Vietnam: Exploring The Vietnamese Corporate Bond

In the meantime, critically needed is a comprehensive regulatory framework that enables not only state-owned but also non-state enterprises to be able to issue a wide range of bonds legitimately. This move will certainly pave the way towards a functional corporate bond market and open up the funding channel for enterprises via bond issues

A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page

Optimization of ROI-based LiDAR sampling in on-road environment for autonomous driving

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. Hyuk-Jae Lee.Light detection and ranging (LiDAR) 센서는 최근 로보틱스와 자율 주행을 비롯한 여러 분야에서 사용되고 있다. 이런 LiDAR 센서는 다른 센서보다 낮은 해상도가 특징으로, 효과적인 샘플링 알고리즘을 설계하는 것이 필수적이다. 자율 주행에 적용되는 LiDAR 샘플링 알고리즘의 경우 도로의 복잡한 환경에서도 강인하게 높은 품질로 reconstruction을 하는 것이 목표이다. 이를 위해 현행 ROI 기반 샘플링 알고리즘은 시멘틱 정보를 이용하고 있다. 하지만, 객체, 도로, 배경 등에 따른 sampling rate는 지금까지 충분히 논의되지 않았고, 이로 인해 종합적인 reconstruction 품질이 저하될 수 있다. 이러한 문제를 해결하기 위해, 본 논문에서는 객체, 도로, 배경에 따른 sampling budget ratio를 도출할 수 있는 방법을 제안한다. 이 방법은 객체, 도로, 배경의 특성이 샘플링 이전에 선행 지식으로 주어져 있다는 가정을 이용한다. 제안하는 sampling budget을 적용한 결과, 현행 알고리즘보다 객체에 대한 mean-absolute-error (MAE)는 최대 45.92% 감소하였을 뿐만 아니라 전반적인 MAE 또한 3.36% 감소하였고, 도로에 대한 MAE는 오직 54.18% 감소하였다.In recent years, light detection and ranging (LiDAR) sensors have been applied in several situations, including robotics and autonomous driving. However, LiDAR sensors have relatively low resolutions. Therefore, it is imperative to design an effective sampling algorithm for LiDAR sensors. To manage complex on-road environments, conventional ROI-based LiDAR sampling algorithm utilizes semantic information to achieve robust and high reconstruction quality. However, the ratio between sampling rates of objects, roads, and background areas is not thoroughly investigated. Therefore, the overall reconstruction quality may be degraded. To address this problem, this study presents a proposed method to examine the sampling budget ratio between objects, roads, and background areas, under the assumption that characteristics of objects, roads, and background areas are known prior to sampling. Experimental results depict a significant reduction in the mean-absolute-error (MAE) of the object region, road region and overall region by up to 45.92%, 54.18% and 3.36% under the proposed method, respectively, compared to the conventional method.Chapter 1. Introduction １ 1.1. Overview １ 1.2. Light detection and ranging sensor LiDAR sampling １ Chapter 2. Background ４ 2.1. Definition of a sampling problem ４ 2.2. Oracle Random Sampling ４ 2.2.1 Sampling Model ４ 2.2.2 Oracle Random Scheme ５ 2.3. ROI-based LiDAR sampling algorithm ６ Chapter 3. Proposed method ８ 3.1. Analytical method ８ Chapter 4. Experimental results １５ 4.1. Dataset １５ 4.2 Quantitative evaluation １６ Chapter 5. Conclusion ２０ Appendix ２１ References ３１ 초 록(Abstract in Korean) ３２Maste

Essays on credit ratings : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Albany, New Zealand

