Voluntary Delisting In Korea: Causes And Impact On Company Performance

This research investigates the attributes of firms that choose to voluntarily delist in Korea, including the evolution of firms after delisting, using performance indicators such as total assets, revenue, and net income. Empirical evidence suggests that the higher the shareholding ratio of the largest shareholder and the higher the growth prospects and liquidity, the greater the incentive for voluntary delisting. In addition, firms in non-high-tech industries choose to delist more often than those in high-tech industries. Further, firms that have delisted show lower total assets, revenues, and net incomes than listed firms, and these gaps increase over time

Nevus-Like Appearance of Primary Malignant Melanoma of the Esophagus

The primary malignant melanoma of the esophagus (PMME) is a rare malignant disease, accounting for only 0.1–0.2% of all esophageal neoplasms, and the majority of the patients are diagnosed at advanced stages with poor prognosis. We present here a case of 56-year-old woman with epigastric pain and her endoscopic finding revealed several flat and black pigmented mucosal lesions within the distal portion of the esophagus which looked like flat nevus. The histopathology and immunohistochemical profile of the tissue specimens were diagnostic of malignant melanoma

Effect Of Changes In The Korean Accounting Environment On The Productivity Of Accounting Firms

To investigate how changes in the accounting environment in Korea affect firm productivity, this study analyzes productivity by firm size and labor type from 2000 to 2014, using a Cobb–Douglas production function. We find that (1) the greater the management advisory (tax) revenue, the greater the total revenue in large (small) accounting firms; and (2) marginal revenue is greatest for partners, followed by certified public accountants and general employees. In particular, partners’ contribution to large accounting firms improved after 2007, whereas general employees made a significant positive contribution to total revenue before 2007

Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014 (poster, 29.88%

Convergence of an iterative algorithm for systems of variational inequalities and nonexpansive mappings with applications

AbstractIn this paper, we consider the problem of convergence of an iterative algorithm for a system of generalized variational inequalities and a nonexpansive mapping. Strong convergence theorems are established in the framework of real Banach spaces

Strong convergence of shrinking projection methods for quasi-ϕ-nonexpansive mappings and equilibrium problems

AbstractThe purpose of this paper is to consider the convergence of a shrinking projection method for a finite family of quasi-ϕ-nonexpansive mappings and an equilibrium problem. Strong convergence theorems are established in a uniformly smooth and strictly convex Banach space which also enjoys the Kadec–Klee property

Metric-based Few-shot Classification in Remote Sensing Image

Target recognition based on deep learning relies on a large quantity of samples, but in some specific remote sensing scenes, the samples are very rare. Currently, few-shot learning can obtain high-performance target classification models using only a few samples, but most researches are based on the natural scene. Therefore, this paper proposes a metric-based few-shot classification technology in remote sensing. First, we constructed a dataset (RSD-FSC) for few-shot classification in remote sensing, which contained 21 classes typical target sample slices of remote sensing images. Second, based on metric learning, a k-nearest neighbor classification network is proposed, to find multiple training samples similar to the testing target, and then the similarity between the testing target and multiple similar samples is calculated to classify the testing target. Finally, the 5-way 1-shot, 5-way 5-shot and 5-way 10-shot experiments are conducted to improve the generalization of the model on few-shot classification tasks. The experimental results show that for the newly emerged classes few-shot samples, when the number of training samples is 1, 5 and 10, the average accuracy of target recognition can reach 59.134%, 82.553% and 87.796%, respectively. It demonstrates that our proposed method can resolve fewshot classification in remote sensing image and perform better than other few-shot classification methods