Abstract

This article asks what it is like for the rural poor to live with coal over time as mines expand and agriculture and forest-dependent ways of living inevitably become more restricted. Research on the expansion of open pit coal mining in India shows a widespread inability to appropriately compensate the rural poor for lost land and access to common property resources. Yet it is simultaneously clear that the growth of coal extraction over time ensures increased community dependence on mainly informal, coal-based livelihoods. What then happens over the long-term for people who live in and around the coalfields? Drawing on evidence from 2011 to 2022, this article explores longer term changes for mine-affected people next to major coal mines in Telangana state, India. Specifically, it examines a) the acquisition of forest and agricultural lands for the expanding mines, b) the operating mines and their environmental implications, but also improved job opportunities, and c) mine closure as an opportunity to rehabilitate the landscape and return the land for alternative community uses post mining. The results add to the understanding of the long-term changes that large-scale mining brings to rural communities, and the challenges to a just transition once coal mining inevitably comes to an end.

Abstract

Editorial note

Abstract

We re-examined whether different time scales such as week, day of week, and hour of day are independently used during memory retrieval as has been previously argued (i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity. We also used pointwise mutual information to directly calculate the degree of dependency between time scales in a formal way. Participants were provided with a smartphone and were asked to wear it around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (i.e., week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life.

Abstract

The role of completely positive–indivisibility (CP-indivisibility) and incompatibility as valuable resources for various information-theoretic tasks is widely acknowledged. This study delves into the intricate relationship between CP-divisibility and channel compatibility. Our investigation focuses on the behavior of incompatibility robustness of quantum channels for a pair of generic dynamical maps. We show that the incompatibility robustness of channels is monotonically nonincreasing for a pair of generic CP-divisible dynamical maps. Further, our explicit study of the behavior of incompatibility robustness with time for some specific dynamical maps reveals nonmonotonic behavior in the CP-indivisible regime. Additionally, we propose a measure of CP-indivisibility based on the incompatibility robustness of quantum channels. Our investigation provides valuable insights into the nature of quantum dynamical maps and their relevance in information-theoretic applications.

Abstract

Advancements in Deep Reinforcement Learning (DRL) have shown significant promise for the development of self directed Unmanned Aerial Vehicles (UAVs). Our research focuses on examining the impact of time-varying noise such as that present in Inertial Measurement Unit (IMU) sensors on the efficacy of DRL-based waypoint navigation and obstacle avoidance in UAVs. Sensor noise affects localization and obstacle detection, and is assumed to follow a Gaussian probability distribution with an unknown non-zero time varying mean and variance. In this study, we consider environment with static obstacles where noise exhibits a time-varying bias, a characteristic commonly associated with IMU sensors. We evaluate the effectiveness of a DRL agent, trained using the Proximal Policy Optimization (PPO) technique, in the presence of such noise.

Abstract

The complex, glinty appearance of detailed normal-mapped surfaces at different scales requires expensive per-pixel Normal Distribution Function computations. Moreover, large light sources further compound this integration and increase the noise in the Monte Carlo renderer. Specialized rendering techniques that explicitly express the underlying normal distribution have been developed to improve performance for glinty surfaces controlled by a fixed material roughness. We present a new method that supports spatially varying roughness based on a neural histogram that computes per-pixel NDFs with arbitrary positions and sizes. Our representation is both memory and compute efficient. Additionally, we fully integrate direct illumination for all light directions in constant time. Our approach decouples roughness and normal distribution, allowing the live editing of the spatially varying roughness of complex normal-mapped objects. We demonstrate that our approach improves on previous work by achieving smaller footprints while offering GPU-friendly computation and compact representation.

Abstract

In this paper, we propose a novel two-step technique for text classification using autoregressive Language Models (LM). In the first step, a set of perplexity and log-likelihood based numeric features are elicited from an LM for a text instance to be classified. Then, in the second step, a classifier based on these features is trained to predict the final label. The classifier used is usually a simple machine learning classifier like Support Vector Machine (SVM) or Logistic Regression (LR) and it is trained using a small set of training examples. We believe, our technique presents a whole new way of exploiting the available training instances, in addition to the existing ways like fine-tuning LMs or in-context learning. Our approach stands out by eliminating the need for parameter updates in LMs, as required in fine-tuning, and does not impose limitations on the number of training examples faced while building prompts for in-context learning. We evaluate our technique across 5 different datasets and compare with multiple competent baselines.

Abstract

To employ Pre-trained Language Models (PLMs) as knowledge containers in niche domains it is important to gauge the knowledge of these PLMs about facts in these domains. It is also an important pre-requisite to know how much enrichment effort is required to make them better. As part of this work, we aim to gauge and enrich small PLMs for knowledge of world geography. Firstly, we develop a moderately sized dataset of masked sentences covering 24 different fact types about world geography to estimate knowledge of PLMs on these facts. We hypothesize that for this niche domain, smaller PLMs may not be well equipped. Secondly, we enrich PLMs with this knowledge through fine-tuning and check if the knowledge in the dataset is infused sufficiently. We further hypothesize that linguistic variability in the manual templates used to embed the knowledge in masked sentences does not affect the knowledge infusion. Finally, we demonstrate the application of PLMs to tourism blog search and Wikidata KB augmentation. In both applications, we aim at showing the effectiveness of using PLMs to achieve competitive performance.

Abstract

The intricate link between brain textbf{Functional Connectivity (FC)} and textbf{Structural Connectivity (SC)} is explored through models performing diffusion on SC to derive FC, using varied methodologies from single to multiple textbf{Graph Diffusion Kernels}. However, existing studies have not correlated diffusion scales with specific brain Regions of Interest (RoIs), limiting the applicability of graph diffusion. We propose a novel approach using textbf{Graph Diffusion Wavelets} to learn the appropriate diffusion scale for each RoI to accurately estimate the SC-FC mapping. Using the open Human Connectome Project (HCP) dataset, we achieve an average textbf{Pearson's correlation} value of 0.833, surpassing the state-of-the-art methods for prediction of FC. It is important to note that the proposed architecture is entirely linear, computationally efficient, and notably demonstrates the textbf{power-law} distribution of diffusion scales. Our results show that the bilateral frontal pole, by virtue of it having large diffusion scale, forms a large community structure. The finding is in line with the current literature on the role of the frontal pole in resting-state networks. Overall, the results underscore the potential of graph diffusion wavelet framework for understanding how the brain structure leads to functional connectivity.

Abstract

Given the challenge of fragmentary or scattered archives, how can we construct a historical and socio-cultural profile of urban micro-minorities that are acutely affected by the “global flows” (Arjun Appadurai 1990) of people, commodities, and capital? Secunderabad, once a town in princely Hyderabad state in colonial South Asia, was shaped critically by the arrival of migrant communities from different parts of the subcontinent. One such group was that of educated, professional, Tamil-speaking migrants from Madras Presidency. Testament to this migration today are multiple localities with a dominance of Tamil-speaking people, cultural events, and related institutions and associations. Using this community as a case study, this paper demonstrates that the biggest challenge in studying the history of a micro-minority is their marginal presence in archives. We find that the keepers of such history are individuals and communities. Hence, we examine private archives, oral histories, and community records in conjunction and contestation with literary texts and official sources, such as census data, civil lists, and gazettes. This paper offers an account of how we addressed methodological challenges by building community networks, and adopting and adapting a mixed-methods, interdisciplinary approach to creatively trace migration, settlement, linguistic, and socio-cultural patterns. Our findings suggest that such approaches have positive implications for city and urban studies and research methods in this area.