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

National Institute of Standards and Technology における超伝導研究及び生活

This survey covers rollback-recovery techniques that do not require special language constructs. In the first part of the survey we classify rollback-recovery protocols into checkpoint-based and log-based.Checkpoint-based protocols rely solely on checkpointing for system state restoration. Checkpointing can be coordinated, uncoordinated, or communication-induced. Log-based protocols combine checkpointing with logging of nondeterministic events, encoded in tuples called determinants. Depending on how determinants are logged, log-based protocols can be pessimistic, optimistic, or causal. Throughout the survey, we highlight the research issues that are at the core of rollback-recovery and present the solutions that currently address them. We also compare the performance of different rollback-recovery protocols with respect to a series of desirable properties and discuss the issues that arise in the practical implementations of these protocols.

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A survey of rollback-recovery protocols in message-passing systems

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

Global Virtual Time (GVT) is a powerful abstraction used to discriminate what events belong (and what do not belong) to the past history of a parallel/distributed computation. For high performance simulation systems based on the Time Warp synchronization protocol, where concurrent simulation objects are allowed to process their events speculatively and causal consistency is achieved via rollback/recovery techniques, GVT is used to determine which portion of the simulation can be considered as committed. Hence it is the base for actuating memory recovery (e.g. of obsolete logs that were taken in order to support state recoverability) and nonrevocable operations (e.g. I/O). For shared memory implementations of simulation platforms based on the Time Warp protocol, the reference GVT algorithm is the one presented by Fujimoto and Hybinette [1]. However, this algorithm relies on critical sections that make it non-wait-free, and which can hamper scalability. In this article we present a waitfree shared memory GVT algorithm that requires no critical section. Rather, correct coordination across the processes while computing the GVT value is achieved via memory atomic operations, namely compare-and-swap. The price paid by our proposal is an increase in the number of GVT computation phases, as opposed to the single phase required by the proposal in [1]. However, as we show via the results of an experimental study, the wait-free nature of the phases carried out in our GVT algorithm pays-off in reducing the actual cost incurred by the proposal in [1].

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Wait-Free Global Virtual Time Computation in Shared Memory TimeWarp Systems

The notion of temporal uncertainty, expressed as the possibility for an event to occur in an interval of simulation time, rather than at a specific instant, has been recently proposed and exploited in order to enhance the performance of parallel/distributed simulation systems. In this paper, we propose the concept of "spatial uncertainty" expressed as the possibility for a simulation event to occur in one of a set of points within the simulated system space. How to handle/exploit space uncertain events in an optimistic simulation system is discussed. Also, experimental results for optimistic simulations of a personal communication system (PCS) modeled with space uncertain events are reported. These results show the ability of spatial uncertainty to increase the performance of the parallel simulation system by providing a more flexible approach to synchronization.

Space uncertain simulation events: some concepts and an application to optimistic synchronization

Time Warp is a synchronization mechanism for parallel/distributed simulation. It allows logical processes (LPs) to execute events without the guarantee of a causally consistent execution. Upon the detection of a causality violation, rollback procedures recover the state of the simulation to a correct value. When a rollback occurs there are two primary sources of performance loss: (1) CPU time must be spent for the execution of the rollback procedures and (2) waste of CPU time arises from the invalidation of event executions. In this paper we present a general framework for the problem of scheduling the next LP to be run on a processor in Time Warp simulations. The framework establishes a class of scheduling algorithms having the twofold aim to keep low the CPU time for the execution of the rollback procedures and also to guarantee low waste of time due to event executions invalidated by rollback. The combination of these two aims should actually lead to short completion time of Time Warp simulations.We collocate existing scheduling algorithms within the framework, pointing out how they miss previous aims, at least partially. Then we instantiate a Window-based Grain Sensitive (WGS) scheduling algorithm relying on the framework, which pursues the above twofold aim. We also identify the proper conditions, associated with the simulation model execution, under which any algorithm exploiting the framework structure is expected to benefit the performance of the Time Warp mechanism. Empirical evidence from an experimental study of WGS on classical benchmarks and on a mobile communication system simulation fully confirms the theoretical outcomes.

On the processor scheduling problem in time warp synchronization

In this tutorial we present the ROme OpTimistic Simulator (ROOT-Sim), a general-purpose Parallel Discrete Event simulation platform built according to the optimistic synchronization scheme, which allows—via the adoption of a simple/reduced API—to implement simulation models via event handlers relying on standard ANSI-C. We present the set of paradigms which ROOT-Sim is built on, and its internal design, along with the offered facilities. We also explain the simulation-model programming paradigm, and give an example of a basic simulation model, which stands as a building block for more complex ones.

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The ROme OpTimistic Simulator: A Tutorial

In this article we present the implementation of an environment supporting Levy's optimal reduction for the λ-calculus on parallel (or distributed) computing systems. In a similar approach to Lamping's, we base our work on a graph reduction technique, known as directed virtual reduction, which is actually a restriction of Danos-Regnier virtual reduction.The environment, which we refer to as PELCR (parallel environment for optimal lambda-calculus reduction), relies on a strategy for directed virtual reduction, namely half combustion. While developing PELCR we adopted both a message aggregation technique, allowing reduction of the communication overhead, and a fair policy for distributing dynamically originated load among processors.We also present an experimental study demonstrating the ability of PELCR to definitely exploit the parallelism intrinsic to λ-terms while performing the reduction. We show how PELCR allows achieving up to 70--80p of the ideal speedup on last generation multiprocessor computing systems. As a last note, the software modules have been developed with the C language and using a standard interface for message passing, that is, MPI, thus making PELCR itself a highly portable software package.

Francesco Quaglia

Papers

Wait-Free Global Virtual Time Computation in Shared Memory TimeWarp Systems

Space uncertain simulation events: some concepts and an application to optimistic synchronization

On the processor scheduling problem in time warp synchronization

The ROme OpTimistic Simulator: A Tutorial

PELCR: Parallel environment for optimal lambda-calculus reduction