On Electrical Equivalence of Aperture-Body and Transmission-Cavity Resonance Phenomena in Subwavelength Conducting Aperture Systems from an Equivalent Circuit Point of View

For a narrow slit structure backed by a conducting strip which is taken as a representative example of an aperture-body resonance (ABR) problem, the transmission resonance condition (i.e., condition for maximum power transmission) and the transmission width (i.e., normalized maximum transmitted power through the slit) are found to be the same as those for narrow slit coupling problem in a thick conducting screen, which is designated as a transmission-cavity resonance (TCR) problem. From a viewpoint of equivalent circuit representation for the transmission resonance condition and the funneling mechanism, the ABR and the TCR problems are thought to be essentially of the same nature.Comment: 14 pages, 3 figure

Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation

Referring Image Segmentation (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. Various recent RIS models have achieved state-of-the-art performance by generating contextual tokens to model multimodal features from pretrained encoders and effectively fusing them using transformer-based cross-modal attention. While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes. To address this issue, we propose a novel bidirectional token-masking autoencoder (BTMAE) inspired by the masked autoencoder (MAE). The proposed model learns the context of image-to-language and language-to-image by reconstructing missing features in both image and language features at the token level. In other words, this approach involves mutually complementing across the features of images and language, with a focus on enabling the network to understand interconnected deep contextual information between the two modalities. This learning method enhances the robustness of RIS performance in complex sentences and scenes. Our BTMAE achieves state-of-the-art performance on three popular datasets, and we demonstrate the effectiveness of the proposed method through various ablation studies

Validation of the Ottawa Ankle Rules in Iran: A prospective survey

BACKGROUND: Acute ankle injuries are one of the most common reasons for presenting to emergency departments, but only a small percentage of patients – approximately 15% – have clinically significant fractures. However, these patients are almost always referred for radiography. The Ottawa Ankle Rules (OARs) have been designed to reduce the number of unnecessary radiographs ordered for these patients. The objective of this study was to validate the OARs in the Iranian population. METHODS: This prospective survey was done among 200 patients with acute ankle injury from January 2004 to April 2004 in the Akhtar Orthopedics Hospital Emergency Department. Main outcome measures of this survey were: sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios (positive and negative) of the OARs. RESULTS: Sensitivity of the OARs for detecting 37 ankle fractures (23 in the malleolar zone and 14 in the midfoot zone) was 100% for each of the two zones, and 100% for both zones. Specificity of the OARs for detecting fractures was 40.50% for both zones, 40.50% for the malleolar zone, and 56.00% for the midfoot zone. Implementation of the OARs had the potential for reducing radiographs by 33%. CONCLUSION: OARs are very accurate and highly sensitive tools for detecting ankle fractures. Implementation of these rules would lead to significant reduction in the number of radiographs, costs, radiation exposure and waiting times in emergency departments

The relative impact of baryons and cluster shape on weak lensing mass estimates of galaxy clusters

Weak gravitational lensing depends on the integrated mass along the line of sight. Baryons contribute to the mass distribution of galaxy clusters and the resulting mass estimates from lensing analysis. We use the cosmo-OWLS suite of hydrodynamic simulations to investigate the impact of baryonic processes on the bias and scatter of weak lensing mass estimates of clusters. These estimates are obtained by fitting NFW profiles to mock data using MCMC techniques. In particular, we examine the difference in estimates between dark matter-only runs and those including various prescriptions for baryonic physics. We find no significant difference in the mass bias when baryonic physics is included, though the overall mass estimates are suppressed when feedback from AGN is included. For lowest-mass systems for which a reliable mass can be obtained ($M_{200} \approx 2 \times 10^{14}$ $M_{\odot}$), we find a bias of $\approx -10$ per cent. The magnitude of the bias tends to decrease for higher mass clusters, consistent with no bias for the most massive clusters which have masses comparable to those found in the CLASH and HFF samples. For the lowest mass clusters, the mass bias is particularly sensitive to the fit radii and the limits placed on the concentration prior, rendering reliable mass estimates difficult. The scatter in mass estimates between the dark matter-only and the various baryonic runs is less than between different projections of individual clusters, highlighting the importance of triaxiality

Prospects for Determining the Mass Distributions of Galaxy Clusters on Large Scales Using Weak Gravitational Lensing

For more than two decades, the Navarro, Frenk, and White (NFW) model has stood the test of time; it has been used to describe the distribution of mass in galaxy clusters out to their outskirts. Stacked weak lensing measurements of clusters are now revealing the distribution of mass out to and beyond their virial radii, where the NFW model is no longer applicable. In this study we assess how well the parameterised Diemer & Kravstov (DK) density profile describes the characteristic mass distribution of galaxy clusters extracted from cosmological simulations. This is determined from stacked synthetic lensing measurements of the 50 most massive clusters extracted from the Cosmo-OWLS simulations, using the Dark Matter Only run and also the run that most closely matches observations. The characteristics of the data reflect the Weighing the Giants survey and data from the future Large Synoptic Survey Telescope (LSST). In comparison with the NFW model, the DK model favored by the stacked data, in particular for the future LSST data, where the number density of background galaxies is higher. The DK profile depends on the accretion history of clusters which is specified in the current study. Eventually however subsamples of galaxy clusters with qualities indicative of disparate accretion histories could be studied

MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.Comment: Accepted by CVPR 2023; Project Page is https://bring728.github.io/mair.project

Metallopanstimulin as a marker for head and neck cancer

BACKGROUND: Metallopanstimulin (MPS-1) is a ribosomal protein that is found in elevated amounts in the sera of patients with head and neck squamous cell carcinoma (HNSCC). We used a test, denoted MPS-H, which detects MPS-1 and MPS-1-like proteins, to determine the relationship between MPS-H serum levels and clinical status of patients with, or at risk for, HNSCC. PATIENTS AND METHODS: A total of 125 patients were prospectively enrolled from a university head and neck oncology clinic. Participants included only newly diagnosed HNSCC patients. Two control groups, including 25 non-smokers and 64 smokers, were studied for comparison. A total of 821 serum samples collected over a twenty-four month period were analyzed by the MPS-H radioimmunoassay. RESULTS: HNSCC, non-smokers, and smokers had average MPS-H values of 41.5 ng/mL, 10.2 ng/mL, and 12.8 ng/mL, respectively (p = 0.0001). CONCLUSION: We conclude that MPS-1 and MPS-1-like proteins are elevated in patients with HNSCC, and that MPS-H appears to be a promising marker of presence of disease and response to treatment in HNSCC patients