Results 91 to 100 of about 2,741,379 (327)

Randomized Matrix Decompositions Using R

Journal of Statistical Software, 2019
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data ...
N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz   +3 more
doaj   +1 more source

Fast Universal Algorithms for Robustness Analysis [PDF]

published in book ``Probabilistic and Randomized Methods for Design under Uncertainty, pp. 415-431, edited by G. Calafiore and F. Dabbene, Springer Verlag, 2006, 2008
In this paper, we develop efficient randomized algorithms for estimating probabilistic robustness margin and constructing robustness degradation curve for uncertain dynamic systems. One remarkable feature of these algorithms is their universal applicability to robustness analysis problems with arbitrary robustness requirements and uncertainty bounding ...
arxiv  

Bone‐wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis.
Arttu Ruohola, Ville Haapamäki, Eero Salli, Tuomas Kaseva, Marko Kangasniemi, Sauli Savolainen   +5 more
wiley   +1 more source

Monte Carlo modeling of radiation dose from radiation therapy with superficial x‐rays

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Introduction Superficial x‐rays (50–100 kVp) are used for treating non‐melanoma skin cancer and intraoperative radiation therapy (IORT). At these energies, the photoelectric effect significantly increases absorbed dose to bone compared to soft tissue.
Reham Barghash, Tiffany W. Martin, Amber R. Prebble, Del Leary   +3 more
wiley   +1 more source

Toward a human‐centric co‐design methodology for AI detection of differences between planned and delivered dose in radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising, Frank Verhaegen, Stefan G. Scheib, Maria J. G. Jacobs, Carol X. J. Ou, Viola Mottarella, Yin‐Ho Chong, Mariangela Zamburlini, Sebastiaan M. J. J. G. Nijsten, Ans Swinnen, Michel Öllers, Cecile J. A. Wolfs   +11 more
wiley   +1 more source

The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread

NeuroImage, 2020
Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference ...
M. Hashemi, A.N. Vattikonda, V. Sip, M. Guye, F. Bartolomei, M.M. Woodman, V.K. Jirsa   +6 more
doaj  

Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra‐hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter‐fraction variability, inconsistent patient adherence still results in OAR variability.
Conor Sinclair Smith, Isabelle Gagne, Karl Otto, Carter Kolbeck, Joshua Giambattista, Abraham Alexander, Sonja Murchison, Andrew Pritchard, Erika Chin   +8 more
wiley   +1 more source

Learning Analytics - Scientific Description and Heuristic Validation of Languages NLG

Je-LKS: Journal of E-Learning and Knowledge Society, 2019
Educators do not have to be mere "Translators" as manufacturers of algorithms for teaching in the infosphere, but must modulate it for everyone's purposes.
Ritamaria Bucciarelli, Roberto Capone, Francesco Saverio Tortoriello, Marianna Greco, Giulia Savarese, Javier Enriquez   +5 more
doaj   +1 more source

Clinical and Imaging Features of Sporadic and Genetic Frontotemporal Lobar Degeneration TDP‐43 A and B

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Certain frontotemporal lobar degeneration subtypes, including TDP‐A and B, can either occur sporadically or in association with specific genetic mutations. It is uncertain whether syndromic or imaging features previously associated with these patient groups are subtype or genotype specific.
Sean Coulborn, Rhiana Schafer, Ashlin R. K. Roy, Andrzej Sokolowski, Noah G. Cryns, Dana Leichter, Argentina Lario Lago, Eliana Marisa Ramos, Yann Cobigo, Salvatore Spina, Lea T. Grinberg, Daniel H. Geschwind, Maria L. Gorno‐Tempini, Joel H. Kramer, Howard J. Rosen, Bruce L. Miller, William W. Seeley, David C. Perry   +17 more
wiley   +1 more source

Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis [PDF]

arXiv, 2017
In this paper we study different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.
arxiv