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

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

/pdf/the-fair-guiding-principles-for-scientific-data-management-1tg0qbz9se.pdf

The FAIR Guiding Principles for scientific data management and stewardship

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

物件導向軟體之架構(Object-Oriented Software Construction)探討

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Engineering Grid Resources' Metadata for Resource and Knowledge Sharing

The soaring number of available online services calls for distributed architectures to promote scalability, fault- tolerance and semantics; to provide meaningful descriptions of services; and to support their efficient retrieval. Current approaches exploit either Semantic Overlay Networks (SONs) or Distributed Hash Tables (DHTs) sweetened with some ”semantic sugar.” SONs enable semantic driven query answering but are less scalable than DHTs, which on their turn, feature efficient but semantic-free query answering based on ”exact” match. This paper presents the ERGOT system combining DHTs and SONs to enable distributed and semantic-based service discovery. A preliminary evaluation of the system performance shows the suitability of the approach both in terms of recall and number of messages.

Combining DHTs and SONs for Semantic-Based Service Discovery

Computing systems are rapidly changing with multicore, graphics processing units (GPUs), clusters, volunteer systems, clouds, and grids offering a confusing dazzling array of opportunities. New programming paradigms such as Google MapReduce and many-task computing have joined the traditional repertoire of workflow and parallel computing for the highest performance systems. Meanwhile, the life sciences are continuing to expand in data generated with continuing improvement in the instruments for high-throughput analysis. This ‘fourth paradigm’ (data driven science) is joined by complex systems or biocomplexity that can build phenomenological models of biological systems and processes. This special issue for Emerging Computational Methods for the Life Sciences Workshop ECMLS2012 [1], juxtaposes these trends seeking those computational methods that will enhance scientific discovery. Within this overall scope, this special issue encouraged researchers to submit and present original work related to the latest trends in parallel and distributed high-performance systems applied to life science problems. Important contributions have been provided by Weber et al. [2], Yang et al. [3], Elllingson et al. [4], Hamacher et al. [5], Cushing et al. [6], and Stanberry et al. [7]. These contributions focus on:

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.3101

Carole Goble

Papers

Engineering Grid Resources' Metadata for Resource and Knowledge Sharing

Combining DHTs and SONs for Semantic-Based Service Discovery

Emerging Computational Methods for the Life Sciences Workshop 2012

Taverna workflows for systems biology

The Elements of a Computational Infrastructure for Social Simulation