A study on the anxiety level and stress during Covid19 lockdown among the general population of West Bengal, India- A must know for primary care physicians.

BACKGROUND: Covid19 pandemic has resulted in drastic changes in human lives across the globe in the form of lockdown and an uncertain future. Information regarding the COVID-19-related anxiety and well-being among the public in India is very limited, especially from the state of West Bengal. We conducted this e-survey among the general population of West Bengal to assess the anxiety levels and the well-being status during lockdown. This information would be helpful to guide family physicians to screen patients for anxiety from the primary care level. AIMS: The main aim of this questionnaire based study was to assess the levels of anxiety and well-being status among the public including the frontline workers in West Bengal, India. MATERIALS AND METHODS: A prospective study was conducted with a validated e-questionnaire after Institutional Ethics committee approval, from 18th April, 2020 to 3rd May, 2020. The questionnaire had 12 questions which included the Generalized Anxiety disorder (GAD)-7 scale and the WHO-5 scale (5 question-items) to assess the well-being of the participants. The survey link was distributed through the social networking sites of WhatsApp, LinkedIn, Facebook and Twitter and e-mails within West Bengal. Microsoft Excel (version 2016) was used to analyse the data. RESULTS: A total of 355 responses were received 15.49% responders were observed to have anxiety and 37.74% participants had low well-being scores. Majority of healthcare workers (89.47%) were seen to have anxiety and a significant (52.03%) had a low well-being status. CONCLUSIONS: We report the presence of anxiety and low well-being among the general population of West Bengal. It is important to understand the current psychological status of the public for the family physicians as many would visit them with vague symptoms. There is a dire need to screen all patients including front line workers visiting primary care physicians for mental health to ensure better clinical outcome.

Large-Scale Crowdsourced Study for Tone-Mapped HDR Pictures.

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip.

No-Reference Quality Assessment of Tone-Mapped HDR Pictures.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip.

Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.

This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.