蒙古北部Gun Nuur湖泊沉积物的地球化学特征及其古环境意义

了解过去是认识现在和预测未来的基础。全新世是距离现今最近的时代，对全新世环境变化的研究是探究人类与环境相互关系的主要内容。蒙古高原位于气候敏感区域，是全球能量传输的重要通道，同时受到3个气候子系统的共同影响（冬季风、夏季风、西风带），决定了此地区高分辨的记录将可为全新世全球气候变化提供重要的区域证据。但是受研究条件所限，目前蒙古高原的研究多限于在其南部的中国境内，很少涉及蒙古国内的古环境研究。 鉴于此，本文选取蒙古北部Gun Nuur湖泊沉积为研究对象，系统探讨了该湖芯沉积物中碳酸盐含量、碳酸盐碳、氧同位素、有机质含量、有机质碳同位素等地球化学指标在高分辨率气候变化重建中的重要应用价值，得出主要结论和认识如下： 1. Gun Nuur湖心沉积物中碳酸盐含量主要受夏季温度的影响，温度升高，碳酸钙和CO2的水中溶解度减小，进而碳酸盐更容易沉淀。 2. Gun Nuur碳酸盐氧同位素与反映湖区降水/蒸发比的湖泊水位有较明确的关系，即降水/蒸发比降低，水位下降，碳酸盐氧同位素值上升。 3. 温度以及由温度引起的蒸发速率的变化，和生物过程共同影响着湖泊碳酸盐碳同位素的组成，使得Gun Nuur湖心碳酸盐δ13C值气候信息不够敏感。 4. Gun Nuur沉积物有机质δ13C含量主要反映湖泊古生产力的变化，湖泊生产力高时，δ13C值偏正，湖泊生产力低时，δ13C偏负。 5. 由于Gun Nuur湖心沉积物有机质含量反映了历史时期该地区冬季的温度和冬季持续时间的长短，所以这可以作为东亚冬季风的一个良好的指标，并据此重建了过去8000年以来东亚冬季风的变化。

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 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