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Abstract—Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a powerful technique for elemental compositional analysis and depth profiling of materials. However, it encounters the problem of matrix effects that hinder its application. In this work, we introduce a pioneering ToF-SIMS calibration method tailored for SixGeySnz ternary alloys. SixGe1-x and Ge1-zSnz binary alloys with known compositions are used as calibration reference samples. Through a systematic SIMS quantification study of SiGe and GeSn binary alloys, we unveil a linear correlation between secondary ion intensity ratio and composition ratio for both SiGe and GeSn binary alloys, effectively mitigating the matrix effects. Extracted relative sensitivity factor (RSF) value from SixGe1-x (0.07<0.83) and Ge1-zSnz (0.066<0.183) binary alloys are subsequently applied to those of SixGeySnz (0.011<0.113, 0.863<0.935 and 0.023<0.103) ternary alloys for elemental compositions quantification. These values are cross-checked by Atom Probe Tomography (APT) analysis, an indication of the great accuracy and reliability of as-developed ToF-SIMS calibration process. The proposed method and its reference sample selection strategy in this work provide a low-cost as well as simple-to-follow calibration route for SiGeSn composition analysis, thus driving the development of next-generation multifunctional SiGeSn-related semiconductor devices. 

Abstract The electric fields of subauroral polarization streams (SAPS) have been suggested to affect energetic charged particles' dynamics in the inner magnetosphere, though their role on radiation belt electrons has never been properly quantified. A moderate geomagnetic storm on 2015‐09‐07 caused the deep injection of 10–100s of keV electrons in Earth's inner magnetosphere to low L* (L* < 4). Using a 2‐D test particle tracer, we present the effects of electric fields given by the Volland‐Stern model, a SAPS (Goldstein et al., 2005,https://doi.org/10.1029/2005ja011135) model, and a modified SAPS model on the energetic electron deep injections. The modified SAPS model reflects the SAPS electric field observations by the Van Allen Probes and is supported by Defense Meteorological Satellite Program observations. Simulations suggest that the SAPS electric field pushes 10–20 MeV/G electrons Earthward to L* ∼ 2.7 in 2.5 hr, much deeper compared to the Volland‐Stern electric field. 

Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in static settings over temporal domains may lead to delayed and adverse effects. To understand the dynamics of performance disparity, we study a fairness problem in Markov decision processes (MDPs). Specifically, we propose return parity, a fairness notion that requires MDPs from different demographic groups that share the same state and action spaces to achieve approximately the same expected time-discounted rewards. We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment using integral probability metrics. We conduct experiments to corroborate our results, showing that the proposed algorithm can successfully close the disparity gap while maintaining the performance of policies on two real-world recommender system benchmark datasets.