Communication between inertial observers with partially correlated reference frames

In quantum communication protocols the existence of a shared reference frame between two spatially separated parties is normally presumed. However, in many practical situations we are faced with the problem of misaligned reference frames. In this paper, we study communication between two inertial observers who have partial knowledge about the Lorentz transformation that relates their frames of reference. Since every Lorentz transformation can be decomposed into a pure boost followed by a rotation, we begin by analysing the effects on communication when the parties have partial knowledge about the transformation relating their frames, when the transformation is either a rotation or pure boost. This then enables us to investigate how the efficiency of communication is affected due to partially correlated inertial reference frames related by an arbitrary Lorentz transformation. Furthermore, we show how the results of previous studies where reference frames are completely uncorrelated are recovered from our results in appropriate limits.Comment: 9 pages, 3 figures, typos corrected, figures update

Generating entanglement between two-dimensional cavities in uniform acceleration

Moving cavities promise to be a suitable system for relativistic quantum information processing. It has been shown that an inertial and a uniformly accelerated one-dimensional cavity can become entangled by letting an atom emit an excitation while it passes through the cavities, but the acceleration degrades the ability to generate entanglement. We show that in the two-dimensional case the entanglement generated is affected not only by the cavity's acceleration but also by its transverse dimension which plays the role of an effective mass

Parameterization of Dark-Energy Properties: a Principal-Component Approach

Considerable work has been devoted to the question of how to best parameterize the properties of dark energy, in particular its equation of state w. We argue that, in the absence of a compelling model for dark energy, the parameterizations of functions about which we have no prior knowledge, such as w(z), should be determined by the data rather than by our ingrained beliefs or familiar series expansions. We find the complete basis of orthonormal eigenfunctions in which the principal components (weights of w(z)) that are determined most accurately are separated from those determined most poorly. Furthermore, we show that keeping a few of the best-measured modes can be an effective way of obtaining information about w(z).Comment: Unfeasibility of a truly model-independent reconstruction of w at z>1 illustrated. f(z) left out, and w(z) discussed in more detail. Matches the PRL versio

Gravitational Lensing as a Probe of Quintessence

A large number of cosmological studies now suggest that roughly two-thirds of the critical energy density of the Universe exists in a component with negative pressure. If the equation of state of such an energy component varies with time, it should in principle be possible to identify such a variation using cosmological probes over a wide range in redshift. Proper detection of any time variation, however, requires cosmological probes beyond the currently studied range in redshift of $\sim$ 0.1 to 1. We extend our analysis to gravitational lensing statistics at high redshift and suggest that a reliable sample of lensed sources, out to a redshift of $\sim$ 5, can be used to constrain the variation of the equation of state, provided that both the redshift distribution of lensed sources and the selection function involved with the lensed source discovery process are known. An exciting opportunity to catalog an adequate sample of lensed sources (quasars) to probe quintessence is now available with the ongoing Sloan Digital Sky Survey. Writing $w(z)\approx w_0 + z (dw/dz)_0$, we study the expected accuracy to which the equation of state today $w_0$ and its rate of change $(dw/dz)_0$ can simultaneously be constrained. Such a determination can rule out some missing-energy candidates, such as classes of quintessence models or a cosmological constant.Comment: Accepted for publication in ApJ Letters (4 pages, including 4 figures

Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs

2020 Fall.Includes bibliographical references.Deep convolutional neural networks (DCNNs) have achieved state of the art performance on a variety of tasks. These high-performing networks require large and diverse training datasets to facilitate generalization when extracting high-level features from low-level data. However, even with the availability of these diverse datasets, DCNNs are not prepared to handle all the data that could be thrown at them. One major challenges DCNNs face is the notion of forced choice. For example, a network trained for image classification is configured to choose from a predefined set of labels with the expectation that any new input image will contain an instance of one of the known objects. Given this expectation it is generally assumed that the network is trained for a particular domain, where domain is defined by the set of known object classes as well as more implicit assumptions that go along with any data collection. For example, some implicit characteristics of the ImageNet dataset domain are that most images are taken outdoors and the object of interest is roughly in the center of the frame. Thus the domain of the network is defined by the training data that is chosen. Which leads to the following key questions: Does a network know the domain it was trained for? and Can a network easily distinguish between in-domain and out-of-domain images? In this thesis it will be shown that for several widely used public datasets and commonly used neural networks, the answer to both questions is yes. The presence of a simple method of differentiating between in-domain and out-of-domain cases has significant implications for work on domain adaptation, transfer learning, and model generalization

Relative measurement error analysis in the process of the Nakagami-m fading parameter estimation

An approach to the relative measurement error analysis in the process of the Nakagami-m fading signal moments estimation will be presented in this paper. Relative error expressions will be also derived for the cases when MRC (Maximal Ratio Combining) diversity technique is performed at the receiver. Capitalizing on them, results will be graphically presented and discussed to show the influence of various parameters, such as diversity order and fading severity on the relative measurement error bounds

Future CMB Constraints on Early, Cold, or Stressed Dark Energy

We investigate future constraints on early dark energy (EDE) achievable by the Planck and CMBPol experiments, including cosmic microwave background (CMB) lensing. For the dark energy, we include the possibility of clustering through a sound speed c_s^2 <1 (cold dark energy) and anisotropic stresses parameterized with a viscosity parameter c_vis^2. We discuss the degeneracies between cosmological parameters and EDE parameters. In particular we show that the presence of anisotropic stresses in EDE models can substantially undermine the determination of the EDE sound speed parameter c_s^2. The constraints on EDE primordial energy density are however unaffected. We also calculate the future CMB constraints on neutrino masses and find that they are weakened by a factor of 2 when allowing for the presence of EDE, and highly biased if it is incorrectly ignored.Comment: 12 pages, 19 figure