Dev Chauhan | Searcharxiv

arXiv2026

A Modular 3D-Printed Design to Investigate Prebiotic Chemical Systems in Hot Spring Pools

The emergence of membranous compartments (protocells) with encapsulated genetic material was a crucial step life's origin and evolution. The hot spring hypothesis for the origin of life suggests that protocells could have formed in hot spring pools and encapsulated organic matter. Previous investigations have focused on mimicking wet-dry (WD) cycles within a single pool, which precludes simulation of many hydrothermal field conditions, such as differential mineralogy, variable temperature and pH and water flow between multiple hot spring pools. Here, we present a modular 3D-printed hydrothermal field simulator that mimics the complex nature of hot spring fields by controlling the variability of a series of linked pools, including WD cycles, temperature, pH, mineralogy, and mixing of different fluids. Results from using the prototype hot spring field design demonstrate the ability to spontaneously form lipid vesicles that encapsulate organic matter within membranous compartments comprised of decanoic acid:decanol (4:1) or the phospholipids POPC:POPG (1:1). We observed distinct morphological differences in the vesicles, ranging from thick-walled multilamellar, thin-walled oligolamellar and unilamellar as well as giant unilamellar vesicles formed under multiple WD cycles in the simulator pools. Cargo encapsulation was favoured in the cell-like giant unilamellar and small oligolamellar vesicles. Overall, hot-spring simulator offers a customisable avenue for studying other hot spring processes such as prebiotic chemical reactions, mineral surface catalysis, and the complexity of hydrothermal field dynamics.

cond-mat.soft↗

arXiv2019

Revisiting Paraphrase Question Generator using Pairwise Discriminator

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.

cs.CL↗