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」

Transactional Memory (TM) implementations guaranteeing disjoint-access parallelism (DAP) are desirable on multi-core architectures because they can exploit low-level parallelism. In this paper we look for a breach in the wall of existing impossibility results on DAP TMs, by identifying the strongest consistency and liveness guarantees that a DAP TM can ensure while maximizing efficiency in read-dominated workloads. Along the path of designing this protocol, we report two impossibility results related to ensuring real-time order in a DAP TM.

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Brief announcement: Breaching the wall of impossibility results on disjoint-access parallel TM

The advent of multi-core machines has lead to the need for revising the architecture of modern simulation platforms. One recent proposal we made attempted to explore the viability of load-sharing for optimistic simulators run on top of these types of machines. In this article, we provide an extensive experimental study for an assessment of the effects on run-time dynamics by a load-sharing architecture that has been implemented within the ROOT-Sim package, namely an open source simulation platform adhering to the optimistic synchronization paradigm. This experimental study is essentially aimed at evaluating possible sources of overheads when supporting load-sharing. It has been based on differentiated workloads allowing us to generate different execution profiles in terms of, e.g., granularity/locality of the simulation events.

/pdf/assessing-load-sharing-within-optimistic-simulation-5bmlju8ezf.pdf

Assessing load-sharing within optimistic simulation platforms

In this paper we present the design and implementation of a Time Management Converter (TiMaC) for HLA based simulation systems. TiMaC is a layer interposed in between the federate and the underlying RTI in order to map the conservative Time Management interface onto the optimistic one. In this way, TiMaC transparently supports optimistic execution for federates originally designed for the conservative approach, which is achieved without the need for developing any ad-hoc RTI system. TiMaC relies on a recently proposed software architecture for transparent treatment of checkpointing/recovery of the federate state, namely Magic State Manager (MASM), and implements a set of additional facilities required to support all the tasks associated with the mapping of conservative onto optimistic Time Management interfaces. The implementation has been tailored to the Georgia Tech B-RTI package, although the underlying design principles would allow it to be integrated with any RTI system. We also report an experimental study demonstrating the viability and effectiveness of our proposal in allowing conservative federates to be supported with highly increased run-time effectiveness in general contexts for what concerns the features of the underlying computing systems (e.g. LAN vsWAN based systems.

Transparent Optimistic Synchronization in HLA via a Time-Management Converter

Common implementations of core memory allocation components, like the Linux buddy system, handle concurrent allocation/release requests by synchronizing threads via spin-locks. This approach is clearly not prone to scale with large thread counts, a problem that has been addressed in the literature by introducing layered allocation services or replicating the core allocators-the bottom most ones within the layered architecture. Both these solutions tend to reduce the pressure of actual concurrent accesses to each individual core allocator. In this article we explore an alternative approach to scalability of memory allocation/release, which can be still combined with those literature proposals. Conflict detection relies on conventional atomic machine instructions in the Read-Modify-Write (RMW) class. Furthermore, beyond improving scalability and performance, it can also avoid wasting clock cycles for spin-lock operations by threads that could in principle carry out their memory allocation/release in full concurrency. Thus, it is resilient to performance degradation---in face of concurrent accesses---independently of the current level of fragmentation of the handled memory blocks.

A Non-blocking Buddy System for Scalable Memory Allocation on Multi-core Machines

Sharing cached documents among cooperative Web proxies is an effective solution to reduce Web traffic and alleviate network bottlenecks. This paper aims at comparing the performance of two cooperation protocols which follow opposite approaches: the Internet Cache Protocol (ICP) and the Full Informed Protocol (FIP). The former activates information exchange among proxies on client demand; the latter guarantees that any proxy is kept informed about the cache content of all the other cooperative proxies. The performance comparison is carried out through analytical models determining under which conditions one protocol outperforms the other. Our analysis shows that ICP is often preferable to FIP, thus pointing out that the client demand based approach is an effective solution for proxy cooperation.

Francesco Quaglia

Papers

Brief announcement: Breaching the wall of impossibility results on disjoint-access parallel TM

Assessing load-sharing within optimistic simulation platforms

Transparent Optimistic Synchronization in HLA via a Time-Management Converter

A Non-blocking Buddy System for Scalable Memory Allocation on Multi-core Machines

An analytical comparison of cooperation protocols for Web proxy servers