Entity Ranking on Graphs: Studies on Expert Finding

Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models

Ex Situ Conservation Of Holstein-Friesian Cattle - Comparing The Dutch, French And USA Germplasm Collections

The establishment of gene banks using cryopreservation to secure the genetic diversity of farm breeds have been widely assessed. France, the Netherlands and the USA were among the first countries to organize national cryobanks and these banks are now 10 to 20 years old. All three countries have started Holstein-Friesian (HF) collections to conserve as much genetic diversity as possible for this globally important breed. In order better understand the diversity captured in these collections, the genetic variability of HF collections within and between countries was assessed, and genetic variability of germplasm collections were compared with active bulls in each country. The overall aim of the project was to determine the breed’s security and to guide future collection activities

PFTijah: text search in an XML database system

This paper introduces the PFTijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PFTijah is part of the open source release of MonetDB/XQuery

Sound ranking algorithms for XML search

Ranking algorithms for XML should reflect the actual combined content and structure constraints of queries, while at the same time producing equal rankings for queries that are semantically equal. Ranking algorithms that produce different rankings for queries that are semantically equal are easily detected by tests on large databases: We call such algorithms not sound. We report the behavior of different approaches to ranking content-and-structure queries on pairs of queries for which we expect equal ranking results from the query semantics. We show that most of these approaches are not sound. Of the remaining approaches, only 3 adhere to the W3C XQuery Full-Text standard

Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product

Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas

Combining Document-and Paragraph-Based Entity Ranking

We study entity ranking on the INEX entity track and pro- pose a simple graph-based ranking approach that enables to combine scores on document and paragraph level. The com- bined approach improves the retrieval results not only on the INEX testset, but similarly on TREC’s expert finding task

Temporal Language Models for the Disclosure of Historical Text

Contains fulltext : 228230.pdf (preprint version ) (Open Access

Complementary Activities of Host Defence Peptides and Antibiotics in Combating Antimicrobial Resistant Bacteria

Due to their ability to eliminate antimicrobial resistant (AMR) bacteria and to modulate the immune response, host defence peptides (HDPs) hold great promise for the clinical treatment of bacterial infections. Whereas monotherapy with HDPs is not likely to become an effective first-line treatment, combinations of such peptides with antibiotics can potentially provide a path to future therapies for AMR infections. Therefore, we critically reviewed the recent literature regarding the antibacterial activity of combinations of HDPs and antibiotics against AMR bacteria and the approaches taken in these studies. Of the 86 studies compiled, 56 featured a formal assessment of synergy between agents. Of the combinations assessed, synergistic and additive interactions between HDPs and antibiotics amounted to 84.9% of the records, while indifferent and antagonistic interactions accounted for 15.1%. Penicillin, aminoglycoside, fluoro/quinolone, and glycopeptide antibiotic classes were the most frequently documented as interacting with HDPs, and Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Enterococcus faecium were the most reported bacterial species. Few studies formally evaluated the effects of combinations of HDPs and antibiotics on bacteria, and even fewer assessed such combinations against bacteria within biofilms, in animal models, or in advanced tissue infection models. Despite the biases of the current literature, the studies suggest that effective combinations of HDPs and antibiotics hold promise for the future treatment of infections caused by AMR bacteria