New and old social risks in Korean social policy: the case of the National Pension Scheme

This is a study of old and new social risks in Korean social policy, in relation to the National Pension Scheme (NPS). It provides a comprehensive overview of the Korean pension structure and the emergence of new social risk groups. Based on the Korean Labour and Income Panel Study undertaken over eleven years and using bivariate and multivariate analysis, this thesis examines the effectiveness of the NPS and its reforms in protecting new social risk groups. The analytical framework of this thesis is based on the New Social Risk theory. Its limitation in explaining developing welfare states like Korea is also highlighted. Over the past two decades, the NPS has undergone dramatic financial cuts as its coverage expands rapidly. Given Korea’s aging population, the reliance on such public schemes will further increase, which will have a profound impact particularly, on those with low income. Societal and economic changes in the Korean society, as a result of de-industrialisation, have given rise to new social risks groups that differ from those that predominate in the post-war welfare era. These new groups are vulnerable because they cannot afford to contribute to their pension even during their working life with the likelihood that they will have little or no benefit from the NPS when they retire. They tend to be the atypical contract holders and workers of small-scale enterprises without unions. Contrary to expectation, women with care responsibility and young workers are less vulnerable

Dynamic Self-Assembly and 3D Fluidic Trap in Rotating Fluids

Department of ChemistryThis thesis describes a dynamical system enabling the formation of ordered structures and particle trapping established in non inertial frame of reference. Lighter particles suspended in a denser rotating fluid filling a cylindrical tube can be localized on the axis of rotation by the axis symmetric centripetal force. Such particles can self assemble into ordered tubular structures with mild rotational accelerations and the types of the resulting packings are dependent on the particle???s concentration. We demonstrated various tubular structures of symmetries ranging from simple helix (????1) to double helix, ????2????, and ????3????. Using the fact that there exists a transient flow along the axis when the rotational rates are abruptly accelerated or decelerated, the structures can interconvert into each other by quickly changing the rotational rates. In addition, the chirality for the chiral structures can be selective when the system???s orientation is adjusted with respect to gravity. We also report unprecedented binary tubular structures by using two types of particles differing in density and/or size. On the other hand, when the particles confined in the axis symmetric potential can experience confinements along the axis of rotation, the particles can be trapped and assemble into 3D ordered structures. We realized the axial confinement with disks which are fitted inside the tube and rotate slower than the surrounding fluids by the external magnetic field (the so called eddy current brake), giving rise to vortices. The strength of the axial confinement by the vortices is inversely proportional to the speed of the disk relative to the fluids. When such two disks are placed near each of the both ends of the tube, the particles can be trapped between the disks if the rotational rates of the tube and the disks are properly adjusted. Depending on the relative strengths of the radial and axial confinements, the trapped particles can exhibit the orbiting trajectory, linear assembly, or ordered packing. Similarly, particle clusters can have geometries of prolate, oblate or spherical symmetric shapes. We showed various types of ordered packings using a few spherical particles and demonstrated various dynamic assemblies of cages and interlocked architectures with non spherical particles as well as jammed colloidal monolith. As we obtained unusual binary tubular structures by using different types of particles, certain combinations of the particles can exhibit structural selectivity for polymorphic system. Last but not least, our system is found to undergo interesting transition behavior such as Hopf bifurcation and others including two limit cycles.clos

Process Safety Management: Optimized Models Influenced by Organization Culture

Companies focus on Process Safety Management (PSM) in order to protect employees and facilities from an accidents, such as explosion and fire. The most elements of PSM are closely related to employees, which determine the organizational culture, and organizational culture directly affects safety culture. Companies put an effort to have a strong safety culture, which is behaviors and responses in regard to emergency and abnormal situation. In this paper, definition and essential theories of PSM were reviewed first, and safety culture in PSM and the safety culture of Indianapolis Power & Light (IPL) were discussed. A method used in IPL was to conduct the safety survey to evaluate their safety culture. To understand the safety culture in Rose-Hulman Institute of Technology (RHIT), two similar safety surveys were performed. The first survey was to understand students’ perceptions about Personal Protective Equipment (PPE) in the laboratory, and the second survey was to study what type of methods are used for the safety training in companies and find current safety problems and solutions of the Chemical Engineering Unit Operations laboratory. Based on the results of the surveys, the safety culture of RHIT was analyzed and possible solutions were suggested

Regularization and Kernelization of the Maximin Correlation Approach

Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction. In correlation-based nearest-neighbor classification, the maximin correlation approach (MCA) provides the worst-case optimal solution by minimizing the maximum misclassification risk through an iterative procedure. Despite the optimality, the original MCA has drawbacks that have limited its wide applicability in practice. That is, the MCA tends to be sensitive to outliers, cannot effectively handle nonlinearities in datasets, and suffers from having high computational complexity. To address these limitations, we propose an improved solution, named regularized maximin correlation approach (R-MCA). We first reformulate MCA as a quadratically constrained linear programming (QCLP) problem, incorporate regularization by introducing slack variables in the primal problem of the QCLP, and derive the corresponding Lagrangian dual. The dual formulation enables us to apply the kernel trick to R-MCA so that it can better handle nonlinearities. Our experimental results demonstrate that the regularization and kernelization make the proposed R-MCA more robust and accurate for various classification tasks than the original MCA. Furthermore, when the data size or dimensionality grows, R-MCA runs substantially faster by solving either the primal or dual (whichever has a smaller variable dimension) of the QCLP.Comment: Submitted to IEEE Acces

After-Hours Block Trading, Short Sales, And Information Leakage: Evidence From Korea

We investigate the impact of insider trading in after-hours block market on stock price and short sales volume, before and after the trading becomes public information. During pre-announcement period, positive (negative) abnormal stock return is generated when insiders buy (sell) their shares but does not when quasi-insiders trade, implying that stock price reflects long-lived private information of corporate governance structure. The impact is most prominent when ownership shares are transferred to (from) corporate insiders. In contrast, short sales volume generally does not depend on the identity of block holders. Short sales volume has a negative correlation with abnormal stock return only during the transaction date, indicating that a short-sale decision of tippees is based on their sole expectation on instantaneous stock returns. We also find evidence that insiders select the timing of their trades with respect to maximizing their realized profits or minimizing their purchasing costs.

깊은 신경망을 이용한 강인한 특징 학습

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 윤성로.최근 기계 학습의 발전으로 인공 지능은 우리에게 한 걸음 더 가까이 다가오게 되었다. 특히 자율 주행이나 게임 플레이 등 최신 인공 지능 프레임워크들에 있어서, 딥 러닝이 중요한 역할을 하고 있는 상황이다. 딥 러닝이란 multi-layered neural networks 과 관련된 기술들을 총칭하는 용어로서, 데이터의 양이 급속하게 증가하며, 사전 지식들이 축적되고, 효율적인 학습 알고리즘들이 개발되며, 고급 하드웨어들이 만들어짐에 따라 빠르게 변화하고 있다. 현재 딥 러닝은 대부분의 인식 문제에서 최첨단 기술로 활용되고 있다. 여러 레이어로 구성된 깊은 신경망은 많은 양의 파라미터를 학습하기 때문에, 방대한 파라미터 집합 속에서 좋은 해를 효율적으로 찾아내는 것이 중요하다. 본 논문에서는 깊은 신경망의 세 가지 이슈에 대해 접근하며, 그것들을 해결하기 위한 regularization 기법들을 제안한다. 첫째로, 신경망 구조는 adversarial perturbations 이라는 내재적인 blind spots 들에 많이 노출되어 있다. 이러한 adversarial perturbations 에 강인한 신경망을 만들기 위하여, 학습 샘플과 그것의 adversarial perturbations 와의 차이를 최소화하는 manifold loss term을 목적 함수에 추가하였다. 둘째로, restricted Boltzmann machines 의 학습에 있어서, 상대적으로 작은 크기를 가지는 클래스를 학습하는 데에 기존의 contrastive divergence 알고리즘은 한계점을 가지고 있었다. 본 논문에서는 작은 클래스에 더 높은 학습 가중치를 부여하는 boosting 개념과 categorical features를 가진 데이터에 적합한 새로운 regularization 기법을 조합하여 기존의 한계점에 접근하였다. 마지막으로, 신경망의 파라미터를 학습하기에 충분하지 않은 데이터가 주어진 경우, 더 정교한 data augmentation 기법을 다룬다. 샘플의 차원이 많을수록, 데이터 생성의 기저에 깔려있는 사전 지식을 활용하여 augmentation을 하는 것이 더욱 더 필요하다. 나아가, 본 논문은 junction splicing signals 학습을 위한 첫 번째 깊은 신경망 모델링 결과를 제시하고 있다. Junction prediction 문제는 positive 샘플 수가 매우 적어 패턴 모델링이 힘들며, 이는 생명정보학 분야에서 가장 중요한 문제 중 하나로서, 전체 gene expression process 를 이해하는 첫 걸음이라고 할 수 있다. 요약하면, 본 논문은 딥 러닝으로 이미지와 대용량 유전체 데이터를 위한 효과적인 표현법을 학습할 수 있는 regularization 기법들을 제안하였으며, 유명한 벤치마크 데이터와 biomedical imaging 데이터를 사용하여 그 실효성을 검증하였다.Recent advances in machine learning continue to bring us closer to artificial intelligence. In particular, deep learning plays a key role in cutting-edge frameworks such as autonomous driving and game playing. Deep learning refers to a class of multi-layered neural networks, which is rapidly evolving as the amount of data increases, prior knowledge builds up, efficient training schemes are being developed, and high-end hardwares are being build. Currently, deep learning is a state-of-the-art technique for most recognition tasks. As deep neural networks learn many parameters, there has been a variety of attempts to obtain reasonable solutions over a wide search space. In this dissertation, three issues in deep learning are discussed and approaches to solve them with regularization techniques are suggested. First, deep neural networks expose the problem of intrinsic blind spots called adversarial perturbations. Thus, we must construct neural networks that resist the directions of adversarial perturbations by introducing an explicit loss term to minimize the differences between the original and adversarial samples. Second, training restricted Boltzmann machines show limited performance when handling minority samples in class-imbalanced datasets. Our approach addresses this limitation and is combined with a new regularization concept for datasets that have categorical features. Lastly, insufficient data handling is required to be more sophisticated when deep networks learn numerous parameters. Given high-dimensional samples, we must augment datasets with adequate prior knowledge to estimate a high-dimensional distribution. Furthermore, this dissertation shows the first application of deep belief networks to identifying junction splicing signals. Junction prediction is one of the major problems in the field of bioinformatics, and is a starting point to understanding the entire gene expression process. In summary, this dissertation proposes a set of deep learning regularization schemes that can learn the meaningful representation underlying large-scale genomic datasets and image datasets. The effectiveness of these methods was confirmed with a number of experimental studies.Chapter 1 Introduction 1 1.1 Deep neural networks 1 1.2 Issue 1: adversarial examples handling 3 1.3 Issue 2: class-imbalance handling 5 1.4 Issue 3: insufficient data handling 5 1.5 Organization 6 Chapter 2 Background 10 2.1 Basic operations for deep networks 10 2.2 History of deep networks 12 2.3 Modern deep networks 14 2.3.1 Contrastive divergence 16 2.3.2 Deep manifold learning 18 Chapter 3 Adversarial examples handling 20 3.1 Introduction 20 3.2 Methods 21 3.2.1 Manifold regularized networks 21 3.2.2 Generation of adversarial examples 25 3.3 Results and discussion 26 3.3.1 Improved classification performance 28 3.3.2 Disentanglement and generalization 30 3.4 Summary 33 Chapter 4 Class-imbalance handling 35 4.1 Introduction 35 4.1.1 Numerical interpretation of DNA sequences 37 4.1.2 Review of junction prediction problem 41 4.2 Methods 44 4.2.1 Boosted contrastive divergence with categorical gradients 44 4.2.2 Stacking and fine-tuning 46 4.2.3 Initialization and parameter setting 47 4.3 Results and discussion 47 4.3.1 Experiment preparation 47 4.3.2 Improved prediction performance and runtime 49 4.3.3 More robust prediction by proposed approach 51 4.3.4 Effects of regularization on performance 53 4.3.5 Efficient RBM training by boosted CD 54 4.3.6 Identification of non-canonical splice sites 57 4.4 Summary 58 Chapter 5 Insufficient data handling 60 5.1 Introduction 60 5.2 Backgrounds 62 5.2.1 Understanding comets 62 5.2.2 Assessing DNA damage from tail shape 65 5.2.3 Related image processing techniques 66 5.3 Methods 68 5.3.1 Preprocessing 70 5.3.2 Binarization 70 5.3.3 Filtering and overlap correction 72 5.3.4 Characterization and classification 75 5.4 Results and discussion 76 5.4.1 Test data preparation 76 5.4.2 Binarization 77 5.4.3 Robust identification of comets 79 5.4.4 Classification 81 5.4.5 More accurate characterization by DeepComet 82 5.5 Summary 85 Chapter 6 Conclusion 87 6.1 Dissertation summary 87 6.2 Future work 89 Bibliography 91Docto

Approximate backwards differentiation of gradient flow

The gradient flow (GF) is an ODE for which its explicit Euler's discretization is the gradient descent method. In this work, we investigate a family of methods derived from \emph{approximate implicit discretizations} of (\GF), drawing the connection between larger stability regions and less sensitive hyperparameter tuning. We focus on the implicit $\tau$-step backwards differentiation formulas (BDFs), approximated in an inner loop with a few iterations of vanilla gradient descent, and give their convergence rate when the objective function is convex, strongly convex, or nonconvex. Numerical experiments show the wide range of effects of these different methods on extremely poorly conditioned problems, especially those brought about in training deep neural networks