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Rohit Roy

Publications and source records attributed to Rohit Roy.

5 recordsLinked to original sources

The Singular Source of Vineyard Monodromy

Vineyards, or time-varying families of persistence diagrams, are widely used in topological data analysis (TDA) pipelines to track how topological features change and evolve as a parameter varies. When the parameter traces a closed loop, a vineyard can exhibit monodromy: diagram points permute over the course of a full traversal, which obstructs feature tracking and can complicate downstream analysis of such data. Chambers et al. considered the periodic vineyards that arise from the radial persistence transform, which maps the manifold to a family of persistence diagrams, where each diagram fixes a base point and considers the filtration that is based on Euclidean distance to that point, and showed that monodromy and knotting can occur. Other recent work by Arya et al. considers geometric conditions that exclude monodromy in two dimensions, in an effort to better understand when this effect happens. That said, understanding when and why monodromy occurs is a fundamental open problem with direct practical consequences for many data analysis pipelines. In this work, we study this question for 1-manifolds in $\mathbb{R}^2$, using a surprising connection with tools from singularity theory, and provide a classification for the causes of monodromy in vineyards. More precisely, we prove that the vineyard of a sufficiently small loop $\gamma$ cannot exhibit monodromy unless it contains a specific singularity of the distance function. The central geometric object in our analysis is the symmetry set, which is the locus of centers of spheres tangent in more than one point to the manifold; this object classifies singularities of the distance function, and in our setting, dictates precisely when monodromy occurs. This characterization opens the door to the development of algorithmic criteria for detecting and utilizing (or avoiding) monodromy in TDA pipelines.

cs.CG

Influence of density-dependent bag function $B(n)$ on strange stars for non-zero strange quark mass ($m_s\neq0$) in $f(R,T)$ gravity consistent with observational validation

In this work, a new class of solution of the Einstein field equation for an isotropic strange star using the modified Mak-Harko type density profile along with the equation of state as proposed in the MIT bag model and considering finite mass of the strange quark ($m_s$) is presented in the framework of $f(R,T)$ gravity with $f(R,T)=R+2\zeta T$, where, $\zeta$ is the coupling parameter. To incorporate the quark matter hypothesis with a physically viable stellar framework, a baryon number density ($n$) dependent bag function $B(n)$ is analysed, using exponential type parametrisation. The energy per baryon ($E_B$) has been investigated to restrict $B(n)$ and corresponding $n$ within a stable window, specifically satisfying the condition $E_B\leq 930.4~MeV$, which corresponds to the binding energy of $\isotope[56]{Fe}$. We note a lower limit of $n$ below which $E_B>930.4~MeV$ as $E_B$ increases with the decrease of $n$. This value, however, depends on $m_s$. Additionally, $n$ has a maximum value of $0.36~fm^{-3}$ irrespective of $m_s$ depending on the range of bag function. All the essential characteristics are satisfactorily fulfilled within the stellar interior for the selected set of parameter space. In this model, the maximum mass and radius are found by solving the TOV equations numerically which yields $M=2.03~M_{\odot}$ with a radius of $11.49~km$ for $m_s=0~MeV$ and $n=0.36~fm^{-3}$ and $\zeta=-0.1$. It is also noted that the maximum mass and the corresponding radius are the function of $m_s$, $\zeta$ and $n$. The proposed model has been shown to comply with the required energy conditions and satisfies the criterion for dynamical stability, thereby confirming its physical plausibility as a physically consistent stellar model within the parameter space used.

gr-qc

Filtration-Based Representation Learning for Temporal Graphs

In this work, we introduce a filtration on temporal graphs based on $\delta$-temporal motifs (recurrent subgraphs), yielding a multi-scale representation of temporal structure. Our temporal filtration allows tools developed for filtered static graphs, including persistent homology and recent graph filtration kernels, to be applied directly to temporal graph analysis. We demonstrate the effectiveness of this approach on temporal graph classification tasks.

cs.LG

Hybrid compact stars with finite strange quark mass and dark energy: implications for astrophysical observations

In this work, a detailed investigation of compact stars composed of deconfined quark matter with finite strange quark mass ($m_s \neq 0$) admixed with dark energy is presented. The quark sector is modeled using the MIT bag model equation of state, while the dark energy component obeys a linear equation of state, $p^{de} = \omega \rho^{de}$ with $\omega$ in the range $-1\leq\omega\leq-frac{1}{3}$. The stellar configuration is explored within the Finch-Skea ansatz for the $g_{rr}$ metric potential. A coupling between quark matter and dark energy is introduced through $\rho^{de} =\beta \rho^Q$, where $\beta$ represents the dark energy coupling parameter. Causality restricts $\beta$ within $0<\beta<-\frac{1}{3\omega}$. The structural features of such compact stars are analysed by varying $\beta$ in this range. Solving the Tolman-Oppenheimer-Volkoff equations yields a maximum mass of $2.012~M_{\odot}$ with a radius of about 11 km. For a fixed $\omega$, both mass and radius decrease as $\beta$ increases. The model satisfies causality, energy and stability conditions, ensuring physical acceptability. Finally, the framework is applied to estimate radii of compact star candidates identified as strange quark stars with dark energy, showing good agreement with observational data.

gr-qc

Automatic Pill Reminder for Easy Supervision

In this paper we present a working model of an automatic pill reminder and dispenser setup that can alleviate irregularities in taking prescribed dosage of medicines at the right time dictated by the medical practitioner and switch from approaches predominantly dependent on human memory to automation with negligible supervision, thus relieving persons from error-prone tasks of giving wrong medicine at the wrong time in the wrong amount.

cs.CY