A New Monte Carlo Method and Its Implications for Generalized Cluster Algorithms

We describe a novel switching algorithm based on a ``reverse'' Monte Carlo method, in which the potential is stochastically modified before the system configuration is moved. This new algorithm facilitates a generalized formulation of cluster-type Monte Carlo methods, and the generalization makes it possible to derive cluster algorithms for systems with both discrete and continuous degrees of freedom. The roughening transition in the sine-Gordon model has been studied with this method, and high-accuracy simulations for system sizes up to $1024^2$ were carried out to examine the logarithmic divergence of the surface roughness above the transition temperature, revealing clear evidence for universal scaling of the Kosterlitz-Thouless type.Comment: 4 pages, 2 figures. Phys. Rev. Lett. (in press

Singular value decomposition in parametrised tests of post-Newtonian theory

Various coefficients of the 3.5 post-Newtonian (PN) phasing formula of non-spinning compact binaries moving in circular orbits is fully characterized by the two component masses. If two of these coefficients are independently measured, the masses can be estimated. Future gravitational wave observations could measure many of the 8 independent PN coefficients calculated to date. These additional measurements can be used to test the PN predictions of the underlying theory of gravity. Since all of these parameters are functions of the two component masses, there is strong correlation between the parameters when treated independently. Using Singular Value Decomposition of the Fisher information matrix, we remove this correlations and obtain a new set of parameters which are linear combinations of the original phasing coefficients. We show that the new set of parameters can be estimated with significantly improved accuracies which has implications for the ongoing efforts to implement parametrised tests of PN theory in the data analysis pipelines.Comment: 17 pages, 6 figures, Accepted for publication in Classical and Quantum Gravity (Matches with the published version

An accurate formula for the period of a simple pendulum oscillating beyond the small-angle regime

A simple approximation formula is derived here for the dependence of the period of a simple pendulum on amplitude that only requires a pocket calculator and furnishes an error of less than 0.25% with respect to the exact period. It is shown that this formula describes the increase of the pendulum period with amplitude better than other simple formulas found in literature. A good agreement with experimental data for a low air-resistance pendulum is also verified and it suggests, together with the current availability/precision of timers and detectors, that the proposed formula is useful for extending the pendulum experiment beyond the usual small-angle oscillations.Comment: 15 pages and 4 figures. to appear in American Journal of Physic

Deep Learning Algorithms for Diagnosing Covid 19 Based on X-Ray and CT Images

An outbreak of a highly pathogenic coronavirus, which can cause chronic respiratory illness and high mortality rates. It takes a considerable amount of time to perform the polymerase chain reaction (PCR) used in COVID tests. Its accuracy ranges from 30% to 70%. In contrast, CT and chest X-ray diagnostics are 98% and 80% accurate in detecting COVID, respectively. A deep learning algorithms was applied to CT and X-ray images to enable rapid and accurately diagnosis of COVID-19 within seconds. In this survey, we revised all state-of-the-art studies of COVID-19 based on CT and X-ray images. Also, we analysed multiple deep learning networks and compared the performance of each technique. The result of the comparison shows that the baseline neural network has better efficiency in the recognition of COVID-19. The detection accuracy of baseline networks ranges between 93% and 98.7%. This shows the efficiency of deep learning techniques in identifying COVID-19

Identifying Medicinal Plant Leaves Using Textures and Optimal Colour Spaces Channel

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT). Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces

Purification of Mixed State with Closed Timelike Curve is not Possible

In ordinary quantum theory any mixed state can be purified in an enlarged Hilbert space by bringing an ancillary system. The purified state does not depend on the state of any extraneous system with which the mixed state is going to interact and on the physical interaction. Here, we prove that it is not possible to purify a mixed state that traverses a closed time like curve (CTC) and allowed to interact in a consistent way with a causality-respecting (CR) quantum system in the same manner. Thus, in general for arbitrary interactions between CR and CTC systems there is no universal 'Church of the larger Hilbert space' for mixed states with CTC. This shows that in quantum theory with CTCs there can exist 'proper' and 'improper' mixtures.Comment: Latex2e, No Figs, 4 + pages, An error corrected, Results unchange

On the Structure of ZnI2{\rm ZnI_2}

A new structure for ${\rm ZnI_2}$ is proposed which it exists in tetragonal state. In this structure the ${\rm ZnI_2}$ molecule exists in a nonlinear array and forms the basis of the tetragonal unit cell with one basis per unit cell. The structural analysis based on the reflections listed in ASTM 30-1479 shows that the proposed structure is correct.Comment: six pages and four figures. Manuscript prepared in RevTe

Quantum superposition of multiple clones and the novel cloning machine

we envisage a novel quantum cloning machine, which takes an input state and produces an output state whose success branch can exist in a linear superposition of multiple copies of the input state and the failure branch exist in a superposition of composite state independent of the input state. We prove that unknown non-orthogonal states chosen from a set $\cal S$ can evolve into a linear superposition of multiple clones by a unitary process if and only if the states are linearly independent. We derive a bound on the success probability of the novel cloning machine. We argue that the deterministic and probabilistic clonings are special cases of our novel cloning machine.Comment: Two column, 5 pages, Latex, some additions, minor changes. Phys. Rev. Lett. (To appear, 1999