Abstract

No Results Found

Abstract

No Results Found

Abstract

This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homoge- neous traffic. Our dataset offers a critical lens into the unique visual attention patterns and corresponding driving decision- making of Indian two-wheeler drivers. The analysis demon- strates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning for this cost-effective mode of transpor

Abstract

Computational History, Trade in Mountain Societies, NLP – NER, Relation Extraction, Application of LLMs.

Abstract

How can we effectively estimate the quality of translated texts in the medical field, where back-translation is usually available and/or recommended for sensitive documents. This paper proposes a novel metric, GATE 1 , for translation quality estimation task, leveraging the Resource Description Framework (RDF) to encode both semantic and syntactical information of the original and back-translated sentences into RDF graphs. The distance between these graphs is measured to get the semantic similarity score to assess the quality of the translation. Unlike traditional metrics like BLEU and METEOR, our approach is reference-less, capturing both semantic and syntactical information for a comprehensive assessment of translation quality. Our results correlate better with human judgment, giving a better Pearson correlation (0.357) as compared to BLEU (0.200), thereby showing ~70% improvement over BLEU. Our research shows that, in the field of translation evaluation, existing resources like back-translation and RDF could be useful.

Abstract

Manuguru, located in Telangana, India, is a significant coal mining hub in the Godavari Valley Coalfield, with over a century of mining history. The region, experiencing rapid industrialization and urbanization, undergoes comprehensive landscape changes, impacting natural ecosystems, agricultural lands, and water resources beyond the coal-bearing areas. This study presents an analysis of the dynamic land use and land cover (LULC) patterns in Manuguru, utilizing multi-spectral/multi-temporal Landsat satellite imagery from 1987 to 2020. Focused on understanding the impact of mining activities on the region's landscape, advanced band indices, namely, Normalized Difference Vegetation Index( NDVI) and Normalized Difference Water Index(NDWI) are employed alongside visual interpretation techniques helps validate these indices by visually inspecting the actual features on the ground.. The interpretation of Landsat images helped delineate 8 land cover categories: agricultural land, barren land, forest, mining sites, built-up areas, rivers, stagnant water bodies, and dried water bodies. In addition to traditional analysis methods, the study incorporates rate of change calculations and transition matrices to quantify and understand the temporal dynamics of LULC composition. Comparative analysis reveals significant shifts in LULC composition over the study period where the expansion of coal mining activities emerges as a dominant driver of change. This resulted in the loss of ecologically rich forests and the proliferation of overburden hills. Despite the prevalent industrial activities, there is a visible dedication to environmental stewardship by reclaiming overburden dumps through reforestation efforts of open scrub areas. The transition of agricultural lands to built-up areas signifies rapid urban expansion, driven by burgeoning population and economic activities. Similarly, the transformation of water bodies into agricultural lands underscores the human demand for arable land, often at the expense of natural ecosystems. The combined effect of transitions within the land use classes can be attributed to the increase of extractive industries and urbanization.

Abstract

The histone methyltransferase ASH1L, first discovered for its role in transcription, has been shown to accelerate the removal of ultraviolet (UV) light-induced cyclobutane pyrimidine dimers (CPDs) by nucleotide excision repair. Previous reports demonstrated that CPD excision is most efficient at transcriptional regulatory elements, including enhancers, relative to other genomic sites. Therefore, we analyzed DNA damage maps in ASH1L-proficient and ASH1L-deficient cells to understand how ASH1L controls enhancer stability. This comparison showed that ASH1L protects enhancer sequences against the induction of CPDs besides stimulating repair activity. ASH1L reduces CPD formation at C–containing but not at TT dinucleotides, and no protection occurs against pyrimidine-(6,4)-pyrimidone photoproducts or cisplatin crosslinks. The diminished CPD induction extends to gene promoters but excludes retrotransposons. This guardian role against CPDs in regulatory elements is associated with the presence of H3K4me3 and H3K27ac histone marks, which are known to interact with the PHD and BRD motifs of ASH1L, respectively. Molecular dynamics simulations identified a DNA-binding AT hook of ASH1L that alters the distance and dihedral angle between neighboring C nucleotides to disfavor dimerization. The loss of this protection results in a higher frequency of C–>T transitions at enhancers of skin cancers carrying ASH1L mutations compared to ASH1L-intact counterparts.

Abstract

The dense and unstructured text in historical manuscripts presents significant challenges for precise line segmentation due to large diversity in sizes, scripts and appearances of the documents. Existing approaches tackle this complexity either by performing dataset-specific processing or training per-dataset models. This strategy hampers maintainability and scalability as newer manuscript collections get digitized and annotated. In this paper, we propose LineTR, a novel two-stage line segmentation approach which can process a diverse variety of challenging handwritten documents in a unified, dataset-agnostic manner. LineTR's first stage processes context-adaptive image patches. It consists of a novel DETR-style network which generates parametric representations of text strike-through lines (scribbles) and a novel hybrid CNN-transformer network which generates a text energy map. A dataset-agnostic and robust post-processing procedure is applied on first-stage outputs to obtain document-level scribbles. In the second stage, these scribbles and the text energy map are used within a seam generation framework to obtain highly precise polygons enclosing the manuscript text lines. We also introduce three new diverse text line segmentation datasets comprising challenging Indic and South-East Asian manuscripts. Through experiments, ablations and evaluations, we show that LineTR generates significantly superior line segmentations - all with a single model. Our results also highlight the effectiveness of our unified model for good quality zero-shot inference on the newly introduced datasets.

Abstract

Lupus Nephritis classification has historically relied on labor-intensive and meticulous glomerular-level labeling of renal structures in whole slide images (WSIs). However, this approach presents a formidable challenge due to its tedious and resource-intensive nature, limiting its scalability and practicality in clinical settings. In response to this challenge, our work introduces a novel methodology that utilizes only slide-level labels, eliminating the need for granular glomerular-level labeling. A comprehensive multi-stained lupus nephritis digital histopathology WSI dataset was created from the Indian population, which is the largest of its kind. LupusNet, a deep learning MIL-based model, was developed to classify LN subtypes. The results underscore its effectiveness, achieving an AUC score of 91.0%, an F1 score of 77.3%, and an accuracy of 81.1% on our dataset in distinguishing membranous and diffused classes of LN

Abstract

Manuguru, a historic coal mining hub in India's Godavari Valley Coalfield, embodies the intricate connections between resource extraction and its wide-ranging impacts. For over a century, mining has carved into the land, leaving a legacy beyond mere geographic alterations. It has reshaped ecosystems, reshuffled communities, and fueled socio-economic changes, often along lines of caste and ethnicity. This study delves into this transformative process through a geospatial analysis of land use and land cover (LULC) changes from 1987-2020, utilizing multi-spectral/multi-temporal Landsat satellite imagery. With a focus on mining's impact, advanced band spatial indices which are coupled with visual interpretation, unveil eight distinct land cover categories: agricultural land, barren land, forest, mining sites, built-up areas, rivers, stagnant water bodies, and dried water bodies, which provide a quantitative understanding of temporal dynamics. Beyond the geographical, field work delves into the human story. We explore the socio-economic realities of Manuguru, examining how mining has intensified existing divisions and fostered caste-based tensions. We further investigate how public policies intended for development might have inadvertently contributed to spatial segregation. By integrating geospatial insights with social realities, this study underscores the complex interplay between extractive industries, environmental concerns, and the social fabric. Understanding these dynamics is crucial for informed decision-making toward a sustainable future for Manuguru and similar resource-rich landscapes, one that balances progress with social equity and environmental well-being.