Credit ratings play an important role as a gatekeeper of capital markets. Firms with higher credit ratings are likely to access the capital markets at a lower cost. Hence, understanding credit rating properties is essential, and this topic is of great importance for academics, regulators, and practitioners. This thesis includes three essays on credit ratings. Traditional issuer-paid credit rating agencies (CRAs hereafter) such as Standard & Poor’s (S&P hereafter), Moody’s and Fitch Ratings (Fitch hereafter) have faced criticisms about the lack of timeliness and accuracy in negative signals due to the conflict of interest in their business model. However, this is not the case for the positive signals. In contrast, investor-paid CRAs, without conflict of interest in their business model, issue more timely and accurate negative signal. The first essay investigates how institutional investors who have advanced trading skills and knowledge respond to credit rating changes issued by two types of CRAs: issuer- and investor-paid CRAs. I find that investors react asymmetrically: they abnormally sell stocks surrounding rating downgrades by investor-paid CRAs, while abnormally buying stocks around rating upgrades by issuer-paid CRAs. In contrast, they have no significant reaction to positive signals from the investor-paid CRA and negative signals from the issuer-paid CRAs. The first essay suggests that, through their trades, institutional investors do capitalize on value-relevant rating information: negative and positive signals provided by investor- and issuer-paid CRAs respectively. More importantly, I further find that a dynamic trading strategy specifically based on rating downgrades by investor-paid CRA and rating upgrades by issuer-paid CRAs generates significant abnormal returns. The second essay focuses on the relationship between politics and credit ratings. Specifically, I investigate whether political similarities between CRAs and bond issuers impact credit ratings. I find that a higher degree of similarity of political affiliation leads to a decrease in timeliness and accuracy of rating downgrades prior to default events. The findings support the notion that CRAs tend to maintain/assign relative rating advantages to politically similar firms via favourable rating activities. I further show that these politically similar firms tend to increase the proportion of political donations to their favoured party following favourable credit ratings. Interestingly, this result is confined to Republican-leaning firms. The results indicate that CRAs successfully use biased credit ratings as an indirect channel of political party support. The second essay thus contributes to the body of knowledge on the importance of political connections in corporate finance as well as CRAs’ rating behaviours. The third essay examines the effect of natural disasters on credit ratings. Natural disasters are exogenous shocks to CRAs’ rating behaviours. I find that firms located in the disaster states (i.e., affected firms) are downgraded by CRAs. I also find the same patterns in changes in stock returns of affected firms. The findings support hypothesis that credit rating changes are driven by firm’s fundamental changes caused by natural disasters. By using instrumental variable (IV) analysis to extract affected firms’ rating changes caused by natural disasters, I further investigate the spill-over effects of natural disasters on rating changes of non-affected firms (i.e., firms are not located in the disaster states). I find that the affected firms’ rating changes positively spill-over to connected firms’ rating changes which are not directly impacted by natural disasters. Connected firms are selected from the same industry, the adjoining states, or supplier-customer relationships with the affected firms. I also find the negative spill-over effects from the affected firms’ rating changes to their competitors’ rating changes. Finally, I replicate the spill-over channels for stock returns, a proxy for market reactions to natural disasters, and find delays in the stock return spill-over. This is significant evidence on CRAs’ sensitivity to natural extreme events

The web - based tool for identification of amphibian and reptiles presented in three western provinces of South - Eastern region, Vietnam

Based on checklist, the keys to the identification updating with the new name of species according to Sang Nguyen Van et al author of the book “Herpetofauna of Vietnam” publisher Chimaira published in 2009 and the biological and ecological data of 120 species of amphibian and reptiles known from the herpetofauna in three western provinces of South - Eastern region, South Vietnam was announced by Hoa Pham Van in 2005 to build the web-based tool for identification of species from this herpetofaunayesBelgorod State Universit

Denoising Diffusion Medical Models

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radiograph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.Comment: Accepted to IEEE ISBI 202

CONCEPT MAPPING INFLUENCING STUDENTS’ ABILITY TO SUMMARIZE READING PASSAGES

Concept mapping has been advocated as a facilitative tool for enhancing meaningful learning of reading comprehension of students in several ways. In particular, this strategy positively influences how students comprehend texts and summarize important ideas or information given in a particular text passage. However, research into the effects of concept mapping on students’ ability to summarize passages has not been explored in the context of teaching and learning English as a foreign language inVietnam. This paper therefore examines the effects of concept mapping on students’ ability to summarize reading passages within a community college context in the Mekong Delta. Using an experimental study, pretest, posttest, and questionnaire were undertaken with twenty six sophomores over the second semester of a reading course. The findings show that concept mapping had positive effects on students’ ability to summarize reading passages and that students perceived the use of this reading strategy as being a facilitative tool for meaningful learning. The paper concludes by discussing the pedagogical implications and insights into the relationship between concept mapping and summarizing skills in reading comprehension in wider contexts.  Article visualizations