Anisotropic strange stars in Tolman-Kuchowicz spacetime

We attempt to study a singularity-free model for the spherically symmetric anisotropic strange stars under Einstein's general theory of relativity by exploiting the Tolman-Kuchowicz metric. Further, we have assumed that the cosmological constant $\Lambda$ is a scalar variable dependent on the spatial coordinate $r$. To describe the strange star candidates we have considered that they are made of strange quark matter (SQM) distribution, which is assumed to be governed by the MIT bag equation of state. To obtain unknown constants of the stellar system we match the interior Tolman-Kuchowicz metric to the exterior modified Schwarzschild metric with the cosmological constant, at the surface of the system. Following Deb et al. we have predicted the exact values of the radii for different strange star candidates based on the observed values of the masses of the stellar objects and the chosen parametric values of the $\Lambda$ as well as the bag constant $\mathcal{B}$. The set of solutions satisfies all the physical requirements to represent strange stars. Interestingly, our study reveals that as the values of the $\Lambda$ and $\mathcal{B}$ increase the anisotropic system becomes gradually smaller in size turning the whole system into a more compact ultra-dense stellar object.Comment: 18 pages, 10 figure

Large diamagnetic persistent currents

In multichannel rings, evanescent modes will always co-exist with propagating modes. The evanescent modes can carry a very large diamagnetic persistent current that can oscillate with energy and are very sensitive to impurity scattering. This provides a natural explanation for the large diamagnetic persistent currents observed in experiments.Comment: 5 figure

Fusion of 6^{6}Li with 159^{159}Tb} at near barrier energies

Complete and incomplete fusion cross sections for $^{6}$Li+$^{159}$Tb have been measured at energies around the Coulomb barrier by the $\gamma$-ray method. The measurements show that the complete fusion cross sections at above-barrier energies are suppressed by $\sim$34% compared to the coupled channels calculations. A comparison of the complete fusion cross sections at above-barrier energies with the existing data of $^{11,10}$B+$^{159}$Tb and $^{7}$Li+$^{159}$Tb shows that the extent of suppression is correlated with the $\alpha$-separation energies of the projectiles. It has been argued that the Dy isotopes produced in the reaction $^{6}$Li+$^{159}$Tb, at below-barrier energies are primarily due to the $d$-transfer to unbound states of $^{159}$Tb, while both transfer and incomplete fusion processes contribute at above-barrier energies.Comment: Phys. Rev. C (accepted

PNEUMOCONIOSIS IN A SPOONBILL - A CASE REPORT

Pneumoconiosis has been identified in an adult dead spoonbill (Platalea leucorodia) from a Zoo in Kolkata, West Bengal, India. An environmental automobile pollutants present around that ambient may be the cause of pneumoconiosis

Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks

State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings, we tackle a more practical problem of generating adversarial perturbations using multi-object (i.e., multiple dominant objects) images as they are representative of most real-world scenes. Our goal is to design an attack strategy that can learn from such natural scenes by leveraging the local patch differences that occur inherently in such images (e.g. difference between the local patch on the object `person' and the object `bike' in a traffic scene). Our key idea is to misclassify an adversarial multi-object image by confusing the victim classifier for each local patch in the image. Based on this, we propose a novel generative attack (called Local Patch Difference or LPD-Attack) where a novel contrastive loss function uses the aforesaid local differences in feature space of multi-object scenes to optimize the perturbation generator. Through various experiments across diverse victim convolutional neural networks, we show that our approach outperforms baseline generative attacks with highly transferable perturbations when evaluated under different white-box and black-box settings.Comment: Accepted at WACV 2023 (Round 1), camera-ready versio