5,877 research outputs found
Possible climates on terrestrial exoplanets
What kind of environment may exist on terrestrial planets around other stars?
In spite of the lack of direct observations, it may not be premature to
speculate on exoplanetary climates, for instance to optimize future telescopic
observations, or to assess the probability of habitable worlds. To first order,
climate primarily depends on 1) The atmospheric composition and the volatile
inventory; 2) The incident stellar flux; 3) The tidal evolution of the
planetary spin, which can notably lock a planet with a permanent night side.
The atmospheric composition and mass depends on complex processes which are
difficult to model: origins of volatile, atmospheric escape, geochemistry,
photochemistry. We discuss physical constraints which can help us to speculate
on the possible type of atmosphere, depending on the planet size, its final
distance for its star and the star type. Assuming that the atmosphere is known,
the possible climates can be explored using Global Climate Models analogous to
the ones developed to simulate the Earth as well as the other telluric
atmospheres in the solar system. Our experience with Mars, Titan and Venus
suggests that realistic climate simulators can be developed by combining
components like a "dynamical core", a radiative transfer solver, a
parametrisation of subgrid-scale turbulence and convection, a thermal ground
model, and a volatile phase change code. On this basis, we can aspire to build
reliable climate predictors for exoplanets. However, whatever the accuracy of
the models, predicting the actual climate regime on a specific planet will
remain challenging because climate systems are affected by strong positive
destabilizing feedbacks (such as runaway glaciations and runaway greenhouse
effect). They can drive planets with very similar forcing and volatile
inventory to completely different states.Comment: In press, Proceedings of the Royal Society A 31 pages, 6 figure
Cluster-Aided Mobility Predictions
Predicting the future location of users in wireless net- works has numerous
applications, and can help service providers to improve the quality of service
perceived by their clients. The location predictors proposed so far estimate
the next location of a specific user by inspecting the past individual
trajectories of this user. As a consequence, when the training data collected
for a given user is limited, the resulting prediction is inaccurate. In this
paper, we develop cluster-aided predictors that exploit past trajectories
collected from all users to predict the next location of a given user. These
predictors rely on clustering techniques and extract from the training data
similarities among the mobility patterns of the various users to improve the
prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility
Predictor), a cluster-aided predictor whose design is based on recent
non-parametric bayesian statistical tools. CAMP is robust and adaptive in the
sense that it exploits similarities in users' mobility only if such
similarities are really present in the training data. We analytically prove the
consistency of the predictions provided by CAMP, and investigate its
performance using two large-scale datasets. CAMP significantly outperforms
existing predictors, and in particular those that only exploit individual past
trajectories
Embedding ethics and ethical practice within and across the curriculum: emerging findings from a TQEF-funded project
Stable variable selection for right censored data: comparison of methods
The instability in the selection of models is a major concern with data sets
containing a large number of covariates. This paper deals with variable
selection methodology in the case of high-dimensional problems where the
response variable can be right censored. We focuse on new stable variable
selection methods based on bootstrap for two methodologies: the Cox
proportional hazard model and survival trees. As far as the Cox model is
concerned, we investigate the bootstrapping applied to two variable selection
techniques: the stepwise algorithm based on the AIC criterion and the
L1-penalization of Lasso. Regarding survival trees, we review two
methodologies: the bootstrap node-level stabilization and random survival
forests. We apply these different approaches to two real data sets. We compare
the methods on the prediction error rate based on the Harrell concordance index
and the relevance of the interpretation of the corresponding selected models.
