Abstract

India is a well-known exception to Duverger’s Law which states that single member plurality electoral systems typically lead to a two-party dominance, due to strong regional parties. We hypothesise that this India level phenomenon is overly dominated by electoral data from densely populated regions like the Indo-Gangetic plains. Drawing on anthropological and historical scholarship, we postulate that mountain societies behave differently. To test our hypothesis we perform quantitative analysis on electoral data comparing India’s Himalayan states to those in the Gangetic and Brahmaputra plains. The results indicate that the plains are diverging while the mountain states are converging towards Duverger’s two-party equilibrium. This suggests that India’s mountain and plains states have a structural dissimilarity in their political culture, which supports the anthropological and historical literature from which we started. We hope to add to both the literature on electoral studies as well as to mountain societies and regional studies.

Abstract

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions typically ignore the surrounding 3D scene. This gap, generating full-body grasping motions that are physically plausible within a 3D scene, remains a significant challenge. To address this, we introduce textbf{MOGRAS} (Human textbf{MO}tion with textbf{GRA}sping in 3D textbf{S}cenes), a large-scale dataset that bridges this gap. MOGRAS provides pre-grasping full-body walking motions and final grasping poses within richly annotated 3D indoor scenes. We leverage MOGRAS to benchmark existing full-body grasping methods and demonstrate their limitations in scene-aware generation. Furthermore, we propose a simple yet effective method to adapt existing approaches to work seamlessly within 3D scenes. Through extensive quantitative and qualitative experiments, we validate the effectiveness of our dataset and highlight the significant improvements our proposed method achieves, paving the way for more realistic human-scene interactions.

Abstract

The land use, land-use changes, and forestry (LULUCF) sector plays a significant role in increasing atmospheric carbon dioxide (CO2) in the current climate change scenario. The present study assessed the longterm CO2 emissions within the LULUCF sectors across India’s climatic zones as described by Köppen. The continuous combustion of fossil fuels has led to a rise in CO2 levels by 53 parts per million (ppm), increasing from 372 to 425 ppm in the Indian region between 2002 to 2022. The investigation reveals a consistent upward trend in CO2 levels across Köppen climatic zones. The semi-arid (steppe) climate zone shows an increasing trend of 2.18 ppm year−1, whereas Tundra exhibits a trend of 2.32 ppm year−1. The rate of change of atmospheric CO2 for India is 2.20 ppm year−1. A regional correlation exists between CO2 concentration and elevated anthropogenic emissions activities in the Indo-Gangetic Plains (IGP). Settlements growth has occurred across various climatic zones, with the Subtropical Highland zone showing the most significant rise at 54.84%. The findings underscore the urgent requirement for sustainable land use practices. This study utilized multivariate linear regression analysis to investigate different CO2 sources across LULUCF classes, using these as independent variables within Köppen climatic zones. The evaluation focused on identifying the classes with the highest and lowest contributions to CO2 emissions throughout the study period. Further training was carried out on different models for India as a whole and by climatic zone, revealing that the settlement class is a significant source of CO2 emission. In contrast, the forest class acts as a substantial sink for CO2.

Abstract

We show that the average of the maximum teleportation fidelities between all pairs of nodes in a large quantum repeater network is a measure of the resourcefulness of the network as a whole. It can be used to rank networks of arbitrary topologies, with and without loops. For demonstration purposes, we use simple Werner state-based models to characterize some fundamental topologies (the star, the chain, some trees, and also the ring and the complete graph) with this measure in three (semi)realistic scenarios. Most of our results are analytic and apply to arbitrary network sizes. We identify the parameter ranges where these networks can achieve quantum advantages and show the large-N behaviors.

Abstract

With the growing adoption of machine learning models in critical domains, techniques for explaining differences between models have become essential for trust, debugging, and informed deployment. Previous approaches address this by identifying input transformations that cause divergent predictions or by learning joint surrogate models to align and contrast behaviors. These methods often require access to training data and do not produce natural language explanations. In this paper, we introduce SLED, a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions. SLED first uses gradient-based optimization to synthesize input samples that highlight divergence and convergence patterns, and then leverages a large language model (LLM) to generate explanations grounded in these synthetic samples. Across both text-based (3 tasks, 7 models) and structured (10 tasks, 4 models) classification tasks, we show that SLED explanations are 18--24% more faithful than the strongest baselines. User studies also indicate that SLED explanations achieve a real-world simulatability of 63.5%. Importantly, SLED requires minimal access to training data and generalizes well to real-world samples, enabling transparent and data-efficient model comparison.

