Foot Bone in Vivo: Its Center of Mass and Centroid of Shape

This paper studies foot bone geometrical shape and its mass distribution and establishes an assessment method of bone strength. Using spiral CT scanning, with an accuracy of sub-millimeter, we analyze the data of 384 pieces of foot bones in vivo and investigate the relationship between the bone's external shape and internal structure. This analysis is explored on the bases of the bone's center of mass and its centroid of shape. We observe the phenomenon of superposition of center of mass and centroid of shape fairly precisely, indicating a possible appearance of biomechanical organism. We investigate two aspects of the geometrical shape, (i) distance between compact bone's centroid of shape and that of the bone and (ii) the mean radius of the same density bone issue relative to the bone's centroid of shape. These quantities are used to interpret the influence of different physical exercises imposed on bone strength, thereby contributing to an alternate assessment technique to bone strength.Comment: 9 pages, 4 figure

Hardware for Speculative Run-Time Parallelization in Distributed Shared-Memory Multiprocessors1

Abstract Run-time parallelization is often the only way to execute the code in parallel when data dependence information is incomplete at compile time. This situation is common in many important applications. Unfortunately, known techniques for run-time parallelization are often computationally expensive or not general enough. To address this problem, we propose new hardware support for efficient run-time parallelization in distributed shared-memory (DSM) multiprocessors. The idea is to execute the code in parallel speculatively and use extensions to the cache coherence protocol hardware to detect any dependence violations. As soon as a dependence is detected, execution stops, the state is restored, and the codeis re-executed serially. This scheme, which we apply to loops, allows iterations to execute and complete in potentially any order. This scheme requires hardware extensions to the cache coherence protocol and memory hierarchy of a DSM. It has low overhead. In this paper, we present the algorithms and ahardware design of the scheme. Overall, the scheme delivers average loop speedups of 7.3 for 16 processors and is 50 % faster than a related software-only method. 1 Introduction While there has been much work on automatic extraction of parallelism at compile time for multiprocessors [3, 5, 8], current parallelizing compilers often have only limited success. One of the reasons for this is that access patternssometimes depend on the input data and, therefore, the information available at compile time is incomplete. This is common in applications with irregular domains or interactions. A few examples are SPICE for circuit simulation, DYNA-3D and PRONTO-3D for structural mechanics modeling, GAUSSIAN and DMOL for quantum mechanical simulation of molecules, CHARMM and DISCOVER for molecular dynamics simulation, and FIDAP for modeling complex fluid flows. Therefore, it has become clear that static analysis must be complemented with methods capable of extracting paral-lelism at run-time

CST Bank: A Corpus for the Study of Cross-document Structural Relationships

Clusters of multiple news stories related to the same topic exhibit a number of interesting properties. For example, when documents have been published at various points in time or by different authors or news agencies, one finds many instances of paraphrasing, information overlap and even contradiction. The current paper presents the Cross-document Structure Theory (CST) Bank, a collection of multi-document clusters in which pairs of sentences from different documents have been annotated for cross-document structure theory relationships. We will describe how we built the corpus, including our method for reducing the number of sentence pairs to be annotated by our hired judges, using lexical similarity measures. Finally, we will describe how CST and the CST Bank can be applied to different research areas such as multi-document summarization. 1

An iterative Bayesian algorithm for block-sparse signal reconstruction

This paper presents a novel iterative Bayesian algorithm, Block Iterative Bayesian Algorithm (Block-IBA), for reconstructing block-sparse signals with unknown block structures. Unlike the other existing algorithms for block sparse signal recovery which assume the cluster structure of the non-zero elements of the unknown signal to be independent and identically distributed (i.i.d.), we use a more realistic Bernoulli-Gaussian hidden Markov model (BGHMM) to capture the burstiness (block structure) of the impulsive noise in practical applications such as Power Line Communication (PLC). The Block-IBA iteratively estimates the amplitudes and positions of the block-sparse signal based on Expectation-Maximization (EM) algorithm which is also optimized with the steepest-ascent method. Simulation results show the effectiveness of our algorithm for block-sparse signal recovery