GIPP: Geophysical Instrument Pool Potsdam

The Geophysical Instrument Pool Potsdam (GIPP) consists of field instruments, sensors and equipment for temporary seismological studies (both controlled source and earthquake seismology) as well as for magnetotelluric (electromagnetic) experiments. These instruments are mainly mobile digital recorders, broadband seis­mometers and short period sensors, and they are used to reveal the subsurface structure and to investigate earth­quakes. Sensors for magnetotellurics include induction coil and fluxgate magnetometers and non-polarizing silver / silver-chloride electrodes. It is operated by the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences. The instru­ment facility is open to all academic applicants, both national and international. Instrument applications are evalu­ated and ranked by an external steering board. Currently, for seismological applications >850 geophysical recorders, >170 broadband seis­mo­meters and >1300 short period geophones are available (among others). Available for magnetotelluric experiments are > 50 real-time data-loggers, >150 induction coils, and >500 electrodes. User guidelines and data policy are in force and data archives are provided (standard exchange formats)

3-D Magnetotelluric Image of Offshore Magmatism at the Walvis Ridge and Rift Basin

Highlights • We report on marine 3D Magnetotelluric study on Walvis Ridge • Derived 3D electrical resistivity model shows a large scale resistive zone, which we link to crustal extension due to local uplift. It might indicate the location where the hot-spot impinged on the crust prior to rifting • Smaller scale resistive region is attributed to magma ascent during rifting • Rift basin is identified by low resistivity region The Namibian continental margin marks the starting point of the Tristan da Cunha hotspot trail, the Walvis Ridge. This section of the volcanic southwestern African margin is therefore ideal to study the interaction of hotspot volcanism and rifting, which occurred in the late Jurassic/early Cretaceous. Offshore magnetotelluric data image electromagnetically the landfall of Walvis Ridge. Two large-scale high resistivity anomalies in the 3-D resistivity model indicate old magmatic intrusions related to hot-spot volcanism and rifting. The large-scale resistivity anomalies correlate with seismically identified lower crustal high velocity anomalies attributed to magmatic underplating along 2-D offshore seismic profiles. One of the high resistivity anomalies (above 500 Ωm) has three arms of approximately 100 km width and 300 km to 400 km length at 120 degree angles in the lower crust. One of the arms stretches underneath Walvis Ridge. The shape is suggestive of crustal extension due to local uplift. It might indicate the location where the hot-spot impinged on the crust prior to rifting. A second, smaller anomaly of 50 km width underneath the continent ocean boundary may be attributed to magma ascent during rifting. We attribute a low resistivity anomaly east of the continent ocean boundary and south of Walvis Ridge to the presence of a rift basin that formed prior to the rifting

The track finding algorithm of the Belle II vertex detectors

The Belle II experiment is a high energy multi purpose particle detector operated at the asymmetric e+e-- collier SuperKEKB in Tsukuba (Japan). In this work we describe the algorithm performing the pattern recognition for inner tracking detector which consists of two layers of pixel detectors and four layers of double sided silicon strip detectors arranged around the interaction region. The track finding algorithm will be used both during the High Level Trigger on-line track reconstruction and during the off-line full reconstruction. It must provide good efficiency down to momenta as low as 50 MeV/c where material effects are sizeable even in an extremely thin detector as the VXD. In addition it has to be able to cope with the high occupancy of the Belle II detectors due to the background. The underlying concept of the track finding algorithm, as well as details of the implementation are outlined. The algorithm is proven to run with good performance on simulated Y (4S) â\u86\u92 BB events with an efficiency for reconstructing tracks of above 90% over a wide range of momentum

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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.Peer reviewe

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost