arXiv subjects

Siddhartha Bhattacharyya

Publications and source records attributed to Siddhartha Bhattacharyya.

At least 19 recordsLinked to original sources

arXiv2026

ALINA: Advanced Line Identification and Notation Algorithm

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

cs.CV↗

arXiv2026

Test-time Prompt Refinement for Text-to-Image Models

Text-to-image (T2I) generation models have made significant strides but still struggle with prompt sensitivity: even minor changes in prompt wording can yield inconsistent or inaccurate outputs. To address this challenge, we introduce a closed-loop, test-time prompt refinement framework that requires no additional training of the underlying T2I model, termed TIR. In our approach, each generation step is followed by a refinement step, where a pretrained multimodal large language model (MLLM) analyzes the output image and the user's prompt. The MLLM detects misalignments (e.g., missing objects, incorrect attributes) and produces a refined and physically grounded prompt for the next round of image generation. By iteratively refining the prompt and verifying alignment between the prompt and the image, TIR corrects errors, mirroring the iterative refinement process of human artists. We demonstrate that this closed-loop strategy improves alignment and visual coherence across multiple benchmark datasets, all while maintaining plug-and-play integration with black-box T2I models. Code is available at https://github.com/hafeezkhan909/Test-time-Image-Refinement.

cs.LG↗

arXiv2026

Wideband Monitoring of FRB 20180916B Across a Half-Decade Bandwidth Using the Upgraded GMRT

With the uGMRT having unprecedented sensitivity and unique capability of providing instantaneous frequency coverage of 250$-$1460 MHz, we studied ${\rm FRB}\,20180916$B over four months sampling during its active phase. We report the detection of $74$ bursts at Band-3 (i.e. 250$-$500 MHz) and $4$ bursts at Band-4 (i.e. 550$-$750 MHz) of uGMRT providing a burst rate of $\sim 4$ bursts/hour above a fluence of 0.05 Jy ms. We find that the source emits maximum energy and luminosity up to a fractional bandwidth of 70 MHz near the middle of its activity window consistent with earlier studies. We see a strong correlation between the excess dispersion measure and excess scattering width. We find that the normalized cumulative distribution of the waiting time can be well-fitted by an exponential function, indicating a stochastic emission process. We also notice that the cumulative burst rate changes rapidly with the intrinsic energy of the bursts near the middle of the activity window considering the full observed window of 0.4-0.6 of this FRB, where this change is much steeper for the high-energy bursts and shallower for the low-energy bursts.

astro-ph.HE↗

arXiv2025

LVADNet3D: A Deep Autoencoder for Reconstructing 3D Intraventricular Flow from Sparse Hemodynamic Data

Accurate assessment of intraventricular blood flow is essential for evaluating hemodynamic conditions in patients supported by Left Ventricular Assist Devices (LVADs). However, clinical imaging is either incompatible with LVADs or yields sparse, low-quality velocity data. While Computational Fluid Dynamics (CFD) simulations provide high-fidelity data, they are computationally intensive and impractical for routine clinical use. To address this, we propose LVADNet3D, a 3D convolutional autoencoder that reconstructs full-resolution intraventricular velocity fields from sparse velocity vector inputs. In contrast to a standard UNet3D model, LVADNet3D incorporates hybrid downsampling and a deeper encoder-decoder architecture with increased channel capacity to better capture spatial flow patterns. To train and evaluate the models, we generate a high-resolution synthetic dataset of intraventricular blood flow in LVAD-supported hearts using CFD simulations. We also investigate the effect of conditioning the models on anatomical and physiological priors. Across various input configurations, LVADNet3D outperforms the baseline UNet3D model, yielding lower reconstruction error and higher PSNR results.

cs.LG↗

arXiv2025

Adapt, But Don't Forget: Fine-Tuning and Contrastive Routing for Lane Detection under Distribution Shift

Lane detection models are often evaluated in a closed-world setting, where training and testing occur on the same dataset. We observe that, even within the same domain, cross-dataset distribution shifts can cause severe catastrophic forgetting during fine-tuning. To address this, we first train a base model on a source distribution and then adapt it to each new target distribution by creating separate branches, fine-tuning only selected components while keeping the original source branch fixed. Based on a component-wise analysis, we identify effective fine-tuning strategies for target distributions that enable parameter-efficient adaptation. At inference time, we propose using a supervised contrastive learning model to identify the input distribution and dynamically route it to the corresponding branch. Our framework achieves near-optimal F1-scores while using significantly fewer parameters than training separate models for each distribution.

