Abstract

Non-Structural Elements (NSEs) in buildings are expected to remain operational after earthquakes. But, numerical models of Structural Elements (SEs) (buildings) seldom include NSEs, thereby ignoring dynamic interaction between SE-NSEs, resulting in unrealistic earthquake demands. This study aims to quantify these earthquake demands, especially in acceleration-sensitive NSEs, by using Floor Response Spectrum (FRS) approach, and employing Decoupled & Coupled Models during Nonlinear Response History Analysis. Results are examined for effectiveness and accuracy in predicting acceleration demands. The findings emphasize importance of incorporating coupled SE-NSE behavior, for improved seismic design and behavior of buildings and NSEs during earthquake events.

Abstract

The Facility Location Problem (FLP) is a well-studied optimization problem with applications in many real-world scenarios. Past literature has explored the solutions from different perspectives to tackle FLPs. These include investigating FLPs under objective functions such as utilitarian, egalitarian, Nash welfare, etc. Also, there is no treatment for asymmetric welfare functions around the facility. We propose a unified framework, FLIGHT, to accommodate a broad class of welfare notions. The framework undergoes rigorous theoretical analysis, and we prove some structural properties of the solution to FLP. Additionally, we provide approximation bounds, which provide insight into an interesting fact: as the number of agents arbitrarily increases, the choice of welfare notion is irrelevant. Furthermore, the paper also includes results around concentration bounds under certain distributional assumptions over the preferred locations of agents.

Abstract

This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ROC of the classifier. The resultant classifier may be unfair to one of the two protected groups in the dataset. It is desirable that no matter what threshold the practitioner uses, the classifier should be fair to both the protected groups; that is, the ℒₚ norm between FPRs and TPRs of both the protected groups should be at most ε. We call such fairness on ROCs of both the protected attributes εₚ-Equalized ROC. Given a classifier not satisfying ε₁-Equalized ROC, we aim to design a post-processing method to transform the given (potentially unfair) classifier's output (score) to a suitable randomized yet fair classifier. That is, the resultant classifier must satisfy ε₁-Equalized ROC. First, we introduce a threshold query model on the ROC curves for each protected group. The resulting classifier is bound to face a reduction in AUC. With the proposed query model, we provide a rigorous theoretical analysis of the minimal AUC loss to achieve ε₁-Equalized ROC. To achieve this, we design a linear time algorithm, namely FROC, to transform a given classifier's output to a probabilistic classifier that satisfies ε₁-Equalized ROC. We prove that under certain theoretical conditions, FROC achieves the theoretical optimal guarantees. We also study the performance of our FROC on multiple real-world datasets with many trained classifiers.

Abstract

Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant im- plications for tasks such as autonomous exploration, long-term perception, and scene understanding, remains underexplored. In this work, we address this gap by proposing a novel dual-path object-instance re-identification transformer architecture that inte- grates multimodal RGB and depth information. By leveraging depth data, we demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions. Additionally, we develop a ReID-based localization framework that enables accurate camera localization and pose identification across different view- points. We validate our methods using two custom-built RGB-D datasets, as well as multiple sequences from the open-source TUM RGB-D datasets. Our approach demonstrates significant improve- ments in both object instance ReID (mAP of 75.18) and localization accuracy (success rate of 83% on TUM-RGBD), highlighting the essential role of object ReID in advancing robotic perception. Our models, frameworks, and datasets have been made publicly avail- able.

Abstract

Computational Linguistics is an interdisciplinary field of computer science and linguistics that focuses on designing computational models and algorithms for processing, analyzing, and generating human language. Over recent years, this field has made substantial progress. While its primary emphasis tends to center around widely spoken languages, there is equal importance in investigating languages that are not commonly spoken but have contributed immensely to the literature, culture, and philosophy of the society. Thus, this survey article comprehensively delves into the exploration of computational tasks undertaken for Sanskrit, an ancient language of the Indian sub-continent steeped in a wealth of literary heritage. The purpose of this study is to provide an overview of the progress made thus far in the computational analysis of Sanskrit, while also reviewing the current digital infrastructure that supports these efforts. Additionally, our study also identifies potential avenues for future research, serving as a reference for anyone interested in advancing their exploration in this field.

