An integrated model of acoustics and language using semantic classification trees

We propose Multi-level Semantic Classification Trees to combine different information sources for predicting speech events (e.g. word chains, phrases, etc.). Traditionally in speech recognition systems these infromation sources (acoustic evidence, language model) are calculated independently and combined via Bayes rule. The proposed approach allows one to combine sources of different types - it is no longer necessary for each source to yield a probability. Moreover the tree can look at several information sources simultaneously. The approach is demonstrated for the prediction of prosodically marked phrase boundaries, combining information about the spoken word chain, word category information, prosodic parameters, and the result of a neural network predicting the boundary on the basis of acoustic-prosodic features. The recognition rates of up to 90% for the two class problem boundary vs. no boundary are already comparable to results achieved with the above mentioned Bayes rule approach that combines the acoustic classifier with a 5-gram categorical language model. This is remarkable, since so far only a small set of questions combining information from different sources have been implemented. (orig.)Available from TIB Hannover: RR 5221(128)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Relativistic deflection of background starlight measures the mass of a nearby white dwarf star (dataset)

HST data used in this paper are available from the Mikulski Archive for Space Telescopes at STScI (https://archive.stsci.edu/hst/search.php), under proposal ID 13457 and 14448

Relativistic deflection of background starlight measures the mass of a nearby white dwarf star (dataset)

HST data used in this paper are available from the Mikulski Archive for Space Telescopes at STScI (https://archive.stsci.edu/hst/search.php), under proposal ID 13457 and 14448

Joint EVS/WVS 2017-2021 Dataset (Joint EVS/WVS)

The European Values Study (EVS) and the World Values Survey (WVS) are two large-scale, cross-national and longitudinal survey research programmes. They include a large number of questions on moral, religious, social, political, occupational and family values which have been replicated since the early eighties. Both organizations agreed to cooperate in joint data collection from 2017. EVS has been responsible for planning and conducting surveys in European countries, using the EVS questionnaire and EVS methodological guidelines. WVSA has been responsible for planning and conducting surveys in countries in the world outside Europe, using the WVS questionnaire and WVS methodological guidelines. Both organisations developed their draft master questionnaires independently. The joint items define the Common Core of both questionnaires. The Joint EVS/WVS is constructed from the two EVS and WVS source datasets: - European Values Study 2017 Integrated Dataset (EVS 2017), ZA7500 Data file Version 4.0.0, doi:10.4232/1.13560 (https://doi.org/10.4232/1.13560). - European Values Study 2017: Ukraine (EVS 2017), ZA7539 Data file Version 1.0.0, doi:10.4232/1.13714 (https://doi.org/10.4232/1.13714). - World Values Survey: Round Seven–Country-Pooled Datafile. Version 2.0.0, doi: 10.14281/18241.13 (https://doi.org/10.14281/18241.13).The European Values Study (EVS) and the World Values Survey (WVS) are two large-scale, cross-national and longitudinal survey research programmes. They include a large number of questions on moral, religious, social, political, occupational and family values which have been replicated since the early eighties. Both organizations agreed to cooperate in joint data collection from 2017. EVS has been responsible for planning and conducting surveys in European countries, using the EVS questionnaire and EVS methodological guidelines. WVSA has been responsible for planning and conducting surveys in countries in the world outside Europe, using the WVS questionnaire and WVS methodological guidelines. Both organisations developed their draft master questionnaires independently. The joint items define the Common Core of both questionnaires. The Joint EVS/WVS is constructed from the two EVS and WVS source datasets: - European Values Study 2017 Integrated Dataset (EVS 2017), ZA7500 Data file Version 4.0.0, doi:10.4232/1.13560 (https://doi.org/10.4232/1.13560). - European Values Study 2017: Ukraine (EVS 2017), ZA7539 Data file Version 1.0.0, doi:10.4232/1.13714 (https://doi.org/10.4232/1.13714). - World Values Survey: Round Seven–Country-Pooled Datafile. Version 2.0.0, doi: 10.14281/18241.13 (https://doi.org/10.14281/18241.13)

CSES Module 1-3 Harmonized Trend File

Für weitere Informationen zur Variablenliste siehe die Dokumentation (Codebook) des CSES Module 1-3 Harmonized Trend File. Informationen zum Inhalt können den Studiennummern ZA5179 CSES Module 1 Full Release, ZA5180 CSES Module 2 Full Release, und ZA5181 CSES Module 3 Full Release entnommen werden

Surface Ocean CO2 Atlas (SOCAT) V6

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis activity by the international marine carbon research community (>100 contributors). SOCAT version 6 has 23.4 million quality-controlled, surface ocean fCO2 (fugacity of carbon dioxide) observations from 1957 to 2017 for the global oceans and coastal seas. Calibrated sensor data are also available. Automation allows annual, public releases. SOCAT data is discoverable, accessible and citable. SOCAT enables quantification of the ocean carbon sink and ocean acidification and evaluation of ocean biogeochemical models. SOCAT represents a milestone in biogeochemical and climate research and in informing policy. 424 datasets Version 5: https://doi.pangaea.de/10.1594/PANGAEA.877863 Version 4: https://doi.pangaea.de/10.1594/PANGAEA.866856 Version 3: https://doi.pangaea.de/10.1594/PANGAEA.849770 Version 2: https://doi.org/10.1594/PANGAEA.81515

Additional file 1 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 1: Table S1. Characteristics of contributing cohorts (as provided by each participating cohort)