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 this work, a hierarchical, service level agreement (SLA) based resource management solution for cloud datacenters is presented, which considers the energy non-proportionality of existing servers, peak power constraints, and cooling power consumption. The goal of this resource manager is to minimize the operational cost of the data center. The hierarchical structure of the proposed solution makes the resource management scalable. The proposed resource management solution simultaneously considers server and cooling power consumption, guarantee-based SLA and complexity of the decision making in the resource management of the cloud computing systems. Considering SLA and state of the datacenter in determining the amount of resource that needs to be allocated to applications results in significant reduction of the operational cost in datacenter. The effectiveness of the proposed management scheme compared to previous work is demonstrated using a comprehensive cloud computing simulation tool. The proposed resource management algorithms reduce the operational cost of a datacenter by about 40 percent while satisfying SLA constraints and decrease the run-time of the management algorithms by up to 86 percent with respect to the state of the art centralized management solution.

Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter

This paper tackles the problem of dynamic power management (DPM) in nanoscale CMOS design technologies that are typically affected by increasing levels of process, voltage, and temperature (PVT) variations and fluctuations. This uncertainty significantly undermines the accuracy and effectiveness of traditional DPM approaches. More specifically, a stochastic framework was propose to improve the accuracy of decision making in power management, while considering the manufacturing process and/or design induced uncertainties. A key characteristic of the framework is that uncertainties are effectively captured by a partially observable semi-Markov decision process. As a result, the proposed framework brings the underlying probabilistic PVT effects to the forefront of power management policy determination. Experimental results with a RISC processor demonstrate the effectiveness of the technique and show that the proposed variability-aware power management technique ensures robust system-wide energy savings under probabilistic variations

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Dynamic power management under uncertain information

Photovoltaic (PV) power generation systems are one of the most promising renewable power sources to reduce carbon footprint. Grid-connected PV power systems do not generally have a battery to store the excess charge. However, due to severe imbalance between the peak PV power generation and peak load demand, battery-less Grid-connected PV systems are much less effective for the purpose of power generation and demand mismatch mitigation. Grid-connected PV systems equipped with a battery indeed require elaborate management. This is the first paper that introduces a systematic battery management optimization that accommodates arbitrary electricity billing policies. We formulate an optimization framework to determine the battery charging current from the Grid and PV array taking into account the limited battery capacity, power converter efficiency, battery's internal resistance and rate capacity effect, and maximum power tracking of the PV array. Experimental results show that the proposed algorithm effectively reduces the electricity bill by as much as 28% when compared with previous state-of-the-art battery management policies.

Battery management for grid-connected PV systems with a battery

This paper presents designs of rapid single flux quantum (RSFQ) logic cells comprising multiple (more than two) inputs and high-fanout splitters, which are subsequently utilized by an automated logic synthesis flow to reduce the area/power costs and logic depth of RSFQ logic circuits. The complex cells include multi-input AND and OR gates as well as specialized gates such as A+BC. The logic synthesis tool builds on the ABC tool, but adds features and capabilities that are unique to RSFQ circuits. Results show a sizeable reduction in the Area-Delay product of large multiplexer, decoder, and carrylook- ahead adder circuits while exhibiting small (manageable) degradation in margins.

Design of Complex Rapid Single-Flux-Quantum Cells with Application to Logic Synthesis

This paper presents an effective technique forcompacting a large sequence of input vectors into a muchsmaller one such that when the two sequences are applied toany circuit, the resulting power dissipations are nearly thesame. Specifically, this paper introduces the hierarchicalmodeling of Markov chains as a flexible framework forcapturing not only complex spatiotemporal correlations, butalso dynamic changes in the characteristics of the inputsequence. The new framework has a high degree ofadaptability, i.e. the hierarchical model is dynamically grownaccording to the sequence behavior. Experimental resultsdemonstrate that large compaction ratios can be obtainedwithout a significant loss in accuracy (less than 5% on average)of power estimates.

/pdf/hierarchical-sequence-compaction-for-power-estimation-2cpgilxyzz.pdf

Massoud Pedram

Papers

Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter

Dynamic power management under uncertain information

Battery management for grid-connected PV systems with a battery

Design of Complex Rapid Single-Flux-Quantum Cells with Application to Logic Synthesis

Hierarchical sequence compaction for power estimation