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1. Giant Apparent Optical Circular Dichroism in Thin Films of Bismuth‐Based Hybrid Organic–Inorganic Metal Halide Semiconductor Through Preferred Orientation

   https://doi.org/10.1002/adom.202302766  

   Yan, Liang ; Xie, Yi ; Mitzi, David B. ; Sercel, Peter C. ; Phillips, Alan J. ; Blackburn, Jeffrey L. ; You, Wei ( January 2024 , Advanced Optical Materials)

   Introducing chirality into organic/inorganic hybrid materials can impart chiroptical properties such as circular dichroism. The ability to tune chiroptical properties in self‐assembled materials can have important implications for spintronic and optoelectronic applications. Here, a chiral organic cation, (R/S)‐4‐methoxy‐α‐methylbenzylammonium, is incorporated to synthesize the bismuth‐based hybrid organic–inorganic metal halide semiconductor, (R/S‐MeOMePMA)BiI₄. Thin films of this Bi‐based compound demonstrate large chiroptical responses, with circular dichroism anisotropy (g_CD) values up to ≈0.1, close to the highest value observed in another chiral metal‐halide semiconductor, (R‐MBA₂CuCl₄). Detailed investigation reveals that this large g_CDin (R/S‐MeOMePMA)BiI₄is caused by the apparent CD effect. Careful selection of deposition conditions and the concomitant thin‐film orientation enables the control of g_CD, with maximum value observed when its thin film has a well‐crystallized preferred (001) orientation parallel to the substrate. The results support a growing body of evidence that low symmetry plays an important role in achieving unusually large g_CDin these chiral metal–halide materials and provides design rules for achieving large chiroptical response via morphology control.

    

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2. Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures

   https://doi.org/10.1287/ijoc.2021.1132  

   Eckman, David J. ; Henderson, Shane G. ( May 2022 , INFORMS Journal on Computing)

   Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly computing a posterior quantity of interest—for example, the posterior probability of good selection or the posterior expected opportunity cost—and terminating when this quantity crosses some threshold. Computing these posterior quantities entails nontrivial numerical computation. Thus, rather than exactly check such posterior-based stopping rules, it is common practice to use cheaply computable bounds on the posterior quantity of interest, for example, those based on Bonferroni’s or Slepian’s inequalities. The result is a conservative procedure that samples more simulation replications than are necessary. We explore how the time spent simulating these additional replications might be better spent computing the posterior quantity of interest via numerical integration, with the potential for terminating the procedure sooner. To this end, we develop several methods for improving the computational efficiency of exactly checking the stopping rules. Simulation experiments demonstrate that the proposed methods can, in some instances, significantly reduce a procedure’s total sample size. We further show these savings can be attained with little added computational effort by making effective use of a Monte Carlo estimate of the posterior quantity of interest. Summary of Contribution: The widespread use of commercial simulation software in industry has made ranking-and-selection (R&S) algorithms an accessible simulation-optimization tool for operations research practitioners. This paper addresses computational aspects of R&S procedures delivering finite-time Bayesian statistical guarantees, primarily the decision of when to terminate sampling. Checking stopping rules entails computing or approximating posterior quantities of interest perceived as being computationally intensive to evaluate. The main results of this paper show that these quantities can be efficiently computed via numerical integration and can yield substantial savings in sampling relative to the prevailing approach of using conservative bounds. In addition to enhancing the performance of Bayesian R&S procedures, the results have the potential to advance other research in this space, including the development of more efficient allocation rules. 

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3. PipeDream: generalized pipeline parallelism for DNN training

   https://doi.org/10.1145/3341301.3359646  

   Narayanan, Deepak ; Harlap, Aaron ; Phanishayee, Amar ; Seshadri, Vivek ; Devanur, Nikhil R. ; Ganger, Gregory R. ; Gibbons, Phillip B. ; Zaharia, Matei ( October 2019 , SOSP)

   DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass. Naïve pipelining can thus result in mismatches in state versions used in the forward and backward passes, or excessive pipeline flushes and lower hardware efficiency. To address these challenges, PipeDream versions model parameters for numerically correct gradient computations, and schedules forward and backward passes of different minibatches concurrently on different workers with minimal pipeline stalls. PipeDream also automatically partitions DNN layers among workers to balance work and minimize communication. Extensive experimentation with a range of DNN tasks, models, and hardware configurations shows that PipeDream trains models to high accuracy up to 5.3X faster than commonly used intra-batch parallelism techniques. 

