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The first scientific observations with adaptive optics (AO) at W. M. Keck Observatory (WMKO) began in 1999. Through 2023, over 1200 refereed science papers have been published using data from the WMKO AO systems. The scientific competitiveness of AO at WMKO has been maintained through a continuous series of AO and instrument upgrades and additions. This tradition continues with AO being a centerpiece of WMKO’s scientific strategic plan for 2035. We will provide an overview of the current and planned AO projects from the context of this strategic plan. The current projects include implementation of new real-time controllers, the KAPA laser tomography system and the HAKA high-order deformable mirror system, the development of multiple advanced wavefront sensing and control techniques, the ORCAS space-based guide star project, and three new AO science instruments. We will also summarize steps toward the future strategic directions which are centered on ground-layer, visible and high-contrast AO. 

Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. 

New materials are advancing the field of soft robotics. Composite films of magnetic iron microparticles dispersed in a shape memory polymer matrix are demonstrated for reconfigurable, remotely actuated soft robots. The composite films simultaneously respond to magnetic fields and light. Temporary shapes obtained through combined magnetic actuation and photothermal heating can be locked by switching off the light and magnetic field. Subsequent illumination in the absence of the magnetic field drives recovery of the permanent shape. In cantilevers and flowers, multiple cycles of locking and unlocking are demonstrated. Scrolls show that the permanent shape of the film can be programmed, and they can be frozen in intermediate configurations. Bistable snappers can be magnetically and optically actuated, as well as biased, by controlling the permanent shape. Grabbers can pick up and release objects repeatedly. Simulations of combined photothermal heating and magnetic actuation are useful for guiding the design of new devices.