Full one-loop electroweak corrections to e+e- to 3 jets at linear colliders

We describe the impact of the full one-loop electroweak terms of O(alpha_s alpha_EM^3) entering the electron-positron into three-jet cross-section from sqrt(s)=M_Z to TeV scale energies. We include both factorisable and non-factorisable virtual corrections and photon bremsstrahlung. Their importance for the measurement of alpha_S from jet rates and shape variables is explained qualitatively and illustrated quantitatively, also in presence of b-tagging.Comment: 6 pages, to appear in the proceedings of the workshop "LC09 -- e+e- Physics at the TeV scale and the Dark Matter Connection", 21-24 September 2009, Perugia (Italy). Minor corrections, references added

Window Expeditions: A playful approach to crowdsourcing natural language descriptions of everyday lived landscapes

Measuring what citizens perceive and value about landscapes is important for landscape monitoring. Capturing temporal, spatial and cultural variation requires collection of data at scale. One potential proxy data source are textual descriptions of landscapes written by volunteers. We implemented a gamified application and crowdsourced a multilingual corpus of in-situ descriptions of everyday lived landscapes. Our implementation focused on the aesthetics of exploration, expression and fellowship in the mechanics, dynamics, aesthetics (MDA) framework. We collected 503 natural language landscape descriptions from 384 participants in English (69.7%), German (25.1%) and French (5.3%) and most contributions were made in urban areas (54.7%). The most frequent noun lemma in English was “tree” and in German “Fenster” (window). By comparing our English collection to corpora of everyday English and landscape descriptions, we identified frequent lemmas such as “tree”, “window”, “light”, “street”, “garden” and “sky” which occurred significantly more than expected. These terms hint as to important components of the everyday landscapes of our users. We suggest a number of ways in which our corpus could be used in ongoing research on landscapes, complementing existing PPGIS approaches, providing data for domain specific lexicons for landscape analysis and as an input to landscape character assessment

Identifying landscape relevant natural language using actively crowdsourced landscape descriptions and sentence-transformers

Natural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora (Geograph and WikiHow) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest