Zobrazeno 1 - 10
of 1 232
pro vyhledávání: '"DENNIS, P. J."'
Computational capability often falls short when confronted with massive data, posing a common challenge in establishing a statistical model or statistical inference method dealing with big data. While subsampling techniques have been extensively deve
Externí odkaz:
http://arxiv.org/abs/2410.22751
Autor:
Merkofer, Julian P., van de Sande, Dennis M. J., Amirrajab, Sina, Nam, Kyung Min, van Sloun, Ruud J. G., Bhogal, Alex A.
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the q
Externí odkaz:
http://arxiv.org/abs/2410.10427
Autor:
Roos, Thomas H. M., Versteeg, Edwin, Klomp, Dennis W. J., Siero, Jeroen C. W., Wijnen, Jannie P.
Purpose: This work aims to address the limitations faced by researchers in developing and sharing new MRI sequences by implementing an interpreter for the open-source MRI pulse sequence format, Pulseq, on a Philips MRI scanner. Methods: The implement
Externí odkaz:
http://arxiv.org/abs/2310.06962
Autor:
Fabris, Alessandro, Baranowska, Nina, Dennis, Matthew J., Graus, David, Hacker, Philipp, Saldivar, Jorge, Borgesius, Frederik Zuiderveen, Biega, Asia J.
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides
Externí odkaz:
http://arxiv.org/abs/2309.13933
Autor:
Merkofer, Julian P., van de Sande, Dennis M. J., Amirrajab, Sina, Drenthen, Gerhard S., Veta, Mitko, Jansen, Jacobus F. A., Breeuwer, Marcel, van Sloun, Ruud J. G.
This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input. The method is invariant to the number of transien
Externí odkaz:
http://arxiv.org/abs/2306.02984
Autor:
van de Sande, Dennis M. J., Merkofer, Julian P., Amirrajab, Sina, Veta, Mitko, van Sloun, Ruud J. G., Versluis, Maarten J., Jansen, Jacobus F. A., Brink, Johan S. van den, Breeuwer, Marcel
This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a
Externí odkaz:
http://arxiv.org/abs/2305.09621
Publikováno v:
SCIENCE CHINA Mathematics (2023)
Sequential Latin hypercube designs have recently received great attention for computer experiments. Much of the work has been restricted to invariant spaces. The related systematic construction methods are inflexible while algorithmic methods are ine
Externí odkaz:
http://arxiv.org/abs/2305.06578
Turbulent pipe flow is still an essentially open area of research, boosted in the last two decades by considerable progress achieved both on the experimental and numerical frontiers, mainly related to the identification and characterization of cohere
Externí odkaz:
http://arxiv.org/abs/2301.11210
Autor:
Jäckel, Robert, Magacho, Bruno, Owolabi, Bayode, Moriconi, Luca, Dennis, David J. C., Loureiro, Juliana B. R.
Turbulent pipe flows exhibit organizational states (OSs) that are labelled by discrete azimuthal wavenumber modes and are reminiscent of the traveling wave solutions of low Reynolds number regimes. The discretized time evolution of the OSs, obtained
Externí odkaz:
http://arxiv.org/abs/2301.05344