Translation and Translanguaging Pedagogies in Intercomprehension and Multilingual Teaching

Since 2007, California State University, Long Beach has developed and offered courses that highlight students’ pre-existing linguistic repertoires in English and in the Romance languages. These courses are unique in that they build upon a multilingual base for the acquisition of new languages through the method of intercomprehension. As an approach that moves among languages, Intercomprehension places learners in conditions that are conducive to translanguaging and translation. This paper discusses the role of translation and translanguaging in Intercomprehension as a pedagogical approach in these courses. Since our students are constantly moving between English and one or more Romance language(s), they actively bring the other Romance languages they are learning into the translingual repertoire they already practice through the multilingual learning strategies deployed in intercomprehension. Depuis 2007, California State University, Long Beach développe et offre des cours qui mettent en avant le répertoire linguistique préexistant des étudiants en anglais et en langues romanes. Ces cours sont uniques, car ils s’appuient sur un répertoire multilingue pour permettre l’acquisition de nouvelles langues à travers la méthode d’intercompréhension. L’intercompréhension, approche transcendant les barrières entre les langues, offre aux apprenants un contexte propice au translanguaging et à la traduction. Cet article discute du rôle de la traduction et du translanguaging dans l’intercompréhension. Étant donné que nos étudiants naviguent constamment entre l’anglais et une (ou, des) langue(s) romanes(s), ils font ainsi entrer de manière active les tierces langues romanes en cours d’apprentissage dans le répertoire translangagier qu’ils utilisent déjà par le biais des stratégies intercompréhensives

Radiotherapy Ergonomic Patient Positioning System

External beam radiotherapy is a powerful tool to combat cancer, but requires precise positioning of patients. The standard practice involves patients lying on treatment tables within immobilization equipment. These systems have limited clearance between treatment sources and the patient that can be uncomfortable or claustrophobic for patients. Patients may move when uncomfortable, leading to improper dose delivery, possibly causing painful side effects or further cancers. We present an alternative, open configuration patient positioning solution for more ergonomic setup options with competitive positioning capabilities compared to conventional systems. Our ergonomic positioning system consists of a seat for patients on a scissor lift for vertical positioning, a gantry system for horizontal positioning, all assembled onto a rotating base for treatment of tumors from any angle. Motion up to ±25 cm in 3D was achieved by controlling stepper motors with a Raspberry Pi computer. Computer-aided design was performed to complete finite element analysis on all load-bearing structures to ensure a maximum load of 200 lbs. The design developed allows new treatment configurations with static radiation sources while the positioning system moves at a distance, which allows greater flexibility in patient positioning. Replacing conventional treatment tables enables patients to be seated during treatment, which could improve patient comfort for more comfortable immobilization and more effective radiotherapy.https://scholarscompass.vcu.edu/capstone/1215/thumbnail.jp

The effects of experienced childhood maternal abuse on adult attachment styles

The relationship between experienced maternal abuse and the development of an insecure attachment style was examined. Data was collected via selfreport questionnaires in a large, urban college campus. The questionnaires used were the Revised Conflict Tactics Scales (CTS-2, Straus, Hamby, Boney-McCoy, & Sugarman, 1996) and Attachment Questionnaire (AQ, Bartholomew & Horowitz, 1991). The sample included 81 females and 86 males, ranging in age from 18-57 years old. No significant correlation was found between the experience of maternal abuse and the development of a fearful attachment style. The results did support a significant correlation between maternal abuse and the development of an insecure-dismissive attachment style. Future research is needed with more diverse samples that consist of more variability in abuse

A Survey of Merger Remnants II: The Emerging Kinematic and Photometric Correlations

This paper is the second in a series exploring the properties of 51 {\it optically} selected, single-nuclei merger remnants. Spectroscopic data have been obtained for a sub-sample of 38 mergers and combined with previously obtained infrared photometry to test whether mergers exhibit the same correlations as elliptical galaxies among parameters such as stellar luminosity and distribution, central stellar velocity dispersion ($\sigma$$_{\circ}$), and metallicity. Paramount to the study is to test whether mergers lie on the Fundamental Plane. Measurements of $\sigma$$_{\circ}$ have been made using the Ca triplet absorption line at 8500 {\AA} for all 38 mergers in the sub-sample. Additional measurements of $\sigma$$_{\circ}$ were made for two of the mergers in the sub-sample using the CO absorption line at 2.29 \micron. The results indicate that mergers show a strong correlation among the parameters of the Fundamental Plane but fail to show a strong correlation between $\sigma$$_{\circ}$ and metallicity (Mg$_{2}$). In contrast to earlier studies, the $\sigma$$_{\circ}$ of the mergers are consistent with objects which lie somewhere between intermediate-mass and luminous giant elliptical galaxies. However, the discrepancies with earlier studies appears to correlate with whether the Ca triplet or CO absorption lines are used to derive $\sigma$$_{\circ}$, with the latter almost always producing smaller values. Finally, the photometric and kinematic data are used to demonstrate for the first time that the central phase-space density of mergers are equivalent to elliptical galaxies. This resolves a long-standing criticism of the merger hypothesis.Comment: Accepted Astronomical Journal (to appear in January 2006

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

Electronic Health Records (EHRs) are commonly used to investigate relationships between patient health information and outcomes. Deep learning methods are emerging as powerful tools to learn such relationships, given the characteristic high dimension and large sample size of EHR datasets. The Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU patients, where researchers investigated the relationships between clinical measurements, and in-hospital mortality. However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs). Although a rich literature exists regarding the treatment of missing data in traditional statistical models, it is unclear how this extends to deep learning architectures. To address these issues, we propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random (MNAR) patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori. We show that the use of our method leads to more realistic imputed values relative to the state-of-the-art, as well as significant differences in fitted downstream models for mortality.Comment: 37 pages, 3 figures, 3 tables, under review (Journal of the American Statistical Association

Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data

Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging