Online Appendix to: The Perception of the Integration of North and South Korea

This study describes South Koreans’ general perceptions of the integration of North and South Korea through a survey of 500 adults living in South Korea. The following multiple-choice questions were asked: one’s general ideas about the integration of North and South Korea; the type of Korean reunification which is mostly supported/opposed; the type of Korean reunification which is most probable; and the pros and cons of reunification as well as necessary factors for reunification. Furthermore, we examined the differences in the perception of Korean reunification among the subgroup based on participants’ demographic information (i.e., gender, age, political orientation). The main results are as follows. First, the most representative thought on integration was “geographical integration of the Korean Peninsula,” followed by “establishment of economic partnerships or communities” and “restoration of common identity.” Meanwhile, there were differences among participants with regard to the detailed representation of Korean reunification. It suggests that when the attitudes toward integration of North and South Korea society are discussed, differences in the perception among people should be considered

The Perception of the Integration of North and South Korea

This study describes South Koreans' general perceptions of the integration of North and South Korea through a survey of 500 adults living in South Korea. The following multiple-choice questions were asked: one's general ideas about the integration of North and South Korea; the type of Korean reunification which is mostly supported/opposed; the type of Korean reunification which is most probable; and the pros and cons of reunification as well as necessary factors for reunification. Furthermore, we examined the differences in the perception of Korean reunification among the subgroup based on participants' demographic information (i.e., gender, age, political orientation). The main results are as follows. First, the most representative thought on integration was "geographical integration of the Korean Peninsula," followed by "establishment of economic partnerships or communities" and "restoration of common identity." Meanwhile, there were differences among participants with regard to the detailed representation of Korean reunification. It suggests that when the attitudes toward integration of North and South Korea society are discussed, differences in the perception among people should be considered

Indoor Propagation of Electromagnetic Waves with Orbital Angular Momentum at 5.8 GHz

Propagation of electromagnetic waves with orbital angular momentum (OAM) is investigated in indoor environments. The OAM modes generated by circular patch array antennas are used. With proper alignment and suppressed multipath, the OAM modes can transport multiple wireless data stream at the same time. Through measurements and ray-tracing simulations, it is found that the advantages of OAM modes are limited if those two conditions are not satisfied. It is also found that multipath effect can be enervated by using narrow beam antennas

SuperNet in Neural Architecture Search: A Taxonomic Survey

Deep Neural Networks (DNN) have made significant progress in a wide range of visual recognition tasks such as image classification, object detection, and semantic segmentation. The evolution of convolutional architectures has led to better performance by incurring expensive computational costs. In addition, network design has become a difficult task, which is labor-intensive and requires a high level of domain knowledge. To mitigate such issues, there have been studies for a variety of neural architecture search methods that automatically search for optimal architectures, achieving models with impressive performance that outperform human-designed counterparts. This survey aims to provide an overview of existing works in this field of research and specifically focus on the supernet optimization that builds a neural network that assembles all the architectures as its sub models by using weight sharing. We aim to accomplish that by categorizing supernet optimization by proposing them as solutions to the common challenges found in the literature: data-side optimization, poor rank correlation alleviation, and transferable NAS for a number of deployment scenarios

Online Hyperparameter Meta-Learning with Hypergradient Distillation

Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradient-based hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length, Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on two different meta-learning methods and three benchmark datasets

Diffusion-based Neural Network Weights Generation

Transfer learning is a topic of significant interest in recent deep learning research because it enables faster convergence and improved performance on new tasks. While the performance of transfer learning depends on the similarity of the source data to the target data, it is costly to train a model on a large number of datasets. Therefore, pretrained models are generally blindly selected with the hope that they will achieve good performance on the given task. To tackle such suboptimality of the pretrained models, we propose an efficient and adaptive transfer learning scheme through dataset-conditioned pretrained weights sampling. Specifically, we use a latent diffusion model with a variational autoencoder that can reconstruct the neural network weights, to learn the distribution of a set of pretrained weights conditioned on each dataset for transfer learning on unseen datasets. By learning the distribution of a neural network on a variety pretrained models, our approach enables adaptive sampling weights for unseen datasets achieving faster convergence and reaching competitive performance.Comment: 14 page

Investigating key attributes in experience and satisfaction of hotel customer using online review data

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers\u27 purchase intention and behavior. Therefore, it is important to understand the customer\u27s experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as Intangible Service , Physical Environment , Purpose , and Location . The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are Access , F&B (Food and Beverage) , Purpose , Tangibles , and Empathy . Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested

A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models

Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. Previous studies have shown that DWT can be effective in the vision domain and natural language processing (NLP) pre-training stage. Specifically, DWT shows promise in practical scenarios, such as enhancing new generation or larger models using pre-trained yet older or smaller models and lacking a resource budget. However, the optimal conditions for using DWT have yet to be fully investigated in NLP pre-training. Therefore, this study examines three key factors to optimize DWT, distinct from those used in the vision domain or traditional knowledge distillation. These factors are: (i) the impact of teacher model quality on DWT effectiveness, (ii) guidelines for adjusting the weighting value for DWT loss, and (iii) the impact of parameter remapping as a student model initialization technique for DWT.Comment: Findings of ACL 202