A Service Late Binding Enabled Solution for Data Integration from Autonomous and Evolving Databases

Integrating data from autonomous, distributed and heterogeneous data sources to provide a unified vision is a common demand for many businesses. Since the data sources may evolve frequently to satisfy their own independent business needs, solutions which use hard coded queries to integrate participating databases may cause high maintenance costs when evolution occurs. Thus a new solution which can handle database evolution with lower maintenance effort is required. This thesis presents a new solution: Service Late binding Enabled Data Integration (SLEDI) which is set into a framework modeling the essential processes of the data integration activity. It integrates schematic heterogeneous relational databases with decreased maintenance costs for handling database evolution. An algorithm, named Information Provision Unit Describing (IPUD) is designed to describe each database as a set of Information Provision Units (IPUs). The IPUs are represented as Directed Acyclic Graph (DAG) structured data instead of hard coded queries, and further realized as data services. Hence the data integration is achieved through service invocations. Furthermore, a set of processes is defined to handle the database evolution through automatically identifying and modifying the IPUs which are affected by the evolution. An extensive evaluation based on a case study is presented. The result shows that the schematic heterogeneities defined in this thesis can be solved by IPUD except the relation isomorphism discrepancy. Ten out of thirteen types of schematic database evolution can be automatically handled by the evolution handling processes as long as the evolution is represented by the designed data model. The computational costs of the automatic evolution handling show a slow linear growth with the number of participating databases. Other characteristics addressed include SLEDI’s scalability, independence of application domain and databases model. The descriptive comparison with other data integration approaches shows that although the Data as a Service approach may result in lower performance under some circumstances, it supports better flexibility for integrating data from autonomous and evolving data sources

On the use of JMAK theory to describe mechanical amorphization: a comparison between experiments, numerical solutions and simulations

The kinetics of amorphization during ball milling is generally analyzed using two different approaches: the classical Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory and Delogu and Cocco’s model for which a region deterministically transforms after it reaches a certain number of collisions. The application of JMAK analysis to the latter model predicts Avrami exponents to be higher than the experimental ones (typically close to one). We develop simulations based on the probabilistic character of the nucleation phenomenon and concave growth of the amorphous phase in the core of a nanocrystal. The predictions of our simulations are in good agreement with the low Avrami exponents and with the size evolution of the remaining crystallites found experimentally. From these values, the parameters involved in the simulated model (growth rate and probability of nucleation) can be estimated.AEI/FEDER-UE (Project MAT-2016-77265-R)Junta de Andalucía (Grupo PAI

Intelligence Processing Units Accelerate Neuromorphic Learning

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units (GPUs) this becomes more expensive than non-spiking networks. The emergence of Graphcore's Intelligence Processing Units (IPUs) balances the parallelized nature of deep learning workloads with the sequential, reusable, and sparsified nature of operations prevalent when training SNNs. IPUs adopt multi-instruction multi-data (MIMD) parallelism by running individual processing threads on smaller data blocks, which is a natural fit for the sequential, non-vectorized steps required to solve spiking neuron dynamical state equations. We present an IPU-optimized release of our custom SNN Python package, snnTorch, which exploits fine-grained parallelism by utilizing low-level, pre-compiled custom operations to accelerate irregular and sparse data access patterns that are characteristic of training SNN workloads. We provide a rigorous performance assessment across a suite of commonly used spiking neuron models, and propose methods to further reduce training run-time via half-precision training. By amortizing the cost of sequential processing into vectorizable population codes, we ultimately demonstrate the potential for integrating domain-specific accelerators with the next generation of neural networks.Comment: 10 pages, 9 figures, journa

Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry

Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural networks over large molecular databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. This paper demonstrates a novel hardware-software co-design approach to scale up the training of graph neural networks for molecular property prediction. We introduce an algorithm to coalesce the batches of molecular graphs into fixed size packs to eliminate redundant computation and memory associated with alternative padding techniques and improve throughput via minimizing communication. We demonstrate the effectiveness of our co-design approach by providing an implementation of a well-established molecular property prediction model on the Graphcore Intelligence Processing Units (IPU). We evaluate the training performance on multiple molecular graph databases with varying degrees of graph counts, sizes and sparsity. We demonstrate that such a co-design approach can reduce the training time of such molecular property prediction models from days to less than two hours, opening new possibilities for AI-driven scientific discovery

