Search results for "Bedding"
showing 10 items of 199 documents
Intersecting Defects and Supergroup Gauge Theory
2021
Journal of physics / A 54(43), 435401 (2021). doi:10.1088/1751-8121/ac2716
The Segre embedding of the quantum conformal superspace
2018
In this paper study the quantum deformation of the superflag Fl(2|0, 2|1,4|1), and its big cell, describing the complex conformal and Minkowski superspaces respectively. In particular, we realize their projective embedding via a generalization to the super world of the Segre map and we use it to construct a quantum deformation of the super line bundle realizing this embedding. This strategy allows us to obtain a description of the quantum coordinate superring of the superflag that is then naturally equipped with a coaction of the quantum complex conformal supergroup SL_q(4|1).
External derivations of internal groupoids
2008
If His a G-crossed module, the set of derivations of Gin H is a monoid under the Whitehead product of derivations. We interpret the Whitehead product using the correspondence between crossed modules and internal groupoids in the category of groups. Working in the general context of internal groupoids in a finitely complete category, we relate derivations to holomorphisms, translations, affine transformations, and to the embedding category of a groupoid. (C) 2007 Elsevier B.V. All rights reserved.
Supravital Uptake of Methylene Blue by Dendritic Cells within Stratified Squamous Epithelia: a Light and Electron Microscope Study
1996
Electron microscopic data on methylene blue staining of dendritic cells in the epithelia of the soft palate and skin of the mouse after supravital dye injection are presented. The ultra-structural details were compared with corresponding light microscopic findings. Methylene blue stained tissue was fixed by immersion in a paraformaldehyde-glutaraldehyde solution containing phosphomolybdic acid. The ensuing dye precipitate was stabilized by ammonium heptamolybdate. The light microscopic investigation revealed that selective staining of dendritic cells depended on the presence of ambient oxygen. In addition, delicate morphological characteristics, like spinous structures of the dendrites, wer…
Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images
2022
This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to t…
Continuous spectrum for a two phase eigenvalue problem with an indefinite and unbounded potential
2020
Abstract We consider a two phase eigenvalue problem driven by the ( p , q ) -Laplacian plus an indefinite and unbounded potential, and Robin boundary condition. Using a modification of the Nehari manifold method, we show that there exists a nontrivial open interval I ⊆ R such that every λ ∈ I is an eigenvalue with positive eigenfunctions. When we impose additional regularity conditions on the potential function and the boundary coefficient, we show that we have smooth eigenfunctions.
An efficient method for clustered multi-metric learning
2019
Abstract Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one.…
ARBUSCULAR MYCORRHIZAL INOCULATION AND SHADING ENHANCE CROP PERFORMANCE AND QUALITY OF GREENHOUSE Begonia semperflorens
2019
Mycorrhizal fungi are gaining interest in the floriculture sector due to the beneficial effects on a crop performance and ornamental quality. The aim of the current study was to assess the effect of inoculation with the arbuscular mycorrhizal (AM) fungi Rhizophagus irregularis on ornamental quality of Begonia × semperflorens-cultorum grown in two different protected cultivation systems: a shadehouse or glasshouse. The inoculated plants incurred a significant increase of plant height by 34.6%, lateral shoot length by 27.9%, number of lateral shoots by 41.2%, number of flowers per plant by 102.9%, flower diameter by 27.5%, and stems dry weight by 263.6%. High temperatures in the glasshouse ne…
Robust stability and stabilization of uncertain T-S fuzzy systems with time-varying delay: An input-output approach
2013
An input-output approach to the stability and stabilization of uncertain Takagi-Sugeno (T-S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria…
Learning with the kernel signal to noise ratio
2012
This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…