Notes

Phase-Programmable Gaussian Boson Sampling Employing Stimulated Compressed Light.
This paper presents Bayesian directional data modeling via the skew-rotationally-symmetric Fisher-von Mises-Langevin (FvML) distribution. The prior distributions for the parameters are a pivotal building block in Bayesian analysis, therefore, the impact of the proposed priors will be quantified using the Wasserstein Impact Measure (WIM) to guide the practitioner in the implementation process. For the computation of the posterior, modifications of Gibbs and slice samplings are applied for generating samples. We demonstrate the applicability of our contribution via synthetic and real data analyses. Our investigation paves the way for Bayesian analysis of skew circular and spherical data.How can the public sector use AI ethically and responsibly for the benefit of people? The sustainable development and deployment of artificial intelligence (AI) in the public sector requires dialogue and deliberation between developers, decision makers, deployers, end users, and the public. This paper contributes to the debate on how to develop persuasive government approaches for steering the development and use of AI. We examine the ethical issues and the role of the public in the debate on developing public sector governance of socially and democratically sustainable and technology-intensive societies. To concretize this discussion, we study the co-development of a Finnish national AI program AuroraAI, which aims to provide citizens with tailored and timely services for different life situations, utilizing AI. With the help of this case study, we investigate the challenges posed by the development and use of AI in the service of public administration. We draw particular attention to the efforts made by the AuroraAI Ethics Board in deliberating the AuroraAI solution options and working toward a sustainable and inclusive AI society.Even though the web environment facilitates our daily life, emotional problems caused by its incompatibility with human cognition are becoming increasingly serious. To alleviate negative emotions during web use, we developed a browser extension that presents memorized product images to users in the form of web advertisements. This system utilizes the cognitive architecture Adaptive Control of Thought-Rational (ACT-R) as a model of human memory and emotion. A heart rate sensor attached to the user modulates the ACT-R model parameters, and the emotional states represented by the model are synchronized (following the chameleon effect) or counterbalanced (following the homeostasis regulation) with the physiological state of the user. An experiment demonstrates that the counterbalanced model suppresses negative ruminative web browsing. The authors claim that this approach, utilizing a cognitive model, is advantageous in terms of explainability.This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the intraday high-frequency returns to forecast daily volatility. Applied to the IBM stock, we find significant improvements in the forecasting performance of models that use this extracted information compared to the forecasts of models that omit the extracted information and some of the most popular alternative models. Furthermore, we find that extracting the information through Long Short Term Memory Recurrent Neural Networks is superior to two Mixed Data Sampling alternatives.Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments.Coronavirus disease 2019 (COVID-19) has exacerbated pre-existing inequities in access to healthy food and land. Programs and policies that eradicate food insecurity by empowering people with agency and dignity instead of providing handouts are essential. Bringing food into the commons can be one strategy to improve food security, equitable land ownership, and land stewardship.The aviation industry has gone through numerous ups and downs in the past decades. Despite the devastating damage caused by the COVID-19 Pandemic, the aviation industry worldwide still manages to bounce back from the abyss of Q2, 2020, though the speed of recovery is less than satisfactory for most regions. Being aware of the existing literature on air travel demands published since March 2020, this study aims to provide US Primary Hub airports with benchmarks that can help airports predict the recovery of air travel demand during the COVID-19 Pandemic. This study uses the passenger numbers going through airport security checkpoints as the input data and the k-shape clustering algorithm to group airports by their travel demand recovery patterns. DMAMCL The clustering analysis results are presented in a circular dendrogram so that any of the 118 subject airports can quickly locate their benchmarking airports. In this process, the geographic location and hub category of an airport are found to play important roles in determining how local outbound traffic recovers during the Pandemic. We also test if state political preference in the 2020 Presidential Election affects local airport traffic but cannot find any convincing results. The method used by this study can be fed with up-to-date data to produce more timely and reliable results to guide airports and other stakeholders through the recovery journey.The COVID-19 outbreak meant that using public transport was potentially unsafe for risk of catching and transmitting the virus. UK anxiety is high with lockdowns preventing a normal way of life for over a year. A lack of ability to travel freely causes numerous declines in quality of life including social isolation and poor physical and mental health. People need crowding information to choose safer travel options and subdue coronavirus. To provide effective guidance, it is essential to empirically formulate messaging to create clarity and trust which can be acted upon in confidence. Behaviour Change Techniques incorporating the Behaviour Change Wheel and COM-B model have been utilised in vast areas of public health intervention development and messaging. There is consensus that public transport information needs to be clearer and more accessible but BCTs have not been utilised in the development of public transport advice. This paper outlines the development of crowding messaging for public transport on a platform available to UK travellers. Barriers and facilitators were explored; related behaviours, intervention functions and behaviour change techniques were mapped. Specific message phrasing was developed utilising the mapped functions and advice from the literature. With the COVID-19 outbreak, having accessible and effective messaging for safely using public transport is a continuation of the work recently conducted examining the best ways to present public health information. It is important to be transparent when developing messaging and interventions accessible to the public and this work forms a basis for continued exploration and development in this area.
The necessity for an equal distribution of the COVID-19 vaccination is critical. Lower-middle and lower income countries may not be able to manufacture their vaccines, nor may they be able to afford to buy them for every inhabitant. Furthermore, the vaccination's potency may wane over time. A booster dosage is recommended. Despite this, certain areas or groups of people are still waiting for their first vaccine dosage.

