Abstract

Extracting work from a physical system is one of the cornerstones of quantum thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete description of the quantum system. This is significantly more challenging when the state of the underlying system is unknown, as quantum tomography is extremely inefficient. In this article, we analyze the number of samples of the unknown state required to extract work. With only a single copy of an unknown state, we prove that ergotropy approaches zero in the asymptotic limit, rendering work extraction nearly impossible. In contrast, when multiple copies are available, we quantify the sample complexity required to estimate extractable work, establishing a scaling relationship that balances the desired accuracy with success probability. Our work develops a sample-efficient protocol to assess the utility of unknown states as quantum batteries and opens avenues for estimating thermodynamic quantities using near-term quantum computers.

Abstract

Excessive groundwater extraction has led to a significant decline in water tables, highlighting the need for continuous monitoring. This study presents an Internet of Things (IoT)-based approach for real-time estimation of groundwater levels in borewells. In addition to water level measurements, parameters such as motor current and rainfall are tracked to evaluate their influence on groundwater dynamics. To assess practical feasibility, five monitoring nodes were deployed across a small educational campus in Hyderabad, India. Data collected over a two-month period indicate that the proposed low-cost system is both reliable and effective in real-world conditions, while also revealing interesting correlations between groundwater level variations and the monitored parameters.

Abstract

Pretrained models are ubiquitous in the current deep learning landscape, offering strong results on a broad range of tasks. Recent works have shown that models differing in various design choices exhibit categorically diverse generalization behavior, resulting in one model grasping distinct data-specific insights unavailable to the other. In this paper, we propose to leverage large publicly available model repositories as an auxiliary source of model improvements. We introduce a data partitioning strategy where pretrained models autonomously adopt either the role of a student, seeking knowledge, or that of a teacher, imparting knowledge. Experiments across various tasks demonstrate the effectiveness of our proposed approach. In image classification, we improved the performance of ViT-B by approximately 1.4% through bidirectional knowledge transfer with ViT-T. For semantic segmentation, our method boosted all evaluation metrics by enabling knowledge transfer both within and across backbone architectures. In video saliency prediction, our approach achieved a new state-of-the-art. We further extend our approach to knowledge transfer between multiple models, leading to considerable performance improvements for all model participants.

Abstract

Residential electricity consumption datasets are essential for applications such as smart grid management, home automation, renewable energy integration, infrastructure planning, and policy-making. However, obtaining high-resolution residential datasets remains challenging due to the high costs and complexities of sensor installation, monitoring, and maintenance, obtaining approvals and related human factors, among other issues. To address this issue with a focus on the Indian residential context, where such data is quite limited, we propose a Residential Electricity Usage Simulator (REUS), to generate synthetic residential electricity usage data. Our approach models electricity usage for 7 different categories of homes using data collected over one year at an hourly interval, from 65 residences. In addition to energy data, we also collected 18 different features for each home to improve our modeling. The data and features are preprocessed using feature selection with Probabilistic Finite State Machines (validated through Multiple Correspondence Analysis) and further refined through systematic data cleaning and imputation. We built simulation models using the popular Machine learning techniques such as Long Short-Term Memory networks, including Vanilla, Stacked, BiDirectional, and Encoder-Decoder LSTMs and Transformer model, including Vanilla Transformer and Temporal Fusion Transformer. In addition, statistical techniques such as Markov Chains of orders (0, 1, 2, 3) and ARIMA were used as benchmarks to evaluate the models’ ability to generate a synthetic residential electricity dataset that is close to real data. Via extensive experimentation and analysis, our results show that (Bi-di) LSTMs capture the trends in electricity consumption more effectively (with the lowest RMSE) than the other models. Simulation and analysis of this nature enables broader, regionspecific energy research, reducing the need for costly or intrusive data collection.

