From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

The simplest possible bouncing quantum cosmological model

We present and expand the simplest possible quantum cosmological bouncing model already discussed in previous works: the trajectory formulation of quantum mechanics applied to cosmology (through the Wheeler-De Witt equation) in the FLRW minisuper- space without spatial curvature. The initial conditions that were previously assumed were such that the wave function would not change its functional form but instead provide a dynamics to its parameters. Here, we consider a more general situation, in practice con- sisting of modified Gaussian wave functions, aiming at obtaining a non singular bounce from a contracting phase. Whereas previous works consistently obtain very symmetric bounces, we find that it is possible to produce highly non symmetric solutions, and even cases for which multiple bounces naturally occur. We also introduce a means of treating the shear in this category of models by quantizing in the Bianchi I minisuperpace.Comment: 10 pages, proceeding of the workshop "Current challenges in cosmology" held in Cali, Colombia, May 18 to 22, 201

Anisotropic multiple bounce models

We analyze the Galileon ghost condensate implementation of a bouncing cosmological model in the presence of a non negligible anisotropic stress. We exhibit its structure, which we find to be far richer than previously thought. In particular, even restricting attention to a single set of underlying microscopic parameters, we obtain, numerically, many qualitatively different regimes: depending on the initial conditions on the scalar field leading the dynamics of the universe, the contraction phase can evolve directly towards a singularity, avoid it by bouncing once, or even bounce many times before settling into an ever-expanding phase. We clarify the behavior of the anisotropies in these various situations.Comment: 11 pages, 9 figures, minor correction

Coupled currents in cosmic strings

We first examine the microstructure of a cosmic string endowed with two simple Abelian currents. This microstructure depends on two state parameters. We then provide the macroscopic description of such a string and show that it depends on an additional Lorentz-invariant state parameter that relates the two currents. We find that in most of the parameter space, the two-current string is essentially equivalent to the single current-carrying string, i.e., only one field condenses onto the defect. In the regions where two currents are present, we find that as far as stability is concerned, one can approximate the dynamics with good accuracy using an analytic model based on either a logarithmic (on the electric side, i.e., for timelike currents) or a rational (on the magnetic side, i.e., for spacelike currents) worldsheet Lagrangian.Comment: 25 pages, 9 figure

Sparse Mixture Conditional Density Estimation by Superficial Regularization

In this paper, the estimation of conditional densities between continuous random variables from noisy samples is considered. The conditional densities are modeled as heteroscedastic Gaussian mixture densities allowing for closed-form solution of Bayesian inference with full-densities. The main contributions of this paper are an improved generalization quality of the estimates by the introduction of a superficial regularizer, the consideration of model uncertainty relative to local data densities by means of adaptive covariances, and the proposition of an efficient distance-based estimation algorithm. This algorithm corresponds to an iterative nested optimization scheme, optimizing hyper-parameters, component placement, and mixture weights. The obtained solutions are sparse, smooth, and generalize well as benchmark experiments, e.g., in nonlinear filtering show

Progressive Correction for Deterministic Dirac Mixture Approximations

Since the advent of Monte-Carlo particle filtering, particle representations of densities have become increasingly popular due to their flexibility and implicit adaptive resolution. In this paper, an algorithm for the multiplication of a systematic Dirac mixture (DM) approximation with a continuous likelihood function is presented, which applies a progressive correction scheme, in order to avoid the particle degeneration problem. The preservation of sample regularity and therefore, representation quality of the underlying smooth density, is ensured by including a new measure of smoothness for Dirac mixtures, the DM energy, into the distance measure. A comparison to common correction schemes in Monte-Carlo methods reveals large improvements especially in cases of small overlap between the likelihood and prior density, as well as for multi-modal likelihoods

Resistance and reconfiguration of natural flexible submerged vegetation in hydrodynamic river modelling

In-stream submerged macrophytes have a complex morphology and several species are not rigid, but are flexible and reconfigure along with the major flow direction to avoid potential damage at high stream velocities. However, in numerical hydrodynamic models, they are often simplified to rigid sticks. In this study hydraulic resistance of vegetation is represented by an adapted bottom friction coefficient and is calculated using an existing two layer formulation for which the input parameters were adjusted to account for (i) the temporary reconfiguration based on an empirical relationship between deflected vegetation height and upstream depth-averaged velocity, and (ii) the complex morphology of natural, flexible, submerged macrophytes. The main advantage of this approach is that it removes the need for calibration of the vegetation resistance coefficient. The calculated hydraulic roughness is an input of the hydrodynamic model Telemac 2D, this model simulates depth-averaged stream velocities in and around individual vegetation patches. Firstly, the model was successfully validated against observed data of a laboratory flume experiment with three macrophyte species at three discharges. Secondly, the effect of reconfiguration was tested by modelling an in situ field flume experiment with, and without, the inclusion of macrophyte reconfiguration. The inclusion of reconfiguration decreased the calculated hydraulic roughness which resulted in smaller spatial variations of simulated stream velocities, as compared to the model scenario without macrophyte reconfiguration. We discuss that including macrophyte reconfiguration in numerical models input, can have significant and extensive effects on the model results of hydrodynamic variables and associated ecological and geomorphological parameters

Ultra-low-noise supercontinuum generation with a flat near-zero normal dispersion fiber

A pure silica photonic crystal fiber with a group velocity dispersion ($\beta_2$) of 4 ps$^2$/km at 1.55 $\mu$m and less than 7 ps$^2$/km from 1.32 $\mu$m to the zero dispersion wavelength (ZDW) 1.80 $\mu$m was designed and fabricated. The dispersion of the fiber was measured experimentally and found to agree with the fiber design, which also provides low loss below 1.83 $\mu$m due to eight outer rings with increased hole diameter. The fiber was pumped with a 1.55 $\mu$m, 125 fs laser and, at the maximum in-coupled peak power (P$_0$) of 9 kW, a 1.34$-$1.82 $\mu$m low-noise spectrum with a relative intensity noise below 2.2\% was measured. The numerical modeling agreed very well with the experiments and showed that P$_0$ could be increased to 26 kW before noise from solitons above the ZDW started to influence the spectrum by pushing high-noise dispersive waves through the spectrum

Expression in the human brain of retinoic acid induced 1, a protein associated with neurobehavioural disorders

Acknowledgements Funding was provided by the Wellcome Trust and Tenovus Scotland. Prof Fragoso is the recipient of a Post Doctoral Science without Borders grant from the Brazilian National Council for Scientific and Technological Development (CNPq, 37450/2012- 7). We also thank Aberdeen Proteomics for assistance with the western blots as well as the Microscopy and Histology Core Facility at the University of Aberdeen for confocal microscopy.Peer reviewedPublisher PD