National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors

Abstract: This paper contributes to a growing body of evidence that—when coupled with appropriate machine-learning techniques–linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.

Biorenewable chemicals: feedstocks, technologies and the conflict with food production.

Abstract: Volatile petroleum product prices along with depleting resources of oil and increasing environmental concerns had prompted several government agencies to promote and subsidize the production of biofuels from edible crops. The alarming rate at which edible crops were being deviated to produce biofuels caused the price of food crops like corn to shoot by a margin of over 100% in the initial three years (2005–2007). The twenty-first century has so far witnessed an enormous growth in production of biorenewable chemicals. The economically more lucrative business of biorenewable chemicals is currently projected to grow at a compounded annual growth rate of 22.4% and account for 45% of the chemicals produced in the US by 2025. This calls for a thoughtful regulation of the parameters, which should be considered for the production of biorenewable chemicals, in order to avoid any direct conflict with food production. This study addresses the possible edible and non-edible feedstock sources, conversion technologies used, conflict with food production accessed in terms of market scenario, environmental concerns and availability of land area for the effective conversion of the individual generation of feedstocks to biorenewable chemicals.

Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model

Abstract: Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors.