evidence from household surveys in Korea

Thesis(Master) --KDI School:Master of Development Policy,2019This paper examines the impacts of pro-natal cash transfer on residential mobility with household survey data in South Korea. With the subgroup analysis, the analysis shows some groups migration decisions have been significantly affected by the cash transfer. It indicates that there are positive impacts of the cash transfer on the probability to move for the households with either the lower-income level or fewer than two children. In addition to the result of no significant impact of the cash transfer on the migration decision for permanent workers, it suggests no significant effect of the cash transfer for different age groups as well.1 INTRODUCTION 2 BACKGROUND 3 DATA 4 EMPIRICAL STRATEGY 5 ESTIMATION RESULTS 6 CONCLUSIONmasterpublishedJongeun PARK

Feasibility of using red cell distribution width for prediction of postoperative mortality in severe burn patients: an association with acute kidney injury after surgery

Background Severe burns cause pathophysiological processes that result in mortality. A laboratory biomarker, red cell distribution width (RDW), is known as a predictor of mortality in critically-ill patients. We examined the association between RDW and postoperative mortality in severe burn patients. Methods We retrospectively analyzed medical data of 731 severely burned patients who underwent surgery under general anesthesia. We evaluated whether preoperative RDW value can predict 3-month mortality after burn surgery using receiver operating characteristic (ROC) curve analysis, logistic regression, and Cox proportional-hazards regression analysis. Mortality was also analyzed according to preoperative RDW values and incidence of postoperative acute kidney injury (AKI). Results The 3-month mortality rate after burn surgery was 27.1% (198/731). The area under the ROC curve of preoperative RDW to predict mortality after burn surgery was 0.701 (95% confidence interval [CI], 0.667–0.734; P 12.9 was 1.238 (95% CI, 1.138–1.347; P 12.9. Preoperative RDW was considered an independent risk factor for mortality (odds ratio, 1.679; 95% CI, 1.378–2.046; P 12.9 and postoperative AKI may further increase mortality after burn surgery

MR Assessment of Acute Pathologic Process after Myocardial Infarction in a Permanent Ligation Mouse Model: Role of Magnetic Nanoparticle-Contrasted MRI

We evaluated the relationship between myocardial infarct size and inflammatory response using cardiac magnetic resonance imaging (CMR) in an acute myocardial infarction (AMI) mouse model. Myocardial infarction (MI) was induced in 14 mice by permanent ligation of the left anterior descending artery. Late gadolinium enhancement (LGE), manganese-enhanced MRI (MEMRI), and magnetofluorescent nanoparticle MRI (MNP-MRI) were performed 1, 2, and 3 days after MI, respectively. The size of the enhanced lesion was quantitatively determined using Otsu’s thresholding method in area-based and sector-based approaches and was compared statistically. Linear correlation between the enhanced lesion sizes was evaluated by Pearson’s correlation coefficients. Differences were compared using Bland-Altman analysis. The size of the inflammatory area determined by MNP-MRI (57.1 ± 10.1%) was significantly larger than that of the infarct area measured by LGE (40.8 ± 11.7%, P<0.0001) and MEMRI (44.1 ± 14.9%, P<0.0001). There were significant correlations between the sizes of the infarct and inflammatory lesions (MNP-MRI versus LGE: r=0.3418, P=0.0099; MNP-MRI versus MEMRI: r=0.4764, P=0.0002). MNP-MRI provides information about inflammatory responses in a mouse model of AMI. Thus, MNP-MRI associated with LGE and MEMRI may play an important role in monitoring the disease progression in MI

MultiMAP: dimensionality reduction and integration of multimodal data.

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics

MultiMAP: dimensionality reduction and integration of multimodal data.

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics

Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution

Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways for more than a decade. In particular, a conventional research named KernelGAN has recently been proposed. To estimate the SR kernel from a single image, KernelGAN introduces generative adversarial networks(GANs) that utilize the recurrence of similar structures across scales. Subsequently, an enhanced version of KernelGAN, named E-KernelGAN, was proposed to consider image sharpness and edge thickness. Although it is stable compared to the earlier method, it still encounters challenges in estimating sizable and anisotropic kernels because the structural information of an input image is not sufficiently considered. In this paper, we propose a kernel estimation algorithm called Total Variation Guided KernelGAN (TVG-KernelGAN), which efficiently enables networks to focus on the structural information of an input image. The experimental results show that the proposed algorithm accurately and stably estimates kernels, particularly sizable and anisotropic kernels, both qualitatively and quantitatively. In addition, we compared the results of the non-blind SR methods, using SR kernel estimation techniques. The results indicate that the performance of the SR algorithms was improved using our proposed method

Centered Symmetric Quantization for Hardware-Efficient Low-Bit Neural Networks

Recent advances in quantized neural networks (QNNs) are closing the performance gap with the full precision neural networks. However at very low precision (i.e., -bits), QNNs often still suffer significant performance degradation. The conventional uniform symmetric quantization scheme allocates unequal numbers of positive and negative quantization levels. We show that this asymmetry in the number of positive and negative quantization levels can result in significant quantization error and performance degradation at low precision. We propose and analyze a quantizer called centered symmetric quantizer (CSQ), which preserves the symmetry of latent distribution by providing equal representations to the negative and positive sides of the distribution. We also propose a novel method to efficiently map CSQ to binarized neural network hardware using bitwise operations. Our analyses and experimental results using state-of-the-art quantization methods on ImageNet and CIFAR-10 show the importance of using CSQ for weight in place of the conventional quantization scheme at extremely low-bit precision (23 bits)

Techniques for Improving Coarse-Grained Reconfigurable Architectures

This paper presents various novel techniques for improving coarse-grained reconfigurable architectures. Specifically, it presents techniques for supporting IEEE single precision floating-point standard, efficient handling of loop-carried dependency with variable-length FIFOs, efficient mapping of control flows, and sharing data with a host processor for transparent binary acceleration. Experiments with benchmark examples demonstrate the effectiveness of the proposed techniques