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.

/pdf/a-survey-of-rollback-recovery-protocols-in-message-passing-bnrpate93a.pdf

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

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

Modern computing platforms are based on multi-processor/multi-core technology. This allows running applications with a high degree of hardware parallelism. However, medium-to-high end machines pose a problem related to the asymmetric delays threads experience when accessing shared data. Specifically, Non-Uniform-Memory-Access (NUMA) is the dominating technology—thanks to its capability for scaled-up memory bandwidth—which however imposes asymmetric distances between CPU-cores and memory banks, making an access by a thread to data placed on a far NUMA node severely impacting performance. In this article, we tackle this problem in the context of shared event-pool management, a relevant aspect in many fields, like parallel discrete event simulation. Specifically, we present a NUMA-aware calendar queue, which also has the advantage of making concurrent threads coordinate via a non-blocking scalable approach. Our proposal is based on work deferring combined with dynamic re-binding of the calendar queue operations (insertions/extractions) to the best suited among the concurrent threads hosted by the underlying computing platform. This changes the locality of the operations by threads in a way positively reflected onto NUMA tasks at the hardware level. We report the results of an experimental study, demonstrating the capability of our solution to achieve the order of 15% better performance compared to state-of-the-art solutions already suited for multicore environments.

NUMA-Aware Non-Blocking Calendar Queue

Reshuffling PDES platforms for multi/many-core machines: A perspective with focus on load sharing.

The continuous progress of electronic equipments has influenced car manufacturers, leading to the integration of the latest infotainment technologies and providing connection to external devices, such as mobile phones. Modern cars work with ECUs (Electronic Control Units) that handle user interactions and sensor data, by also sending information to actuators using simple, reliable and efficient networks with fast protocols, like CAN (Controller Area Network). This is the most used vehicular protocol, which allows interconnecting different ECUs, making them interact in a synergic manner. On the down side, there is a security risk related to the exposition of malicious ECU's frames—possibly generated by compromised devices—which can lead to the possibility to remote control all the car equipments (like brakes and others) by an attacker. We propose a solution to this problem, designing an authentication and encryption system above CAN, called AuthentiCAN. Our proposal is tailored for the evolution of CAN called CAN-FD, and avoids the possibility for an attacker to inject malicious frames that are not discarded by the destination ECUs. Also, we avoid the possibility for an attacker to learn the interactions that occur across ECUs, with the objective of maliciously replaying messages—which would lead the actuator's logic to be no longer compliant with the actual data sources. We also present a simulation study of our solution, where we provide an assessment of its overhead, e.g. in terms of reduction of the throughput of data-unit transfer over CAN-FD, caused by the added security features.

AuthentiCAN: a Protocol for Improved Security over CAN

A restriction of the elastic time algorithm

Concurrency control protocols based on read-validation schemes allow transactions which are doomed to abort to still run until a subsequent validation check reveals them as invalid. These late aborts do not favor the reduction of wasted computation and can penalize performance. To counteract this problem, we present an analytical model that predicts the abort probability of transactions handled via read-validation schemes. Our goal is to determine what are the suited points-along a transaction lifetime-to carry out a validation check. This may lead to early aborting doomed transactions, thus saving CPU time. We show how to exploit the abort probability predictions returned by the model in combination with a threshold-based scheme to trigger read-validations. We also show how this approach can definitely improve performance-leading up to 14 % better turnaround-as demonstrated by some experiments carried out with a port of the TPC-C benchmark to Software Transactional Memory.

/pdf/model-based-proactive-read-validation-in-transaction-1les8mao01.pdf

Francesco Quaglia

Papers

NUMA-Aware Non-Blocking Calendar Queue

Reshuffling PDES platforms for multi/many-core machines: A perspective with focus on load sharing.

AuthentiCAN: a Protocol for Improved Security over CAN

A restriction of the elastic time algorithm

Model-Based Proactive Read-Validation in Transaction Processing Systems