Juan Marcos Ramirez | Searcharxiv

arXiv2026

What Causes COVID-19 Fear? General Drivers of Fear During a Health Crisis

The COVID-19 pandemic triggered not only a global health crisis but also an infodemic, where exposure to heterogeneous information sources influenced public emotional responses. In this work, we investigate the determinants of self-reported fear of infection using data from the Delphi US CTIS survey. In particular, we analyze how demographic variables, epidemiological conditions, and exposure to different information sources shape fear levels. We introduce a Probabilistic Causal Model to estimate causal relationship strengths, identifying the variables that most strongly influence fear. Our results indicate that exposure to information sources accounts for a greater proportion of the variance in fear than demographic and epidemiological variables do. We further compute the Average Treatment Effect to quantify the impact of different information sources on fear. After causal adjustment, institutional and expert-driven sources are associated with increased fear levels, whereas politicians, religious leaders, and alternative information channels are associated with reduced fear. These findings highlight both the central role of the information ecosystem in shaping emotional responses during public health crises and the value of causal inference approaches for studying behavioral responses to pandemics.

cs.SI↗

arXiv2020

Feature Fusion via Dual-resolution Compressive Measurement Matrix Analysis For Spectral Image Classification

In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture the relevant information of the spectral image, various methods that extract classification features from compressive samples have been recently proposed. However, these techniques require a feature extraction procedure that reorders measurements using the information embedded in the coded aperture patterns. In this paper, a method that fuses features directly from dual-resolution compressive measurements is proposed for spectral image classification. More precisely, the fusion method is formulated as an inverse problem that estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To this end, the decimation matrices that describe the compressive measurements as degraded versions of the fused features are mathematically modeled using the information embedded in the coded aperture patterns. Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the accuracy of pixel-based classifiers. To solve the fusion problem, we describe an algorithm based on the accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally, a classification approach that includes the developed fusion method and a multilayer neural network is introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set of compressive measurements captured in the laboratory. Extensive simulations show that the proposed classification approach outperforms other approaches under various performance metrics.

eess.IV↗