Empirical comparison of diffusion kurtosis imaging and diffusion basis spectrum imaging using the same acquisition in healthy young adults

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals

Porsonify: A Portable System for Data Sonification

This document can be read in two ways: as a guide to using the current software, or as a reference manual for those wishing to extend the system to support new features. If you have not yet built and installed the system, before reading further you will probably want to consult Appendix C for installation instructions. The system software (e.g., g++, X11R5/Motif) necessary to build and run Porsonify is described in Appendix B. Throughout this text, the following font conventions are used

The relevance of long-range dependence in disk traffic and implications for trace synthesis

Accurate disk workloads are crucial for storage systems design, but I/O traces are difficult to obtain, unwieldy to work with, and unparameterizable. Unfortunately, I/O traces are extremely bursty and difficult to characterize. Although good models of I/O workloads would be extremely useful, traces cannot accurately be modeled using exponential or Poisson arrival times. Much experimental evidence shows that I/O traces are self-similar, which researchers have hoped might help to model bursty traces. In this paper, we show that self-similarity at large time scales does not significantly affect disk behavior with respect to response times. This allows us to generate synthetic arrival patterns at relatively small time scales, improving the accuracy of trace generation. The relative error of our method, with input parameters suitable for the workload, ranges from approximately 8 % to 12%.

Physical Modeling of Probe-Based Storage

Magnetic disks may be reaching physical performance limits due to the superparamagnetic effect. To close the performance gap between processors and storage, researchers are exploring a variety of new storage technologies [17]. Among these new technologies, probe-based micro-electrical mechanical systems (MEMS) magnetic storage arrays are attractive [3]. Probe-based storage is dense and highly parallel

Data Sonification: Do You See What I Hear?

Using sound to complement graphics and to present data is becoming increasingly popular. Sound can potentially reveal patterns and anomalies in data that are difficult to perceive visually. Moreover, psychological studies show that some types of data are more quickly assimilated when presented with sound; an audio alarm is the classic example. Unfortunately, the lack of standards governing the software interface to sound hardware, as well as the diversity of sound hardware, make it difficult to portably integrate mappings of data to sound (sonifications) with existing data visualization tools. This paper describes the design of a sonification toolkit, Porsonify, created to simplify this task. 1 Introduction In addition to speech and music, incidental and ambient sounds are important, though frequently unrecognized, contributors to our perception of the world. As you read this article, your subconscious is processing and eliding ambient sound. You are unlikely to notice the sound of a..

Cluster-based input/output trace synthesis

hongbo,tara,bing£ I/O traces are crucial for understanding the performance of new storage architectures. Unfortunately, traces are extremely bursty and difficult to characterize. They are large, difficult to obtain, and unwieldy. In this paper, we examine a method of trace synthesis based on cluster analysis of the time-varying characteristics of the trace. Representative trace segments are selected, and a synthesized trace is reconstructed from the segments. We show that we can achieve a 5–10 % demerit factor for I/O response times with a reduction of data volume of 75–90%.

Informed prefetching of collective input/output requests

Optimizing collective input/output (I/O) is important for improving throughput of parallel scientific applications. Current research suggests that a specialized collective application programming interface, coupled with system-level optimizations, is necessary to obtain good I/O performance. Unfortunately, collective interfaces require an application to disclose its entire access pattern to fully reorder I/O requests, and cannot flexibly utilize additional memory to improve performance. In this paper we propose and analyze a method of optimizing collective access patterns using informed prefetching that is capable of exploiting any amount of available memory to overlap I/O with computation. We compare this approach to diskdirected I/O, an efficient implementation of a collective I/O interface. Moreover, we prove that under certain conditions, a per-processor prefetch depth equal to the number of drives can guarantee sequential disk accesses for any collectively accessed file. In empirical studies, a prefetch horizon of one to two times the number of disks per processor is sufficient to match the performance of disk-directed I/O for sequentially allocated files. Finally, we develop accurate analytical models to predict the throughput of informed prefetching for collective reads as a function of the per-processor prefetch depth.