Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

A method is described for sampling the surface duller seen in the receiver of an airborne pulse Doppler radar. The clutter return is modelled as a two dimensional Guassian random field with uncorrelated increments and is characterised by the mean clutter power of the field. The elutter signal in the radar receiver is also modelled as a random process and is obtained by sampling a realisation of ihe clutter return and convolving with the impulse response of the radar. The impulse response of the radar is described in terms of the ambiguity function of the receiver. Examples of realisations ofthe clutter signal am used to illustrate the key ideas.

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Figure 1: Our novel interactive approach for shape detection in point clouds allows for sophisticated interactions: Left: A Lasso selection selects only points that lie on the support shape as shown in the top image. Points in front and back of the support shape are not selected (bottom). Middle: A volumetric brush selection is performed on the selected support shape (top). Points are only selected if they belong to the support shape and intersect the brush (bottom). Right: Interactive LoD increment interaction along the selected support shape (drawn in red). The top image shows the original rendering model of the point cloud; the bottom image shows the point cloud with the additional points.

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