cs.CV↗

arXiv2025

Low-frequency, wideband study of an active repeater, FRB 20240114A, with the GMRT

We report the detection of 167 bursts from an active repeater, FRB 20240114A, using the uGMRT. The observations were carried over a frequency range of 300 $-$ 750 MHz, and on 4 different dates over a period of 6 months. Our analysis indicates that the FRB's emission properties are evolving, with both the median flux and emission rate of the bursts decreasing over time. The properties of the bursts also vary widely, with a wide range of intrinsic widths (0.246 to 39.364 ms), scattering timescales (0.004 to 28.289 ms), dispersion measures (524.07 to 533.56 pc cm$^{-3}$), and band occupancies (9 to 180 MHz). The distribution for the waiting time, $t_{\mathrm{wait}}$, is bi-modal, and follows a Weibull distribution, with a shape parameter $k = 0.33$; however, when the two distributions are fit separately, they follow a Weibull distribution with $k = 0.63$ for $t_{\mathrm{wait}} < 1$ s, and a log-normal distribution with a width of $σ= 1.28$ for $t_{\mathrm{wait}} > 1$ s. The isotropic energy distribution is seen to follow a log-normal distribution as well, with a width of $σ= 0.83$.

astro-ph.HE↗

arXiv2025

Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging

Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Propagation-Reconstruction Network (PRNet) for few-shot classification and localization, respectively, in Single Photon Emission Computed Tomography (SPECT) images. For the proof of concept we used a 2D-sliced image cropped around heart. The Prototypical Network, with a pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for 2D imaging with an encoder-decoder architecture and skip connections, achieved a training loss of 1.395, accurately reconstructing patches and capturing spatial relationships. These results highlight the potential of Prototypical Networks for tissue classification with limited labeled data and PRNet for anatomical landmark localization, paving the way for improved performance in deep learning frameworks.

cs.CV↗

arXiv2025

Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.

cs.CV↗

arXiv2025

Design and Validation of Learning Aware HMI For Learning-Enabled Increasingly Autonomous Systems

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

cs.HC↗

arXiv2025

Automating Physics-Based Reasoning for SysML Model Validation

System and software design benefits greatly from formal modeling, allowing for automated analysis and verification early in the design phase. Current methods excel at checking information flow and component interactions, ensuring consistency, and identifying dependencies within Systems Modeling Language (SysML) models. However, these approaches often lack the capability to perform physics-based reasoning about a system's behavior represented in SysML models, particularly in the electromechanical domain. This significant gap critically hinders the ability to automatically and effectively verify the correctness and consistency of the model's behavior against well-established underlying physical principles. Therefore, this paper presents an approach that leverages existing research on function representation, including formal languages, graphical representations, and reasoning algorithms, and integrates them with physics-based verification techniques. Four case studies (coffeemaker, vacuum cleaner, hairdryer, and wired speaker) are inspected to illustrate the model's practicality and effectiveness in performing physics-based reasoning on systems modeled in SysML. This automated physics-based reasoning is broken into two main categories: (i) structural, which is performed on BDD and IBD, and (ii) functional, which is then performed on activity diagrams. This work advances the field of automated reasoning by providing a framework for verifying structural and functional correctness and consistency with physical laws within SysML models.

eess.SY↗

arXiv2024

Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy.

cs.CV↗

arXiv2024

AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.

cs.CV↗

arXiv2024

Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection

Transfer Learning has become one of the standard methods to solve problems to overcome the isolated learning paradigm by utilizing knowledge acquired for one task to solve another related one. However, research needs to be done, to identify the initial steps before inducing transfer learning to applications for further verification and explainablity. In this research, we have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. Lane detection is an important aspect of autonomous vehicles driving assistance system. In most circumstances, modern deep-learning-based lane recognition systems are successful, but they struggle with lanes with complex topologies. The proposed architecture, ERFCondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies like dense, curved and fork lines. The newly proposed technique was tested on two common lane detecting benchmarks, CULane and CurveLanes respectively, and two different backbones, ResNet and ERFNet. The researched technique with ERFCondLaneNet, exhibited similar performance in comparison to ResnetCondLaneNet, while using 33% less features, resulting in a reduction of model size by 46%.

