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Browsing by Subject "Multimodality"
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Item Approaching Composition as Showing–Telling through Translanguaging: Weaving Multilingualism, Multimodality, and Multiliteracies in a Digital Collage Proyecto Final(MDPI, 2022) Prada, Josh; World Languages and Cultures, School of Liberal ArtsCouched in theories of translanguaging, multimodality, and multiliteracies, this article explores digital compositions (i.e., digital collages) as spaces for identity representation through the proyectos finales produced by 22 students in a Spanish composition class for heritage/native speakers in a U.S. university. Each digital collage was accompanied by two written documents: one describing the processes leading to its creation, and another one explaining the meaning of the collage and its components. Qualitative content analysis was used to investigate the submissions, with particular attention paid to instances of identity, experience, and self-representation through complex orchestrations of flexible multilingual and multimodal meaning- and sense-making. The proyecto final is discussed in terms of the curricular innovation for courses designed for racialized language-minoritized multilingual students, describing the nature and affordances of translanguaging in this context, and advancing an approach to digital composing as showing–telling.Item M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data(BMC, 2019-12-20) Zhang, Yu; Wan, Changlin; Wang, Pengcheng; Chang, Wennan; Huo, Yan; Chen, Jian; Ma, Qin; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of MedicineBackground Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.Item Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills(MDPI, 2023-04-28) Lim, Chiho; Barragan, Juan Antonio; Farrow, Jason Michael; Wachs, Juan P.; Sundaram, Chandru P.; Yu, Denny; Urology, School of MedicinePrevious studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.