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」

On August 14, 2018, a new set of vulnerabilities collectively named “L1 terminal fault” were announced. Systems with microprocessors utilizing out-of-order execution could allow unauthorized disclosure of information residing in the L1data cache, by tweaking the virtual memory abstraction. The vulnerability was therein mentioned for three different scenarios. In this demo-paper, we provide practical evidence about the feasibility of the most complex “VMM” case of an attacker residing in a Virtual Machine (VM), and targeting information leakage from the host OS and other independent VMs.

Foreshadow-VMM: Feasibility and Network Perspective

In this paper we propose a software architecture for achieving completely transparent optimistic synchronization in the High-Level-Architecture (HLA), namely the emerging standard for the interoperability and the integration of heterogeneous simulators. The software architecture is based on a layer called MAgic State Manager (MASM), which we have designed and implemented for transparently handling state related operations (i.e. checkpointing and recovery), and on an additional software layer, we propose in this paper, acting as Rollback Controller (RC) and taking care of all the other operations (e.g. event retractions) required to support optimistic synchronization. Compared to existing proposals, which only partially move synchronization related operations outside the application level code, our proposal has the advantage of completely removing the need for modules in support of optimistic synchronization at the application level, thus strongly simplifying the application level programmer job.

Towards Transparent Optimistic Synchronization in HLA

A rollback operation in a speculative parallel discrete event simulator has traditionally targeted the perfect reconstruction of the state to be restored after a timestamp-order violation. This imposes that the rollback support entails specific capabilities and consequently pays given costs. In this article we propose approximated rollbacks, which allow a simulation object to perfectly realign its virtual time to the timestamp of the state to be restored, but lead the reconstructed state to be an approximation of what it should really be. The advantage is an important reduction of the cost for managing the state restore task in a rollback phase, as well as for managing the activities (i.e. state saving) that actually enable rollbacks to be executed. Our proposal is suited for stochastic simulations, and explores a tradeoff between the statistical representativeness of the outcome of the simulation run and the execution performance. We provide mechanisms that enable the application programmer to control this tradeoff, as well as simulation-platform level mechanisms that constitute the basis for managing approximate rollbacks in general simulation scenarios. A study on the aforementioned tradeoff is also presented.

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Approximated Rollbacks

In this report we tackle transparent deploy and seamless execution of sequentially-coded Parallel Discrete Event Simulation (PDES) models on distributed computing architectures. We present an innovative distributed synchronization protocol which allows, in conjunction with ad-hoc Operating System memory management facilities, to access the simulation state of any concurrent Logical Process (LP) running on any node of the distributed computing environment, as if it were locally hosted by a unique node - more specifically, by a unique address space. By relying on our facilities, the simulation model developer is not required to implement neither explicit message passing, nor to rely on annotations or specific programming constructs. He can simply code the accesses to the LPs' states in place (e.g. via pointers), which significantly simplifies the software development process. The burden of synchronization and correct handling of these accesses is demanded from our user-space and kernel-space runtime environment. Our proposal targets Linux on x86 64 systems and has been integrated within the ROOT-Sim open-source optimistic simulation platform, although its design principles, and most parts of the developed software, are of general relevance.

Transparent Distributed Cross-State Synchronization in Optimistic Parallel Discrete Event Simulation

In-memory (transactional) data stores are recognized as a first-class data management technology for cloud platforms, thanks to their ability to match the elasticity requirements imposed by the pay-as-you-go cost model. On the other hand, defining the well-suited amount of cache servers to be deployed, and the degree of in-memory replication of slices of data, in order to optimize reliability/availability and performance tradeoffs, is far from being a trivial task. Yet, it is an essential aspect of the provisioning process of cloud platforms, given that it has an impact on how well cloud resources are actually exploited. To cope with the issue of determining optimized configurations of cloud in-memory data stores, in this article we present a flexible simulation framework offering skeleton simulation models that can be easily specialized in order to capture the dynamics of diverse data grid systems, such as those related to the specific protocol used to provide data consistency and/or transactional guarantees. Besides its flexibility, another peculiar aspect of the framework lies in that it integrates simulation and machine-learning (black-box) techniques, the latter being essentially used to capture the dynamics of the data-exchange layer (e.g. the message passing layer) across the cache servers. This is a relevant aspect when considering that the actual data-transport/networking infrastructure on top of which the data grid is deployed might be unknown, hence being not feasible to be modeled via white-box (namely purely simulative) approaches. We also provide an extended experimental study aimed at validating instances of simulation models supported by our framework against execution dynamics of real data grid systems deployed on top of either private or public cloud infrastructures.

Francesco Quaglia

Papers

Foreshadow-VMM: Feasibility and Network Perspective

Towards Transparent Optimistic Synchronization in HLA

Approximated Rollbacks

Transparent Distributed Cross-State Synchronization in Optimistic Parallel Discrete Event Simulation

A Flexible Framework for Accurate Simulation of Cloud In-Memory Data Stores