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

Understanding the evolutionary history of living organisms is a central problem in biology. Until recently the ability to infer evolutionary relationships was limited by the amount of DNA sequence data available, but new DNA sequencing technologies have largely removed this limitation. As a result, DNA sequence data are readily available or obtainable for a wide spectrum of organisms, thus creating an unprecedented opportunity to explore evolutionary relationships broadly and deeply across the Tree of Life. Unfortunately, the algorithms used to infer evolutionary relationships are NP-hard, so the dramatic increase in available DNA sequence data has created a commensurate increase in the need for access to powerful computational resources. Local laptop or desktop machines are no longer viable for analysis of the larger data sets available today, and progress in the field relies upon access to large, scalable high-performance computing resources. This paper describes development of the CIPRES Science Gateway, a web portal designed to provide researchers with transparent access to the fastest available community codes for inference of phylogenetic relationships, and implementation of these codes on scalable computational resources. Meeting the needs of the community has included developing infrastructure to provide access, working with the community to improve existing community codes, developing infrastructure to insure the portal is scalable to the entire systematics community, and adopting strategies that make the project sustainable by the community. The CIPRES Science Gateway has allowed more than 1800 unique users to run jobs that required 2.5 million Service Units since its release in December 2009. (A Service Unit is a CPU-hour at unit priority).

/pdf/creating-the-cipres-science-gateway-for-inference-of-large-nmwbumhj3p.pdf

Creating the CIPRES Science Gateway for inference of large phylogenetic trees

The widely used CHARMM additive all-atom force field includes parameters for proteins, nucleic acids, lipids, and carbohydrates. In the present article, an extension of the CHARMM force field to drug-like molecules is presented. The resulting CHARMM General Force Field (CGenFF) covers a wide range of chemical groups present in biomolecules and drug-like molecules, including a large number of heterocyclic scaffolds. The parametrization philosophy behind the force field focuses on quality at the expense of transferability, with the implementation concentrating on an extensible force field. Statistics related to the quality of the parametrization with a focus on experimental validation are presented. Additionally, the parametrization procedure, described fully in the present article in the context of the model systems, pyrrolidine, and 3-phenoxymethylpyrrolidine will allow users to readily extend the force field to chemical groups that are not explicitly covered in the force field as well as add functional groups to and link together molecules already available in the force field. CGenFF thus makes it possible to perform "all-CHARMM" simulations on drug-target interactions thereby extending the utility of CHARMM force fields to medicinally relevant systems.

/pdf/charmm-general-force-field-a-force-field-for-drug-like-4mhn8ejv47.pdf

CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields.

MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

/pdf/spark-cluster-computing-with-working-sets-2mqxkxxuqt.pdf

Spark: cluster computing with working sets

The term "the Grid" was coined in the mid-1990s to denote a proposed distributed computing infrastructure for advanced science and engineering [4]. Considerable progress has since been made on the construction of such an infrastructure (e.g., [1,6,7]) but the term "Grid" has also been conflated, at least in popular perception, to embrace everything from advanced networking to artificial intelligence. One might wonder whether the term has any real substance and meaning. Is there really a distinct "Grid problem" and hence a need for new "Grid technologies"? If so, what is the nature of these technologies, and what is their domain of applicability? While numerous groups have interest in Grid concepts and share, to a significant extent, a common vision of Grid architecture, we do not see consensus on the answers to these questions.

/pdf/the-anatomy-of-the-grid-enabling-scalable-virtual-1pvp7w8260.pdf

The anatomy of the grid: enabling scalable virtual organizations

Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources.In this paper we create a taxonomy of data provenance characteristics and apply it to current research efforts in e-science, focusing primarily on scientific workflow approaches. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. The survey culminates with an identification of open research problems in the field.

A survey of data provenance in e-science

Workflows and e-Science: An overview of workflow system features and capabilities

This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners.

Workflows for e-Science

Workflows have emerged as a paradigm for representing and managing complex distributed computations and are used to accelerate the pace of scientific progress. A recent National Science Foundation workshop brought together domain, computer, and social scientists to discuss requirements of future scientific applications and the challenges they present to current workflow technologies.

/pdf/examining-the-challenges-of-scientific-workflows-1r1gvl9aik.pdf

Examining the Challenges of Scientific Workflows

The Characteristics of Parallel Algorithms.

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