Abstract

The simulation of appropriate ground motion is the main objective in earthquake-resistant designing to mitigate the impact of landslides and structural damages. The seismic hazard studies serve as the basis for quantifying the hazard associated within the particular area over a given time period. In this study, the detailed site specific hazard analysis of Tindharia site as well as Darjeeling Sikkim Himalaya (DSH) region has been carried out to assure the safety of human and infrastructure against seismic risk. The study area is mountainous region on north western side of India, falls in seismic zone IV and V, and is one of the most seismically active regions with vulnerable earthquakes along with fatal landslides. The future large earthquakes are likely to occur in the region due to constant movement of Indian plate towards Eurasian plate.Therefore, the heightened seismic risk increases the demand for seismic studies near the Himalayan belt. Thus, the assessment of associated seismic hazard in the study area has been carried out by deterministic and probabilistic seismic hazard approaches. From the results, the seismo-tectonic map of a 300km radial distance of the study area is developed. The deterministic hazard distribution in terms of peak ground acceleration (PGA) and the PGA and peak spectral acceleration (PSA) hazard maps for 2% and 10% probability of exceedance in 50 years at different time periods have been generated. The updated hazard maps developed in this study play pivotal roles in mitigation measures with better pre-disaster prevention by earthquake resistant design and in preparedness and post disaster rescue in the study area

Abstract

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to frst perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. The depth of the transformed quantum circuits is minimized by utilizing the Monte Carlo tree search (MCTS) to perform qubit routing by making it both construct each action and search over the space of all actions. It is aided in performing these tasks by a Graph neural network that evaluates the value function and action probabilities for each state. Along with this, we propose a new method of adding mutex-lock like variables in our state representation which helps factor in the parallelization of the scheduled operations, thereby pruning the depth of the output circuit. Overall, our procedure (referred to as QRoute) performs qubit routing in a hardware agnostic manner, and it outperforms other available qubit routing implementations on various circuit benchmarks.

Abstract

Recent advancements in deep learning have enabled 3D human body reconstruction from a monocular image, which has broad applications in multiple domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People in loose clothing), a novel end-to-end trainable network that accurately recovers the 3D geometry and appearance of humans in loose clothing from a monocular image. SHARP uses a sparse and efficient fusion strategy to combine parametric body prior with a non-parametric 2D representation of clothed humans. The parametric body prior enforces geometrical consistency on the body shape and pose, while the non-parametric representation models loose clothing and handles self-occlusions as well. We also leverage the sparseness of the non-parametric representation for faster training of our network while using losses on 2D maps. Another key contribution is 3DHumans, our new life-like dataset of 3D human body scans with rich geometrical and textural details. We evaluate SHARP on 3DHumans and other publicly available datasets, and show superior qualitative and quantitative performance than existing state-of-the-art methods.

Abstract

The present study compared various empirical and data-driven algorithms to predict Actual Evapotranspiration (AET) using various hydro climatic variables. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India, and Waipara (New Zealand) was estimated using different empirical methods-based PET using Budyko and Turc models. Modelled PET from five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor, Random Forest, and Support Vector Regression were trained to predict AET using meteorological variables. The results show simple empirical-based AET models, Budyko and Turc, can estimate AET very well. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models based on PET have been noted as the most robust models for estimating AET with minimal climate data. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed, and precipitation for estimating AET for semi-arid climatology.

Abstract

The task of multilingual news article similarity en- tails determining the degree of similarity of a given pair of news articles in a language-agnostic setting. This task aims to determine the extent to which the articles deal with the entities and events in ques- tion without much consideration of the subjective aspects of the discourse. Considering the supe- rior representations being given by these models as validated on other tasks in NLP across an array of high and low-resource languages and this task not having any restricted set of languages to focus on, we adopted using the encoder representations from these models as our choice throughout our experiments. For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture. In experimentation, we investigated on several fronts including features passed to the encoder model, data augmentation and ensembling among our major experiments. We found data aug- mentation to be the most effective working strategy among our experiments.

Abstract

This paper provides a comparison of different deep learning methods for identifying misogy- nous memes for SemEval-2022 Task 5: Multi- media Automatic Misogyny Identification. In this task, we experiment with architectures in the identification of misogynous content in memes by making use of text and image-based information. The different deep learning meth- ods compared in this paper are: (i) unimodal image or text models (ii) fusion of unimodal models (iii) multimodal transformers models and (iv) transformers further pretrained on a multimodal task. From our experiments, we found pretrained multimodal transformer ar- chitectures to strongly outperform the models involving fusion of representation from both the modalities.

Abstract

Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong. With not much prior work being done toward the generation of legal contracts, this problem was proposed as a first step toward the bigger problem of contract generation. As an open-ended text generation prob-lem, the distinguishing characteristics of this problem lie in the nature of legal lan-guage as a sublanguage and the considerable similarity of textual content within the clauses of a specific type. This similarity aspect in legal clauses drives us to investi-gate the importance of similar contracts’ representation for recommending clauses. In our work, we experiment with generating clauses for 15 commonly occurring clause types in contracts expanding upon the previous work on this problem and analyzing clause recommendations in varying settings using information derived from similar contracts.

Abstract

Code-mixing (CM) is a frequently observed phenomenon on social media platforms in mul- tilingual societies such as India. While the increase in code-mixed content on these plat- forms provides good amount of data for study- ing various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natu- ral Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete infor- mation. In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named en- tity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features.

Abstract

We present a method to control the emotional prosody of Text to Speech (TTS) systems by us- ing phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to dis- entangle features relating to affective prosody from those arising due to acoustics conditions and speaker identity. With thorough exper- imental studies, we show that the proposed method improves over the prior art in accu- rately emulating the desired emotions while retaining the naturalness of speech. We ex- tend the traditional evaluation of using indi- vidual sentences for a more complete evalua- tion of HCI systems. We present a novel ex- perimental setup by replacing an actor with a TTS system in offline and live conversa- tions. The emotion to be rendered is either predicted or manually assigned. The results show that the proposed method is strongly pre- ferred over the state-of-the-art TTS system and adds the much-coveted “human touch” in ma- chine dialogue. Audio samples for our exper- iments and the code are available at: https: //emtts.github.io/tts-demo/

Abstract

A tool Smart Glazing Simulator (SGLSim), has been developed to perform parametric simulation analysis of different window systems with several window-to-wall ratios and orientations to compute and compare the annual energy performance. The net annual energy performance of the building is based on the electricity consumption in heating, cooling, interior lighting, and appliances, along with the electricity generation by the photovoltaic (PV) glazing, which is used to evaluate the energy performance of smart glazing. Performing parametric energy simulations and calculating the net annual electricity consumption of different combinations requires building modeling and energy simulation expertise. A web-based parametric tool can assist the user in carrying out the desired studies without requiring extensive technical knowledge. A case study is prepared for India’s warm and humid climatic zone. This study examines the benefits of double pane semi-transparent photovoltaics (STPV) glazing, STPV glaz-ing with dynamic internal blind, and electrochromic (EC) glazing over other traditional glazing systems. The study shows that the optimal net annual electricity consumption in the case of STPV windows is 10–12% less than the optimal value obtained in a sim-ple glazing case. Additionally, the result suggested that glare-controlled interior blinds in the STPV window further reduce the net annual electricity consumption by up to 15% compared to conventional glazing. Similarly, installing the EC glazing reduces the yearly electricity consumption by up to 5% compared to standard glazing.