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While academics increasingly point to the value of engaged scholarship, we describe a more extreme form which we label as "deep partnering"-a long-term, holistic, and dynamic collaboration between academics and practitioners to achieve shared goals. Deep partnering involves interdependent and evolving interactions between academics and practitioners over an extended time period. While such relationships enable generative impact on important issues, these relationships remain challenging as academics spend time in the practitioners' complex worlds, surfacing paradoxes due to the partners' conflicting roles, time horizons, and goals, as well as uncertainty in the partnership's evolution.
View Article and Find Full Text PDFUncertainty-Aware Multimodal Trajectory Prediction via a Single Inference from a Single Model.
Sensors (Basel)
January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
View Article and Find Full Text PDFCoordinating uncertainty in the political economy of cyber threat intelligence.
Soc Stud Sci
January 2025
King's College London, London, UK.
Cyber threat intelligence firms play a powerful role in producing knowledge, uncertainty, and ignorance about threats to organizations and governments globally. Drawing on historical and ethnographic methods, we show how cyber threat intelligence analysts navigate distinctive types of uncertainty as they transform digital traces into marketable products and services. We make two related contributions and arguments.
View Article and Find Full Text PDFLike milk on the stove: Healthcare professionals navigating uncertainty when caring for families with 22q11DS.
PLoS One
January 2025
Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.
Introduction: 22q11 deletion syndrome (22q11DS) results from a microdeletion on chromosome 22 and is the most common microdeletion disorder in humans, affecting 1 in 2148 live births. Clinical manifestations vary widely among individuals and across different life stages. Effective management requires the involvement of a specialized multidisciplinary team.
View Article and Find Full Text PDFArtificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178.
Med Teach
January 2025
Department of Medical Education, Dartmouth College Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies.
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