Research on machine learning approaches for upper limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge, because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.
We present a large set of benchmarks for automated theorem provers that require inductive reasoning. Motivated by the need to compare first-order theorem provers, SMT solvers and inductive theorem provers, the setting of our examples follows the SMT-LIB standard. Our benchmark set contains problems with inductive data types as well as integers. In addition to SMT-LIB encodings, we provide translations to some other less common input formats.
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