There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

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Convex Analysisの二,三の進展について

In the past the major concerns of the VLSI designer were area speed and cost power consideration was typically of secondary importance In recent years how ever this has begun to change and increasingly power is being given comparable weight to other design considerations Several factors have contributed to this trend including the remarkable success and growth of the class of battery powered per sonal computing devices and wireless communications systems that demand high speed computation and complex functionality with low power consumption In these applications extending the battery service life is a critical design concern There also exists a signi cant pressure for producers of high end products to reduce their power consumption The main driving factors for lower power dissipation in these products are the cost associated with packaging and cooling as well as the circuit reliability System designers have started to respond to the requirement of power constrained system designs by a combination of architectural improvements and advanced design automation methodologies and technqiues for low power In parallel researchers and CAD tool developers have introduced a variety of models algorithms and techniques for estimating the power dissipation in VLSI circuits and systems in support of the low power optimization and synthesis techniques This article describes representative contributions to the power modeling and esti mation of VLSI circuits at various levels of design abstraction

Power simulation and estimation in VLSI circuits

For modern high performance systems, aggressive technology and voltage scaling has drastically increased their susceptibility to soft errors. At the grand scale of cloud computing, it is clear that soft error induced failures will occur far more frequently, but it is unclear as to how to effectively apply current error detection and fault tolerance techniques in scale. In this paper, we focus on energy-aware fault tolerant scheduling in public, multi-user cloud systems, and explore the three-way tradeoff between reliability (in terms of soft error resiliency), performance and energy. Through a systematically optimized resource allocation, error detection approach selection, virtual machine placement, spatial/temporal redundancy augmentation and task scheduling process, the cloud service provider can achieve high error coverage and fault tolerance confidence while minimizing global energy costs under user deadline constraints. Our scheduling algorithm includes a static scheduling phase that operates on task graph based workload inputs prior to execution, and a light-weight dynamic scheduler that migrates tasks during execution in case of excessive re-executions. All schedules are evaluated on a runtime simulation engine that (1) mimics the performance fluctuations in cloud systems, and (2) supports the injection of arbitrary fault patterns. Compared to current virtual machine or task replication techniques, we are able to reduce overall application failure rates by over 50% with approximately 76% total energy overhead.

An energy-aware fault tolerant scheduling framework for soft error resilient cloud computing systems

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of virtual machines are given as scalar variables, this paper treats these demands as random variables with known means and standard deviations because the demands are not deterministic in many situations. These random variables may be correlated with one another, and there are several types of resources which can be performance bottlenecks. Therefore, both correlations and resource type heterogeneity must be considered. The virtual machine consolidation problem is thus formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we present a heuristic method to efficiently solve the problem. Simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.

Hierarchical, Portfolio Theory-Based Virtual Machine Consolidation in a Compute Cloud

An energy–quality scalable coarse grain reconfigurable architecture (CGRA) based on the voltage overscaling (VOS) technique is presented. The approximation level of each processing element (PE) in the CGRA is determined by the applied VOS-determined voltage level. By employing the technique, the architecture may be configured for accurate or approximate modes of computation depending on a user-specified output quality-of-service target for a given application. More precisely, operating voltages used for performing various operations in the application dataflow graph are minimized subject to the output quality constraint by using an energy–quality tradeoff algorithm. To make the hardware implementation of the scheme more efficient, PEs are clustered into groups of (e.g., $3\times 1$ and $2\times 1$ ) voltage islands. To assess the efficacy of the proposed method in improving the power (energy) consumption and reliability of CGRAs, different combinations of minimum output quality constraints, voltage levels, and cluster sizes for several benchmarks are studied. Simulation results indicate considerable reductions in energy consumption (up to 43%) and aging rate (up to 73%) when compared with the conventional CGRA with perfect output quality (i.e., with no approximate computations).

Energy and Reliability Improvement of Voltage-Based, Clustered, Coarse-Grain Reconfigurable Architectures by Employing Quality-Aware Mapping

A hybrid electrical energy storage (HEES) system that consists of multiple, heterogeneous electrical energy storage (EES) elements is a promising solution to achieve a cost-effective EES system because no storage element has ideal characteristics. The state-of-the-art HEES systems are based on a shared-bus charge transfer interconnect (CTI) architecture. Consequently, they are quite limited in scalability which is a function of the number of EES banks. This paper is the first introduction of a HEES system based on a networked CTI architecture, which is highly scalable and is capable of accommodating multiple, concurrent charge transfers. The paper starts by presenting a router architecture for the networked CTI and an effective on-line routing algorithm for multiple charge transfers. In the proposed algorithm, negotiated congestion (NC) routing for multiple charge transfers is performed and any lack of routing resources is addressed by merging two or more charge transfers while maximizing the overall energy efficiency by setting the optimal voltage level for the shared CTI. Examples of the proposed networked CTI are presented and the efficacy of the routing algorithm is demonstrated on a mesh-grid networked CTI.

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Massoud Pedram

Papers

Power simulation and estimation in VLSI circuits

An energy-aware fault tolerant scheduling framework for soft error resilient cloud computing systems

Hierarchical, Portfolio Theory-Based Virtual Machine Consolidation in a Compute Cloud

Energy and Reliability Improvement of Voltage-Based, Clustered, Coarse-Grain Reconfigurable Architectures by Employing Quality-Aware Mapping

Networked architecture for hybrid electrical energy storage systems