A time-varying true individual effects model with endogenous regressors

We propose a fairly general individual effects stochastic frontier model, which allows both heterogeneity and inefficiency to change over time. Moreover, our model handles the endogeneity problems if either at least one of the regressors or one-sided error term is correlated with the two-sided error term. Our Monte Carlo experiments show that our estimator performs well. We employed our methodology to the US banking data and found a negative relationship between return on revenue and cost efficiency. Estimators ignoring time-varying heterogeneity or endogeneity did not perform well and gave very different estimates compared to our estimator

Unknown Latent Structure and Inefficiency in Panel Stochastic Frontier Models

This paper extends the fixed effect panel stochastic frontier models to allow group heterogeneity in the slope coefficients. We propose the first-difference penalized maximum likelihood (FDPML) and control function penalized maximum likelihood (CFPML) methods for classification and estimation of latent group structures in the frontier as well as inefficiency. Monte Carlo simulations show that the proposed approach performs well in finite samples. An empirical application is presented to show the advantages of data-determined identification of the heterogeneous group structures in practice

Endogeneity in stochastic frontier models:copula approach without external instruments

This papers considers an alternative estimation procedures for estimating stochastic frontier models with endogenous regressors when no external instruments are available. The approach we propose is based on copula function to directly model the correlation between the endogenous regressors and the composed errors. Estimation of model parameters is done using maximum likelihood. Monte Carlo simulations are used to assess and compare the finite sample performances of the proposed estimation procedures

Bayesian inference in threshold stochastic frontier models

In this paper, we generalize the stochastic frontier model to allow for heterogeneous technologies and inefficiencies in a structured way that allows for learning and adapting. We propose a general model and various special cases, organized around the idea that there is switching or transition from one technology to the other(s), and construct threshold stochastic frontier models. We suggest Bayesian inferences for the general model proposed here and its special cases using Gibbs sampling with data augmentation. The new techniques are applied, with very satisfactory results, to a panel of world production functions using, as switching or transition variables, human capital, age of capital stock (representing input quality), as well as a time trend to capture structural switchin

R-MAE: Regions Meet Masked Autoencoders

In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-training baseline, we propose masked region autoencoding to learn from groups of pixels or regions. Specifically, we design an architecture which efficiently addresses the one-to-many mapping between images and regions, while being highly effective especially with high-quality regions. When integrated with MAE, our approach (R-MAE) demonstrates consistent improvements across various pre-training datasets and downstream detection and segmentation benchmarks, with negligible computational overheads. Beyond the quantitative evaluation, our analysis indicates the models pre-trained with masked region autoencoding unlock the potential for interactive segmentation. The code is provided at https://github.com/facebookresearch/r-mae

Robust Entanglement in Atomic Systems via Lambda-Type Processes

It is shown that the system of two three-level atoms in $\Lambda$ configuration in a cavity can evolve to a long-lived maximum entangled state if the Stokes photons vanish from the cavity by means of either leakage or damping. The difference in evolution picture corresponding to the general model and effective model with two-photon process in two-level system is discussed.Comment: 10 pages, 3 figure

Enhanced insulin sensitivity associated with provision of mono and polyunsaturated fatty acids in skeletal muscle cells involves counter modulation of PP2A

International audienceAims/Hypothesis: Reduced skeletal muscle insulin sensitivity is a feature associated with sustained exposure to excess saturated fatty acids (SFA), whereas mono and polyunsaturated fatty acids (MUFA and PUFA) not only improve insulin sensitivity but blunt SFA-induced insulin resistance. The mechanisms by which MUFAs and PUFAs institute these favourable changes remain unclear, but may involve stimulating insulin signalling by counter-modulation/repression of protein phosphatase 2A (PP2A). This study investigated the effects of oleic acid (OA; a MUFA), linoleic acid (LOA; a PUFA) and palmitate (PA; a SFA) in cultured myotubes and determined whether changes in insulin signalling can be attributed to PP2A regulation. Principal Findings: We treated cultured skeletal myotubes with unsaturated and saturated fatty acids and evaluated insulin signalling, phosphorylation and methylation status of the catalytic subunit of PP2A. Unlike PA, sustained incubation of rat or human myotubes with OA or LOA significantly enhanced Akt-and ERK1/2-directed insulin signalling. This was not due to heightened upstream IRS1 or PI3K signalling nor to changes in expression of proteins involved in proximal insulin signalling, but was associated with reduced dephosphorylation/inactivation of Akt and ERK1/2. Consistent with this, PA reduced PP2Ac demethylation and tyrosine 307 phosphorylation-events associated with PP2A activation. In contrast, OA and LOA strongly opposed these PA-induced changes in PP2Ac thus exerting a repressive effect on PP2A.Conclusions/Interpretation: Beneficial gains in insulin sensitivity and the ability of unsaturated fatty acids to oppose palmitate-induced insulin resistance in muscle cells may partly be accounted for by counter-modulation of PP2A

Dark States and Interferences in Cascade Transitions of Ultra-Cold Atoms in a Cavity

We examine the competition among one- and two-photon processes in an ultra-cold, three-level atom undergoing cascade transitions as a result of its interaction with a bimodal cavity. We show parameter domains where two-photon transitions are dominant and also study the effect of two-photon emission on the mazer action in the cavity. The two-photon emission leads to the loss of detailed balance and therefore we obtain the photon statistics of the cavity field by the numerical integration of the master equation. The photon distribution in each cavity mode exhibits sub- and super- Poissonian behaviors depending on the strength of atom-field coupling. The photon distribution becomes identical to a Poisson distribution when the atom-field coupling strengths of the modes are equal.Comment: 15 pages including 7 figures in Revtex, submitted to PR

Scattering of massless particles in one-dimensional chiral channel

We present a general formalism describing a propagation of an arbitrary multiparticle wave packet in a one-dimensional multimode chiral channel coupled to an ensemble of emitters which are distributed at arbitrary positions. The formalism is based on a direct and exact resummation of diagrammatic series for the multiparticle scattering matrix. It is complimentary to the Bethe Ansatz and to approaches based on equations of motion, and it reveals a simple and transparent structure of scattering states. In particular, we demonstrate how this formalism works on various examples, including scattering of one- and two-photon states off two- and three-level emitters, off an array of emitters as well as scattering of coherent light. We argue that this formalism can be constructively used for study of scattering of an arbitrary initial photonic state off emitters with arbitrary degree of complexity.Comment: 25 pages, 5 figure