arXiv subjects

Kshitij Gupta

Publications and source records attributed to Kshitij Gupta.

23 records · Page 2Linked to original sources

arXiv2021

Penrose process for a charged black hole in a uniform magnetic field

Spinning black holes create electromagnetic storms when immersed in ambient magnetic fields, illuminating the otherwise epically dark terrain. In an electromagnetic extension of the Penrose process, tremendous energy can be extracted, boosting the energy of radiating particles far more efficiently than the mechanical Penrose process. We locate the regions from which energy can be mined and demonstrate explicitly that they are no longer restricted to the ergosphere. We also show that there can be toroidal regions that trap negative energy particles in orbit around the black hole. We find that the effective charge coupling between the black hole and the super-radiant particles decreases as energy is extracted, much like the spin of a black hole decreases in the mechanical analogue. While the effective coupling decreases, the actual charge of the black hole increases in magnitude reaching the energetically-favored Wald value, at which point energy extraction is impeded. We demonstrate the array of orbits for products from the electromagnetic Penrose process.

gr-qc↗

arXiv2021

ViTA: Visual-Linguistic Translation by Aligning Object Tags

Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. It has gained popularity in recent years, and several pipelines have been proposed in the same direction. Yet, the task lacks quality datasets to illustrate the contribution of visual modality in the translation systems. In this paper, we propose our system under the team name Volta for the Multimodal Translation Task of WAT 2021 from English to Hindi. We also participate in the textual-only subtask of the same language pair for which we use mBART, a pretrained multilingual sequence-to-sequence model. For multimodal translation, we propose to enhance the textual input by bringing the visual information to a textual domain by extracting object tags from the image. We also explore the robustness of our system by systematically degrading the source text. Finally, we achieve a BLEU score of 44.6 and 51.6 on the test set and challenge set of the multimodal task.

cs.CL↗

arXiv2021

Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning

Tables are widely used in various kinds of documents to present information concisely. Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning. In this paper, we present our systems to solve Task 9 of SemEval-2021: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a statement, predicting whether the table supports the statement and (B) Predicting which cells in the table provide evidence for/against the statement. We fine-tune TAPAS (a model which extends BERT's architecture to capture tabular structure) for both the subtasks as it has shown state-of-the-art performance in various table understanding tasks. In subtask A, we evaluate how transfer learning and standardizing tables to have a single header row improves TAPAS' performance. In subtask B, we evaluate how different fine-tuning strategies can improve TAPAS' performance. Our systems achieve an F1 score of 67.34 in subtask A three-way classification, 72.89 in subtask A two-way classification, and 62.95 in subtask B.

cs.CL↗

arXiv2021

Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble

Memes are one of the most popular types of content used to spread information online. They can influence a large number of people through rhetorical and psychological techniques. The task, Detection of Persuasion Techniques in Texts and Images, is to detect these persuasive techniques in memes. It consists of three subtasks: (A) Multi-label classification using textual content, (B) Multi-label classification and span identification using textual content, and (C) Multi-label classification using visual and textual content. In this paper, we propose a transfer learning approach to fine-tune BERT-based models in different modalities. We also explore the effectiveness of ensembles of models trained in different modalities. We achieve an F1-score of 57.0, 48.2, and 52.1 in the corresponding subtasks.

cs.CL↗

arXiv2018

White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

The Blue Whale Challenge is a series of self-harm causing tasks that are propagated via online social media under the disguise of a "game." The list of tasks must be completed in a duration of 50 days and they cause both physical and mental harm to the player. The final task is to commit suicide. The game is supposed to be administered by people called "curators" who incite others to cause self-mutilation and commit suicide. The curators and potential players are known to contact each other on social networking websites and the conversations between them are suspected to take place mainly via direct messages which are difficult to track. Though, in order to find curators, the players make public posts containing certain hashtags/keywords to catch their attention. Even though a lot of these social networks have moderated posts talking about the game, yet some posts manage to pass their filters. Our research focuses on (1) understanding the social media spread of the challenge, (2) spotting the behaviour of the people taking interest in Blue Whale challenge and, (3) analysing demographics of the users who may be involved in playing the game.

cs.SI↗