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Augmented Reality (AR) glasses separate dyadic interactions on diferent sides of the lens, where the person wearing the glasses (primary user) sees an AR world overlaid on their partner (secondary actor). The secondary actor interacts with the primary user under- standing they are seeing both physical and virtual worlds. We use grounded theory to study interaction tasks, participatory design ses- sions, and in-depth interviews of 10 participants and explore how AR real-time modifcations afect them. We observe a power imbalance attributed to the: (1) lack of transparency of the primary user’s view, (2) violation of agency over self-presentation, and (3) discreet record- ing capabilities of AR glasses. This information asymmetry leads to a negotiation of behaviors to reach a silently understood equilibrium. This paper addresses underlying design issues that contribute to power imbalances in dyadic interactions and ofers nuanced insights into the dynamics between primary users and secondary actors. 

Ultraluminous X-ray sources (ULXs) in globular clusters (GCs) are low-mass X-ray binaries that achieve high X-ray luminosities through a currently uncertain accretion mechanism. Using archival Chandra and Hubble Space Telescope observations, we perform a volume-limited search (≲70 Mpc) of 21 of the most massive ($\gt 10^{11.5} \, \mathrm{M}_\odot$) early-type galaxies to identify ULXs hosted by GC candidates. We find a total of 34 ULX candidates above the expected background within five times the effective radius of each galaxy, with 10 of these ($\sim 29.4{{\ \rm per\ cent}}$) potentially hosted by a GC. A comparison of the spatial and luminosity distributions of these new candidate GC ULXs with previously identified GC ULXs shows that they are similar: both samples peak at LX ∼ a few × 1039 erg s−1 and are typically located within a few effective radii of their host galaxies.

 

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system. 

We introduce SearchGazer, a web-based eye tracker for remote web search studies using common webcams already present in laptops and some desktop computers. SearchGazer is a pure JavaScript library that infers the gaze behavior of searchers in real time. The eye tracking model self-calibrates by watching searchers interact with the search pages and trains a mapping of eye features to gaze locations and search page elements on the screen. Contrary to typical eye tracking studies in information retrieval, this approach does not require the purchase of any additional specialized equipment, and can be done remotely in a user's natural environment, leading to cheaper and easier visual attention studies. While SearchGazer is not intended to be as accurate as specialized eye trackers, it is able to replicate many of the research findings of three seminal information retrieval papers: two that used eye tracking devices, and one that used the mouse cursor as a restricted focus viewer. Charts and heatmaps from those original papers are plotted side-by-side with SearchGazer results. While the main results are similar, there are some notable differences, which we hypothesize derive from improvements in the latest ranking technologies used by current versions of search engines and diligence by remote users. As part of this paper, we also release SearchGazer as a library that can be integrated into any search page.