Analyzing Prosody with Legendre Polynomial Coefficients

This investigation demonstrates the effectiveness of Legendre polynomial coefficients representing prosodic contours within the context of two different tasks: nativeness classification and sarcasm detection. By making use of accurate representations of prosodic contours to answer fundamental linguistic questions, we contribute significantly to the body of research focused on analyzing prosody in linguistics as well as modeling prosody for machine learning tasks. Using Legendre polynomial coefficient representations of prosodic contours, we answer prosodic questions about differences in prosody between native English speakers and non-native English speakers whose first language is Mandarin. We also learn more about prosodic qualities of sarcastic speech. We additionally perform machine learning classification for both tasks, (achieving an accuracy of 72.3% for nativeness classification, and achieving 81.57% for sarcasm detection). We recommend that linguists looking to analyze prosodic contours make use of Legendre polynomial coefficients modeling; the accuracy and quality of the resulting prosodic contour representations makes them highly interpretable for linguistic analysis

북한의 경제계층과 한국민의 통일의식에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 사회과학대학 경제학부, 2023. 2. 김병연.시장화는 최근 북한 경제 관련 연구에서 가장 활발히 논의되고 있는 주제 중 하나이다. 1990년대의 '고난의 행군' 이후 시장화는 북한 일반주민들의 가장 중요한 소득원으로 자리잡고 있으며, 국가경제 차원에서도 경제성장에 가장 큰 동력으로 자리잡고 있을 것으로 추정된다. 또 북한의 시장화 규모는 이전 사회주의국가들에 비교해 보아도 그 규모와 비중이 유례없이 큰 것으로 추정되고 있다. 이러한 북한 시장화의 독특함과 규모를 반영하여 그 동안 북한 시장화에 대한 많은 선행연구들이 진행되었다. 관련 연구들은 북한 시장화에 대하여 주로 규모 추정, 비교분석, 시장화 확대의 결정요소, 그리고 북한의 경제성장에 대한 기여 등을 분석하여 왔다. 하지만 시장화와 동반하여 생겨난 여러 경제적 요인들이 실제 북한 주민들의 삶에 어떠한 영향을 미쳤는지에 대한 정량적, 실증적 연구는 관련 자료 부족으로 인해 아직 많이 이루어지지 않고 있다. 이 논문은 이러한 자료부족에 대한 어려움을 극복하기 위하여 서울대 통일평화연구원에서 매년 발표되고 있는 '북한이탈주민 의식조사' 자료를 활용하여 다음 두 가지의 연구를 수행한다. 첫 번째 장에서는 북한의 뇌물이 시장소득으로 대표되는 비공식소득에 미치는 영향에 대하여 알아본다. 도구변수(Instrumental Variable)를 활용한 2SLS 추정 결과 뇌물은 비공식소득을 증가시키는 경향이 있는 것으로 나타났다. 추가로 진행된 도구변수 분위수 회귀분석(IV quantile regression) 결과 뇌물의 수익성은 비공식소득 분위가 높을수록 높은 것으로 추정되었다. 이러한 추정결과는 Kim (2010) 연구가 주장하는 북한의 부패 균형(corruption equilibrium)의 취약성(fragility)을 실증적으로 뒷받침한다고 할 수 있다. 또한 뇌물의 비공식소득 수준에 따른 차별적 수익성은 북한 주민 수입의 대부분을 차지하고 있는 비공식소득의 불평등에 기여하고 있을 것으로 추정된다. 두 번째 장은 앞선 분석과 같은 자료를 활용하여 북한의 비공식소득에 대한 분포분석을 진행한다. 상대분포분석(relative distribution analysis) 방법과 중위상대양극화 지수(median relative polarization index) 방법을 통하여 시장화 확대 기간 동안 북한의 비공식소득이 더욱 양극화되었음을 보인다. 또한 이러한 양극화의 대부분은 상위 소득 분포의 상대적 비중 증가보다는 하위 소득 분포의 상대적 비중 증가에 기인하는 것으로 파악되었다. 이러한 결과는 이번 설문조사 자료가 내포하고 있는 표본선택편향(sample selection bias)에 대응하기 위한 표본의 재추출(resampling) 처리와 '성향점수매칭(propensity score matching)' 방법론 적용 이후에도 강건한 것으로 나타났다. 북한의 비공식소득의 양극화 확대는 장기적으로 시장의 제도권 편입에 대한 압력 요인으로 작용할 수 있음을 시사한다. 마지막으로 이 논문은 한국인의 통일의식에 대한 분석을 진행한다. 최근 한국인의 통일의식에 대한 통계자료에 의하면 통일에 대한 부정적 인식이 늘어나고 있는 추세에 있는 것으로 나타난다. 통일의식과 민족의식에 대한 선행 연구들은 이러한 부정적 통일의식 확산의 원인을 주로 북한주민들에 대한 '타자화', 그리고 늘어나는 젊은세대의 부정적 통일의식에서 찾고 있다. 또한 최근 일부 연구들은 북한주민들에 대한 타자화로 한국사회에서의 통일문제가 점차 일반적인 이민문제화 되고 있음을 주장한다. 이러한 측면에서 이 연구는 일반적인 '반이민정서 이론(anti-immigration sentiment theories)' 중 하나인 '경제적 경쟁 모형(economic competition theory)'을 주요 분석대상으로 설정한다. 경제적 경쟁 모형은 저숙련 노동자의 경우 향후 기대되는 저숙련 이민자와의 노동시장 경쟁에 대한 우려로 이민에 대하여 비교적 부정적 태도를 취할 가능성이 높음을 설명하는 모형이다. 이 연구는 위 모형이 한국인의 통일의식에도 유의한 영향을 미치는지 살펴보고, 특히 기성세대 대비 젊은세대에서 더 유효하게 작용하고 있는지에 대한 실증분석을 진행한다. 실증분석 결과 저숙련 집단일수록 통일에 대한 필요성 인식이 상대적으로 낮은 것으로 추정되었으며, 이러한 경향에는 세대효과가 존재하는 것으로 나타났다. 즉, 젊은세대의 경우에는 통일의식 형성에 자신의 숙련도가 유효한 변수인 것으로 추정된 반면, 기성세대의 경우에는 유효하지 않은 변수인 것으로 추정되었다. 이러한 결과는 젊은세대 일수록 통일을 보다 실질적인 측면에서 바라보고 있을 가능성을 제기한다. 또한, 이는 우리사회에서 논의되고 있는 통일에 대한 담론이 기존의 전통적인 '한민족 통일'에 대한 것에서 경제적 비용과 편익 등 통일의 보다 실질적인 측면에 대한 것으로 변화해야 함을 시사한다.Marketization is the most prominent economic issue discussed most frequently in the North Korean economy literature. Since the Arduous March of 1990s, marketization has been expanding to become both the most important source of income for the normal North Koreans, and the main engine for growth of the North Korean economy. Furthermore, the level of marketization in North Korea is unprecedented even in comparison to the previous examples of Soviet Union and former socialist states. Reflecting the uniqueness and sheer size of marketization, there have been many attempts to study the phenomenon. Most of the previous studies on North Korean marketization thus far have mainly focused on topics such as estimating its size, comparative level, finding the determinants of expansion, and its contribution to growth of the North Korean economy. However, there are only limited