Translation Effectiveness of Offset Heart Rate Biofeedback as a Mindless Intervention for Alcohol Craving Among Risky Drinkers: Controlled Experiment.

Background: Digital and wearable intervention systems promise to improve how people manage their behavioral health conditions by making interventions available when the user can best benefit from them. However, existing interventions are obtrusive because they require attention and motivation to engage in, limiting the effectiveness of such systems in demanding contexts, such as when the user experiences alcohol craving. Mindless interventions, developed by the human-computer interaction community, offer an opportunity to intervene unobtrusively.

Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation.

Background: Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility.

Application of microscopic smartphone attachment for remote preoperative lab testing.

Introduction: Current preoperative exam guidelines utilize extensive lab tests, including blood tests and urine analysis, which are crucial for assessing surgical readiness. However, logistical challenges, especially for patients traveling long distances for high-quality medical care, create significant delays and burdens. This study aims to address these challenges by applying a previously developed point-of-care (POC) device system to perform accurate and rapid lab tests.

Human visual clustering of point arrays.

Although the importance of unsupervised learning has been recognized since William James's "blooming, buzzing confusion," it has received less attention in the literature than supervised learning. An important form of unsupervised learning is clustering, which involves determining the groups of distinct objects that belong together. Visual clustering is foundational for ensemble perception, numerosity judgments, spatial problem-solving, understanding information visualizations, and other forms of visual cognition, and yet surprisingly few researchers have directly investigated this human ability.

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph networks to deliver robust predictions of cardiomyocyte dynamics. Embedded with inductive physical priors, the proposed constraint-based interaction neural projection (CINP) algorithm can uncover hidden physical constraints from sparse image data on a small set of beating cardiac cells and provide robust predictions for heterogenous large-scale cell sets.

Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level.

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates.

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation.

Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data.

Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.

Introduction: Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of visual and sonified (pixel to sound) data. The addition of an audio technology enhances malignant features of lesions, increases sensitivity and was previously validated under a prospective clinical setting in FS.

Strategy variability in computational estimation and its association with mathematical achievement.

Computational estimation requires a breadth of strategies and selection of the relevant strategy given a problem's features. We used the new Test of Estimation Strategies (TES), composed of 20 arithmetic problems (e.g.

Towards reconciling usability and usefulness of policy explanations for sequential decision-making systems.

Safefy-critical domains often employ autonomous agents which follow a sequential decision-making setup, whereby the agent follows a policy to dictate the appropriate action at each step. AI-practitioners often employ reinforcement learning algorithms to allow an agent to find the best policy. However, sequential systems often lack clear and immediate signs of wrong actions, with consequences visible only in hindsight, making it difficult to humans to understand system failure.

Recruitment of magnitude representations to understand graded words.

Language understanding and mathematics understanding are two fundamental forms of human thinking. Prior research has largely focused on the question of how language shapes mathematical thinking. The current study considers the converse question.

Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills.

Importance: Assessing nontechnical skills in operating rooms (ORs) is crucial for enhancing surgical performance and patient safety. However, automated and real-time evaluation of these skills remains challenging.

Objective: To explore the feasibility of using motion features extracted from surgical video recordings to automatically assess nontechnical skills during cardiac surgical procedures.

Mastery of assistive technology: What is it? How do we measure it?

This paper presents the results of a one-year study on mastery of assistive technology (AT). This study sought to develop a conceptual framework for talking about mastery of AT and to create an instrument for measuring individual mastery. A Delphi Study was conducted with individuals with disabilities considered to be "power users" of AT, practitioners, and researchers.

Using mHealth to reduce disparities in Black maternal health: Perspectives from Black rural postpartum mothers.

Background: Racial disparities are evident in maternal morbidity and mortality rates globally. Black women are more likely to die from pregnancy and childbirth than any other race or ethnicity. This leaves one of the largest gaps in women's health to date.

The psychological reality of the learned "p < .05" boundary.

The .05 boundary within Null Hypothesis Statistical Testing (NHST) "has made a lot of people very angry and been widely regarded as a bad move" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the .

Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis.

Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences.

Generative Adversarial Network Model to Classify Human Induced Pluripotent Stem Cell-Cardiomyocytes based on Maturation Level.

Objective: Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions.

Methods: Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates.

Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks.

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks.

Making Space in Geographical Analysis.

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for . We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high-level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.