According to Grau and Joynes (2005), (1) the current classification of types of learning is based on methodology and assumes a correspondence between types of learning and distinct neural-functional mechanisms; (2) this assumption is wrong because experiments show that different mechanisms may underlie the same type of learning; consequently, (3) we should change the teaching of the psychology of learning. I argue that because Grau and Joynes misunderstood the nature of the classification of learning phenomena and cloaked their research findings with a garb of conceptual errors and infelicities, their recommendations concerning the teaching of learning should be rejected.

Despite the link between school-lunch participation and student health, only half of adolescents with the program in their schools participate on a given day. A multi-pronged school lunch promotion intervention was implemented in middle and high schools in an urban, low-income school district to improve school lunch participation and reduce food waste. This dissertation assesses the impact of the intervention on student and teacher perceptions of school lunch and explores how leadership shaped intervention implementation.

Using student survey data, paper 1 explores the theoretical pathways (perceptions of school-lunch quality and convenience) through which the intervention was expected to increase school-lunch participation and decrease plate waste. We observed modest changes in perceptions of school-lunch quality and no changes in perceptions of school-lunch convenience. Results suggest that additional efforts are needed to improve school-lunch participation and dietary intake.

Paper 2 utilizes teacher survey data to assess the impact of the intervention on teacher perceptions, modeling, and encouragement related to school lunch. Results indicate that the intervention had a modest effect on teacher-reported frequency of eating in the cafeteria with students and on encouraging students to participate in the school lunch program. There was no change, however, in teacher perceptions of or participation in the school lunch program. The paper concludes that improvements in teacher perceptions of school meals may not be needed in order for teachers to promote the school-lunch program to students.

Paper 3 focuses on the implementation of the intervention. Through qualitative interviews, the paper characterizes implementation leadership in the context of the intervention; and contextualizes emergent findings in the existing implementation science literature. Four major themes emerged reflecting the ways in which leaders are perceived to influence implementation effectiveness: (1) leader understanding of the technical and operational details of the intervention; (2) leader ability to develop and communicate intervention plans; (3) leader supervisory oversight over implementation staff and contractors; and (4) leader acknowledgment that ‘innovation’ requires time and stakeholder input —the absence of which can threaten cohesion and buy in. Implementation leadership functions in a school nutrition context appear similar to those identified in the implementation science literature. Results are the first to illustrate how these leadership dimensions are performed in a school nutrition setting.

The past few decades have witnessed an explosive development of atomic, molecular and optical (AMO) platforms. Previously restricted to a single qubit, quantum coherent manipulation is now available across large arrays of quantum particles. This unprecedented control over many-body quantum matter has enabled the direct study of isolated quantum dynamics away from equilibrium, and has opened an entirely new landscape for exploring different physical phenomena in AMO systems. In this thesis, I explore this landscape by focusing on three different classes of dynamics: many-body localization, prethermalization, and hydrodynamics. Using a combination of analytical and numerical tools, I will discuss the stability of different out-of-equilibrium phases of matter, as well as different universal dynamical phenomena. Examples include, time crystalline order in both many-body localized and periodically driven prethermal systems, prethermal phases of matter in long-range interacting systems systems, and diffusive and superdiffusive dynamics in isolated quantum systems. At the same time, I will also discuss the experimental realization of different dynamical phenomena in AMO platforms.

Information retrieval techniques have been applied to biomedical research for a variety of purposes, such as textual document retrieval and molecular data retrieval. As biomedical research evolves over time, information retrieval is also constantly facing new challenges, including the growing number of available data, the emerging new data types, the demand for interoperability between data resources, and the change of users’ search behaviors. To help solve the challenges, I studied three solutions in my dissertation: (a) using information collected from online resources to enrich the representation models for biomedical datasets; (b) exploring rule-based and deep learning-based methods to help users formulate effective queries for both dataset retrieval and publication retrieval; and (c) developing a “retrieval plus re-ranking” strategy to identify relevant datasets, and rank them using customized ranking models.

In a biomedical dataset retrieval study, we developed a pipeline to automatically analyze users’ free-text requests, and rank relevant datasets using a “retrieval plus re-ranking” strategy. To improve the representation model of biomedical datasets, we explored online resources and collected information to enrich the metadata of datasets. The rule-based query formulation module extracted keywords from users’ free-text requests, expanded the keywords using NCBI resources, and finally formulated Boolean queries using pre-designed templates. The novel “retrieval plus re-ranking” strategy captured relevant datasets in the retrieval step, and ranked datasets using the customized relevance scoring functions that model unique properties of the metadata of biomedical datasets. The solutions proved to be successful for biomedical dataset retrieval, and the pipeline achieved the highest inferred Normalized Discounted Cumulative Gain (infNDCG) score in the 2016 bioCADDIE Biomedical Dataset Retrieval Challenge.

In a biomedical publication retrieval study, we developed the eXtended PubMed Related Citation (XPRC) algorithm to find similar articles in PubMed. Currently, similar articles in PubMed are determined by the PubMed Related Citation (PRC) algorithm. However, when the distributions of term counts are similar between articles, the PRC algorithm may conclude that the articles are similar, even though they may be about different topics. On the other hand, when two articles discuss the same topic but use different terms, the PRC algorithm may miss the similarity. For the above problem, we implemented a term expansion method to help capture the similarity. Unlike popular ontology-based expansion methods, we used a deep learning method to learn distributed representations of terms over one million articles from PubMed Central, and identified similar terms using the Euclidean distance between distributed representation vectors. We showed that, under certain conditions, using XPRC can improve precision, and helps find similar articles from PubMed.

In conclusion, information retrieval techniques in biomedical research have helped researchers find desired publications, datasets, and other information. Further research on developing robust representation models, intelligent query formulation systems, and effective ranking models will lead to smarter and more friendly information retrieval systems that will further promote the transformation from data to knowledge in biomedicine.