다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining Practical Machine Learning Tools and Techniques

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since text itself often contains incomplete and ambiguous information. Fortunately, knowledge graphs provide a golden mine for enhancing the quality of learned word representations. In particular, a knowledge graph, usually composed by entities (words, phrases, etc.), relations between entities, and some corresponding meta information, can supply invaluable relational knowledge that encodes the relationship between entities as well as categorical knowledge that encodes the attributes or properties of entities. Hence, in this paper, we introduce a novel framework called RC-NET to leverage both the relational and categorical knowledge to produce word representations of higher quality. Specifically, we build the relational knowledge and the categorical knowledge into two separate regularization functions, and combine both of them with the original objective function of the skip-gram model. By solving this combined optimization problem using back propagation neural networks, we can obtain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic prediction, have all demonstrated that our model can significantly improve the quality of word representations.

/pdf/rc-net-a-general-framework-for-incorporating-knowledge-into-52zz92lzwh.pdf

RC-NET: A General Framework for Incorporating Knowledge into Word Representations

Most of the modern hard disk drives support Self-Monitoring, Analysis and Reporting Technology (SMART), which can monitor internal attributes of individual drives and predict impending drive failures by a thresholding method. As the prediction performance of the thresholding algorithm is disappointing, some researchers explored various statistical and machine learning methods for predicting drive failures based on SMART attributes. However, the failure detection rates of these methods are only up to 50% ~ 60% with low false alarm rates (FARs). We explore the ability of Backpropagation (BP) neural network model to predict drive failures based on SMART attributes. We also develop an improved Support Vector Machine (SVM) model. A real-world dataset concerning 23,395 drives is used to verify these models. Experimental results show that the prediction accuracy of both models is far higher than previous works. Although the SVM model achieves the lowest FAR (0.03%), the BP neural network model is considerably better in failure detection rate which is up to 95% while keeping a reasonable low FAR.

/pdf/proactive-drive-failure-prediction-for-large-scale-storage-4p6n98z2as.pdf

Proactive drive failure prediction for large scale storage systems

Recently, in order to improve reactive fault tolerance techniques in large scale storage systems, researchers have proposed various statistical and machine learning methods based on SMART attributes. Most of these studies have focused on predicting failures of hard drives, i.e., labeling the status of a hard drive as “good” or not. However, in real-world storage systems, hard drives often deteriorate gradually rather than suddenly. Correspondingly, their SMART attributes change continuously towards failure. Inspired by this observation, we introduce a novel method based on Recurrent Neural Networks (RNN) to assess the health statuses of hard drives based on the gradually changing sequential SMART attributes. Compared to a simple failure prediction method, a health status assessment is more valuable in practice because it enables technicians to schedule the recovery of different hard drives according to the level of urgency. Experiments on real-world datasets for disks of different brands and scales demonstrate that our proposed method can not only achieve a reasonable accurate health status assessment, but also achieve better failure prediction performance than previous work.

Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks

Some statistical and machine learning methods have been proposed to build hard drive prediction models based on the SMART attributes, and have achieved good prediction performance However, these models were not evaluated in the way as they are used in real-world data centers Moreover, the hard drives deteriorate gradually, but these models can not describe this gradual change precisely This paper proposes new hard drive failure prediction models based on Classification and Regression Trees, which perform better in prediction performance as well as stability and interpretability compared with the state-of the-art model, the Back propagation artificial neural network model Experiments demonstrate that the Classification Tree (CT) model predicts over 95% of failures at a false alarm rate (FAR) under 01% on a real-world dataset containing 25,792 drives Aiming at the practical application of prediction models, we test them with different drive families, with fewer number of drives, and with different model updating strategies The CT model still shows steady and good performance We propose a health degree model based on Regression Tree (RT) as well, which can give the drive a health assessment rather than a simple classification result Therefore, the approach can deal with warnings raised by the prediction model in order of their health degrees We implement a reliability model for RAID-6 systems with proactive fault tolerance and show that our CT model can significantly improve the reliability and/or reduce construction and maintenance cost of large-scale storage systems

Hard Drive Failure Prediction Using Classification and Regression Trees

Major web search engines answer thousands of queries per second requesting information about billions of web pages. The data sizes and query loads are growing at an exponential rate. To manage the heavy workload, we consider techniques for utilizing a Graphics Processing Unit (GPU). We investigate new approaches to improve two important operations of search engines -- lists intersection and index compression.For lists intersection, we develop techniques for efficient implementation of the binary search algorithm for parallel computation. We inspect some representative real-world datasets and find that a sufficiently long inverted list has an overall linear rate of increase. Based on this observation, we propose Linear Regression and Hash Segmentation techniques for contracting the search range. For index compression, the traditional d-gap based compression schemata are not well-suited for parallel computation, so we propose a Linear Regression Compression schema which has an inherent parallel structure. We further discuss how to efficiently intersect the compressed lists on a GPU. Our experimental results show significant improvements in the query processing throughput on several datasets.

/pdf/efficient-parallel-lists-intersection-and-index-compression-7evuuk23tg.pdf

Gang Wang

Papers

RC-NET: A General Framework for Incorporating Knowledge into Word Representations

Proactive drive failure prediction for large scale storage systems

Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks

Hard Drive Failure Prediction Using Classification and Regression Trees

Efficient parallel lists intersection and index compression algorithms using graphics processing units