Clinorotation Increases the Growth of Utricular Otoliths of Developing Cichlid Fish

It has been shown earlier that hypergravity slows down inner ear otolith growth in developing fish as an adaptation towards increased environmental gravity. Suggesting that otolith growth is regulated by the central nervous system, thus adjusting otolithic weight to produce a test mass, applying functional weightlessness should yield an opposite effect, i.e. larger than normal otoliths. Therefore, larval siblings of cichlid fish (Oreochromis mossambicus) were housed for 7 days in a submersed, two-dimensional clinostat, which provided a residual gravity of approximately 0.007g. After the experiment, otoliths were dissected and their size (area grown during the experiment) was determined. Maintenance in the clinostat resulted in significantly larger utricular otoliths (lapilli, involved in graviperception). There were no statistical significant differences regarding saccular otoliths obtained (sagittae, involved in transmitting linear acceleration and, especially, in the hearing process). These results indicated, that the animals had in fact received functional weightlessness. In line and contrasting results on the otoliths of other teleost species kept at actual microgravity (spaceflight) or within rotating wall vessels are discussed.     

Augmentation

A semantic and pragmatic interpreter that combines automatic and interactive disambiguation is described. This augmentor has an interactive disambiguation component that is called upon to aid automatic disambiguation when automated strategies prove inadequate. In addition to interactive disambiguation, the augmentor also provides the user interface for the KBMT-89 project.     

Internal and external algorithms for the points-in-regions problem—the inside join of geo-relational algebra

We consider the problem of collectively locating a set of points within a set of disjoint polygonal regions when neither for points nor for regions preprocessing is allowed. This problem arises in geometric database systems. More specifically it is equivalent to computing theinside join of geo-relational algebra, a conceptual model for geo-data management. We describe efficient algorithms for solving this problem based on plane-sweep and divide-and-conquer, requiringO(n(logn) +t) andO(n(log² n) +t) time, respectively, andO(n) space, wheren is the total number of points and edges, and (is the number of reported (point, region) pairs. Since the algorithms are meant to be practically useful we consider as well as the internal versions-running completely in main memory-versions that run internally but use much less than linear space and versions that run externally, that is, require only a constant amount of internal memory regardless of the amount of data to be processed. Comparing plane-sweep and divide-and-conquer, it turns out that divide-and-conquer can be expected to perform much better in the external case even though it has a higher internal asymptotic worst-case complexity. An interesting theoretical by-product is a new general technique for handling arbitrarily large sets of objects clustered on a singlex-coordinate within a planar divide-and-conquer algorithm and a proof that the resulting “unbalanced” dividing does not lead to a more than logarithmic height of the tree of recursive calls.     

Segmenting images using localized histograms and region merging

A working system for segmenting images of complex scenes is presented. The system integrates techniques that have evolved out of many years of research in low-level image segmentation at the University of Massachusetts and elsewhere. This paper documents the result of this historical evolution. Segmentations produced by the system are used extensively in related image interpretation research.The system first produces segmentations based upon an analysis of spatially localized feature histograms. These initial segmentations are then simplified using a region merging algorithm. Parameter selection for the local histogram segmentation algorithm is facilitated by mapping the multidimensional parameter space to a one-dimensional parameter which regulates region fragmentation. An extension of this algorithm to multiple features is also presented. Experience with roughly 100 images from different domains has shown the system to be robust and effective. Samples of these results are included.