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.

It is clear that closed-loop control for clinical pharmacology would significantly advance our understanding of the wide effects of pharmacological agents and anesthetics, as well as advance the state-of-the-art in drug delivery systems. While our focus in this paper has been to survey the developments of active control methods to deliver sedation to critically ill patients in an acute care environment and outline some of the future challenges of active sedation control, these control methods will have implications for other uses of closed-loop control of drug delivery. There are numerous potential applications such as control of glucose, heart rate, blood pressure, etc., that may be improved as a result of active drug dosing control. Payoffs would arise from improvements in medical care, health care, reliability of drug dosing equipment, as well as reduced cost for health care.

/pdf/closed-loop-control-in-clinical-pharmacology-paradigms-23n639bbq3.pdf

Closed-loop control in clinical pharmacology paradigms, benefits, and challenges

In agent systems, an agent's recovery from, execution problems is often complicated by constraints that are not present in a more traditional distributed, database systems environment. An analysis of agent-related crash recovery issues is presented, and requirements for achieving 'acceptable' agent crash recovery are discussed. Motivated by this analysis, a novel approach to managing agent recovery is presented. It utilises an event-and task-driven model for employing semantic compensation; task retries, and checkpointing. The compensation/retry model requires a situated model of action and failure, and provides the agent with an emergent unified, treatment of both crash recovery and run-time failure-handling. This approach helps the agent to recover acceptably from crashes and execution problems; improve system predictability; manage inter-task dependencies; and address the way in which exogenous events or crashes can trigger the need for a re-decomposition of a task. Agent architecture is then presented, which uses pair processing to leverage these recovery techniques and increase the agent's availability on crash restart.

/pdf/semantic-compensation-based-recovery-in-multi-agent-systems-439nncpw8c.pdf

Semantic-compensation-based recovery in multi-agent systems

Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy. A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size. In recent years, this motivated the development of optimal classification tree algorithms that globally optimise the decision tree in contrast to heuristic methods that perform a sequence of locally optimal decisions. We follow this line of work and provide a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our algorithm supports constraints on the depth of the tree and number of nodes. The success of our approach is attributed to a series of specialised techniques that exploit properties unique to classification trees. Whereas algorithms for optimal classification trees have traditionally been plagued by high runtimes and limited scalability, we show in a detailed experimental study that our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances, providing several orders of magnitude improvements and notably contributing towards the practical use of optimal decision trees.

MurTree: Optimal Decision Trees via Dynamic Programming and Search

Contrast data mining is a key tool for finding differences between sets of objects, or classes, and contrast patterns are a popular method for discrimination between two classes. However, such patterns can be limited in two primary ways: i) They do not readily allow second order differentiation - i.e. discovering contrasts of contrasts, ii) Mining contrast patterns often results in an overwhelming volume of output for the user. To address these limitations, this paper proposes a method which can identify contrast behaviour across both classes and also groups of classes. Furthermore, to increase interpretability for the user, it presents a new technique for finding the attributes which represent the key underlying factors behind the contrast behaviour. The associated mining task is computationally challenging and we describe an efficient algorithm to handle it, based on binary decision diagrams. Experimental results demonstrate that our technique can efficiently identify and explain contrast behaviour which would be difficult or impossible to isolate using standard techniques.

Mining influential attributes that capture class and group contrast behaviour

This paper focuses on multistability theory for discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters In this paper, we extend the definition and theory of multistability to discontinuous autonomous dynamical systems In particular, nontangency Lyapunov-based tests for multistability of discontinuous systems with Filippov solutions are established The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a theoretical foundation for general anesthesia using the network properties of the brain

Multistability, bifurcations, and biological neural networks: A synaptic drive firing model for cerebral cortex transition in the induction of general anesthesia

James Bailey

Papers

Closed-loop control in clinical pharmacology paradigms, benefits, and challenges

Semantic-compensation-based recovery in multi-agent systems

MurTree: Optimal Decision Trees via Dynamic Programming and Search

Mining influential attributes that capture class and group contrast behaviour

Multistability, bifurcations, and biological neural networks: A synaptic drive firing model for cerebral cortex transition in the induction of general anesthesia