Neural Topic Modeling of Psychotherapy Sessions

In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve psychotherapy effectiveness.Comment: This work extends our research series in computational linguistics for psychiatry (e.g. working alliance analysis in arXiv:2204.05522) with a systematic investigation of neural topic modeling approaches to provide interpretable insights in psychotherap

Language-Grounded Control for Coordinated Robot Motion and Speech

Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during collaborative contact tasks. We first derive principles from analyzing physical therapists' movements and speech during patient exercises. These principles are translated into control objectives to: 1) guide users through trajectories, 2) control motion and speech pace to align completion times with varying user cooperation, and 3) dynamically paraphrase speech along the trajectory. We then propose a Language Controller that synchronizes motion and speech, modulating both based on user cooperation. Experiments with 12 users show the Language Controller successfully aligns motion and speech compared to baselines. This provides a framework for fluent human-robot collaboration.Comment: Under review in ICRA 202

Social Interactions as Recursive MDPs

While machines and robots must interact with humans, providing them with social skills has been a largely overlooked topic. This is mostly a consequence of the fact that tasks such as navigation, command following, and even game playing are well-defined, while social reasoning still mostly re- mains a pre-theoretic problem. We demonstrate how social interactions can be effectively incorporated into MDPs (Markov decision processes) by reasoning recursively about the goals of other agents. In essence, our method extends the reward function to include a combination of physical goals (something agents want to accomplish in the configuration space, a traditional MDP) and social goals (something agents want to accomplish relative to the goals of other agents). Our Social MDPs allow specifying reward functions in terms of the estimated reward functions of other agents, modeling interactions such as helping or hindering another agent (by maximizing or minimizing the other agent’s reward) while bal- ancing this with the actual physical goals of each agent. Our formulation allows for an arbitrary function of another agent’s estimated reward structure and physical goals, enabling more complex behaviors such as politely hindering another agent or aggressively helping them. Extending Social MDPs in the same manner as I-POMDPs (Interactive-partially observed Markov decision processes) extension would enable interactions such as convincing another agent that something is true. To what extent the Social MDPs presented here and their potential Social POMDPs variant account for all possible social interactions is unknown, but having a precise mathematical model to guide questions about social in- teractions has both practical value (we demonstrate how to make zero-shot social inferences and one could imagine chatbots and robots guided by Social MDPs) and theoretical value by bringing the tools of MDP that have so successfully organized research around navigation to shed light on what social interactions really are given their extreme importance to human well-being and human civilization.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216

Use of Molecular Simulation in Calculating a Characteristic Relative Growth Effect Curvature to Correlate Factors Influencing Crystalline Growth and Other Properties

The following report outlines a simplified method to predict the effect on the relative growth rates of crystal facets resulting from different solvents. The method uses molecular dynamics (MD) techniques which are coupled to a Monte Carlo (MC) scheme to generate distributions of estimates of molecular binding energies at the crystal surface. We then use these calculated binding energies to make inferences on how solvent may affect the relative growth rate of the crystal facets (i.e., solvent effect on growth). We support the analysis by revisiting the growth of adipic acid. It is demonstrated that there is a remarkable increase in the sensitivity of the expected values used to represent the “solvent effect on growth” when a very simple correction for the molecular size between solute and solvent is implemented into the Monte Carlo scheme. The use of single point energy calculations (potential energy) displays limited sensitivity to the expected solvent effect in comparison to the use of distributions of MD derived values (binding free energy). Thus, the combination of relative binding free energy data and the proposed MC scheme is believed to be an effective path forward to providing insight into a solvent or additive effect on growth for more complex molecular systems that is simple to implement and does not come at a significantly high computational expense. In order to make an assessment of the data from simulation, plotting of the relative growth effect curvature is also introduced

Migratable AI: Effect of identity and information migration on users' perception of conversational AI agents

© 2020 IEEE. Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments

Impact of a month-long training program on the clinical skills of ophthalmology residents and practitioners

A cohort study was performed to assess the impact of an intensive, hands-on, supervised training program in ophthalmic clinical evaluation, for ophthalmology residents and private practitioners. All students underwent one-month training in comprehensive ophthalmology examination and investigations at a tertiary care center between January 2004 and January 2006. The training methodology included didactic lectures, video-demonstrations and hands-on training. The participants completed a self-assessment with a set of 23 questions designed to assess the level of confidence in various skills on the first and last day of the training. Of a total of 118 students, 67 (56.8%) were residents and 51 (43.2%) were practitioners. The mean score pre-training was 38.3 out of 92 (S.D. ±16.9), and was 70.6 out of 92 (S.D.± 10.1) post-training. The mean increase in the scores was 32.3 (P value < 0.001). We concluded that intensive, short-term training programs could improve the self-perceived level of confidence of ophthalmology residents and practitioners