Abstract

In the Structural Health Monitoring (SHM) framework, model updating procedure is an iterative or cyclical process which gives numerical model of structure having modal behaviour close to the dynamic behaviour of real structure. Normally for structures with or without any expansion joints typical model updating procedure is used. But the typical procedure may not be applicable for long uniform buildings having expansion joints since the individual blocks in structure connected with expansion joints tend to vibrate in coherence. This behaviour is confirmed by observing the identicalness of modal frequencies of individual blocks in long uniform buildings. This phenomenon was observed in three selected long uniform buildings. When structures connected with expansion joints vibrate in coherence or when the modal frequency of the connected structures are identical, the numerical models of these structures cannot be updated using typical procedure for the following reasons: (1) Modal parameters from Modal identification of structure doesn’t represent the actual parameters of the individual block since the ambient vibration is not independent and is influenced by interaction between blocks. (2) Modal analysis of analytical model of individual structural blocks gives dynamic parameters which are true to the model. And these two sets of parameters can’t be compared for convergence in model updating procedure. Therefore, to update a numerical model of long uniform buildings with expansion joints having the coherence phenomenon among its individual blocks, a different is needed. Hence, a new approach is proposed where instead of using modal parameters from analytical analysis, the parameters obtained for the structure from modelled floor responses to weak earthquake using Frequency Domain Decomposition (FDD) algorithm are used for checking convergence in model updating procedure. To demonstrate the proposed procedure, two long uniform buildings in which the coherence phenomenon was observed were updated by applying the proposed procedure and results are discussed. Here for the selected buildings, only modal frequencies were used for checking convergence, since mode shapes obtained were not reliable as floor vibration data of intermediate floors were not available.

Abstract

Large-scale deployment of fully autonomous vehicles re- quires a very high degree of robustness to unstructured traf- fic, weather conditions, and should prevent unsafe mispre- dictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, cap- tured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called “Safe mean Intersection over Union (Safe mIoU)” for hierarchical datasets which penalizes danger- ous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The re- sults show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io

Abstract

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs’ cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model’s encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

Abstract

Counter-intuitive to classical notions, quantum conditional entropy can be negative, playing a pivotal role in information-processing tasks. This article delves deeply into quantum channels, em- phasizing negative conditional entropy breaking channels (NCEB) and introducing negative condi- tional entropy annihilating channels (NCEA). We characterize these channels from both topological and information-theoretic perspectives, examining their properties when combined serially and in parallel. Our exploration extends to complimentary channels associated with NCEB, leading to the introduction of information-leaking channels. Utilizing the parameters of the standard depolarizing channel, we provide tangible examples and further characterization. We demonstrate the relation- ship of NCEB and NCEA with newly introduced channels like coherent information breaking (CIB) and mutual information breaking (MIB), along with standard channels like zero capacity channels. Preservation of quantum resources is an integral constituent of quantum information theory. Rec- ognizing this, we lay prescriptions to detect channels that do not break the negativity of conditional entropy, ensuring the conservation of this quantum resource

Abstract

The global impact of coal mining and associated activities on land use/land cover (LULC) changes is significant. This study used Landsat satellite images from 1990 to 2020 to assess LULC changes and their impact on land surface temperature (LST) in four districts of Chhattisgarh state, India. Over three decades, Korba and Raigarh districts saw expansion in coal mines, built-up areas, and water bodies, while forest areas diminished by 711.3 km2 and 212.87 km2, respectively. Koriya district saw coal mine expansion of 5.68 km2 (1990–2010), later declining to 2.85 km2, alongside growth in built-up regions, and forest cover reduction by 251.31 km2 in 2020. Surguja district experienced coal mine and built-up area expansion (1990–2020), with initial forest decline of 160.21 km2 in 2010 followed by recovery in 2020. LST was determined using the Mono-window algorithm. LST increased during winter and summer, with the most significant rise in summer. Vegetation-rich regions had lower LST, while coal mines had the highest temperatures. There was a positive relationship between mining land patch size and patch temperatures. This study underscores the need for vegetation restoration in mining areas, particularly abandoned sites, and sustainable mining practices to mitigate coal mining's warming effects.

