scholarly journals Applying Time-Dependent Data for Fluorescence Tomography

2007 ◽  
Author(s):  
Ralf B. Schulz ◽  
Martin Schweiger ◽  
Cosimo D'Andrea ◽  
Gianluca Valentini ◽  
Jörg Peter ◽  
...  
Keyword(s):  
Time Dependent ◽  
Dependent Data ◽  
Fluorescence Tomography

scholarly journals Applying time-dependent data for fluorescence tomography

2007 ◽  
Author(s):  
Ralf B. Schulz ◽  
Martin Schweiger ◽  
Cosimo D'Andrea ◽  
Gianluca Valentini ◽  
Jörg Peter ◽  
...  
Keyword(s):  
Time Dependent ◽  
Dependent Data ◽  
Fluorescence Tomography

Time Dependent Data Mining in RAVEN

2016 ◽  
Author(s):  
Joshua Joseph Cogliati ◽  
Jun Chen ◽  
Japan Ketan Patel ◽  
Diego Mandelli ◽  
Daniel Patrick Maljovec ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Dependent ◽  
Dependent Data

scholarly journals Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Mingchen Yao ◽  
Chao Zhang ◽  
Wei Wu
Keyword(s):  
Time Dependent ◽  
Dependent Data ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Practical Applications ◽  
Empirical Risk ◽  
Learning Tasks ◽  
Generalization Bound ◽  
Erm Principle ◽  
Dependent Samples

Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.). However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM) principle for sequences of time-dependent samples (TDS). In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

scholarly journals Progress and Regress of Time Dependent Data and Application in Bank Branch

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
F. Hosseinzadeh Lotfi ◽  
Z. Taeb ◽  
S. Abbasbandy
Keyword(s):  
Decision Making ◽  
Commercial Bank ◽  
Spline Function ◽  
Time Dependent ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Dependent Data ◽  
Bank Branch ◽  
Decision Making Unit ◽  
Cubic Spline Function

To evaluate each decision making unit having time dependent inputs and outputs data, a new method has been developed and reported here. This method uses the Malmquist productivity index, and is a very simple function based on Cubic Spline function to determine the progress and regress of that unit. To show the capability of this developed method, the data of 9 branches of a commercial bank has been used, evaluated, and reported.

Modeling the magnetic structure of CMEs in the inner heliosphere based on data-driven time-dependent simulations of active region evolution

2021 ◽  
Author(s):  
Jens Pomoell ◽  
Emilia Kilpua ◽  
Daniel Price ◽  
Eleanna Asvestari ◽  
Ranadeep Sarkar ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Active Region ◽  
Magnetic Structure ◽  
Interplanetary Space ◽  
Coronal Magnetic Field ◽  
Time Dependent ◽  
Data Driven ◽  
Dependent Data ◽  
Heliospheric Physics ◽  
The Magnetic Field

<p>Characterizing the detailed structure of the magnetic field in the active corona is of crucial importance for determining the chain of events from the formation to the destabilisation and subsequent eruption and propagation of coronal structures in the heliosphere. A comprehensive methodology to address these dynamic processes is needed in order to advance our capabilities to predict the properties of coronal mass ejections (CMEs) in interplanetary space and thereby for increasing the accuracy of space weather predictions. A promising toolset to provide the key missing information on the magnetic structure of CMEs are time-dependent data-driven simulations of active region magnetic fields. This methodology permits self-consistent modeling of the evolution of the coronal magnetic field from the emergence of flux to the birth of the eruption and beyond. </p><p>In this presentation, we discuss our modeling efforts in which time-dependent data-driven coronal modeling together with heliospheric physics-based modeling are employed to study and characterize CMEs, in particular their magnetic structure, at various stages in their evolution from the Sun to Earth. </p>

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