Dynamics of confined water and its interplay with alkali cations in sodium aluminosilicate hydrate gel: insights from reactive force field molecular dynamics

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibits a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5–20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers

Automatic modulation classification for cognitive radios using cumulants based on fractional lower order statistics

Automatic modulation classification (AMC) finds various applications in cognitive radios. This paper presents a method for the automatic classification using cumulants derived using fractional lower order statistics. The performance of the classifier is presented in the form of probability of correct classification under noisy and fading conditions. Unlike many of the conventional methods, the proposed method does not require a priori knowledge of signal parameters. The proposed method is also more robust to different noises. Simulation results show that the proposed method can achieve better classification accuracy when compared to conventional cumulant based AMC method, in various impulsive noise conditions. 1

Fracture toughness of sodium aluminosilicate hydrate (NASH) gels: Insights from molecular dynamics simulations

This paper evaluates the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash via molecular dynamics (MD) simulations. The short- and medium-range order of the constructed N-A-S-H structures shows good correlation with the experimental observations, signifying the viability of the N-A-S-H structures. The simulated fracture toughness values of N-A-S-H (0.4–0.45 MPa m0.5) appear to be of the same order as the available experimental values for fly ash-based geopolymer mortars and concretes. These results suggest the efficacy of the MD simulation toward obtaining a realistic fracture toughness of N-A-S-H, which is otherwise very challenging to obtain experimentally, and no direct experimental fracture toughness values are yet available. To further assess the fracture behavior of N-A-S-H, the number of chemical bonds formed/broken during elongation and their relative sensitivity to crack growth are evaluated. Overall, the fracture toughness of N-A-S-H presented in this paper paves the way for a multiscale simulation-based design of tougher geopolymers

IndicBART: A Pre-trained Model for Indic Natural Language Generation

In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller. It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning. Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model.Comment: Published at ACL 2022, 15 page

Genetic Diversity Analysis of Mutant Lines of Oat (\u3cem\u3eAvena sativa\u3c/em\u3e L.) Based on RAPD and ISSR Analysis

The genus Avena belongs to the grass family Poaceae and has ploidy levels of diploid, tetraploid and hexaploid with basic chromosome number of 7 (n=7). Oat (Avena sativa L.) is one of the most important forage and feed crops of the world. Oat is used as green fodder, straw, hay or silage. Oat grain makes a good balanced concentrate in the rations for poultry, cattle, sheep and other animals. Green fodder contains about 10 to 13% protein and 30 to 35% dry matter. Despite being high fed fodder crop, it is now gaining importance as food due to its unique and important quality characteristics, particularly the lipid and protein in grains (Ruwali et al., 2013). The existing genetic variability for the traits of agronomic importance, such as plant vegetative cycle, is considered restricted. The narrow of the genetic base in cultivated oat varieties can be a constraint on the efficacy of genotype selection in segregating generations (Carvalho and Federizzi, 1989). Genetic variability in existing oat cultivars is not high enough; it hampers the selection of superior genotypes for breeding. Modifications in the genetic structure of plants and an organisms occurs naturally, though at low frequency, but can be increased through physical or chemical mutagens. Advances in molecular biology have introduced an alternative for variety/genotype identification. The genetic characterization of germplasm helps in their effective conservation and reveals the extent of relationship among the accessions and the estimates of genetic diversity (Singh et al., 2012). The selection of RAPD and ISSR were based on their relative technical simplicity, level of polymorphism they detect, cost effective, easily applicable to any plant species and target those sequence which are abundant throughout the eukaryotic genome and are rapidly evolved. A series of studies have indicated that ISSR could be able to produce more reliable and reproducible bands because of the higher annealing temperature and longer sequence of ISSR primers considered superior than RAPD (Bornet et al., 2001). ISSR has proved to be useful to study of population genetic studies gene mapping germplasm identification and characterize gene bank accessions as well as to identify closely related cultivars (Cortesi et al., 2004). The present research had the following objectives: Assessment of diversity of mutant lines of oat (Avena sativa L.) based on RAPD and ISSR analysis

IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages

Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. In this paper, we present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence models. Our results exhibit the strong performance of multilingual language-specific pre-trained models, and the utility of models trained on our dataset for other related NLG tasks. Our dataset creation methods can be easily applied to modest-resource languages as they involve simple steps such as scraping news articles and Wikipedia infoboxes, light cleaning, and pivoting through machine translation data. To the best of our knowledge, the IndicNLG Benchmark is the first NLG benchmark for Indic languages and the most diverse multilingual NLG dataset, with approximately 8M examples across 5 tasks and 11 languages. The datasets and models are publicly available at https://ai4bharat.iitm.ac.in/indicnlg-suite.Comment: Accepted at EMNLP 202

IndicXTREME: A Multi-Task Benchmark For Evaluating Indic Languages

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL

Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. The dataset contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location, and, Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language translation. We also create manually annotated testsets for 9 languages. We demonstrate the utility of the obtained dataset on the Naamapadam-test dataset. We also release IndicNER, a multilingual IndicBERT model fine-tuned on Naamapadam training set. IndicNER achieves an F1 score of more than $80$ for $7$ out of $9$ test languages. The dataset and models are available under open-source licences at https://ai4bharat.iitm.ac.in/naamapadam.Comment: ACL 202