Results 1 to 10 of about 2,558 (93)

Geometric valuation theory [PDF]

In: European Congress of Mathematics. Proceedings of the 8th Congress (8ECM). EMS Press, Berlin, 2023, 2021
A brief introduction to geometric valuation theory is given. The focus is on classification results for valuations on convex bodies and on function spaces.
arxiv   +1 more source

Remembering Ludwig Dmitrievich Faddeev, our lifelong partner in mathematical physics [PDF]

pp.21-32 in "Ludwig Faddeev Memorial Volume. A Life in Mathematical Physics", World Scientific (2018), free access, 2023
We briefly recount the long friendship that developed between Ludwig and us (Moshe Flato and I), since we first met at ICM 1966 in Moscow. That friendship extended to his school and family, and persists to this day. Its strong personal impact and main scientific components are sketched, including reflexions on what mathematical physics is (or should be)
arxiv   +1 more source

Valuations on Convex Functions [PDF]

Int. Math. Res. Not. IMRN 2019, no. 8, 2384-2410, 2017
All continuous, SL$(n)$ and translation invariant valuations on the space of convex functions on ${\mathbb R}^n$ are completely classified.
arxiv   +1 more source

Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling [PDF]

arXiv, 2022
Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computational model is a vital step towards tissue engineering, enhancing the research of coronary diseases and developing medical treatment and ...
arxiv  

Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling [PDF]

arXiv, 2021
In-Stent Restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute coronary syndrome. An uncertainty quantification of a model for In-Stent Restenosis with four uncertain parameters (endothelium regeneration time, the ...
arxiv  

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures [PDF]

AMIA Annual Symposium 2020, 2020
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. This study evaluated the potential to extract these symptoms from physician notes using the Bidirectional Encoder from Transformers language model fine-tuned on a domain-specific corpus.
arxiv  

Novel Classification of Ischemic Heart Disease Using Artificial Neural Network [PDF]

Computing in Cardiology 2020, 2020
Ischemic heart disease (IHD), particularly in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. Machine learning techniques applied to parameters extracted form heart rate variability (HRV) signal seem to be a valuable support in the early ...
arxiv  

Unstable Angina is a syndrome correlated to mixed Th17 and Th1 immune disorder [PDF]

arXiv, 2013
Unstable angina is common clinical manifestation of atherosclerosis. However, the detailed pathogenesis of unstable angina is still not known. Here, I propose that unstable angina is a mixed TH17 and TH1 immune disorder. By using microarray analysis, I find out that TH1 and TH17 related cytokine, cytokine receptor, chemokines, complement, immune ...
arxiv  

Non-Linear Dynamics In Patients With Stable Angina Pectoris [PDF]

arXiv, 2001
We investigate the clinical and prognostic significance of fractal dimension and detrended fluctuation analysis by comparing the group of patients with stable angina pectoris without previous myocardial infarction with the group of age-matched healthy controls.
arxiv  

A Representation of Uncertainty to Aid Insight into Decision Models [PDF]

arXiv, 2013
Many real world models can be characterized as weak, meaning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users of computer decision aids to understand and have confidence in the models.
arxiv