arXiv subjects

Gurpreet Singh

Publications and source records attributed to Gurpreet Singh.

At least 37 records · Page 2Linked to original sources

arXiv2024

AB Dor: Coronal imaging and activity cycles

Using long-term X-ray observations, we present the short-term and long-term X-ray variability analysis of the ultra-fast rotating active star AB Dor. Flaring events are common in X-ray observations of AB Dor and occupy a substantial portion of the total observation time, averaging around 57$\pm$23\%. The flare-free X-ray light curves show rotational modulation, indicating the presence of highly active regions in its corona. We have developed a light curve inversion code to image the corona of active fast rotating stars. The results of coronal imaging reveal the presence of two active regions of different brightness that are separated by $\sim$180°in longitude. These active regions are also found to migrate along the longitude and also show variation in their brightness. Our analysis of long-term X-ray data spanning from 1979 to 2022 shows multiple periodicities. The existence of a $\sim$19.2 yr cycle and its first harmonic indicates the presence of a Solar-like, long-term pattern. In comparison, the periodicities of $\sim$3.6 and $\sim$5.4 yr are possibly due to the presence of a flip-flop cycle in X-rays, which is also supported by similar periods findings from the optical data in the earlier studies. Further confirmation of the existence of the X-ray flip-flop cycle requires long-term observations at regular intervals in the quiescent state.

astro-ph.SR↗

arXiv2024

Narrow-linewidth Fano microcavities with resonant subwavelength grating mirror

We report on the theoretical and experimental investigations of optical microcavities consisting in the plane-plane arrangement of a broadband high-reflectivity mirror and a suspended one-dimensional grating mirror possessing a high-quality factor Fano resonance. By varying the length of these cavities from the millimeter to the few-micron range, we observe at short lengths the reduction of the spectral linewidth predicted to occur for such a Fano cavity as compared to a conventional broadband mirror cavity with the same length and internal losses. Such narrow linewidth and small modevolume microcavities with high-mechanical quality ultrathin mirrors will be attractive for a wide range of applications within optomechanics and sensing.

physics.optics↗

arXiv2024

Tracking an Untracked Space Debris After an Inelastic Collision Using Physics Informed Neural Network

With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris' trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN) - based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN )based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better prediction for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

astro-ph.EP↗

arXiv2023

AstroSat investigation of X-ray flares on two active K-M systems: CC Eri and AB Dor

We present an X-ray and UV investigation of five X-ray flares detected on two active systems, CC Eri and AB Dor, using the AstroSat observatory. The peak X-ray luminosities of the flares in the 0.3$-$7.0 keV band are found to be within 10$^{31-33}$ erg s$^{-1}$. Preliminary spectral analysis indicates the presence of three and four-temperature corona for CC Eri and AB Dor, respectively, where the highest temperature is found to vary with flare. The flare temperatures peaked at 51$-$59 MK for CC Eri and 29$-$44 MK for AB Dor. The peak emission measures of the flaring loops are estimated to be $\sim$10$^{54}$ for CC Eri and $\sim$10$^{55}$ cm$^{-3}$ for AB Dor. Global metallic abundances were also found to increase during flares.

astro-ph.SR↗

arXiv2023

Pose Estimation and Tracking for ASIST

Aircraft Ship Integrated Secure and Traverse (ASIST) is a system designed to arrest helicopters safely and efficiently on ships. Originally, a precision Helicopter Position Sensing Equipment (HPSE) tracked and monitored the position of the helicopter relative to the Rapid Securing Device (RSD). However, using the HPSE component was determined to be infeasible in the transition of the ASIST system due to the hardware installation requirements. As a result, sailors track the position of the helicopters with their eyes with no sensor or artificially intelligent decision aid. Manually tracking the helicopter takes additional time and makes recoveries more difficult, especially at high sea states. Performing recoveries without the decision aid leads to higher uncertainty and cognitive load. PETA (Pose Estimation and Tracking for ASIST) is a research effort to create a helicopter tracking system prototype without hardware installation requirements for ASIST system operators. Its overall goal is to improve situational awareness and reduce operator uncertainty with respect to the aircrafts position relative to the RSD, and consequently increase the allowable landing area. The authors produced a prototype system capable of tracking helicopters with respect to the RSD. The software included a helicopter pose estimation component, camera pose estimation component, and a user interface component. PETA demonstrated the potential for state-of-the-art computer vision algorithms Faster R-CNN and HRNet (High-Resolution Network) to be used to estimate the pose of helicopters in real-time, returning ASIST to its originally intended capability. PETA also demonstrated that traditional methods of encoder-decoders could be used to estimate the orientation of the helicopter and could be used to confirm the output from HRNet.

