Axioms for multilevel objects

1980 ◽  
Vol 3 (2) ◽  
pp. 171-180
Author(s):  
J.A. Bergstra ◽  
H.J.M. Goeman ◽  
A. Ollongren ◽  
G.A. Terpstra ◽  
Th.P. van der Weide
Keyword(s):  
Structured Objects

A set of axioms for structured objects of data is presented. In the structured objects components and levels are distinguished. Change of level is the result of a special application operator, components are accessible by successive selections. The set of access paths is also axiomatized. The set of axioms is uniform in the sense that features of various known classes of datastructures are combined.

scholarly journals Spatial Clustering of Structured Objects

2005 ◽  
pp. 227-245 ◽  
Author(s):  
Donato Malerba ◽  
Annalisa Appice ◽  
Antonio Varlaro ◽  
Antonietta Lanza
Keyword(s):  
Spatial Clustering ◽  
Structured Objects

Modeling of Object-Oriented Programs with Petri Net Structured Objects

2017 ◽  
Vol 36 (5) ◽  
pp. 1063-1087 ◽  
Author(s):  
Dmitriy Kharitonov ◽  
George Tarasov ◽  
Evgeniy Golenkov
Keyword(s):  
Petri Net ◽  
Object Oriented ◽  
Structured Objects

N.m.r. imaging of intact biological systems

1980 ◽  
Vol 289 (1037) ◽  
pp. 471-481 ◽  
Keyword(s):  
Biological Systems ◽  
Relaxation Times ◽  
Heterogeneous Systems ◽  
Three Dimensions ◽  
Acquisition Time ◽  
One Dimensional ◽  
Tissue Discrimination ◽  
Structured Objects ◽  
Ionizing Radiations

Internal images of structured objects may be obtained with n.m.r. by labelling component parts with different magnetic field strengths and therefore recognizably different n.m.r. frequencies. A linear field gradient generates a one-dimensional projection of nuclear density and a variety of techniques are employed to manipulate this one-dimensional probe to yield internal images in two and three dimensions. In the past few years, n.m.r. imaging, sometimes also called zeugmatography or spin mapping, has been applied progressively to provide proton images of small phantoms, fruit, vegetables and small animals, and finally to in vivo imaging of the human body; it promises to provide a valuable means of interior investigation of intact biological systems generally. For medical imaging the method is non-invasive, does not use ionizing radiations, appears to be without hazard and penetrates bony cavities without attenuation. Furthermore, other n.m.r. parameters, for example, relaxation times and fluid flow, may also be mapped; there is evidence that the relaxation times from tumours are significantly longer than those from corresponding normal tissue. Effort to date has mostly been concentrated on proton n.m.r., but some work has been done with other nuclei. Three examples are shown of n.m.r. images of intact biological systems: a fruit, an animal and a human system. The discussion includes the quantitative nature of the images, tissue discrimination, the relation between resolution in the image and image acquisition time, attenuation and phase shift of the r.f. field in the biological tissue, and magnets suitable for n.m.r. imaging. In principle, all conventional n.m.r. techniques can be combined with n.m.r. imaging methods in order to investigate heterogeneous systems. Overhauser imaging is briefly discussed.

Reasoning about structured objects: Knowledge representation meets databases

1995 ◽  
Vol 10 (1) ◽  
pp. 73-76 ◽  
Author(s):  
Franz Baader ◽  
Martin Buchheit ◽  
Manfred A. Jeusfeld ◽  
Werner Nutt
Keyword(s):  
Knowledge Representation ◽  
Computer System ◽  
Database Design ◽  
System Research ◽  
The World ◽  
Structured Objects

Structured objects are items with defined properties that are to be represented in a computer system. Research in Knowledge Representation (KR) and in Database Design (DB) has produced languages for describing structured objects. Although different in the particular means for defining properties, both areas share the goal of representing a part of the world in a structured way. Moreover, the rise of object-centred formalisms in the last decade has significantly influenced the convergence of languages.

scholarly journals Predicting structured objects with support vector machines

2009 ◽  
Vol 52 (11) ◽  
pp. 97-104 ◽  
Author(s):  
Thorsten Joachims ◽  
Thomas Hofmann ◽  
Yisong Yue ◽  
Chun-Nam Yu
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Structured Objects
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