Growth and North-South Wage Gap

We study the sources of long-run growth and wage gap in a North-South (N-S) model with trade and foreign direct investment (FDI). Although R&D is the engine of global growth, increased share of R&D spending need not be accompanied by higher growth rate, and vice versa. Although, investment is induced by productivity growth, investment-output ratio need not rise monotonically with productivity growth. Lower investment-output ratio may accompany higher productivity growth, so higher growth rate need not entail lower share of consumption. We argue that existing models may exaggerate or under-estimate the role of R&D in growth. We also show that higher growth rate is normally accompanied by greater N–S wage gap in the long run. The effect of country size on wage gap is generally ambiguous, depending on the direction and magnitude of scale effects in R&D. Both FDI and S-N migration may increase global growth rate and N-S wage gap.Endogenous Growth; North-South Wage Gap; R&D; Investment

Modified T-F Function Method for Finding Global Minimizer on Unconstrained Optimization

This paper indicates that the filled function which appeared in one of the papers by Y. L. Shang et al. (2007) is also a tunneling function; that is, we prove that under some general assumptions this function has the characters of both tunneling function and filled function. A solution algorithm based on this T-F function is given and numerical tests from test functions show that our T-F function method is very effective in finding better minima

UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

Recent medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture. However, one potential drawback of these approaches is that they failed to provide an intuitive explanation of why this hybrid combination manner is beneficial, making it difficult for subsequent work to make improvements on top of them. To address this issue, we first analyze the differences between the weight allocation mechanisms of the self-attention and convolution. Based on this analysis, we propose to construct a parallel non-isomorphic block that takes the advantages of self-attention and convolution with simple parallelization. We name the resulting U-shape segmentation model as UNet-2022. In experiments, UNet-2022 obviously outperforms its counterparts in a range segmentation tasks, including abdominal multi-organ segmentation, automatic cardiac diagnosis, neural structures segmentation, and skin lesion segmentation, sometimes surpassing the best performing baseline by 4%. Specifically, UNet-2022 surpasses nnUNet, the most recognized segmentation model at present, by large margins. These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.Comment: Code is available at https://bit.ly/3ggyD5

μ-3-Thienylmalonato-κ2 O 1:O 3-bis­[triphenyl­tin(IV)]

The title compound, [Sn2(C6H5)6(C7H4O4S)], contains two molecules with similar conformations in the asymmetric unit. In each mol­ecule, the Sn atoms adopt a distorted tetra­hedral geometry arising from three C atoms of three phenyl rings and one O atom from the bridging 3-thienylmalonato ligand. The mol­ecules lie about inversion centers with the ligands facing each other, with C⋯O distances of 3.417 (10) and 3.475 (10) Å