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

In this paper, a method is proposed for finding a pixel transformation function that maximizes backlight dimming while maintaining a pre-specified image distortion level for a liquid crystal display. This is achieved by finding a pixel transformation function, which maps the original image histogram to a new histogram with lower dynamic range. Next the contrast of the transformed image is enhanced so as to compensate for brightness loss that would arise from backlight dimming. The proposed approach relies on an accurate definition of the image distortion which takes into account both the pixel value differences and a model of the human visual system and is amenable to highly efficient hardware realization. Experimental results show that the histogram equalization for backlight scaling method results in about 45% power saving with an effective distortion rate of 5% and 65% power saving for a 20% distortion rate. This is significantly higher power savings compared to previously reported backlight dimming approaches.

HEBS: Histogram Equalization for Backlight Scaling

Multi-level implementations of digital circuits are in general smaller and consume less power compared to two-level implementations of the same logic. In fact, in some cases where a two-level implementation has exponential size in the number of inputs (i.e. a multi-input XOR function), the multi-level implementation provides a non-exponential implementation. Logic restructuring techniques are an important part of logic synthesis as they allow for creation of multi-level designs from two-level representation of the circuit. Logic restructuring techniques are also used in optimizing the multi-level representation of circuits. Logic restructuring techniques include common sub-function extraction and factorization.

Logic Restructuring for Low Power

This paper presents BlendNet, a neural network architecture employing a novel building block called Blend module, which relies on performing binary and fixed-point convolutions in its main and skip paths, respectively. There is a judicious deployment of batch normalizations on both main and skip paths inside the Blend module and in between consecutive Blend modules. This paper also presents a compiler for mapping various BlendNet models obtained by replacing some blocks/modules in various vision neural network models with BlendNet modules to FPGA devices with the goal of minimizing the end-to-end inference latency while achieving high output accuracy. BlendNet-20, derived from ResNet-20 trained on the CIFAR-10 dataset, achieves 88.0% classification accuracy (0.8% higher than the state-of-the-art binary neural network) while it only takes 0.38ms to process each image (1.4x faster than state-of-the-art). Similarly, our BlendMixer model trained on the CIFAR-10 dataset achieves 90.6% accuracy (1.59% less than full precision MLPMixer) while achieving a 3.5x reduction in the model size. Moreover, The reconfigurability of DSP blocks for performing 48-bit bitwise logic operations is utilized to achieve low-power FPGA implementation. Our measurements show that the proposed implementation yields 2.5x lower power consumption.

BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions

Single flux quantum (SFQ) logic family is an attractive alternative to CMOS technology with the promise of more than two–three orders of magnitude improvement in energy-delay product. However, component-level parameter variations that arise during the fabrication process of SFQ logic circuits tend to be very high, which means that the fabricated circuits may run into major functional and/or performance issues. Therefore, optimizing SFQ logic cells to maximize their operating parameter margins under different variability sources is required. In this article, a swarm optimization technique combining the best of automatic niching particle swarm optimization and fireworks algorithm is presented where the objective is to maximize the summation of the upper and lower bound margins over all design parameters of an SFQ logic cell. The proposed method eliminates all functional errors and improves the critical margin range and parametric yield values for six different logic cells by 22.83 and 15.22% on average, when compared to a previously optimized open-source cell library.

Margin and Yield Optimization of Single Flux Quantum Logic Cells Using Swarm Optimization Techniques

This paper illustrates, analytically and quantitatively, the effect of high-order temporal correlations on steady-state and transition probabilities in Finite State Machines (FSMs). As the main theoretical contribution, we extend the previous work done on steady-state probability calculation in FSMs to account for complex spatiotemporal correlations which are present at the primary inputs when the target machine models real hardware and receives data from real applications. More precisely: 1) using the concept of constrained reachability analysis, the correct set of Chapman-Kolmogorov equations are constructed; and 2) based on stochastic complementation and iterative aggregation/disaggregation techniques, exact and approximate techniques for finding the state occupancy probabilities in the target machine are presented. From a practical point of view, we show that assuming temporal independence or even using first-order temporal models is not sufficient, that is, the inaccuracies induced in steady-state and transition probability calculations are significant for most of the analyzed benchmarks. Experimental results show that, if the order of the source is underestimated, not only the set of reachable sets is incorrectly determined, but also the obtained probability values can be more than 100% off from the correct ones.

Massoud Pedram

Papers

HEBS: Histogram Equalization for Backlight Scaling

Logic Restructuring for Low Power

BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions

Margin and Yield Optimization of Single Flux Quantum Logic Cells Using Swarm Optimization Techniques

Steady-state probability estimation in fsms considering high-order temporal effects