Search

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., Comment: 16 Pages, 4 figures, comments and suggestions are welcome

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., Comment: Paper has been accepted to The 3rd CVPR Workshop on Vision Datasets Understanding, 2024

We report the detection of a total of 135 bursts from a recently discovered active, repeating fast radio burst, FRB 20240114A with the GMRT over a frequency range of 300$-$750 MHz. The bursts were detected with intrinsic widths ranging from 0.308 to 39.364 ms, a median scattering timescale of 2.059 ms at 400 MHz and 1.372 ms at 650 MHz. The fluences of the detected bursts range from 36.81 mJy ms to 7.47 Jy ms. Both the energy and waiting time distributions of the bursts can be fitted with broken power laws, indicating the presence of two distinct populations of bursts. The energy distributions were modeled via broken power law with $\alpha_{1} = -0.62 \pm 0.01$ and $\alpha_{2} = -1.98 \pm 0.11$, while the waiting time distribution was modeled via a broken power law with $\alpha_{1} = -0.71 \pm 0.01$ and $\alpha_{2} = -2.09 \pm 0.09$. Both the energy and waiting time distributions of FRB 20240114A are comparable to high-energy bursts from magnetars, and giant radio pulses from pulsars, indicating that such objects could be likely progenitors., Comment: Submitted for publication in ApJ

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., Comment: Presented at ICMLA 2023, Special Session: Machine Learning in Health, 8 pages, 6 figures, 7 tables

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., Comment: 15 Pages, 6 Tables, 9 figures

We characterize the intrinsic properties of any FRB using its redshift $z$, spectral index $\alpha$ 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 $(\alpha, E_{33})$ 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 $n(E_{33} \mid \alpha)$ the conditional energy distribution i.e. the number of FRBs in the interval $\Delta E_{33}$ given a value of $\alpha$. Considering $-10 \le \alpha \le 10$, we find that the entire energy scale shifts to higher energy values as $\alpha$ is increased. For all values of $\alpha$, 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 $\alpha$. 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., Comment: 7 pages, 9 figures, Submitted to MNRAS

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., Comment: In Proceedings FMAS 2021, arXiv:2110.11527

We consider a sample of $82$ non-repeating FRBs detected at Parkes, ASKAP, CHIME and UTMOST each of which operates over a different frequency range and has a different detection criteria. Using simulations, we perform a maximum likelihood analysis to determine the FRB population model which best fits this data. Our analysis shows that models where the pulse scatter broadening increases moderately with redshift ($z$) are preferred over those where this increases very sharply or where scattering is absent. Further, models where the comoving event rate density is constant over $z$ are preferred over those where it follows the cosmological star formation rate. Two models for the host dispersion measure ($DM_{\rm host}$) distribution (a fixed and a random $DM_{\rm host}$) are found to predict comparable results. We obtain the best fit parameter values $\alpha=-1.53^{+0.29}_{-0.19}$, $\overline{E}_{33}=1.55^{+0.26}_{-0.22}$ and $\gamma=0.77\pm 0.24$. Here $\alpha$ is the spectral index, $\gamma$ is the exponent of the Schechter luminosity function and $\overline{E}_{33}$ is the mean FRB energy in units of $10^{33} \, {\rm J}$ across $2128 - 2848\; {\rm MHz}$ in the FRB rest frame., Comment: 5 pages, 3 figures, Accepted for Publication in the MNRAS Letter

The Parkes, ASKAP, CHIME and UTMOST telescopes, which have all detected FRBs, each works at a different frequency and has a different detection criteria. Using simulations, we have combined the constraints from all four telescopes to identify an allowed range of model parameters $(\alpha, \E33)$ for the FRB source population. Here $\alpha$ is the spectral index and $\E33$ is the mean FRB energy in units of $10^{33} \, {\rm J}$ across a $2128 - 2848\; {\rm MHz}$ band in the FRB rest frame. We have considered several different FRB energy distributions, and also different scenarios for the scattering pulse broadening, the event rate density variation with $z$ and the host dispersion measure. We find that in all cases, the common allowed region includes the range $-3.9\leq\alpha\leq-1.3$ and $0.42\leq\E33\leq1$. In all case, large values $\alpha > 4$ and $\E33 >60$ are ruled out. Considering the allowed $(\alpha, \E33)$ parameter range, we predict that CHIME is unlikely to detect an FRB with extra-galactic dispersion measure $(DM_{Ex})$ exceeding $3700\,{\rm pc\,cm}^{-3}$. A substantially larger $DM_{Ex}$ in the large FRB sample anticipated from CHIME would falsify the assumptions of the present analysis. Our analysis is expected to yield tighter parameter constraints with the advent of more FRB data., Comment: 11 pages, 13 figures, accepted for publication in MNRAS

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bi-level quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system referred to as Quantum Fully Self-Supervised Neural Network (QFS-Net) is presented for automated segmentation of brain MR images. The QFS-Net model comprises a trinity of a layered structure of qutrits inter-connected through parametric Hadamard gates using an 8-connected second-order neighborhood-based topology. The non-linear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counter-propagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on Cancer Imaging Archive (TCIA) data set collected from Nature repository and also compared with state of the art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model. Results shed promising segmented outcome in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources.

There are numerous models of quantum neural networks that have been applied to variegated problems such as image classification, pattern recognition etc.Quantum inspired algorithms have been relevant for quite awhile. More recently, in the NISQ era, hybrid quantum classical models have shown promising results. Multi-feature regression is common problem in classical machine learning. Hence we present a comparative analysis of continuous variable quantum neural networks (Variational circuits) and quantum backpropagating multi layer perceptron (QBMLP). We have chosen the contemporary problem of predicting rise in COVID-19 cases in India and USA. We provide a statistical comparison between two models , both of which perform better than the classical artificial neural networks., Comment: Comparative analysis of two quantum neural network models to solve a multi-dimensional regression problem