cs.CV↗

arXiv2023

Computer Vision for Carriers: PATRIOT

Deck tracking performed on carriers currently involves a team of sailors manually identifying aircraft and updating a digital user interface called the Ouija Board. Improvements to the deck tracking process would result in increased Sortie Generation Rates, and therefore applying automation is seen as a critical method to improve deck tracking. However, the requirements on a carrier ship do not allow for the installation of hardware-based location sensing technologies like Global Positioning System (GPS) sensors. PATRIOT (Panoramic Asset Tracking of Real-Time Information for the Ouija Tabletop) is a research effort and proposed solution to performing deck tracking with passive sensing and without the need for GPS sensors. PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets. PATRIOT would allow for faster, more accurate, and less laborious asset tracking for aircraft, people, and support equipment. PATRIOT is anticipated to benefit the warfighter by reducing cognitive workload, reducing manning requirements, collecting data to improve logistics, and enabling an automation gateway for future efforts to improve efficiency and safety. The authors have developed and tested algorithms to perform pose estimations of assets in real-time including OpenPifPaf, High-Resolution Network (HRNet), HigherHRNet (HHRNet), Faster R-CNN, and in-house developed encoder-decoder network. The software was tested with synthetic and real-world data and was able to accurately extract the pose of assets. Fusion, tracking, and real-world generality are planned to be improved to ensure a successful transition to the fleet.

cs.CV↗

arXiv2023

Effect of many-body interaction on de Haas-van Alphen oscillations in insulators

De Haas-van Alphen (dHvA) oscillations are oscillations in the magnetization as a function of the inverse magnetic field. These oscillations are usually considered to be a property of the Fermi surface and, hence, a metallic property. Recently, however, such oscillations have been shown to arise, both experimentally and theoretically, in certain insulators which have a narrow gap and an inverted band structure. In this work, we develop a theory to study the effect of many-body interaction on these unconventional oscillations. We consider weak interaction, focusing on the effect of renormalization of the quasiparticle spectrum on these oscillations. We find that interaction has an unusual effect: unlike in metals, in a certain regime the amplitude of oscillations may be enhanced substantially, both at zero and nonzero temperatures, even when the interaction is perturbatively weak.

cond-mat.mes-hall↗

arXiv2023

Study of the Energetic X-ray Superflares from the active fast rotator AB Doradus

We present the analyses of intense X-ray flares detected on the active fast rotator AB Dor using observations from the XMM-Newton. A total of 21 flares are detected, and 13 flares are analysed in detail. The total X-ray energy of these flares is found to be in the range of 10$^{34-36}$ erg, in which the peak flare flux increased up to 34 times from the pre-/post-flaring states for the strongest observed flare. The duration of these flaring events is found to be 0.7 to 5.8 hrs. The quiescent state X-ray spectra are found to be explained by a three-temperature plasma with average temperatures of 0.29, 0.95, and 1.9 keV, respectively. The temperatures, emission measures, and abundances are found to be varying during the flares. The peak flare temperature was found in the 31-89 MK range, whereas the peak emission measure was 10$^{52.5-54.7}$ $cm^{-3}$. The abundances vary during the flares and increase by a factor of $\sim$3 from the quiescent value for the strongest detected flare. The variation in individual abundances follows the inverse-FIP effect in quiescent and flare phases. The X-ray light curves of AB Dor are found to exhibit rotational modulation. The semi-loop lengths of the flaring events are derived in the range of 10$^{9.9-10.7}$ cm, whereas the minimum magnetic field to confine the plasma in the flaring loop is estimated between 200 and 700 G.

astro-ph.SR↗

arXiv2022

Blow-up radial solutions for elliptic systems with monotonic non-linearities

We are concerned with the existence and boundary behaviour of positive radial solutions for the system \begin{equation*} \left\{ \begin{aligned} Δu&=g(|x|,v(x)) &&\quad\mbox{in}\ Ω, \\ Δv&=f(|x|,|\nabla u(x)|) &&\quad\mbox{in}\ Ω, \end{aligned} \right. \end{equation*} where $Ω\subset \mathbb{R}^N$ is either a ball centered at the origin or the whole space $\mathbb{R}^N$, and $f,g\in C^{1}([0,\infty)\times [0,\infty))$, are non-negative, and increasing. Firstly, we study the existence of positive radial solutions in the case when the system is posed in a ball corresponding to their behaviour at the boundary. Next, we discuss the existence of positive radial solutions in case when $g(|x|,v(x)) = |x|^{a} v^p$ and $f(|x|, |\nabla u (x)|) = |x|^{b} h(|\nabla u|)$. Finally, we take $h(t) = t^s$, $s> 1$, $Ω= \mathbb{R}^N$ and by the use of dynamical system techniques we are able to describe the behaviour at infinity of such positive radial solutions.

