Scratch the Scratch-off: Testing Prepaid and Conditional Incentives With Postcard and Letter Invitations in a Web-push Design With an Address-based Sample

We tested a novel extension to mailed invitations to a web-push survey, using a postcard invitation to deliver a scratch-off giftcode incentive similar to an instant-win lottery ticket. Scratch-off postcards were included as one of five conditions in randomized survey experiment varying two mailing types (letter and postcard) and three incentive types (prepaid cash, prepaid giftcodes, and conditional giftcodes). Invitations were sent to a sample of 17,808 addresses in Boston, Massachusetts, recruiting for a new online panel study of city residents. We report response rates and costs for each condition. Findings suggest that letters achieve higher response rates than postcards and are more cost effective overall. We also find that conditional incentives achieve higher response rates and are more cost effective, although conflating factors do not permit clear inferences. Notably, the novel scratch-off postcard condition achieved the lowest response rate and the highest costs per completed survey.

COVID-19 detection using cough sound analysis and deep learning algorithms

Reliable and rapid non-invasive testing has become essential for COVID-19 diagnosis and tracking statistics. Recent studies motivate the use of modern machine learning (ML) and deep learning (DL) tools that utilize features of coughing sounds for COVID-19 diagnosis. In this paper, we describe system designs that we developed for COVID-19 cough detection with the long-term objective of embedding them in a testing device. More specifically, we use log-mel spectrogram features extracted from the coughing audio signal and design a series of customized deep learning algorithms to develop fast and automated diagnosis tools for COVID-19 detection. We first explore the use of a deep neural network with fully connected layers. Additionally, we investigate prospects of efficient implementation by examining the impact on the detection performance by pruning the fully connected neural network based on the Lottery Ticket Hypothesis (LTH) optimization process. In general, pruned neural networks have been shown to provide similar performance gains to that of unpruned networks with reduced computational complexity in a variety of signal processing applications. Finally, we investigate the use of convolutional neural network architectures and in particular the VGG-13 architecture which we tune specifically for this application. Our results show that a unique ensembling of the VGG-13 architecture trained using a combination of binary cross entropy and focal losses with data augmentation significantly outperforms the fully connected networks and other recently proposed baselines on the DiCOVA 2021 COVID-19 cough audio dataset. Our customized VGG-13 model achieves an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%.

Verteilungsgerechtigkeit, Verantwortung und Zufall: Die Bedeutung von Risikoakzeptanz für den Glücksegalitarismus

Luck egalitarians like Ronald Dworkin and G. A. Cohen claim that the influence of luck on the distribution of goods or welfare has to be equalized, whereas inequality must be accepted if the subjects bear responsibility for it. The paper investigates how this claim should be interpreted and if it is plausible. Analyzing several examples, I try to show that the spectrum of pertinent cases is much more limited than Dworkin and Cohen think. Sometimes it seems as if someone is responsible for being in a worse position than someone else, while in fact they didn't have equal opportunities. Fitting cases of responsibility for inequality can be found when we focus on the outcomes of calculated gambles (Dworkin: option luck). But why, and what exactly are cases in point? What distinguishes the risk-taking of someone who buys a lottery ticket from that of a peasant who cultivates a piece of land, knowing that a storm might ruin the crop? I try to demonstrate that an ethically relevant difference occurs when the attitude towards risk differs. Would the agent prefer to receive the expected utility safely, or would she rather gamble?

shinyJackpot: Visualizing lottery gambling in a large Canadian city

Lottery gambling is widely enjoyed by Canadians and is the most popular form of legal gambling. As such, discovering and analyzing patterns in lottery gambling data is an important but nontrivial task. In this work, three methods were presented to process and visualize it to the end user to allow for faster pattern discovery. A bubble graph was utilized for the comparative analysis of lottery sales per each neighbourhood of the city of Toronto, Canada. As well, a scatter plot was used to explore the relationship between different neighbourhoods, lottery game product, year, lottery ticket sales, and demographic information. Lastly, a line graph was deployed to compare the jackpot size and ticket sales over time. shinyJackpot is deployed at https://andrewcli.shinyapps.io/shinyJackpot/ for online use. The repository is available at https://github.com/andr3wli/shinyapps.

Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting

Long-term forecasting involves predicting a horizon that is far ahead of the last observation. It is a problem of high practical relevance, for instance for companies in order to decide upon expensive long-term investments. Despite the recent progress and success of Gaussian processes (GPs) based on spectral mixture kernels, long-term forecasting remains a challenging problem for these kernels because they decay exponentially at large horizons. This is mainly due to their use of a mixture of Gaussians to model spectral densities. Characteristics of the signal important for long-term forecasting can be unravelled by investigating the distribution of the Fourier coefficients of (the training part of) the signal, which is non-smooth, heavy-tailed, sparse, and skewed. The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain. Motivated by these observations, we propose to model spectral densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of its peaks, sparsity, non-smoothness, and heavy tail characteristics. By applying the inverse Fourier Transform to this spectral density we obtain a new GP kernel for long-term forecasting. In addition, we adapt the lottery ticket method, originally developed to prune weights of a neural network, to GPs in order to automatically select the number of kernel components. Results of extensive experiments, including a multivariate time series, show the beneficial effect of the proposed SLSM kernel for long-term extrapolation and robustness to the choice of the number of mixture components.

Infertile Women's Perceptions Of Infertility: A Phenomenological Study Based On Metaphor Analysis

Backround: Infertility is an increasing health problem which affects 10-15% of couples across the world. Infertility can cause various psychosocial problems such as stress, anxiety, depression, loneliness, social isolation and stigmatization during the diagnosis and treatment process. Objective: The study was designed to reveal what kinds of cognitive images (metaphors) women produce in defining infertility, why they produce them and whether the metaphors produced by them differ depending on their professional culture. Methods: This study is a qualitative research carried out based on metaphor analysis in phenomenology design. The study was conducted with 15 participants. The data were collected by the researcher between May-July 2019. Results: The metaphors revealed by participants on what infertility meant to them, according to their Professional culture, included dry and fruitless tree, a defective human, cube, cactus and rose, empty tin can, hope and snowdrop, orzo, parasite, national lottery ticket, stock market, figures, surprise egg, bird, a slowly filled lake, and a disease that is very difficult to treat for infertility. Conclusion: Metaphors reflect the beliefs, values and norms of individuals. According to the findings, we can understand and comment infertile women’s perceptions on infertility and what the infertility means for them.