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

One of the main design constraints in mobile ad hoc networks (MANETs) is that they are power constrained. Hence, every effort is to be channeled towards reducing power. More precisely, network lifetime is a key design metric in MANETs. Since every node has to perform the functions of a router, if some nodes die early due to lack of energy, it will not be possible for other nodes to communicate with each other. Hence, the network will get disconnected and the network lifetime will be adversely affected. This paper presents a lifetime prediction routing protocol for MANETs that maximizes the network lifetime by finding routing solutions that minimize the variance of the remaining energies of the nodes in the network. Although this scheme introduces some additional traffic, simulations show that it improves the network lifetime by about 20-30%.

/pdf/lifetime-prediction-routing-in-mobile-ad-hoc-networks-2mtbswhs1m.pdf

Lifetime prediction routing in mobile ad hoc networks

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data flow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.

Energy minimization using multiple supply voltages

Cloud computing systems (or hosting datacenters) have attracted a lot of attention in recent years. Utility computing, reliable data storage, and infrastructure-independent computing are example applications of such systems. Electrical energy cost of a cloud computing system is a strong function of the consolidation and migration techniques used to assign incoming clients to existing servers. Moreover, each client typically has a service level agreement (SLA), which specifies constraints on performance and/or quality of service that it receives from the system. These constraints result in a basic trade-off between the total energy cost and client satisfaction in the system. In this paper, a resource allocation problem is considered that aims to minimize the total energy cost of cloud computing system while meeting the specified client-level SLAs in a probabilistic sense. The cloud computing system pays penalty for the percentage of a client's requests that do not meet a specified upper bound on their service time. An efficient heuristic algorithm based on convex optimization and dynamic programming is presented to solve the aforesaid resource allocation problem. Simulation results demonstrate the effectiveness of the proposed algorithm compared to previous work.

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SLA-based Optimization of Power and Migration Cost in Cloud Computing

In the past, the major concern of the VLSI designers werearea, performance, cost, and reliability.In recent years,however, this has changed and, increasingly, power is beinggiven comparable weight to area and speed.This is mainlydue to the remarkable success of personal computing devicesand wireless communication systems, which demandhigh-speed computation and complex functionality with lowpower consumption.In addition, there exists a strong pressurefor manufacturers of high-end products to keep powerunder control.The main driving factors for lower powerdissipation in these products are the costs associated withpackaging and cooling, and circuit reliability.Tools for the automatic design of low-power VLSI systemshave thus become mandatory.More specifically, followinga natural trend, interests of researchers have latelyshifted to the investigation of high-level power modeling,estimation, synthesis, and optimization techniques that accountfor power dissipation as the primary cost factor.This paper provides a non-exhaustive survey of the mostsuccessful and innovative ideas in this area that have appearedin the literature in the last few years.

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High-level power modeling, estimation, and optimization

This paper presents an intra-process dynamic voltage and frequency scaling (DVFS) technique targeted toward non real-time applications running on an embedded system platform. The key idea is to make use of runtime information about the external memory access statistics in order to perform CPU voltage and frequency scaling with the goal of minimizing the energy consumption while translucently controlling the performance penalty. The proposed DVFS technique relies on dynamically-constructed regression models that allow the CPU to calculate the expected workload and slack time for the next time slot, and thus, adjust its voltage and frequency in order to save energy while meeting soft timing constraints. This is in turn achieved by estimating and exploiting the ratio of the total off-chip access time to the total on-chip computation time. The proposed technique has been implemented on an XScale-based embedded system platform and actual energy savings have been calculated by current measurements in hardware. For memory-bound programs, a CPU energy saving of more than 70% with a performance degradation of 12% was achieved. For CPU-bound programs, 15/spl sim/60% CPU energy saving was achieved at the cost of 5-20% performance penalty.

/pdf/fine-grained-dynamic-voltage-and-frequency-scaling-for-2l38mcysm4.pdf

Fine-grained dynamic voltage and frequency scaling for precise energy and performance trade-off based on the ratio of off-chip access to on-chip computation times

Massoud Pedram

Papers

Lifetime prediction routing in mobile ad hoc networks

Energy minimization using multiple supply voltages

SLA-based Optimization of Power and Migration Cost in Cloud Computing

High-level power modeling, estimation, and optimization

Fine-grained dynamic voltage and frequency scaling for precise energy and performance trade-off based on the ratio of off-chip access to on-chip computation times