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」

In this paper, we address reliability issues in three-tier systems with stateless application servers. For these systems, a framework called e-Transaction has been recently proposed, which specifies a set of desirable end-to-end reliability guarantees. In this article, we propose an innovative distributed protocol providing e-Transaction guarantees in the general case of multiple, autonomous back-end databases (typical of scenarios with multiple parties involved within a same business process). Differently from existing proposals coping with the e-Transaction framework, our protocol does not rely on any assumption on the accuracy of failure detection. Hence, it reveals suited for a wider class of distributed systems. To achieve such a target, our protocol exploits an innovative scheme for distributed transaction management (based on ad hoc demarcation and concurrency control mechanisms), which we introduce in this paper. Beyond providing the proof of protocol correctness, we also discuss hints on the protocol integration with conventional systems (e.g., database systems) and show the minimal overhead imposed by the protocol.

Providing e-Transaction Guarantees in Asynchronous Systems with No Assumptions on the Accuracy of Failure Detection

The order according to which the different tasks are carried out within a Time Warp platform has a direct impact on performance, given that event processing is speculative, thus being subject to the possibility of being rolled-back. It is typically recognized that not-yet-executed events having lower timestamps should be given higher CPU-schedule priority, since this contributes to keep low the amount of rollbacks. However, common Time Warp platforms usually execute events as atomic actions. Hence control is bounced back to the underlying simulation platform only at the end of the current event processing routine. In other words, CPU-scheduling of events resembles classical batch-multitasking scheduling, which is recognized not to promptly react to variations of the priority of pending tasks (e.g. associated with the injection of new events in the system). In this article we present the design and implementation of a time-sharing Time Warp platform, to be run on multi-core machines, where the platform-level software is allowed to take back control on a periodical basis (with fine grain period), and to possibly preempt any ongoing event processing activity in favor of dispatching (along the same thread) any other event that is revealed to have higher priority. Our proposal is based on an ad-hoc kernel module for Linux, which implements a fine grain timer-interrupt mechanism with lightweight management, which is fully integrated with the modern top/bottom-half timer-interrupt Linux architecture, and which does not induce any bias in terms of relative CPU-usage planning across Time Warp vs non-Time Warp threads running on the machine. Our time-sharing architecture has been integrated within the open source ROOT-Sim optimistic simulation package, and we also report some experimental data for an assessment of our proposal.

/pdf/time-sharing-time-warp-via-lightweight-operating-system-djh756kxp2.pdf

Time-Sharing Time Warp via Lightweight Operating System Support

Supporting Function Calls within PELCR

APART (A Posteriori Active ReplicaTion) is a recently proposed active replication protocol specifically tailored for multi-tier data acquisition systems. It ensures consistency of middle-tier sink replicas by means of an a-posteriori synchronization phase based on reconciliation, which is activated only in case replicas react to an input message from the sensors by generating an output event destined to the back-end tier. This paper enhances APART via a novel non-blocking synchronization scheme which prevents replicas from stalling while waiting for the outcome of an on-going synchronization phase. Contrarily, replicas are allowed to optimistically process data from the sensors, and to immediately propagate any output event towards the back-end tier. The removal of the blocking synchronization phase from the critical path gives rise to striking performance gains via an effective overlapping of event processing and synchronization. On the other hand, system consistency is ensured by enhancing the back-end tier synchronization logic in order to filter out optimistically produced output events that are incompatible with the reconciled state trajectory

/pdf/apart-boosting-apart-performance-via-optimistic-pipelining-12e4tghtiz.pdf

APART + : Boosting APART performance via optimistic pipelining of output events

PELCR is an environment for ?-terms reduction on parallel/ distributed computing systems. The computation performed in this environment is a distributed graph rewriting and a major optimization to achieve efficient execution consists of a message aggregation technique exhibiting the potential for strong reduction of the communication overhead. In this paper we discuss the interaction between the effectiveness of aggregation and the schedule sequence of rewriting operations. Then we present a Priority Based (BP) scheduling algorithm well suited for the specific aggregation technique. Results on a classical benchmark ?-term demonstrate that PB allows PELCR to achieve up to 88% of the ideal speedup while executing on a shared memory parallel architecture.

/pdf/scheduling-vs-communication-in-pelcr-4cblchr8ux.pdf

Francesco Quaglia

Papers

Providing e-Transaction Guarantees in Asynchronous Systems with No Assumptions on the Accuracy of Failure Detection

Time-Sharing Time Warp via Lightweight Operating System Support

Supporting Function Calls within PELCR

APART + : Boosting APART performance via optimistic pipelining of output events

Scheduling vs Communication in PELCR