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Although metal halide perovskites are candidate high-performance light-emitting diode (LED) materials, blue perovskite LEDs are problematic: mixed-halide materials are susceptible to phase segregation and bromide-based perovskite quantum dots (QDs) have low stability. Herein, a novel strategy for highly efficient, stable cesium lead bromide (CsPbBr

We propose a loglinear present-value identity in which investment ("scale"), profitability ("yield"), and discount rates determine a firm's market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (i) Both investment and profitability are important contributors to the value spread and stock return news variance. (ii) Asset growth predicts the cross-section of returns primarily because it predicts a long-run decline in future profitability. (iii) The investment spread significantly improves the ability to forecast time-series variation in the value premium and helps justify the poor performance of value in recent years.

We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple moving objects. We start by extracting a global motion tendency that reflects the scene context by tracking movements of objects in the scene. We then use Gaussian process regression to represent the extracted motion tendency as a stochastic vector field. The generated stochastic field is robust to noise and can handle a video from an uncalibrated moving camera. We use the stochastic field for predicting important future regions of interest as the scene evolves dynamically. We evaluate our approach on a variety of videos of team sports and compare the detected regions of interest to the camera motion generated by actual camera operators. Our experimental results demonstrate that our approach is computationally efficient and provides better predictions than previously proposed RBF-based approaches.

A three-body potential function can account for interactions among triples of particles which are uncaptured by pairwise interaction functions such as Coulombic or Lennard-Jones potentials. Likewise, a multibody potential of order $n$ can account for interactions among $n$-tuples of particles uncaptured by interaction functions of lower orders. To date, the computation of multibody potential functions for a large number of particles has not been possible due to its $O(N^n)$ scaling cost. In this paper we describe a fast tree-code for efficiently approximating multibody potentials that can be factorized as products of functions of pairwise distances. For the first time, we show how to derive a Barnes-Hut type algorithm for handling interactions among more than two particles. Our algorithm uses two approximation schemes: 1) a deterministic series expansion-based method; 2) a Monte Carlo-based approximation based on the central limit theorem. Our approach guarantees a user-specified bound on the absolute or relative error in the computed potential with an asymptotic probability guarantee. We provide speedup results on a three-body dispersion potential, the Axilrod-Teller potential., Comment: To appear in Journal of Computational Physics