191,789 research outputs found
pymc-devs/pymc-bart: 0.5.8
No full text<h2>What's Changed</h2>
<ul>
<li>Ruff linter + pre-commit integration by @juanitorduz in https://github.com/pymc-devs/pymc-bart/pull/140</li>
<li>Improve CONTRIBUTING guidelines by @juanitorduz in https://github.com/pymc-devs/pymc-bart/pull/141</li>
<li>Add Usage and Table of Contents, to the README file, enhance Installation section, and fix top header by @NicholasLindner in https://github.com/pymc-devs/pymc-bart/pull/143</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li>@NicholasLindner made their first contribution in https://github.com/pymc-devs/pymc-bart/pull/143</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc-bart/compare/0.5.7...0.5.8</p>
pymc-devs/pymc-bart: 0.5.9
No full text<h2>What's Changed</h2>
<ul>
<li>Ruff linter + pre-commit integration by @juanitorduz in https://github.com/pymc-devs/pymc-bart/pull/140</li>
<li>Improve CONTRIBUTING guidelines by @juanitorduz in https://github.com/pymc-devs/pymc-bart/pull/141</li>
<li>Add Usage and Table of Contents, to the README file, enhance Installation section, and fix top header by @NicholasLindner in https://github.com/pymc-devs/pymc-bart/pull/143</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li>@NicholasLindner made their first contribution in https://github.com/pymc-devs/pymc-bart/pull/143</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc-bart/compare/0.5.7...0.5.9</p>
BART: version 0.2.04
No full text<p>The Berkeley Advanced Reconstruction Toolbox (BART) is a framework for image reconstruction in Magnet Resonance Imaging (MRI). It consists of a programming library and a toolbox of command-line programs. The library provides common operations on multi-dimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The command-line tools provide direct access to basic operations on multi-dimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for MRI.</p>
pymc-devs/pymc-bart: 0.5.10
No full text<p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc-bart/compare/0.5.8...0.5.10</p>
BART: version 0.3.00
No full text<p>The Berkeley Advanced Reconstruction Toolbox (BART) is a framework for image reconstruction in Magnet Resonance Imaging (MRI). It consists of a programming library and a toolbox of command-line programs. The library provides common operations on multi-dimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The command-line tools provide direct access to basic operations on multi-dimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for MRI.</p>
BART: version 0.3.01
No full text<p>The Berkeley Advanced Reconstruction Toolbox (BART) is a framework for image reconstruction in Magnet Resonance Imaging (MRI). It consists of a programming library and a toolbox of command-line programs. The library provides common operations on multi-dimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The command-line tools provide direct access to basic operations on multi-dimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for MRI.</p>
Evaluating the Balloon Analogue Risk Task (BART) as a Predictor of Risk Taking in Adolescent and Adult Male Drivers.
No full textAbstract
Young drivers between the ages of 15 and 24 are overrepresented in automobile crash statistics worldwide. Despite the common assumption that young drivers are more at risk of crashing than older drivers due to inexperience, age appears to be the main factor influencing crash risk, even after experience has been taken into account. It is possible that young drivers are involved in a high number of crashes because of their risk-taking tendencies. Accident involvement is not so much influenced by errors and lapses by the driver, but by the willingness to commit driving violations intentionally. However, studies that attempted to measure the risk-taking tendencies of drivers have so far used mainly self-report questionnaires, which are limited in their ability to predict real-world behaviour. This thesis used a new behavioural measure of risk-taking known as the Balloon Analogue Risk Task (BART). In this task, participants engage in computer simulation where a balloon is pumped in order to accumulate money, but when the balloon is pumped too high it explodes, and the money that could have been gained is lost. A group of 50 male drivers were the participants of this study, and these were separated into three age groups: adolescents, aged 16-17, young adults, aged 20-21, and older adults, aged 25 years and over. In addition to the BART, the participants answered a series of questionnaires that focused on risk-related constructs, such as impulsiveness and subjective risk assessment, as well as driving attitudes and intentions. The expectation was that younger drivers would be shown to have greater risk-taking tendencies than older drivers. The results showed that the BART showed no relationship with either driving attitude scores (apart from a small correlation with attitudes towards close following), or any of the self-reported measures of risk. The other self-report risk measures, however, showed many correlations with various aspects of driving