Masking Strategies for Image Manifolds

We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data. Such masking implements a form of compressive sensing through emerging imaging sensor platforms for which the power expense grows with the number of pixels acquired. Our goal is for the manifold learned from masked images to resemble its full image counterpart as closely as possible. More precisely, we show that one can indeed accurately learn an image manifold without having to consider a large majority of the image pixels. In doing so, we consider two masking methods that preserve the local and global geometric structure of the manifold, respectively. In each case, the process of finding the optimal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated by a fast greedy algorithm. Numerical experiments show that the relevant manifold structure is preserved through the data-dependent masking process, even for modest mask sizes

Spectral Compressive Sensing with Polar Interpolation

Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.Comment: v1: 5 pages, 2 figures, accepted for publication at ICASSP 2013. v2,v3: This version corrects minor typos in Algorithm 1 from the published versio

Post-discharge health assessment in survivors of coronavirus disease: a time-point analysis of a prospective cohort study

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.PURPOSE: The objective of this study was to quantitatively evaluate psychological and quality of life-related complications at three months following discharge in hospitalized coronavirus disease 2019 (COVID-19) patients during the pandemic in Iran. METHODS: In this time-point analysis of prospective cohort study data, adult patients hospitalized with symptoms suggestive of COVID-19 were enrolled. Patients were stratifed in analyses based on severity. The primary outcomes consisted of psychological problems and pulmonary function tests (PFTs) in the three months following discharge, with Health-related quality of life (HRQoL) as the secondary outcome. Exploratory predictors were determined for both primary and secondary outcomes. Results 283 out of 900 (30%) eligible patients were accessible for the follow-up assessment and included in the study. The mean age was 53.65±13.43 years, with 68% experiencing a severe disease course. At the time of the fnal follow-up, participants still reported persistent symptoms, among which fatigue, shortness of breath, and cough were the most common. Based on the regression-adjusted analysis, lower levels of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio was associated with higher levels of depression (standardized ß=- 0.161 (SE=0.042), P=0.017) and stress levels (standardized ß=- 0.110 (SE=0.047), P=0.015). Furthermore, higher levels of anti-SARS-CoV-2 immunoglobulinM (IgM) were associated with signifcantly lower levels of depression (standardized ß=- 0.139 (SE=0.135), P=0.031). CONCLUSIONS: There is an association between lung damage during COVID-19 and the reduction of pulmonary function for up to three months from acute infection in hospitalized patients. Varying degrees of anxiety, depression, stress, and low HRQoL frequently occur in patients with COVID-19. More severe lung damage and lower COVID-19 antibodies were associated with lower levels of psychological health.Isfahan University of Medical Sciences, IR.MUI.MED.REC.1399.517, Ramin SamiPeer ReviewedPostprint (published version

Learning from Pairwise Proximity Data

In many areas of machine learning, the characterization of the input data is given by a form of proximity measure between data points. Examples of such representations are pairwise differences, pairwise distances, and pairwise comparisons. In this work, we investigate different learning problems on data represented in terms of such pairwise proximities. More specifically, we consider three problems: masking (feature selection) for dimensionality reduction, extension of the dimensionality reduction for time series, and online collaborative filtering. For each of these problems, we start with a form of pairwise proximity which is relevant in the problem at hand. We evaluate the performance of the proposed algorithms in terms of both theoretical metrics and in practical applications such as eye gaze estimation and movie recommendations

Masking Schemes for Image Manifolds

We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the dimensions of the image space that preserves the manifold structure present in the original data. Such masking implements a form of compressed sensing that reduces power consumption in emerging imaging sensor platforms. Our goal is for the manifold learned from masked images to resemble the manifold learned from full images as closely as possible. We show that the process of finding the optimal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated by a fast greedy algorithm. Numerical experiments show that the manifolds learned from masked images resemble those learned from full images for modest mask sizes. Furthermore, our greedy algorithm performs similarly to the exhaustive search from integer programming at a fraction of the computational cost