Continuum Surrogate Software Interface for Teleoperation of Continuum Robots

This thesis presents a novel teleoperation interface for continuum robots. Previous tele-operation interface methods for continuum robots did not include a natural mapping due to a degree-of-freedom mismatch, using non continuum input devices with fewer degrees-of-freedom than the robot that was being controlled. The approach introduced in this thesis involves creating a 3D model of the robot using graphics libraries and a continuum kinematic model, then manipulating that graphical 3D model on screen to directly control the continuum robot. This thesis details the development of both the model and software. The teleoperation interface was developed specifi-cally for a nine degree-of-freedom pneumatically-driven extensible continuum robot (OctArm), but it applies to any continuum robot with an arbitrary number of sections due to its modular design. Experiments using the aforementioned system on two different continuum robots are reported and areas for future work and improvement are detailed

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper

Restrictive Emotionality and Marital Satisfaction

The relationship between restrictive emotionality and marital satisfaction was examined in a sample of 112 married couples. Individuals were asked to rate their own and their spouses' restrictive emotionality, rate the extent to which they believed their spouses were a good fit for them in terms of emotional expression, and rate their own marital satisfaction. Analyses focused on the relationship of men's and women's restrictive emotionality to marital satisfaction, and the relationship between similarity among spouses to marital satisfaction. Results indicated that men's restrictive emotionality was related to both men's and women's marital satisfaction, but women's restrictive emotionality was not related to marital satisfaction. Only individuals' perceptions of similarity were related to satisfaction for both men and women

Interaction Tree Specifications: A Framework for Specifying Recursive, Effectful Computations That Supports Auto-Active Verification (Artifact)

This paper presents a specification framework for monadic, recursive, interactive programs that supports auto-active verification, an approach that combines user-provided guidance with automatic verification techniques. This verification tool is designed to have the flexibility of a manual approach to verification along with the usability benefits of automatic approaches. We accomplish this by augmenting Interaction Trees, a Coq datastructure for representing effectful computations, with logical quantifier events. We show that this yields a language of specifications that are easy to understand, automatable, and are powerful enough to handle properties that involve non-termination. Our framework is implemented as a library in Coq. We demonstrate the effectiveness of this framework by verifying real, low-level code

Interaction Tree Specifications: A Framework for Specifying Recursive, Effectful Computations That Supports Auto-Active Verification

This paper presents a specification framework for monadic, recursive, interactive programs that supports auto-active verification, an approach that combines user-provided guidance with automatic verification techniques. This verification tool is designed to have the flexibility of a manual approach to verification along with the usability benefits of automatic approaches. We accomplish this by augmenting Interaction Trees, a Coq datastructure for representing effectful computations, with logical quantifier events. We show that this yields a language of specifications that are easy to understand, automatable, and are powerful enough to handle properties that involve non-termination. Our framework is implemented as a library in Coq. We demonstrate the effectiveness of this framework by verifying real, low-level code

Combined action observation and motor imagery therapy: a novel method for post-stroke motor rehabilitation

Cerebral vascular accidents (strokes) are a leading cause of motor deficiency in millions of people worldwide. While a complex range of biological systems is affected following a stroke, in this paper we focus primarily on impairments of the motor system and the recovery of motor skills. We briefly review research that has assessed two types of mental practice, which are currently recommended in stroke rehabilitation. Namely, action observation (AO) therapy and motor imagery (MI) training. We highlight the strengths and limitations in both techniques, before making the case for combined action observation and motor imagery (AO + MI) therapy as a potentially more effective method. This is based on a growing body of multimodal brain imaging research showing advantages for combined AO + MI instructions over the two separate methods of AO and MI. Finally, we offer a series of suggestions and considerations for how combined AO + MI therapy could be employed in neurorehabilitation