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   https://doi.org/10.1021/acs.jpca.4c08576  

   Moonkaen, Pattarapon; McCoy, Anne B (March 2025, The Journal of Physical Chemistry A)
2. Observation of magneto-electric rectification at non-relativistic intensities

   https://doi.org/10.1038/s41467-020-19125-w  

   Trinh, M. Tuan; Smail, Gregory; Makhal, Krishnandu; Yang, Da Seul; Kim, Jinsang; Rand, Stephen C. (December 2020, Nature Communications)

   null (Ed.)

   Abstract The subject of electromagnetism has often been called electrodynamics to emphasize the dominance of the electric field in dynamic light–matter interactions that take place under non-relativistic conditions. Here we show experimentally that the often neglected optical magnetic field can nevertheless play an important role in a class of optical nonlinearities driven by both the electric and magnetic components of light at modest (non-relativistic) intensities. We specifically report the observation of magneto-electric rectification, a previously unexplored nonlinearity at the molecular level which has important potential for energy conversion, ultrafast switching, nano-photonics, and nonlinear optics. Our experiments were carried out in nanocrystalline pentacene thin films possessing spatial inversion symmetry that prohibited second-order, all-electric nonlinearities but allowed magneto-electric rectification. 

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3. Erlang mixture modeling for Poisson process intensities

   https://doi.org/10.1007/s11222-021-10064-0  

   Kim, Hyotae; Kottas, Athanasios (November 2021, Statistics and Computing)

   Abstract We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments of a cumulative intensity function which is modeled nonparametrically with a gamma process prior. Such model specification provides a novel extension of Erlang mixtures for density estimation to the intensity estimation setting. The prior model structure supports general shapes for the point process intensity function, and it also enables effective handling of the Poisson process likelihood normalizing term resulting in efficient posterior simulation. The Erlang mixture modeling approach is further elaborated to develop an inference method for spatial Poisson processes. The methodology is examined relative to existing Bayesian nonparametric modeling approaches, including empirical comparison with Gaussian process prior based models, and is illustrated with synthetic and real data examples. 

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4. Credit portfolio selection with decaying contagion intensities

   https://doi.org/10.1111/mafi.12177  

   Bo, Lijun; Capponi, Agostino; Chen, Peng‐Chu (February 2018, Mathematical Finance)

   Abstract We develop a fixed‐income portfolio framework capturing the exponential decay of contagious intensities between successive default events. We show that the value function of the control problem is the classical solution to a recursive system of second‐order uniformly parabolic Hamilton–Jacobi–Bellman partial differential equations. We analyze the interplay between risk premia, decay of default intensities, and their volatilities. Our comparative statics analysis finds that the investor chooses to go long only if he is capturing enough risk premia. If the default intensities deteriorate faster, the investor increases the size of his position if he goes short, or reduces the size of his position if he goes long. 

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5. Theoretical rovibrational characterization of HAlNP: Weak bonding but strong intensities

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   Fortenberry, Ryan C.; Trabelsi, Tarek; Francisco, Joseph S. (March 2021, Journal of Molecular Spectroscopy)

   null (Ed.)