Anisotropic Lifshitz Point at O(ϵL2)O(\epsilon_L^2)

We present the critical exponents $\nu_{L2}$, $\eta_{L2}$ and $\gamma_{L}$ for an $m$-axial Lifshitz point at second order in an $\epsilon_{L}$ expansion. We introduced a constraint involving the loop momenta along the $m$-dimensional subspace in order to perform two- and three-loop integrals. The results are valid in the range $0 \leq m < d$. The case $m=0$ corresponds to the usual Ising-like critical behavior.Comment: 10 pages, Revte

A spatiotemporal deep learning approach for automatic pathological Gait classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.info:eu-repo/semantics/publishedVersio

Neutrino Telescopes as a Direct Probe of Supersymmetry Breaking

We consider supersymmetric models where the scale of supersymmetry breaking lies between 5 $\times 10^6$ GeV and 5 $\times 10^8$ GeV. In this class of theories, which includes models of gauge mediated supersymmetry breaking, the lightest supersymmetric particle is the gravitino. The next to lightest supersymmetric particle is typically a long lived charged slepton with a lifetime between a microsecond and a second, depending on its mass. Collisions of high energy neutrinos with nucleons in the earth can result in the production of a pair of these sleptons. Their very high boost means they typically decay outside the earth. We investigate the production of these particles by the diffuse flux of high energy neutrinos, and the potential for their observation in large ice or water Cerenkov detectors. The relatively small cross-section for the production of supersymmetric particles is partially compensated for by the very long range of heavy particles. The signal in the detector consists of two parallel charged tracks emerging from the earth about 100 meters apart, with very little background. A detailed calculation using the Waxman-Bahcall limit on the neutrino flux and realistic spectra shows that km$^3$ experiments could see as many as 4 events a year. We conclude that neutrino telescopes will complement collider searches in the determination of the supersymmetry breaking scale, and may even give the first evidence for supersymmetry at the weak scale.Comment: 4 pages, 3 figure

Casimir effect for massless minimally coupled scalar field between parallel plates in de Sitter spacetime

Casimir effect for massless minimally coupled scalar field is studied. An explicit answer for de Sitter spacetime is obtained and analized. Cosmological implications of the result are discussed.Comment: 7 pages, 2 figure

Efeitos a curto prazo da cobertura morta com residuos de bananeira sobre caracteristicas quimicas do solo e teor de nutrientes em videira.

Avaliou-se a influência imediata da cobertura morta com resíduos de cultivos de bananeira

Remote Gait type classification system using markerless 2D video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating 5 types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating 5 types of gait, at 2 severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.info:eu-repo/semantics/publishedVersio

Desenvolvimento e calibração de uma guia de onda para TDR.

A medicao da umidade do solo e crucial para pesquisa e manejo de irrigacao. Entre os metodos mais recentes de determinacao de umidade esta o da TDR ("time domain reflectometry") que se baseia na correlacao entre a propriedade dieletrica do solo e a sua umidade. A obtencao do perfil de umidade do solo requer a utilizaco de um grande numero de guias de onda, cujo custo ainda e elevado. Por outro lado, as curvas de calibracao relacionando umidade com constante dieletrica do solo foram desenvolvidas para solos de paises temperados. Os objetivos desse trabalho foram construir uma guia de onda com materiais facilmente encontrados no mercado nacional e calibra-la para solos tropicais. As curvas de calibracao para areia quartzosa e latossolo vermelho escuro sao distintas e diferentes dos modelos apresentados na literatura. A modificacao de um modelo matematico fisicamente embasado permitiu a geracao de curvas de calibracao com bom ajuste, porem independentes para cada tipo de solo

Reação de espécies de Piper a dois isolados de Nectria haematococca f. sp. piperis.

bitstream/item/39511/1/Com-Tec-7-Am-Oriental.pd