The Design Space of Generative Models

Card et al.'s classic paper "The Design Space of Input Devices" established the value of design spaces as a tool for HCI analysis and invention. We posit that developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices. We explore what it means to develop an AI model design space by proposing two design spaces relating to generative AI models: the first considers how HCI can impact generative models (i.e., interfaces for models) and the second considers how generative models can impact HCI (i.e., models as an HCI prototyping material)

Leaf Carbon Export and Nonstructural Carbohydrates in Relation to Diurnal Water Dynamics in Mature Oak Trees

Trees typically experience large diurnal depressions in water potential, which may impede carbon export from leaves during the day because the xylem is the source of water for the phloem. As water potential becomes more negative, higher phloem osmotic concentrations are needed to draw water in from the xylem. Generating this high concentration of sugar in the phloem is particularly an issue for the ∼50% of trees that exhibit passive loading. These ideas motivate the hypothesis that carbon export in woody plants occurs predominantly at night, with sugars that accumulate during the day assisting in mesophyll turgor maintenance or being converted to starch. To test this, diurnal and seasonal patterns of leaf nonstructural carbohydrates, photosynthesis, solute, and water potential were measured, and carbon export was estimated in leaves of five mature (\u3e20 m tall) red oak (Quercus rubra) trees, a species characterized as a passive loader. Export occurred throughout the day at equal or higher rates than at night despite a decrease in water potential to −1.8 MPa at midday. Suc and starch accumulated over the course of the day, with Suc contributing ∼50% of the 0.4 MPa diurnal osmotic adjustment. As a result of this diurnal osmotic adjustment, estimates of midday turgor were always \u3e0.7 MPa. These findings illustrate the robustness of phloem functioning despite diurnal fluctuations in leaf water potential and the role of nonstructural carbohydrates in leaf turgor maintenance

Human-Centered Responsible Artificial Intelligence: Current & Future Trends

In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research communities may use different terminology to discuss similar topics, all of this work is ultimately aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends to advance this important area of research by fostering collaboration and sharing ideas.Comment: To appear in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing System

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Building artificial intelligent (AI) products that make sense

Developing AI products for the mass market has been an ongoing challenge considering product-market fit, limited technology know-how, and user adoption. This paper provides a list of challenges and explores how we can launch AI products from idea to market by applying design thinking and lean-startup strategies. © 2018 Copyright is held by the owner/author(s)

Raman spectroscopy reveals high phloem sugar content in leaves of canopy red oak trees

A robust understanding of phloem functioning in tall trees evades us because current methods for collecting phloem sap do not lend themselves to measuring actively photosynthesizing canopy leaves. We show that Raman spectroscopy can be used as a quantitative tool to assess sucrose concentration in leaf samples. Specifically, we found that Raman spectroscopy can predict physiologically relevant sucrose concentrations (adjusted R2 of 0.9) in frozen leaf extract spiked with sucrose. We then apply this method to estimate sieve element sucrose concentration in rapidly frozen petioles of canopy red oak (Quercus rubra) trees and found that sucrose concentrations are \u3e 1100 mM at midday and midnight. This concentration is predicted to generate a sieve element turgor pressure high enough to generate bulk flow through the phloem, but is potentially too high to allow for sucrose diffusion from photosynthetic cells. Our findings support the Münch hypothesis for phloem transport once the carbon is in the phloem and challenge the passive-loading hypothesis for carbon movement into the phloem for red oak. This study provides the first ˜in-situ (frozen in the functioning state) source sieve element sucrose concentration characterization in any plant, opening a new avenue for investigation of phloem functioning