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Augmented reality head-worn displays (AR HWDs) have the potential to assist personal computing and the acquisition of everyday information. With advancements in hardware and tracking, these devices are becoming increasingly lightweight and powerful. They could eventually have the same form factor as normal pairs of eyeglasses, be worn all-day, overlaying information pervasively on top of the real-world anywhere and anytime to continuously assist people’s tasks. However, unlike traditional mobile devices, AR HWDs are worn on the head and always visible. If designed without care, the displayed virtual information could also be distracting, overwhelming, and take away the user’s attention from important real- world tasks. In this dissertation, we research methods for appropriate information displays and interactions with future all-day AR HWDs by seeking answers to four questions: (1) how to mitigate distractions of AR content to the users; (2) how to prevent AR content from occluding the real-world environment; (3) how to support scalable on-the-go access to AR content; and (4) how everyday users perceive using AR systems for daily information acquisition tasks. Our work builds upon a theory we developed called Glanceable AR, in which digital information is displayed outside the central field of view of the AR display to minimize distractions, but can be accessed through a quick glance. Through five projects covering seven studies, this work provides theoretical and empirical knowledge to prepare us for a pervasive yet unobtrusive everyday AR future, in which the overlaid AR information is easily accessible, non-invasive, responsive, and supportive. Doctor of Philosophy Augmented reality (AR) refers to a technology in which digital information is overlaid on the real-world environment. This provides great potential for everyday uses, because users can view and interact with digital apps anywhere and anytime even when physical screens are unavailable. However, depending on how the digital information is displayed, it could quickly occupy the user’s view, block the real-world environment, and distract or overwhelm users. In this dissertation work, we research ways to deliver and interact with virtual information displayed in AR head-worn displays (HWDs). Our solution centers around the Glanceable AR concept, in which digital information is displayed in the periphery of users’ views to remain unobtrusive, but can be accessed through a glance when needed. Through empirical evaluations, we researched the feasibility of such solutions, and distilled lessons learned for future deployment of AR systems in people’s everyday lives.

Replication of paper entitled 'Skillful decadal prediction skill over the Southern Ocean based on SPEAR Model-Analogs'. This file contains four datasets used to replicate figures in apaper published in Environmental Research Communications. These datasets are in .mat format, which can be easily read by Matlab software using load command.

Additional file 1: Table S1. Primer sequence. Table S2. Antibody information.

Additional file 2.

The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.

Imagine in the future people comfortably wear augmented reality (AR) displays all day, how do we design interfaces that adapt to the contextual changes as people move around? In current operating systems, the majority of AR content defaults to staying at a fxed location until being manually moved by the users. However, this approach puts the burden of user interface (UI) transition solely on users. In this paper, we frst ran a bodystorming design workshop to capture the limitations of existing manual UI transition approaches in spatially diverse tasks. Then we addressed these limitations by designing and evaluating three UI transition mechanisms with different levels of automation and controllability (low-efort manual, semi-automated, fully-automated). Furthermore, we simulated imperfect contextual awareness by introducing prediction errors with diferent costs to correct them. Our results provide valuable lessons about the trade-ofs between UI automation levels, controllability, user agency, and the impact of prediction errors. Published version

Additional file 1: Fig. S1. Comparison of CD19+ B cells and CD20+ B cells among DCM, HF and HC groups. Fig. S2. Comparison of CD5+ B cells and Breg cells among DCM, HF and HC groups. Fig. S3. Comparison of B cell subsets in patients with DCM with an LVEF 30%-45% and LVEF less than 30%. Fig. S4. Comparison of B1 cells and Tr cells in HF subgroups. Fig. S5. Correlation between the percentages of B1 and Tr cells and the disease duration in patients with DCM.