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

Autonomous driving has become a major goal of automobile manufacturers and an important driver for the vehicular technology. Hybrid electric vehicles (HEVs), which represent a trade-off between conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), have gained popularity due to their high fuel economy, low pollution, and excellent compatibility with the current fossil fuel dispensing and electric charging infrastructures. To facilitate autonomous driving, an autonomous HEV controller is needed for determining the power split between the powertrain components (including an ICE and an electric motor) while simultaneously managing the power consumption of auxiliary systems (e.g., air-conditioning and lighting systems) such that the overall electromobility is enhanced. Certain (partial) prior knowledge of the future driving profile is useful information for the automatic HEV control. In this paper, methods for predicting driving profile characteristics to enhance HEV power control are first presented. Based on the prediction results and the observed HEV system state (e.g. velocity, battery state-of-charge, propulsion power demand), we propose a reinforcement learning method to determine the power source split between the ICE and electric motor while also controlling the power consumptions of the air-conditioning and lighting systems in the automobile. Experimental results demonstrate significant improvement in the overall HEV system efficiency.

Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles

With switching speeds in hundreds of GHz and energy dissipation of 10_19 Joules per JJ transition, single flux quantum (SFQ) circuits can provide the speed and energy efficiency for beyond-Exascale computing. This article provides a review of the basic SFQ technology and reports recent advances in SFQ optimizations using qPALACE.

Superconductive Single FlUX Quantum Logic Devices and Circuits: Status, Challenges, and Opportunities

A technique for converting an arbitrary sum-of-products expression into a subnetwork of two-input NAND gates such that, given a fixed linear order on the inputs to the expression, the signal arrival time at the output of the subnetwork is minimum, is presented. The procedure, which is based on an algorithm for constructing optimal binary trees for alphabetic codes, is optimal for complex nodes with sum-of-products expressions having noncrossing literal support. This procedure has been said to recursively NAND-decompose a Boolean network based on the input ordering information derived from a companion placement solution resulting in reduced chip area and improved performance after technology mapping, placement and routing. >

Technology decomposition using optimal alphabetic trees

This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making problem.1

Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems

In this paper, a forecasting-based dynamic virtual channel allocation technique for reducing the power consumption of network on chips is introduced. Based on the network traffic as well as past link and total virtual channel utilizations, the technique dynamically forecasts the number of virtual channels that should be active. It is based on an exponential smoothing forecasting method that filters out short-term traffic fluctuations. In this technique, for low (high) traffic loads, a small (large) number of VCs are allocated to the corresponding input channel. To assess the efficacy of the proposed method, the network on chip has been simulated using uniform, transpose, hotspot, NED, and realistic GSM voice codec traffic profiles. Simulation results show that up to a 35% reduction in the buffer power consumption and up to 20% savings in the overall router power consumption may be achieved. The area and power dissipation overheads of the technique are negligible.

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Massoud Pedram

Papers

Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles

Superconductive Single FlUX Quantum Logic Devices and Circuits: Status, Challenges, and Opportunities

Technology decomposition using optimal alphabetic trees

Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems

Forecasting-Based Dynamic Virtual Channel Management for Power Reduction in Network-on-Chips