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1. Toward Predicting Infant Developmental Outcomes from Day-Long Inertial Motion Recordings

   https://doi.org/10.1109/TNSRE.2020.3016916  

   Fitter, Naomi T; Funke, Rebecca; Pulido, José Carlos; Matarić, Maja J; Smith, Beth A. (January 2020, IEEE transactions on neural systems and rehabilitation engineering)

   As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges. 

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2. Ethical considerations of urban ecological design and planning experiments

   https://doi.org/10.1002/ppp3.10204  

   Pataki, Diane E.; Santana, Carlos G.; Hinners, Sarah J.; Felson, Alexander J.; Engebretson, Jesse (June 2021, PLANTS, PEOPLE, PLANET)

   Societal Impact Statement It is increasingly common for plant scientists and urban planning and design professionals to collaborate on interdisciplinary teams that integrate scientific experiments into public and social urban spaces. However, neither the procedural ethics that govern scientific experimentation, nor the professional ethics of urban design and planning practice, fully account for the possible impacts of urban ecological experiments on local residents and communities. Scientists that participate in design and planning teams act as decision‐makers, and must expand their domain of ethical consideration accordingly. Conversely, practitioners who engage in ecological experiments take on the moral responsibilities inherent in generation of knowledge. To avoid potential harm to human and non‐human inhabitants of cities while maintaining scientific and professional integrity in research and practice, an integrated ethical framework is needed for urban ecological planning and design. SummaryWhile there are many ethical and procedural guidelines for scientists who wish to inform decision‐making and public policy, urban ecologists are increasingly embedded in planning and design teams to integrate scientific measurements and experiments into urban landscapes. These scientists are not just informing decision‐making – they are themselves acting as decision‐makers. As such, researchers take on additional moral obligations beyond scientific procedural ethics when designing and conducting ecological design and planning experiments. We describe the growing field of urban ecological design and planning and present a framework for expanding the ethical considerations of socioecological researchers and urban practitioners who collaborate on interdisciplinary teams. Drawing on existing ethical frameworks from a range of disciplines, we outline possible ways in which ecologists, social scientists, and practitioners should expand the traditional ethical considerations of their work to ensure that urban residents, communities, and non‐human entities are not harmed as researchers and practitioners carry out their individual obligations to clients, municipalities, and scientific practice. We present an integrated framework to aid in the development of ethical codes for research, practice, and education in integrated urban ecology, socioenvironmental sciences, and design and planning. 

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3. RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios

   Chen, Z.; Chiappalupi, D.; Lin, T.; Beyer, J.; Yang, Y.; Pfister, H. (January 2023, IEEE transactions on visualization and computer graphics)

   Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them occlusion-free while keeping visual linkings legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers the current and predicted future states of objects and labels, such as positions and velocities, as well as the user’s viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. Our experiments on two real-world datasets show that RL-LABEL effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on AR labels within dynamic scenes. 

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4. Evaluating Layout Dimensionalities in PC+VR Asymmetric Collaborative Decision Making

   https://doi.org/10.1145/3698130  

   Enriquez, Daniel; Tong, Wai; North, Chris; Qu, Huamin; Yang, Yalong (October 2024, Proceedings of the ACM on Human-Computer Interaction)

   With the commercialization of virtual/augmented reality (VR/AR) devices, there is an increasing interest in combining immersive and non-immersive devices (e.g., desktop computers) for asymmetric collaborations. While such asymmetric settings have been examined in social platforms, significant questions around layout dimensionality in data-driven decision-making remain underexplored. A crucial inquiry arises: although presenting a consistent 3D virtual world on both immersive and non-immersive platforms has been a common practice in social applications, does the same guideline apply to lay out data? Or should data placement be optimized locally according to each device's display capacity? This study aims to provide empirical insights into the user experience of asymmetric collaboration in data-driven decision-making. We tested practical dimensionality combinations between PC and VR, resulting in three conditions: PC2D+VR2D, PC2D+VR3D, and PC3D+VR3D. The results revealed a preference for PC2D+VR3D, and PC2D+VR2D led to the quickest task completion. Our investigation facilitates an in-depth discussion of the trade-offs associated with different layout dimensionalities in asymmetric collaborations. 

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5. Instructional Set Moderates the Effect of GRE on Faculty Appraisals of Applicant Competence: A Vignette Study With Implications for Holistic Review

   https://doi.org/10.3389/fpsyg.2021.749621  

   Hernández-Colón, Isabelle Rios; Caño, Annmarie; Wurm, Lee H.; Sanders, Gavin; Nava, Jennifer (October 2021, Frontiers in Psychology)

   While there is movement to create more equitable and holistic admission review processes, faculty continue to place strong emphasis on a single piece of information when making admissions decisions: standardized test scores. This study used an experimental design to test whether instructions provided to faculty prior to assessing doctoral applicants could support holistic review by reducing the weight of the general record examination (GRE) in faculty appraisals of competence and merit for graduate study. Tenured and/or tenure-track faculty ( N =271) were randomly assigned to one of three instructional conditions: Control (no instruction), “Diamond in the Rough,” and “Weed Out.” In addition, faculty participants were randomly assigned to read one of two vignettes of a prospective first-generation student who either received high or average GRE scores. Faculty then rated the applicant’s competence using a three-item survey. As expected, faculty who read the vignette describing the candidate with the high GRE rated him as more competent than faculty who read the average GRE vignette. In addition, being instructed to seek out diamonds in the rough buffered the effect of the GRE score on competence. Faculty were also asked to indicate whether they would need additional information to make an admissions decision. They were more likely to ask about grades and research skills than about psychosocial factors that might contextualize the candidate’s performance and perceived competence. The results of this study have implications for creating more equitable doctoral admissions processes that center equity, diversity, and inclusion in decision making. 

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