arXiv subjects

Tushar Verma

Publications and source records attributed to Tushar Verma.

7 recordsLinked to original sources

arXiv2026

Experimental Determination of Slow-Neutron Detection Efficiency and Background Discrimination in Mixed Radiation Fields Using Differential CR-39 Track Detectors

Measuring slow neutrons is difficult when the radiation field also contains charged particles and fast neutrons, especially when the radiation composition is not known in advance. In this work, we present a tested method to measure slow neutron fluence using CR-39 solid state nuclear track detectors. Two detectors are used together: a boron coated CR-39 detector and an uncoated CR-39 detector.The uncoated detector records tracks from charged particles and fast neutrons but does not respond to slow neutrons. The boron coated detector additionally detects charged particles produced when slow neutrons react with boron and generate lithium and alpha particles. Subtracting the track density of the uncoated detector from that of the boron coated detector provides a reliable and conservative measure of slow neutrons.Experiments using a reference thermal neutron source show that the difference between the two detectors increases linearly with exposure time. Statistical analysis gives a slow neutron equivalent track rate of 5.84 plus or minus 0.18 tracks per minute, clearly different from zero. The slope of this response is used to determine the detection efficiency of the boron coated detector. The uncoated detector measures the background caused by fast neutron leakage from the source. These results show that boron coated CR-39 detectors cannot be used alone for accurate slow neutron measurements. Reliable neutron fluence determination requires the simultaneous use of an uncoated detector. The difference between the two detectors provides a correct estimate of the thermal neutron flux in mixed radiation fields and where conventional neutron detectors cannot be used.

nucl-ex↗

arXiv2026

CR-39 track detector signatures of slow neutron like signals in Heavy-water electrolysis

We report reproducible track-detector signals consistent with slow neutron capture events, recorded in D$_2$O electrolysis involving D-Pd deposited on Pt cathode. Sensitivity to slow neutrons was achieved using boron-coated CR-39 (BCR) detectors, which register charged particle tracks arising from the $^{10}$B$(n,α)^{7}$Li reaction. These detectors were positioned adjacent to identically prepared uncoated CR-39 control detectors (CCR), which are effectively insensitive to slow neutrons and serve to quantify background contributions from charged particles and fast neutrons under the present experimental conditions. A reproducible differential detector signature (BCR $>$ CCR) would thus indicative of slow neutron fluences. Across multiple independent D$_2$O electrolysis experiments in $0.25~\mathrm{T}$ field, the BCR exhibited significantly excess track signals relative to CCRs. Under these conditions, the observed differential response corresponds to an inferred detector-equivalent slow neutron flux of approximately $(6.7 \pm 0.2)~\mathrm{cm^{-2},s^{-1}}$. Removal of the magnetic field resulted in a reduction of the differential signal by a factor of $\sim6$, indicating a strong empirical dependence on the applied field. In contrast, H$_2$O electrolysis performed under otherwise identical conditions produced no measurable differential detector response, establishing the necessity of deuterated electrochemical conditions for the observed effect. The results are reported strictly as detector signatures consistent with slow neutron capture and do not assert any theoretical explanation. Instead, this work establishes a control verified and detector validated experimental protocol for detecting low flux slow neutrons, and provides empirical constraints relevant to slow neutron studies in experiments involving metal-deuteride systems.

physics.ins-det↗

arXiv2024

Veagle: Advancements in Multimodal Representation Learning

Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks.

cs.CV↗

arXiv2024

Cluster-Segregate-Perturb (CSP): A Model-agnostic Explainability Pipeline for Spatiotemporal Land Surface Forecasting Models

