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 fully entangled fraction (FEF) measures the proximity of a quantum state to maximally entangled states. FEF , in systems, is a significant benchmark for various quantum information processing protocols including teleportation. Quantum conditional entropy (QCE) on the other hand is a measure of correlation in quantum systems. Conditional entropies for quantum systems can be negative, marking a departure from conventional classical systems. The negativity of quantum conditional entropies plays a decisive role in tasks like state merging and dense coding. In the present work, we investigate the relation of these two important yardsticks. Our probe is mainly done in the ambit of states with maximally mixed marginals, with a few illustrations from other classes of quantum states. We start our study in two-qubit systems, where for the Werner states, we obtain lower bounds to its FEF when the conditional Rényi entropy is negative. We then obtain relations between FEF and QCE for two-qubit Weyl states. Moving on to two qudit states, we find a necessary and sufficient condition based on FEF, for the isotropic state to have negative conditional entropy. In two qudit systems, the relation between FEF and QCE is probed for the rank-deficient and generalized Bell diagonal states. FEF is intricately linked with k-copy nonlocality and k- copy steerability. The relations between FEF and QCE facilitates to find conditions for k- copy nonlocality and k- copy steerability based on QCE. We obtain such conditions for certain classes of states in two qubits and two qudits. Applications of the relations obtained are provided in the context of work extraction, faithful entanglement and entropic uncertainty relations.

Abstract

Linearly Transformed Spherical Distributions (LTSDs), a superset of the commonly known Linearly Transformed Cosines (LTCs) for analytic area light rendering, applied in the context of analytic area light rendering with participating media.

Abstract

Effective decision-making in complex environments with multi-discrete action spaces poses significant challenges for agent architectures, particularly in image-based settings. While Decision Transformers have shown promise in various domains, their performance often suffers in environments where agents must handle multi-discrete actions. Existing enhancements to Decision Transformer architectures have yet to address this critical issue, limiting their ability to support agents in learning robust policies in these environments. To address this gap, we propose Multi-State Action Tokenisation (M-SAT), a novel approach designed to improve agent decision-making by tokenising actions at the individual action level and incorporating auxiliary state information. This disentanglement of actions improves both the performance of agents and the interpretability of individual actions within attention layers, fostering better visibility into agent decision processes. Importantly, M-SAT facilitates the development of more interpretable and transparent agents capable of making complex decisions in dynamic environments involving multi-discrete action spaces. We evaluate M-SAT on the challenging ViZDoom environments, focusing on scenarios with multi-discrete action spaces and image-based observations, such as Deadly Corridor, My Way Home and Death Match. Our approach demonstrates superior performance compared to baseline Decision Transformers, with no additional data or significant computational overheads. Furthermore, we observe that M-SAT does not require positional encoding to achieve high performance, with its removal occasionally leading to further improvements. These findings suggest that M-SAT enables more efficient and interpretable agent-based decision-making in multi-discrete action spaces.

Abstract

Time-series data from sensor networks often has missing entries due to sensor failures, poor communication, scheduled downtime, etc. Missing data imputation improves perfor- mance of downstream tasks such as forecasting and anomaly detection. Therefore, highly accurate imputation methods are desirable. Multi-variate time series imputation methods using interpolation [1–3], or matrix factor- ization [4, 5] have limited accuracy. Auto-regressive methods like ARIMA [6] and RNNs like GRIN [7] are subject to error propagation and are slow. More recently, attention [8] has been successfully used to design effective networks for various time series tasks – imputation (SPIN, SPIN-H and Transformer) [9], forecasting [10, 11] and anomaly detection [12]. However, as evident from Figure 2, these state-of-the-art (SOTA) networks are effective only on certain datasets. It motivates a more general meta-network which can effectively aggregate these experts using a gating network. We provide the scope of improvement when these frozen experts are selected optimally using an oracle. The possible reduction in error can be as high as 28 − 40 percent by using existing imputation networks.