Data management for interdisciplinary field experiments: OTTER project support

The ability of investigators of an interdisciplinary science project to properly manage the data that are collected during the experiment is critical to the effective conduct of science. When the project becomes large, possibly including several scenes of large-format remotely sensed imagery shared by many investigators requiring several services, the data management effort can involve extensive staff and computerized data inventories. The OTTER (Oregon Transect Ecosystem Research) project was supported by the PLDS (Pilot Land Data System) with several data management services, such as data inventory, certification, and publication. After a brief description of these services, experiences in providing them are compared with earlier data management efforts and some conclusions regarding data management in support of interdisciplinary science are discussed. In addition to providing these services, a major goal of this data management capability was to adopt characteristics of a pro-active attitude, such as flexibility and responsiveness, believed to be crucial for the effective conduct of active, interdisciplinary science. These are also itemized and compared with previous data management support activities. Identifying and improving these services and characteristics can lead to the design and implementation of optimal data management support capabilities, which can result in higher quality science and data products from future interdisciplinary field experiments

European ancient settlements - A guide to their composition and morphology based on soil micromorphology and associated geoarchaeological techniques; introducing the contrasting sites of Chalcolithic Bordus, ani-Popina, Borcea River, Romania and Viking Age Heimdaljordet, Vestfold, Norway

Specific soil micromorphological, broader geoarchaeological and environmental archaeology signatures of settlement activities and land use have been identified from numerous case studies across Europe – from Romania to western Norway. In order to demonstrate how such investigations contribute to our understanding of settlement morphology and its wider landscape, an improved way of organising site-specific information or guide was created (Macphail and Goldberg, in press). Activities and land use are divided into ‘Within Settlement’, ‘Peripheral to Settlement’ and ‘The Settlement's Wider Landscape’. Major themes identified are: Constructions (and materials), Trackways and paths (and other communication/transport-associated features), Animal Management, Water Management, Waste Disposal (1: middening; 2: human waste), Specialist Domestic and Industrial Activities and Funerary Practices. In the case of trackway deposits, their characterisation aids the identification of intensely occupied areas compared to rural communications, although changing land use within urban areas has also produced ‘rural signatures’ (e.g. as associated with animal management), for example in Late Roman cities. Specialist activities such as fish and crop processing or working with lead and other metals, in-field and within-wall manuring, stabling and domestic occupation floor-use evidence, and identification of different funerary practice – cremations, boat graves and other inhumations, and excarnation features – and peripheral constructions such as boat-houses, are also noted. New information from the Chalcolithic tell site of Borduşani-Popină, Romania and seasonally occupied Viking settlement of Heimdaljordet, Norway, is introduced

Face Class Modeling in Eigenfaces Space

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies and discuss their effectiveness in the case of face-class modeling

Higher Order Autocorrelations for Pattern Classification

The use of higher-order local autocorrelations as features forpattern recognition has been acknowledge since many years, but their applicability was restricted to relatively low orders (2 or 3) and small local neighborhoods, due to combinatorial increase in computational costs. In this paper a new method for using these features is presented, which allows the use of autocorrelations of any order and of larger neighborhoods. The method is closely related to the classifier used, a Support Vector Machine

PCA in Autocorrelation Space

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component Analysis (PCA) which, however, cannot be applied directly in the autocorrelation space. In this paper we develop a new method for performing PCA in autocorrelation space, without explicitly computing the autocorrelations. The connections with the nonlinear PCA and possible extensions are also discussed

Pattern Recognition using Higer-Order Local Autocorrelation Coefficients

The autocorrelations have been previously used as features fo

Face Detection using SVM Trained in Eigenfaces Space

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling

Adaptive Kernel Matching Pursuit for Pattern Classification

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over th