arXiv subjects

Amit Kumar

Publications and source records attributed to Amit Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2025

Visible Emission Line Coronagraph (VELC) onboard ADITYA-L1

Aditya-L1, India's first dedicated mission to study the Sun and its atmosphere from the Sun-Earth Lagrangian L1 location was successfully launched on 2023 September 2. It carries seven payloads. The Visible Emission Line Coronagraph (VELC) is a major payload on Aditya-L1. VELC is designed to carry out imaging and spectroscopic observations (the latter in three emission lines of the corona), simultaneously. Images of the solar corona in the continuum at 5000 A, with a field of view (FoV) from 1.05 Ro to 3 Ro can be obtained at variable intervals depending on the data volume that can be downloaded. Spectroscopic observations of the solar corona in three emission lines, namely 5303 A Fexiv, 7892 A Fexi, and 10747 A Fexiii are possible simultaneously, with different exposure times and cadence. Four slits, each of width 50 um, separated by 3.75 mm help to simultaneously obtain spectra at four positions in the solar corona at all the aforementioned lines. A Linear Scan Mechanism (LSM) makes it possible to scan the solar corona up to +/-1.5 Ro. The instrument has the facility to carry out spectropolarimetric observations at 10747 A also in the FoV range 1.05 - 1.5 Ro. Various components of the instrument were tested interferometrically on the optical bench before installation. The individual components were aligned and performance of the payload was checked in the laboratory using a laser source and tungsten lamp. Wavelength calibration of the instrument was verified using Sun as a light source. All the detectors were calibrated for different parameters such as dark current and its variation with exposure time. Here, we discuss the various features of the VELC, alignment, calibration, performance, possible observations, initial data analysis and results of initial tests conducted in-orbit.

astro-ph.SR↗

arXiv2025

A Scalable Procedure for $\mathcal{H}_{\infty}-$Control Design

This paper proposes a novel gradient based scalable procedure for $\mathcal{H}_{\infty}-$control design. We compute the gradient using algebraic Riccati equation and then couple it with a novel Armijo rule inspired step-size selection procedure. We perform numerical experiments of the proposed solution procedure on an exhaustive list of benchmark engineering systems to show its convergence properties. Finally we compare our proposed solution procedure with available semi-definite programming based gradient-descent algorithm to demonstrate its scalability.

math.OC↗

arXiv2025

Robust-Sorting and Applications to Ulam-Median

Sorting is one of the most basic primitives in many algorithms and data analysis tasks. Comparison-based sorting algorithms, like quick-sort and merge-sort, are known to be optimal when the outcome of each comparison is error-free. However, many real-world sorting applications operate in scenarios where the outcome of each comparison can be noisy. In this work, we explore settings where a bounded number of comparisons are potentially corrupted by erroneous agents, resulting in arbitrary, adversarial outcomes. We model the sorting problem as a query-limited tournament graph where edges involving erroneous nodes may yield arbitrary results. Our primary contribution is a randomized algorithm inspired by quick-sort that, in expectation, produces an ordering close to the true total order while only querying $\tilde{O}(n)$ edges. We achieve a distance from the target order $π$ within $(3 + ε)|B|$, where $B$ is the set of erroneous nodes, balancing the competing objectives of minimizing both query complexity and misalignment with $π$. Our algorithm needs to carefully balance two aspects: identify a pivot that partitions the vertex set evenly and ensure that this partition is "truthful" and yet query as few "triangles" in the graph $G$ as possible. Since the nodes in $B$ can potentially hide in an intricate manner, our algorithm requires several technical steps. Additionally, we demonstrate significant implications for the Ulam-$k$-Median problem, a classical clustering problem where the metric is defined on the set of permutations on a set of $d$ elements. Chakraborty, Das, and Krauthgamer gave a $(2-\varepsilon)$ FPT approximation algorithm for this problem, where the running time is super-linear in both $n$ and $d$. We use our robust sorting framework to give the first $(2-\varepsilon)$ FPT linear time approximation algorithm for this problem.

cs.DS↗

arXiv2025

Co-Change Graph Entropy: A New Process Metric for Defect Prediction

Process metrics, valued for their language independence and ease of collection, have been shown to outperform product metrics in defect prediction. Among these, change entropy (Hassan, 2009) is widely used at the file level and has proven highly effective. Additionally, past research suggests that co-change patterns provide valuable insights into software quality. Building on these findings, we introduce Co-Change Graph Entropy, a novel metric that models co-changes as a graph to quantify co-change scattering. Experiments on eight Apache projects reveal a significant correlation between co-change entropy and defect counts at the file level, with a Pearson correlation coefficient of up to 0.54. In filelevel defect classification, replacing change entropy with co-change entropy improves AUROC in 72.5% of cases and MCC in 62.5% across 40 experimental settings (five machine learning classifiers and eight projects), though these improvements are not statistically significant. However, when co-change entropy is combined with change entropy, AUROC improves in 82.5% of cases and MCC in 65%, with statistically significant gains confirmed via the Friedman test followed by the post-hoc Nemenyi test. These results indicate that co-change entropy complements change entropy, significantly enhancing defect classification performance and underscoring its practical importance in defect prediction.

