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The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of >10 million DESI Bright Galaxy Survey galaxies. Each posterior distribution will be inferred from joint Bayesian modeling of observed photometry and spectroscopy using Markov Chain Monte Carlo sampling and the Hahn et al. stellar population synthesis (SPS) model. To make this computationally feasible, PROVABGS will use a neural emulator for the SPS model to accelerate the posterior inference. In this work, we present how we construct the emulator using the Alsing et al. approach and verify that it can be used to accurately infer galaxy properties. We confirm that the emulator is in excellent agreement with the original SPS model with ≪1% error and is 100× faster. In addition, we demonstrate that the posteriors of galaxy properties derived using the emulator are also in excellent agreement with those inferred using the original model. The neural emulator presented in this work is essential in bypassing the computational challenge posed in constructing the PROVABGS catalog. Furthermore, it demonstrates the advantages of emulation for scaling sophisticated analyses to millions of galaxies.

 

The spread of misinformation through a variety of communication channels has amplified society’s challenge to manage the COVID-19 pandemic. While existing studies have examined how misinformation spreads, few studies have examined the role of psychological distance in people’s mental processing of a rumor and their propensity to accept self-transformed narratives of the message. Based on an open-ended survey data collected in the U.S. ( N = 621) during an early phase of the pandemic, the current study examines how psychological distance relates to the transformation and acceptance of conspiratorial narratives in the context of the COVID-19 pandemic. Two instances of misinformation are examined, both of which were widely heard at the time of data collection: the role of (a) Bill Gates and (b) government during the outbreak of the pandemic. This study uses topic modeling techniques to capture distinctive topical attributes that emerged from rumor narratives. In addition, statistical analyses estimate the psychological distance effects on the salience of topical attributes of a rumor story and an individual’s propensity to believe them. Findings reveal that psychological distance to the threats of COVID-19 influences how misinformation evolves through word-of-mouth, particularly in terms of who is responsible for the pandemic and why the world finds itself in the current situation. Psychological distance also explains why people accept the message to be true. Implications for misinformation and rumor psychology research, as well as avenues for future research, are discussed. 

This study introduces an Augmented-Reality-based learning system that aims to support young students’ embodied learning in block-based programming activities where they learn computational concepts and create meaningful chunks of codes. Students are going to perform episode-embedded path-finding tasks, which are designed to practice their capacities of applying computational thinking in a reasonable manner to solve problems within different scenarios. Grounded on an embodied cognition approach, the AR integration creates a concrete and tangible environment for young students to understand abstract conceptual knowledge in an engaging and interactive way, with a close connection built between the real and virtual worlds. 

Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.