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In this work, we present numerical simulations of Stream Interaction Regions (SIRs) and Corotating Interaction Regions (CIRs) using the sunrunner3d tool that employs as a coronal model the boundary conditions obtained by corhel/mas with the pluto code that describes the global 3D structure of the solar wind using the magnetohydrodynamics (MHD) approach in the inner heliosphere. Specifically, we selected a set of SIRs and CIRs observed by the Parker Solar Probe (PSP) and STEREO-A (STA) missions during the Carrington rotations (CRs) 2207 to 2210 and CRs from 2020 to 2022. In order to describe the dynamics of the plasma that constitutes the solar wind background conditions for the selected CRs, we solve the ideal MHD equations in an inertial frame of reference, managing the solar rotation by rotating the boundary values in ϕ (longitude) at a rate corresponding to the sidereal rotation rate of the solar equator. We show that our results using sunrunner3d can globally reproduce the plasma parameters, such as radial velocity, number proton density, and radial magnetic field strength of these large-scale structures, observed by PSP and STA at distances near the Sun and around 1 au, respectively. These results allow exploring the global evolution of SIRs/CIRs in the inner heliosphere using sunrunner3d.

 

Despite increasing advocacy for gender equality, gender prejudice and discrimination persist. The origins of these biases develop in early childhood, but it is less clear whether (1) children's gender attitudes predict discrimination and (2) gender attitudes and discrimination vary by ethnicity and US region. We examine these questions with an ethnically (Asian, Black, Latinx and White) and geographically (Northeast, Pacific Northwest, West, Southeast and Hawaii) diverse sample of 4‐ to 6‐year‐old children (N = 605) who completed measures of gender attitudes and discrimination in a preregistered study. Children, across groups, demonstrated more positive attitudes towards their gender ingroup. Children who showed more pro‐ingroup attitudes also showed more pro‐ingroup behavioural discrimination. Girls showed stronger ingroup favouritism than boys, but ethnic and regional groups generally did not vary in levels of bias. These findings contribute to our understanding of how gender intergroup biases develop and highlight the generalizability of these processes.

 

The growing frequency, intensity, and duration of extreme heat events necessitates interventions to reduce heat exposures. Local opportunities for heat adaptation may be optimally identified through collection of both quantitative exposure metrics and qualitative data on perceptions of heat. In this study, we used mixed methods to characterize heat exposure among urban residents in the area of Boston, Massachusetts, US, in summer 2020. Repeated interviews ofN = 24 study participants ascertained heat vulnerability and adaptation strategies. Participants also used low-cost sensors to collect temperature, location, sleep, and physical activity data. We saw significant differences across temperature metrics: median personal temperature exposures were 3.9 °C higher than median ambient weather station temperatures. Existing air conditioning (AC) units did not adequately control indoor temperatures to desired thermostat levels: even with AC use, indoor maximum temperatures increased by 0.24 °C per °C of maximum outdoor temperature. Sleep duration was not associated with indoor or outdoor temperature. On warmer days, we observed a range of changes in time-at-home, expected given our small study size. Interview results further indicated opportunities for heat adaptation interventions including AC upgrades, hydration education campaigns, and amelioration of energy costs during high heat periods. Our mixed methods design informs heat adaptation interventions tailored to the challenges faced by residents in the study area. The strength of our community-academic partnership was a large part of the success of the mixed methods approach.

 

Machine learning models are updated as new data is acquired or new architectures are developed. These updates usually increase model performance, but may introduce backward compatibility errors, where individual users or groups of users see their performance on the updated model adversely affected. This problem can also be present when training datasets do not accurately reflect overall population demographics, with some groups having overall lower participation in the data collection process, posing a significant fairness concern. We analyze how ideas from distributional robustness and minimax fairness can aid backward compatibility in this scenario, and propose two methods to directly address this issue. Our theoretical analysis is backed by experimental results on CIFAR-10, CelebA, and Waterbirds, three standard image classification datasets. 

We perform a search for galaxy–galaxy strong lens systems using a convolutional neural network (CNN) applied to imaging data from the first public data release of the DECam Local Volume Exploration Survey, which contains ∼520 million astronomical sources covering ∼4000 deg²of the southern sky to a 5σpoint–source depth ofg= 24.3,r= 23.9,i= 23.3, andz= 22.8 mag. Following the methodology of similar searches using Dark Energy Camera data, we apply color and magnitude cuts to select a catalog of ∼11 million extended astronomical sources. After scoring with our CNN, the highest-scoring 50,000 images were visually inspected and assigned a score on a scale from 0 (not a lens) to 3 (very probable lens). We present a list of 581 strong lens candidates, 562 of which are previously unreported. We categorize our candidates using their human-assigned scores, resulting in 55 Grade A candidates, 149 Grade B candidates, and 377 Grade C candidates. We additionally highlight eight potential quadruply lensed quasars from this sample. Due to the location of our search footprint in the northern Galactic cap (b> 10 deg) and southern celestial hemisphere (decl. < 0 deg), our candidate list has little overlap with other existing ground-based searches. Where our search footprint does overlap with other searches, we find a significant number of high-quality candidates that were previously unidentified, indicating a degree of orthogonality in our methodology. We report properties of our candidates including apparent magnitude and Einstein radius estimated from the image separation.

 

Abstract An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, was constructed and tested in a low-background pulse-tube-based cryostat at the Canfranc underground laboratory in Spain. Performance of the scintillating bolometers was studied depending on the size of phonon NTD-Ge sensors glued to both LMO and Ge absorbers, shape of the Ge light detectors (circular vs. square, from two suppliers), in different light collection conditions (with and without reflector, with aluminum coated LMO crystal surface). The scintillating bolometer array was operated over 8 months in the low-background conditions that allowed to probe a very low, μBq/kg, level of the LMO crystals radioactive contamination by 228 Th and 226 Ra.