There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Adaptive control for nonlinear compartmental dynamical systems with applications to clinical pharmacology

Data mining is one of the most important areas in the 21st century with many wide ranging applications. These include medicine, finance, commerce and engineering. Pattern mining is amongst the most important and challenging techniques employed in data mining. Patterns are collections of items which satisfy certain properties. Emerging patterns are those whose frequencies change significantly from one dataset to another. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust classifiers. In this paper, we examine various kinds of contrast patterns. We also investigate efficient pattern mining techniques and discuss how to exploit patterns to construct effective classifiers

Efficient Mining of Contrast Patterns and Their Applications to Classification

The widespread use of localization technologies produces increasing quantities of trajectory data. An important task in the analysis of trajectory data is the discovery of moving object clusters, i.e., moving objects that travel together for a period of time. Algorithms for the discovery of moving object clusters operate by applying constraints on the consecutiveness of timestamps. However, existing approaches either use a very strict timestamp constraint, which may result in the loss of interesting patterns, or a very relaxed timestamp constraint, which risks discovering noisy patterns. To address this challenge, we introduce a new type of moving object pattern called the platoon pattern.We propose a novel algorithm to efficiently retrieve platoon patterns in large trajectory databases, using several pruning techniques. Our experiments on both real data and synthetic data evaluate the effectiveness and efficiency of our approach and demonstrate that our algorithm is able to achieve several orders of magnitude improvement in running time, compared to an existing method for retrieving moving object clusters.

Efficient mining of platoon patterns in trajectory databases

One of the main challenges for online learners is knowing how to effectively manage their time. Highly autonomous settings, such as Massive Open Online Courses (MOOCs), put additional pressure on learners in this regard. However, little is known about how learners organise their time in terms of sessions or blocks of time across a MOOC. This study examined session behavioural data of 9272 learners in a MOOC and its relation to their engagement, grade and self-report data measuring aspects of self-regulated learning (SRL). From an exploratory temporal approach using clustering and group comparison tests, we examined how learners distributed sessions in relation to their length and frequency across the course (macro aspect), and which types of activities they prioritised within these sessions (micro aspect). We then investigated if these patterns of sessions were related to learners' level of engagement, achievement and use of self-regulated learning (SRL) skills. We found that successful learners had more frequent and longer sessions across the course, mixed up activities within sessions, and changed the focus of activities within sessions across the course. In addition, session distribution was found to be a meaningful proxy for learners’ use of SRL skills related to time management and effort regulation. That is, learners with higher levels of time management and effort regulation had longer and more sessions across the course. Based on the results, implications for supporting specific session behaviours to promote effective learning in MOOCs are discussed.

The importance and meaning of session behaviour in a MOOC

Background 
High-throughput DNA sequencing techniques offer the ability to rapidly and cheaply sequence material such as whole genomes. However, the short-read data produced by these techniques can be biased or compromised at several stages in the sequencing process; the sources and properties of some of these biases are not always known. Accurate assessment of bias is required for experimental quality control, genome assembly, and interpretation of coverage results. An additional challenge is that, for new genomes or material from an unidentified source, there may be no reference available against which the reads can be checked.
Results 
We propose analytical methods for identifying biases in a collection of short reads, without recourse to a reference. These, in conjunction with existing approaches, comprise a methodology that can be used to quantify the quality of a set of reads. Our methods involve use of three different measures: analysis of base calls; analysis of k-mers; and analysis of distributions of k-mers. We apply our methodology to wide range of short read data and show that, surprisingly, strong biases appear to be present. These include gross overrepresentation of some poly-base sequences, per-position biases towards some bases, and apparent preferences for some starting positions over others.
Conclusions 
The existence of biases in short read data is known, but they appear to be greater and more diverse than identified in previous literature. Statistical analysis of a set of short reads can help identify issues prior to assembly or resequencing, and should help guide chemical or statistical methods for bias rectification.

/pdf/reference-free-validation-of-short-read-data-4oyau8wv1y.pdf

James Bailey

Papers

Adaptive control for nonlinear compartmental dynamical systems with applications to clinical pharmacology

Efficient Mining of Contrast Patterns and Their Applications to Classification

Efficient mining of platoon patterns in trajectory databases

The importance and meaning of session behaviour in a MOOC

Reference-Free Validation of Short Read Data