Shrinkage estimation in general linear models

We propose a James-Stein-type shrinkage estimator for the parameter vector in a general linear model when it is suspected that some of the parameters may be restricted to a subspace. The James-Stein estimator is shown to demonstrate asymptotically superior risk performance relative to the conventional least squares estimator under quadratic loss. An extensive simulation study based on a multiple linear regression model and a logistic regression model further demonstrates the improved performance of this James-Stein estimator in finite samples. The application of this new estimator is illustrated using Ontario newborn infants data spanning four fiscal years.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.     

Tactical combat movements: inter-individual variation in performance due to the effects of load carriage

An examination into the effects of carried military equipment on the performance of two tactical combat movement simulations was conducted. Nineteen Airfield Defence Guards performed a break contact (five 30-m sprints) and a fire and movement simulation (16 6-m bounds) in five load conditions (10–30 kg). Heavier loads significantly increased movement duration on the break contact (0.8%/kg load) and fire and movement (1.1%/kg). Performance deterioration was observed from the beginning to the end of the series of movements (bounds or sprints) with deterioration becoming significantly greater in heavier load conditions. Inter-individual variation between slower and faster participants showed a range in load effects; 0.6, 0.8%/kg for fast and 1.0, 1.4%/kg for slow (break contact, fire and movement, respectively). Velocity profiles revealed that the initial acceleration and peak velocity were the primary determinants of performance. As the duration of these tactical combat movements reflects periods of heightened vulnerability, these findings highlight important implications for commanders.

Practitioner Summary: Increasing amounts of carried military equipment impairs the performance of tactical combat movements. Examination of inter-individual variation in velocity profiles identified that the initial acceleration and the peak velocity achieved during sprints and bounds are key determinants of overall performance.     

Resilient overlay network for real-time interactive multimedia sessions in corporate networks

Real-time interactive multimedia communications are becoming increasingly useful for education, business, e-commerce and e-government, providing an enriched user experience in teleconferencing, e-meetings, distance training and product demonstrations. Large corporations are usually located at several sites, so real-time multipoint sessions within corporations are especially difficult. IP multicast is available or feasible within each site of an organization. Thus, corporate networks can be considered as various multicast-capable networks interconnected through a wide area network without multicast connectivity. This paper proposes a resilient self-managed overlay network to support real-time multipoint interactive sessions within corporate networks. The proposed overlay takes advantage of the configuration of corporate networks to self-organize and provide an efficient media delivery service, making use of multicast communications wherever available. Various self-healing techniques are implemented allowing for the continuity of ongoing sessions in spite of network disruptions and entity failures. Extensive simulations and tests have been carried out to assess the performance and resilience of the overlay facing several types of disruptions.     

Fovea center detection based on the retina anatomy and mathematical morphology

In this work, we present a new fovea center detection method for color eye fundus images. This method is based on known anatomical constraints on the relative locations of retina structures, and mathematical morphology. The detection of this anatomical feature is a prerequisite for the computer aided diagnosis of several retinal diseases, such as Diabetic Macular Edema. The proposed method is adaptive to local illumination changes, and it is robust to local disturbances introduced by pathologies in digital color eye fundus images (e.g. exudates). Our experimental results using the DRIVE image database indicate that our method is able to detect the fovea center in 37 out of 37 images (i.e. with a success rate of 100%). Using the DIARETDB1 database, our method was able to detect the fovea center in 92.13% of all tested cases (i.e. in 82 out of 89 images). These results indicate that our approach potentially can achieve a better performance than comparable methods proposed in the literature.     

Scientific sketching for collaborative VR visualization design

We present four studies investigating tools and methodologies for artist-scientist-technologist collaboration in designing multivariate, virtual reality (VR) visualizations. Design study 1 identifies the promise of 3D drawing-style interfaces for VR design and also establishes limitations of these tools with respect to precision and support for animation. Design study 2 explores animating artist-created visualization designs with scientific 3D fluid flow data. While results captured an accurate sense of flow that was advantageous as compared to the results of study 1, the potential for visual exploration using the design tools tested was limited. Design study 3 reveals the importance of a new 3D interface that overcomes the precision limitation found in study 1 while remaining accessible to artist collaborators. Drawing upon previous results, design study 4 engages collaborative teams in a design process that begins with traditional paper sketching and moves to animated, interactive, VR prototypes "sketched" by designers in VR using interactive 3D tools. Conclusions from these four studies identify important characteristics of effective artist-accessible VR visualization design tools and lead to a proposed formalized methodology for successful collaborative design that we expect to be useful in guiding future collaborations. We call this proposed methodology Scientific Sketching.     

The impact of process standardization on business process outsourcing success

What is the impact of business process standardization on business process outsourcing (BPO) success? This paper argues that there is a direct impact of process standardization on BPO success, due to production cost economies, and also an indirect effect via improved contractual and relational governance resulting from better monitoring opportunities and facilitated communication and coordination. This threefold impact of standardization on BPO success is empirically confirmed using data from 335 BPO ventures in 215 German banks.     

Deformation models for image recognition

We present the application of different nonlinear image deformation models to the task of image recognition. The deformation models are especially suited for local changes as they often occur in the presence of image object variability. We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks. We show experimentally that the model performs very well for four different handwritten digit recognition tasks and for the classification of medical images, thus showing high generalization capacity. In particular, an error rate of 0.54 percent on the MNIST benchmark is achieved, as well as the lowest reported error rate, specifically 12.6 percent, in the 2005 international ImageCLEF evaluation of medical image specifically categorization.