Increased effort during use of the paretic arm and hand can provoke involuntary abnormal synergy patterns and amplify stiffness effects of muscle tone for individuals after stroke, which can add difficulty for user-controlled devices to assist hand movement during functional tasks. We study how volitional effort, exerted in an attempt to open or close the hand, affects resistance to robot-assisted movement at the finger level. We perform experiments with three chronic stroke survivors to measure changes in stiffness when the user is actively exerting effort to activate ipsilateral EMG-controlled robot-assisted hand movements, compared with when the fingers are passively stretched, as well as overall effects from sustained active engagement and use. Our results suggest that active engagement of the upper extremity increases muscle tone in the finger to a much greater degree than through passive-stretch or sustained exertion over time. Potential design implications of this work suggest that developers should anticipate higher levels of finger stiffness when relying on user-driven ipsilateral control methods for assistive or rehabilitative devices for stroke.

Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood. In particular, predicting if knowledge can be transferred between two given tasks is still an unresolved problem. In this work, we explore the use of network distillation as a feature extraction method to better understand the context in which transfer can occur. Notably, we show that distillation does not prevent knowledge transfer, including when transferring from multiple tasks to a new one, and we compare these results with transfer without prior distillation. We focus our work on the Atari benchmark due to the variability between different games, but also to their similarities in terms of visual features.

Reinforcement learning, and specifically bandit optimization, in dynamically changing environments has remained an active topic of study in machine learning. A variety of non-stationary bandit settings have been studied incorporating a range of structural assumptions. At one end are classical stochastic models such as restless bandits [Whittle, 1988], where the state of the arms governs the bandit problem at any instant, but the transitions between these problems (states) follow probabilistic dynamics. At the other extreme are settings with non-stochastic and arbitrarily changing rewards such as prediction with expert advice (and the EXP3 algorithm)[Cesa-Bianchi and Lugosi, 2006; Auer et al., 2002]. In between these extremes lie settings of changing environments where the adversary (environment) is assumed to be limited in its ability to change the rewards, i.e., a structural constraint is put on the amount of change in the rewards across time. These include the abrupt change (or switching experts) model [Garivier and Moulines, 2011], where at most k arbitrary changes to the reward distributions are allowed in the entire time horizon, and the variation-budgeted (drifting) change model [Besbes et al., 2014], in which the total magnitude of changes (of rewards) across successive time steps is constrained to be within an overall budget. In this paper, we focus on slowly-varying bandits - a different and arguably commonly encountered, yet less studied, model of non-stationary bandits. In this setting, the arms are allowed to change arbitrarily over time as long as the amount of change in their mean rewards between two successive time steps is bounded uniformly across the horizon. Many real-world settings naturally involve observables whose distributions are'smooth' over time, in the sense that their instantaneous rate of change is not too large, e.g., slowly drifting distributions in natural language tasks [Lu et al., 2020], data from physical transducers (position, velocity, power, temperature, chemical concentration), and slowly fading wireless

We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a top-down sensing procedure can identify these partitions, and hence the activations, using few measurements. By exploiting the cluster structure, we are able to provide localization guarantees at weaker signal to noise ratios than in the unstructured setting. We complement this performance guarantee with an information theoretic lower bound, providing a necessary signal-to-noise ratio for any algorithm to successfully localize the cluster. We verify our analysis with some simulations, demonstrating the practicality of our algorithm.