In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a split architecture and use a pseudo-ground truth for abundances to guide the 'unmixing network' (UN) optimization. Preceding the UN, an 'approximation network' (AN) is proposed, which will improve the association between the centre pixel and its neighbourhood. Hence, it will accentuate spatial correlation in the abundances as its output is the input to the UN and the reference for the 'mixing network' (MN). In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods. Furthermore, we release the single-layer constraint on MN by introducing the UN generated abundances in contrast to the standard AE for HU. Secondly, we experimented with two modifications on the pre-trained network using the GAUSS method. In GAUSS blind , we have concatenated the UN and the MN to back-propagate the reconstruction error gradients to the encoder. Then, in the GAUSSprime, abundance results of a signal processing (SP) method with reliable abundance results were used as the pseudo-ground truth with the GAUSSarchitecture. According to quantitative and graphical results for four experimental datasets, the three architectures either transcended or equated the performance of existing HU algorithms from both DL and SP domains.
Objectives: The impact of the COVID-19 pandemic was diverse and disproportionate among nations and population segments. The impacts of the disease and the containment strategies adopted are broad and cut across multiple facets of life, society, and the economy, which are intimately interlinked. To ascertain the socioeconomic impact and human behavior changes due to the pandemic and the containment strategies adopted a large household survey was conducted covering all the provinces in Sri Lanka. Data description: We conducted a cross-sectional Household survey covering all 9 provinces, including 20 districts in Sri Lanka from August 2021 to September 2021. This dataset consists of the data collected from 3020 households, on the impact of the pandemic through three distinctly identified pandemic waves in Sri Lanka. The questionnaire was designed to capture COVID-19 impact in 2 primary sections (socioeconomic impact and behavioral impact) which were further divided into 8 sub-sections: educational impact, impact on mobility, access to health services, economic impact, human interactions, food consumption, religious and cultural, and psychological impact. This dataset will enable researchers and policymakers to analyze the impact of the pandemic through a multifaceted perspective enabling a more holistic approach to decision-making.
It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they are highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.
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