Abstract

This study explored the cognitive status of community-dwelling Indian older adults. Our objective was to observe the association of age-related cognitive change with other physiological health parameters like, glycated hemoglobin (HbA1c), and vitamin B12 in older adults in India. Urban community dwelling, consenting older adults (55–85years, n = 123), with no clinical history of cognitive or neurological problems participated in the study. The participants underwent a detailed demographic documentation and cognitive assessment comprising of tests from different cognitive domains and blood-based assessment of glycated hemoglobin (HbA1c) and vitamin B12. As expected, performance in all cognitive domains declined with increasing age. HbA1c levels correlated inversely with processing speed and executive function. Vitamin B12 levels did not correlate with performance on any cognitive test. Interestingly, geriatric depression correlated inversely with visuospatial abilities. A stepwise multiple regression revealed that HbA1c and geriatric depression contributed to 28 % variance on Montreal Cognitive Assessment while age did not qualify as a significant contributor. Using Petersen’s criteria, Mild Cognitive Impairment (MCI) was observed in 17 % of participants. Participants classified as MCI had higher levels of HbA1c and geriatric depression, and lower performance in all cognitive domains compared to non-MCI participants. In conclusion, although cognitive performance declined with age, HbA1c and geriatric depression had a greater role in cognitive decline than age. With a high incidence of diabetes in India, this study highlights the prevalence of metabolism-linked changes in cognition, which are often ignored in community dwelling older adults in India.

Abstract

Accurate molecular subtyping of cancer is crucial for advancing personalized medicine. Although multiomics data contain valuable predictive information, effectively integrating them is challenging domics sources, providing a robust framework for molecular characterizations-modal biological interactions. We propose HyperCLSA (Hypergraph Contrastive Learning with Self-Attention), a novel deep learning framework for efficient multi-omics integration in breast cancer subtyping. HyperCLSA combines hypergraph-based sample encoding, supervised contrastive learning for latent space alignment, and multi-head self-attention for adaptive fusion of omics modalities. Evaluated on The Cancer Genome Atlas Breast Cancer dataset (TCGA-BRCA) forPAM50 subtype classification, HyperCLSA achieves a state-of-the-art accuracy of 90.1%, significantly outperforming established baselines. Our results demonstrate HyperCLSA’s effectiveness in extracting complementary information across heterogeneous omics sources, providing a robust framework for molecular characterization of breast cancer.

Abstract

Amid the surge of Non-Fungible Tokens (NFTs) in blockchain, this study introduces a meticulous methodology focusing on transaction behaviors to unveil rug pulls — a critical issue impacting financial security and trust in the NFT landscape. Using a Graph Isomorphism Network (GIN) model with 6 behavioral patterns obtained from transaction sequences, we create a “Rug Pull Pattern Matcher” model. We provide a comprehensive analysis by applying the model on two datasets — creator’s transactions from 50 reputable NFT projects and 32 reported rug pulls. Our work utilizes automated labeling to categorize addresses and our analysis reveals several interconnected NFT creator activities. We present an in-depth mapping of fund flows and creator interactions exposing suspicious behaviors like artificial inflation and intricate network collaborations among creators. The results of our proposed model demonstrate the efficacy of our methodology with 75.4% accuracy and 85.9% precision on the dataset of reported rug pulls. This work provides comparative analyses of genuine and malicious creator networks to elucidate their structural differences, helping to identify genuine and potentially fraudulent NFT activities.

Abstract

Recent advances in text-to-image models have enabled a new era of creative and controllable image generation. However, generating compositional scenes with multiple subjects and attributes remains a significant challenge. To enhance user control over subject placement, several layout-guided methods have been proposed. However, these methods face numerous challenges, particularly in compositional scenes. Unintended subjects often appear outside the layouts, generated images can be out-of-distribution and contain unnatural artifacts, or attributes bleed across subjects, leading to incorrect visual outputs. In this work, we propose MALeR, a method that addresses each of these challenges. Given a text prompt and corresponding layouts, our method prevents subjects from appearing outside the given layouts while being in-distribution. Additionally, we propose a masked, attribute-aware binding mechanism that prevents attribute leakage, enabling accurate rendering of subjects with multiple attributes, even in complex compositional scenes. Qualitative and quantitative evaluation demonstrates that our method achieves superior performance in compositional accuracy, generation consistency, and attribute binding compared to previous work. MALeR is particularly adept at generating images of scenes with multiple subjects and multiple attributes per subject.

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.