The Dirac point with a double-cone structure for optical fields, an optical analogy Dirac fermions in graphene, Klein paradox [16], which were predicted in quantum electrodynamics for relativistic electrons in vacuum but never observed directly.

A Monte Carlo (MC) model was developed and implemented to simulate the thermalization of electrons in inorganic scintillator materials. The model incorporates electron scattering with both longitudinal optical and acoustic phonons. In this paper, the MC model was applied to simulate electron thermalization in CsI, both pure and doped with a range of thallium concentrations. The inclusion of internal electric fields was shown to increase the fraction of recombined electron-hole pairs and to broaden the thermalization distance and thermalization time distributions. The MC simulations indicate that electron thermalization, following γ-ray excitation, takes place within approximately 10 ps in CsI and that electrons can travel distances up to several hundreds of nanometers. Electron thermalization was studied for a range of incident γ-ray energies using electron-hole pair spatial distributions generated by the MC code NWEGRIM (NorthWest Electron and Gamma Ray Interaction in Matter). These simulations revealed that the partition of thermalized electrons between different species (e.g., recombined with self-trapped holes or trapped at thallium sites) vary with the incident energy. Implications for the phenomenon of nonlinearity in scintillator light yield are discussed.

A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-ofthe-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data.In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.

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