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

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

Traditionally, dysarthric speech intelligibility assessment systems have focused on speech as the primary input, utilizing methods such as extraction of relevant speech features, classification models, alignment of Automatic Speech Recognition (ASR) outputs, and comparisons between speech representations of dysarthric and healthy speakers. However, to achieve an automated intelligibility assessment that closely mirrors the auditory-perceptual evaluations conducted by clinicians, a model that captures both the acoustic characteristics of dysarthric speech and the linguistic structure related to word pronunciation are needed. Inspired by the practices of clinicians, this study introduces a novel text-guided dysarthric speech intelligibility assessment framework that leverages custom keyword spotting (DySIA-CKWS). The model evaluates intelligibility by detecting specific keywords and is extensively tested using UA-Speech database for speaker-wise analysis and across word groups of varying complexity. To ensure robustness, the system's performance is further validated on TORGO database, demonstrating its adaptability in cross-database settings. Statistical analysis demonstrates strong alignment between predicted and subjective intelligibility scores, with a Pearson Correlation Coefficient (PCC) of 0.9588 and a Spearman's Correlation Coefficient (SCC) of 0.9141, achieved using the proposed system on the UA-Speech database. The findings emphasize the importance of word selection and showcase the model's effectiveness in diagnosing dysarthric speech, offering a significant advancement in intelligibility assessment methodologies.

Abstract

Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker's visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy.

Abstract

Automatic syllable stress detection traditionally relies on heuristic features like energy, duration, and pitch. Recent self-supervised models, such as wav2vec 2.0, capture stress cues more effectively, enhancing scalability. These models encode different information across layers, yet existing research focuses mainly on final-layer features, leaving intermediate layers unexplored. Stress detection is particularly challenging for non-native speakers due to deviations from correct stress patterns. This study analyzes all layers of self-supervised models to identify the most effective for stress detection. We evaluate wav2vec 2.0 (base, large, xlsr) on supervised and unsupervised classifiers using the ISLE corpus of non-native German and Italian speakers. Results show that wav2vec 2.0-large achieves the highest accuracy (96.43% for German, 96.68% for Italian) at layers 16 and 13, respectively, improving by 2.9% and 3.3% over the baseline.

Abstract

Brachiation, inspired by ape locomotion, involves swinging from one substrate to another. Existing approaches typically rely on simple grippers and computationally expensive optimal control to compute feasible states and control trajectories. In contrast, learning-based methods often lack physical modeling and require extensive training data. We present a brachiating system using high degree-of-freedom anthropomorphic hands to generate swing trajectories and perform stable grasps in a physics-based simulation environment (MuJoCo). An optimal open-loop trajectory is first generated via trajectory optimization based on a desired grasp location. A tracking controller follows this reference, while a grasping controller activates upon proximity to ensure secure contact. To reduce computational cost, we train a Generative Adversarial Imitation Learning (GAIL) policy using expert trajectories from the optimization framework. The GAIL-based controller generalizes to perturbed conditions and eliminates the need for repeated re-optimization, significantly lowering computation time. It also adapts to varying initial configurations, removing the requirement to rerun optimization for each case. We compare the learned model with a traditional optimal controller and demonstrate marked improvements in both computational efficiency and versatility.