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We have fabricated and characterized asymmetric gap-engineered junctions and transmon devices. To create Josephson junctions with asymmetric gaps, Ti was used to proximitize and lower the superconducting gap of the Al counter-electrode. DC IV measurements of these small, proximitized Josephson junctions show a reduced gap and larger excess current for voltage biases below the superconducting gap when compared to standard Al/AlOx/Al junctions. The energy relaxation time constant for an Al/AlOx/Al/Ti 3D transmon was T1 = 1 {\mu}s, over two orders of magnitude shorter than the measured T1 = 134 {\mu}s of a standard Al/AlOx/Al 3D transmon. Intentionally adding disorder between the Al and Ti layers reduces the proximity effect and subgap current while increasing the relaxation time to T1 = 32 {\mu}s., Comment: 5 pages, 3 figures, to be published in IEEE Transactions on Applied Superconductivity

To test the contribution of non-equilibrium quasiparticles to qubit relaxation, we have repeatedly measured the relaxation time T_1 in Al/AlOx/Al transmons with electrodes that have different superconducting gaps. In one device, the first layer electrode was formed by thermal evaporation of nominally pure Al, while the counter-electrode was formed by deposition of oxygen-doped Al, which gave a larger gap value. The relaxation time was long, but showed large fluctuations, with T_1 varying between about 100 and 300 {\mu}s at 20 mK. In other transmons, we formed the first layer electrode by deposition of oxygen-doped Al, while the counter-electrode was formed by deposition of nominally pure Al. These devices showed a similar range of large and fluctuating T_1 values, with maximum T_1 values over 200 {\mu}s. The relaxation time of the devices did not depend strongly on temperature below about 150 mK, but dropped rapidly above this due to thermally-generated quasiparticles.

We present a few Euclidean wormhole configurations using both the analytic and numerical solutions of the field equations considering Einstein Gauss-Bonnet dilaton interaction in 4-dimensional Robertson Walker Euclidean background. In one analytic solution we present transition from a wormhole to an exponential expansion with Lorentzian time $t$ using $\tau=it$ after passing through an era of oscillating Euclidean wormhole. The numerical solutions of the scale factor $a(\tau)$ show multiple local maxima and minima about a global minimum for inverse power law potentials, while for exponential potential the wormholes have a single minimum. The Hubble parameter and deceleration parameter obtained by curve fit of $a(\tau)$ of the numerical solution show an inflation away from the throat of the wormhole invoking analytic continuation by $\tau=it$. A sharp decay of the potential is also observed., Comment: 17 pages, 17 figures, communicated in MPLA. arXiv admin note: text overlap with arXiv:2111.13913

Regular and uninterrupted operation of critical infrastructures such as power, transport, communication etc. are essential for proper functioning of a country. Cyber-attacks causing disruption in critical infrastructure service in the past, are considered as a significant threat. With the advancement in technology and the progress of the critical infrastructures towards IP based communication, cyber-physical systems are lucrative targets of the attackers. In this paper, we propose a robust multivariate intrusion detection system called RAD for detecting attacks in the cyber-physical systems in O(d) space and time complexity, where d is the number parameters in the system state vector. The proposed Intrusion Detection System(IDS) is developed in an unsupervised learning setting without using labelled data denoting attacks. It allows a fraction of the training data to be corrupted by outliers or under attack, by subscribing to robust training procedure. The proposed IDS outperforms existing anomaly detection techniques in several real-world datasets and attack scenarios., Comment: 9pages, 14 figures

The study of a high-lying resonant state of $^{9}$B is carried out using supersymmetric quantum mechanics (SQM). The resulting isospectral potentials are very deep and narrow and the generated wave functions identify the resonance at 16.84 MeV with a width of 69 keV. The present work shows that SQM can be successfully applied for detection of high-lying resonances in unstable nuclei., Comment: 3 pages, 3 figures

The Diffuse Optical Tomography (DOT) has received considerable attention in the recent years in the field of biomedical imaging and disease detection. However, imaging through highly diffusive medium is a challenge and stability is always an issue due to the inverse problem. Here a non-linear continous wave (CW) semi-analytic reconstruction method is discussed that used curved-beam paths for tomographic imaging with no assumption on inclusion, unlike iterative methods.The non-linear Rosenbrock's function is used to approximate the paths followed by majority photons as curved ones. The modified Beer-Lambert Law (MBLL) in proposed differntial form is used to calculate the absorption coefficient of all the avilable photon paths. The computed values are back-projected along these channels and serve as the basis for image reconstruction without solving the inverse problem. For three-dimensional (3-D) imaging, measurements at three different depths covering the entire depth of the phantom are taken. These slices are stacked together followed by interpolation to form the volumetric image of the phantom to complete the tomographic imaging in its true sense. Numerical simulations, wax phantom experiments with different geometries and contrast are carried out with satisfactory results. This semi-analytic reconstruction method is simple and efficient and is suitable for real-time applications that reqiure fast absorption image reconstruction. The method is compared with the Greedy algorithms for further validation. Also, different performance evaluation matrices are estimated to assess the accuracy of the reconstructed images and the results are rather satisfactory.

