Abstract

In this study we carried out the early distribution of clades and subclades state-wise based on shared mutations in Indian SARS-CoV-2 isolates collected (27th Jan – 27th May 2020). Phylogenetic analysis of these isolates indicates multiple independent sources of introduction of the virus in the country, while principal component analysis revealed some state-specific clusters. It is observed that clade 20A defining mutations C241T (ORF1ab: 5’ UTR), C3037T (ORF1ab: F924F), C14408T (ORF1ab: P4715L), and A23403G (S: D614G) are predominant in Indian isolates during this period. Higher number of coronavirus cases were observed in certain states, viz., Delhi, Tamil Nadu, and Telangana. Genetic analysis of isolates from these states revealed a cluster with shared mutations, C6312A (ORF1ab: T2016K), C13730T (ORF1ab: A4489V), C23929T, and C28311T (N: P13L). Analysis of region-specific shared mutations carried out to understand the large number of deaths in Gujarat and Maharashtra identified shared mutations defining subclade, I/GJ-20A (C18877T, C22444T, G25563T (ORF3a: H57Q), C26735T, C28854T (N: S194L), C2836T) in Gujarat and two sets of co-occurring mutations C313T, C5700A (ORF1ab: A1812D) and A29827T, G29830T in Maharashtra. From the genetic analysis of mutation spectra of Indian isolates, the insights gained in its transmission, geographic distribution, containment, and impact are discussed.

Abstract

A fast, robust, parallel, and parameter free version of a frequency filtering preconditioner is proposed for linear systems corresponding to diffusion equation on a structured grid. Proposed solver is faster than the state-of-the-art solvers

Abstract

Datasets play an important role in driving innovation in algorithms and architectures for supervised deep learning tasks. Numerous datasets exist for images, language translation, etc. One of the interesting challenge problems for deep learning is to solve high school problems in mathematics and sciences. To this end, a comprehensive set of dataset containing hundreds of millions of samples, and the generation modules is required that can propel research for these problems. In this paper, a large set of datasets covering mathematics and science problems is proposed, and the dataset generation codes are proposed. Test results on the proposed datasets for character-to-character transformer architecture show promising results with test accuracy above 95%, however, for some datasets it shows test accuracy of below 30%. Dataset will be available at: www.github.com/misterpawan/scimat2.

Abstract

Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircraft

Abstract

Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes. Since data annotation is time-consuming and costly, reducing the label requirement even further is an important goal. To that end, our paper presents a more challenging few-shot setting with almost no class label access. By leveraging self-supervision to learn image representations and similarity for classification at test time, we achieve competitive baselines while using almost zero (0-5) class labels. Compared to existing state-of-the-art approaches which use 60,000 labels, this is a four orders of magnitude (10,000 times) difference. This work is a step towards developing few-shot learning methods that do not depend on annotated data. Our code is publicly released at https: //github.com/adbugger/FewShot. 3

Abstract

Handwritten documents from communities like cultural heritage, judiciary, and modern journals remain largely unexplored even today. To a great extent, this is due to the lack of retrieval tools for such unlabeled document collections. This work considers such collections and presents a simple, robust retrieval framework for easy information access. We achieve retrieval on unlabeled novel collections through invariant features learned for handwritten text. These feature representations enable zero-shot retrieval for novel queries on unlabeled collections. We improve the framework further by supporting search via text and exemplar queries. Four new collections written in English, Malayalam, and Bengali are used to evaluate our text retrieval framework. These collections comprise 2957 handwritten pages and over 300K words. We report promising results on these collections, despite the zero-shot constraint and huge collection size. Our framework allows the addition of new collections without any need for specific finetuning or labeling. Finally, we also present a demonstration of the retrieval framework. [Project Page] Keywords: Document retrieval· Keyword Spotting· Zero-shot retrieval

Abstract

Earthquake-induced landslides can affect people and structures as a result of significant ground shaking or regardless of the intensity. A suitable design ground motion helps to mitigate the impact of such landslides. The most commonly used design ground motion in slope stability analysis is based on probabilistic seismic hazard maps. Uncertainties in the selection of expected ground motion levels have been ignored. The present study is conducted based on improvised fully probabilistic approach, which provides the total probability of slope failure in a particular period under seismic loading by addressing all possible scenarios. This approach is applied to the seismically active Tindharia slope located in Darjeeling, India. The total probability of seismic slope failure obtained in the next 50 years is 30% and the most probable peak-ground acceleration that triggers a landslide is 0.12g. Design peak-ground acceleration predicted from the next 475-year probabilistic seismic hazard map is 1.02g. In the present study, the significant difference in the design peak-ground acceleration from probabilistic seismic-hazard analysis and fully probabilistic approach is observed. The study suggests that the seismic landslide hazard may be overestimated or underestimated when used in the design of ground intensity obtained by conducting PSHA.

Abstract

The northern part of INDIA is most vulnerable in failure of slopes due to hill region with complex geology, high intensity rainfall and frequently occurrence of earthquakes. The stability of slopes has traditionally analyzed using deterministic methods. But uncertainty plays major role in geotechnical engineering the probabilistic characterization of uncertainties in input variables for the assessment of slope stability is very helpful. In this paper, the safety of slope using deterministic and probabilistic methods under static and pseudo static loads is car- ried for Lungchok landslide which is in south district of Sikkim state, India. The Factor of safe- ty calculated by deterministic cannot represent the stability of slope exactly as it is limited to the single value of parameters. The stability of slope is independent of single soil parameter but dependent on random process with distribution of soil parameters. Rather than the conventional factor of safety against the sliding failure the probability of sliding failure is most useful. The stability of slope is analyzed using two approaches deterministic and probabilistic analysis under the effects of static and pseudo static forces. The deterministic approach with Morgen-stern Price limit equilibrium method and the probabilistic approach with Monte Carlo simula- tion have been performed for the stability analysis of Lungcok landslide using Geo-Studio Software. The dependence and efficiency of the probabilistic strategies in the investigation of slope stability was highlighted in this paper.

Abstract

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with wellestablished, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate.

Abstract

Advances in deep learning have been instrumental in enhancing the performance of face verification systems. Despite their ability to attain high accuracy, most of these systems fail to provide interpretations of their decisions. With the increased demands in making deep learning models more interpretable, numerous post-hoc methods have been proposed to probe the workings of these systems. Yet, the quest for face verification systems that inherently provide interpretations still remains largely unexplored. Additionally, most of the existing face recognition models are highly susceptible to adversarial attacks. In this work, we propose a face verification system which addresses the issue of interpretability by employing modular neural networks. In this, representations for each individual facial parts such as nose, mouth, eyes etc. are learned separately. We also show that our method is significantly more resistant to adversarial attacks, thereby addressing another crucial weakness concerning deep learning models.