Achieving accurate and context‐sensitive timing for code optimization

Key computational kernels must run near their peak efficiency for most high‐performance computing (HPC) applications. Getting this level of efficiency has always required extensive tuning of the kernel on a particular platform of interest. The success or failure of an optimization is usually measured by invoking a timer. Understanding how to build reliable and context‐sensitive timers is one of the most neglected areas in HPC, and this results in a host of HPC software that looks good when reported in the papers, but delivers only a fraction of the reported performance when used by actual HPC applications. In this paper, we motivate the importance of timer design and then discuss the techniques and methodologies we have developed in order to accurately time HPC kernel routines for our well‐known empirical tuning framework, ATLAS. Copyright © 2008 John Wiley & Sons, Ltd.     

An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.     

New product development team intelligence: Antecedents and consequences

Our study investigated the effect of team knowledge on new product development (NPD). By investigating 207 NPD projects, we found that the declarative and procedural knowledge of the team and their use of IT had a positive influence on the team's knowledge base; and that the higher the functional diversity of the project team, the greater their overall knowledge. We also found that team knowledge positively impacted new product creativity and success in the market place.     

Enhancing self-scheduling algorithms via synchronization and weighting

Existing dynamic self-scheduling algorithms, used to schedule independent tasks on heterogeneous clusters, cannot handle tasks with dependencies because they lack the support for internode communication. To compensate for this deficiency we introduce a synchronization mechanism that provides inter-processor communication, thus, enabling self-scheduling algorithms to handle efficiently nested loops with dependencies. We also present a weighting mechanism that significantly improves the performance of dynamic self-scheduling algorithms. These algorithms divide the total number of tasks into chunks and assign them to processors. The weighting mechanism adapts the chunk sizes to the computing power and current run-queue state of the processors. The synchronization and weighting mechanisms are orthogonal, in the sense that they can simultaneously be applied to loops with dependencies. Thus, they broaden the application spectrum of dynamic self-scheduling algorithms and improve their performance. Extensive testing confirms the efficiency of the synchronization and weighting mechanisms and the significant improvement of the synchronized–weighted versions of the algorithms over the synchronized-only versions.     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

Dynamic Shape and Appearance Modeling via Moving and Deforming Layers

We propose a model of the shape, motion and appearance of a scene, seen through a sequence of images, that captures occlusions, scene deformations, unconstrained viewpoint variations and changes in its radiance. This model is based on a collection of overlapping layers that can move and deform, each supporting an intensity function that can change over time. We discuss the generality and limitations of this model in relation to existing ones such as traditional optical flow or motion segmentation, layers, deformable templates and deformotion. We then illustrate how this model can be used for inference of shape, motion, deformation and appearance of the scene from a collection of images. The layering structure allows for automatic inpainting of partially occluded regions. We illustrate the model on synthetic and real sequences where existing schemes fail, and show how suitable choices of constants in the model yield existing schemes, from optical flow to motion segmentation and inpainting.     

A learning classifier system for mazes with aliasing clones

Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure. Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and analyse the results to discover which features of the environments play the most significant role in the learning process. We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing on learning algorithms.     

A wireless embedded tongue tactile biofeedback system for balance control

We describe the architecture of an original biofeedback system for balance improvement for fall prevention and present results of a feasibility study. The underlying principle of this biofeedback consists of providing supplementary information related to foot sole pressure distribution through a wireless embedded tongue-placed tactile output device.Twelve young healthy adults voluntarily participated in this experiment. They were asked to stand as immobile as possible with their eyes closed in two conditions of no-biofeedback and biofeedback. Centre of foot pressure (CoP) displacements were recorded using a force platform. Results showed reduced CoP displacements in the biofeedback relative to the no-biofeedback condition.On the whole, the present findings evidence the effectiveness of this system in improving postural control on young healthy adults. Further investigations are needed to strengthen the potential clinical value of this device.     

Toward the Proteome of the Human Peripheral Blood Eosinophil

Eosinophils (EOSs) are granular leukocytes that have significant roles in many inflammatory and immunoregulatory responses, especially asthma and allergic diseases. We have undertaken a fairly comprehensive proteomic analysis of purified peripheral blood EOSs from normal human donors primarily employing 2‐DE with protein spot identification by MALDI‐MS. Protein subfractionation methods employed included IEF (Zoom^® Fractionator) and subcellular fractionation using differential protein solubilization. We have identified 3141 proteins, which had Mascot expectation scores of 10^?3 or less. Of these 426 were unique and non‐redundant of which 231 were novel proteins not previously reported to occur in EOSs. Ingenuity Pathway Analysis showed that some 70% of the non‐redundant proteins could be subdivided into categories that are clearly related to currently known EOS biological activities. Cytoskeletal and associated proteins predominated among the proteins identified. Extensive protein posttranslational modifications were evident, many of which have not been previously reported that reflected the dynamic character of the EOS. This data set of eosinophilic proteins will prove valuable in comparative studies of disease versus normal states and for studies of gender differences and polymorphic variation among individuals.