Transmission spectra and the effective parameters for planar metamaterials with omega shaped metallic inclusions

Cataloged from PDF version of article.Planar metamaterials with omega shaped metallic inclusions were studied experimentally and theoretically. Our results show that when the incidence is perpendicular to the plane of the omega structure, the omega medium acts effectively as an electric resonator metamaterial. The stop band of the omega medium is due to the negative part of the electric resonance of the omega structure. The transmission band of the composite metamaterial (CMM) that is based on the omega medium is due to the strong positive part of the electric resonance of the omega structure. Consequently, the transmission band of the CMM does not coincide with the stop band of the omega medium. Furthermore, the transmission band of the CMM is a band with positive refractive indices. Our experimental and numerical results are in good agreement. (C) 2010 Elsevier B.V. All rights reserved

Generalizable Embeddings with Cross-batch Metric Learning

Global average pooling (GAP) is a popular component in deep metric learning (DML) for aggregating features. Its effectiveness is often attributed to treating each feature vector as a distinct semantic entity and GAP as a combination of them. Albeit substantiated, such an explanation's algorithmic implications to learn generalizable entities to represent unseen classes, a crucial DML goal, remain unclear. To address this, we formulate GAP as a convex combination of learnable prototypes. We then show that the prototype learning can be expressed as a recursive process fitting a linear predictor to a batch of samples. Building on that perspective, we consider two batches of disjoint classes at each iteration and regularize the learning by expressing the samples of a batch with the prototypes that are fitted to the other batch. We validate our approach on 4 popular DML benchmarks.Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Negative phase advance in polarization independent, multi-layer negative-index metamaterials

Cataloged from PDF version of article.We demonstrate a polarization independent negative-index metamaterial (NIM) at microwave frequencies. Transmission measurements and simulations predict a left-handed transmission band with negative permittivity and negative permeability. A negative-index is verified by using the retrieval procedure. Effective parameters of single-layer and twolayer NIMs are shown to be different. Negative phase advance is verified within the negative-index regime by measuring the phase shift between different sized negative-index metamaterials. Backward wave propagation is observed in the numerical simulations at frequencies where the phase advance is negative. ©2008 Optical Society of Americ

Feature Embedding by Template Matching as a ResNet Block

Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perform local feature embedding via template matching once batch normalization (BN) followed by a rectified linear unit (ReLU) is interpreted as arg-max optimizer. Following this perspective, we tailor a residual block that explicitly forces semantically meaningful local feature embedding through using label information. Specifically, we assign a feature vector to each local region according to the classes that the corresponding region matches. We evaluate our method on three popular benchmark datasets with several architectures for image classification and consistently show that our approach substantially improves the performance of the baseline architectures.Comment: Accepted at the British Machine Vision Conference 2022 (BMVC 2022

From Psoriasis to Psoriatic Arthritis: Insights from Imaging on the Transition to Psoriatic Arthritis and Implications for Arthritis Prevention

Purpose of Review: To describe the recent advances in the field towards the prevention and early recognition of Psoriatic Arthritis (PsA). Recent Findings: Defining the preclinical phase of PsA remains challenging since up to 50% of subjects with psoriasis have subclinical imaging enthesopathy, but many of these do not progress to PsA. Nevertheless, there is evidence that subjects with subclinical imaging enthesopathy are at increased risk of developing PsA. In recent years, it has been shown that both PsA and anti-citrullinated protein antibodies (ACPA) positive rheumatoid arthritis (RA) are characterized by a subclinical phase of non-specific or brief duration arthralgia with shared imaging features accounting for joint symptomatology. Sonographically determined tenosynovitis and enthesitis are the key imaging features present in non-specific PsO arthralgia that are at risk of future PsA development. Furthermore, the early phases of PsA are complicated by factors including body mass index (BMI), which is a risk factor for PsA, but BMI is also associated with imaging abnormalities on enthesopathy. Fully disentangling these clinical and imaging factors will be important for enrichment for imminent PsA so that disease prevention strategies can be investigated. Summary: Psoriasis patients with arthralgia have a higher prevalence of tenosynovitis and imaging enthesopathy is at higher risk of transitioning to overt PsA