arXiv subjects

Sayak Chakraborty

Publications and source records attributed to Sayak Chakraborty.

2 recordsLinked to original sources

arXiv2026

On the altitude profile of lower ionospheric D-region response time delay during solar flares

The D-region ionosphere is sluggish in nature while responding to the incoming solar radiation. We study the altitude (h) profile of mid-latitude D-region response time delay (Delta t) during three chosen solar flares, namely, C5.2, M5.2, and X2.2 classes. We solve "electron continuity equation" numerically at each and every h to obtain Delta t-h profile. We investigate the 1) latitudinal variation (over both Northern and Southern hemispheres) and 2) seasonal variation (throughout the year) of Delta t-h profiles of each of these solar flares separately. We go over noteworthy variations of Delta t-h profile for both 1) and 2) with reasonably different results over different hemispheres. Also, we study some contrasting behaviours of Delta t-h profile of X2.2 class for both 1) and 2) in comparison to C5.2 or M5.2 classes. We conclude the outcome with possible explanations.

astro-ph.SR↗

arXiv2020

A more abstractive summarization model

Pointer-generator network is an extremely popular method of text summarization. More recent works in this domain still build on top of the baseline pointer generator by augmenting a content selection phase, or by decomposing the decoder into a contextual network and a language model. However, all such models that are based on the pointer-generator base architecture cannot generate novel words in the summary and mostly copy words from the source text. In our work, we first thoroughly investigate why the pointer-generator network is unable to generate novel words, and then address that by adding an Out-of-vocabulary (OOV) penalty. This enables us to improve the amount of novelty/abstraction significantly. We use normalized n-gram novelty scores as a metric for determining the level of abstraction. Moreover, we also report rouge scores of our model since most summarization models are evaluated with R-1, R-2, R-L scores.

cs.CL↗