The aim is to find a compromise between a good prediction performance and ease
to interpretation for clinicians. Results suggest that in the case of a small
number of individuals, a bootstrapping adapted to L1-penalization in the Cox
model or a bootstrap node-level stabilization in survival trees give a good
alternative to the random survival forest methodology, known to give the
smallest prediction error rate but difficult to interprete by
non-statisticians. In a clinical perspective, the complementarity between the
methods based on the Cox model and those based on survival trees would permit
to built reliable models easy to interprete by the clinician.Comment: nombre de pages : 29 nombre de tableaux : 2 nombre de figures :
Impact of Resonant Magnetic Perturbations on Zonal Modes, Drift-Wave Turbulence and the L-H Transition Threshold
We study the effects of Resonant Magnetic Perturbations (RMPs) on turbulence,
flows and confinement in the framework of resistive drift-wave turbulence. This
work was motivated, in parts, by experiments reported at the IAEA 2010
conference [Y. Xu {\it et al}, Nucl. Fusion \textbf{51}, 062030] which showed a
decrease of long-range correlations during the application of RMPs. We derive
and apply a zero-dimensional predator-prey model coupling the Drift-Wave Zonal
Mode system [M. Leconte and P.H. Diamond, Phys. Plasmas \textbf{19}, 055903] to
the evolution of mean quantities. This model has both density gradient drive
and RMP amplitude as control parameters and predicts a novel type of transport
bifurcation in the presence of RMPs. This model allows a description of the
full L-H transition evolution with RMPs, including the mean sheared flow
evolution. The key results are: i) The L-I and I-H power thresholds \emph{both}
increase with RMP amplitude |\bx|, the relative increase of the L-I threshold
scales as \Delta P_{\rm LI} \propto |\bx|^2 \nu_*^{-2} \gyro^{-2}, where
is edge collisionality and \gyro is the sound gyroradius. ii) RMPs
are predicted to \emph{decrease} the hysteresis between the forward and
back-transition. iii) Taking into account the mean density evolution, the
density profile - sustained by the particle source - has an increased turbulent
diffusion compared with the reference case without RMPs which provides one
possible explanation for the \emph{density pump-out} effect.Comment: 30 pages, IAEA-based articl
Understanding exoplanet formation, structure and evolution in 2010
In this short review, we summarize our present understanding (and
non-understanding) of exoplanet formation, structure and evolution, in the
light of the most recent discoveries. Recent observations of transiting massive
brown dwarfs seem to remarkably confirm the predicted theoretical mass-radius
relationship in this domain. This mass-radius relationship provides, in some
cases, a powerful diagnostic to distinguish planets from brown dwarfs of same
mass, as for instance for Hat-P-20b. If confirmed, this latter observation
shows that planet formation takes place up to at least 8 Jupiter masses.
Conversely, observations of brown dwarfs down to a few Jupiter masses in young,
low-extinction clusters strongly suggest an overlapping mass domain between
(massive) planets and (low-mass) brown dwarfs, i.e. no mass edge between these
two distinct (in terms of formation mechanism) populations. At last, the large
fraction of heavy material inferred for many of the transiting planets confirms
the core-accretion scenario as been the dominant one for planet formation.Comment: Invited review, IAU Symposium No. 276, The Astrophysics of Planetary
Systems: Formation, Structure, and Dynamical Evolutio
A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population
In survey analysis, the estimation of the cumulative distribution function
(cdf) is of great interest: it allows for instance to derive quantiles
estimators or other non linear parameters derived from the cdf. We consider the
case where the response variable is a right censored duration variable. In this
framework, the classical estimator of the cdf is the Kaplan-Meier estimator. As
an alternative, we propose a nonparametric model-based estimator of the cdf in
a finite population. The new estimator uses auxiliary information brought by a
continuous covariate and is based on nonparametric median regression adapted to
the censored case. The bias and variance of the prediction error of the
estimator are estimated by a bootstrap procedure adapted to censoring. The new
estimator is compared by model-based simulations to the Kaplan-Meier estimator
computed with the sampled individuals: a significant gain in precision is
brought by the new method whatever the size of the sample and the censoring
rate. Welfare duration data are used to illustrate the new methodology.Comment: 18 pages, 5 figure
Unconventional Features in the Quantum Hall Regime of Disordered Graphene: Percolating Impurity States and Hall Conductance Quantization
We report on the formation of critical states in disordered graphene, at the
origin of variable and unconventional transport properties in the quantum Hall
regime, such as a zero-energy Hall conductance plateau in the absence of an
energy bandgap and Landau level degeneracy breaking. By using efficient
real-space transport methodologies, we compute both the dissipative and Hall
conductivities of large size graphene sheets with random distribution of model
single and double vacancies. By analyzing the scaling of transport coefficients
with defect density, system size and magnetic length, we elucidate the origin
of anomalous quantum Hall features as magnetic-field dependent impurity states,
which percolate at some critical energies. These findings shed light on
unidentified states and quantum transport anomalies reported experimentally.Comment: 7 pages, 7 figures. Accepted in PR
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