number of empirical studies on how the consequent factors of the marketization affect the economies of the North Korean people due to data deficiency. To overcome the difficulty, the first two chapters of this dissertation utilizes the survey dataset of North Korean Refugee Survey. The dataset is annually published by the Institute for Peace and Unification Studies (IPUS) at Seoul National University. The first chapter explores the effect of bribe on informal income in North Korea. By selecting an instrumental variable, the 2SLS estimation results suggest that bribe increases informal income. Furthermore, subsequent IV quantile regression results show that the profitability of bribes increases with the informal income quantile. These results empirically confirm the fragility of corruption equilibrium argued by Kim (2010). Furthermore, result of the disproportionate profitability of bribes suggest that it may has contributions to informal income inequality. The second chapter provides distributional analysis on informal income of North Korea with the same data source as the first chapter. By relative distribution analysis and median relative polarization index estimation methods, an increased level of informal income polarization during the period of market expansion is observed. More importantly, the contribution of increase in the share of lower tail distribution to overall increased polarization overwhelms that of increase in the share of upper tail distribution. The results are robust after resampling of the data, and also after matching of the samples by propensity score matching methodology which were conducted in an effort to alleviate the sample selection bias. The result of increased level of informal income inequality may put pressure for institutionalization of the markets in the long-run. This dissertation also conducts additional analysis on the unification perceptions of the South Koreans. Specifically, the last chapter seeks to analyze growing pessimism on unification. According to statistics on unification perception of the South Koreans, there seems to be a trend where negative perceptions are growing over time. The growing pessimism is especially alarming because some of the suggested reasons behind it, analyzed by previous literature, are increasing alienation of the North Koreans and salient pessimism among the younger generations. In light of this, the last chapter investigates whether there exists generation effect on economic determinants of unification perception by analyzing the Unification Perception Survey of IPUS. More specifically, empirical tests were conducted to discover whether the economic competition theory, one of the established anti-immigration sentiment theories, are a valid determinant of unification perception. The theory argues that low-skilled individuals are more likely to harbor negative attitudes toward immigration over the concerns for potential competitions with low-skilled immigrants. In addition, whether the younger generations are especially susceptible to it compared to the older generation was investigated. Results of empirical analyses suggest that there exists generation effect in the negative effect of skill level. In other words, the younger generations consider their skill levels significantly when considering unification, whereas for the older generations, the skill level turned out to be immaterial. This implies that the context in which the unification is discussed in the society should change from traditional justification of mono-ethnicity to more practical aspects of unification such as economic costs and benefits.Introduction 1 Chapter I. Bribery and Informal Income of North Korea: An Instrumental Variable Approach 4 1. Introduction 4 2. Literature Review 7 3. Data 10 3.1 IPUS North Korean Refugee Survey 10 3.2 Main Variables 14 4. Empirical Analysis 20 4.1 Baseline Model 20 4.2 Instrumental Variable Approach 23 4.3 Quantile Regression Analysis 29 5. Conclusion 32 Chapter II. Marketization and Informal Income Distribution of North Korea 34 1. Introduction 34 2. Distributional Analysis 37 2.1 Threshold Year Analysis 39 2.2 Distributional Analysis 45 2.3 Robustness Checks 51 3. Discussion 62 4. Conclusion 64 Chapter III. Economic Status and Unification Perception of the South Koreans 67 1. Introduction 67 2. Data 72 2.1 Unification Perception Survey 72 2.2 Variable Construction 74 3. Empirical Analysis 80 3.1 Baseline Model 80 3.2 Age Effect Model 83 3.3 Generation Effect Model 86 3.4 Limitations 91 4. Conclusion 92 Concluding Remarks 94 References 97 Appendix 103박

