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Cross-cultural research provides invaluable information about the origins of and explanations for cognitive and behavioral diversity. Interest in cross-cultural research is growing, but the field continues to be dominated by WEIRD (Western, Educated, Industrialized, Rich, and Democratic) researchers conducting WEIRD science with WEIRD participants, using WEIRD protocols. To make progress toward improving cognitive and behavioral science, we argue that the field needs (1) data workflows and infrastructures to support long-term high-quality research that is compliant with open-science frameworks; (2) process and participation standards to ensure research is valid, equitable, participatory, and inclusive; (3) training opportunities and resources to ensure the highest standards of proficiency, ethics, and transparency in data collection and processing. Here we discuss infrastructures for cross-cultural research in cognitive and behavioral sciences which we call Cross-Cultural Data Infrastructures (CCDIs). We recommend building global networks of psychologists, anthropologists, demographers, experimental philosophers, educators, and cognitive, learning, and data scientists to distill their procedural and methodological knowledge into a set of community standards. We identify key challenges including protocol validity, researcher diversity, community inclusion, and lack of detail in reporting quality assurance and quality control (QAQC) workflows. Our objective is to help promote dialogue and efforts towards consolidating robust solutions by working with a broad research community to improve the efficiency and quality of cross-cultural research. 

The ever increasing size of deep neural network (DNN) models once implied that they were only limited to cloud data centers for runtime inference. Nonetheless, the recent plethora of DNN model compression techniques have successfully overcome this limit, turning into a reality that DNN-based inference can be run on numerous resource-constrained edge devices including mobile phones, drones, robots, medical devices, wearables, Internet of Things devices, among many others. Naturally, edge devices are highly heterogeneous in terms of hardware specification and usage scenarios. On the other hand, compressed DNN models are so diverse that they exhibit different tradeoffs in a multi-dimension space, and not a single model can achieve optimality in terms of all important metrics such as accuracy, latency and energy consumption. Consequently, how to automatically select a compressed DNN model for an edge device to run inference with optimal quality of experience (QoE) arises as a new challenge. The state-of-the-art approaches either choose a common model for all/most devices, which is optimal for a small fraction of edge devices at best, or apply device-specific DNN model compression, which is not scalable. In this paper, by leveraging the predictive power of machine learning and keeping end users in the loop, we envision an automated device-level DNN model selection engine for QoE-optimal edge inference. To concretize our vision, we formulate the DNN model selection problem into a contextual multi-armed bandit framework, where features of edge devices and DNN models are contexts and pre-trained DNN models are arms selected online based on the history of actions and users' QoE feedback. We develop an efficient online learning algorithm to balance exploration and exploitation. Our preliminary simulation results validate our algorithm and highlight the potential of machine learning for automating DNN model selection to achieve QoE-optimal edge inference. 

Abstract Recent decades have seen a rapid acceleration in global participation in formal education, due to worldwide initiatives aimed to provide school access to all children. Research in high income countries has shown that school quality indicators have a significant, positive impact on numeracy and literacy—skills required to participate in the increasingly globalized economy. Schools vary enormously in kind, resources, and teacher training around the world, however, and the validity of using diverse school quality measures in populations with diverse educational profiles remains unclear. First, we assessed whether children's numeracy and literacy performance across populations improves with age, as evidence of general school‐related learning effects. Next, we examined whether several school quality measures related to classroom experience and composition, and to educational resources, were correlated with one another. Finally, we examined whether they were associated with children's (4–12‐year‐olds,N = 889) numeracy and literacy performance in 10 culturally and geographically diverse populations which vary in historical engagement with formal schooling. Across populations, age was a strong positive predictor of academic achievement. Measures related to classroom experience and composition were correlated with one another, as were measures of access to educational resources and classroom experience and composition. The number of teachers per class and access to writing materials were key predictors of numeracy and literacy, while the number of students per classroom, often linked to academic achievement, was not. We discuss these results in the context of maximising children's learning environments and highlight study limitations to motivate future research. RESEARCH HIGHLIGHTSWe examined the extent to which four measures of school quality were associated with one another, and whether they predicted children's academic achievement in 10 culturally and geographically diverse societies.Across populations, measures related to classroom experience and composition were correlated with one another as were measures of access to educational resources to classroom experience and composition.Age, the number of teachers per class, and access to writing materials were key predictors of academic achievement across populations.Our data have implications for designing efficacious educational initiatives to improve school quality globally. 

Abstract Self‐regulation is a widely studied construct, generally assumed to be cognitively supported by executive functions (EFs). There is a lack of clarity and consensus over the roles of specific components of EFs in self‐regulation. The current study examines the relations between performance on (a) a self‐regulation task (Heads, Toes, Knees Shoulders Task) and (b) two EF tasks (Knox Cube and Beads Tasks) that measure different components of updating: working memory and short‐term memory, respectively. We compared 107 8‐ to 13‐year‐old children (64 females) across demographically‐diverse populations in four low and middle‐income countries, including: Tanna, Vanuatu; Keningau, Malaysia; Saltpond, Ghana; and Natal, Brazil. The communities we studied vary in market integration/urbanicity as well as level of access, structure, and quality of schooling. We found that performance on the visuospatial working memory task (Knox Cube) and the visuospatial short‐term memory task (Beads) are each independently associated with performance on the self‐regulation task, even when controlling for schooling and location effects. These effects were robust across demographically‐diverse populations of children in low‐and middle‐income countries. We conclude that this study found evidence supporting visuospatial working memory and visuospatial short‐term memory as distinct cognitive processes which each support the development of self‐regulation.