This essay’s content is rendered by the titles of the successive sections. 1. Effective solvability versus intuitive solvability. — 2. Decidability, i.e. effective solvability, in predicate logic. The speedup phenomenon — 3. Contributions of the second-order logic to the problems of solvability — 4. The infinite progress of science in the light of Turing’s idea of the oracle. The term “oracle” is a technical counterpart of the notion of mathematical intuition.
A more detailed summary can be obtained through juxtaposing the textboxes labelled with letters A...F. Conclusion: in the progress of science an essential role is played by the feedback between intellectual intuitions (intuitive solvability) and algorithmic procedures (effective solvability).
world applications, a set of discrete points in ℝ 3 does not involve a regular geometric structure. In this paper, we also present an algorithmicprocedure to approximate normal fields of such sets which are sampled on a smooth surface. Our procedure first start with the geometric closeness relation of discrete points. This relation is expressed as a finite graph. Then, by using the neighborhood of points in the graph, we fit a quadratic surface to approximate the parameter of underlying smooth surface. Afterwards, we assign a non-regular rectangular grid like
In this paper, a comparative assessment of the Image Divide and Link Algorithm (ID&L) in different color spaces is presented. This, in order to show the significance of choosing a specific color space when the algorithm computes the dissimilarity measure between adjacent pixels. Specifically, the algorithm procedure is based on treating a digital image as a graph, assigning a weight to each edge based on the dissimilarity measure between adjacent pixels. Then, the algorithm constructs a spanning forest through a Kruskal scheme to order the edges successively while partitions are obtained. This process is driven until all the pixels of the image are segmented, that is, there are as many regions as pixels. The results of the algorithm which have been compared with those generated using different color spaces are shown.
Association. Mao Y. Lu Z. 2013 Predicting clicks of PubMed articles. In AMIA Annual Symposium Proceedings 947 956 Bethesda, Maryland American Medical Informatics Association Mao, Y., & Lu, Z. (in press). MeSH now: Automatic MeSH indexing at PubMed scale via learning to rank. Journal of Biomedical Semantics. Mao Y. Lu Z. MeSH now: Automatic MeSH indexing at PubMed scale via learning to rank Journal of Biomedical Semantics Pudovkin, A. I., & Garfield, E. (2002). Algorithmicprocedure for finding semantically related journals. Journal of the American Society for Information
., & Garfield, E. (2002). Algorithmicprocedure for finding semantically related journals. Journal of the American Society for Information Science and Technology , 53 (13), 1113–1119. https://doi.org/10.1002/asi.10153 Pudovkin A. I. Garfield E. 2002 Algorithmicprocedure for finding semantically related journals Journal of the American Society for Information Science and Technology 53 13 1113 1119 https://doi.org/10.1002/asi.10153 Ramirez, A. M., Garcia, A. O., & Del Rio, J. A. (2000). Renormalized impact factor. Scientometrics , 47 (1), 3–9. https://doi.org/10.1023/A
reported. If the data process procedure finds records without the expected number of strings, the entire record is removed. The use of incomplete records causes the failure of the data processing. Point cloud analysis and noises filtering: The second step of the algorithmprocedure foresees the displacement of each point collected by the two LiDARs into the space. As it knows the distances between the LiDARs and targets as well as the scanning angle, the system is able to build a point cloud related to the scanned semi-row. The obtained data set is corrected using the
al. (1999) as:
Herbaceous: if CHM < 0.5 m Riparian shrubs: if 0.5 m ≤ CHM < 5 m Riparian arboreal: if CHM ≥ 5 m As in the previous analysis, in this case also, the result is shown in a georeferenced thematic map, on which the classification of the riparian and floodplain vegetation are summarized ( Figure 9 ), together with a table where the percentage of soil and classified vegetation of the riverbanks are reported. Figure 9 Classification layer overlapping an RGB image in the study area. Graphical result of algorithmprocedures for riverbanks, riparian and
interdisciplinarity Scientometrics 72 1 117 147 Pudovkin, A.I., & Garfield, E. (2002). Algorithmicprocedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53(13), 1113–1119. Pudovkin A.I. Garfield E. 2002 Algorithmicprocedure for finding semantically related journals Journal of the American Society for Information Science and Technology 53 13 1113 1119 Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and