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Table 3: Structure correlation length `: GCAU alphabet, F refers to Fontana et al. 1992a.

in RNA Multi-Structure Landscapes - A Study Based on Temperature Dependent Partition Functions
by Sebastian Bonhoeffer, John S. McCaskill, Peter F. Stadler, Peter Schuster

Table 4: Structure correlation length `: GC alphabet, F refers to Fontana et al. 1992a.

in RNA Multi-Structure Landscapes - A Study Based on Temperature Dependent Partition Functions
by Sebastian Bonhoeffer, John S. McCaskill, Peter F. Stadler, Peter Schuster

Table 5: Characteristic lengths of RNA secondary structure landscapes for various alphabets and chain lengths. Energy parameters are taken from [23].

in RNA Folding and Combinatory Landscapes
by Walter Fontana, Peter F. Stadler, Erich G. Bornberg-Bauer, Thomas Griesmacher, Ivo L. Hofacker, Manfred Tacker, Pedro Tarazona, Edward D. Weinberger, Peter Schuster

Table 1: Average time complexity comparison between PATRICIA trie and other common data structures, where n is the number of records in the data structure and h is the entropy of the alphabet [8, 22]

in Constructing a DNS-Based Client Redirector for Generic Load Balancing
by Marie Charisse L. Gascon, Clifford Ian G. Lim, William Yu, Pierre Tagle
"... In PAGE 8: ...Table1 0: Performance times for the different component processes. A /n filter means that the pre-processor was configured to exclude subnets with values larger than n.... ..."

Table S1. False TEs found by RM in protein coding regions. The phylogenies of host genes do not support these matches as real TE cassettes

in
by Valer Gotea, Wojciech Makałowski

Table 14: ASCII code alphabet for the binning method from [8].

in Statistical Analysis of RNA Backbone£
by Eli Hershkovitz, Guillermo Sapiro, Loren Dean Williams, Eli Hershkovitzý, Guillermo Sapiroþ, Allen Tannenbaumü, Loren Dean Williamsß 2004
"... In PAGE 12: ...epresent a large fraction of the RNA. Approximately 60% of globular RNA is found in the A-conformation. The results of the proposed algorithm (VQ followed by merging of non-tagged clusters, or modified vector quantization) are presented in Table 3. Each row contains the ASCII code of the bin that matches the coding method of [8]7 (see Table14 in Appendix C), and the enumeration of the peaks (numbers as obtained from the scalar quantization). In addition to being fully automatic and capable of handling all the torsion angles at once, a clear advantage of the VQ method as compared to manual binning is the smaller numbers of classes that are needed to classify the structure.... ..."

Table 2: Raw observations before statistical processing for the rst 75 verbs in alphabetical order.

in Surface Cues and Robust Inference as a Basis for the Early Acquisition of Subcategorization Frames
by Michael Brent 1994
"... In PAGE 14: ... This information is kept in a data structure called an observations table. An alphabetically contiguous portion of the observations table from an exper- imental run is shown in Table2 . Each row represents data collected from one pair of word forms, an {ing form and its stem form.... In PAGE 33: ...) Another possibility is that children understand utterances well enough to recognize that certain words stand for actions or states, and that they have an innate predisposition to classify these as verbs (Grimshaw, 1981). The importance of the statistical inference component can be seen clearly by comparing its input, the raw observations shown in Table2 , with its output, the lexicon shown in Table 5. The segment of the observations table shown in Table 2 contains a number of cases where a verb cooccurs with cues for a frame that the verb does not in fact have in its lexical entry.... In PAGE 33: ... The importance of the statistical inference component can be seen clearly by comparing its input, the raw observations shown in Table 2, with its output, the lexicon shown in Table 5. The segment of the observations table shown in Table2 contains a number of cases where a verb cooccurs with cues for a frame that the verb does not in fact have in its lexical entry. These include come and look with a cue for NP, color, come and get with cues for a tensed clause, and come with a cue for an in nitive.... ..."
Cited by 8

Table 1. Comparison of space, counting time, and restrictions on the alphabet size among the best known self-indexes.

in Compressed representations of sequences and full-text indexes
by Paolo Ferragina, Giovanni Manzini, Veli Mäkinen, Gonzalo Navarro 2007
"... In PAGE 5: ... To summarize, our index has asymptotically the smallest known space occupancy and processes all queries faster than the data structure in [18], which is the only other compressed index known to date with essentially nHk(T ) space occupancy. Table1 summarizes our contribution.... ..."
Cited by 16

Table 1. Comparison of space, counting time, and restrictions on the alphabet size among the best known self-indexes.

in Compressed representations of sequences and full-text indexes
by Paolo Ferragina, Giovanni Manzini, Veli Mäkinen, Gonzalo Navarro 2007
"... In PAGE 5: ... To summarize, our index has asymptotically the smallest known space occupancy and processes all queries faster than the data structure in [17], which is the only other compressed index known to date with essentially nHk(T ) space occupancy. Table1 summarizes our contribution.... ..."
Cited by 16

Table 4: Running times for doubles (preprocessing times are in parentheses). and 3) it is more e cient to perform the search with the actual alphabet and for longer words it is better to decompose. The best speed-up gained by decomposing is 1.97 for words of length 640. Structures Each individual symbol is composed of 32 bytes as follows: a short integer on 2 bytes, a real number on 4 bytes, a real number on 8 bytes and a string on 18 bytes. When two symbols are compared, the comparisons is done component by component, beginning with the short integer, then the real number and nally the string. The length of the text when considered over a bounded alphabet is 6400000. In each position the average frequency value for one symbol is 200000=256 = 781:25. Overall it is 6400000=256 = 25000. The best results are always reached by the TBM algorithm meaning that even when the text has a special structure on some positions if there are enough positions with a \good quot; distribution, the use of heuristic on the alphabet is e cient. See Reference [13] for complete results.

in Experiments on String Matching in Memory Structures
by Thierry Lecroq 1998
Cited by 4
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