Satellite images have become increasingly valuable for modelling regional climate change effects. Earth surface forecasting represents one such task that integrates satellite images with meteorological data to capture the joint evolution of regional climate change effects. However, understanding the complex relationship between specific meteorological variables and land surface evolution poses a significant challenge. In light of this challenge, our paper introduces a pipeline that integrates principles from both perturbation-based explainability techniques like LIME and global marginal explainability techniques like PDP, besides addressing the constraints of using such techniques when applying them to high-dimensional spatiotemporal deep models. The proposed pipeline simplifies the undertaking of diverse investigative analyses, such as marginal sensitivity analysis, marginal correlation analysis, lag analysis, etc., on complex land surface forecasting models In this study we utilised Convolutional Long Short-Term Memory (ConvLSTM) as the surface forecasting model and did analyses on the Normalized Difference Vegetation Index (NDVI) of the surface forecasts, since meteorological variables like temperature, pressure, and precipitation significantly influence it. The study area encompasses various regions in Europe. Our analyses show that precipitation exhibits the highest sensitivity in the study area, followed by temperature and pressure. Pressure has little to no direct effect on NDVI. Additionally, interesting nonlinear correlations between meteorological variables and NDVI have been uncovered.

cs.LG↗

arXiv2024

AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automation

In recent advancements within the domain of Large Language Models (LLMs), there has been a notable emergence of agents capable of addressing Robotic Process Automation (RPA) challenges through enhanced cognitive capabilities and sophisticated reasoning. This development heralds a new era of scalability and human-like adaptability in goal attainment. In this context, we introduce AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical techniques to facilitate autonomous navigation and task execution on web interfaces, thereby obviating the necessity for predefined scripts or manual intervention. Our engine empowers agents to comprehend and implement complex workflows, adapting to dynamic web environments with unparalleled efficiency. Our methodology synergizes cognitive functionalities with robotic automation, endowing AUTONODE with the ability to learn from experience. We have integrated an exploratory module, DoRA (Discovery and mapping Operation for graph Retrieval Agent), which is instrumental in constructing a knowledge graph that the engine utilizes to optimize its actions and achieve objectives with minimal supervision. The versatility and efficacy of AUTONODE are demonstrated through a series of experiments, highlighting its proficiency in managing a diverse array of web-based tasks, ranging from data extraction to transaction processing.

cs.AI↗

arXiv2024

SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients

Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noise. Traditional methods using transformer-based models often face limitations stemming from the lack of specialized databases, which adversely affect their performance with objects of varying orientations and scales. This underscores the need for more adaptable, lightweight models. In response, this paper introduces two innovative approaches that significantly enhance detection and segmentation capabilities for small aerial objects. Firstly, we explore the use of the SAHI framework on the newly introduced lightweight YOLO v9 architecture, which utilizes Programmable Gradient Information (PGI) to reduce the substantial information loss typically encountered in sequential feature extraction processes. The paper employs the Vision Mamba model, which incorporates position embeddings to facilitate precise location-aware visual understanding, combined with a novel bidirectional State Space Model (SSM) for effective visual context modeling. This State Space Model adeptly harnesses the linear complexity of CNNs and the global receptive field of Transformers, making it particularly effective in remote sensing image classification. Our experimental results demonstrate substantial improvements in detection accuracy and processing efficiency, validating the applicability of these approaches for real-time small object detection across diverse aerial scenarios. This paper also discusses how these methodologies could serve as foundational models for future advancements in aerial object recognition technologies. The source code will be made accessible here.

cs.CV↗

arXiv2024

Evolution of urban areas and land surface temperature

With the global population on the rise, our cities have been expanding to accommodate the growing number of people. The expansion of cities generally leads to the engulfment of peripheral areas. However, such expansion of urban areas is likely to cause increment in areas with increased land surface temperature (LST). By considering each summer as a data point, we form LST multi-year time-series and cluster it to obtain spatio-temporal pattern. We observe several interesting phenomena from these patterns, e.g., some clusters show reasonable similarity to the built-up area, whereas the locations with high temporal variation are seen more in the peripheral areas. Furthermore, the LST center of mass shifts over the years for cities with development activities tilted towards a direction. We conduct the above-mentioned studies for three different cities in three different continents.

physics.soc-ph↗