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A method for estimating theoretical predictability of time series is presented, based on information-theoretic functionals---redundancies and surrogate data technique. The redundancy, designed for a chosen model and a prediction horizon,…
The photoproduction reaction of $\gamma p \to K^+\Lambda(1405)$ is investigated based on an effective Lagrangian approach at the tree-level approximation with the purpose of understanding the reaction mechanism and extracting the resonance…
In genetic networks, information of relevance to the organism is represented by the concentrations of transcription factor molecules. In order to extract this information the cell must effectively "measure"' these concentrations, but there…
Early data on $K^-$ induced reactions off protons are collected and used in a coupled-channel partial wave analysis (PWA). Data which had been published in the form of Legendre coefficients are included in the PWA. In a {\it primary} fit…
In a mathematical model of interacting biological organisms, where external interventions may alter behavior over time, traditional models that assume fixed parameters usually do not capture the evolving dynamics. In oncology, this is…
A recent Letter attempted to reconcile the disagreement between neutron resonance data and random matrix theory (RMT). To this end, a new formula was derived for transforming measured ({\Gamma}_{{\lambda}n}) to reduced…
This study presents a new strategy for the identification of material parameters in the case of restricted or redundant data, based on a hybrid approach combining a genetic algorithm and the Levenberg-Marquardt method. The proposed…
Unstructured notes within the electronic health record (EHR) contain rich clinical information vital for cancer treatment decision making and research, yet reliably extracting structured oncology data remains challenging due to extensive…
The most general exclusion single species reaction-diffusion models with nearest-neighbor interactions one a one dimensional lattice are investigated, for which the evolution of full intervals are closed. Using a generating function method,…
We preprocess the raw NMR spectrum and extract key characteristic features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition; meanwhile may provide the…
This paper describes an efficient rule generation algorithm, called rule generation from artificial neural networks (RGANN) to generate symbolic rules from ANNs. Classification rules are sought in many areas from automatic knowledge…
Fitting model parameters to experimental data is a common yet often challenging task, especially if the model contains many parameters. Typically, algorithms get lost in regions of parameter space in which the model is unresponsive to…
In order to determine the chemical freeze-out parameters of the hadron-emitting source in relativistic heavy ion collisions some studies in literature perform fits by using as data input a subsample of ratios calculated out of…
Atomic data determined by analysis of observed atomic spectra are essential for plasma diagnostics. For each low-ionisation open d- and f-subshell atomic species, around $10^3$ fine structure level energies can be determined through years…
This chapter deals with approaches for protein three-dimensional structure prediction, starting out from a single input sequence with unknown struc- ture, the 'query' or 'target' sequence. Both template based and template free modelling…
When a fault occurs in nuclear facilities, accurately reconstructing gamma radiation fields through measurements from the mobile radiation detection (MRD) system becomes crucial to enable access to internal facility areas for essential…
We evaluate a co-evolutionary calibration framework for the Heston model in which a genetic algorithm (GA) over parameters is coupled to an evolving neural inverse map from option surfaces to parameters. While GA-history sampling can reduce…
Low-energy data on the three charge states in $\gamma p \to K^+(\Sigma\pi)$ from CLAS at JLab, on $K^-p\to \pi^0\pi^0\Lambda$ and $\pi^0\pi^0\Sigma$ from the Crystal Ball at BNL, bubble chamber data on…
Designing models that produce accurate predictions is the fundamental objective of machine learning (ML). This work presents methods demonstrating that when the derivatives of target variables (outputs) with respect to inputs can be…
We investigate the photoproduction of $\Lambda(1405)\equiv\Lambda^*$ hyperon resonance, i.e., $\gamma p\to K^+\Lambda^*$, employing the effective Lagrangian approach with the $t$-channel Regge trajectories at tree level. We extensively…