Drawing Big Graphs using Spectral Sparsification

Spectral sparsification is a general technique developed by Spielman et al. to reduce the number of edges in a graph while retaining its structural properties. We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs. We show that spectral sparsifiers are more effective than random edge sampling. Our results lead to guidelines for using spectral sparsification in big graph visualization.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Understanding the interplay of lies, violence, and religious values in folktales

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character. A violent act committed to serving Buddhist mission results in a happy ending for the committer

Healthcare consumers’ sensitivity to costs: a reflection on behavioural economics from an emerging market

Decision-making regarding healthcare expenditure hinges heavily on an individual's health status and the certainty about the future. This study uses data on propensity of general health exam (GHE) spending to show that despite the debate on the necessity of GHE, its objective is clear—to obtain more information and certainty about one’s health so as to minimise future risks. Most studies on this topic, however, focus only on factors associated with GHE uptake and overlook the shifts in behaviours and attitudes regarding different levels of cost. To fill the gap, this study analyses a dataset of 2068 subjects collected from Hanoi (Vietnam) and its vicinities using the baseline-category logit method. We evaluate the sensitivity of Vietnamese healthcare consumers against two groups of factors (demographic and socioeconomic-cognitive) regarding payment for periodic GHE, which is not covered by insurance. Our study shows that uninsured, married and employed individuals are less sensitive to cost than their counterparts because they value the information in reducing future health uncertainty. The empirical results challenge the objections to periodic health screening by highlighting its utility. The relevance of behavioural economics is further highlighted through a look at the bounded rationality of healthcare consumers and private insurance companies in using and providing the service, respectively

Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering

The problems in machine translation are related to the characteristics of a family of languages, especially syntactic divergences between languages. In the translation task, having both source and target languages in the same language family is a luxury that cannot be relied upon. The trained models for the task must overcome such differences either through manual augmentations or automatically inferred capacity built into the model design. In this work, we investigated the impact of multiple methods of differing word orders during translation and further experimented in assimilating the source languages syntax to the target word order using pre-ordering. We focused on the field of extremely low-resource scenarios. We also conducted experiments on practical data augmentation techniques that support the reordering capacity of the models through varying the target objectives, adding the secondary goal of removing noises or reordering broken input sequences. In particular, we propose methods to improve translat on quality with the denoising autoencoder in Neural Machine Translation (NMT) and pre-ordering method in Phrase-based Statistical Machine Translation (PBSMT). The experiments with a number of English-Vietnamese pairs show the improvement in BLEU scores as compared to both the NMT and SMT systems

ENVIRONMENTAL IMPACTS OF SHRIMP CULTURE IN THE MANGROVE AREAS OF VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Modellierung der Nährstoffdynamik bei Hochwasser in tropischen Einzugsgebieten

The aim of this dissertation is to study how catchment water quality modeling can be applied in the tropical conditions (e.g. Vietnam), especially looking at the aspect of transferring current knowledge from developed countries to developing countries. In order to achieve this aim, the PhD study conducts the following work: (1) review on the state of the art of water quality modeling at catchment scale; (2) selection of a suitable study area for testing and validating concepts; (3) utilization of available complex model codes; (4) development of an innovative and robust adapted to the specific condition of tropical regions in developing countries; (5) proposition of a model-based framework for water resources management. The experience gained from modeling and the recommendations will be used in an ongoing joint research project (German Ministry of Education and Research – BMBF and Vietnam Ministry of Science and Technology – MOST) about “Water pollution control management in key economics zones of South Vietnam”. Coordinators of this project are the Leichtweiß-Institute for Hydraulics and Water Resources (LWI), University of Braunschweig and the Institute for Environment and Resources (IER), Vietnam National University of Ho Chi Minh city.Die vorgelegte Doktorarbeit beinhaltet folgende Themen: (1) Eine Literaturstudie über den aktuellen Stand der Gewässergütemodellierung auf Einzugsgebietsebene; (2) Auswahl eines geeigneten Einzugsgebietes zum Testen und Validieren von Modellkonzepten; (3) Anwendung von vorhandenen komplexen Modellen; (4) Entwicklung eines innovativen, robusten und an die spezifischen Bedingungen tropischer Regionen angepassten Modellkonzeptes; (5) Vorschläge für ein modellbasiertes Wasserressourcenmanagementkonzept. Die Erkenntnisse aus den Modellanwendungen sowie die Empfehlungen zum Managementkonzept werden in einem laufenden Verbundforschungsprojekt (Bundesministerium für Bildung und Forschung – BMBF und Vietnam Ministry of Science and Technology – MOST), über "nachhaltiges Gewässerschutzmanagement in der Hauptwirtschaftszone in Südvietnam", verwendet. Koordinator dieses Projektes ist das Leichtweiß-Institut für Wasserbau (LWI) der Technischen Universität Braunschweig und das Institute for Environment and Resources (IER), Vietnam National University of Ho Chi Minh city

Development of predictive models for the coalescence of fused deposition modeling fibers

Field of study: Mechanical & aerospace engineering.Dr. A. Sherif El-Gizawy, Thesis Supervisor."December 2017."Fused deposition modeling (FDM) is the prominent manufacturing method for fabricating end-use parts due to the ability to build complicated structures. In order to be used confidentially in the industry requires a thorough understanding of mechanical behavior of FDM parts under working conditions. The strength of FDM parts is negatively influenced by the insufficient bond strength achieved between fibers, the weakest links in the FDM parts are the weak inter-layer bonds and intra-layer bonds. The aim of this study is to create models that can accurately predict bond length and bond strength between fibers. Analytical equations describing the sintering processes and heat transfer between FDM fibers and surrounding environment are developed and presented. By comparing the predicted value to the actual bond length, the models are found to be moderately accurate. To validate the relation between bond length and bond strength and also determine the process parameters that affect the bond strength, design of experiments (DOE) and analysis of variance (ANOVA) were applied. The result showed that the extrusion temperature to be statistically significant. Further research is recommended to take in to account more factors that could affect the cooling and sintering process that will help improve the precision of predictive models.Includes bibliographical references (pages 50-53)

Cultural evolution in Vietnam’s early 20th century: a Bayesian networks analysis of Franco-Chinese house designs

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p\u3c0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular

The trilemma of sustainable industrial growth: evidence from a piloting OECD’s Green city

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies has been effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hints at the limits of the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data, as well as to invest in the application of big data