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Table 4: Field content of some orbifolds of W-algebras In the simplest cases we also determined some primary generators of the orbifold and calculated the corresponding structure constants but omit the results since they do not provide new insights (for details see [Bl5]).

in unknown title
by unknown authors
"... In PAGE 52: ... Results From the result of the preceding paragraph and the character argument explained above one can predict the generating set for many orbifolds. Table4 contains a collection of results 7). Strictly speaking, one would have to check in each case separately that the cancellation mechanism of [BFH] indeed takes place.... ..."

Table 2.2: Some Desirable Properties and Corresponding Kernel Constraints smooth. A simple, but useful, observation is that by using a kernel that acts as a low pass lter, one can attenuate cross terms and retain the auto terms. This was rst observed by Choi and Williams [15] when they created a Gaussian kernel of the form: ( ; ) = e? 2 2= where the parameter controls the cut o frequency of the lter. This work provided new insight into creating time-frequency distributions and inspired many other methods for creating kernels which we shall not go into here. Further work in this area has shown that many of the desirable properties of time-frequency distributions can be satis ed by placing constraints on the kernel function. In Table 2.2 we list su cient conditions for time- frequency distributions in Cohen apos;s class to satisfy the desirable properties in Table 2.1. All time-frequency distributions in Cohen apos;s class will be covariant to time shifts and frequency shifts. In addition, time-frequency distributions in Cohen apos;s class will also be scale covariant if the kernel satis es:

in Shift Covariant Time-Frequency Distributions of Discrete Signals
by Jeffrey C. O'neill, Jeffrey C. O'neill, Chair William, J. Williams 1999
Cited by 11

Table 6: Average iteration counts for the Nesterov-Todd (NT) and the new methods on logarithmic Chebychev approximation problems with random data. structured data are similar, as seen from Table 7. 9 Conclusion Primal{dual a ne{scaling methods were analysed in a potential reduction framework. This yielded new proofs of the polynomial worst{case iteration bounds of the short step algorithms, as well as insight into 21

in Primal-Dual Potential Reduction Methods for Semidefinite Programming Using Affine-Scaling Directions
by E. de Klerk, C. Roos, T. Terlaky
"... In PAGE 23: ... Given data A = [a1; : : :; ap]T 2 IRp k and b 2 IRp, the problem becomes minx max i=1;:::;p logaT i x ? logbi which is equivalent to min t : 1=t aT i x=bi t; i = 1; : : :; p which in turn is equivalent to mint subject to 2 6 6 6 4 t ? aT i x=bi 0 0 0 aT i x=bi 1 0 1 t 3 7 7 7 5 0; i = 1; : : :; p; which is an SDP problem of dimension n = 3p, m = k + 1. The results are shown for problems with random data in Table6 . Here the NT method performs signi- cantly better, requiring four to ve fewer iterations on average in most cases.... ..."

Table 3: Insights and Opportunities

in References
by Steve Sawyer
"... In PAGE 9: ...Insights and Opportunities Responding to the second question we discuss some of the insights and opportunities that a social perspective on software development provides. Table3 summarizes this discussion. Table 3: Insights and Opportunities... ..."

Table 6. Insight evolution sensitivity.

in Projects by
by Eduardo Málaga Chocano, Eduardo Malaga Chocano, Olivier De Weck, Eduardo Malaga Chocano 2004
"... In PAGE 10: ...able 5 Default scenario results............................................................................................. 51 Table6 .... In PAGE 62: ...igure 25. Insight evolution curves. Fig. 26 and Table6 show that a faster insight evolution helps to finish the project earlier and reduce the number of errors not detected at the end of the project. It is also observed that changes on the insight have a greater impact in the iterative approach than in the sequential ... ..."

Table 6: Unexpected, hypothesis, and incorrect insights.

in unknown title
by unknown authors 2004
"... In PAGE 5: ...onger (p lt;0.01). In general, Clusterview users finished quickly while GeneSpring users took twice as long. Table6 shows the total number of unexpected insights, hypotheses generated, and incorrect insights from the insight occurrences for each tool. Unexpected Insights: The participants using HCE with the Viral dataset noticed several facts about the data that were completely unrelated to their initial list of questions.... ..."
Cited by 20

Table 7: Total number of insights in each category

in unknown title
by unknown authors 2004
"... In PAGE 6: ...4 Insight Categories Though a wide variety of insights were made, most could be categorized into a few basic groups through a clustering process. Table7 summarizes the number of each type of insight by tool. Overall Gene Expression: These described and compared overall expression distributions for a particular experimental condition.... ..."
Cited by 20

Table 1. Insight and macro constraint summary

in Harmonizing Architectural Dissonance in REST-based Architectures
by Justin R. Erenkrantz, Michael Gorlick, Girish Suryanarayana, Richard N. Taylor, Justin R. Erenkrantz, Michael Gorlick, Girish Suryanarayana, Richard N. Taylor 2006
"... In PAGE 7: ... 5. Computational REST architectural style Driven by the insights from the previous sections and summarized in Table1 , we formulated three addi- tional macro constraints for REST to better explain the phenomena we identified. These macro constraints, in turn, lead to a larger body of micro constraints, acting on the level of a single active element (server, cache, client, etc.... ..."

Table 1: Portfolio insight of the SI customers

in The
by Bart Larivière, Dirk Van Den Poel 1111
"... In PAGE 11: ... Although the fourth SI category only encompasses one particular SI product, we thought it to be appealing to investigate this product as a separate and unique category. INSERT TABLE 1 ABOUT HERE Table1 provides insight into the portfolio of the SI customers during their lifecycle (i.e.... In PAGE 12: ... SI customer. Given the fact that this category is the most popular (i.e. possessed) within the range of SI products (see Table1 ) we can ascertain the need to find the appropriate marketing strategy and actions to revert this SI behavior. - The most retention prone SI customers are those that own high-risk products in the long run (i.... In PAGE 13: ...ince the savings account customers (i.e. SI category 2) not only represent the largest group of customers (cf. Table1 ), but at the same time also the most alarming in terms of defection rates (cf.... ..."

Table 6. Neuroanatomic and Neuropsychological Correlates of Insight in Dementias

in unknown title
by unknown authors 2007
"... In PAGE 8: ...gies (Gainotti 1975; Ownsworth and others 2006; Trouillet and others 2003), educational level, and pro- fessional status before the illness (Spitznagel and Tremont 2005). Another line of research focuses on the associations between anosognosia in AD and neuropsychological dimensions (see Table6 ). For instance, Starkstein and col- leagues (2006) examined a large sample of AD patients with variable severities of dementia and found a significant positive correlation between anosognosia and deficits in verbal memory and verbal comprehension.... ..."
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