Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio

Physiological Parameters of Latex from Controlled Upward Tapping of Hevea Brasiliensis Stimulated With Ethephon

A study was carried out to evaluate the effect of several ethephon concentrations (5%, 10% and 20%) on yield and some physiological parameters of latex obtained from Controlled Upward Tapping (CUT) of clone RRlM 600. The relationships between yield, physiological parameters and their interactions were investigated. A good response t o stimulation on yield was observed. The yields in stimulated treatments were from 192.5% to 267.7% of control over the time of the study

Leadership skills development in theological seminary: crucial factors in creating effective local church leadership

Leadership is a challenge for almost every organization. Like everyone else, the church is faced with similar challenges. Being leaders of the local congregation, pastors are the key element in church leadership. Church members are demanding more of their pastors than ever. Many pastors feel ill-prepared for these tasks. The theological seminary has been the primary place to prepare church leaders to do ministry. Previous researchers have stated that there is compelling evidence that many theological seminaries were not appropriately preparing men and women for the job the local congregation expects them to do for the church. The purpose of this study is to explore whether theological seminaries make leadership learning explicit through course offerings. The study is grounded in related literature on the foundation of leadership and attempts to determine the leadership components of a graduate education in Divinity. The curriculum of the Master\u27s of Divinity program from 10 theological seminaries are examined using catalog content analysis to determine the extent that theological seminaries offer leadership courses to their students to prepare them for future ministry. This is a qualitative study, semantically focused upon the vocabularies in the catalogs. Semantic feature analysis is used with quadrilateral instruments acting as data collection tools. The investigation revealed that graduate studies in Divinity do not universally offer leadership as a component of their curricula. The investigation concluded that the majority of theological seminaries have placed more emphasis on Biblical and theological education. This emphasis did not focus on leadership-skills development. Students were left to make the critical decision on taking leadership courses to prepare for their future ministry. For further study, the researcher recommends replicating this investigation with a larger and more representative sampling of theological seminaries from many more denominations

Evaluation of Using the Bootstrap Procedure to Estimate the Population Variance

The bootstrap procedure is widely used in nonparametric statistics to generate an empirical sampling distribution from a given sample data set for a statistic of interest. Generally, the results are good for location parameters such as population mean, median, and even for estimating a population correlation. However, the results for a population variance, which is a spread parameter, are not as good due to the resampling nature of the bootstrap method. Bootstrap samples are constructed using sampling with replacement; consequently, groups of observations with zero variance manifest in these samples. As a result, a bootstrap variance estimator will carry a bias to the low side. This work will attempt to demonstrate the bias issue with simulations, as well as explore possible approaches to correct for any such bias. In addition, these approaches will be evaluated for more general performance through simulations

POSITIVISM OF ERNST MACH: VLADIMIR LENIN’S CRITIQUE FROM PHILOSOPHICAL WORK MATERIALISM AND EMPIRIO–CRITICISM

Philosophical views of Ernst Mach represent the second-generation of Positivism. Developed by the end of the 19th century, Mach’s positivism associated with the turning point of Scientific Revolution which marked the emergence of modern science. From Ernst Mach’s viewpoint, philosophy was intertwined with natural sciences and turned into its core concepts. He advocated the absolute role of sensation, focused on “the principle of economy of thought” definition; opposed atomism and metaphysics. In the philosophical work “Materialism and Empirio-criticism”, Vladimir Lenin severely criticized Ernst Mach’s position of subjective idealism and philosophical realists.  Article visualizations

