Car Racing Game

This project is a racing and car collecting video game, which can be played by one or more players. A player can buy cars, upgrade owned cars, tune owned cars, and use owned cars to race for money to buy, upgrade, and tune cars. The game runs on a personal computer in a Windows environment and can be played with keyboard, mouse, or a joystick. It can also be ported to other platforms such as Android or iOS. It was developed using Unity3D 5.4 Game Engine and features 3D graphic and physic simulation

Influences on Throughput and Latency in Stream Programs

Vu Thien Nga Nguyen and Raimund Kirner, 'Influences on Throughput and Latency in Stream Programs' paper presented at the 2nd Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures. Berlin, Germany. 22 January 2013Stream programming is a promising approach to execute programs on parallel hardware such as multi-core systems. It allows to reuse sequential code at component level and to extend such code with concurrency-handling at the communication level. In this paper we investigate in the performance of stream programs in terms of throughput and latency. We identify factors that affect these performance metrics and propose an efficient scheduling approach to obtain the maximal performance

The Possible Connection of Gamma Oscillation and 3-D Object Representation

We process and encode for different features of a particular object (shape, color, texture, etc.) in distinct areas of the brain. How we bind these attributes together into a unified perception of an object is unknown. Past research suggests that synchronized activity between brain areas, particularly induced gamma activity (~ 40 Hz), may account for this binding process and the basis of our conscious perceptual experience, specifically through object representation. In this study, participants were asked to look at a series of 2-D pictures of cars from distinctive rotations (00, 900, 1800) and were asked to distinguish whether two pictures are of the same or different cars; meanwhile, electroencephalography (EEG) was used to measure electrical activity on participants’ scalps. Our preliminary analysis showed a difference in gamma oscillation after the stimulus onset when comparing 1800 rotations to no rotation in one participant. This suggests the possible relationship between induced gamma oscillation and 3-D object representation

Forming the Conscience of Young Vietnamese

Many current theories (e.g., individualism, materialism, relativism, etc.) exalt individual freedom as an absolute. They ignore the voice of universal truth as a principle of conscience and instead place conscience underneath individual choice. The concept of individual freedom in this way is influencing the conscience of many young Vietnam people to make decisions that destroy and jeopardize moral human life (e.g., abortion, transgender, same-sex marriage, murder, etc.). Educating young people to see themselves as God’s children by forming their conscience is an urgent obligation for the Vietnamese Catholic Church. Thus, my PSP is to follow past practices of the Church’s faith tradition as well as developing new resources that specially address the situation of the younger generation in Vietnam. To accomplish this, I intend to work with the presbyterate to cultivate a culture of reconciliation to help young people restore a sensitivity of guilt within one’s conscience. I also intend to work with the parents within my diocese to cultivate a culture of love where the younger generation can first flourish before tackling social and cultural challenges

The Impact of Salary and Social Welfare on Working Motivation of Vietnamese Official-Lecturers: Passion Overcome the Difficulties

Studies on how salaries and social welfare policies affect the working motivation of lecturers are areas of interest in education and human resource management Because of the differences in Vietnamese political regimes and sociocultural characteristics there are different salary and social welfare policies according to historical periods for officiallecturers Data for this article were collected through life-history interviews in which the two participating lecturers were encouraged to tell stories about their experience in salary and social welfare policies The findings of the study reveal that Vietnamese lecturers face many difficulties in terms of salary and social welfare to work well in their profession Salary and social welfare policies for official-lecturers are greatly influenced by the legal documents prescribed by the Government so many lecturers are not satisfied and have not devoted themselves Based on the findings the study emphasizes the need to adjust official-lecturer salary and social welfare policies based on the university autonomy fund Besides the issue of changing lecturers perceptions of professional values also needs to be considered and studied effective solutions for creating working motivation in the new educational contex

Monitoring framework for stream-processing networks

Vu Thien Nga Nguyen, Raimund Kirner, and Frank Penczek, 'Monitoring framework for stream-processing networks'. Paper presented at the Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures (FD-COMA 2012), Berlin, Germany. 21-23 January 2013.In this paper we present a monitoring framework that exploits special characteristics of stream-processing networks in order to reason the performance. The novelty of the framework is to trace the non-deterministic execution which is reflected in i) the dynamic mapping and scheduling of network components at the operating system level and ii) the dynamic message routing across the network at runtime. We evaluate the efficiency with an implementation for the coordination language S-Net, showing negligible overhead in most cases

