arXiv subjects

Johan Vegelius

Publications and source records attributed to Johan Vegelius.

3 recordsLinked to original sources

arXiv2025

Sensitivity of x-ray absorption at $5d$ edges of high-valent light actinides to crystal-field strength and covalency effects

The $5f$ states were probed in ThO$_2$ and U(VI) and U(V) oxides using x-ray absorption spectroscopy at the actinide $O_{4,5}$ edges. Measured data were analyzed by several approaches including atomic, crystal-field theory and the Anderson impurity model to take into account the hybridization of actinide valence states with oxygen $2p$ states. For Th(IV), the $5f$ states are mainly affected by the crystal-field interaction with the closest neighbors as can be seen from the corresponding spectrum of ThO$_2$. In turn, for U(VI) and U(V) oxides, the U $5f$-O $2p$ hybridization and high degree of covalency in the chemical bonding play a decisive role increasing the $5f$ occupancy and consequently governing the ground and excited state properties. That is additionally illustrated by results from the calculations of the U $5d$ x-ray photoemission spectra for U(V) in the lattice of La-doped UO$_2$.

cond-mat.str-el↗

arXiv2024

Forecasting Densities of Fatalities from State-based Conflicts using Observed Markov Models

In this contribution to the VIEWS 2023 prediction challenge, we propose using an observed Markov model for making predictions of densities of fatalities from armed conflicts. The observed Markov model can be conceptualized as a two-stage model. The first stage involves a standard Markov model, where the latent states are pre-defined based on domain knowledge about conflict states. The second stage is a set of regression models conditional on the latent Markov-states which predict the number of fatalities. In the VIEWS 2023/24 prediction competition, we use a random forest classifier for modeling the transitions between the latent Markov states and a quantile regression forest to model the fatalities conditional on the latent states. For the predictions, we dynamically simulate latent state paths and randomly draw fatalities for each country-month from the conditional distribution of fatalities given the latent states. Interim evaluation of out-of-sample performance indicates that the observed Markov model produces well-calibrated forecasts which outperform the benchmark models and are among the top performing models across the evaluation metrics.

stat.ME↗

arXiv2024

The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty

This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023.

stat.AP↗