Psychological research has long centered around questionnaire assessments, but now digital devices, especially smartphones, enable the collection of real-world behavioral data through mobile sensing. While this data collection method offers unique opportunities, it also introduces new methodological challenges, as mobile-sensing data are highly complex and high in dimensionality (i.e., time-stamped events with millisecond resolution), requiring advanced preprocessing to derive psychologically meaningful variables. This article highlights these challenges by reviewing the current state of data preprocessing based on app usage logs from smartphones. Afterwards, it presents three preprocessing cases that vary in complexity across the dimensions of data enrichment—which involves adding context to raw data by integrating information from external and internal sources (including Ecological Momentary Assessments)—and data aggregation—which entails summarizing data in different ways, from basic descriptive statistics to sophisticated machine learning models. For each case, potential pitfalls are identified, and extensions are discussed to refine our preprocessing pipelines and accommodate different data types and research questions. By outlining these preprocessing strategies, this manuscript demonstrates the rich potential of mobile-sensing data for extracting nuanced behavioral variables beyond simple person-level summaries, and aims to inspire the development of more advanced research questions based on sensing data.
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised learning methods due to their independence from label annotation. This survey explores the interplay between diffusion models and representation learning. It provides an overview of diffusion models' essential aspects, including mathematical foundations, popular denoising network architectures, and guidance methods. Various approaches related to diffusion models and representation learning are detailed. These include frameworks that leverage representations learned from pre-trained diffusion models for subsequent recognition tasks and methods that utilize advancements in representation and self-supervised learning to enhance diffusion models. This survey aims to offer a comprehensive overview of the taxonomy between diffusion models and representation learning, identifying key areas of existing concerns and potential exploration. Github link: https://github.com/dongzhuoyao/Diffusion-Representation-Learning-Survey-Taxonomy
Economic shocks have been shown to affect social and political outcomes. Here, I show that U.S. counties that faced greater economic shocks within the last 30 years were less likely to comply with the advice/orders of public health officials during the COVID-19 pandemic. Analyzing county-level vaccination rates and then compliance rates with stay-at-home orders, I show that compliance with these initiatives was lower in counties that had experienced trade exposure to China, excess unemployment from the Great Recession, and a greater risk of job automation. These shocks are comparable in importance to factors such as income, age, and education.
INTRODUCTION:Current guidelines recommend prophylaxis for patients with non-severe haemophilia with severe bleeding phenotype (SBPT) but there is no consensus how to define a SBPT and when to recommend prophylaxis in patients with non-severe haemophilia. METHOD:A Delphi consensus procedure among the members of the Standing Committee Hemophilia of the German, Austrian and Swiss Society of Hemostasis and Thrombosis Research (GTH) was conducted. After defining 41 statements in a steering committee, 26 haemophilia experts participated. Statements were scored on a scale of 1-9, and agreement was defined as a score of ≥ 7. Consensus was defined as ≥ 75%, and strong consensus as ≥ 95% agreement. RESULTS:After 3 rounds of consent, five major and three minor criteria for SBPT, five recommendations for starting long-term and six recommendations for starting intermittent prophylaxis were consented. Major criteria included life-threatening bleeding in critical regions or organs, severe bleeding that occurs spontaneously, repeatedly, or after inadequate trauma, development of haemophilic arthropathy, presence of chronic synovitis, and Hb-relevant menstrual bleeding. Long-term prophylaxis should be recommended in patients with a residual factor activity < 3 IU/dL, in patients with a residual activity > 3 IU/dL and a SBPT, following intracranial haemorrhage after assessing the individual risk of recurrence, in cases of comorbidities and medications that cause a permanently increased bleeding tendency, and in the presence of risk factors for severe bleeding or arthropathy. CONCLUSION:Consensus was reached on criteria for SBPT and recommendations to initiate prophylaxis in patients with non-severe haemophilia that can be used in daily practice.
Das initiale Zusammenwirken von Polizei und Rettungsdienst bei lebensbedrohlichen Einsatzlagen (LbEL) konzentriert sich bisher auf die fixe Einteilung von Zonen und die dort stattfindende medizinische Versorgung und Evakuierung der Patienten. Das größte Problem bei der Ordnung des Raumes nach Zonen besteht in der veränderlichen Dynamik eines Einsatzes und der nicht geometrischen Verteilung der zu definierenden Bereiche in der Einsatzrealität. Viel entscheidender als die Zonen an sich sind hingegen die Aufträge der Einsatzkräfte, die es mit den Zielen „Rettung möglichst vieler“ und „größtmöglicher Schutz der Einsatzkräfte“ zu erfüllen gilt. Diese ReAktionen müssen exakt aufeinander abgestimmt sein und deren Ausführung hängt von der jeweilig herrschenden Gefahrenlage ab. Die Weiterentwicklung des Zonenmodells setzt genau hier an und fokussiert mehr auf die Aufträge als auf die Festlegung von Zonen. Das hier vorgestellte ReAktionsmodell beschreibt die Weiterentwicklung des Zonenmodells und soll als Diskussionsgrundlage für eine stetige Verbesserung bestehender Einsatzkonzepte dienen.