A scramble for value:On the interpretation and application of value-based health care in the Netherlands

In many health care systems across the globe, value-based health care has quickly become a remarkably popular concept. Yet, despite its global popularity, the meaning of the concept remains shrouded in ambiguity, and efforts to put value-based health care into practice are characterized by a high degree of local variability. This makes it rather challenging to grasp the essence of this seemingly influential concept, let alone evaluate its effects within health care systems. A Scramble for Value addresses that challenge.The thesis traces the journey of value-based health care from its original conception by Harvard business scholar Michael Porter to its global popularity, and zooms in on its interpretation and application in the Netherlands. As the original set of ideas runs into the historically rooted institutions of the Dutch health care system, the meaning of value-based health care gets moderated, and its application conforms to the very structures it once so boldly set out to reform. It is by overlaying rather than overthrowing those traditional structures that value-based health care has ignited a renewed focus on outcomes that matter to patients, and amplified multidisciplinary efforts to improve those outcomes. All in all, this can be seen as quite an accomplishment..<br/

Machine learning and inferencing for the decomposition of speech mixtures

In this dissertation, we present and evaluate a novel approach for incorporating machine learning and inferencing into the time-frequency decomposition of speech signals in the context of speaker-independent multi-speaker pitch tracking. The pitch tracking performance of the resulting algorithm is comparable to that of a state-of-the-art machine-learning algorithm for multi-pitch tracking while being significantly more computationally efficient and requiring much less training data. Multi-pitch tracking is a time-frequency signal processing problem in which mutual interferences of the harmonics from different speakers make it challenging to design an algorithm to reliably estimate the fundamental frequency trajectories of the individual speakers. The current state-of-the-art in speaker-independent multi-pitch tracking utilizes 1) a deep neural network for producing spectrograms of individual speakers and 2) another deep neural network that acts upon the individual spectrograms and the original audio’s spectrogram to produce estimates of the pitch tracks of the individual speakers. However, the implementation of this Multi-Spectrogram Machine- Learning (MS-ML) algorithm could be computationally intensive and make it impractical for hardware platforms such as embedded devices where the computational power is limited. Instead of utilizing deep neural networks to estimate the pitch values directly, we have derived and evaluated a fault recognition and diagnosis (FRD) framework that utilizes machine learning and inferencing techniques to recognize potential faults in the pitch tracks produced by a traditional multi-pitch tracking algorithm. The result of this fault-recognition phase is then used to trigger a fault-diagnosis phase aimed at resolving the recognized fault(s) through adaptive adjustment of the time-frequency analysis of the input signal. The pitch estimates produced by the resulting FRD-ML algorithm are found to be comparable in accuracy to those produced via the MS-ML algorithm. However, our evaluation of the FRD-ML algorithm shows it to have significant advantages over the MS-ML algorithm. Specifically, the number of multiplications per second in FRD-ML is found to be two orders of magnitude less while the number of additions per second is about the same as in the MS-ML algorithm. Furthermore, the required amount of training data to achieve optimal performance is found to be two orders of magnitude less for the FRD-ML algorithm in comparison to the MS-ML algorithm. The reduction in the number of multiplications per second means it is more feasible to implement the MPT solution on hardware platforms with limited computational power such as embedded devices rather than relying on Graphics Processing Units (GPUs) or cloud computing. The reduction in training data size makes the algorithm more flexible in terms of configuring for different application scenarios such as training for different languages where there may not be a large amount of training data