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  1. Abstract

    The zero-age main sequence (ZAMS) is a critical phase for stellar angular momentum evolution, as stars transition from contraction-dominated spin-up to magnetic wind-dominated spin-down. We present the first robust observational constraints on rotation for FGK stars at ≈40 Myr. We have analyzed TESS light curves for 1410 members of five young open clusters with ages between 25 and 55 Myr: IC 2391, IC 2602, NGC 2451A, NGC 2547, and Collinder 135. In total, we measure 868 rotation periods, including 96 new, high-quality periods for stars around 1M. This is an increase of ten times the existing literature sample at the ZAMS. We then use theτ2method to compare our data to models for stellar angular momentum evolution. Although the ages derived from these rotation models do not match isochronal ages, we show that these observations can clearly discriminate between different models for stellar wind torques. Finally,τ2fits indicate that magnetic braking and/or internal angular momentum transport significantly impact rotational evolution even on the pre-main sequence.

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  2. We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communities in social and information networks. The idea of $\mu$-conductance is similar to the traditional graph conductance, but disregards sets with small volume. We derive a sequence of optimization problems including a low-rank semi-definite program from which we can derive a lower bound on the optimal $\mu$-conductance value. These ideas give the first theoretically sound bound on the behavior of the network community profile for a wide range of cluster sizes. The algorithm scales up to graphs with hundreds of thousands of nodes and we demonstrate how our framework validates the predicted structures of real-world graphs. 
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    Free, publicly-accessible full text available July 10, 2024
  3. Math performance continues to be an important focus for improve- ment. Many districts adopted educational technology programs to support student learning and teacher instruction. The ASSISTments program provides feedback to students as they solve homework problems and automatically prepares reports for teachers about student performance on daily assignments. During the 2018- 19 and 2019-20 school years, we conducted a large-scale randomized controlled trial to replicate the effects of ASSISTments in 63 schools in North Carolina in the US. 32 treatment schools implemented ASSISTments in 7th-grade math class- rooms. Recently, we conducted a follow-up analysis to measure the long-term effects of ASSISTments on student performance one year after the intervention, when the students were in 8th grade. The initial results suggested that implement- ing ASSISTments in 7th grade improved students’ performance in 8th grade and minority students benefited more from the intervention. 
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    Free, publicly-accessible full text available July 1, 2024
  4. Double heterojunction nanorods enable both electroluminescence and light harvesting capabilities within the same device structure, providing a promising platform for energy-scavenging displays and related applications. However, the efficiency of the photovoltaic mode remains modest for useful power conversion and may be challenging to improve without sacrificing performance in electroluminescence. Through a facile on-film partial ligand exchange with benzenethiol integrated into the device fabrication step, we achieve an average of more than threefold increase in power conversion efficiency while maintaining the maximum external quantum efficiency and the maximum luminance in the LED mode. The improved photovoltaic performance is mainly due to the increase in the short circuit current, which we attribute to the enhanced charge separation afforded by the partial ligand exchange. The recovery of the photoluminescence lifetime under the forward bias suggests that the hole traps introduced by benzenethiols are filled prior to reaching the voltage at which light emission begins, allowing LED performance to be maintained and possibly improved. 
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    Free, publicly-accessible full text available June 22, 2024
  5. Free, publicly-accessible full text available January 1, 2025
  6. Abstract The coupling between superconductors and oscillation cycles of light pulses, i.e., lightwave engineering, is an emerging control concept for superconducting quantum electronics. Although progress has been made towards terahertz-driven superconductivity and supercurrents, the interactions able to drive non-equilibrium pairing are still poorly understood, partially due to the lack of measurements of high-order correlation functions. In particular, the sensing of exotic collective modes that would uniquely characterize light-driven superconducting coherence, in a way analogous to the Meissner effect, is very challenging but much needed. Here we report the discovery of parametrically driven superconductivity by light-induced order-parameter collective oscillations in iron-based superconductors. The time-periodic relative phase dynamics between the coupled electron and hole bands drives the transition to a distinct parametric superconducting state out-of-equalibrium. This light-induced emergent coherence is characterized by a unique phase–amplitude collective mode with Floquet-like sidebands at twice the Higgs frequency. We measure non-perturbative, high-order correlations of this parametrically driven superconductivity by separating the terahertz-frequency multidimensional coherent spectra into pump–probe, Higgs mode and bi-Higgs frequency sideband peaks. We find that the higher-order bi-Higgs sidebands dominate above the critical field, which indicates the breakdown of susceptibility perturbative expansion in this parametric quantum matter. 
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  7. Summary This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer’s disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer’s disease study. 
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