cs.CV↗

arXiv2024

Constraining the parameters of an isolated neutron star using the lensed HI signal at uGMRT

The strength of the HI signal originating from a distant galaxy at a cosmological distance is several orders of magnitude lower than the foreground and background noise and hence it is difficult to observe this signal at a given radio telescope. However, a few recent studies reported the detection of that signal at the radio band suggests the strength of this signal is somehow magnified. In this analysis, we study the prospects of detecting this signal at different frequency bands of the uGMRT where this signal is supposed to be amplified through the strong gravitational lensing by an isolated neutron star located in a cosmological distance. Our study shows the effects of the lensing parameters on the observables of that amplified signal and discusses its variation with the frequency bands considered here. We present a method to estimate the position and size of an isolated neutron star using the signal-to-noise ratio of that signal supposed to be detected at different frequency bands of the uGMRT. We discuss the scope of multi-messenger astronomy in the era of HI observation where the estimated lensing parameters can be cross-validated using the pulsar detection at the X-ray band from the same location in the sky. Our analysis is equally applicable to any radio telescope with given specifications.

astro-ph.CO↗

arXiv2023

Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using artificial intelligence (AI) techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art language model, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using large language models during inference.

cs.CL↗

arXiv2023

DQNAS: Neural Architecture Search using Reinforcement Learning

Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem.

cs.LG↗

arXiv2022

Modelling the energy distribution in CHIME/FRB Catalog-1

We characterize the intrinsic properties of any FRB using its redshift $z$, spectral index $α$ and energy $E_{33}$ in units of $10^{33} \, {\rm J}$ emitted across $2128 - 2848\; {\rm MHz}$ in the FRB's rest frame. Provided that $z$ is inferred from the measured extra-galactic dispersion measure $DM_{\rm Ex}$, the fluence $F$ of the observed event defines a track in $(α, E_{33})$ space which we refer to as the "energy track". Here we consider the energy tracks for a sample of $254$ non-repeating low dispersion measure FRBs from the CHIME/FRB Catalog-1, and use these to determine $n(E_{33} \mid α)$ the conditional energy distribution i.e. the number of FRBs in the interval $ΔE_{33}$ given a value of $α$. Considering $-10 \le α\le 10$, we find that the entire energy scale shifts to higher energy values as $α$ is increased. For all values of $α$, we can identify two distinct energy ranges indicating that there are possibly two distinct FRB populations. At high energies, the distribution is well fitted by a modified Schechter function whose slope and characteristic energy both increase with $α$. At low energies, the number of FRBs are in excess of the predictions of the modified Schechter function indicating that we may have a distinctly different population of low-energy FRBs. We have checked that our main findings are reasonably robust to the assumptions regarding the Galactic Halo and Host galaxy contributions to the dispersion measure.

astro-ph.HE↗

arXiv2021

Assuring Increasingly Autonomous Systems in Human-Machine Teams: An Urban Air Mobility Case Study

As aircraft systems become increasingly autonomous, the human-machine role allocation changes and opportunities for new failure modes arise. This necessitates an approach to identify the safety requirements for the increasingly autonomous system (IAS) as well as a framework and techniques to verify and validate that an IAS meets its safety requirements. We use Crew Resource Management techniques to identify requirements and behaviors for safe human-machine teaming behaviors. We provide a methodology to verify that an IAS meets its requirements. We apply the methodology to a case study in Urban Air Mobility, which includes two contingency scenarios: unreliable sensor and aborted landing. For this case study, we implement an IAS agent in the Soar language that acts as a copilot for the selected contingency scenarios and performs takeoff and landing preparation, while the pilot maintains final decision authority. We develop a formal human-machine team architecture model in the Architectural Analysis and Design Language (AADL), with operator and IAS requirements formalized in the Assume Guarantee REasoning Environment (AGREE) Annex to AADL. We formally verify safety requirements for the human-machine team given the requirements on the IAS and operator. We develop an automated translator from Soar to the nuXmv model checking language and formally verify that the IAS agent satisfies its requirements using nuXmv. We share the design and requirements errors found in the process as well as our lessons learned.

cs.SE↗