A Theory of Unilateral Trade Policy

We integrate strategic-trade and political-economy considerations in a unified framework to analyze unilateral trade policy. Foreign firms compete on Home´s market through export or foreign direct investment (FDI). They also lobby Home´s government which sets trade (tariff) and industrial (tax) policies to maximize a weighted sum of domestic welfare and lobby contributions. We show that protection by a low-cost Home may improve global welfare by inducing a more cost-efficient global production pattern. The strategic-trade motive for unilateral intervention to increase domestic welfare may prevail even without domestic firms, and may be enhanced by the presence of FDI firms. The political motive to induce lobby contributions may mitigate or even reverse strategic-trade motivated policy deviations, and trade policy deviation need not benefit special interests to be politically optimal. If the government cares more about lobby contributions than about domestic welfare, it is more likely to adopt a liberal rather than a protectionist trade policy, regardless of its impact on lobbies.Trade Policy; Political Economy; Strategic Trade Policy; FDI

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

Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features

This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting. Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation. Specifically, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. Secondly, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph jointly within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive experiments on three publicly available datasets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semi-supervised classification and discriminative analysis, which verifies the effectiveness of our proposed method

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

Advancing Radiograph Representation Learning with Masked Record Modeling

Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins.Comment: Camera ready at ICLR 2023. Code and models are available at https://github.com/RL4M/MRM-pytorc