A new approach to the study of quasi-normal modes of rotating stars

We propose a new method to study the quasi-normal modes of rotating relativistic stars. Oscillations are treated as perturbations in the frequency domain of the stationary, axisymmetric background describing a rotating star. The perturbed quantities are expanded in circular harmonics, and the resulting 2D-equations they satisfy are integrated using spectral methods in the (r,theta)-plane. The asymptotic conditions at infinity, needed to find the mode frequencies, are implemented by generalizing the standing wave boundary condition commonly used in the non rotating case. As a test, the method is applied to find the quasi-normal mode frequencies of a slowly rotating star.Comment: 24 pages, 7 figures, submitted to Phys. Rev.

Unstable g-modes in Proto-Neutron Stars

In this article we study the possibility that, due to non-linear couplings, unstable g-modes associated to convective motions excite stable oscillating g-modes. This problem is of particular interest, since gravitational waves emitted by a newly born proto-neutron star pulsating in its stable g-modes would be in the bandwidth of VIRGO and LIGO. Our results indicate that nonlinear saturation of unstable modes occurs at relatively low amplitudes, and therefore, even if there exists a coupling between stable and unstable modes, it does not seem to be sufficiently effective to explain, alone, the excitation of the oscillating g-modes found in hydrodynamical simulations.Comment: 10 pages, 3 figures, to appear on Class. Quant. Gra

Empirical orbit determination using Apollo 14 data

An empirical orbit determination method is shown to yield highly accurate navigation results when applied to lunar orbit tracking data. Regressions and predictions of free flight Apollo 14 tracking data exhibit minimal residual growth, and the solution orbital elements behave in a very consistent manner. Solutions from data acquired during propulsive maneuvers result in degraded predictions. The residual patterns from free flight processing are shown to be consistent from pass to pass and are correlated with lunar topographic features

A Conditional Random Field for Multiple-Instance Learning

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets. 1

On the validity of the adiabatic approximation in compact binary inspirals

Using a semi-analytical approach recently developed to model the tidal deformations of neutron stars in inspiralling compact binaries, we study the dynamical evolution of the tidal tensor, which we explicitly derive at second post-Newtonian order, and of the quadrupole tensor. Since we do not assume a priori that the quadrupole tensor is proportional to the tidal tensor, i.e. the so called "adiabatic approximation", our approach enables us to establish to which extent such approximation is reliable. We find that the ratio between the quadrupole and tidal tensors (i.e., the Love number) increases as the inspiral progresses, but this phenomenon only marginally affects the emitted gravitational waveform. We estimate the frequency range in which the tidal component of the gravitational signal is well described using the stationary phase approximation at next-to-leading post-Newtonian order, comparing different contributions to the tidal phase. We also derive a semi-analytical expression for the Love number, which reproduces within a few percentage points the results obtained so far by numerical integrations of the relativistic equations of stellar perturbations.Comment: 13 pages, 1 table, 2 figures. Minor changes to match the version appearing on Phys. Rev.

Learning Visual Attributes

We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ‘striped’, or ‘spotted’. The model sees attributes as patterns of image segments, repeatedly sharing some characteristic properties. These can be any combination of appearance, shape, or the layout of segments within the pattern. Moreover, attributes with general appearance are taken into account, such as the pattern of alternation of any two colors which is characteristic for stripes. To enable learning from unsegmented training images, the model is learnt discriminatively, by optimizing a likelihood ratio. As demonstrated in the experimental evaluation, our model can learn in a weakly supervised setting and encompasses a broad range of attributes. We show that attributes can be learnt starting from a text query to Google image search, and can then be used to recognize the attribute and determine its spatial extent in novel real-world images.

Theoretical and numerical investigations of inverse patchy colloids in the fluid phase

We investigate the structural and thermodynamic properties of a new class of patchy colloids, referred to as inverse patchy colloids (IPCs) in their fluid phase via both theoretical methods and simulations. IPCs are nano- or micro- meter sized particles with differently charged surface regions. We extend conventional integral equation schemes to this particular class of systems: our approach is based on the so-called multi-density Ornstein-Zernike equation, supplemented by the associative Percus-Yevick approximation (APY). To validate the accuracy of our framework, we compare the obtained results with data extracted from $NpT$ and $NVT$ Monte Carlo simulations. In addition, other theoretical approaches are used to calculate the properties of the system: the reference hypernetted-chain (RHNC) method and the Barker-Henderson thermodynamic perturbation theory. Both APY and RHNC frameworks provide accurate predictions for the pair distribution functions: APY results are in slightly better agreement with MC data, in particular at lower temperatures where the RHNC solution does not converge.Comment: 41 pages, 9 figure

Machine learning-based Raman amplifier design

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.Comment: conferenc