8,070 research outputs found
Bridging Micro- and Macro-Analyses of the EU Sugar Program: Methods and Insights
Crop Production/Industries,
Do Convolutional Networks need to be Deep for Text Classification ?
We study in this work the importance of depth in convolutional models for
text classification, either when character or word inputs are considered. We
show on 5 standard text classification and sentiment analysis tasks that deep
models indeed give better performances than shallow networks when the text
input is represented as a sequence of characters. However, a simple
shallow-and-wide network outperforms deep models such as DenseNet with word
inputs. Our shallow word model further establishes new state-of-the-art
performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%)
Quantum confinement effects in Pb Nanocrystals grown on InAs
In the recent work of Ref.\cite{Vlaic2017-bs}, it has been shown that Pb
nanocrystals grown on the electron accumulation layer at the (110) surface of
InAs are in the regime of Coulomb blockade. This enabled the first scanning
tunneling spectroscopy study of the superconducting parity effect across the
Anderson limit. The nature of the tunnel barrier between the nanocrystals and
the substrate has been attributed to a quantum constriction of the electronic
wave-function at the interface due to the large Fermi wavelength of the
electron accumulation layer in InAs. In this manuscript, we detail and review
the arguments leading to this conclusion. Furthermore, we show that, thanks to
this highly clean tunnel barrier, this system is remarkably suited for the
study of discrete electronic levels induced by quantum confinement effects in
the Pb nanocrystals. We identified three distinct regimes of quantum
confinement. For the largest nanocrystals, quantum confinement effects appear
through the formation of quantum well states regularly organized in energy and
in space. For the smallest nanocrystals, only atomic-like electronic levels
separated by a large energy scale are observed. Finally, in the intermediate
size regime, discrete electronic levels associated to electronic wave-functions
with a random spatial structure are observed, as expected from Random Matrix
Theory.Comment: Main 12 pages, Supp: 6 page
Global sensitivity analysis for the boundary control of an open channel
The goal of this paper is to solve the global sensitivity analysis for a
particular control problem. More precisely, the boundary control problem of an
open-water channel is considered, where the boundary conditions are defined by
the position of a down stream overflow gate and an upper stream underflow gate.
The dynamics of the water depth and of the water velocity are described by the
Shallow Water equations, taking into account the bottom and friction slopes.
Since some physical parameters are unknown, a stabilizing boundary control is
first computed for their nominal values, and then a sensitivity anal-ysis is
performed to measure the impact of the uncertainty in the parameters on a given
to-be-controlled output. The unknown physical parameters are de-scribed by some
probability distribution functions. Numerical simulations are performed to
measure the first-order and total sensitivity indices
Sparse classification boundaries
Given a training sample of size from a -dimensional population, we
wish to allocate a new observation to this population or to the
noise. We suppose that the difference between the distribution of the
population and that of the noise is only in a shift, which is a sparse vector.
For the Gaussian noise, fixed sample size , and the dimension that tends
to infinity, we obtain the sharp classification boundary and we propose
classifiers attaining this boundary. We also give extensions of this result to
the case where the sample size depends on and satisfies the condition
, , and to the case of non-Gaussian
noise satisfying the Cram\'er condition
- …