cs.SE↗

arXiv2025

Clustering What Matters in Constrained Settings

Constrained clustering problems generalize classical clustering formulations, e.g., $k$-median, $k$-means, by imposing additional constraints on the feasibility of clustering. There has been significant recent progress in obtaining approximation algorithms for these problems, both in the metric and the Euclidean settings. However, the outlier version of these problems, where the solution is allowed to leave out $m$ points from the clustering, is not well understood. In this work, we give a general framework for reducing the outlier version of a constrained $k$-median or $k$-means problem to the corresponding outlier-free version with only $(1+\varepsilon)$-loss in the approximation ratio. The reduction is obtained by mapping the original instance of the problem to $f(k,m, \varepsilon)$ instances of the outlier-free version, where $f(k, m, \varepsilon) = \left( \frac{k+m}{\varepsilon}\right)^{O(m)}$. As specific applications, we get the following results: - First FPT (in the parameters $k$ and $m$) $(1+\varepsilon)$-approximation algorithm for the outlier version of capacitated $k$-median and $k$-means in Euclidean spaces with hard capacities. - First FPT (in the parameters $k$ and $m$) $(3+\varepsilon)$ and $(9+\varepsilon)$ approximation algorithms for the outlier version of capacitated $k$-median and $k$-means, respectively, in general metric spaces with hard capacities. - First FPT (in the parameters $k$ and $m$) $(2-δ)$-approximation algorithm for the outlier version of the $k$-median problem under the Ulam metric. Our work generalizes the known results to a larger class of constrained clustering problems. Further, our reduction works for arbitrary metric spaces and so can extend clustering algorithms for outlier-free versions in both Euclidean and arbitrary metric spaces.

cs.DS↗

arXiv2025

Excision and idealization of a multiplicative Lie algebra

In this article, we introduce the concepts of excision and idealization for a multiplicative Lie algebra (also for a Lie algebra), which provides two new multiplicative Lie algebras (or Lie algebras) from a given multiplicative Lie algebra (or Lie algebra) and an ideal, under certain conditions. These concepts may assist in classifying all multiplicative Lie algebras (or Lie algebras) of a specified order (or dimension).

math.GR↗

arXiv2025

Jordan Decomposition for WBV-functions in Ordered Normed Spaces

In this paper, we define two relations one by orthogonality in vector lattices named as strong relation and the other by bounded linear functionals in normed spaces named as weak relation. It turns out that strong relation is an equivalence relation. We study some of the characterizations of these relations. Given a non-zero element in a normed space, we construct an extensible cone which makes that normed space, an ordered normed space. This extensible cone induces the weak relation in the normed space. Later, we prove a Jordan Decomposition Theorem in a normed space by the weak relation induced by the extensible cone.

math.FA↗

arXiv2025

Shock propagation in a driven hard sphere gas: molecular dynamics simulations and hydrodynamics

The continuous injection of energy in a stationary gas creates a shock wave that propagates radially outwards. We study the hydrodynamics of this disturbance using event driven molecular dynamics of a hard sphere gas in two and three dimensions, the numerical solution of the Euler equation with a virial equation of state for the gas, and the numerical solution of the Navier-Stokes equation, for the cases when the driving is localised in space and when it is uniform throughout the shock. We show that the results from the Euler equation do not agree with the data from hard sphere simulations when the driving is uniform and has singularities when the driving is localised. Including dissipative terms through the Navier-Stokes equation results in reasonably good description of the data, when the coefficients of dissipation are chose parametrically.

cond-mat.stat-mech↗

arXiv2025

Splash in an inhomogeneous gas in one dimension: Exact analysis and molecular dynamics simulations

We investigate the splash phenomenon resulting from the energy input at the interface between a vacuum and an inhomogeneous gas with density profile $ρ(r) = ρ_0 r^{-β}$. The energy input causes the formation of ballistic spatters that propagate into the vacuum, leading to a decay of the total energy in the inhomogeneous medium following a power law, $E(t) \sim t^{-δ_s}$. We determine exactly the exponents $δ_s$ by solving the Euler equation using a self-similar solution of second kind for different values of $β$. These exponents are further validated through event-driven molecular dynamics simulations. The determination of these exponents also allows us to numerically determine the spatio-temporal dependence of the density, velocity and temperature.

