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

The objective of this paper is to provide lower and upper bounds for the switching activity on the state lines in Finite State Machines (FSMs). Using a Markov chain model for the behavior of the states of the FSM, we derive theoretical bounds for the average Hamming distance on the state lines which are valid irrespective of the state encoding used in the final implementation. Such lower and upper bounds, in addition to providing a target for any state assignment algorithm, can also be used as parameters in a high-level model of power, and thus provide an early indication about the performance limits of the target FSM. Experimental results obtained for the mcnc'91 benchmark suite show that our bounds are tighter than the bounds reported previously by other researchers and can be effectively used in a high-level power estimation framework.

Theoretical bounds for switching activity analysis in finite-state machines

In this paper we introduce a new low power address bus encoding technique, and the resulting code, named ALBORZ. The ALBORZ code is constructed based on transition signaling the limited-weight codes and, with enhancements to make it adaptive and irredundant, results in up to 89% reduction in the instruction bus switching activity, at the expense of a small area overhead.

/pdf/alborz-address-level-bus-power-optimization-1gkkfk1wvu.pdf

ALBORZ: Address Level Bus Power Optimization

This paper presents a state assignment technique to reduce dynamic power consumption in finite state machines (FSM). The key idea is to decompose the state machine into a set of cycles that are collectively equivalent to the original FSM, and perform state assignment based on the cycle realization of the state machine using Gray codes. A new implementation of state machines by using a combination of D and T flip-flops is thereby proposed, which in conjunction with the proposed encoding algorithm, reduces power consumption by an average of 15%.

/pdf/low-power-synthesis-of-finite-state-machines-with-mixed-d-p9aks88va1.pdf

Low power synthesis of finite state machines with mixed D and T flip-flops

In standard single flux quantum (SFQ) logic circuits, it is required to insert path balancing D-flip-flops (DFFs) to guarantee correct circuit operation. DFF insertion should be done in a way that any path from a primary input to a primary output has the same length in terms of the clocked element count. This is known as full path balancing (FPB) method. The FPB method usually requires insertion of many path balancing DFFs that can be even more than the actual gate count in the original circuit. This significantly increases the chip area, lowers the local clock frequency, and increases both static and dynamic power dissipation. This article presents an architecture for realizing SFQ circuits which removes all path balancing DFFs, resulting in a huge reduction in total area, node and Josephson junction count, and power consumption. The drawback is a degradation of the peak throughput of the circuit, which can be overcome by performing partial path balancing in the circuit, thereby recovering the circuit throughput by adding some path balancing DFFs.

An Efficient Pipelined Architecture for Superconducting Single Flux Quantum Logic Circuits Utilizing Dual Clocks

In static random access memory (SRAM), some cells are not selected for writing, but due to the distribution of the word line signals in the SRAM array, their word line signal is activated. Therefore, they may be mistakenly written. Such cells are called half-selected cells. This study presents two schemes, one for single-ended and the other for differential sensing SRAMs, to eliminate the half-selection disturbance. In the first proposed scheme, the content of the desired row of the SRAM array is read before the write operation and is written back on the corresponding write bitlines. This operation results in eliminating the possibility for noise to be written onto the half-selected cells. In the second scheme, a simple read operation is performed before the write operation. The authors applied their half-selection resilient schemes to 8 and 6 T SRAMs. Simulation results show that in the presence of radioactive particles, by applying their write-back scheme to 8 T SRAM and their read-before-write scheme to the conventional 6 T SRAM, the failure rate is reduced from an average of 56 and 20%, respectively, to 0. The proposed schemes do not degrade write-ability of the SRAM cells, and are bit-addressable. Moreover, their proposed schemes consume smaller amounts of power compared with their rivals.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cds.2017.0227

Massoud Pedram

Papers

Theoretical bounds for switching activity analysis in finite-state machines

ALBORZ: Address Level Bus Power Optimization

Low power synthesis of finite state machines with mixed D and T flip-flops

An Efficient Pipelined Architecture for Superconducting Single Flux Quantum Logic Circuits Utilizing Dual Clocks

Internal write-back and read-before-write schemes to eliminate the disturbance to the half-selected cells in SRAMs