12,357 research outputs found
First CLADAG data mining prize : data mining for longitudinal data with different marketing campaigns
The CLAssification and Data Analysis Group (CLADAG) of the Italian
Statistical Society recently organised a competition, the 'Young Researcher Data
Mining Prize' sponsored by the SAS Institute. This paper was the winning entry
and in it we detail our approach to the problem proposed and our results. The main
methods used are linear regression, mixture models, Bayesian autoregressive and
Bayesian dynamic models
Electron - positron cascades in multiple-laser optical traps
We present an analytical and numerical study of multiple-laser QED cascades
induced with linearly polarised laser pulses. We analyse different polarisation
orientations and propose a configuration that maximises the cascade
multiplicity and favours the laser absorption. We generalise the analytical
estimate for the cascade growth rate previously calculated in the field of two
colliding linearly polarised laser pulses and account for multiple laser
interaction. The estimate is verified by a comprehensive numerical study of
four-laser QED cascades across a range of different laser intensities with QED
PIC module of OSIRIS. We show that by using four linearly polarised 30 fs laser
pulses, one can convert more than 50 % of the total energy to gamma-rays
already at laser intensity . In this
configuration, the laser conversion efficiency is higher compared with the case
with two colliding lasers
Ion dynamics and acceleration in relativistic shocks
Ab-initio numerical study of collisionless shocks in electron-ion
unmagnetized plasmas is performed with fully relativistic particle in cell
simulations. The main properties of the shock are shown, focusing on the
implications for particle acceleration. Results from previous works with a
distinct numerical framework are recovered, including the shock structure and
the overall acceleration features. Particle tracking is then used to analyze in
detail the particle dynamics and the acceleration process. We observe an energy
growth in time that can be reproduced by a Fermi-like mechanism with a reduced
number of scatterings, in which the time between collisions increases as the
particle gains energy, and the average acceleration efficiency is not ideal.
The in depth analysis of the underlying physics is relevant to understand the
generation of high energy cosmic rays, the impact on the astrophysical shock
dynamics, and the consequent emission of radiation.Comment: 5 pages, 3 figure
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
Electromagnetic field generation in the downstream of electrostatic shocks due to electron trapping
A new magnetic field generation mechanism in electrostatic shocks is found,
which can produce fields with magnetic energy density as high as 0.01 of the
kinetic energy density of the flows on time scales . Electron trapping during the shock formation process
creates a strong temperature anisotropy in the distribution function, giving
rise to the pure Weibel instability. The generated magnetic field is
well-confined to the downstream region of the electrostatic shock. The shock
formation process is not modified and the features of the shock front
responsible for ion acceleration, which are currently probed in laser-plasma
laboratory experiments, are maintained. However, such a strong magnetic field
determines the particle trajectories downstream and has the potential to modify
the signatures of the collisionless shock
The impact of kinetic effects on the properties of relativistic electron-positron shocks
We assess the impact of non-thermally shock-accelerated particles on the
magnetohydrodynamic (MHD) jump conditions of relativistic shocks. The adiabatic
constant is calculated directly from first principle particle-in-cell
simulation data, enabling a semi-kinetic approach to improve the standard fluid
model and allowing for an identification of the key parameters that define the
shock structure. We find that the evolving upstream parameters have a stronger
impact than the corrections due to non-thermal particles. We find that the
decrease of the upstream bulk speed yields deviations from the standard MHD
model up to 10%. Furthermore, we obtain a quantitative definition of the shock
transition region from our analysis. For Weibel-mediated shocks the inclusion
of a magnetic field in the MHD conservation equations is addressed for the
first time
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