Abd AlRahman R. AlMomani
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View article: Generalizing geometric partition entropy for the estimation of mutual information in the presence of informative outliers
Generalizing geometric partition entropy for the estimation of mutual information in the presence of informative outliers Open
The recent introduction of geometric partition entropy brought a new viewpoint to non-parametric entropy quantification that incorporated the impacts of informative outliers, but its original formulation was limited to the context of a one…
View article: Generalizing Geometric Partition Entropy for the Estimation of Mutual Information in the Presence of Informative Outliers
Generalizing Geometric Partition Entropy for the Estimation of Mutual Information in the Presence of Informative Outliers Open
The recent introduction of geometric partition entropy brought a new viewpoint to non-parametric entropy quantification that incorporated the impacts of informative outliers, but its original formulation was limited to the context of a one…
View article: Boltzmann–Shannon interaction entropy: A normalized measure for continuous variables with an application as a subsample quality metric
Boltzmann–Shannon interaction entropy: A normalized measure for continuous variables with an application as a subsample quality metric Open
The recent introduction of geometric partition entropy offered an alternative to differential Shannon entropy for the quantification of uncertainty as estimated from a sample drawn from a one-dimensional bounded continuous probability dist…
View article: Data-driven learning of Boolean networks and functions by optimal causation entropy principle
Data-driven learning of Boolean networks and functions by optimal causation entropy principle Open
Boolean functions, and networks thereof, are useful for analysis of complex data systems, including from biological systems, bioinformatics, decision making, medical fields, and finance. However, automated learning of a Boolean networked f…
View article: Geometric Partition Entropy: Coarse-Graining a Continuous State Space
Geometric Partition Entropy: Coarse-Graining a Continuous State Space Open
Entropy is re-examined as a quantification of ignorance in the predictability of a one dimensional continuous phenomenon. Although traditional estimators for entropy have been widely utilized in this context, we show that both the thermody…
View article: The essential synchronization backbone problem
The essential synchronization backbone problem Open
Network optimization strategies for the process of synchronization have generally focused on the re-wiring or re-weighting of links in order to (1) expand the range of coupling strengths that achieve synchronization, (2) expand the basin o…
View article: Response Letter
Response Letter Open
This paper presents a data-driven methodology for detecting early-warning signs of critical transitions on ice sheets.The approach is based on a spectral partitioning
View article: Response Letter
Response Letter Open
This paper presents a data-driven methodology for detecting early-warning signs of critical transitions on ice sheets.The approach is based on a spectral partitioning
View article: Entropic Causal Inference for Neurological Applications
Entropic Causal Inference for Neurological Applications Open
The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. Key to this study is understanding structure of both the structural and functional connectiv…
View article: ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations
ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations Open
Data-driven sparse system identification becomes the general framework for a wide range of problems in science and engineering. It is a problem of growing importance in applied machine learning and artificial intelligence algorithms. In th…
View article: ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System\n Identification of Underlying Dynamic Equations
ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System\n Identification of Underlying Dynamic Equations Open
Data-driven sparse system identification becomes the general framework for a\nwide range of problems in science and engineering. It is a problem of growing\nimportance in applied machine learning and artificial intelligence algorithms.\nIn…
View article: An Early Warning Sign of Critical Transition inThe Antarctic Ice Sheet –A New Data Driven Tool for Spatiotemporal Tipping Point
An Early Warning Sign of Critical Transition inThe Antarctic Ice Sheet –A New Data Driven Tool for Spatiotemporal Tipping Point Open
In this paper, we introduce a new tool for data-driven discovery of early warning signs of critical transitions in ice shelves, from remote sensing data. Our approach adopts principles of directed spectral clustering methodology considerin…
View article: Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)
Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE) Open
Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field an…
View article: Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19
Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19 Open
We develop here a data-driven approach for disease recognition based on given symptoms, to be efficient tool for anomaly detection. In a clinical setting and when presented with a patient with a combination of traits, a doctor may wonder i…
View article: Informative Ranking of Stand Out Collections of Symptoms: A New\n Data-Driven Approach to Identify the Strong Warning Signs of COVID 19
Informative Ranking of Stand Out Collections of Symptoms: A New\n Data-Driven Approach to Identify the Strong Warning Signs of COVID 19 Open
We develop here a data-driven approach for disease recognition based on given\nsymptoms, to be efficient tool for anomaly detection. In a clinical setting and\nwhen presented with a patient with a combination of traits, a doctor may wonder…
View article: How entropic regression beats the outliers problem in nonlinear system identification
How entropic regression beats the outliers problem in nonlinear system identification Open
In this work, we developed a nonlinear System Identification (SID) method that we called Entropic Regression. Our method adopts an information-theoretic measure for the data-driven discovery of the underlying dynamics. Our method shows rob…
View article: Go With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories
Go With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories Open
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspect…
View article: Go With the Flow, on Jupiter and Snow. Coherence From Video Data without Trajectories
Go With the Flow, on Jupiter and Snow. Coherence From Video Data without Trajectories Open
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspect…