A compiler approach to scalable concurrent program design

The programmer's most powerful tool for controlling complexity in program design is abstraction. We seek to use abstraction in the design of concurrent programs, so as to separate design decisions concerned with decomposition, communication, synchronization, mapping, granularity, and load balancing. This paper describes programming and compiler techniques intended to facilitate this design strategy. The programming techniques are based on a core programming notation with two important properties: the ability to separate concurrent programming concerns, and extensibility with reusable programmer-defined abstractions. The compiler techniques are based on a simple transformation system together with a set of compilation transformations and portable run-time support. The transformation system allows programmer-defined abstractions to be defined as source-to-source transformations that convert abstractions into the core notation. The same transformation system is used to apply compilation transformations that incrementally transform the core notation toward an abstract concurrent machine. This machine can be implemented on a variety of concurrent architectures using simple run-time support. The transformation, compilation, and run-time system techniques have been implemented and are incorporated in a public-domain program development toolkit. This toolkit operates on a wide variety of networked workstations, multicomputers, and shared-memory multiprocessors. It includes a program transformer, concurrent compiler, syntax checker, debugger, performance analyzer, and execution animator. A variety of substantial applications have been developed using the toolkit, in areas such as climate modeling and fluid dynamics

A Primer for Program Composition Notation

This primer describes a notation for program composition. Program composition is putting programs together to get larger ones. PCN (Program Composition Notation) is a programming language that allows programmers to compose programs so that composed programs execute efficiently on uniprocessors, distributed-memory multicomputers or shared-memory multiprocessors. (Revised December 12, 1990

Post-selection point and interval estimation of signal sizes in Gaussian samples

We tackle the problem of the estimation of a vector of means from a single vector-valued observation $y$. Whereas previous work reduces the size of the estimates for the largest (absolute) sample elements via shrinkage (like James-Stein) or biases estimated via empirical Bayes methodology, we take a novel approach. We adapt recent developments by Lee et al (2013) in post selection inference for the Lasso to the orthogonal setting, where sample elements have different underlying signal sizes. This is exactly the setup encountered when estimating many means. It is shown that other selection procedures, like selecting the $K$ largest (absolute) sample elements and the Benjamini-Hochberg procedure, can be cast into their framework, allowing us to leverage their results. Point and interval estimates for signal sizes are proposed. These seem to perform quite well against competitors, both recent and more tenured. Furthermore, we prove an upper bound to the worst case risk of our estimator, when combined with the Benjamini-Hochberg procedure, and show that it is within a constant multiple of the minimax risk over a rich set of parameter spaces meant to evoke sparsity.Comment: 27 pages, 13 figure

Investigating the influence of music training on verbal memory

Previous research has shown that musical training is associated with enhanced verbal memory. The current study investigated the generality of this association by presenting undergraduates who had received musical training (n = 20) and undergraduates with no formal music training (n = 20) with four types of word list; high visual imagery, high auditory imagery, high tactile imagery, and abstract. Those who had received music training showed enhanced memory for all word lists, suggesting that music training leads to a general enhancement in verbal memory that is not restricted to specific types of words (e.g., those invoking auditory imagery). The findings support previous research in showing that music training enhances cognitive skills beyond those that are specific to the domain of music. The possible cognitive and neural factors underpinning this effect are discussed

The productivity of organic dairy herds

This report was presented at the UK Organic Research 2002 Conference. Organically managed ruminant systems place particular emphasis on maximising production from forage. Therefore, it is important that efficiency measures take full account of forage inputs. The Livestock Production Efficiency Calculator (LPEC) calculates the total metabolisable energy (ME) required by livestock and estimates forage inputs as the difference between total requirement and that supplied by other measured feeds. Productivity is expressed in terms of output (£) minus other valued inputs (£) per unit of forage ME. Estimates of production parameters were used to produce productivity indices for thirteen organic dairy herds. The productivity of herds was compared, and sensitivity analyses were conducted to examine the potential impact of a number of economic and production scenario. The relationships between yield, fertility, culling and herd productivity were examined. The advantages of this productivity analysis of organic production systems is that full account is taken of the most important input i.e. grazed and conserved forage and that all of the determinants of productivity and their interactions are considered

Early Precambrian crustal evolution in Eastern India: The ages of the Singhbhum granite and included remnants of older gneiss

Geochronology of samples from the Indian Shield was discussed. New Sm-Nd data was given for the Singhbhum granite, which give model ages (T sub DM of 3.36 to 3.40 Ga, essentially equivalent to ages of included gneissic remnants of the older metamorphic group (OMG) (T sub DM = 3.35 to 3.41 Ga). Lead-lead and Rb-Sr ages of the granite and OMG range between 3.28 to 3.38 Ga. These results are considerably younger than the 3775 + or - 89 Ma Sm-Nd isochron of Basu et al., which Taylor and colleagues interpret as an artifact caused by regressing two suites of unrelated rock samples

Bayesian cross validation for gravitational-wave searches in pulsar-timing array data

Gravitational-wave data analysis demands sophisticated statistical noise models in a bid to extract highly obscured signals from data. In Bayesian model comparison, we choose among a landscape of models by comparing their marginal likelihoods. However, this computation is numerically fraught and can be sensitive to arbitrary choices in the specification of parameter priors. In Bayesian cross validation, we characterize the fit and predictive power of a model by computing the Bayesian posterior of its parameters in a training dataset, and then use that posterior to compute the averaged likelihood of a different testing dataset. The resulting cross-validation scores are straightforward to compute; they are insensitive to prior tuning; and they penalize unnecessarily complex models that overfit the training data at the expense of predictive performance. In this article, we discuss cross validation in the context of pulsar-timing-array data analysis, and we exemplify its application to simulated pulsar data (where it successfully selects the correct spectral index of a stochastic gravitational-wave background), and to a pulsar dataset from the NANOGrav 11-year release (where it convincingly favors a model that represents a transient feature in the interstellar medium). We argue that cross validation offers a promising alternative to Bayesian model comparison, and we discuss its use for gravitational-wave detection, by selecting or refuting models that include a gravitational-wave component.Comment: 7 pages, 4 figures. Submitted to MNRA