Optimization of Mixed-precision Neural Architecture with Knowledge Distillation

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이혁재.Quantization은 메모리와 계산 능력이 제한된 edge device에서 deep neural network를 수행하기 위해 필수적인 프로세스이다. 그러나 bit-width 및 transformation function을 포함하여 똑같은 quantization 을 모든 레이어에 그대로 적용하는 uniform-precision quantization은 심각한 성능 저하를 겪는 것으로 널리 알려져 있다. 한편 각각의 레이어에 서로 다른 quantization 을 찾아서 적용하는 것은 후보군의 수가 레이어 수에 따라 기하급수적으로 증가하기 때문에 적용하기 어렵다. 이 문제를 해결하기 위해, 본 연구에서는 Knowledge Distillation 기법을 활용하여 선형시간 내에 검색 공간을 효율적으로 탐색하는 방법을 제안한다. 특히, 제안된 방법은 대상 레이어에 대한 quantization 의 영향을 추정하기 위해 레이어별로 loss function을 공식화하였다. 레이어간에 의존성이 최소라는 가정에 근거하여 각 레이어별 적용을 개별적으로 결정해서 성능의 손실을 최소화한다. 하드웨어 친화적인 quantization 만 사용하여 image classification과 object detection 에 대한 실험을 실시하였다. 그 결과, CIFAR-10 데이터 세트에서 크기가 13.65배 작은 가장 효율적인 mixed-precision의 ResNet20 모델로 93.62%의 정확도를 달성하였으며, 이는 기본 full-precision 모델보다 0.19% 낮은 수준이다. VOC 데이터 세트에서, 제안된 방법은 평균 precision 이 63.87% 인 mixed-precision Sim-YOLOv2-FPGA 모델을 생성하였고, 동일 압축률의 모든 uniform-precision 모델보다 성능이 뛰어나다. 제안하는 방법은 다른 최첨단 mixed-precision quantization 접근법과 유사한 효율을 달성하면서도 실행은 단순하다.Quantization is an essential process in the deployment of deep neural networks on edge devices which only have limited memory and computation capacity. However, it is widely known that the straightforward uniform-precision quantization method, which applies the same quantization scheme including bit-width and transformation function to all layers, suffers a severe performance degradation. Meanwhile, finding and assigning different quantization schemes to different layers is challenging as the number of candidates is exponential to the the number of layers. To address this problem, this study proposes a method that utilizes Knowledge Distillation technique to efficiently explore the search space in linear time. In particular, the proposed method formulates a per-layer loss function to estimate the impact of a quantization scheme on a target layer. Based on the assumption that the dependence among layers is minimal, the assignment is then decided for each layer separately to minimize the performance loss. Experiments are conducted for both image classification and object detection task, using only hardware-friendly quantization schemes. The results show that the most efficient mixed-precision ResNet20 model with 13.65 times smaller size can still achieve up to 93.62% accuracy on CIFAR-10 dataset, which is only 0.19% lower than the baseline full-precision model. On VOC dataset, the proposed method generates a mixed-precision Sim-YOLOv2-FPGA model with a mean average precision of 63.87, which outperforms all uniform-precision models with the same compression rate. The proposed method is practically simple to carry out while still achieving a comparable efficiency to other state-of-the-art approaches on mixed-precision quantization.Chapter 1: Introduction 1 Chapter 2: Related Work 4 2.1. Quantization techniques 4 2.2. Uniform quantization 5 2.3. Shifter quantization 6 2.4. Knowledge Distillation 8 2.5. Neural Architecture Search 10 Chapter 3: Knowledge-Distillation Mixed-Precision 11 3.1. Complexity challenge and solution 11 3.2. Impact assessment via Knowledge Distillation 15 3.3. Parallel Knowledge Distillation training 17 3.4. Candidate architecture generation 18 3.5. Final model fine-tuning 20 3.6. Summary 21 Chapter 4: Experimental Results 23 4.1. Final model fine-tuning time reduction 23 4.2. Scalable and flexible training 25 4.3. Effectiveness of Knowledge Distillation Mixed Precision 27 4.4. Intra-layer mixed precision 30 Chapter 5: Conclusion and Future Work 32 Appendix 33 A. Experiment with Sim-YOLOv2-FPGA on VOC dataset 33 B. Experiment with ResNet20 and ResNet32 on CIFAR-10 34 Reference 35 초 록 38 Acknowledgement 40Maste

Comparative analysis of sustainability policies regarding forestry in selected multinational home improvement and wood furniture companies