In a recent paper [El 13], M.E. Kahoui has shown that if $R$ is a polynomial ring over $\mathbb{C}$, $A$ an $\mathbb{A}^3$-fibration over $R$, and $W$ a residual variable of $A$ then $A$ is stably polynomial over $R[W]$. In this article we show that the above result holds over any Noetherian domain $R$ provided the module of differentials $\Omega_R(A)$ of the affine fibration $A$ (which is necessarily a projective $A$-module by a theorem of Asanuma) is a stably free $A$-module., Comment: 6 pages

In this paper we extend an epimorphism theorem of D. Wright to the case of discrete valuation rings. We will show that if $(R, t)$ is a discrete valuation ring, $n \ge 2$ is an integer not divisible by the characteristic of the residue field $R/tR$, and $g \in R[X, Y, Z]$ is a polynomial of the form $g = b(X,Y)Z^n - a(X,Y)$ such that $R[X, Y, Z]/(g)$ is a polynomial algebra in two variables, then $g$ and $Z$ form a pair of variables in $R[X, Y, Z]$. We will also show that the result holds over any Noetherian domain containing $\mathbb{Q}$., Comment: 16 pages

We propose to use high numerical aperture single mode optical fibers like photonic crystal fiber for lensless in-line holographic microscopy. Highly divergent beam helps to overcome the spatial sampling limitation of the image sensor. In this paper, a submicron lateral resolution has been demonstrated, with an imaging sensor of pixel pitch 1.12 micrometer and a photonic crystal fiber of mode field diameter 1.8 micrometer. In earlier methods of single-shot lensless imaging, submicron resolution has been obtained at very small working distance and field of view. The proposed method improves the resolution without compromising the working distance. A working distance of (but not limited to) ~1.7 mm with a field of View ~1.4 mm has been demonstrated.

The theory of the nuclear energy-density functional is used to provide a unified and thermodynamically consistent treatment of all regions of cold non-accreting neutron stars. In order to assess the impact of our lack of complete knowledge of the density dependence of the symmetry energy on the constitution and the global structure of neutron stars, we employ four different functionals. All of them were precision fitted to essentially all the nuclear-mass data with the Hartree-Fock-Bogoliubov method and two different neutron-matter equations of state based on realistic nuclear forces. For each functional, we calculate the composition, the pressure-density relation, and the chemical potentials throughout the star. We show that uncertainties in the symmetry energy can significantly affect the theoretical results for the composition and global structure of neutron stars. To facilitate astrophysical applications, we construct analytic fits to our numerical results., Comment: 36 pages, 31 figures, 29 tables; corrected according to an erratum (2019, accepted)

We demonstrate hierarchical assembly of plasmonic toroidal metamolecules, which exhibit tailored optical activity in the visible spectral range. Each metamolecule consists of four identical origami-templated helical building blocks. Such toroidal metamolecules show stronger chiroptical response than monomers and dimers of the helical building blocks. Enantiomers of the plasmonic structures yield opposite circular dichroism spectra. The experimental results agree well with the theoretical simulations. We also demonstrate that given the circular symmetry of the structures, distinct chiroptical response along their axial orientation can be uncovered via simple spin-coating of the metamolecules on substrates. Our work provides a new strategy to create plasmonic chiral platforms with sophisticated nanoscale architectures for potential applications such as chiral sensing using chemically-based assembly systems., Comment: 4 figures

Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks. Finding the best deep model requires a combination of finding both the right architecture and the correct set of parameters appropriate for that architecture. In addition, this complexity (in terms of layer types, number of neurons, and number of layers) also present problems with generalization since larger networks are easier to overfit to the data. In this paper, we propose a search framework for finding effective architectural building blocks for convolutional neural networks (CNN). Our approach is much faster at finding models that are close to state-of-the-art in performance. In addition, the models discovered by our approach are also smaller than models discovered by similar techniques. We achieve these twin advantages by designing our search space in such a way that it searches over a reduced set of state-of-the-art building blocks for CNNs including residual block, inception block, inception-residual block, ResNeXt block and many others. We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case). We also show that learned models are transferable between datasets.

The theoretical procedure of supersymmetric quantum mechanics is adopted to generate the resonance state wave functions of the unbound nucleus $^{15}$Be. In this framework, we used a density dependent M3Y microscopic potential and arrived at the energy and width of the 1.8 MeV (5/2$^+$) resonance state. We did not find any other nearby resonances for $^{15}$Be. It becomes apparent that the present framework is a powerful tool to theoretically complement the increasingly important accelerator based experiments with unbound nuclei., Comment: 5 pages, 4 figures, Phys. Lett. B (2017)