math.AP↗

arXiv2022

Existence and boundary behaviour of radial solutions for weighted elliptic systems with gradient terms

We are concerned with the existence and boundary behaviour of positive radial solutions for the system \begin{equation*} \left\{ \begin{aligned} Δu&=|x|^{a}v^{p} &&\quad\mbox{ in } Ω, \\ Δv&=|x|^{b}v^{q}f(|\nabla u|) &&\quad\mbox{ in } Ω, \end{aligned} \right. \end{equation*} where $Ω\subset \bR^N$ is either a ball centered at the origin or the whole space $\bR^N$, $a$, $b$, $p$, $q> 0$, and $f \in C^1[0, \infty)$ is an increasing function such that $f(t)> 0$ for all $t> 0$. Firstly, we study the existence of positive radial solutions in case when the system is posed in a ball corresponding to their behaviour at the boundary. Next, we take $f(t) = t^s$, $s> 1$, $Ω= \bR^N$ and by the use of dynamical system techniques we are able to describe the behaviour at infinity for such positive radial solutions.

math.AP↗

arXiv2022

An X-ray study of coronally connected active eclipsing binaries

We present a detailed X-ray analysis and imaging of stellar coronae of five coronally connected eclipsing binaries, namely, 44 Boo, DV Psc, ER Vul, XY UMa, and TX Cnc. Both components of these binaries are found to be active. The X-ray light curves of detached and semidetached type systems show eclipsed-like features, whereas no evidence for coronal eclipsing is shown by the contact type systems. The X-ray light curve of DV Psc shows the O'Connell-like effect where the first maximum is found to be brighter than that of the second. Results of the coronal imaging using three-dimensional deconvolution of X-ray light curves show the coronae of all these binaries are either in the contact or over-contact configuration, with the primary being 1.7 $-$ 4 times X-ray brighter than its companion. In the current sample, a minimum of 30$-$50 \% of total UV emission is found to originate from the photosphere and positively correlated with the X-ray emission. X-ray spectra of these systems are well explained by two-temperature plasma models. The temperature corresponding to cool and hot components of plasma are found to be in the ranges of 0.25$-$0.64 and 0.9$-$1.1 keV, respectively. For the majority of binaries in the sample, the phase-resolved X-ray spectral analysis shows the orbital modulation in X-ray luminosity and emission measure corresponding to the hot component. A total of seven flaring events are also detected in the four systems with the flare energy in the range of (1.95$-$27.0)$\times$10$^{33}$ erg and loop length of the order of 10$^{9-11}$ cm.

astro-ph.SR↗

arXiv2022

Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints

Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints. The Pareto optimal solution set defines all possible optimal trade-offs over such objectives. In this work, we present a novel method for Pareto-front learning: inducing the full Pareto manifold at train-time so users can pick any desired optimal trade-off point at run-time. Our key insight is to exploit Fritz-John Conditions for a novel guided double gradient descent strategy. Evaluation on synthetic benchmark problems allows us to vary MOO problem difficulty in controlled fashion and measure accuracy vs. known analytic solutions. We further test scalability and generalization in learning optimal neural model parameterizations for Multi-Task Learning (MTL) on image classification. Results show consistent improvement in accuracy and efficiency over prior MTL methods as well as techniques from operations research.

cs.LG↗

arXiv2021

SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing

Hyperspectral unmixing involves separating a pixel as a weighted combination of its constituent endmembers and corresponding fractional abundances, with the current state of the art results achieved by neural models on benchmark datasets. However, these networks are severely over-parameterized and consequently, the invariant endmember spectra extracted as decoder weights have a high variance over multiple runs. These approaches perform substantial post-processing while requiring an exact specification of the number of endmembers and specialized initialization of weights from other algorithms like VCA. We show for the first time that a two-layer autoencoder (SCA), with $2FK$ parameters ($F$ features, $K$ endmembers), achieves error metrics that are scales apart ($10^{-5})$ from previously reported values $(10^{-2})$. SCA converges to this low error solution starting from a random initialization of weights. We also show that SCA, based upon a bi-orthogonal representation, performs a self-correction when the number of endmembers are over-specified. Numerical experiments on Samson, Jasper, and Urban datasets demonstrate that SCA outperforms previously reported error metrics for all the cases while being robust to noise and outliers.

cs.LG↗

arXiv2021

An Interactive Dashboard for Real-Time Analytics and Monitoring of COVID-19 Outbreak in India: A proof of Concept

Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality, from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports have been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner which includes time trends, epidemic curves, key epidemiological parameters such as growth rate, doubling time, and effective reproduction number have been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision aiding tools.

cs.CY↗

arXiv2021

A Systematic Approach to Cleaning Routine Health Surveillance Datasets: An Illustration Using National Vector Borne Disease Control Programme Data of Punjab, India

