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

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

With increasing demand for high performance computing and data storage, distributed computing systems have attracted a lot of attention. Resource allocation is one of the most important challenges in the distributed systems specially when the clients have some Service Level Agreements (SLAs) and the total profit in the system depends on how the system can meet these SLAs. In this paper, an SLA-based resource allocation problem for cloud computing is considered and a distributed solution to this problem is presented. The processing, data storage, and communication resources are considered as three dimensions in which optimizations are performed. Simulation results demonstrate that the proposed heuristic algorithm is robust (produces high quality solutions independent of the initial solution provided) and produces solutions very close to the "optimum" (best solution found by Monte Carlo simulation).

/pdf/maximizing-profit-in-cloud-computing-system-via-resource-45zn2iwomq.pdf

Maximizing Profit in Cloud Computing System via Resource Allocation

In this paper, we present a probabilistic simulation technique to estimate the power consumption of a CMOS circuit under a general delay model. This technique is based on the notion of a tagged (probability) waveform, which models the set of all possible events at the output of each circuit node. Tagged waveforms are obtained by partitioning the logic waveform space of a circuit node according to the initial and final values of each logic waveform and compacting all logic waveforms in each partition by a single tagged waveform. To improve the efficiency of tagged probabilistic simulation, only tagged waveforms at the circuit inputs are exactly computed. The tagged waveforms of the remaining nodes are computed using a compositional scheme that propagates the tagged waveforms from circuit inputs to circuit outputs. We obtain significant speed up over explicit simulation methods with an average error of only 6%. This also represents a factor of 2-3/spl times/ improvement in accuracy of power estimates over previous probabilistic simulation approaches.

/pdf/gate-level-power-estimation-using-tagged-probabilistic-3rkqprslno.pdf

Gate-level power estimation using tagged probabilistic simulation

In this paper, two static random access memory (SRAM) cells that reduce the static power dissipation due to gate and subthreshold leakage currents are presented. The first cell structure results in reduced gate voltages for the NMOS pass transistors, and thus lowers the gate leakage current. It reduces the subthreshold leakage current by increasing the ground level during the idle (inactive) mode. The second cell structure makes use of PMOS pass transistors to lower the gate leakage current. In addition, dual threshold voltage technology with forward body biasing is utilized with this structure to reduce the subthreshold leakage while maintaining performance. Compared to a conventional SRAM cell, the first cell structure decreases the total gate leakage current by 66% and the idle power by 58% and increases the access time by approximately 2% while the second cell structure reduces the total gate leakage current by 27% and the idle power by 37% with no access time degradation.

/pdf/design-and-analysis-of-two-low-power-sram-cell-structures-qgvtep5f3z.pdf

Design and Analysis of Two Low-Power SRAM Cell Structures

The problem of estimating the energy consumption at register transfer level is addressed from an information theoretical point of view. It is shown that the average switching activity can be predicted without simulation using either entropy or informational energy averages. Consequently, two new measures relying on these concepts are developed. The accuracy of these models is investigated using common benchmarks and the results are promising.

http://www.cecs.uci.edu/~papers/compendium94-03/papers/1995/islpd95/pdffiles/3_1.pdf

Information theoretic measures of energy consumption at register transfer level

An accurate model for the prediction of interconnection lengths for standard cell layouts is presented. On the designs in the test suite estimates are within 10% of the actual layouts. The model abstracts the important features of placement, global routing, and channel routing. The predicted results are obtained from analysis of the net list. No prior knowledge of the functionality of the design is used. Accurate prediction of the interconnection length is useful for estimating the actual layout area, for evaluating the fit of a logic design to a fabrication technology, and for solving placement and routing algorithms. >

/pdf/interconnection-length-estimation-for-optimized-standard-1xcbvflaeh.pdf

Massoud Pedram

Papers

Maximizing Profit in Cloud Computing System via Resource Allocation

Gate-level power estimation using tagged probabilistic simulation

Design and Analysis of Two Low-Power SRAM Cell Structures

Information theoretic measures of energy consumption at register transfer level

Interconnection length estimation for optimized standard cell layouts