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In order to explore the consequences of spin–orbit coupling on spin–phonon interactions in a set of chemically similar mixed metal oxides, we measured the infrared vibrational properties of Co4B2O9 (B = Nb, Ta) as a function of temperature and compared our findings with lattice dynamics calculations and several different models of spin–phonon coupling. Frequency vs temperature trends for the Co2+ shearing mode near 150 cm−1 reveal significant shifts across the magnetic ordering temperature that are especially large in relative terms. Bringing these results together and accounting for noncollinearity, we obtain spin–phonon coupling constants of −3.4 and −4.3 cm−1 for Co4Nb2O9 and the Ta analog, respectively. Analysis reveals that these coupling constants are derived from interlayer (rather than intralayer) exchange interactions and that the interlayer interactions contain competing antiferromagnetic and ferromagnetic contributions. At the same time, beyond-Heisenberg terms are minimized due to fortuitous symmetry considerations, different from most other 4d- and 5d-containing oxides. Comparison with other contemporary oxides shows that spin–phonon coupling in this family of materials is among the strongest ever reported, suggesting an origin for magnetoelectric coupling.

Deep neural networks implementing generative models for dimensionality reduction have been extensively used for the visualization and analysis of genomic data. One of their key limitations is lack of interpretability: it is challenging to quantitatively identify which input features are used to construct the embedding dimensions, thus preventing insight into why cells are organized in a particular data visualization, for example. Here we present a scalable, interpretable variational autoencoder (siVAE) that is interpretable by design: it learns feature embeddings that guide the interpretation of the cell embeddings in a manner analogous to factor loadings of factor analysis. siVAE is as powerful and nearly as fast to train as the standard VAE but achieves full interpretability of the embedding dimensions. Using siVAE, we exploit a number of connections between dimensionality reduction and gene network inference to identify gene neighborhoods and gene hubs, without the explicit need for gene network inference. We observe a systematic difference in the gene neighborhoods identified by dimensionality reduction methods and gene network inference algorithms in general, suggesting they provide complementary information about the underlying structure of the gene co-expression network. Finally, we apply siVAE to implicitly learn gene networks for individual iPSC lines and uncover amore »correlation between neuronal differentiation efficiency and loss of co-expression of several mitochondrial complexes, including NADH dehydrogenase, cytochrome C oxidase, and cytochrome b.« less

ABSTRACT We use the SMASH survey to obtain unprecedented deep photometry reaching down to the oldest main-sequence turn-offs in the colour–magnitude diagrams (CMDs) of the Small Magellanic Cloud (SMC) and quantitatively derive its star formation history (SFH) using CMD fitting techniques. We identify five distinctive peaks of star formation in the last 3.5 Gyr, at ∼3, ∼2, ∼1.1, ∼0.45 Gyr ago, and one presently. We compare these to the SFH of the Large Magellanic Cloud (LMC), finding unequivocal synchronicity, with both galaxies displaying similar periods of enhanced star formation over the past ∼3.5 Gyr. The parallelism between their SFHs indicates that tidal interactions between the MCs have recurrently played an important role in their evolution for at least the last ∼3.5 Gyr, tidally truncating the SMC and shaping the LMC’s spiral arm. We show, for the first time, an SMC–LMC correlated SFH at recent times in which enhancements of star formation are localized in the northern spiral arm of the LMC, and globally across the SMC. These novel findings should be used to constrain not only the orbital history of the MCs but also how star formation should be treated in simulations.

University-based makerspaces are receiving increasing attention as promising innovations that may contribute to the development of future engineers. Using a theory of social boundary spaces, we investigated whether the diverse experiences offered at university-based makerspaces may contribute to students’ learning and development of various “soft” or “21st century” skills that go beyond engineering-specific content knowledge. Through interviews with undergraduate student users at two university-based makerspaces in the United States we identified seven different types of boundary spaces (where multiple communities, and the individuals and activities affiliated with those communities, come together). We identified students engaging in the processes of identification, reflection, and coordination, which allowed them to make sense of, and navigate, the various boundary spaces they encountered in the makerspaces. These processes provided students with opportunities to engage with, and learn from, individuals and practices affiliated with various communities and disciplines. These opportunities can lead to students’ development of necessary skills to creatively and collaboratively address interdisciplinary socio-scientific problems. We suggest that universitybased makerspaces can offer important developmental experiences for a diverse body of students that may be challenging for a single university department, program, or course to offer. Based on these findings, we recommend university programs and facultymore »intentionally integrate makerspace activities into undergraduate curricula to support students’ development of skills, knowledge, and practices relevant for engineering as well as 21st century skills more broadly.« less

Abstract We report the detection of three RR Lyrae (RRL) stars (two RRc and one RRab) in the ultra-faint dwarf (UFD) galaxy Centaurus I (Cen I) and two Milky Way (MW) δ Scuti/SX Phoenicis stars based on multi-epoch giz DECam observations. The two RRc stars are located within two times the half-light radius ( r h ) of Cen I, while the RRab star (CenI-V3) is at ∼6 r h . The presence of three distant RRL stars clustered this tightly in space represents a 4.7 σ excess relative to the smooth distribution of RRL in the Galactic halo. Using the newly detected RRL stars, we obtain a distance modulus to Cen I of μ 0 = 20.354 ± 0.002 mag ( σ = 0.03 mag), a heliocentric distance of D ⊙ = 117.7 ± 0.1 kpc ( σ = 1.6 kpc), with systematic errors of 0.07 mag and 4 kpc. The location of the Cen I RRL stars in the Bailey diagram is in agreement with other UFD galaxies (mainly Oosterhoff II). Finally, we study the relative rate of RRc+RRd (RRcd) stars ( f cd ) in UFD and classical dwarf galaxies. The full sample of MW dwarf galaxiesmore »gives a mean of f cd = 0.28. While several UFD galaxies, such as Cen I, present higher RRcd ratios, if we combine the RRL populations of all UFD galaxies, the RRcd ratio is similar to the one obtained for the classical dwarfs ( f cd ∼ 0.3). Therefore, there is no evidence for a different fraction of RRcd stars in UFD and classical dwarf galaxies.« less