreme Multi-label Learning

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace. Still, leading embedding approaches have been unable to deliver high prediction accuracies or scale to large problems as the low rank assumption is violated in most real world applications. This paper develops the X-One classifier to address both limitations. The main technical contribution in X-One is a formulation for learning a small ensemble of local distance preserving embeddings which can accurately predict infrequently occurring (tail) labels. This allows X-One to break free of the traditional low-rank assumption and boost classification accuracy by learning embeddings which preserve pairwise distances between only the nearest label vectors. We conducted extensive experiments on several real-world as well as benchmark data sets and compared our method against state-of-the-art methods for extreme multi-label classification. Experiments reveal that X-One can make significantly more accurate predictions then the state-of-the-art methods including both embeddings (by as much as 35%) as well as trees (by as much as 6%). X-One can also scale efficiently to data sets with a million labels which are beyond the pale of leading embedding methods

Hot electron effects and non-linear transport in hole doped manganites

We show that strong non--linear electron transport in the ferromagnetic insulating (FMI) state of manganites, responsible for phenomena such as colossal electroresistance and current induced resistance switching, can occur due to a hot electron effect. In the FMI state, which we show is an insulator with a Coulomb gap, the temperature of the electron and lattice baths can decouple at high input power levels, leading to heating of the electron bath. Parameters of the hot electron effect model were independently determined via time dependence experiments and are in good agreement with the experimental values.Comment: Preprint generated using RevTeX4. 3 Figure

A Review on Study of Hepatoprotective Activity of Chenopodium Album Linnon CCl4 Induced Hepatotoxicity in Rats

The hepatoprotective activity of Chenopodium album Linn leaves against carbon tetrachloride (CCl4)-induced hepatotoxicity was investigated. Possibilities of Rat hepatocyte monolayer culture and rats were used as in vitro and in vivo hepatoprotective screening models also very useful. In the in vitro studies, different extracts and fraction we can screened. Silymarin can be used as reference drug. In the in vivo studies, hepatotoxicity was induced in wistar rats species give satisfactory results as per reported methods and administering a mixture of CCl4: olive oil (1:1, 2 ml/kg, s.c.) can be used for the inducible purpose. The extent of hepatotoxicity can be assessed by measuring the serum enzyme levels. So overall parameters consider for the CCl4 induced hapatoxicity in rats. Keywords: Antioxidant; Carbon tetrachloride; Chenopodium album; Hepatoprotectiv