The Effects of Innovation on Performance of Korean Firms

This study empirically examines the relationship between knowledge capital and performance heterogeneity at the firm level. The model is based on a knowledge production function comprising of four interdependent equations linking innovativeness to innovation input, innovation output and productivity. The empirical part is based on Korean firm level innovation data. The model is estimated using advanced econometric methods. We investigate whether innovation is a significant and contributing determinant of performance heterogeneity among firms. In examining the relationship between innovation and productivity we correct for selectivity and simultaneity biases. The results show that there is a two-way causal relationship between knowledge capital and labor productivity. Firm-specific effects positively contribute to innovation output but they are negatively related to productivity. Industry heterogeneity does not affect innovation output or productivity.Innovation Input; Innovation Output; Productivity; Korea

Dynamics of Capital Structure: The Case of Korean Listed Manufacturing Companies

firms is developed in this paper and results compared with the classical static model. This paper specifies and estimates the unobservable optimal capital structure using a wide range of observable determinants. Uunbalanced panel data of Korean listed firms for the period 1985 to 2002 is used in this study. In addition to identification and estimation of the effects of the determinants of capital structure and capital structure optimality, some Korea-specific features such as the structural break before and after the financial crisis, as well as affiliation to a chaebol business groups, are taken into account to verify whether the optimal capital structure was affected by the financial crisis or whether belonging to a chaebol has any effect, and if so, to what extent.Capital structure; debt; firm; panel data; adjustment; Korea

Three-Dimensional Numerical Simulations of Thermal-Gravitational Instability in Protogalactic Halo Environment

We study thermal-gravitational instability in simplified models for protogalactic halos using three-dimensional hydrodynamic simulations. The simulations followed the evolution of gas with radiative cooling down to T = 10^4 K, background heating, and self-gravity. Then cooled and condensed clouds were identified and their physical properties were examined in detail. During early stage clouds start to form around initial density peaks by thermal instability. Small clouds appear first and they are pressure-bound. Subsequently, the clouds grow through compression by the background pressure as well as gravitational infall. During late stage cloud-cloud collisions become important, and clouds grow mostly through gravitational merging. Gravitationally bound clouds with mass M_c > ~6 X 10^6 Msun are found in the late stage. They are approximately in virial equilibrium and have radius R_c = \~150 - 200 pc. Those clouds have gained angular momentum through tidal torque as well as merging, so they have large angular momentum with the spin parameter ~ 0.3. The clouds formed in a denser background tend to have smaller spin parameters. We discuss briefly the implications of our results on the formation of protoglobular cluster clouds in protogalactic halos. (abridged)Comment: To appear in ApJ 20 September 2005, v631 1 issue. Pdf with full resolution figures can be downloaded from http://canopus.cnu.ac.kr/ryu/baeketal.pd

Effects of Rotation on Thermal-Gravitational Instability in the Protogalactic Disk Environment

Thermal-gravitational instability (TGI) is studied in the protogalactic environment. We extend our previous work, where we found that dense clumps first form out of hot background gas by thermal instability and later a small fraction of them grow to virialized clouds of mass M_c >~ 6X10^6 M_sun by gravitational infall and merging. But these clouds have large angular momentum, so they would be difficult, if not impossible, to further evolve into globular clusters. In this paper, through three-dimensional hydrodynamic simulations in a uniformly rotating frame, we explore if the Coriolis force due to rotation in protogalactic disk regions can hinder binary merging and reduce angular momentum of the clouds formed. With rotation comparable to the Galactic rotation at the Solar circle, the Coriolis force is smaller than the pressure force during the early thermal instability stage. So the properties of clumps formed by thermal instability are not affected noticeably by rotation, except increased angular momentum. However, during later stage the Coriolis force becomes dominant over the gravity, and hence the further growth to gravitationally bound clouds by gravitational infall and merging is prohibited. Our results show that the Coriolis force effectively destroys the picture of cloud formation via TGI, rather than alleviate the problem of large angular momentum.Comment: To appear in ApJ Lett. (June 1, 2006, v643n2). Pdf with full resolution figures can be downloaded from http://canopus.cnu.ac.kr/ryu/baeketal.pd

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.Comment: presented at the 32nd Conference on Neural Information Processing Systems (NIPS 2018), 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2), Montr\'eal, Canad

THE IMPACT OF ACTIVE LEARNING WITH ADAPTIVE LEARNING SYSTEMS IN GENERAL EDUCATION INFORMATION TECHNOLOGY COURSES

An adaptive learning system is an effective educational tool that meets the individual needs of students, but it is limited in fostering student learning by itself. With active engagement, students learn better than with adaptive learning systems alone. In this study, we investigate the impact of an adaptive learning system with active learning projects on student learning in general education information technology courses. We believe that today\u27s classroom calls for adaptive learning to serve the needs of diverse student populations. Active learning through real-life hands-on learning activities can enhance student learning by allowing them to apply their knowledge to authentic projects. In the classroom, we often find that learning computing with authentic hands-on activities is not only useful, but it contributes to improving student motivation and confidence

Comparison of CORSIKA and COSMOS simulations

Ultra-high-energy cosmic rays (UHECRs) refer to cosmic rays with energy above 10^{18} eV. UHECR experiments utilize simulations of extensive air shower to estimate the properties of UHECRs. The Telescope Array (TA) experiment employs the Monte Carlo codes of CORSIKA and COSMOS to obtain EAS simulations. In this paper, we compare the results of the simulations obtained from CORSIKA and COSMOS and report differences between them in terms of the longitudinal distribution, Xmax-value, calorimetric energy, and energy spectrum at ground.Comment: 4 pages, 6 figures, to appear in proceedings of UHECR2010 (AIP Conference Series

Data Envelopment Analysis for establishing the financial benchmark of Korean hotels

In the wake of catastrophic natural disasters and rising threats of terrorism, the hotel industry has seen a decline in revenue and an increase in competition. To avoid a downward spiral, the hotel industry needs to develop more competitive business strategies in order to make its operations lean and robust. These strategies may include: customer relationship management, yield management, niche marketing and continuous improvement of financial health. The success of these strategies hinges on the ability of hotel managers to assess the financial efficiency of their hotel in comparison to competition. In an effort to help hotel management enhance its financial efficiency in an increasingly competitive hotel industry, this paper proposes a Data Envelopment Analysis (DEA), which develops a meaningful set of benchmarks that will dictate best practices and form a successful hotel business model. Using the examples of 39 international and regional hotels in Korea, this paper illustrates the usefulness of DEA for the continuous improvement of hotel business practice