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」

Whether it is for audit or for recovery purposes, data checkpointing is an important problem of distributed database systems. Actually, transactions establish dependence relations on data checkpoints taken by data object managers. So, given an arbitrary set of data checkpoints (including at least a single data checkpoint from a data manager, and at most a data checkpoint from each data manager), an important question is the following one: ``Can these data checkpoints be members of a same consistent global checkpoint?''. This paper answers this question by providing a necessary and sufficient condition suited for database systems. Moreover, to show the usefulness of this condition, two {\em non-intrusive} data checkpointing protocols are derived from this condition. It is also interesting to note that this paper, by exhibiting ``correspondences'', establishes a bridge between the data object/transaction model and the process/message-passing model.

Consistent Checkpointing in Distributed Databases: Towards a Formal Approach

We present a multi-word atomic (1,N) register for multi-core machines exploiting Read-Modify-Write (RMW) instructions to coordinate the writer and the readers in a wait-free manner. Our proposal, called Anonymous Readers Counting (ARC), enables large-scale data sharing by admitting up to $2^{32}-2$ concurrent readers on off-the-shelf 64-bits machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers. Further, ARC avoids multiple copies of the register content when accessing it---this affects classical register's algorithms based on atomic read/write operations on single words. Thus it allows for higher scalability with respect to the register size. Moreover, ARC explicitly reduces improves performance via a proper limitation of RMW instructions in case of read operations, and by supporting constant time for read operations and amortized constant time for write operations. A proof of correctness of our register algorithm is also provided, together with experimental data for a comparison with literature proposals. Beyond assessing ARC on physical platforms, we carry out as well an experimentation on virtualized infrastructures, which shows the resilience of wait-free synchronization as provided by ARC with respect to CPU-steal times, proper of more modern paradigms such as cloud computing.

A Wait-free Multi-word Atomic (1,N) Register for Large-scale Data Sharing on Multi-core Machines

Input characterization to describe the flow of incoming traffic in network systems, such as the grid and the WWW, is often performed by using Markov modulated poisson processes (MMPP). Therefore, to enact capacity planning and quality-of-service (QoS) oriented design, the model of the servers that receive the incoming traffic is often described as a MMPP/M/1 queue. In a work we have provided an approximate solution for the response time distribution of the MMPP/M/1 queue, which is based on a hyper-exponential process obtained via a weighted superposition of the response time distributions of M/M/l queues. Compared to exact solution methods, or simulative techniques, the aim of this approximation is to provide the potential for more efficient model solution, so to enable, e.g., real-time what-if analysis in system reconfiguration scenarios. In this paper, we show how fast the computation can be supported in practical settings by ad-hoc techniques allowing the hyper-exponential model to be solved with no iterative or numerical costly steps, which would otherwise be required in order to compute the length of transient phases due to state switches in the MMPP arrival process. An application to the context of performance analysis of a grid system is also shown, supporting the efficiency of our proposal.

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Fast Computation of Hyper-exponential Approximations of the Response Time Distribution of MMPP/M/1 Queues

In this article we present Mutable Locks, a synchronization construct with the same execution semantic of traditional locks (such as spin locks or sleep locks), but with a self-tuned optimized trade off between responsiveness---in the access to a just released critical section---and CPU-time usage during threads' wait phases. It tackles the need for modern synchronization supports, in the era of multi-core machines, whose runtime behavior should be optimized along multiple dimensions (performance vs resource consumption) with no intervention by the application programmer. Our proposal is intended for exploitation in generic concurrent applications where scarce or none knowledge is available about the underlying software/hardware stack and the actual workload, an adverse scenario for static choices between spinning and sleeping faced by mutable locks just thanks to their hybrid waiting phases and self-tuning capabilities.

Mutable locks: Combining the best of spin and sleep locks

Software Transactional Memory (STM) allows encapsulating shared-data accesses within transactions, executed with atomicity and isolation guarantees. The assessment of the consistency of a running transaction is performed by the STM layer at specific points of its execution, such as when a read or write access to a shared object occurs, or upon a commit attempt. However, performance and energy efficiency issues may arise when no shared-data read/write operation occurs for a while along a thread running a transaction. In this scenario, the STM layer may not regain control for a considerable amount of time, thus not being able to early detect if such transaction has become inconsistent in the meantime. To tackle this problem we present an STM architecture that, thanks to a lightweight operating system support, is able to perform a fine-grain periodic (hence prompt) revalidation of running transactions. Our proposal targets Linux and x86 systems and has been integrated with the open source TinySTM package. Experimental results with a port of the TPC-C benchmark to STM environments show the effectiveness of our solution.

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Francesco Quaglia

Papers

Consistent Checkpointing in Distributed Databases: Towards a Formal Approach

A Wait-free Multi-word Atomic (1,N) Register for Large-scale Data Sharing on Multi-core Machines

Fast Computation of Hyper-exponential Approximations of the Response Time Distribution of MMPP/M/1 Queues

Mutable locks: Combining the best of spin and sleep locks

Prompt application-transparent transaction revalidation in software transactional memory