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

Workow technology continues to play an important role as a means for specifying and enacting computational experiments in modern Science. Reusing and repurposing workows

Common Motifs in Scientic Workows: An Empirical Analysis

Increased public awareness and usage of the Web suggests that a commensurate Web presence is required from providers of cultural heritage information such as Galleries and Museums. While galleries have traditionally supplied goal-driven search facilities to specialists, they must now provide browsing and query facilities to casual users, with less precise information seeking requirements. Hypertext systems provide an appropriate technology to support the networks of associations required in order to provide path-based browsing. Requirements are twofold: browsing support for the users; and authoring support in the creation of pathways. In out prototype, we combine techniques from Hypertext and Information Retrieval to provide access to artifacts drawn from the costume collection of the Manchester City Art Gallery. We provide similarity based browsing, using terms from the artifacts' metadata to calculate similarities. The approach is simple, yet effective as the results of a user evaluation show.

Supporting Public Browsing of an Art Gallery Collections Database

During the past decade, data-driven science and engineering have emerged as a key paradigm for performing scientific research, enabling innovations through new kinds of experiments that were earlier impossible. Today’s science has access to advanced instruments like next generation genome sequencers, gigapixel survey telescopes, and networks of sensors that monitor cyber-physical systems, and these are generating datasets that are growing exponentially in complexity and data volume. Big Data, across all dimensions of volume, velocity, variety, and veracity, are offering unique opportunities to enable scientific discovery as well as novel challenges to scientific platforms. Such dynamic, distributed, and data-intensive applications hold the solutions to vital scientific and societal problems of the 21st century. In order to achieve breakthrough in new knowledge, there is a need to develop data-driven system models, perform analytics at large scales, manage data from instruments and analyses, and share and visualize the results with scientific peers and the society at large. To this end, cloud computing offers a computing model for running such data-intensive scientific and engineering applications. Clouds have democratized resource access to underserved disciplines, making it possible to perform nontrivial scientific explorations for just a few hundred dollars. Clouds are particularly cost-effective for Big Data applications due to their co-location of elastic compute resources with data, and their use of commodity hardware, which economizes on costs for non-high performance computing (HPC) workloads. Many contemporary Big Data platforms that have emerged from online enterprises such as Google and Twitter are also optimized for such commodity hardware, as found in their own data centers. Of course, there are costs associated with data transfer and storage, in keeping with the pay-as-you-go model, that may not be well suited for applications requiring frequent transfer of and long-term storage of terabytes of data. Likewise, it is valuable to understand how data-intensive or even HPC applications that have been developed for computing grids, at one end, and applications developed for workstation tools like MATLAB and R, at the other end, can be effectively run on clouds. These are some of the practical realities that are worth exploring on the relevance of clouds for data-driven scientific applications. In this special issue, we have compiled a set of articles that discusses new research, development, and deployment efforts in running eScience and eEngineering workloads on cloud infrastructures and platforms. The open solicitation, which followed the 3rd Workshop on Scientific Cloud Computing (ScienceCloud), invited research and case studies on a variety of topics relevant to data-driven scientific computing on clouds: use of cloud-based technologies to address innovative compute and data-driven scientific problems that are not well served by current HPC clusters and grids, programming platforms for elastic and Big Data applications, performance and cost-effective computing on clouds, and gaps in diverse cloud fabrics and service offerings, among others. In all, the special issue received 28 articles, of which six were selected for publication after multiple rounds of reviews and revisions. The special issue starts with two articles that explore the runtime platform support required for executing Big Data science on clouds. In TomusBlobs: Scalable Data-Intensive Processing on Azure Clouds [1], the authors address the limitations of data storage within IaaS clouds such as Amazon S3 and Microsoft Azure BLOBs that are, while co-located in the data center, not present in the virtual machines (VMs) and need to be accessed over the network. Their distributed storage on VMs is optimized for concurrent access and elastic scaling, even

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.3668

Cloud computing for data-driven science and engineering

Semantics for Grid Databases, First International IFIP Conference on Semantics of a Networked World: ICSNW 2004, Paris, France, June 17-19, 2004. Revised Selected Papers

Two households, both alike in dignity, In fair Genomics, where we lay our scene, (One, comforted by its logic's rigour, Claims ontology for the realm of pure, The other, with blessed scientist's vigour, Acts hastily on models that endure), From ancient grudge break to new mutiny, When 'being' drives a fly-man to blaspheme. From forth the fatal loins of these two foes, Researchers to unlock the book of life; Whole misadventured piteous overthrows, Can with their work bury their clans' strife. The fruitful passage of their GO-mark'd love, And the continuance of their studies sage, Which, united, yield ontologies undreamed-of, Is now the hour's traffic of our stage; The which if you with patient ears attend, What here shall miss, our toil shall strive to mend.

Carole Goble

Papers

Common Motifs in Scientic Workows: An Empirical Analysis

Supporting Public Browsing of an Art Gallery Collections Database

Cloud computing for data-driven science and engineering

Semantics for Grid Databases, First International IFIP Conference on Semantics of a Networked World: ICSNW 2004, Paris, France, June 17-19, 2004. Revised Selected Papers

The montagues and the capulets : Comparative and Functional Genomics