Abstract

Flexible capacitive pressure sensors have simple sensing structure, zero-temperature drift, linear response, and high sensitivity, leading to applications in human healthcare including soft and surgical robotics, human machine interaction (HMI) and wearable devices. In this paper, we present a highly compressible multilayer flexible capacitive pressure sensor, based on air/vapour bubble-induced PDMS foam thin-film as dielectric and polypyrrole coated cotton textile as electrodes. An in-situ chemical oxidative polymerisation method was used to synthesize a conducting, large area polypyrrole coated cotton textile. We used this as parallel electrodes on the top and bottom of the bubble-induced PDMS foam. The bubble-induced PDMS film was made by casting of PDMS, mix with specific amounts of ethanol. The top and bottom textile electrodes were then attached to it with the help of silicone adhesive. We observed low hysteresis, high repeatability, and good step response in our sensor characterisation. Bubble characterisation was also performed including surface profilometer and scanning electron microscopy (SEM). We showcase two applications of our sensor including mouse click and grasping of a cup. The sensor can be fabricated in different shapes and sizes for various applications of free-form electronics such as soft and surgical robotics, wearable, biomedical, human-machine interaction, and so on.

Abstract

Audio descriptions (ADs) narrate important visual details in movies, enabling Blind and Low Vision (BLV) users to understand narratives and appreciate visual details. Existing works in automatic AD generation mostly focus on few-second trimmed clips, and evaluate them by comparing against a single ground-truth reference AD. However, writing ADs is inherently subjective. Through alignment and analysis of two independent AD tracks for the same movies, we quantify the subjectivity in when and whether to describe, and what and how to highlight. Thus, we show that working with trimmed clips is inadequate. We propose ADQA, a QA benchmark that evaluates ADs at the level of few-minute long, coherent video segments, testing whether they would help BLV users understand the story and appreciate visual details. ADQA features visual appreciation (VA) questions about visual facts and narrative understanding (NU) questions based on the plot. Through ADQA, we show that current AD generation methods lag far behind human-authored ADs. We conclude with several recommendations for future work and introduce a public leaderboard for benchmarking.

Abstract

This article seeks to understand how experiences of coal extraction and use shape local perspectives on energy transitions. It does this by exploring community struggles over land and labour in India's largest coalfield Korba in Chhattisgarh state. While the Indian government has announced that the country will have net zero emissions by 2070, continuing coal mine expansions built on the dispossession of rural poor and indigenous groups dramatically shape lives, economies and aspirations, and with them expectations around a potential transition away from coal. At the moment coal provides stability and continuity in the context of a depressed agricultural sector and limited non-farm employment opportunities. The coal sector is in this manner a source of hope and aspirations for many, while simultaneously creating enormous social and ecological disruptions. In the article we place specific focus on the interlinked roles of land and labour in the production of fossil-free futures situated within agrarian relations. Long-term resistance to land acquisition for coal mining is in recent years accompanied by the emergence of new relationships that coal communities are forging around land as a transactional asset, to be bartered for mining company jobs, or simply used as a speculative asset which may yield future pay-off as mining continues to expand. Based on a close reading of everyday micro-level negotiations, this paper argues that the possibilities for justice in a post-coal future is rendered complicated by existing coal economy dependencies and narrow conceptions of compensation.

Abstract

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications.

Abstract

Multi-signature protocols allow a group of signers to collectively generate a single signature for a shared message. In the context of a decentralized blockchain, multi-signature schemes play a pivotal role in reducing the signature size. Recently, several multi-signature methods have emerged in the literature, some operating in discrete-log settings and others in lattice settings. However, many of the existing lattice-based multi-signature schemes incur high computation costs and online round complexity. Traditional public key-based multi-signature schemes are susceptible to quantum threats, and they are computationally intensive as well. A lattice-based multi-signature can provide robust security, which often falls short in terms of efficiency when it comes to round complexity. In this article, we aim to introduce a single-round lattice-based multi-signature scheme specifically designed for decentralized public blockchains. What sets the proposed scheme apart is its ability to function without the need for trapdoor commitments or sample pre-images, which are common features in existing lattice-based signature methods. Furthermore, we explore some potential applications in a generic Internet of Things (IoT) environment and their integration of the proposed scheme with the blockchain technology. The security of the proposed scheme is based on lattice-hard problems, like Ring-SIS (Shortest Integer Solution) and Ring-LWE (Learning with Errors).