Dual Route Model of Idiom Processing in the Bilingual Context

The dual route model predicts that idiomatic phrases show a processing advantage over matched novel phrases. This model postulates that familiar phrases are processed by a faster direct route, and novel phrases are processed by an indirect route. This thesis investigated the role of familiar form and concept in direct route activation. Study 1 provided norming evidence for experimental stimuli selection. Study 2 examined whether direct route can be activated for translated Chinese idioms in Chinese-English bilinguals. Bilinguals listened to the idiom up until the last word (e.g., draw a snake and add), then saw either the idiom ending (e.g., feet) or the matched control ending (e.g., hair); to which they made lexical decision and reaction times were recorded. Results showed evidence for dual route model and provided preliminary support for both familiar concept and lexical association as drivers of direct route activation

Temperament and Individual Differences in Category Learning

Objectives. Individuals can differ in their strategic approach in learning the same categorization task, researchers have sought to study what specific stable individual differences traits can help explain these differences. This dissertation first surveyed extant literature on the impact of trait differences on category learning then examined the effect of temperament traits on these dependent variables. Chapter 2 (scoping review): This scoping review synthesized the past literature that examined the relationship between sources of stable individual differences and category learning performance and strategy use outcomes. Five database platforms were searched to identify relevant articles, cross-referencing was also performed. Sixty-nine studies met inclusion criteria with 3 major sources of individual differences identified: (1) developmental, (2) aging, (3) working memory. The results of this scoping review suggest that (1) children tend to show both performance and task-appropriate strategy-use disadvantage in both rule-based and similarity-based category learning tasks compared to young adults. (2) Older adults also showed a performance disadvantage, but results were less consistent with regards to whether they used different strategies than young adults. (3) Working memory was associated with better performance on both types of tasks, but it was not associated with strategy choice on rule-based tasks, and results were inconsistent in terms of strategy choice on similarity-based tasks. Chapter 3 (two studies): In two studies, I examined affective temperament traits to see whether the tendency to experience negative and positive affect is predictive of category learning performance and strategy use. Temperamental effortful control and working memory were measured as covariates. There were minimal effects of affective temperament traits and temperamental effortful control may be negatively associated with learning on both types of category learning. Working memory may be positively associated with learning on both types of category learning. However, these findings were not consistent across studies. The results may either reflect a lack of relationship or low data quality due to the pandemic. Conclusions: Neither previous studies nor the present dissertation provided a firm answer to the mystery behind individual differences in category learning strategy use. Future research should replicate the studies in Chapter 3 of this dissertation in the laboratory to see whether temperament effects would emerge

Cognitive changes in conjunctive rule-based category learning: An ERP approach

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.Comment: To appear at Machine Learning for Healthcare Conference (MLHC2022

Measurement of Source Star Colors with the K2C9-CFHT Multi-color Microlensing Survey

K2 Campaign 9 (K2C9) was the first space-based microlensing parallax survey capable of measuring microlensing parallaxes of free-floating planet candidate microlensing events. Simultaneous to K2C9 observations we conducted the K2C9 Canada-France-Hawaii Telescope Multi-Color Microlensing Survey (K2C9-CFHT MCMS) in order to measure the colors of microlensing source stars to improve the accuracy of K2C9's parallax measurements. We describe the difference imaging photometry analysis of the K2C9-CFHT MCMS observations, and present the project's first data release. This includes instrumental difference flux lightcurves of 217 microlensing events identified by other microlensing surveys, reference image photometry calibrated to PanSTARRS data release 1 photometry, and tools to convert between instrumental and calibrated flux scales. We derive accurate analytic transformations between the PanSTARRS bandpasses and the Kepler bandpass, as well as angular diameter-color relations in the PanSTARRS bandpasses. To demonstrate the use of our data set, we analyze ground-based and K2 data of a short timescale microlensing event, OGLE-2016-BLG-0795. We find the event has a timescale $t_{\rm E}=4.5 \pm 0.1$~days and microlens parallax $\pi_{\rm E}=0.12 \pm 0.03$ or $0.97 \pm 0.04$, subject to the standard satellite parallax degeneracy. We argue that the smaller value of the parallax is more likely, which implies that the lens is likely a stellar-mass object in the Galactic bulge as opposed to a super-Jupiter mass object in the Galactic disk.Comment: Submitted to PAS

Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.Comment: 8 pages, 10 figures, 1 table, Accepted to IROS 202