131 research outputs found
Batalin-Vilkovisky Integrals in Finite Dimensions
The Batalin-Vilkovisky method (BV) is the most powerful method to analyze
functional integrals with (infinite-dimensional) gauge symmetries presently
known. It has been invented to fix gauges associated with symmetries that do
not close off-shell. Homological Perturbation Theory is introduced and used to
develop the integration theory behind BV and to describe the BV quantization of
a Lagrangian system with symmetries. Localization (illustrated in terms of
Duistermaat-Heckman localization) as well as anomalous symmetries are discussed
in the framework of BV.Comment: 35 page
Academic and clinical preparation in speech-language pathology and audiology: a global training consortium
ABSTRACT: Purpose: To describe a research-based global\ud
curriculum in speech-language pathology and audiology that\ud
is part of a funded cross-linguistic consortium among 2 U.S.\ud
and 2 Brazilian universities.\ud
Method: The need for a global curriculum in speechlanguage\ud
pathology and audiology is outlined, and different\ud
funding sources are identified to support development of a\ud
global curriculum. The U.S. Department of Education’s Fund\ud
for the Improvement of Post-Secondary Education (FIPSE), in\ud
conjunction with the Brazilian Ministry of Education (Fundacao\ud
Coordenacao de Aperfeicoamento de Pessoal de Nivel\ud
Superior; CAPES), funded the establishment of a shared\ud
research curriculum project, “Consortium for Promoting\ud
Cross-Linguistic Understanding of Communication Disabilities\ud
in Children” for East Tennessee State University and the University of Northern Iowa and 2 Brazilian universities\ud
(Universidade Federal de Santa Maria and Universidade de\ud
São Paulo-Baurú).\ud
Results: The goals and objectives of the research-based\ud
global curriculum are summarized, and a description of an\ud
Internet-based course, “Different Languages, One World,” is\ud
provided\ud
Conclusion: Partnerships such as the FIPSE–CAPES consortium\ud
provide a foundation for training future generations of\ud
globally and research-prepared practitioners in speechlanguage\ud
pathology and audiology.U.S. Department of Education Fund for the Improvement of Post-Secondary Education P116M100014Brazilian Ministry of Education Fundacao Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior 094/1
Status of the Superconducting Magnets for FAIR
Silicon microdosimetry measurements in Fast Neutron Therapy were simulated using the GEANT4 Monte Carlo toolkit. The possibility of using silicon microdosimeters for verification of Monte Carlo based treatment planning systems in hadron therapy is suggested
Generalized Force Model of Traffic Dynamics
Floating car data of car-following behavior in cities were compared to
existing microsimulation models, after their parameters had been calibrated to
the experimental data. With these parameter values, additional simulations have
been carried out, e.g. of a moving car which approaches a stopped car. It
turned out that, in order to manage such kinds of situations without producing
accidents, improved traffic models are needed. Good results have been obtained
with the proposed generalized force model.Comment: For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.htm
Technical Report on the Design, Construction, Commissioning and Operation of the Super-FRS of FAIR
An analysis-ready and quality controlled resource for pediatric brain white-matter research
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets
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