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4. SDSS-IV MaStar: theoretical atmospheric parameters for the MaNGA stellar library

   https://doi.org/10.1093/mnras/stab3263  

   Hill, Lewis ; Thomas, Daniel ; Maraston, Claudia ; Yan, Renbin ; Neumann, Justus ; Lundgren, Andrew ; Lazarz, Daniel ; Chen, Yan-Ping ; Cappellari, Michele ; Holtzman, Jon A. ; et al ( November 2021 , Monthly Notices of the Royal Astronomical Society)

   We calculate the fundamental stellar parameters effective temperature, surface gravity, and iron abundance – Teff, log g, [Fe/H] – for the final release of the Mapping Nearby Galaxies at APO (MaNGA) Stellar Library (MaStar), containing 59 266 per-visit-spectra for 24 290 unique stars at intermediate resolution (R ∼ 1800) and high S/N (median = 96). We fit theoretical spectra from model atmospheres by both MARCS and BOSZ-ATLAS9 to the observed MaStar spectra, using the full spectral fitting code pPXF. We further employ a Bayesian approach, using a Markov Chain Monte Carlo (MCMC) technique to map the parameter space and obtain uncertainties. Originally in this paper, we cross match MaStar observations with Gaia photometry, which enable us to set reliable priors and identify outliers according to stellar evolution. In parallel to the parameter determination, we calculate corresponding stellar population models to test the reliability of the parameters for each stellar evolutionary phase. We further assess our procedure by determining parameters for standard stars such as the Sun and Vega and by comparing our parameters with those determined in the literature from high-resolution spectroscopy (APOGEE and SEGUE) and from lower resolution matching template (LAMOST). The comparisons, considering the different methodologies and S/N of the literature surveys, are favourable in all cases. Our final parameter catalogue for MaStar cover the following ranges: 2592 ≤ Teff ≤ 32 983 K; −0.7 ≤ log g ≤ 5.4 dex; −2.9 ≤ [Fe/H] ≤ 1.0 dex and will be available with the last SDSS-IV Data Release, in 2021 December.

    

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5. Spatial Variations of Upper Crustal Anisotropy Along the San Jacinto Fault Zone in Southern California: Constraints From Shear Wave Splitting Analysis

   https://doi.org/10.1029/2020JB020876  

   Jiang, Enyuan ; Liu, Kelly H. ; Gao, Yuan ; Fu, Xiaofei ; Gao, Stephen S. ( April 2021 , Journal of Geophysical Research: Solid Earth)

   To discern spatial and explore possible existence of temporal variations of upper crustal anisotropy in an ∼15 km section of the San Jacinto Fault Zone (SJFZ) that is composed of the Buck Ridge and Clark faults in southern California, we conduct a systematic shear wave splitting investigation using local S‐wave data recorded by three broadband seismic stations located near the surface expression of the SJFZ. An automatic data selection and splitting measurement procedure is first applied, and the resulting splitting measurements are then manually screened to ensure reliability of the results. Strong spatial variations in crustal anisotropy are revealed by 1,694 pairs of splitting parameters (fast polarization orientation and splitting delay time), as reflected by the dependence of the resulting splitting parameters on the location and geometry of the raypaths. For raypaths traveling through the fault zones, the fast orientations are dominantly WNW‐ESE which is parallel to the faults and may be attributed to fluid‐filled fractures in the fault zones. For non‐fault‐zone crossing raypaths, the fast orientations are dominantly N–S which are consistent with the orientation of the regional maximum compressive stress. A three‐dimensional model of upper crustal anisotropy is constructed based on the observations. An increase in the raypath length normalized splitting times is observed after the 03/11/2013 M4.7 earthquake, which is probably attributable to changes in the spatial distribution of earthquakes before and after the M4.7 earthquake rather than reflecting temporal changes of upper crustal anisotropy.

    

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