Advances in ICT4D and data science facilitate systematic, reproducible, and scalable data cleaning for strengthening routine health information systems. A logic model for data cleaning was used and it included an algorithm for screening, diagnosis, and editing datasets in a rule-based, interactive, and semi-automated manner. Apriori computational workflows and operational definitions were prepared. Model performance was illustrated using the dengue line-list of the National Vector Borne Disease Control Programme, Punjab, India from 01 January 2015 to 31 December 2019. Cleaning and imputation for an estimated date were successful for 96.1% and 98.9% records for the year 2015 and 2016 respectively, and for all cases in the year 2017, 2018, and 2019. Information for age and sex was cleaned and extracted for more than 98.4% and 99.4% records. The logic model application resulted in the development of an analysis-ready dataset that can be used to understand spatiotemporal epidemiology and facilitate data-based public health decision making.

cs.CY↗

arXiv2021

Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications

SVD serves as an exploratory tool in identifying the dominant features in the form of top rank-r singular factors corresponding to the largest singular values. For Big Data applications it is well known that Singular Value Decomposition (SVD) is restrictive due to main memory requirements. However, a number of applications such as community detection, clustering, or bottleneck identification in large scale graph data-sets rely upon identifying the lowest singular values and the singular corresponding vectors. For example, the lowest singular values of a graph Laplacian reveal the number of isolated clusters (zero singular values) or bottlenecks (lowest non-zero singular values) for undirected, acyclic graphs. A naive approach here would be to perform a full SVD however, this quickly becomes infeasible for practical big data applications due to the enormous memory requirements. Furthermore, for such applications only a few lowest singular factors are desired making a full decomposition computationally exorbitant. In this work, we trivially extend the previously proposed Range-Net to \textbf{Tail-Net} for a memory and compute efficient extraction of lowest singular factors of a given big dataset and a specified rank-r. We present a number of numerical experiments on both synthetic and practical data-sets for verification and bench-marking using conventional SVD as the baseline.

cs.LG↗

arXiv2021

Range-Net: A High Precision Streaming SVD for Big Data Applications

In a Big Data setting computing the dominant SVD factors is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has exponential decay. This is seldom true for any practical data. Although these methods are claimed to be applicable to scientific computations due to associated tail-energy error bounds, the approximation errors in the singular vectors and values are high when the aforementioned assumption does not hold. Furthermore from a practical perspective, oversampling can still be memory intensive or worse can exceed the feature dimension of the data. To address these issues, we present Range-Net as an alternative to randomized SVD that satisfies the tail-energy lower bound given by Eckart-Young-Mirsky (EYM) theorem. Range-Net is a deterministic two-stage neural optimization approach with random initialization, where the main memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample dimension. The data samples are read in a streaming setting with the network minimization problem converging to the desired rank-r approximation. Range-Net is fully interpretable where all the network outputs and weights have a specific meaning. We provide theoretical guarantees that Range-Net extracted SVD factors satisfy EYM tail-energy lower bound at machine precision. Our numerical experiments on real data at various scales confirms this bound. A comparison against the state of the art streaming Randomized SVD shows that Range-Net accuracy is better by six orders of magnitude while being memory efficient.

math.NA↗

arXiv2021

A Hybrid 2-stage Neural Optimization for Pareto Front Extraction

Classification, recommendation, and ranking problems often involve competing goals with additional constraints (e.g., to satisfy fairness or diversity criteria). Such optimization problems are quite challenging, often involving non-convex functions along with considerations of user preferences in balancing trade-offs. Pareto solutions represent optimal frontiers for jointly optimizing multiple competing objectives. A major obstacle for frequently used linear-scalarization strategies is that the resulting optimization problem might not always converge to a global optimum. Furthermore, such methods only return one solution point per run. A Pareto solution set is a subset of all such global optima over multiple runs for different trade-off choices. Therefore, a Pareto front can only be guaranteed with multiple runs of the linear-scalarization problem, where all runs converge to their respective global optima. Consequently, extracting a Pareto front for practical problems is computationally intractable with substantial computational overheads, limited scalability, and reduced accuracy. We propose a robust, low cost, two-stage, hybrid neural Pareto optimization approach that is accurate and scales (compute space and time) with data dimensions, as well as number of functions and constraints. The first stage (neural network) efficiently extracts a weak Pareto front, using Fritz-John conditions as the discriminator, with no assumptions of convexity on the objectives or constraints. The second stage (efficient Pareto filter) extracts the strong Pareto optimal subset given the weak front from stage 1. Fritz-John conditions provide us with theoretical bounds on approximation error between the true and network extracted weak Pareto front. Numerical experiments demonstrates the accuracy and efficiency on a canonical set of benchmark problems and a fairness optimization task from prior works.

cs.LG↗