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Table 1.1: JVMPI events used by xdProf.

in
by unknown authors

Table 1.2: xdProf analysis tool interface

in
by unknown authors

Table 5. Statistical analysis of the performance of Prof using different three states decompositionsa

in structure prediction
by M Ouali, Rd King, Mohammed Ouali, Ross D. King 1999
"... In PAGE 9: ... They translated E as ~E!,Has~H!, and the rest into ~C!, including EE and HHHH. Table5 shows the results. With this decomposition, Prof achieves an accuracy per residue of 77.... ..."

Table 1. SM-prof classi cation of cache line accesses [1]

in Cautious, machine-independent performance tuning for shared-memory multiprocessors
by Sarah A. M. Talbot, Andrew J. Bennett, Paul H. J. Kelly 1996
"... In PAGE 3: ...1 Using CLARISSA The clarissa tool is based on [5]. Input parameters include cache line size, class threshold (the N value in Table1 ), phase type (barrier or time-slot), time- slot length and overlap. A classi cation system is needed for summarising the wealth of data.... In PAGE 3: ... A classi cation system is needed for summarising the wealth of data. Table1 gives the classi cation used in the SM-prof performance debugging tool, which reports cache line access for xed time-slots in terms of read or write accesses and the number of CPUs involved [1]. In clarissa,an enhanced version of this categorisation is used, where the sharing categories... ..."
Cited by 1

Table 1. Run Nt Nn TWB Adap. Prof. Size PRF Docs

in Topic tracking at RMIT University
by Vaughan R. Shanks, Hugh E. Williams 2002
"... In PAGE 2: ... Table1 : Run parameters for RMIT runs submitted. 4.... In PAGE 2: ... 4. Results A comparison of results of the runs shown in Table1 is shown in Table 2 (these are taken from the official NIST TDT evaluation). Statistics displayed are topic weighted and macroaveraged.... ..."
Cited by 2

Table 5.3: nProfWidth and nPixSearch, 1D pro les.

in To my parents, Geoff and Libby.
by Stephen Milborrow 2007

Table 5.13: nProfWidth and nPixSearch, 2D pro les.

in To my parents, Geoff and Libby.
by Stephen Milborrow 2007

Table 3. Statistical analysis of all the classifiers forming the second stage of Prof a

in structure prediction
by M Ouali, Rd King, Mohammed Ouali, Ross D. King 1999
"... In PAGE 5: ... Using such a procedure, it is possible to boost the GOR method to 71.4% ~using the per-residue accuracy! for the unbal- anced trained network and to 70% for the balanced one, which represents an improvement of 2% over linear discrimination and more than 5% over any individual GOR algorithm; the Sov is also improved ~ Table3 !. The increase of the global accuracy is ex- plained by the fact that the subset of residues without consensus is predicted correctly at 61% after the neural network step, which represents an improvement of 7% on this subset.... In PAGE 5: ... The architecture of these networks is the same as the one used for single sequences. This produces different classifiers whose characteristics are shown in Table3 . Their accuracies per residue are at ;71%, which represents an improvement of 5% over the neural networks using only single sequences, as in the case of GOR.... In PAGE 6: ...6 and 72.5%, respectively, which represents an im- provement of 2% over NN-GOR and 2 to 3% over the neural network using a standard profile ~profile 1 or 2!~ Table3 !. It is also an improvement of more than 7 to 8% over the neural network using only single sequences.... ..."

Table 1: The input data used by the ANNs in the different ProfNet methods.

in Methodology article
by Bmc Bioinformatics, Tomas Ohlson, Varun Aggarwal, Arne Elofsson, Robert M Maccallum, Robert M Maccallum 2006

Table 3 Profile and structural parameters for the hygroscopic and monohydrate phases of -lactose obtained with FullProf after Rietveld refinements.

in unknown title
by unknown authors
Results 11 - 20 of 116