Abstract

Generative Large Language Models (LLMs) have achieved remarkable advancements in var- ious NLP tasks. In this work, our aim is to ex- plore the multilingual capabilities of large lan- guage models by using machine translation as a task involving English and 22 Indian languages. We first investigate the translation capabilities of raw large language models, followed by ex- ploring the in-context learning capabilities of the same raw models. We fine-tune these large language models using parameter efficient fine- tuning methods such as LoRA and additionally with full fine-tuning. Through our study, we have identified the best performing large lan- guage model for the translation task involving LLMs, which is based on LLaMA.

Abstract

The task of headline generation within the realm of Natural Language Processing (NLP) holds immense significance, as it strives to dis- till the true essence of textual content into con- cise and attention-grabbing summaries. While noteworthy progress has been made in head- line generation for widely spoken languages like English, there persist numerous challenges when it comes to generating headlines in low- resource languages, such as the rich and diverse Indian languages. A prominent obstacle that specifically hinders headline generation in In- dian languages is the scarcity of high-quality annotated data. To address this crucial gap, we proudly present Mukhyansh, an extensive mul- tilingual dataset, tailored for Indian language headline generation. Comprising an impressive collection of over 3.39 million article-headline pairs, Mukhyansh spans across eight promi- nent Indian languages, namely Telugu, Tamil, Kannada, Malayalam, Hindi, Bengali, Marathi, and Gujarati. We present a comprehensive evaluation of several state-of-the-art baseline models. Additionally, through an empirical analysis of existing works, we demonstrate that Mukhyansh outperforms all other mod- els, achieving an impressive average ROUGE-L score of 31.43 across all 8 languages.

Abstract

Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS modelling for the Indian context. Prior works characterised IE variabilities by reporting qualitative phonetic rules relative to Received Pronunciation (RP). However, such characterisations lack quantitative descriptors and data-driven analysis of diverse IE pronunciations, which could be due to the scarcity of phonetically labelled data. Furthermore, the versatility of IE stems from the influence of a large diversity of the speakers’ mother tongues and demographic region differences. To address these issues, we consider the corpus Indic TIMIT and manually obtain 13, 632 phonetic transcriptions in addition to those parts of the corpus. By performing a data-driven analysis on 15, 974 phonetic transcriptions of 80 speakers from diverse regions of India, we present a new set of phonetic rules and validate them against the existing phonetic rules to identify their relevance. Finally, we test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.

Abstract

Automatic syllable stress detection is helpful in assessing L2 learners’ pronunciation. In this work, for stress detection, we propose a representation learning framework by jointly optimizing VAE and DNN. The obtained representations from the proposed VAE plus DNN framework are compared with the implicit representations learned from DNN-based stress detection. Further, we compare the obtained representations from VAE plus DNN with those obtained from autoencoder (AE) plus DNN, and sparse-autoencoder (SAE) plus DNN considering with/without implicit representations from DNN. We perform the experiments on the ISLE corpus consisting of English utterances from German and Italian native speakers. We observe that the detection performance with the learned representations from VAE plus DNN is significantly better than that with the state-of-the-art method without any representation learning with the highest improvement of 2.2%, 5.1%, and 1.4% under matched, combined, and cross scenarios, respectively.

Abstract

This work proposes the utilization of a self-supervised pre-trained network for developing a Language Identification (LID) system catering to low-resource Indian languages. The framework employed is Wav2vec2.0-XLSR-53, pre-trained on 53k hours of unlabeled speech data. The unsupervised training of the model enables it to learn the acoustic patterns specific to a language. Given that languages share phonetic space, multi-lingual pre-training is instrumental in learning cross-lingual information and building systems that cater to low-resource languages. Further fine-tuning with a limited amount of labeled data significantly boosts the model’s accuracy. The results showcase a relative improvement of 33.2% over the DNN-A (DNN with attention) model and 19.04% over Dense Resnets for the Language Identification task on the IIITH-ILSC database using the proposed features (Shivang Gupta and Kowshik Siva Sai Motepalli share first authorship).