Explaining Import Variety and Quality: The Role of the Income Distribution

We examine a generalized version of Flam and Helpman’s (1987) model of vertical differentiation that maps cross-country differences in income distributions to variations in import variety and price distributions. The theoretical predictions are examined and confirmed using micro data on income from the Luxemburg Income Study for 30 countries over 20 years. The pairs of importers whose income distributions look more similar have more export partners in common and more similar import price distributions. Similarly, the importers whose income distributions look more like the world buy from more exporters and have import price distributions that look more like the world.

Who will win the Nobel Prize?

This paper identifies the determinants of the Nobel Prize Award. The analysis is analogous in spirit to Hamermesh and Schmidt (Econometrica, 2003) on the election of Econometric Society fellows. It is found that the number of citations, age and nationality have significant impacts on the odds of winning the Nobel. We provide the first statistical evidence that John Bates Clark medalists and individuals affiliated with the University of Chicago have a higher chance of winning the Prize.Nobel Prize; John Bates Clark Medal; Logit Model.

Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overfitting in certain dimensions in the first projection matrix, as training samples may not cover the entire range of graph filters (hyperplanes). To address this, we propose a novel data augmentation strategy. Specifically, by flipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the first attempt to mitigate the overfitting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classification accuracy by up to 46.5% relatively

Is Signed Message Essential for Graph Neural Networks?

Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes, achieve satisfying results on homophilic graphs. However, their performances are dismal in heterophilous graphs, and many researchers have proposed a plethora of schemes to solve this problem. Especially, flipping the sign of edges is rooted in a strong theoretical foundation, and attains significant performance enhancements. Nonetheless, previous analyses assume a binary class scenario and they may suffer from confined applicability. This paper extends the prior understandings to multi-class scenarios and points out two drawbacks: (1) the sign of multi-hop neighbors depends on the message propagation paths and may incur inconsistency, (2) it also increases the prediction uncertainty (e.g., conflict evidence) which can impede the stability of the algorithm. Based on the theoretical understanding, we introduce a novel strategy that is applicable to multi-class graphs. The proposed scheme combines confidence calibration to secure robustness while reducing uncertainty. We show the efficacy of our theorem through extensive experiments on six benchmark graph datasets

Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods

Medial Meniscal Tears in Anterior Cruciate Ligament-Deficient Knees: Effects of Posterior Tibial Slope on Medial Meniscal Tear

PURPOSE: To evaluate the incidence of meniscal tears in patients with chronic anterior cruciate ligament (ACL)-deficient knees, and to determine the influence of posterior tibial slope (PTS) on medial meniscal tears in ACL-deficient knees. MATERIALS AND METHODS: We reviewed 174 patients (174 knees) with a mean age of 30.7 years who underwent ACL reconstruction for chronic ACL tears. We divided the patients into two groups: low group (135 knees with a PTS or =13degrees). RESULTS: The incidence of medial meniscus tears was 44% (77/174), and that of lateral meniscus tears was 35% (61/174). The mean PTS in patients with medial meniscal tears was 11.4degrees+/-3.0degrees, whereas that in patients without medial meniscal tears was 9.8degrees+/-2.4degrees. The incidence of meniscal tears was 57.8% (78/135) in the low group and 89.7% (35/39) in the high group (p or =13degrees is a risk factor for secondary medial meniscal tears in ACL-deficient knees. So, we suggest that PTS is one of the considerations for determining early ACL reconstruction to prevent secondary meniscal tears.ope