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Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases. 

A ditopic nitrogen ligand (E)-N′-(pyridin-4-ylmethylene)isonicotinohydrazide (L) containing both divergent pyridyl coordination sites and a hydrogen-bonding hydrazide–hydrazone moiety was synthesized. The Co(NCS)2-mediated self-assembly of L has resulted in the synthesis of a novel 3-dimensional (3D) supramolecular framework (1) that features both coordination and hydrogen bonding interactions. X-ray structural analysis reveals the formation and coordination mode of 1 in the solid state. The rational utilization of coordination bonds and hydrogen bonding interactions is confirmed and responsible for constructing the 3D materials. Catalytic studies using 1 in the presence of an activator are performed for the hydroboration and hydrosilylation reactions of ketones and aldehydes, and the results are compared with previously reported cobalt-based polymeric catalysts. 

Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains -- or by using approximate heuristics to extract them from everyday text -- which frequently yields unfaithful summaries. In this work, we turn to a slower but more straightforward approach to developing summarization benchmark data: We hire highly-qualified contractors to read stories and write original summaries from scratch. To amortize reading time, we collect five summaries per document, with the first giving an overview and the subsequent four addressing specific questions. We use this protocol to collect SQuALITY, a dataset of question-focused summaries built on the same public-domain short stories as the multiple-choice dataset QuALITY (Pang et al., 2021). Experiments with state-of-the-art summarization systems show that our dataset is challenging and that existing automatic evaluation metrics are weak indicators of quality. 

Importance Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A 1c (HbA 1c ) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes. Objective To determine whether HbA 1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM. Design, Setting, and Participants This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children’s Hospital, a single-site, freestanding children’s hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022. Exposures All eligible participants were offered CGM within 1 month of diabetes diagnosis. Main Outcomes and Measures To assess HbA 1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. Results The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants’ race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA 1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, −0.26% [95% CI, −1.05% to 0.43%], −0.60% [−1.46% to 0.21%], and −0.15% [−1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, −0.27% [95% CI, −0.62% to 0.10%], −0.50% [−0.81% to −0.11%], and −0.47% [−0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA 1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, −0.52% [95% CI, −1.22% to 0.15%], −0.38% [−1.26% to 0.33%], and −0.57% [−2.08% to 0.74%]) and privately insured individuals (estimated difference, −0.34% [95% CI, −0.67% to 0.03%], −0.57% [−0.85% to −0.26%], and −0.43% [−0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA 1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, −0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, −0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [−0.09% to 2.13%]). Conclusions and Relevance The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA 1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA 1c for all youths but is unlikely to eliminate disparities entirely. Trial Registration ClinicalTrials.gov Identifier: NCT04336969 

We prove the equivalence of two different Hessian evaluation algorithms in AD. The first is the Edge Pushing algorithm of Gower and Mello, which may be viewed as a second order Reverse mode algorithm for computing the Hessian. In earlier work, we have derived the Edge Pushing algorithm by exploiting a Reverse mode invariant based on the concept of live variables in compiler theory. The second algorithm is based on eliminating vertices in a computational graph of the gradient, in which intermediate variables are successively eliminated from the graph, and the weights of the edges are updated suitably. %The algorithm can be performed on any order of eliminating vertices. We prove that if the vertices are eliminated in a reverse topological order while preserving symmetry in the computational graph of the gradient, then the Vertex Elimination algorithm and the Edge Pushing algorithm perform identical computations. In this sense, the two algorithms are equivalent. This insight that unifies two seemingly disparate approaches to Hessian computations could lead to improved algorithms and implementations for computing Hessians. 

Abstract The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules.During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector.Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2.It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%.Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.