Achievable efficiencies for probabilistically cloning the states

We present an example of quantum computational tasks whose performance is enhanced if we distribute quantum information using quantum cloning. Furthermore we give achievable efficiencies for probabilistic cloning the quantum states used in implemented tasks for which cloning provides some enhancement in performance.Comment: 9 pages, 8 figure

Iterative maximum-likelihood reconstruction in quantum homodyne tomography

I propose an iterative expectation maximization algorithm for reconstructing a quantum optical ensemble from a set of balanced homodyne measurements performed on an optical state. The algorithm applies directly to the acquired data, bypassing the intermediate step of calculating marginal distributions. The advantages of the new method are made manifest by comparing it with the traditional inverse Radon transformation technique

Mass hierarchy, 2-3 mixing and CP-phase with Huge Atmospheric Neutrino Detectors

We explore the physics potential of multi-megaton scale ice or water Cherenkov detectors with low ($\sim 1$ GeV) threshold. Using some proposed characteristics of the PINGU detector setup we compute the distributions of events versus neutrino energy $E_\nu$ and zenith angle $\theta_z$, and study their dependence on yet unknown neutrino parameters. The $(E_\nu - \theta_z)$ regions are identified where the distributions have the highest sensitivity to the neutrino mass hierarchy, to the deviation of the 2-3 mixing from the maximal one and to the CP-phase. We evaluate significance of the measurements of the neutrino parameters and explore dependence of this significance on the accuracy of reconstruction of the neutrino energy and direction. The effect of degeneracy of the parameters on the sensitivities is also discussed. We estimate the characteristics of future detectors (energy and angle resolution, volume, etc.) required for establishing the neutrino mass hierarchy with high confidence level. We find that the hierarchy can be identified at $3\sigma$ -- $10\sigma$ level (depending on the reconstruction accuracies) after 5 years of PINGU operation.Comment: 39 pages, 21 figures. Description of Fig.3 correcte

Star-galaxy separation by far-infrared color-color diagrams for the AKARI FIS All-Sky Survey (Bright Source Catalogue Version beta-1)

To separate stars and galaxies in the far infrared AKARI All-Sky Survey data, we have selected a sample with the complete color information available in the low extinction regions of the sky and constructed color-color plots for these data. We looked for the method to separate stars and galaxies using the color information. We performed an extensive search for the counterparts of these selected All-Sky Survey sources in the NED and SIMBAD databases. Among 5176 objects, we found 4272 galaxies, 382 other extragalactic objects, 349 Milky Way stars, 50 other Galactic objects, and 101 sources detected before in various wavelengths but of an unknown origin. 22 sources were left unidentified. Then, we checked colors of stars and galaxies in the far-infrared flux-color and color-color plots. In the resulting diagrams, stars form two clearly separated clouds. One of them is easy to be distinguished from galaxies and allows for a simple method of excluding a large part of stars using the far-infrared data. The other smaller branch, overplotting galaxies, consists of stars known to have an infrared excess, like Vega and some fainter stars discovered by IRAS or 2MASS. The color properties of these objects in any case make them very difficult to distinguish from galaxies. We conclude that the FIR color-color diagrams allow for a high-quality star-galaxy separation. With the proposed simple method we can select more that 95 % of galaxies rejecting at least 80 % of stars.Comment: 20 pages, 41 figures, "Astronomy & Astrophysics", accepted, to appear in the AKARI special issu

Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1–3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC–FEF–LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load

Resistivity due to low-symmetrical defects in metals

The impurity resistivity, also known as the residual resistivity, is calculated ab initio using multiple-scattering theory. The mean-free path is calculated by solving the Boltzmann equation iteratively. The resistivity due to low-symmetrical defects, such as an impurity-vacancy pair, is calculated for the FCC host metals Al and Ag and the BCC transition metal V. Commonly, 1/f noise is attributed to the motion of such defects in a diffusion process.Comment: 24 pages in REVTEX-preprint format, 10 Postscript figures. Phys. Rev. B, vol. 57 (1998), accepted for publicatio

An action principle for the quantization of parametric theories and nonlinear quantum cosmology

By parametrizing the action integral for the standard Schrodinger equation we present a derivation of the recently proposed method for quantizing a parametrized theory. The reformulation suggests a natural extension from conventional to nonlinear quantum mechanics. This generalization enables a unitary description of the quantum evolution for a broad class of constrained Hamiltonian systems with a nonlinear kinematic structure. In particular, the new theory is applicable to the quantization of cosmological models where a chosen gravitational degree of freedom acts as geometric time. This is demonstrated explicitly using three cosmological models: the Friedmann universe with a massless scalar field and Bianchi type I and IX models. Based on these investigations, the prospect of further developing the proposed quantization scheme in the context of quantum gravity is discussed.Comment: 14 page

Bayesian alignments of warped multi-output Gaussian processes

We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We present an efficient varia-tional approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem. We show results for an artificial data set and real-world data of two wind turbines