104 research outputs found
Modelling of gas dynamical properties of the KATRIN tritium source and implications for the neutrino mass measurement
The KATRIN experiment aims to measure the effective mass of the electron
antineutrino from the analysis of electron spectra stemming from the beta-decay
of molecular tritium with a sensitivity of 200 meV. Therefore, a daily
throughput of about 40 g of gaseous tritium is circulated in a windowless
source section. An accurate description of the gas flow through this section is
of fundamental importance for the neutrino mass measurement as it significantly
influences the generation and transport of beta-decay electrons through the
experimental setup. In this paper we present a comprehensive model consisting
of calculations of rarefied gas flow through the different components of the
source section ranging from viscous to free molecular flow. By connecting these
simulations with a number of experimentally determined operational parameters
the gas model can be refreshed regularly according to the measured operating
conditions. In this work, measurement and modelling uncertainties are
quantified with regard to their implications for the neutrino mass measurement.
We find that the systematic uncertainties related to the description of gas
flow are represented by eV,
and that the gas model is ready to be used in the analysis of upcoming KATRIN
data.Comment: 28 pages, 13 figure
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies outside of their predefined set, thus, making it a necessity to retrain the classifier with additional data when faced with novel classes. In contrast, we investigate direct text supervision to break away from this closed set assumption. By doing so, we avoid noisy label extraction via text classifiers and incorporate more contextual information. We employ a contrastive global-local dual-encoder architecture to learn concepts directly from unstructured medical reports while maintaining its ability to perform free form classification. We investigate relevant properties of open set recognition for radiological data and propose a method to employ currently weakly annotated data into training. We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR, CheXpert, and ChestX-Ray14 for disease classification. We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
When reading images, radiologists generate text reports describing the
findings therein. Current state-of-the-art computer-aided diagnosis tools
utilize a fixed set of predefined categories automatically extracted from these
medical reports for training. This form of supervision limits the potential
usage of models as they are unable to pick up on anomalies outside of their
predefined set, thus, making it a necessity to retrain the classifier with
additional data when faced with novel classes. In contrast, we investigate
direct text supervision to break away from this closed set assumption. By doing
so, we avoid noisy label extraction via text classifiers and incorporate more
contextual information.
We employ a contrastive global-local dual-encoder architecture to learn
concepts directly from unstructured medical reports while maintaining its
ability to perform free form classification.
We investigate relevant properties of open set recognition for radiological
data and propose a method to employ currently weakly annotated data into
training.
We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR,
CheXpert, and ChestX-Ray14 for disease classification. We show that despite
using unstructured medical report supervision, we perform on par with direct
label supervision through a sophisticated inference setting.Comment: Provisionally Accepted at MICCAI202
-Decay Spectrum, Response Function and Statistical Model for Neutrino Mass Measurements with the KATRIN Experiment
The objective of the Karlsruhe Tritium Neutrino (KATRIN) experiment is to
determine the effective electron neutrino mass with an
unprecedented sensitivity of (90\% C.L.) by precision electron
spectroscopy close to the endpoint of the decay of tritium. We present
a consistent theoretical description of the electron energy spectrum in
the endpoint region, an accurate model of the apparatus response function, and
the statistical approaches suited to interpret and analyze tritium
decay data observed with KATRIN with the envisaged precision. In addition to
providing detailed analytical expressions for all formulae used in the
presented model framework with the necessary detail of derivation, we discuss
and quantify the impact of theoretical and experimental corrections on the
measured . Finally, we outline the statistical methods for
parameter inference and the construction of confidence intervals that are
appropriate for a neutrino mass measurement with KATRIN. In this context, we
briefly discuss the choice of the energy analysis interval and the
distribution of measuring time within that range.Comment: 27 pages, 22 figures, 2 table
Электропривод питателя сушильного барабана
Разработка и исследование систем векторного управления асинхронным электроприводом питателя сушильного барабана. Исследование электропривода будет произведено с учетом ШИМ преобразователя и квантованием сигналов управления и регуляторов во времени.Development and research of vector control systems of asynchronous electric drive of the dryer drum feeder. The study of the electric drive will be made taking into account the PWM Converter and quantization of control signals and controllers in time
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web
crawls or domain-specific data. However, recent successes of generative large
language models, have shed light on the benefits of cross-domain datasets. To
examine the significance of prioritizing data diversity over quality, we
present a German dataset comprising texts from five domains, along with another
dataset aimed at containing high-quality data. Through training a series of
models ranging between 122M and 750M parameters on both datasets, we conduct a
comprehensive benchmark on multiple downstream tasks. Our findings demonstrate
that the models trained on the cross-domain dataset outperform those trained on
quality data alone, leading to improvements up to over the previous
state-of-the-art. The models are available at
https://huggingface.co/ikim-uk-essenComment: 13 pages, 1 figure, accepted at Findings of the Association for
Computational Linguistics: EMNLP 202
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Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN.
We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9}) eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation
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