An Operating Environment for the Jellybean Machine

The Jellybean Machine is a scalable MIMD concurrent processor consisting of special purpose RISC processors loosely coupled into a low latency network. I have developed an operating system to provide the supportive environment required to efficiently coordinate the collective power of the distributed processing elements. The system services are developed in detail, and may be of interest to other designers of fine grain, distributed memory processing networks.

Parallel Networks for Machine Vision

The amount of computation required to solve many early vision problems is prodigious, and so it has long been thought that systems that operate in a reasonable amount of time will only become feasible when parallel systems become available. Such systems now exist in digital form, but most are large and expensive. These machines constitute an invaluable test-bed for the development of new algorithms, but they can probably not be scaled down rapidly in both physical size and cost, despite continued advances in semiconductor technology and machine architecture. Simple analog networks can perform interesting computations, as has been known for a long time. We have reached the point where it is feasible to experiment with implementation of these ideas in VLSI form, particularly if we focus on networks composed of locally interconnected passive elements, linear amplifiers, and simple nonlinear components. While there have been excursions into the development of ideas in this area since the very beginnings of work on machine vision, much work remains to be done. Progress will depend on careful attention to matching of the capabilities of simple networks to the needs of early vision. Note that this is not at all intended to be anything like a review of the field, but merely a collection of some ideas that seem to be interesting.

The M-Machine Multicomputer

The M-Machine is an experimental multicomputer being developed to test architectural concepts motivated by the constraints of modern semiconductor technology and the demands of programming systems. The M- Machine computing nodes are connected with a 3-D mesh network; each node is a multithreaded processor incorporating 12 function units, on-chip cache, and local memory. The multiple function units are used to exploit both instruction-level and thread-level parallelism. A user accessible message passing system yields fast communication and synchronization between nodes. Rapid access to remote memory is provided transparently to the user with a combination of hardware and software mechanisms. This paper presents the architecture of the M-Machine and describes how its mechanisms maximize both single thread performance and overall system throughput.

Supporting Dynamic Languages on the Java Virtual Machine

In this note, I propose two extensions to the Java virtual machine (or VM) to allow dynamic languages such as Dylan, Scheme and Smalltalk to be efficiently implemented on the VM. These extensions do not affect the performance of pure Java programs on the machine. The first extension allows for efficient encoding of dynamic data; the second allows for efficient encoding of language-specific computational elements.

Vision by Man and Machine

The development of increasingly sophisticated and powerful computers in the last few decades has frequently stimulated comparisons between them and the human brain. Such comparisons will become more earnest as computers are applied more and more to tasks formerly associated with essentially human activities and capabilities. The expectation of a coming generation of "intelligent" computers and robots with sensory, motor and even "intellectual" skills comparable in quality to (and quantitatively surpassing) our own is becoming more widespread and is, I believe, leading to a new and potentially productive analytical science of "information processing". In no field has this new approach been so precisely formulated and so thoroughly exemplified as in the field of vision. As the dominant sensory modality of man, vision is one of the major keys to our mastery of the environment, to our understanding and control of the objects which surround us. If we wish to created robots capable of performing complex manipulative tasks in a changing environment, we must surely endow them with (among other things) adequate visual powers. How can we set about designing such flexible and adaptive robots? In designing them, can we make use of our rapidly growing knowledge of the human brain, and if so, how at the same time, can our experiences in designing artificial vision systems help us to understand how the brain analyzes visual information?

Rapid Losses of Surface Elevation following Tree Girdling and Cutting in Tropical Mangroves.

Discussion Conclusions Materials and Methods Acknowledgments Author Contributions References Reader Comments (0) Figures Abstract The importance of mangrove forests in carbon sequestration and coastal protection has been widely acknowledged. Large-scale damage of these forests, caused by hurricanes or clear felling, can enhance vulnerability to erosion, subsidence and rapid carbon losses. However, it is unclear how small-scale logging might impact on mangrove functions and services. We experimentally investigated the impact of small-scale tree removal on surface elevation and carbon dynamics in a mangrove forest at Gazi bay, Kenya. The trees in five plots of a Rhizophora mucronata (Lam.) forest were first girdled and then cut. Another set of five plots at the same site served as controls. Treatment induced significant, rapid subsidence (−32.1±8.4 mm yr−1 compared with surface elevation changes of +4.2±1.4 mm yr−1 in controls). Subsidence in treated plots was likely due to collapse and decomposition of dying roots and sediment compaction as evidenced from increased sediment bulk density. Sediment effluxes of CO2 and CH4 increased significantly, especially their heterotrophic component, suggesting enhanced organic matter decomposition. Estimates of total excess fluxes from treated compared with control plots were 25.3±7.4 tCO2 ha−1 yr−1 (using surface carbon efflux) and 35.6±76.9 tCO2 ha−1 yr−1 (using surface elevation losses and sediment properties). Whilst such losses might not be permanent (provided cut areas recover), observed rapid subsidence and enhanced decomposition of soil sediment organic matter caused by small-scale harvesting offers important lessons for mangrove management. In particular mangrove managers need to carefully consider the trade-offs between extracting mangrove wood and losing other mangrove services, particularly shoreline stabilization, coastal protection and carbon storage.

Rapid detection method for microbial spoilage using FT-IR and machine learning

Ellis, D.I., Broadhurst, D., Rowland, J.J. and Goodacre, R. (2005) Rapid detection method for microbial spoilage using FT-IR and machine learning. In: Rapid Methods for Food and Feed Quality Determination, (Eds) van Amerongen, A., Barug, D and Lauwaars, M., Wageningen Academic Publishers, Wageningen, Netherlands, in press.

Application of metabolomics to plant genotype discrimination using statistics and machine learning

Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248).

Metabolic Engineering, metabolite profiling and machine learning to investigate the phloem-mobile signal in systemic acquired resistance in tobacco

Draper, J., Darby, R.M., Beckmann, M., Maddison, A.L., Mondhe, M., Sheldrick, C., Taylor, J., Goodacre, R., and Kell, D.B. (2002) Metabolic Engineering, metabolite profiling and machine learning to investigate the phloem-mobile signal in systemic acquired resistance in tobacco. First International Congress on Plant Metabolomics, Wageningen, The Netherlands

Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning

Ellis, D. I., Broadhurst, D., Kell, D. B., Rowland, J. J., Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning. ? Applied and Environmental Microbiology, 68, (6), 2822-2828 Sponsorship: BBSRC

Machine learning of functional class from phenotype data

Clare, A. and King R.D. (2002) Machine learning of functional class from phenotype data. Bioinformatics 18(1) 160-166

Homology Induction: the use of machine learning to improve sequence similarity searches

Karwath, A. King, R. Homology induction: the use of machine learning to improve sequence similarity searches. BMC Bioinformatics. 23rd April 2002. 3:11 Additional File Describes the title organims species declaration in one string [http://www.biomedcentral.com/content/supplementary/1471- 2105-3-11-S1.doc] Sponsorship: Andreas Karwath and Ross D. King were supported by the EPSRC grant GR/L62849.

Using spectral information and machine vision for bruise detection on peaches and apricots

R. Zwiggelaar, Q. Yang, E. Garcia-Pardo and C.R. Bull, 'Using spectral information and machine vision for bruise detection on peaches and apricots', Journal of Agricultural Engineering Research 63 (4), 323-332 1996)

Machine Translation and the Internet

Jones, E. H. G., Thomas, Ned, and King, Alan, 'Machine Translation and the Internet', Mercator Media Forums (2001) 5(1) pp.84-98 RAE2008