Polarization alignments of radio quasars in JVAS/CLASS surveys

We test the hypothesis that the polarization vectors of flat-spectrum radio sources (FSRS) in the JVAS/CLASS 8.4-GHz surveys are randomly oriented on the sky. The sample with robust polarization measurements is made of $4155$ objects and redshift information is known for $1531$ of them. We performed two statistical analyses: one in two dimensions and the other in three dimensions when distance is available. We find significant large-scale alignments of polarization vectors for samples containing only quasars (QSO) among the varieties of FSRS's. While these correlations prove difficult to explain either by a physical effect or by biases in the dataset, the fact that the QSO's which have significantly aligned polarization vectors are found in regions of the sky where optical polarization alignments were previously found is striking.Comment: 13 pages, 9 figures, submitted to MNRA

Percolation without FKG

We prove a Russo-Seymour-Welsh theorem for large and natural perturbative families of discrete percolation models that do not necessarily satisfy the Fortuin-Kasteleyn-Ginibre condition of positive association. In particular, we prove the box-crossing property for the antiferromagnetic Ising model with small parameter, and for certain discrete Gaussian fields with oscillating correlation function

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity. Using these results as a benchmark, we discuss the role that the discount factor may play in the quality of the learning process of a deep Q-network (DQN). When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps. When used in conjunction with a varying learning rate, we empirically show that it outperforms original DQN on several experiments. We relate this phenomenon with the instabilities of neural networks when they are used in an approximate Dynamic Programming setting. We also describe the possibility to fall within a local optimum during the learning process, thus connecting our discussion with the exploration/exploitation dilemma.Comment: NIPS 2015 Deep Reinforcement Learning Worksho

Alignment of quasar polarizations with large-scale structures

We have measured the optical linear polarization of quasars belonging to Gpc-scale quasar groups at redshift z ~ 1.3. Out of 93 quasars observed, 19 are significantly polarized. We found that quasar polarization vectors are either parallel or perpendicular to the directions of the large-scale structures to which they belong. Statistical tests indicate that the probability that this effect can be attributed to randomly oriented polarization vectors is of the order of 1%. We also found that quasars with polarization perpendicular to the host structure preferentially have large emission line widths while objects with polarization parallel to the host structure preferentially have small emission line widths. Considering that quasar polarization is usually either parallel or perpendicular to the accretion disk axis depending on the inclination with respect to the line of sight, and that broader emission lines originate from quasars seen at higher inclinations, we conclude that quasar spin axes are likely parallel to their host large-scale structures.Comment: Accepted for publication in Astronomy and Astrophysic

Investigating the impact of optical selection effects on observed rest frame prompt GRB properties

Measuring gamma-ray burst (GRB) properties in their rest-frame is crucial to understand the physics at work in gamma-ray bursts. This can only be done for GRBs with known redshift. Since redshifts are usually measured from the optical spectrum of the afterglow, correlations between prompt and afterglow emissions may introduce biases in the distribution of rest-frame properties of the prompt emission. Our analysis is based on a sample of 90 GRBs with good optical follow-up and well measured prompt emission. 76 of them have a measure of redshift and 14 have no redshift. We estimate their optical brightness with their R magnitude measured two hours after the trigger and compare the rest frame prompt properties of different classes of GRB afterglow brightness. We find that the optical brightness of GRBs in our sample is mainly driven by their intrinsic afterglow luminosity. We show that GRBs with low and high afterglow optical fluxes have similar Epi , Eiso , Liso , indicating that the rest-frame distributions computed from GRBs with a redshift are not significantly distorted by optical selection effects. However we found that the rest frame T90 distribution is not immune to optical selection effect, which favor the selection of GRBs with longer durations. Finally, we note that GRBs in the upper part of the Epi-Eiso plane have fainter optical afterglows and we show that optical selection effects strongly favor the detection of GRBs with bright afterglows located close or below the best-fit Epi-Eiso relation, whose redshift is easily measurable.Comment: 41 pages, 10 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:1503.0276

Fast Selection of Spectral Variables with B-Spline Compression

The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward-backward procedure applied to the coefficients of a B-Spline representation of the spectra. The criterion used in the forward-backward procedure is the mutual information, allowing to find nonlinear dependencies between variables, on the contrary of the generally used correlation. The spline representation is used to get interpretability of the results, as groups of consecutive spectral variables will be selected. The experiments conducted on NIR spectra from fescue grass and diesel fuels show that the method provides clearly identified groups of selected variables, making interpretation easy, while keeping a low computational load. The prediction performances obtained using the selected coefficients are higher than those obtained by the same method applied directly to the original variables and similar to those obtained using traditional models, although using significantly less spectral variables

Recurrent Neural Networks with more flexible memory: better predictions than rough volatility

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short term memory networks (LSTMs) to predict asset price volatility, known to have a long memory. Generally, the number of epochs needed to train extended LSTMs is divided by two, while the variation of validation and test losses among models with the same hyperparameters is much smaller. We also show that the model with the smallest validation loss systemically outperforms rough volatility predictions by about 20% when trained and tested on a dataset with multiple time series.Comment: 9 page

On overfitting and asymptotic bias in batch reinforcement learning with partial observability

This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability. Our theoretical analysis formally characterizes that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk of overfitting. This analysis relies on expressing the quality of a state representation by bounding L1 error terms of the associated belief states. Theoretical results are empirically illustrated when the state representation is a truncated history of observations, both on synthetic POMDPs and on a large-scale POMDP in the context of smartgrids, with real-world data. Finally, similarly to known results in the fully observable setting, we also briefly discuss and empirically illustrate how using function approximators and adapting the discount factor may enhance the tradeoff between asymptotic bias and overfitting in the partially observable context.Comment: Accepted at the Journal of Artificial Intelligence Research (JAIR) - 31 page

Electric-field control of the magnetic anisotropy in an ultrathin (Ga,Mn)As/(Ga,Mn)(As,P) bilayer

We report on the electric control of the magnetic anisotropy in an ultrathin ferromagnetic (Ga,Mn)As/(Ga,Mn)(As,P) bilayer with competing in-plane and out-of-plane anisotropies. The carrier distribution and therefore the strength of the effective anisotropy is controlled by the gate voltage of a field effect device. Anomalous Hall Effect measurements confirm that a depletion of carriers in the upper (Ga,Mn)As layer results in the decrease of the in-plane anisotropy. The uniaxial anisotropy field is found to decrease by a factor ~ 4 over the explored gate-voltage range, so that the transition to an out-of-plane easy-axis configuration is almost reached