Abstract

Abstract— Transgenic crops have become widespread but have associated health concerns. Testing for transgenic corn is slow and lengthy. In this paper, we used UV-Vis spectroscopy in combination with chemometrics and the PCA-SVM classifier to identify different types of corn samples from each other, including transgenic varieties. The presented methodology and the classifier can differentiate very well between the three different varieties of corn considered in this paper. Although there is a slight improvement in prediction accuracies compared to the state-of-the-art methods, our method is considerably more cost-effective and less time-consuming.

Abstract

Disenchanted Tales from the Old Home in Qissa: A Tale of a Lonely Ghost Abstract: The temporal fixity of the event of Independence/Partition in nationalist historiography has allowed generations of Indians to imagine the Partition and the accompanying genocide as restricted to the year and event of 1947, and yet continue to repeat its violence in an attempt to complete the task of securing religious identities to their assigned national spaces. Just as the political histories of the nations created by the Partition continue to live with the spectres of its violence through repeated wars, so do citizen-subjects spawned by the new nations live in the shadows of old identities. This paper proposes that Singh’s Qissa: A Tale of a Lonely Ghost presents the viewer with the spectre of a partition that never ended. This paper shall explore the function of memory in Anup Singh’s 2013 feature Qissa: A Tale of a Lonely Ghost in producing and de-stabilizing gender, communal and familial identities, and real and imaginary histories. The paper shall look at specific stylistic choices of the film’s form to suggest that this historic cleavage the film populates with an indeterminacy of the corporal/non-corporal body, of the tentative presence of a ghost of days past, and the dead who didn’t stay buried. The site of memory here is inhabited by fluid bodies that lie between genders, in between the past, present and the future, and ultimately in between the living and the dead. Memory here serves as an imaginary history.

Abstract

Graph Neural Network (GNN) research is rapidly advancing due to GNNs’ capacity to learn distributed representations from graph-structured data. However, centralizing large volumes of real-world graph data for GNN training is often impractical due to privacy concerns, regulatory restrictions, and commercial competition. Federated learning (FL), a distributed learning paradigm, offers a solution by preserving data privacy with collaborative model training. Despite progress in training huge vision and language models, federated learning for GNNs remains underexplored. To address this challenge, we present a novel method for federated learning on GNNs based on spectral GNNs equipped with neural ordinary differential equations (ODE) for better information capture, showing promising results across both homophilic and heterophilic graphs. Our approach effectively handles non-Independent and Identically Distributed (non-IID) data, while also achieving performance comparable to existing methods that only operate on IID data. It is designed to be privacy-preserving and bandwidth-optimized, making it suitable for real-world applications such as social network analysis, recommendation systems, and fraud detection, which often involve complex, non-IID, and heterophilic graph structures. Our results in the area of federated learning on non-IID heterophilic graphs demonstrate significant improvements while also achieving better performance on homophilic graphs. This work highlights the potential of federated learning in diverse and challenging graph settings.

Abstract

Mann ki Baat is a series of speeches made by the 14th Prime Minister of India, Narendra Modi addressing the nation. Mann ki Baat holds significant sway over the Indian population as it is broadcast directly by the Prime Minister. As such, it is possible for this stream of speeches to carry hidden political intent or to act as a symbol to show that the government is in touch with the people. For this purpose, the English transcripts of 84 speeches from this series starting from October 2014 to March 2022 were analysed. For an overarching general analysis, Natural Language Processing tools were employed and later on, the speeches were juxtaposed with important events that have occurred in the country to investigate the responses as shown in Mann ki Baat. Moreover, 25 speeches were analysed individually in connection with 3 major events, each spanning a course of multiple months to validate the observations found through the employment of NLP tools and add to them. Observations have shown that Mann ki Baat does indeed carry political intent and works to better the image the people have of the Prime Minister and the ruling government, even at the cost of bending facts at times. However, it has also come to light that Mann ki Baat also works as a highly efficient means of spreading general awareness such as the instances observed during the Covid pandemic. Keywords: Mann ki Baat, NLP, Textual Analysis, Indian Politics

Abstract

The possibility of a quantum system to exhibit properties that are akin to both the classically held notions of being a particle and a wave, is one of the most intriguing aspects of the quantum description of nature. These aspects have been instrumental in understanding paradigmatic natural phenomena as well as to provide non-classical applications. A conceptual foundation for the wave nature of a quantum state has recently been presented, through the notion of quantum coherence. We introduce here a parallel notion for the particle nature of a quantum state of an arbitrary physical system. We provide elements of a resource theory of particleness, and give a quantification of the same. Finally, we provide evidence for a complementarity between the particleness thus introduced, and the coherence of an arbitrary quantum state.