1,986 research outputs found
Focal plane wavefront sensor achromatization : The multireference self-coherent camera
High contrast imaging and spectroscopy provide unique constraints for
exoplanet formation models as well as for planetary atmosphere models. But this
can be challenging because of the planet-to-star small angular separation and
high flux ratio. Recently, optimized instruments like SPHERE and GPI were
installed on 8m-class telescopes. These will probe young gazeous exoplanets at
large separations (~1au) but, because of uncalibrated aberrations that induce
speckles in the coronagraphic images, they are not able to detect older and
fainter planets. There are always aberrations that are slowly evolving in time.
They create quasi-static speckles that cannot be calibrated a posteriori with
sufficient accuracy. An active correction of these speckles is thus needed to
reach very high contrast levels (>1e7). This requires a focal plane wavefront
sensor. Our team proposed the SCC, the performance of which was demonstrated in
the laboratory. As for all focal plane wavefront sensors, these are sensitive
to chromatism and we propose an upgrade that mitigates the chromatism effects.
First, we recall the principle of the SCC and we explain its limitations in
polychromatic light. Then, we present and numerically study two upgrades to
mitigate chromatism effects: the optical path difference method and the
multireference self-coherent camera. Finally, we present laboratory tests of
the latter solution.
We demonstrate in the laboratory that the MRSCC camera can be used as a focal
plane wavefront sensor in polychromatic light using an 80 nm bandwidth at 640
nm. We reach a performance that is close to the chromatic limitations of our
bench: contrast of 4.5e-8 between 5 and 17 lambda/D.
The performance of the MRSCC is promising for future high-contrast imaging
instruments that aim to actively minimize the speckle intensity so as to detect
and spectrally characterize faint old or light gaseous planets.Comment: 14 pages, 20 figure
A linear speed-up theorem for cellular automata
AbstractIbarra (1985) showed that, given a cellular automaton of range 1 recognizing some language in time n+1+R(n), we can obtain another CA of range 1 recognizing exactly the same language but in time n+1+R(n)/k (k⩾2 arbitrary). Their proof proceeds indirectly (through the simulation of CAs by a special kind of sequential machines, the STMs) and we think it misses that way some of the deep intuition of the problem. We, therefore, provide here a direct proof of this result (extended to the case of CAs of arbitrary range) involving the explicit construction of a CA working in time n+1+R(n)/k. This speeded-up automaton first gathers the cells of the line k by k in n+1 steps which then enables it to start computing by “leaps” of k steps, thus completing the R(n) remaining steps in time R(n)/k. The major problem arising from the obligation to pass from one phase to the other synchronously is solved using a synchronization process derived from the solutions of the well-known “firing-squad synchronization problem” (FSSP)
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
The joint analysis of biomedical data in Alzheimer's Disease (AD) is
important for better clinical diagnosis and to understand the relationship
between biomarkers. However, jointly accounting for heterogeneous measures
poses important challenges related to the modeling of the variability and the
interpretability of the results. These issues are here addressed by proposing a
novel multi-channel stochastic generative model. We assume that a latent
variable generates the data observed through different channels (e.g., clinical
scores, imaging, ...) and describe an efficient way to estimate jointly the
distribution of both latent variable and data generative process. Experiments
on synthetic data show that the multi-channel formulation allows superior data
reconstruction as opposed to the single channel one. Moreover, the derived
lower bound of the model evidence represents a promising model selection
criterion. Experiments on AD data show that the model parameters can be used
for unsupervised patient stratification and for the joint interpretation of the
heterogeneous observations. Because of its general and flexible formulation, we
believe that the proposed method can find important applications as a general
data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with
MICCAI 2018, September 20, Granada, Spai
Exploring Millions of 6-State FSSP Solutions: the Formal Notion of Local CA Simulation
In this paper, we come back on the notion of local simulation allowing to
transform a cellular automaton into a closely related one with different local
encoding of information. This notion is used to explore solutions of the Firing
Squad Synchronization Problem that are minimal both in time (2n -- 2 for n
cells) and, up to current knowledge, also in states (6 states). While only one
such solution was proposed by Mazoyer since 1987, 718 new solutions have been
generated by Clergue, Verel and Formenti in 2018 with a cluster of machines. We
show here that, starting from existing solutions, it is possible to generate
millions of such solutions using local simulations using a single common
personal computer
A Simple n-Dimensional Intrinsically Universal Quantum Cellular Automaton
We describe a simple n-dimensional quantum cellular automaton (QCA) capable
of simulating all others, in that the initial configuration and the forward
evolution of any n-dimensional QCA can be encoded within the initial
configuration of the intrinsically universal QCA. Several steps of the
intrinsically universal QCA then correspond to one step of the simulated QCA.
The simulation preserves the topology in the sense that each cell of the
simulated QCA is encoded as a group of adjacent cells in the universal QCA.Comment: 13 pages, 7 figures. In Proceedings of the 4th International
Conference on Language and Automata Theory and Applications (LATA 2010),
Lecture Notes in Computer Science (LNCS). Journal version: arXiv:0907.382
Dynamics of particles and cages in an experimental 2D glass former
We investigate the dynamics of a glass forming 2D colloidal mixture and show
the existence of collective motions of the particles. We introduce a mean
square displacement MSD with respect to the nearest neighbors which shows
remarkable deviations from the usual MSD quantifying the individual motion of
our particles. Combined with the analysis of the self part of the Van Hove
function this indicates a coupled motion of particles with their cage as well
as intra cage hopping processes.Comment: Submitted to EP
New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems
We propose two uniform solutions to an open question: the Firing Squad
Synchronization Problem (FSSP), for hyperdag and symmetric neural P systems,
with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively,
where e_c is the eccentricity of the commander cell of the dag or digraph
underlying these P systems. The first and fast solution is based on a novel
proposal, which dynamically extends P systems with mobile channels. The second
solution is substantially longer, but is solely based on classical rules and
static channels. In contrast to the previous solutions, which work for
tree-based P systems, our solutions synchronize to any subset of the underlying
digraph; and do not require membrane polarizations or conditional rules, but
require states, as typically used in hyperdag and neural P systems
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