453 research outputs found
Natural history specimens collected and/or identified and deposited.
No full textNatural history specimen data collected and/or identified by Karl Moritz Ernst Gustav Wilhelm Adametz, <a href="http://www.wikidata.org/entity/Q64946788">http://www.wikidata.org/entity/Q64946788</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.http://www.wikidata.org/entity/Q6494678
Natural history specimens collected and/or identified and deposited.
No full textNatural history specimen data collected and/or identified by Karl Moritz Ernst Gustav Wilhelm Adametz, <a href="http://www.wikidata.org/entity/Q64946788">http://www.wikidata.org/entity/Q64946788</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.http://www.wikidata.org/entity/Q6494678
Invariances for Gaussian models
No full textAt the heart of a statistical analysis, we are interested in drawing conclusions about random variables and the laws they follow. For this we require a sample, therefore our approach is best described as learning from data. In many instances, we already have an intuition about the generating process, meaning the space of all possible models reduces to a specific class that is defined up to a set of unknown parameters. Consequently, learning becomes the task of inferring these parameters given observations. Within this scope, the thesis answers the following two questions:
Why are invariances needed? Among all parameters of the model, we often distinguish between those of interest and the so-called nuisance. The latter does not carry any meaning for our purposes, but may still play a crucial role in how the model supports the parameters of interest. This is a fundamental problem in statistics which is solved by finding suitable transformations such that the model becomes invariant against unidentifiable properties. Often, the application at hand already decides upon the necessary requirements: a Euclidean distance matrix, for example, does not carry translational information of the underlying coordinate system.
Why Gaussian models? The normal distribution constitutes an important class in statistics due to frequent occurrences in nature, hence it is highly relevant for many research disciplines including physics, astronomy, engineering, but also psychology and social sciences. Besides fundamental results like the central limit theorem, a significant part of its appeal is rooted in convenient mathematical properties which permit closed-form solutions to numerous problems.
In this work, we develop and discuss generalizations of three established models: a Gaussian mixture model, a Gaussian graphical model and the Gaussian information bottleneck. On the one hand, all of these are analytically convenient, but on the other hand they suffer from strict normality requirements which seriously limit their range of application. To this end, our focus is to explore solutions and relax these restrictions. We successfully show that with the addition of invariances, the aforementioned models gain a substantial leap forward while retaining their core concepts of the Gaussian foundation
Modelling the mass transfer of a polysaccharide-based protein A chromatography membrane with a bimodal porous structure
No full textZum Einfluss sportartspezifischer Rotationsexpertise bei der Wahrnehmung von Drehbewegungen
No full textZum Einfluss sportartspezifischer Rotationsexpertise bei der Wahrnehmung von Drehbewegungen
No full textRealizacja prawa wglądu do sprawdzonej i ocenionej pracy egzaminacyjnej z egzaminu maturalnego przez osoby z niepełnosprawnościami
No full textThe article cites the procedure for inspecting the graded examination paper of the Matura exam conducted in Poland in accordance with Article 44zzz of the Act of 7 September 1991 on education system, pointing out the inadequacy of this procedure to the needs of persons with disabilities. The author presents the still existing problem of duplication law and its impact on the state administration, resulting in this work from the analysis of acts issued in the field of education law. The thesis of the insufficiency of regulations in this area, as presented in the article, also highlights the solutions resulting from the Directory, which ignore – with one exception – the need to adapt this procedure to persons with near-complete disabilities, which is a potential violation of the principles stemming from the Polish Constitution and international law.W artykule przytoczono procedurę wglądu do ocenionej pracy egzaminacyjnej z egzaminu maturalnego przeprowadzanego w Polsce zgodnie z art. 44zzz ustawy z dnia 7 września 1991 r. o systemie oświaty, wskazując niedostosowania tej procedury do potrzeb osób z niepełnosprawnościami. Autor przedstawia wciąż istniejący problem prawa powielaczowego i jego oddziaływania na administrację państwową, wynikły w tej pracy z przeprowadzonej analizy aktów wydanych w zakresie prawa oświatowego. Postawiona w artykule teza niedostatku regulacji w tym zakresie uwypukla również rozwiązania wynikające z Informatora, które pomijają – oprócz jednego wyjątku – potrzeby dostosowań tej procedury do osób z niepełnosprawnościami w stopniu niemal zupełnym, co stanowi potencjalne naruszenie zasad wynikających z Konstytucji RP i prawa międzynarodowego
Measurement of tau decays into a charged hadron accompanied by neutral pi-mesons and determination of the CKM matrix element |V_us|
No full textThis thesis presents the branching fraction measurement of the tau^-->K^- npi^0 nu_tau (n = 0,1,2,3) and tau^-->pi^- npi^0 nu_tau (n = 3,4) decays. The measurement is based on a data sample of 435 million tau pairs produced in e^+e^- collisions and collected with the Babar detector in 1999-2008. The analysis is validated using precisely known tau decays as control modes. The measured branching fractions are BR( tau^-->K^-nu_tau) = (7.100 +- 0.033 +- 0.156) x 10^{-3}, BR( tau^-->K^- pi^0 nu_tau) =(5.000 +- 0.020 +- 0.139) x 10^{-3}, BR( tau^-->K^- 2pi^0 nu_tau) = (5.654 +- 0.144 +- 0.323) x 10^{-4}, BR( tau^-->K^- 3pi^0 nu_tau) = (1.642 +- 0.279 +- 0.375) x 10^{-4}, BR(tau^-->pi^- 3pi^0 nu_tau) = (1.216 +- 0.010 +- 0.047) x 10^{-2}, BR(tau^-->pi^- 4pi^0 nu_tau) = (1.041 +- 0.067 +- 0.090) x 10^{-3}, where the first uncertainty is statistical and the second systematic. The branching fraction BR(tau^-->pi^- 4pi^0 nu_tau) is measured for the first time. The precision of the results is comparable or significantly improved with respect to previous measurements. The branching fraction BR(tau^-->K^- nu_tau) is combined with a lattice QCD calculation of the kaon decay constant to obtain the Cabibbo-Kobayashi-Maskawa matrix element |V_us| = 0.2224 +- 0.0025(exp) +- 0.0029(theo). The branching fractions of the tau decays into a kaon are combined with the current world averages. The resulting averages are used in the determination of the total tau branching fraction, BR_{strange}, into strangeness |S| = 1 final states. BR_{strange} is used in conjunction with |V_ud| and a small SU(3)-symmetry breaking correction to compute |V_us| = 0.2176 +- 0.0025(exp) +- 0.0010(theo)
Jean-François Laslier and M. Remzi Sanver (Eds.) : Handbook on Approval Voting, Springer, 2010
No full textInternational audienc
Threat object classification with a close range polarimetric imaging system by means of H-α decomposition
No full textIn this paper, an approach to differentiate between various dielectric threat objects in security applications is investigated. The scattering information in form of the Sinclair matrix of relevant scenarios is gained from a fully polarimetric, synthetic aperture radar. Both monostatic and multistatic array configurations are examined. A possible polarimetric calibration procedure is presented. The radar data are processed with the H-α decomposition algorithm. The H-α scattering characteristics of threat objects are analyzed in terms of a weighted averaging. It is shown that an object classification is possible even for threat objects conceiled under thick layers of clothing. Measurement results are presented to illustrate the topic
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