282 research outputs found
Quantum-electrodynamical approach to the Casimir force problem
We derive the Casimir force expression from Maxwell's stress tensor by means
of original quantum-electro-dynamical cavity modes. In contrast with similar
calculations, our method is straightforward and does not rely on intricate
mathematical extrapolation relations
Wiretapping and the Confines of the Marital Home: What Part of Any Don\u27t You Understand;Legislative Reform
Should Post-Arrest Drug Rehabilitation Be a Consideration in Granting a Downward Departure under the United States Sentencing Guidelines;Legislative Reform
GANIL Status report
The GANIL-Spiral facility (Caen, France) is dedicated to the acceleration of
heavy ion beams for nuclear physics, atomic physics, radiobiology and material
irradiation. The production of radioactive ion beams for nuclear physics
studies represents the main part of the activity. The facility possesses a
versatile combination of equipments, which permits to produce accelerated
radioactive ion beams with two complementary methods: Isotope Separation In
Line (ISOL) and In-Flight Separation techniques (IFS). Considering the future
of GANIL, SPIRAL II projects aims to produce high intensity secondary beams, by
fission induced with a 5 mA deuteron beam on an uranium target.Comment: 5 pages, 5 figures, to be appear in the proceedings of the 17th
International Conference on Cyclotrons and their Application
Should Post-Arrest Drug Rehabilitation Be a Consideration in Granting a Downward Departure under the United States Sentencing Guidelines;Legislative Reform
Violation of the equivalence principle from light scalar fields: from Dark Matter candidates to scalarized black holes
Tensor-scalar theory is a wide class of alternative theory of gravitation
that can be motivated by higher dimensional theories, by models of dark matter
or dark ernergy. In the general case, the scalar field will couple
non-universally to matter producing a violation of the equivalence principle.
In this communication, we review a microscopic model of scalar/matter coupling
and its observable consequences in terms of universality of free fall, of
frequencies comparison and of redshifts tests. We then focus on two models: (i)
a model of ultralight scalar dark matter and (ii) a model of scalarized black
hole in our Galactic Center. For both these models, we present constraints
using recent measurements: atomic clocks comparisons, universality of free fall
measurements, measurement of the relativistic redshift with the short period
star S0-2 orbiting the supermassive black hole in our Galactic Center.Comment: 8 pages, 1 figure, contribution to the 2019 Gravitation session of
the 54th Rencontres de Morion
Prelicensure Nursing Students’ Perceptions of a Rapid Transition to an Online Learning Environment
The transition to a fully online environment after the spread of the COVID-19 pandemic left all educators with the need to quickly transition didactic classes to the online environment and attempt to understand and utilize online teaching modalities. Online learning has only recently become integrated into nursing education, creating a gap in literature in relation to the most appropriate strategies for online course delivery, specifically in prelicensure nursing. This chapter explores the background to the project, types of learning environments, definitions of terms utilized, and purpose of the secondary analysis
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection
Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling'
(MP) layers to extract deformation-invariant features, but we argue in favor of
a more refined treatment. First, we introduce epitomic convolution as a
building block alternative to the common convolution-MP cascade of DCNNs; while
having identical complexity to MP, Epitomic Convolution allows for parameter
sharing across different filters, resulting in faster convergence and better
generalization. Second, we introduce a Multiple Instance Learning approach to
explicitly accommodate global translation and scaling when training a DCNN
exclusively with class labels. For this we rely on a `patchwork' data structure
that efficiently lays out all image scales and positions as candidates to a
DCNN. Factoring global and local deformations allows a DCNN to `focus its
resources' on the treatment of non-rigid deformations and yields a substantial
classification accuracy improvement. Third, further pursuing this idea, we
develop an efficient DCNN sliding window object detector that employs explicit
search over position, scale, and aspect ratio. We provide competitive image
classification and localization results on the ImageNet dataset and object
detection results on the Pascal VOC 2007 benchmark.Comment: 13 pages, 7 figures, 5 tables. arXiv admin note: substantial text
overlap with arXiv:1406.273
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