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.

This book constitutes the proceedings of the 9th International Conference on Web Information Systems Engineering, WISE 2008, held in Auckland, New Zealand, in September 2008. The 17 revised full papers and 14 revised short papers presented together with two keynote talks were carefully reviewed and selected from around 110 submissions. The papers are organized in topical sections on grid computing and peer-to-peer systems; Web mining; rich Web user interfaces; semantic Web; Web information retrieval; Web data integration; queries and peer-to-peer systems; and Web services.

Web Information Systems Engineering - WISE 2008: 9th International Conference, Auckland, New Zealand, September 1-3, 2008, Proceedings

This work aims to characterize students’ writing processes using keystroke logs and understand how the extracted characteristics influence the text quality at specific moments of writing. Earlier works have proposed predictive models characterizing students’ writing processes and mainly rely on distribution-based measures of pauses obtained from the overall keystroke logs. However, the effect of isolated phases of writing has not been evaluated in these models. Moreover, current theories on writing suggest that the quality of writing depends on when specific writing behaviours are performed. This view is not examined in the keystroke logging analysis literature. Addressing the mentioned challenges, the two contributions of this work are: a) characterizing students’ writing processes connected to isolated writing phases and examining their influence on writing quality; and b) temporal analysis of keystrokes and investigating whether the significance of writing characteristics varies as students progress in their writing task. Our results suggest that characterizing students’ writing based on isolated writing phases is slightly more predictive of writing quality. Additionally, the effect of several writing characteristics on writing quality changes when considering the time dimension.

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Characterising Students' Writing Processes Using Temporal Keystroke Analysis.

Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour. Existing work cannot handle the tasks well since they model such dynamics either directly on observations or enforce the availability of complete longitudinal individual-level trajectories. However, in most of the practical applications, these requirements are unrealistic: the evolving dynamics may be too complex to be modeled directly on observations, and individual-level trajectories may not be available due to technical limitations, experimental costs and/or privacy issues. To address these challenges, we formulate a model of diffusion dynamics as the {\em hidden stochastic process} via the introduction of hidden variables for flexibility, and learn the hidden dynamics directly on {\em aggregate observations} without any requirement for individual-level trajectories. We propose a dynamic generative model with Wasserstein distance for LEarninG dEep hidden Nonlinear Dynamics (LEGEND) and prove its theoretical guarantees as well. Experiments on a range of synthetic and real-world datasets illustrate that LEGEND has very strong performance compared to state-of-the-art baselines.

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data.

Though recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effect of the modality choice on learning. In this paper, we compared two note-taking input modalities—keyboard and voice—to study their effects on participants’ understanding of learning content. We conducted a study with 60 participants in which they were asked to take notes using voice or keyboard on two independent digital text passages while also making a judgment about their performance on an upcoming test. We built mixed-effects models to examine the effect of the note-taking modality on learners’ text comprehension, the content of notes and their meta-comprehension judgement. Our findings suggest that taking notes using voice leads to a higher conceptual understanding of the text when compared to typing the notes. We also found that using voice triggers generative processes that result in learners taking more elaborate and comprehensive notes. The findings of the study imply that note-taking tools designed for digital learning environments could incorporate voice as an input modality to promote effective note-taking and higher conceptual understanding of the text.

To type or to speak? The effect of input modality on text understanding during note-taking

Plasma density measurements are key to a wide variety of high-energy-density (HED) and laboratory astrophysics experiments. We present a creative application of photonic Doppler velocimetry (PDV) from which time- and spatially resolved electron density measurements can be made. PDV has been implemented for the first time in close proximity, ∼6 cm, to the high-intensity radiation flux produced by a z-pinch dynamic hohlraum on the Z-machine. Multiple PDV probes were incorporated into the photoionized gas cell platform. Two probes, spaced 4 mm apart, were used to assess plasma density and uniformity in the central region of the gas cell during the formation of the plasma. Electron density time histories with subnanosecond resolution were extracted from PDV measurements taken from the gas cells fielded with neon at 15 Torr. As well, a null shot with no gas fill in the cell was fielded. A major achievement was the low noise high-quality measurements made in the harsh environment produced by the mega-joules of x-ray energy emitted at the collapse of the z-pinch implosion. To evaluate time dependent radiation induced effects in the fiber optic system, two PDV noise probes were included on either side of the gas cell. The success of this alternative use of PDV demonstrates that it is a reliable, precise, and affordable new electron density diagnostic for radiation driven experiments and more generally HED experiments.

James Bailey

Papers

Web Information Systems Engineering - WISE 2008: 9th International Conference, Auckland, New Zealand, September 1-3, 2008, Proceedings

Characterising Students' Writing Processes Using Temporal Keystroke Analysis.

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data.

To type or to speak? The effect of input modality on text understanding during note-taking

Development and integration of photonic Doppler velocimetry as a diagnostic for radiation driven experiments on the Z-machine.