More Like this

1. Generative factor chaining: Coordinated manipulation with diffusion-based factor graph

   Mishra, Utkarsh Aashu; Chen, Yongxin; Xu, Danfei (November 2024, Proceedings of The 8th Conference on Robot Learning)
2. How Do the Purcell Factor, the Q ‐Factor, and the Beta Factor Affect the Laser Threshold?

   https://doi.org/10.1002/lpor.202000250  

   Khurgin, Jacob B.; Noginov, Mikhail A. (February 2021, Laser & Photonics Reviews)

   Abstract As lasers get more and more miniaturized and their dimensions become comparable to the wavelength, two interconnected phenomena take place: the fraction of spontaneous radiation going into a specific laser mode (β‐factor) increases and can ultimately reach unity, while the radiative lifetime gets shortened by the Purcell factorF_p. Often it is assumed that an increase of these two factors, along with the quality factor (Q‐factor), almost invariably causes reduction of the lasing threshold. This assumption is tested on various photonic and plasmonic lasers, demonstrating that, while there is obvious correlation between the aforementioned factors and the laser threshold, the dependence is far from being straightforward and omnipresent. Depending on specific laser material and geometry, the threshold can decrease, increase, or stay unchanged whenβ‐factor,Q‐factor, andF_pincrease. For the most part, the reduction of threshold is achieved simply by reducing the laser volume and this volume reduction can concurrently cause the increase inβ‐factor and/or Purcell factor, but it would be imprudent to say that the increase in either of these factors is the cause of the threshold reduction. 

   more » « less
3. On Bayes factor functions

   Datta, Saptati; Guha, Riana; Shudde, Rachael; Johnson, Valen E (June 2025, arXiv: arXiv:2506.16674)

   We describe Bayes factors functions based on the sampling distributions of z, t, χ2, and F statistics, using a class of inverse-moment prior distributions to define alternative hypotheses. These non-local alternative prior distributions are centered on standardized effects, which serve as indices for the Bayes factor function. We compare the conclusions drawn from resulting Bayes factor functions to those drawn from Bayes factors defined using local alternative prior specifications and examine their frequentist operating characteristics. Finally, an application of Bayes factor functions for replicated experimental designs in psychology are provided. 

   more » « less
4. Factor-Adjusted Regularized Model Selection

   https://doi.org/10.2139/ssrn.3248047  

   Fan, Jianqing; Ke, Yuan; Wang, Kaizheng (January 2020, SSRN Electronic Journal)
5. LIMIT THEOREMS FOR FACTOR MODELS

   https://doi.org/10.1017/S0266466620000468  

   Anatolyev, Stanislav; Mikusheva, Anna (October 2021, Econometric Theory)

   This paper establishes central limit theorems (CLTs) and proposes how to perform valid inference in factor models. We consider a setting where many counties/regions/assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous microparameters estimated separately for each entity. The CLT applies for quantities involving both cross-sectional and time series aggregation, as well as for quadratic forms in time-aggregated errors. This paper studies the conditions when one can consistently estimate the asymptotic variance, and proposes a bootstrap scheme for cases when one cannot. A small simulation study illustrates performance of the asymptotic and bootstrap procedures. The results are useful for making inferences in two-step estimation procedures related to factor models, as well as in other related contexts. Our treatment avoids structural modeling of cross-sectional dependence but imposes time-series independence. 

   more » « less