Order invariant spectral properties for several matrices

The collections of m n-by-n matrices over a field such that the products in any of the m! orders share a common similarity class (resp. spectrum, trace) are studied. The spectral and trace order invariant properties are characterized and the similarity invariant one is related to them in several cases. A complete explicit description is given in case m = 3 and n = 2. (C) 2009 Elsevier Inc. All rights reserved

On aether terms in a space-time with a compact extra dimension

In this paper, for the CPT-even and CPT-odd extensions of the QED, we explicitly obtain the aether-like corrections for the electromagnetic field in the case when the space-time involves an extra compact spatial dimension besides of usual four dimensions. Our methodology is based on an explicit summation over the Kaluza-Klein tower of fields which is no more difficult than the finite-temperature calculations. The quantum corrections turn out to be large as the extra dimension is small. We demonstrate that in the CPT-even case, the extra dimension manifests itself through a new scalar particle.Comment: 9 page

Influence des facteurs abiotiques du milieu sur la croissance et la reproduction de deux coccinelles aphidiphages Coccinella septempunctata et Adonia variegata Goeze (Col., Coccinellidae).

Rapport du stage effectué à Station de Zoologie et de Lutte Biologique (I.N.R.A.) d'Antibes du 1.10.1984 au 31.12.1984.[...]. Le travail presénté ci-dessous s'insere dans ie cadre d'un programme de recherches assuré par plusieurs scientifiques de la station (MM. FERRAN, IPERTI, LAPCHIN, LYON, MALAUSA et RABASSE) et qui est destiné à estimer 1 'efficacité des prédateurs aphidiphages dans un champ de céréales. Les principaux objectifs de ce programme tiennent compte des remarques précédentes: mise au point de méthodes d'échanti1lonnage des populations prédatrices qui soient propres à chaque espèce ou à un groupe d'espèces présentant des caractéristiques comportementales voisines, quantification de l'efficacité prédatrice a partir de critères physiologiques, c'est-à-dire le poids frais chez les larves et l'intensité de la ponte chez les adultes (FERRAN et LARROQUE, 1977; FERRAN et al., 1984), comparaison de cette efficacité avec la production naturelle d'aphides et integration de ce facteur biotique limitant, le predatisme, dans un modèle d'évolution des populations aphidiennes. [...

A deep learning approach to detect diabetic retinopathy in fundus images.

Background: Diabetic retinopathy is a disease caused due by complications of diabetes mellitus which can lead to blindness. About 33% of the US population with diabetes also show symptoms for diabetes retinopathy. If not treated, diabetic retinopathy worsens over time by progressing through two main pathological stages of non-proliferative and proliferative and four clinical stages. While the diagnostic accuracy of detecting diabetic retinopathy through machine learning have shown to be successful for OCT images, the accuracy of ultra-widefield fundus images have yet to be fully reported. This paper describes a method to non-invasively detect and diagnose diabetic retinopathy from ultra-widefield fundus images. Methods: A total of 62 graded-images were obtained from the Cleveland Clinic. A deep learning algorithm was developed to identify and extract features from the images. The algorithm was then simulated to classify the test images into one of three clinical classes. Data was collected on the accuracy and probability of the diagnosis/classification. Results: The classification algorithm had an average accuracy that ranged from 92% to 97% for the training images and 50% for the test images. Confusion matrices were created to obtain statistical measures of performance such as sensitivity, false negative rate, precision, and the false discovery rate. The sensitivity decreased from 70% to 50% as the image size increased. The precision also decreased from 65% to 50% as the image size increased. Validation methods such as image normalization and transfer learning showed no improvement in classification accuracy. Conclusion: This study demonstrates the potential for applying deep learning algorithms to classify ultra-widefield images. This study also demonstrates the need for doctors to further examine the diagnosis to account for false positives and/or misdiagnosis. Additionally, limitations and their impact on the simulation of the deep learning algorithm were explored