Scalable data abstractions for distributed parallel computations

The ability to express a program as a hierarchical composition of parts is an essential tool in managing the complexity of software and a key abstraction this provides is to separate the representation of data from the computation. Many current parallel programming models use a shared memory model to provide data abstraction but this doesn't scale well with large numbers of cores due to non-determinism and access latency. This paper proposes a simple programming model that allows scalable parallel programs to be expressed with distributed representations of data and it provides the programmer with the flexibility to employ shared or distributed styles of data-parallelism where applicable. It is capable of an efficient implementation, and with the provision of a small set of primitive capabilities in the hardware, it can be compiled to operate directly on the hardware, in the same way stack-based allocation operates for subroutines in sequential machines

Factor tame: Does Britannia Still Rule the Waves?

Factor tame: Does Britannia Still Rule the Waves

A Computational Theory of Contextual Knowledge in Machine Reading

Machine recognition of off–line handwriting can be achieved by either recognising words as individual symbols (word level recognition) or by segmenting a word into parts, usually letters, and classifying those parts (letter level recognition). Whichever method is used, current handwriting recognition systems cannot overcome the inherent ambiguity in writingwithout recourse to contextual information. This thesis presents a set of experiments that use Hidden Markov Models of language to resolve ambiguity in the classification process. It goes on to describe an algorithm designed to recognise a document written by a single–author and to improve recognition by adaptingto the writing style and learning new words. Learning and adaptation is achieved by reading the document over several iterations. The algorithm is designed to incorporate contextual processing, adaptation to modify the shape of known words and learning of new words within a constrained dictionary. Adaptation occurs when a word that has previously been trained in the classifier is recognised at either the word or letter level and the word image is used to modify the classifier. Learning occurs when a new word that has not been in the training set is recognised at the letter level and is subsequently added to the classifier. Words and letters are recognised using a nearest neighbour classifier and used features based on the two–dimensional Fourier transform. By incorporating a measure of confidence based on the distribution of training points around an exemplar, adaptation and learning is constrained to only occur when a word is confidently classified. The algorithm was implemented and tested with a dictionary of 1000 words. Results show that adaptation of the letter classifier improved recognition on average by 3.9% with only 1.6% at the whole word level. Two experiments were carried out to evaluate the learning in the system. It was found that learning accounted for little improvement in the classification results and also that learning new words was prone to misclassifications being propagated

Alien Registration- Hanlon, James H. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/23234/thumbnail.jp

Ammonia borane-based nanocomposites as solid state hydrogen stores for portable power applications

Ammonia borane (AB) based nanocomposites have been investigated with the aim of developing a promising solid-state hydrogen store that complies with the requirements of a modular polymer electrolyte membrane fuel cell (PEM FC) in a portable power pack system. AB-carbon nanocomposites (prepared via ball milling or solution-impregnation) demonstrate improved hydrogen release performance compared to AB itself in terms of onset temperature and hydrogen purity, while maintaining a gravimetric density of more than 5 wt. % H2. The most promising of these materials is an AB-AC (activated carbon) composite, synthesised via solution-impregnation with an optimal dehydrogenation temperature of 96 °C. When combined with an external nickel chloride filter downstream, no evolved gaseous by-products can be detected above 100 ppb. The feasibility of an AB-AC storage tank has been further endorsed by simulations in which the reaction rate and the hydrogen flux was found to be almost constant as the temperature front propagated from the bottom to the top of the tank after initiation