Robust nearest-neighbor methods for classifying high-dimensional data

We suggest a robust nearest-neighbor approach to classifying high-dimensional data. The method enhances sensitivity by employing a threshold and truncates to a sequence of zeros and ones in order to reduce the deleterious impact of heavy-tailed data. Empirical rules are suggested for choosing the threshold. They require the bare minimum of data; only one data vector is needed from each population. Theoretical and numerical aspects of performance are explored, paying particular attention to the impacts of correlation and heterogeneity among data components. On the theoretical side, it is shown that our truncated, thresholded, nearest-neighbor classifier enjoys the same classification boundary as more conventional, nonrobust approaches, which require finite moments in order to achieve good performance. In particular, the greater robustness of our approach does not come at the price of reduced effectiveness. Moreover, when both training sample sizes equal 1, our new method can have performance equal to that of optimal classifiers that require independent and identically distributed data with known marginal distributions; yet, our classifier does not itself need conditions of this type.Comment: Published in at http://dx.doi.org/10.1214/08-AOS591 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Specification Language for the WIDE Workflow Model

This paper presents a workflow specification language developed in the WIDE project. The language provides a rich organisation model, an information model including presentation details, and a sophisticated process model. Workflow application developers should find the language a useful and compact means to capture and investigate design details. Workflow system developers would discover the language a good vehicle to study the interaction between different features as well as facilitate the development of more advanced features. Others would attain a better understanding of the workflow paradigm and could use the language ms a basis of evaluation for the functionality of workflow systems

Skin thickness of the anterior, anteromedial, and anterolateral thigh: a cadaveric study for split-skin graft donor sites

Background: The depth of graft harvest and the residual dermis available for reepithelization primarily influence the healing of split-skin graft donor sites. When the thigh region is chosen, the authors hypothesize based on thickness measurements that the anterolateral region is the optimal donor site. Methods: Full-thickness skin specimens were sampled from the anteromedial, anterior, and anterolateral regions of human cadavers. Skin specimens were cut perpendicularly with a custom-made precision apparatus to avoid the overestimation of thickness measurements. The combined epidermal and dermal thicknesses (overall skin thickness) were measured using a digital calliper. The specimens were histologically stained to visualize their basement membrane, and microscopy images were captured. Since the epidermal thickness varies across the specimen, a stereological method was used to eliminate observer bias. Results: Epidermal thickness represented 2.5% to 9.9% of the overall skin thickness. There was a significant difference in epidermal thickness from one region to another (P<0.05). The anterolateral thigh region had the most consistent and highest mean epidermal thickness (60±3.2 µm). We observed that overall skin thickness increased laterally from the anteromedial region to the anterior and anterolateral regions of the thigh. The overall skin thickness measured 1,032±435 µm in the anteromedial region compared to 1,220±257 µm in the anterolateral region. Conclusions: Based on skin thickness measurements, the anterolateral thigh had the thickest epidermal and dermal layers. We suggest that the anterolateral thigh region is the optimal donor site for split-skin graft harvests from the thigh