Mudit Sinha

HC
h-index1
4papers
1citation
Novelty15%
AI Score19

4 Papers

4.2AIJun 15
Rhythm of the Deep: A Computational-Linguistic Test of Duality of Patterning in Sperm Whale Codas

Mudit Sinha, Sanika Chavan

Human language has often been described as combining structure at two levels: lower-level units combine into larger units, which then combine into larger sequences. We test for this design feature, duality of patterning, in sperm whale codas using 1,483 codas from the Dominica Sperm Whale Project. Because acoustic similarity can imitate symbolic structure, we treat the problem as computational-linguistic structure discovery from continuous audio rather than as a direct claim about language or meaning. We use a consensus of frozen audio encoders, held-out structural tests, per-statistic nulls, and acoustic-null recoverability gates. The evidence supports a narrow two-tier architecture. At the lower tier, clicks compose into codas not by a stable ordered rule, but by which clicks are present together with their inter-click rhythm. At the upper tier, coda tokens show bout-level sequential dependence, with an NSB second-order transfer-entropy lift of 0.132 bits (p = 0.002). Under tempo scaling, encoder-derived click identity is strongly rate-bound, while coda identity remains substantially more stable, yielding a measurable abstraction gradient across the click-to-coda step. Rhythm-only baselines recover substantial lower-tier structure but fail to reproduce the upper-tier sequential-dependence signal. We do not claim language, semantics, perception, or human-like phonemes. Instead, we report representation-level evidence for a duality-of-patterning-like architecture whose lower tier is rhythmic rather than segmental, and provide a portable null-controlled framework for testing combinatorial structure in induced acoustic token systems.

3.1LGApr 26, 2021
Continual Distributed Learning for Crisis Management

Aman Priyanshu, Mudit Sinha, Shreyans Mehta

Social media platforms such as Twitter, Facebook etc can be utilised as an important source of information during disaster events. This information can be used for disaster response and crisis management if processed accurately and quickly. However, the data present in such situations is ever-changing, and using considerable resources during such a crisis is not feasible. Therefore, we have to develop a low resource and continually learning system that incorporates text classification models which are robust against noisy and unordered data. We utilised Distributed learning which enabled us to learn on resource-constrained devices, then to alleviate catastrophic forgetting in our target neural networks we utilized regularization. We then applied federated averaging for distributed learning and to aggregate the central model for continual learning.

3.3HCMar 11, 2015Code
Bublz! : Playing with Bubbles to Develop Mathematical Thinking

Dhruv Chand, Karthik Gopalakrishnan, Nisha KK et al.

We encounter mathematical problems in various forms in our lives, thus making mathematical thinking an important human ability. In this paper, we present Bublz!, a simple, click-driven game for children to engage in and develop mathematical thinking in an enjoyable manner.

3.3HCMar 11, 2015Code
Get 'em Moles! : Learning Spelling and Pronunciation through an Educational Game

Dhruv Chand, Karthik Gopalakrishnan, Nisha KK et al.

Get 'em Moles! is a single-player educational game inspired by the classic arcade game Whac-A-Mole. Primarily designed for touchscreen devices, Get 'em Moles! aims to teach English spelling and pronunciation through engaging game play. This paper describes the game, design decisions in the form of elements that support learning, preliminary play-testing results, and future work.