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

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

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