The Study on Effects of Foreign Ownership on Innovation

In developing countries, government actively promotes foreign investment in order to adapt the new and latest technology. This leads to greater R&D activities, thus this creates knowledge and technology spillover. In this paper, we look at Korea where the R&D has been the main factor of rapid growth. We study the effects of foreign ownership on technological performance by looking at 756 R&D intensive Korean firms from 1999 to 2009. We look the number of applied and registered patents are dependent variables (as a technological performance) and observe statistically significant and positive correlation with foreign ownership due to three main reasons: (a) knowledge and technology spillover, (b) relatively more risk-taking investment behavior of institutional investors, and (c) cherry-picking strategy of investing in firms that perform well. Furthermore, we also observe the R&D expenditure has a strong and positive correlation with the number of applied and registered patents, and R&D expenditure could serve as a proxy variable for technologically advanced industries. Lastly, we observe that the coefficients increase for applied and registered patents for different technology index sub-groups

Anonymization for Skeleton Action Recognition

Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skeleton-based action recognition is a safer way to protect the privacy of subjects while having competitive recognition performance. However, due to improvements in skeleton estimation algorithms as well as motion- and depth-sensors, more details of motion characteristics can be preserved in the skeleton dataset, leading to potential privacy leakage. To investigate the potential privacy leakage from skeleton datasets, we first train a classifier to categorize sensitive private information from trajectories of joints. Our preliminary experiments show that the gender classifier achieves 87% accuracy on average and the re-identification task achieves 80% accuracy on average for three baseline models: Shift-GCN, MS-G3D, and 2s-AGCN. We propose an adversarial anonymization algorithm to protect potential privacy leakage from the skeleton dataset. Experimental results show that an anonymized dataset can reduce the risk of privacy leakage while having marginal effects on action recognition performance

Wave equation calculation of most energetic traveltimes and amplitudes for Kirchhoff prestack migration

This work was conceived during a visit by Kurt Marfurt to Seoul National University, sponsored by the Korean Ministry of Science andTechnology.This work was financially supported by the Brain Korea 21 Project of the Ministry of Education of Korea and the National Research Laboratory project of the Ministry of Science and Technology. The authors acknowledge the support of the Korea Institute of Science and Technology Information (KISTI) under the Grand Challenge Support Program and the use of the Supercomputing Center

Prestack depth migration using straight ray technique(SRT)

Kirchhoff prestack depth migration requires an elaborate book-keeping effort and a massive IO process to construct Kirchhoff hyperbolas. In order to avoid the complexity of the programming code and the massive IO process, we propose a straight ray technique (SRT) for traveltimi calculations in Kirchhoff migration. Since all the rays are straight in. polar coordinates for the 2D velocity model,or in sphericalc oordinatesf or the 3D velocity model, traveltimesc an be simply computed along a straight ray for a given source-receiver configuration,without suffering from shadow zones and caustics, and used directly for building Kirchhoff hyperbolas. In this way, we clrcumvent the substantial IO process required for reading traveltimes on a disk and save computationals torage.N umerical examplesd emonstrateth at SRT computest raveltimesi ntermediate between first-arrival traveltimes and the most energetic arrival traveltimes, resulting in better images than the first arrival traveltimes for the 2D IFP Marmousi data. With the implementation of SRT for 2D Kirchhoff migration, we successfully extend our SRT to 3D Kirchhoff misration for the SECiEAGE salr dome data.This work was financially supported by the National Laboratory Project of the Ministry of Science and Technology, Brain Korea 21 Project of the Ministry of Education, grant No. R05-2000-00003 from the Basic Research Program of the Korea Science & Engineering Foundation, and grant No. PM10300 from the Korea Ocean Research & Development Institute

Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753)

Efficient calculation of a partial-derivative wavefield using reciprocity for seismic imaging and inversion

Linearized inversion of surface seismic data for a model of the earths subsurface requires estimating the sensitivity of the seismic response to perturbations in the earths subsurface. This sensitivity, or Jacobian, matrix is usually quite expensive to estimate for all but the simplest model parameterizations.We exploit the numerical structure of the finite-element method, modern sparse matrix technology, and source–receiver reciprocity to develop an algorithm that explicitly calculates the Jacobian matrix at only the cost of a forward model solution. Furthermore, we show that we can achieve improved subsurface images using only one inversion iteration through proper scaling of the image by a diagonal approximation of the Hessian matrix, as predicted by the classical Gauss-Newton method. Our method is applicable to the full suite of wave scattering problems amenable to finiteelement forward modeling.We demonstrate our method through some simple 2-D synthetic examples

Evaluation of partial cranial cruciate ligament rupture with positive contrast computed tomographic arthrography in dogs

Computed tomographic arthrography (CTA) of four cadaveric canine stifles was performed before and after partial cranial cruciate ligament rupture in order to verify the usefulness of CTA examination for the diagnosis of partial cranial cruciate ligament rupture. To obtain the sequential true transverse image of a cranial cruciate ligament, the computed tomography gantry was angled such that the scanning plane was parallel to the fibula. True transverse images of cranial cruciate ligaments were identified on every sequential image, beginning just proximal to the origin of the cranial cruciate ligament distal to the tibial attachment, after the administration of iodinated contrast medium. A significant decrease in the area of the cranial cruciate ligament was identified on CTA imaging after partial surgical rupture of the cranial cruciate ligament. This finding implies that CTA can be used for assessing partial cranial cruciate ligament ruptures in dogs