Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

A kernel-based approach to differentially private image generation

The gold standard privacy notion, differential privacy (DP), has gained widespread adoption in academic research, industry products, and government databases due to its mathematically provable privacy guarantee. However, the composability property of DP leads to privacy degradation with multiple accesses to the same data. Differentially private data generation has emerged as a solution, creating synthetic datasets resembling private data while allowing repeated access without additional privacy loss. Existing methods often assume specific use cases for synthetic data, limiting flexibility. This thesis addresses the challenge of producing flexible synthetic data by leveraging deep generative modeling and addressing privacy loss in other methods such as generative adversarial networks (GAN). we propose utilizing public data to learn perceptual features (PFs) for comparing real and synthetic data distributions, employing a non-adversarial generator training scheme based on Maximum Mean Discrepancy (MMD) to mitigate privacy loss. Experimental results reveal the efficacy of our method. it successfully generates samples for CIFAR-10, CelebA, MNIST, and FashionMNIST. Theoretical analysis of our privacy-preserving loss function clarifies the privacy-accuracy trade-offs.Science, Faculty ofComputer Science, Department ofGraduat

Breaking bad news in medical services: a comprehensive systematic review

Objective: This study was performed with the aims of screening the previous studies on breaking bad news in all medical wards. Methods: Eligible observational studies were selected. The quality of the studies was assessed using the STROBE checklist. The findings were reported using Garrard's table. All the stages of the present study were performed in terms of the PRISMA statement. Results: Totally, 40 articles were included in the study and 96 items were extracted. The results show that breaking bad news is a recipient-centered process. Respect, empathy, and support were reported. The news presenters are better to use guidelines based on evidence-based findings. It is suggested that the presenter should use simple and understandable content. Moreover, suitable time and space are important to present the news. The results show the importance of paying enough attention to the emotions of the recipient and the need to provide support after breaking bad news. Conclusion: The recipient must be the center of the programs. It is necessary to pay attention to the characteristics of the news presenter, the news content, and finally the support.Practice Implication: Understand the recipient, trained presenter, and use of the evidence-based results, improve the breaking bad news outcome

The Assessment of Share of Banks, Insurance and Investment Companies in Systemic Risk

The aim of this research is to focus on systemic risk by assessing the extent to which the distress caused by the main different financial sectors including banks, insurance and investment companies can be spread over the risk of financial system. For this purpose, a method of measuring changes in Conditional Value at Risk (CoVaR) based on the financial sectors return is used and its value is estimated using quantile regression. Also, two-sample Kolmogorov-Smirnov test was used to determine the impact of risk imposed by financial institutions to financial system and to achieve a ranking of financial sectors contributing to systemic risk. In this paper, 24 financial institutions listed in Tehran Stock Exchange during the time period of 2011-15 was selected. The results show that all 3 sectors contribute significantly to systemic risk in Iran during this time period and the investment companies have the highest share in creating systemic risk and then the banking and insurance sectors, respectively

Differentially Private Data Generation Needs Better Features

Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large. We advocate instead building off a good, relevant representation on public data, then using private data only for "transfer learning." In particular, we minimize the maximum mean discrepancy (MMD) between private target data and the generated distribution, using a kernel based on perceptual features from a public dataset. With the MMD, we can simply privatize the data-dependent term once and for all, rather than introducing noise at each step of optimization as in DP-SGD. Our algorithm allows us to generate CIFAR10-level images faithfully with $\varepsilon \approx 2$, far surpassing the current state of the art, which only models MNIST and FashionMNIST at $\varepsilon \approx 10$. Our work introduces simple yet powerful foundations for reducing the gap between private and non-private deep generative models

One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model

Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most existing SWSSS algorithms extract pixel-level pseudo-labels from an image classifier - a very difficult task to do well, hence requiring complicated architectures and extensive hyperparameter tuning on fully-supervised validation sets. We propose a method called prediction filtering, which instead of extracting pseudo-labels, just uses the classifier as a classifier: it ignores any segmentation predictions from classes which the classifier is confident are not present. Adding this simple post-processing method to baselines gives results competitive with or better than prior SWSSS algorithms. Moreover, it is compatible with pseudo-label methods: adding prediction filtering to existing SWSSS algorithms further improves segmentation performance