Abstract

Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems that are data-efficient is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel framework leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by upto 70%, enabling effective performance at new locations without relying on any past data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. By dramatically reducing the forecasting setup timeline, SPIRIT accelerates solar farm deployment in all potential global sites, most of which lack historical data, thereby democratizing access to clean energy and enabling participation in the renewable energy transition.

Abstract

Natural fibre composites (NFCs) have gained prominence as sustainable, cost-effective alternatives to synthetic fibre reinforcements in masonry structural retrofitting. This paper reviews the extraction, treatment, and fabrication of natural fibres such as jute, flax, sisal, hemp, and basalt into Fibre Reinforced Polymer (FRP) and Textile Reinforced Mortar (TRM) systems suited for masonry appli-cations. Experimental and field studies demonstrate that natural FRP composites improve tensile strength, ductility, and seismic resilience of unreinforced masonry walls, while natural TRM composites offer enhanced environmental compatibil-ity, fire resistance, and vapour permeability, which are essential for heritage ma-sonry preservation. The review assesses mechanical performance, durability chal-lenges related to moisture and ultraviolet (UV) exposure, and sustainability bene-fits, including reduced embodied energy and biodegradability. Practical case stud-ies confirm the feasibility of locally sourced natural fibre retrofits in seismic zones. Challenges such as variability in fibre properties and the need for stand-ardized design codes are discussed alongside future research directions focusing on hybrid composites, bio-based matrices, and sensor integration for structural health monitoring. This review consolidates current advances and prospects, positioning NFCs in form of FRP and TRM composites as viable, eco-friendly retrofit technologies for resilient and sustainable masonry infrastructure.

Abstract

Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce Segmentation Schödinger Bridge (SSB), the first application of Schödinger Bridge for ambiguous medical image segmentation, modelling joint image-mask dynamics to enhance performance. SSB preserves structural integrity, delineates unclear boundaries without additional guidance, and maintains diversity using a novel loss function. We further propose the Diversity Divergence Index () to quantify inter-rater variability, capturing both diversity and consensus. SSB achieves state-of-the-art performance on LIDC-IDRI, COCA, and RACER (in-house) datasets.

Abstract

Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost when researching vast, unlabeled data lakes. We introduce Labeling Copilot, the first data curation deep research agent for computer vision. A central orchestrator agent, powered by a large multimodal language model, uses multi-step reasoning to execute specialized tools across three core capabilities: (1) Calibrated Discovery sources relevant, in-distribution data from large repositories; (2) Controllable Synthesis generates novel data for rare scenarios with robust filtering; and (3) Consensus Annotation produces accurate labels by orchestrating multiple foundation models via a novel consensus mechanism incorporating non-maximum suppression and voting. Our large-scale validation proves the effectiveness of Labeling Copilot's components. The Consensus Annotation module excels at object discovery: on the dense COCO dataset, it averages 14.2 candidate proposals per image-nearly double the 7.4 ground-truth objects-achieving a final annotation mAP of 37.1%. On the web-scale Open Images dataset, it navigated extreme class imbalance to discover 903 new bounding box categories, expanding its capability to over 1500 total. Concurrently, our Calibrated Discovery tool, tested at a 10-million sample scale, features an active learning strategy that is up to 40x more computationally efficient than alternatives with equivalent sample efficiency. These experiments validate that an agentic workflow with optimized, scalable tools provides a robust foundation for curating industrial-scale datasets.

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

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

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.