The purposes of this study were to assess the safety and tolerability of patients who received a fractionated intradermal administration (ID) of PFE-BNT as a booster dose in a group of people who had previously finished full doses of Verocell and to determine the antibody response after the injection.

An open-label experiment was carried out. Participants were at least 18years old. Participants received 6 ug of PFE-BNT vaccination through intradermal injection. The safety and adverse reactions were monitored at immediate after injection, 30min later, day 1, day 7, and day 30. Venous blood tests for specific IgG concentration against SARS-CoV-2 spike S1 were received prior to injection and day 30.

42 participants completed the study. The mean age was 48 (the range; 23-62). The average duration after completing the 2nd dose of Verocell was 78.3days (95% CI; 73.9-82.8). There was no serious adverse event. Almost 50% of participants reported minor adverse reactions on day 1 and roughly 30% still reporting on day 7. Systemic reactions were foundless than5%. The antibody level at day 30 was 16669.8 (95% CI; 3692.6-51238.9), which was 40 times higher.

PFE-BNT at a dose of 6 ug (1/5 of the typical dose) was shown to be safe and well tolerated when given intradermally. The antibody reaction was very strong. The ID administration could potentially save vaccine doses.
PFE-BNT at a dose of 6 ug (1/5 of the typical dose) was shown to be safe and well tolerated when given intradermally. The antibody reaction was very strong. The ID administration could potentially save vaccine doses.
The current SARS-CoV-2 pandemic created an urgent need for rapid, infection screening applied to large numbers of asymptomatic individuals. To date, nasal/throat swab polymerase chain reaction (PCR) is considered the "gold standard". However, this is inconducive to mass, point-of-care (POC) testing due to person discomfort during sampling and a prolonged result turnaround. Breath testing for disease specific organic compounds potentially offers a practical, rapid, non-invasive, POC solution. The study compares the Breath of Health, Ltd. (BOH) breath analysis system to PCR's ability to screen asymptomatic individuals for SARS-CoV-2 infection. The BOH system is mobile and combines Fourier-transform infrared (FTIR) spectroscopy with artificial intelligence (AI) to generate results within 2min and 15s. In contrast to prior SARS-CoV-2 breath analysis research, this study focuses on diagnosing SARS-CoV-2 via disease specific spectrometric profiles rather than through identifying the disease specific molecules.

Asymptomatic emergency room patients with suspected SARS-CoV-2 exposure in two leading Israeli hospitals were selected between February through April 2021.
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