OBJECTIVE: The changes in neural drive to muscles associated with modulation of inter-muscular coordination in the upper extremity have not yet been investigated. Such information could help elucidate the neural mechanisms behind motor skill learning. METHODS: Six young, neurologically healthy participants underwent a six-week training protocol to decouple two synergist elbow flexor muscles as a newly learned motor skill in the isometric force generation in upward and medial directions. Concurrent electroencephalography and surface electromyography from twelve upper extremity muscles were recorded in two conditions (As-Trained & Habitual) across two assessments (Week 0 vs. Week 6). Changes to inter-muscular connectivity (IMC), functional muscle networks, cortico-muscular connectivity (CMC), cortico-cortical connectivity (CCC) as well as functional brain network controllability (FBNC) associated with the modulation of inter-muscular coordination patterns were assessed to provide a perspective on the neural mechanisms for the newly learned motor skills. RESULTS: Significant decreases in elbow flexor IMC, CMC, and increases in CCC were observed. No significant changes were observed for FBNC. CONCLUSION: The results of this study suggest that modulating the inter-muscular coordination of the elbow flexor muscle synergy during isometric force generation is associated with multiple yet distinct changes in functional connectivity across the central and peripheral perspectives. SIGNIFICANCE: Understanding the neural mechanisms of modulating inter-muscular coordination patterns can help inform motor rehabilitation regimens.

Static timing analysis is the state-of-the-art practice of ascertaining the timing behavior of current-generation real-time embedded systems. The adoption of more complex hardware to respond to the increasing demand for computing power in next-generation systems exacerbates some of the limitations of static timing analysis. In particular, the effort of acquiring (1) detailed information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behavior of the program in the presence of varying hardware conditions, such as those dependent on the history of previously executed instructions. We call these problems the timing analysis walls. In this vision-statement article, we present probabilistic timing analysis , a novel approach to the analysis of the timing behavior of next-generation real-time embedded systems. We show how probabilistic timing analysis attacks the timing analysis walls; we then illustrate the mathematical foundations on which this method is based and the challenges we face in the effort of efficiently implementing it. We also present experimental evidence that shows how probabilistic timing analysis reduces the extent of knowledge about the execution platform required to produce probabilistically accurate WCET estimations.

The rigorous application of static timing analysis requires a large and costly amount of detail knowledge on the hardware and software components of the system. Probabilistic Timing Analysis has potential for reducing the weight of that demand. In this paper, we present a sound measurement-based probabilistic timing analysis technique based on Extreme Value Theory. In all the experiments made as part of this work, the timing bounds determined by our technique were less than 15% pessimistic in comparison with the tightest possible bounds obtainable with any probabilistic timing analysis technique. As a point of interest to industrial users, our technique also requires a comparatively low number of measurement runs of the program under analysis, less than 650 runs were needed for the benchmarks presented in this paper.

Simulating the expansion of a Type II supernova using an adaptive computational fluid dynamics (CFD) engine yields a complex mixture of turbulent flow with dozens of physical properties. The dataset shown in this sketch was initially simulated on iVEC's EPIC supercomputer (a 9600 core Linux cluster) using FLASH [Fryxell et al. 2000] to model the thermonuclear explosion, and later post-processed using a novel integration technique to derive the radio frequency emission spectra of the expanding shock-wave front [Potter et al. 2011]. Model parameters have been chosen to simulate the asymmetric properties of the SN 1987A remnant [Potter et al. 2009].

Modern processors are evolving into hybrid, heterogeneous processors with both CPU and GPU cores used for general purpose computation. Several languages such as Brook, CUDA, and more recently OpenCL are being developed to fully harness the potential of these processors. These languages typically involve the control code running on the CPU and the performance-critical, data-parallel kernel code running on the GPUs.

Segmentation of structures from measured volume data, such as anatomy in medical imaging, is a challenging data-dependent task. In this paper, we present a segmentation method that leverages the parallel processing capabilities of modern programmable graphics hardware in order to run significantly faster than previous methods. In addition, collocating the algorithm computation with the visualization on the graphics hardware circumvents the need to transfer data across the system bus, allowing for faster visualization and interaction. This algorithm is unique in that it utilizes sophisticated graphics hardware functionality (i.e., floating point precision, render to texture, computational masking, and fragment programs) to enable fast segmentation and interactive visualization.