Heteroatom Oxidation Controls Singlet-Triplet Energy Splitting in Singlet Fission Buildings Blocks

Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the...

A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).

The probability of exitfrom registered unemployment, based on the example of Szczecin

The aim of the paper is to determine the influence of sex, age and education on the probability of exit from the registered unemployment in Szczecin. For the purposes of the study, the authors employed the survival analysis method, where they used survival trees built on the basis of the Kaplan-Meier estimators and adopted the statistic of the log-rank test as the splitting criterion. The research analysed the two most frequent reasons for deregistration, namely starting a job and the unemployed person’s failure to meet the conditions for being registered as unemployed. In addition, the study extracted subgroups of persons whom it took shortest and longestto start a job or deregister froma labour office. The analysis was based on the microdata from the Powiat Labour Office in Szczecin concerning persons who registered as unemployed in 2013 and were moni-tored until the end of 2014. The calculations were made in the R computer programme, using the partykit package and the ctree function. The research demonstrated that the probability of deregistration from the unemployment register because of finding a job depends solely on the age and education of the unemployed person, while the probability of getting removed from the unemployment register –on the two former determinants plus sex.

Inter-node Hellinger Distance based Decision Tree

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.