Labor Market Developments in China: A Neoclassical View

This paper assesses the applicability of two alternative theories in understanding labor market developments in China: the classical view featuring a Lewis turning point in wage growth versus a neoclassical framework emphasizing rational choices of individuals and equilibrating forces of the market. Empirical evidence based on multiple data sources fails to validate the arrival of the Lewis turning point in China, showing continuous and coordinated wage growth across rural and urban sectors instead. Consistent with the neoclassical view, we find that rural workers expanded off-farm work when mobility restrictions were lifted, interprovincial migration responded to expected earnings and local employment conditions, and returns to education converged gradually to the international standard. These findings suggest major progresses in the integration of labor markets in China.labor markets, rural-urban migration, wage growth, schooling returns, Lewis turning point, China

Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play

A Homotopy Algorithm for the Combined H-squared/H-to Infinity Model Reduction Problem

The problem of finding a reduced order model, optimal in the H-squared sense, to a given system model is a fundamental one in control system analysis and design. The addition of a H-to infinity constraint to the H-squared optimal model reduction problem results in a more practical yet computationally more difficult problem. Without the global convergence of probability-one homotopy methods the combined H-squared/H-to infinity model reduction problem is difficult to solve. Several approaches based on homotoppy methods have been proposed. The issues are the number of degrees of freedom, the well posedness of the finite dimensional optimization problem, and the numerical robustness of the resulting homotopy algorithm. Homotopy algorithms based on two formulations - input normal form; Ly, Bryson, and Cannon's 2 x 2 block parametrization - are developed and compared here

Comparison of Wide and Compact Fourth Order Formulations of the Navier-Stokes Equations

In this study the numerical performances of wide and compact fourth order formulation of the steady 2-D incompressible Navier-Stokes equations will be investigated and compared with each other. The benchmark driven cavity flow problem will be solved using both wide and compact fourth order formulations and the numerical performances of both formulations will be presented and also the advantages and disadvantages of both formulations will be discussed

PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models

Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general, and for clinical applications in particular, is slow sampling. Due to their iterative nature, the number of function evaluations (NFE) required is usually on the order of $10-10^3$, both for conditional and unconditional generation. In this paper, we present posterior sampling Poisson flow generative models (PPFM), a novel image denoising technique for low-dose and photon-counting CT that produces excellent image quality whilst keeping NFE=1. Updating the training and sampling processes of Poisson flow generative models (PFGM)++, we learn a conditional generator which defines a trajectory between the prior noise distribution and the posterior distribution of interest. We additionally hijack and regularize the sampling process to achieve NFE=1. Our results shed light on the benefits of the PFGM++ framework compared to diffusion models. In addition, PPFM is shown to perform favorably compared to current state-of-the-art diffusion-style models with NFE=1, consistency models, as well as popular deep learning and non-deep learning-based image denoising techniques, on clinical low-dose CT images and clinical images from a prototype photon-counting CT system

Numerical Performance of Compact Fourth Order Formulation of the Navier-Stokes Equations

In this study the numerical performance of the fourth order compact formulation of the steady 2-D incompressible Navier-Stokes equations introduced by Erturk et al. (Int. J. Numer. Methods Fluids, 50, 421-436) will be presented. The benchmark driven cavity flow problem will be solved using the introduced compact fourth order formulation of the Navier-Stokes equations with two different line iterative semi-implicit methods for both second and fourth order spatial accuracy. The extra CPU work needed for increasing the spatial accuracy from second order (O(x2)) to fourth order (O(x4)) formulation will be presented

State-resolved rotational cross sections and thermal rate coefficients for ortho-/para-H2+HD at low temperatures and HD+HD elastic scattering

Results for quantum mechanical calculations of the integral cross sections and corresponding thermal rate coefficients for para-/ortho-H2+HD collisions are presented. Because of significant astrophysical interest in regard to the cooling of primodial gas the low temperature limit of para-/ortho-H2+HD is investigated. Sharp resonances in the rotational state-resolved cross sections have been calculated at low energies. These resonances are important and significantly contribute to the corresponding rotational state-resolved thermal rate coefficients, particularly at low temperatures, that is less than $T \sim 100$K. Additionally in this work, the cross sections for the elastic HD+HD collision have also been calculated. We obtained quite satisfactory agreement with the results of other theoretical works and experiments.Comment: 16 pages, 5 figures, additional results include

Marine optical characterizations

During the past three months, the MOCE Team conducted two field experiments in Mill Creek,Chesapeake Bay, from July 24 to August 4, and at the MOBY operations site at Snug Harbor, Honolulu, Hawaii, from August 15-30, prepared two technical memoranda, and continued MOCE-2 and MOCE-3 data reduction. The primary purposes of the experiments were to test the SeaWiFS 'remote sensing reflectance' protocol, obtain turbid water data for ocean color satellite algorithm development, perform calibration for both Near Infrared (NIR) and Visible Rainbow Spectrometer system, continue assembling the operational Marine Optical Buoy, and to test the MOBY cellular phone communications link at the Lanai mooring site

A Homotopy Algorithm for the Combined H2/H&infin Model Reduction Problem

The problem of finding a reduced order model, optimal in the H2 sense, to a given system model is a fundamental one in control system analysis and design. The addition of an H∞ constraint to the H2 optimal model reduction problem results in a more practical yet computationally more difficult problem. Without the global convergence of probability-one homotopy methods the combined H2 /H∞ model reduction problem is difficult to solve. Several approaches based on homotopy methods have been proposed. The issues are the number of degrees of freedom, the well posedness of the finite dimensional optimization problem, and the numerical robustness of the resulting homotopy algorithm. Homotopy algorithms based on two formulations---input normal form; Ly, Bryson, and Cannon's 2x2 block parametrization are developed and compared