Mahidol University International College

About: Mahidol University International College is a based out in . It is known for research contribution in the topics: Tourism & Corporate governance. The organization has 240 authors who have published 485 publications receiving 6095 citations.

Fdi and income inequality: evidence from a panel of u.s. states

Abstract: This study employs state-level panel data to explore the relationship between inward foreign direct investment (FDI) and income inequality in the United States. Using panel cointegration techniques that allow for cross-sectional heterogeneity, cross-sectional dependence, and endogenous regressors, we find that the short-run effects of FDI on income inequality are insignificant or weakly significant and negative. In the long run, however, FDI exerts a significant and robust negative effect on income inequality in the United States. This result for the United States as a whole does not imply that FDI narrows income gaps in the long run in each individual state. There is considerable heterogeneity in the long-run effects of FDI on income inequality across states, with some states (21 out of 48 cases) exhibiting a positive relationship between FDI in income inequality.

General incremental sliding-window aggregation

Abstract: Stream processing is gaining importance as more data becomes available in the form of continuous streams and companies compete to promptly extract insights from them. In such applications, sliding-window aggregation is a central operator, and incremental aggregation helps avoid the performance penalty of re-aggregating from scratch for each window change.This paper presents Reactive Aggregator (RA), a new framework for incremental sliding-window aggregation. RA is general in that it does not require aggregation functions to be invertible or commutative, and it does not require windows to be FIFO. We implemented RA as a drop-in replacement for the Aggregate operator of a commercial streaming engine. Given m updates on a window of size n, RA has an algorithmic complexity of O(m + m log (n/m)), rivaling the best prior algorithms for any m. Furthermore, RA's implementation minimizes overheads from allocation and pointer traversals by using a single flat array.

Sentiment Classification Using Document Embeddings Trained with Cosine Similarity.

Abstract: In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead of dot product. Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naive Bayes weighted bag of n-grams achieves a new state of the art accuracy of 97.42%. Code to reproduce all experiments is available at https://github.com/tanthongtan/dv-cosine