cond-mat.stat-mech↗

arXiv2025

The Type I Superluminous Supernova Catalogue II: Spectroscopic Evolution in the Photospheric Phase, Velocity Measurements, and Constraints on Diversity

Hydrogen-poor superluminous supernovae (SLSNe) are among the most energetic explosions in the universe, reaching luminosities up to 100 times greater than those of normal supernovae. Detailed spectral analysis hold the potential to reveal their progenitors and underlying energy sources. This paper presents the largest compilation of SLSN photospheric spectra to date, encompassing data from ePESSTO+, the FLEET search and all published spectra up to December 2022. The dataset includes a total of 974 spectra of 234 SLSNe. By constructing average phase binned spectra, we find SLSNe initially exhibit high temperatures (10000 to 11000 K), with blue continua and weak lines. A rapid transformation follows, as temperatures drop to 5000 to 6000 K by 40 days post peak, leading to stronger P-Cygni features. These averages also suggest a fraction of SLSNe may contain some He at explosion. Variance within the dataset is slightly reduced when defining the phase of spectra relative to explosion, rather than peak, and normalising to the population's median e-folding time. Principal Component Analysis (PCA) supports this, requiring fewer components to explain the same level of variation when binning data by scaled days from explosion, suggesting a more homogeneous grouping. Using PCA and K-Means clustering, we identify outlying objects with unusual spectroscopic evolution and evidence for energy input from interaction, but find not support for groupings of two or more statistically significant subpopulations. We find Fe II λ5169 lines velocities closely track the radius implied from blackbody fits, indicating formation near the photosphere. We also confirm a correlation between velocity and velocity gradient, which can be explained if all SLSNe are in homologous expansion but with different scale velocities. This behaviour aligns with expectations for an internal powering mechanism.

astro-ph.HE↗

arXiv2025

CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators

Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations affecting collaborative investor behaviors; 2) overall market sentiment, heavily influenced by news impacting investor strategies; and 3) technical indicators, offering insights into overbought or oversold conditions, momentum, and market trends, which are crucial for short-term price movements. This paper proposes a dual prediction mechanism that forecasts the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Additionally, a novel refinement mechanism enhances predictions through market sentiment-based rescaling and fusion. Experiments demonstrate that the proposed model achieves state-of-the-art performance, consistently outperforming ten comparison methods.

cs.LG↗

arXiv2025

Decorum: A Language-Based Approach For Style-Conditioned Synthesis of Indoor 3D Scenes

3D indoor scene generation is an important problem for the design of digital and real-world environments. To automate this process, a scene generation model should be able to not only generate plausible scene layouts, but also take into consideration visual features and style preferences. Existing methods for this task exhibit very limited control over these attributes, only allowing text inputs in the form of simple object-level descriptions or pairwise spatial relationships. Our proposed method Decorum enables users to control the scene generation process with natural language by adopting language-based representations at each stage. This enables us to harness recent advancements in Large Language Models (LLMs) to model language-to-language mappings. In addition, we show that using a text-based representation allows us to select furniture for our scenes using a novel object retrieval method based on multimodal LLMs. Evaluations on the benchmark 3D-FRONT dataset show that our methods achieve improvements over existing work in text-conditioned scene synthesis and object retrieval.

cs.CV↗

arXiv2025

Current Flow Mapping in Conducting Ferroelectric Domain Walls using Scanning NV-Magnetometry

The electrical conductivity of parallel plate capacitors, with ferroelectric lithium niobate as the dielectric layer, can be extensively and progressively modified by the controlled injection of conducting domain walls. Domain wall-based memristor devices hence result. Microstructures, developed as a result of partial switching, are complex and so simple models of equivalent circuits, based on the collective action of all conducting domain wall channels acting identically and in parallel, may not be appropriate. Here, we directly map the current density in ferroelectric domain wall memristors in-situ, by mapping Oersted fields, using nitrogen vacancy centre microscopy. Current density maps were found to directly correlate with the domain microstructure, revealing that a strikingly small fraction of the total domain wall network is responsible for the majority of the current flow. This insight forces a two order of magnitude correction to the carrier densities, previously inferred from standard scanning probe or macroscopic electrical characterisation.

cond-mat.mes-hall↗

arXiv2025

Impact of Perfect Fluid Dark Matter on the Thermodynamics of $AdS$ Ayón--Beato--García Black Holes

In this paper, we derive the black hole solution in the context of nonlinear electrodynamics (NLED) coupled to a perfect fluid dark matter (PFDM) field. The resulting black hole solution interpolates between the $AdS$ Ayón--Beato--García (ABG) black hole in the absence of the PFDM field and the Schwarzschild black hole devoid of magnetic monopole charges and PFDM influence. A numerical investigation of the horizon structure and thermodynamic properties, including both local and global stability, is conducted for the obtained black hole solution. The thermodynamic quantities are shown to be modified by the presence of the NLED and PFDM fields. We observe that the behaviour of thermodynamical quantities of black holes depends on these parameters significantly. We also discuss the stability and phase transition dependency on these parameters.