An Efficient Execution Model for Reactive Stream Programs

Stream programming is a paradigm where a program is structured by a set of computational nodes connected by streams. Focusing on data moving between computational nodes via streams, this programming model fits well for applications that process long sequences of data. We call such applications reactive stream programs (RSPs) to distinguish them from stream programs with rather small and finite input data. In stream programming, concurrency is expressed implicitly via communication streams. This helps to reduce the complexity of parallel programming. For this reason, stream programming has gained popularity as a programming model for parallel platforms. However, it is also challenging to analyse and improve the performance without an understanding of the program's internal behaviour. This thesis targets an effi cient execution model for deploying RSPs on parallel platforms. This execution model includes a monitoring framework to understand the internal behaviour of RSPs, scheduling strategies for RSPs on uniform shared-memory platforms; and mapping techniques for deploying RSPs on heterogeneous distributed platforms. The foundation of the execution model is based on a study of the performance of RSPs in terms of throughput and latency. This study includes quantitative formulae for throughput and latency; and the identification of factors that influence these performance metrics. Based on the study of RSP performance, this thesis exploits characteristics of RSPs to derive effective scheduling strategies on uniform shared-memory platforms. Aiming to optimise both throughput and latency, these scheduling strategies are implemented in two heuristic-based schedulers. Both of them are designed to be centralised to provide load balancing for RSPs with dynamic behaviour as well as dynamic structures. The first one uses the notion of positive and negative data demands on each stream to determine the scheduling priorities. This scheduler is independent from the runtime system. The second one requires the runtime system to provide the position information for each computational node in the RSP; and uses that to decide the scheduling priorities. Our experiments show that both schedulers provides similar performance while being significantly better than a reference implementation without dynamic load balancing. Also based on the study of RSP performance, we present in this thesis two new heuristic partitioning algorithms which are used to map RSPs onto heterogeneous distributed platforms. These are Kernighan-Lin Adaptation (KLA) and Congestion Avoidance (CA), where the main objective is to optimise the throughput. This is a multi-parameter optimisation problem where existing graph partitioning algorithms are not applicable. Compared to the generic meta-heuristic Simulated Annealing algorithm, both proposed algorithms achieve equally good or better results. KLA is faster for small benchmarks while slower for large ones. In contrast, CA is always orders of magnitudes faster even for very large benchmarks

Probabilistic uncertainty quantification and experiment design for nonlinear models: Applications in systems biology

Despite the ever-increasing interest in understanding biology at the system level, there are several factors that hinder studies and analyses of biological systems. First, unlike systems from other applied fields whose parameters can be effectively identified, biological systems are usually unidentifiable, even in the ideal case when all possible system outputs are known with high accuracy. Second, the presence of multivariate bifurcations often leads the system to behaviors that are completely different in nature. In such cases, system outputs (as function of parameters/inputs) are usually discontinuous or have sharp transitions across domains with different behaviors. Finally, models from systems biology are usually strongly nonlinear with large numbers of parameters and complex interactions. This results in high computational costs of model simulations that are required to study the systems, an issue that becomes more and more problematic when the dimensionality of the system increases. Similarly, wet-lab experiments to gather information about the biological model of interest are usually strictly constrained by research budget and experimental settings. The choice of experiments/simulations for inference, therefore, needs to be carefully addressed. ^ The work presented in this dissertation develops strategies to address theoretical and practical limitations in uncertainty quantification and experimental design of non-linear mathematical models, applied in the context of systems biology. This work resolves those issues by focusing on three separate but related approaches: (i) the use of probabilistic frameworks for uncertainty quantification in the face of unidentifiability (ii) the use of behavior discrimination algorithms to study systems with discontinuous model responses and (iii) the use of effective sampling schemes and optimal experimental design to reduce the computational/experimental costs. ^ This cumulative work also places strong emphasis on providing theoretical foundations for the use of the proposed framework: theoretical properties of algorithms at each step in the process are investigated carefully to give more insights about how the algorithms perform, and in many cases, to provide feedback to improve the performance of existing approaches. Through the newly developed procedures, we successfully created a general probabilistic framework for uncertainty quantification and experiment design for non-linear models in the face of unidentifiability, sharp model responses with limited number of model simulations, constraints on experimental setting, and even in the absence of data. The proposed methods have strong theoretical foundations and have also proven to be effective in studies of expensive high-dimensional biological systems in various contexts

Throughput-driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

This is an Open Access article. © 2015 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Graph partitioning is an important problem in computer science and is of NP-hard complexity. In practice it is usually solved using heuristics. In this article we introduce the use of graph partitioning to partition the workload of stream programs to optimise the throughput on heterogeneous distributed platforms. Existing graph partitioning heuristics are not adequate for this problem domain. In this article we present two new heuristics to capture the problem space of graph partitioning for stream programs to optimise throughput. The first algorithm is an adaptation of the well-known Kernighan-Lin algorithm, called KL-Adapted (KLA), which is relatively slow. As a second algorithm we have developed the Congestion Avoidance (CA) partitioning algorithm, which performs reconfiguration moves optimised to our problem type. We compare both KLA and CA with the generic meta-heuristic Simulated Annealing (SA). All three methods achieve similar throughput results for most cases, but with significant differences in calculation time. For small graphs KLA is faster than SA, but KLA is slower for larger graphs. CA on the other hand is always orders of magnitudes faster than both KLA and SA, even for large graphs. This makes CA potentially useful for re-partitioning of systems during runtime.Peer reviewedFinal Published versio