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This preliminary report shares an outcome from a summer professional development (PD) activity with university instructors. Instructors participated in four PD meetings, then immediately taught a five-day summer workshop using inquiry, working primarily with first-generation minoritized students. While instructor participants’ exit interviews of the project identified their experience in the summer PD as pivotal to their development, we know little of how students experienced the instructors’ teaching during the workshop. Our analysis focuses on two items from student post-workshop survey wherein students shared their feedback of their instructor and their experiences more broadly. This analysis allowed us to get a good sense of the instructors’ individual practices and revealed convergence in their practices. Pedagogically, instructors utilized group work and deemphasized direct instructions, while prioritizing students’ engagement in discussions and struggling through conceptual ideas. Relationally, instructors were responsive to students’ mathematical needs and created a respectful, safe, and welcoming classroom environment. 

Despite the importance of computational thinking (CT) as a problem-solving process (Wing, 2008) and the growing spread in teacher education (Yadav et al., 2017), existing initiatives for preservice teachers (PSTs) tend to focus on the computer science domain without making explicit connections to disciplinary classroom settings and promoting critical perspectives. As a cohesive unit, this learning representation aims to assist PSTs in integrating CT into their work as they design and implement science-focused lessons.Centered around a contextual issue: accessing, growing, and sustaining food, this learning representation employs 2D and 3D block-based programming languages coupled with unplugged activities that demonstrate CT practices, processes, and concepts. PSTs’ group designs, lesson modifications, and full lesson plans provide opportunities for assessment. 

Ultracold fermionic atoms in optical lattices offer pristine realizations of Hubbard models1, which are fundamental to modern condensed-matter physics2,3. Despite notable advancements4–6, the accessible temperatures in these optical lattice material analogues are still too high to address many open problems7–10. Here we demonstrate a several-fold reduction in temperature6,11–13, bringing large-scale quantum simulations of the Hubbard model into an entirely new regime. This is accomplished by transforming a low-entropy product state into strongly correlated states of interest via dynamic control of the model parameters14,15, which is extremely challenging to simulate classically10. At half-filling, the long-range antiferromagnetic order is close to saturation, leading to a temperature of T /t =0.05−0.05 +0.06 based on comparisons with numerically exact simulations. Doped away from half-filling, it is exceedingly challenging to realize systematically accurate and predictive numerical simulations9. Importantly, we are able to use quantum simulation to identify a new pathway for achieving similarly low temperatures with doping. This is confirmed by comparing short-range spin correlations to state-of-the-art, but approximate, constrainedpath auxiliary-field quantum Monte Carlo simulations16–18. Compared with the cuprates2,19,20, the reported temperatures correspond to a reduction from far above to below room temperature, at which physics such as the pseudogap and stripe phases may be expected3,19,21–24. Our work opens the door to quantum simulations that solve open questions in material science, develop synergies with numerical methods and theoretical studies, and lead to discoveries of new physics8,10. 

We propose a reformulation of the streaming dynamic mode decomposition method that requires maintaining a single orthonormal basis, thereby reducing computational redundancy. The proposed efficient streaming dynamic mode decomposition method results in a constant-factor reduction in computational complexity and memory storage requirements. Numerical experiments on representative canonical dynamical systems show that the enhanced computational efficiency does not compromise the accuracy of the proposed method. 

Male baboons mature more slowly than females, reaching full adult maturity at around 10-12 years of age. After the onset of puberty at 5-7 years, the sub-adult period lasts 3-5 years while the male continues to grow, though there is considerable variation between individuals. Here, we present data on the behavioral changes that accompany the physical maturation of male olive baboons (Papio anubis) as they transition through each developmental stage. This research was conducted on a fully habituated wild troop at the Uaso Ngiro Baboon Project in Laikipia, Kenya. We use long-term grooming data (2018-2023) to show that males have significantly more grooming partners as they get older (n=48, p<.001). We then use behavioral data collected in June and July 2023 to compare the social behaviors of males from three developmental stages: juveniles (n=5), males who recently became sub-adults (n=4), and males who have been sub-adults for over a year (n=5). The differences between these three groups show the effect of puberty on behavior: juveniles were observed in social play significantly more often than sub-adults (p=.006), while males who recently underwent puberty tended to groom less often than either juveniles or older sub-adults (p=.091). Our focal data also revealed variation in the age at which males reached each developmental stage. Further research is needed to determine causes and consequences of the variation in age at puberty and the potential long-term consequences of this variation on the males’ social behavior.