gr-qc↗

arXiv2025

Jet transport coefficient $\hat{q}$ in lattice QCD

We present the first calculation of the jet transport coefficient $\hat{q}$ in quenched and (2+1)-flavor QCD on a 4-D Euclidean lattice. The light-like propagation of an energetic parton is factorized from the mean square gain in momentum transverse to the direction of propagation, which is expressed in terms of the thermal field-strength field-strength correlator. The leading-twist term in its operator product expansion is calculated on the lattice. Continuum extrapolated quenched results, and full QCD estimates based on un-renormalized lattice data, over multiple lattice sizes, are compared with (non) perturbative calculations and phenomenological extractions of $\hat{q}$. The lattice data for $\hat{q}$ show a temperature dependence similar to the entropy density. Within uncertainties, these are consistent with phenomenological extractions, contrary to calculations using perturbation theory.

hep-lat↗

arXiv2025

Signatures of the Shock Interaction as an Additional Power Source in the Nebular Spectra of SN 2023ixf

Red supergiants may lose significant mass during the final 100-1000 years before core collapse, shaping their circumstellar environment. The supernova (SN) shockwave propagating through this environment forms a shock-swept dense shell that interacts with the surrounding circumstellar material (CSM), generating secondary shocks that energise the ejecta and may power the SN during the nebular phase. In the present work, we investigate the nebular spectrum of SN 2023ixf, observed one-year post-explosion (at +363 d) with the recently commissioned WEAVE instrument on the 4.2m William Herschel Telescope. This marks the first supernova spectrum captured with WEAVE. In this spectrum, H$α$ exhibits a peculiar evolution, flanked by blueward and redward broad components centred at $\sim\pm 5650\,\mathrm{km\,s^{-1}}$, features that have been observed in only a few SNe as early as one-year post-explosion. These features may indicate energy deposition from shock generated by the interaction of shock-swept dense shell with CSM expelled a few hundred years prior to the explosion. Comparisons of the +363 d spectrum with model spectra from the literature suggest a shock power of at least $\sim5 \times 10 ^{40}\,\mathrm{erg\,s^{-1}}$ at this epoch. Additionally, analysis of the [O I] doublet and other emission lines helped to constrain the oxygen mass ($\lesssim 0.07-0.30 M_\odot$), He-core mass ($\lesssim 3 M_\odot$), and zero-age main sequence mass ($\lesssim 12 M_\odot$) for SN~2023ixf. The comparison with other Type II SNe highlights SN 2023ixf's unique shock interaction signatures and evidence of dust formation, setting it apart in terms of evolution and dynamics.

astro-ph.HE↗

arXiv2025

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level

Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard for CKD diagnosis and treatment, the lack of comprehensive benchmarks for kidney pathology segmentation hinders progress in the field. To address this, we organized the Kidney Pathology Image Segmentation (KPIs) Challenge, introducing a dataset that incorporates preclinical rodent models of CKD with over 10,000 annotated glomeruli from 60+ Periodic Acid Schiff (PAS)-stained whole slide images. The challenge includes two tasks, patch-level segmentation and whole slide image segmentation and detection, evaluated using the Dice Similarity Coefficient (DSC) and F1-score. By encouraging innovative segmentation methods that adapt to diverse CKD models and tissue conditions, the KPIs Challenge aims to advance kidney pathology analysis, establish new benchmarks, and enable precise, large-scale quantification for disease research and diagnosis.

cs.CV↗

arXiv2025

Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers

E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and perceived safety of both users and non-users. Artificial intelligence (AI), as a cutting-edge and widely applied technology, has demonstrated potential to enhance transportation safety, particularly in driver assistance systems. The integration of AI into e-scooters presents a promising approach to addressing these safety concerns. This study aims to explore the factors influencing individuals willingness to use AI-assisted e-scooters. Data were collected using a structured questionnaire, capturing responses from 405 participants. The questionnaire gathered information on demographic characteristics, micromobility usage frequency, road users' perception of safety around e-scooters, perceptions of safety in AI-enabled technology, trust in AI-enabled e-scooters, and involvement in e-scooter crash incidents. To examine the impact of demographic factors on participants' preferences between AI-assisted and regular e-scooters, decision tree analysis is employed, indicating that ethnicity, income, and age significantly influence preferences. To analyze the impact of other factors on the willingness to use AI-enabled e-scooters, a full-scale Structural Equation Model (SEM) is applied, revealing that the perception of safety in AI enabled technology and the level of trust in AI-enabled e-scooters are the strongest predictors.

cs.HC↗