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1. Attention and choice across domains.

   https://doi.org/10.1037/xge0000482  

   Smith, Stephanie M.; Krajbich, Ian (December 2018, Journal of Experimental Psychology: General)

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2. Neuroimaging and biofluid biomarkers across race and ethnicity in older adults across the spectrum of cognition

   https://doi.org/10.1016/j.arr.2024.102507  

   Wang, Wei-en; Asken, Breton M; DeSimone, Jesse C; Levy, Shellie-Anne; Barker, Warren; Fiala, Jacob A; Velez-Uribe, Idaly; Curiel_Cid, Rosie E; Rósselli, Monica; Marsiske, Michael; et al (November 2024, Ageing Research Reviews)

   Neuroimaging and biofluid biomarkers provide a proxy of pathological changes for Alzheimer’s disease (AD) and are useful in improving diagnosis and assessing disease progression. However, it is not clear how race/ethnicity and different prevalence of AD risks impact biomarker levels. In this narrative review, we survey studies focusing on comparing biomarker differences between non-Hispanic White American(s) (NHW), African American(s) (AA), Hispanic/Latino American(s) (HLA), and Asian American(s) with normal cognition, mild cognitive impairment, and dementia. We found no strong evidence of racial and ethnic differences in imaging biomarkers after controlling for cognitive status and cardiovascular risks. For biofluid biomarkers, in AA, higher levels of plasma Aβ42/Aβ40, and lower levels of CSF total tau and p-tau 181, were observed after controlling for APOE status and comorbidities compared to NHW. Examining the impact of AD risks and comorbidities on biomarkers and their contributions to racial/ethnic differences in cognitive impairment are critical to interpreting biomarkers, understanding their generalizability, and eliminating racial/ethnic health disparities. 

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3. Stochastic Sensitivities across Scales and Physics

   Wang, Z.; Ghanem, R. (July 2019, EMI 2019)

   The polynomial chaos expansions (PCE) provide stochastic representations of quantities of interest (QoI) in terms of a vector of standardized random variables that represent all uncertainties influencing the QoI. These uncertainties could reflect statistical scatter in estimated probabilistic model (of which the mean, variance, or PCE coefficients are but examples), or errors in the underlying functional model between input and output (e.g. physics models). In this paper, we show how PCE permit the evaluation of sensitivities with respect to all these uncertainties, and provide a rational paradigm for resource allocation aimed at model validation. We will demonstrate the methodologies on examples drawn across science and engineering. 

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4. Perceptual and mnemonic differences across cultures.

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5. Adapting Entities across Languages and Cultures

   Denis Peskov, Viktor Hangya (January 2021, Findings of the Association for Computational Linguistics: EMNLP 2021)

   How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community (Vinay and Darbelnet, 1995). Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector arithmetic and orthogonal embedding mappings identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset1 of human adaptations. 

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