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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

We present an algorithm for scheduling distributed data intensive bag-of-task applications on data grids that have costs associated with requesting, transferring and processing datasets. We evaluate the algorithm on a data grid testbed and present the results.

/pdf/cost-based-scheduling-for-data-intensive-applications-on-4ocnt1mvq6.pdf

Cost-based scheduling for data-intensive applications on global grids

Book Review: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers.

Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid environment. However, in this paper, we present a distributed list heuristic for decentralized scheduling of workflow applications in global Grids. The simulation results show that the proposed scheduling approach is scalable with respect to increased workload on the system.

/pdf/a-distributed-heuristic-for-decentralized-workflow-ahcyjhzbhf.pdf

A distributed heuristic for decentralized workflow scheduling in global Grids

Recent developments of the enhanced hardware on mobile devices such as quad-core CPUs and various sensors have made it possible to build a powerful heterogeneous mobile cloud offloading service that consists of a mobile ad-hoc cloud, nearby servers and public cloud services. However, the availability and mobility management of mobile devices in the network can significantly hinder the performance of mobile cloud systems due to the frequent system faults caused by dynamic changes, and prevent applications from offloading to mobile ad-hoc networks. In order to improve the mobile cloud service reliability, we propose a group based fault tolerant mechanism GFT-mCloud that classifies mobile devices into groups based on its processing capacity, mobility, and reliability. Different fault tolerance techniques are then devised adaptively based on the task offloading schedules and the specific group of machines it's offloaded. GFT-mCloud is designed as a standalone module that can work with existing mobile cloud code offloading systems. Extensive experiments have been conducted to evaluate the proposed mechanism. The results show that our fault tolerant mechanism is able to outperform conventional fault tolerant algorithms in the mobile cloud offloading environment.

A Group-based Fault Tolerant Mechanism for Heterogeneous Mobile Clouds

With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namely LYRIC (deadLine and budget aware spatio-temporal querY pRocessing In Cloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11%, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by ≈23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches.

Rajkumar Buyya

Papers

Cost-based scheduling for data-intensive applications on global grids

Book Review: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers.

A distributed heuristic for decentralized workflow scheduling in global Grids

A Group-based Fault Tolerant Mechanism for Heterogeneous Mobile Clouds

LYRIC: Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud