The Experts below are selected from a list of 21489 Experts worldwide ranked by ideXlab platform
S. Torres-lópez - One of the best experts on this subject based on the ideXlab platform.
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pySCu: a new Python Code for analyzing remagnetizations directions by means of Small Circle utilities
Computers & Geosciences, 2017Co-Authors: Pablo Calvín, Juan José Villalaín, Antonio M. Casas-sainz, Lisa Tauxe, S. Torres-lópezAbstract:Abstract The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction. The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame. In this paper, some utilities for SCs analysis are automatized by means of a new Python Code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly.
Pablo Calvín - One of the best experts on this subject based on the ideXlab platform.
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pySCu: a new Python Code for analyzing remagnetizations directions by means of Small Circle utilities
Computers & Geosciences, 2017Co-Authors: Pablo Calvín, Juan José Villalaín, Antonio M. Casas-sainz, Lisa Tauxe, S. Torres-lópezAbstract:Abstract The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction. The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame. In this paper, some utilities for SCs analysis are automatized by means of a new Python Code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly.
Lisa Tauxe - One of the best experts on this subject based on the ideXlab platform.
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pySCu: A new Python Code for analyzing remagnetizations directions by means of small circle utilities
Elsevier, 2019Co-Authors: Calvín Ballester Pablo, Lisa Tauxe, Villalaín Santamaria, Juan José, Antonio ,casas M. . Sainz, Torres López SaraAbstract:The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction. The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame. In this paper, some utilities for SCs analysis are automatized by means of a new Python Code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly.CGL2012-38481 and CGL2016-77560 of the MINECO (Spanish Ministry of Economy and Competitiveness) with also FEDER founding (European Union). PC acknowledges the MINECO for the F.P.I. research grant BES-2013-062988. LT acknowledges support from National Science Foundation grant # EAR 1345003
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pySCu: a new Python Code for analyzing remagnetizations directions by means of Small Circle utilities
Computers & Geosciences, 2017Co-Authors: Pablo Calvín, Juan José Villalaín, Antonio M. Casas-sainz, Lisa Tauxe, S. Torres-lópezAbstract:Abstract The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction. The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame. In this paper, some utilities for SCs analysis are automatized by means of a new Python Code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly.
David Poole - One of the best experts on this subject based on the ideXlab platform.
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AAAI - AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence.
2020Co-Authors: Chenliang Zhou, Dominic Kuang, Jingru Liu, Hanbo Yang, Zijia Zhang, Alan K. Mackworth, David PooleAbstract:AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python Code that is designed to be as close as possible to pseudoCode. AISpace2, visualized in JupyterLab, keeps the simple Python Code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level algorithms in Python, and to visualize the output in a graphical form, aiming to better help them to build confidence and comfort in AI concepts and algorithms. So far we have tools for search, constraint satisfaction problems (CSP), planning and Bayesian network. In this paper we outline the tools and give some evaluations based on user feedback.
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AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Chenliang Zhou, Dominic Kuang, Jingru Liu, Hanbo Yang, Zijia Zhang, Alan K. Mackworth, David PooleAbstract:AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python Code that is designed to be as close as possible to pseudoCode. AISpace2, visualized in JupyterLab, keeps the simple Python Code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level algorithms in Python, and to visualize the output in a graphical form, aiming to better help them to build confidence and comfort in AI concepts and algorithms. So far we have tools for search, constraint satisfaction problems (CSP), planning and Bayesian network. In this paper we outline the tools and give some evaluations based on user feedback.
Juan José Villalaín - One of the best experts on this subject based on the ideXlab platform.
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pySCu: a new Python Code for analyzing remagnetizations directions by means of Small Circle utilities
Computers & Geosciences, 2017Co-Authors: Pablo Calvín, Juan José Villalaín, Antonio M. Casas-sainz, Lisa Tauxe, S. Torres-lópezAbstract:Abstract The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction. The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame. In this paper, some utilities for SCs analysis are automatized by means of a new Python Code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly.
