Warped space-time for phonons moving in a perfect nonrelativistic fluid

We construct a kinematical analogue of superluminal travel in the ``warped'' space-times curved by gravitation, in the form of ``super-phononic'' travel in the effective space-times of perfect nonrelativistic fluids. These warp-field space-times are most easily generated by considering a solid object that is placed as an obstruction in an otherwise uniform flow. No violation of any condition on the positivity of energy is necessary, because the effective curved space-times for the phonons are ruled by the Euler and continuity equations, and not by the Einstein field equations.Comment: 7 pages, 1 figure. Version as published; references update

Multi-mode entanglement of N harmonic oscillators coupled to a non-Markovian reservoir

Multi-mode entanglement is investigated in the system composed of $N$ coupled identical harmonic oscillators interacting with a common environment. We treat the problem very general by working with the Hamiltonian without the rotating-wave approximation and by considering the environment as a non-Markovian reservoir to the oscillators. We invoke an $N$-mode unitary transformation of the position and momentum operators and find that in the transformed basis the system is represented by a set of independent harmonic oscillators with only one of them coupled to the environment. Working in the Wigner representation of the density operator, we find that the covariance matrix has a block diagonal form that it can be expressed in terms of multiples of $3\times 3$ and $4\times 4$ matrices. This simple property allows to treat the problem to some extend analytically. We illustrate the advantage of working in the transformed basis on a simple example of three harmonic oscillators and find that the entanglement can persists for long times due to presence of constants of motion for the covariance matrix elements. We find that, in contrast to what one could expect, a strong damping of the oscillators leads to a better stationary entanglement than in the case of a weak damping.Comment: 21 pages, 4 figure

Entropy in the NUT-Kerr-Newman Black Holes Due to an Arbitrary Spin Field

Membrane method is used to compute the entropy of the NUT-Kerr-Newman black holes. It is found that even though the Euler characteristic is greater than two, the Bekenstein-Hawking area law is still satisfied. The formula $S=\chi A/8$ relating the entropy and the Euler characteristic becomes inapplicable for non-extreme four dimensional NUT-Kerr-Newman black holes

What is the Geometry of Superspace ?

We investigate certain properties of the Wheeler-DeWitt metric (for constant lapse) in canonical General Relativity associated with its non-definite nature. Contribution to the conference on Mach's principle: "From Newtons Bucket to Quantum Gravity", July 26-30 1993, Tuebingen, GermanyComment: 10 pages, Plain Te

Halo stochasticity in global clustering analysis

In the present work we study the statistics of haloes, which in the halo model determines the distribution of galaxies. Haloes are known to be biased tracer of dark matter, and at large scales it is usually assumed there is no intrinsic stochasticity between the two fields. Following the work of Seljak & Warren (2004), we explore how correct this assumption is and, moving a step further, we try to qualify the nature of stochasticity. We use Principal Component Analysis applied to the outputs of a cosmological N-body simulation to: (1) explore the behaviour of stochasticity in the correlation between haloes of different masses; (2) explore the behaviour of stochasticity in the correlation between haloes and dark matter. We show results obtained using a catalogue with 2.1 million haloes, from a PMFAST simulation with box size of 1000h^{-1}Mpc. In the relation between different populations of haloes we find that stochasticity is not-negligible even at large scales. In agreement with the conclusions of Tegmark & Bromley (1999) who studied the correlations of different galaxy populations, we found that the shot-noise subtracted stochasticity is qualitatively different from `enhanced' shot noise and, specifically, it is dominated by a single stochastic eigenvalue. We call this the `minimally stochastic' scenario, as opposed to shot noise which is `maximally stochastic'. In the correlation between haloes and dark matter, we find that stochasticity is minimized, as expected, near the dark matter peak (k ~ 0.02 h Mpc^{-1} for a LambdaCDM cosmology) and, even at large scales, it is of the order of 15 per cent above the shot noise. Moreover, we find that the reconstruction of the dark matter distribution is improved when we use eigenvectors as tracers of the bias. [Abridged]Comment: 9 pages, 12 figures. Submitted to MNRA

DEVELOPMENT OF COMPLEX TECHNOLOGIES FOR PROCESSING OF RICE BRAN WITH OBTAINING OF THERAPEUTIC OIL AND MEDICINE – PHYTIN

In this article, showed effectiveness of obtaining therapeutic, edible oil and medicine-phytin and researched the viability of using waste generated during processing of rice - rice bran. Carried out research on the qualitative and physical - chemical characteristics of rice bran and defined quantitative content of phytin in raw materials and substances

DEVELOPMENT OF COMPLEX TECHNOLOGIES FOR PROCESSING OF RICE BRAN WITH OBTAINING OF THERAPEUTIC OIL AND MEDICINE – PHYTIN

In this article, showed effectiveness of obtaining therapeutic, edible oil and medicine-phytin and researched the viability of using waste generated during processing of rice - rice bran. Carried out research on the qualitative and physical - chemical characteristics of rice bran and defined quantitative content of phytin in raw materials and substances

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201