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

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Data Mining - Concepts and Techniques.

http://brainimaging.waisman.wisc.edu/~chung/neuro.processing/literature/rubinov.2010.NI.network.pdf

Complex network measures of brain connectivity: uses and interpretations.

Preface. 1. Scheduling: Setting the Seen. 2. Parallel Computing: Experimental Platform. 3. Task Scheduling: Highlights and Framework. 4. Static Scheduling: Mean-Field Annealing. 5. Dynamic Scheduling: A Fuzzy Logic Approach. 6. Single-Row Routing: Another NP-Hard Problem. 7. Epilogue. Appendices: A. Graph Multipartitioning Using Mean-Field Annealing. B. General List Heuristic. C. Single Row Routing (Tarng et al. 1984). D. Single Row Routing (Du and Liu 1984). References. Index.

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques

The Internet of Things (IoT) is a new paradigm consisting of heterogeneous entities that communicate with each other by sending and receiving messages in heterogeneous formats through heterogeneous protocols to achieve a common goal. When designing IoT applications, there are two main challenges: the complexity to represent such heterogeneous entities, message formats, and protocols in an unambiguous manner, and the lack of methodologies to verify QoS (Quality of Service) properties. This paper introduces a design and analysis process supported by a framework to assist IoT application engineers to precisely model IoT applications and verify their properties. The framework is composed of the SysML4IoT, a SysML profile based on the IoT-A Reference Model, and the SysML2NuSMV, a model-to-text translator that converts the model and QoS properties specified on it to be executed by NuSMV, a mature model checker that allows entering a system model comprising a number of communicating Finite State Machines (FSM) and automatically checks its properties specified as Computational Tree Logic (CTL) or Linear Temporal Logic (LTL) formulas. Our approach is evaluated through a proof of concept implementation that analyzes the QoS property of reliability in a Building Energy Conservation (BEC) IoT application.

Design and Analysis of IoT Applications: A Model-Driven Approach

Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows

Over the past decades, caching has become the key technology used for bridging the performance gap across memory hierarchies via temporal or spatial localities; in particular, the effect is prominent in disk storage systems. Applications that involve heavy I/O activities, which are common in the cloud, probably benefit the most from caching. The use of local volatile memory as cache might be a natural alternative, but many well-known restrictions, such as capacity and the utilization of host machines, hinder its effective use. In addition to technical challenges, providing cache services in clouds encounters a major practical issue (quality of service or service level agreement issue) of pricing. Currently, (public) cloud users are limited to a small set of uniform and coarse-grained service offerings, such as High-Memory and High-CPU in Amazon EC2. In this paper, we present the cache as a service (CaaS) model as an optional service to typical infrastructure service offerings. Specifically, the cloud provider sets aside a large pool of memory that can be dynamically partitioned and allocated to standard infrastructure services as disk cache. We first investigate the feasibility of providing CaaS with the proof-of-concept elastic cache system (using dedicated remote memory servers) built and validated on the actual system, and practical benefits of CaaS for both users and providers (i.e., performance and profit, respectively) are thoroughly studied with a novel pricing scheme. Our CaaS model helps to leverage the cloud economy greatly in that 1) the extra user cost for I/O performance gain is minimal if ever exists, and 2) the provider's profit increases due to improvements in server consolidation resulting from that performance gain. Through extensive experiments with eight resource allocation strategies, we demonstrate that our CaaS model can be a promising cost-efficient solution for both users and providers.

Albert Y. Zomaya

Papers

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques

Design and Analysis of IoT Applications: A Model-Driven Approach

Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows

Cashing in on the Cache in the Cloud