arXiv subjects

Sourav Ghosh

Publications and source records attributed to Sourav Ghosh.

42 records · Page 3Linked to original sources

arXiv2021

Comparison of the electrochemical performance of CeO2 and rare earth-based mixed metallic oxide (Ce0.9Zr0.1O2) for supercapacitor applications

CeO2 and Ce0.9Zr0.1O2 are prepared from the sol-gel method to investigate and compare their electrochemical properties for supercapacitor applications. Structural, morphological, and elemental studies have been done for CeO2 and Ce0.9Zr0.1O2 by XRD, SEM, and EDX. Cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy techniques are used to study the electrochemical performance of these materials. Doping enhances the electrochemical performance of the electrode, by improving the specific capacitance (~150%, 243 F g-1 from 96 F g-1) for the doped system @2 mV s-1 Vs. Ag/AgCl reference electrode in 2 mol L-1 KOH electrolyte solution. Ce0.9Zr0.1O2 shows only ~30% of capacitance degradation for a ten folds increase in current densities. Ce0.9Zr0.1O2 also shows 16% capacitance degradation after 800 cycles with excellent Columbic efficiency (~100%) @2 A g-1 current density. Partial replacement of Ce4+ ion (0.97 Å) with Zr4+ ion (0.84 Å) results in a decrease in lattice parameter, as confirmed by Rietveld refinement. Ce0.9Zr0.1O2 has provided good energy, and power density of 1.128 Wh kg-1and 112.5 W kg-1 respectively. Furthermore, better diffusivity of the Ce0.9Zr0.1O2 in KOH electrolyte (indicated using Randles-Sevcik equation-based analysis) is correlated with better electrochemical performance. These insights presented here clearly indicate that Zr doping into CeO2 results in a promising candidate material for electrochemical and supercapacitive applications.

cond-mat.mtrl-sci↗

arXiv2021

Real-Time Optimized N-gram For Mobile Devices

With the increasing number of mobile devices, there has been continuous research on generating optimized Language Models (LMs) for soft keyboard. In spite of advances in this domain, building a single LM for low-end feature phones as well as high-end smartphones is still a pressing need. Hence, we propose a novel technique, Optimized N-gram (Op-Ngram), an end-to-end N-gram pipeline that utilises mobile resources efficiently for faster Word Completion (WC) and Next Word Prediction (NWP). Op-Ngram applies Stupid Backoff and pruning strategies to generate a light-weight model. The LM loading time on mobile is linear with respect to model size. We observed that Op-Ngram gives 37% improvement in Language Model (LM)-ROM size, 76% in LM-RAM size, 88% in loading time and 89% in average suggestion time as compared to SORTED array variant of BerkeleyLM. Moreover, our method shows significant performance improvement over KenLM as well.

cs.CL↗

arXiv2021

Language Detection Engine for Multilingual Texting on Mobile Devices

More than 2 billion mobile users worldwide type in multiple languages in the soft keyboard. On a monolingual keyboard, 38% of falsely auto-corrected words are valid in another language. This can be easily avoided by detecting the language of typed words and then validating it in its respective language. Language detection is a well-known problem in natural language processing. In this paper, we present a fast, light-weight and accurate Language Detection Engine (LDE) for multilingual typing that dynamically adapts to user intended language in real-time. We propose a novel approach where the fusion of character N-gram model and logistic regression based selector model is used to identify the language. Additionally, we present a unique method of reducing the inference time significantly by parameter reduction technique. We also discuss various optimizations fabricated across LDE to resolve ambiguity in input text among the languages with the same character pattern. Our method demonstrates an average accuracy of 94.5% for Indian languages in Latin script and that of 98% for European languages on the code-switched data. This model outperforms fastText by 60.39% and ML-Kit by 23.67% in F1 score for European languages. LDE is faster on mobile device with an average inference time of 25.91 microseconds.

cs.CL↗

arXiv2020

EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts

Word emphasis in textual content aims at conveying the desired intention by changing the size, color, typeface, style (bold, italic, etc.), and other typographical features. The emphasized words are extremely helpful in drawing the readers' attention to specific information that the authors wish to emphasize. However, performing such emphasis using a soft keyboard for social media interactions is time-consuming and has an associated learning curve. In this paper, we propose a novel approach to automate the emphasis word detection on short written texts. To the best of our knowledge, this work presents the first lightweight deep learning approach for smartphone deployment of emphasis selection. Experimental results show that our approach achieves comparable accuracy at a much lower model size than existing models. Our best lightweight model has a memory footprint of 2.82 MB with a matching score of 0.716 on SemEval-2020 public benchmark dataset.

cs.CL↗

arXiv2016

The Pressure Metric on the Margulis Multiverse

This paper defines the pressure metric on the Moduli space of Margulis spacetimes without cusps and shows that it is positive definite on the constant entropy sections. It also demonstrates an identity regarding the variation of the cross-ratios.

math.DG↗

arXiv2015

Anosov Structure on Margulis Space Time

In this paper we describe the stable and unstable leaves for the geodesic flow on the space of non-wandering spacelike geodesics of a Margulis Space Time and prove contraction properties of the leaves under the flow. We also show that monodromy of Margulis Space Times are "Anosov representations in non semi-simple Lie groups".

math.DG↗