Highly directional emission from photonic crystals with a wide bandwidth

Cataloged from PDF version of article.The authors numerically and experimentally demonstrated highly directional emission from photonic crystals. This was achieved by first splitting the incident electromagnetic wave into multiple beams using photonic crystal waveguide structures. The beams were then emitted out of the surface of a photonic crystal with the same phase, which resulted in a highly directional radiation pattern. The measured half power beam width was 4.8 degrees, which was in good agreement with the calculated value of 4.1 degrees. In contrast to the traditional beaming structures, their design did not involve gratinglike structures, which resulted in a wider operation bandwidth. (c) 2007 American Institute of Physics

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

Deep Metric Learning with Chance Constraints

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding image. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxy-based DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by iterative projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We apply our method with the well-accepted losses and evaluate on four popular benchmark datasets for image retrieval. Outperforming state-of-the-art, our method consistently improves the performance of the applied losses. Code is available at: https://github.com/yetigurbuz/ccp-dmlComment: Under review at IEEE Transactions on Neural Networks and Learning System