This study touched a specific and sensitive question: the implementation of sustainability policies regarding forestry of leading companies in a forestry-related sector. Due to a lack of reference and previous researches, it appeared that this was the first time such a kind of study was carried out. Five companies including Home Depot, Carrefour, B&Q, IKEA and Wal-Mart were chosen for this study based on the following conditions: they are multinationals, they have long tradition in business in forest selected sector and they are major global competitors in the retailer market for home improvement and wood furniture. In this study different approaches such as survey with questionnaires, sustainability report analysis, web impression analysis, communication friendliness analysis and Google search statistic were used for getting data. The study found that the two US-based companies (Home Depot and Wal-Mart) are worse not only in providing, presenting the information regarding forestry but also in communicating with researcher. Based on analyses of different approaches it was proved that all selected companies have clear sustainability policies and sustainability reports regarding forestry as an obligatory part in their activities. While study could indicate that three EU-based companies (Carrefour, B&Q, IKEA) have implemented key policies in their supply chains (e.g. start-up requirements to wood suppliers, compulsory documents for clarifying wood sources and procedure of auditing) as efforts to prevent illegal timber, it could not conclude that two US-based companies have the same motivation due to the lack of information and communication from them. All selected companies have used certified raw material and planned to use majority of certified wood products in the business, and FSC scheme was preferred by these companies because of its credibility. Some more interesting findings are being paid attention. Firstly, all informants chosen for survey were not full ready for such a kind of sensitive topics. Secondly, while companies avoided presentation of negative information on themselves, it was easy to find ebullient debate about their involvement in illegal logging. Thirdly, the study results showed the difference between two groups companies in dealing with sustainability regarding forestry: three EU-based companies performed better than their two US-based competitors, and this picture was comparable to what have been found by other previous researches investigating sustainability in general in these 3 groups. Since the topic was quite sensitive and specific for everyday business in selected multinationals, there were a great deal of issues to be discussed regarding the methodology and reliability of data

Diversity Of Corynespora Cassiicola Isolates And Changes In Rubber (Hevea Brasiliensis) Leaf Protein Profiles In Response To Pathogen Inoculation

Corynespora leaf fall, caused by Corynespora cassiicola, is one of the most important diseases in rubber (Hevea brasiliensis) plantations. A study was conducted to analyse the diversity among C. cassiicola isolates and to investigate the changes in rubber leaf protein profiles in response to this pathogen. Inter Simple Sequence Repeat (ISSR) and rDNA-ITS sequence markers along with morphological characteristics and detached leaf assay were employed to analyse 21 isolates of C. cassiicola collected from different rubber clones grown in several states of Malaysia. Variations in morphological features were observed within and among isolates with no inclination to either clonal or geographical origins of the isolates. The ISSR and rDNA-ITS sequence analyses segregated the studied isolates into two distinct groups. Group 1 includes 12 isolates from the states of Johor and Selangor (this group was split into 2 subgroups 1A and 1B, subgroup 1B includes a unique isolate, CKT05D); and group 2 includes 9 isolates obtained from the other states. AMOVA analysis showed 84% of total genetic variation was attributed to variation between two groups with highly significant difference. The detached leaf assay performed on selected rubber clones grouped the isolates in subcluster 1A into Race 1; the isolates in cluster 2 into Race 2 while the pathogenicity of the isolate CKT5D was dissimilar to either Race 1 or Race 2. Two Single Nucleotide Polymorphisms (SNPs) were discovered from the rDNA-ITS region of the studied isolates. They are correlated to the races that were identified in Malaysia. The BLAST search results revealed that the nucleotide sequences in the rDNA-ITS region of C. cassiicola fungus are highly conserved. Seven SNPs and two indels were detected in the rDNA-ITS region of the studied and deposited C. cassiicola isolates obtained from several countries on diverse hosts and their presence may be correlated with the race of this fungus. The changes in the leaf protein profiles of two rubber clones RRIM 600 and PB 260 in response to inoculation with the spores of two isolates representing two races of this fungus were analysed using two-dimensional gel electrophoresis (2-DE). Several differentially expressed proteins were detected at different time points after inoculation. Dissimilarities in expression patterns were observed within and among the four clone/isolate interaction systems. The number of differentially expressed proteins was also different among the systems. These proteins differed in their estimated isoelectric points (pI) and molecular weights (MW) with the exception of three detected identical proteins. In conclusion, morphological analysis could identify but not differentiate the races of C. cassiicola; ISSR markers proved useful to distinguish the races while rDNA-ITS sequence markers could not only identify but could also infer the races of this fungus. This study confirmed that at least two distinct groups of C. cassiicola infect rubber trees in Malaysia. The changes in the 2-DE protein profiles of the rubber leaf proteomes in response to inoculation with C. cassiicola are highly dependent on the compatibility reactions of the rubber clone to a particular isolate. Differences in protein profiles implied the complexity of the interactions