Quantum Interference in the Field Ionization of Rydberg Atoms

We excite ultracold rubidium atoms in a magneto-optical trap to a coherent superposition of the three |mj | sublevels of the 37d5/2 Rydberg state. After some delay, during which the relative phases of the superposition components can evolve, we apply an electric field pulse to ionize the Rydberg electron and send it to a detector. The electron traverses many avoided crossings in the Stark levels as it ionizes. The net effect of the transitions at these crossings is to mix the amplitudes of the initial superposition into the same final states at ionization. Similar to a Mach-Zehnder interferometer, the three initial superposition components have multiple paths by which they can arrive at ionization and, since the phases of those paths differ, we observe quantum beats as a function of the delay time between excitation and initiation of the ionization pulse. We present a fully quantum-mechanical calculation of the electron’s path to ionization and the resulting interference pattern

Supporting Self-Regulation of Children with ADHD Using Wearables: Tensions and Design Challenges

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch in various contexts. These results indicate key considerations—from both the child and caregiver viewpoints—for designing technological interventions supporting children with ADHD

Measuring Ethical Values with AI for Better Teamwork

Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task