attitudes and intentions. Over age groups, the level of impulsiveness was found to decrease, and the attitudes became less in favour of taking physical risks. Adolescents were also found to be more approving of using a cell phone while driving, and of overtaking in risky circumstances. They had greater intentions to commit violations in the future, and were more likely to get a thrill from driving. The failure of the BART to reveal any significant findings may have been because so far it has only been shown to correlate with self-reported real-world behaviour, and not so much with attitudes and risk-related constructs. The other suggestion of this thesis was that the BART does not simulate risk-taking in the truest sense because there are no specific negative consequences for risk taking, only the removal of a possible benefit. The finding of greater risk taking in adolescent drivers was discussed in relation to Risk Homeostasis Theory and Problem Behaviour Theory, with a focus on how age-related factors might influence driver risk taking. As further discussed, these age-related factors might include the effect of incomplete brain development, the motives for driving, and the lifestyle of the individual
BART: version 0.4.02
No full text<p>The Berkeley Advanced Reconstruction Toolbox (BART) is a framework for image reconstruction in Magnet Resonance Imaging (MRI). It consists of a programming library and a toolbox of command-line programs. The library provides common operations on multi-dimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The command-line tools provide direct access to basic operations on multi-dimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for MRI.</p>
<p>Changes:</p>
<ul>
<li>new tools: std var vec wavepsf whiten</li>
<li>std: compute standard deviation (Jonathan Tamir)</li>
<li>var: compute variance (Jonathan Tamir)</li>
<li>vec: create vectors from the command line</li>
<li>wavepsf: create PSF for wave acquisition (Siddharth Iyer)</li>
<li>whiten: compute/apply whitening matrix (Jonathan Tamir)</li>
<li>pics: basis pursuit formulation (Jonathan Tamir)</li>
<li>nlinv: support for simultaneous multi-slice (Sebastian Rosenzweig)</li>
<li>nlinv: various enhancements and fixes (Christian Holme)</li>
<li>pics: support for simultaneous multi-slice (Sebastian Rosenzweig)</li>
<li>traj: radial simultaneous multi-slice trajectories (Sebastian Rosenzweig)</li>
<li>fft: uncentered option (Jonathan Tamir)</li>
<li>nufft: use Toepliz-mode by default</li>
<li>nufft: add GPU option</li>
<li>python 3 version for bartview (Siddharth Iyer)</li>
<li>fix compilation for Cygwin on Windows (Johannes Töger)</li>
<li>include relevant parts of LAPACKE in BART</li>
<li>library: add NIHT algorithm (Sofia Dimoudi)</li>
<li>library: add Chambolle-Pock primal dual algorithm for F(Ax) + G(x) (Jonathan Tamir)</li>
<li>library: add md_zss function for sum-of-squares (Jonathan Tamir)</li>
<li>library: improved parallelization (Michael Anderson)</li>
<li>library: joint l1-wavelet regularization</li>
<li>library: rename wavelet3 to wavelet</li>
<li>library: Hamming and Hann windows (Jonathan Tamir)</li>
<li>library: png write functions (Christian Holme)</li>
<li>library: initial interface for nonlinear operators</li>
<li>many other bug fixes and improvements</li>
</ul>
The Review of Economic Performance and Social Progress 2002: Towards a Social Understanding of Productivity
No full textProductivity and income growth rates and differentials vary widely among OECD countries. In this chapter, Bart van Ark develops a framework for the understanding of these productivity and income differences. The framework breaks GDP per capita into two basic drivers: labour supply and labour productivity. Labour supply is in turn determined by the hours worked per person employed, the share of employment in the working age population, and the share of the working age population in the total population. Labour productivity is determined by within-industry productivity growth rates and inter-sectoral shifts in employment shares. The former is affected by the efficiency in factor use, that is total factor productivity, investment in physical capital, and investment in intangible capital.Productivity, Labor Productivity, Labour Productivity, Growth, Level, Levels, Living Standards, ICT, Information, Communication, Technology, Canada, United States, European Union, Hours Worked, Average Hours, Knowledge, Knowledge Capital, Human Capital, Intangible Capital, Research, Development, Investment, Income, Income Differentials
BART: A multilingual anaphora resolution system
No full textBART (Versley et al., 2008) is a highly mod-
ular toolkit for coreference resolution that
supports state-of-the-art statistical approaches
and enables efficient feature engineering. For
the SemEval task 1 on Coreference Resolution, BART runs have been submitted for German, English, and Italian.
BART relies on a maximum entropy-based classifier for pairs of mentions. A novel entity-mention approach based on Semantic Trees is
at the moment only supported for English
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