25,463 research outputs found
Technical advantages for weak value amplification: When less is more
The technical merits of weak value amplification techniques are analyzed. We
consider models of several different types of technical noise in an optical
context and show that weak value amplification techniques (which only use a
small fraction of the photons) compare favorably with standard techniques
(which uses all of them). Using the Fisher information metric, we demonstrate
that weak value techniques can put all of the Fisher information about the
detected parameter into a small portion of the events and show how this fact
alone gives technical advantages. We go on to consider a time correlated noise
model, and find that a Fisher information analysis indicates that while the
standard method can have much larger information about the detected parameter
than the postselected technique. However, the estimator needed to gather the
information is technically difficult to implement, showing that the inefficient
(but practical) signal-to-noise estimation of the parameter is usually
superior. We also describe other technical advantages unique to imaginary weak
value amplification techniques, focusing on beam deflection measurements. In
this case, we discuss combined noise types (such as detector transverse jitter,
angular beam jitter before the interferometer and turbulence) for which the
interferometric weak value technique gives higher Fisher information over
conventional methods. We go on to calculate the Fisher information of the
recently proposed photon recycling scheme for beam deflection measurements, and
show it further boosts the Fisher information by the inverse postselection
probability relative to the standard measurement case
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
Non-monotonic entanglement of physical EM field states in non-inertial frames
We develop a general technique to analyse the quantum effects of acceleration
on realistic spatially-localised electromagnetic field states entangled in the
polarization degree of freedom. We show that for this setting, quantum
entanglement may build up as the acceleration increases, providing a clear
signature of the quantum effects of relativistic acceleration.Comment: 5 pages, 3 figure
Van der Waals spin valves
We propose spin valves where a 2D non-magnetic conductor is intercalated
between two ferromagnetic insulating layers. In this setup, the relative
orientation of the magnetizations of the insulating layers can have a strong
impact on the in-plane conductivity of the 2D conductor. We first show this for
a graphene bilayer, described with a tight-binding model, placed between two
ferromagnetic insulators. In the anti-parallel configuration, a band gap opens
at the Dirac point, whereas in the parallel configuration, the graphene bilayer
remains conducting. We then compute the electronic structure of graphene
bilayer placed between two monolayers of the ferromagnetic insulator CrI,
using density functional theory. Consistent with the model, we find that a gap
opens at the Dirac point only in the antiparallel configuration.Comment: 5 pages, 4 figure
An Improved GPU Simulator For Spiking Neural P Systems
Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
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