"... In PAGE 27: ... It should be noted that complexity comparisons between this and other approaches will be strongly in u- enced by the relative costs of shifts and exclusive OR operations verus addition and increment operations on a speci c architecture. Table15 shows how this procedure performs when incorporated in a scheme like that of Cooley[7], nbrv1, and in one like that of Evans[10], nbrv2. One other, rather obvious idea occurred to me while looking at these algorithms.... In PAGE 27: ... However, the data movement can be done in place if we simply use the index vector to control which elements get swapped. Wemay not see muchimprovementonavector processor, but, as the numbers in Table15 show, the saving is considerable on a cache based machine.... ..."

"... In PAGE 55: ... Table10 C. Estimation of the Rate of Return to Sesame Research and Extension in Uganda (1999-2006) Category 1999 2000 2001 2002 2003 2004 2005 2006 BENEFITS without Research Area in local varieties (ha) 130,000 130,000 130,000 130,000 130,000 130,000 130,000 130,000 Yield, local varieties (kg) 400 400 400 400 400 400 400 400 Production (t) 52,000 52,000 52,000 52,000 52,000 52,000 52,000 52,000 Export price, local vars.... ..."

"... In PAGE 12: ... Subplans encourage modularity and re-use. Once written, a plan can be used as an operator in any number of Figure 5: Calling a subplan PLAN P2 { INPUT: w, x OUTPUT: z BODY { Op5 (w : y) P1 (x, y : z) } } Table2 : Monitoring operators Name Purpose dbquery Fetches relation from DB based on query dbappend Append to existing relation in DB dbexport Export relation to DB dbupdate Processes an update query (no results returned) email Emails data to specified e-mail address fax Faxes data to specified fax number phone Sends text message to specified cell phone number null Conditionally routes stream based on if another is empty Name Purpose wrapper Extracts web page data as relation xml2rel Converts XML document into a relation rel2xml Converts a relation to an XML document xquery Manipulates attributes that are XML documents select Filters relation based on specified criteria project Extracts specified attributes from relation join Combines relations based on specified criteria union Performs set union of two relations minus Performs set minus of two relations intersect Performs set intersect of two relations pack Embeds relation in single attribute tuple unpack Expands embedded relation from single attribute tuple Table1: Data manipulation operators Name Purpose apply Apply single row function to each relation tuple aggregate Apply multi-row function to relation Table 3: Extensibility operators... In PAGE 28: ... Return the set of successor belief states. Table2 : The Successors algorithm. Given an applicable operator, the function simulates the effects of applying it to the current belief state a66 a67a15a69 a12 .... In PAGE 29: ... This ensures we have a valid plan regardless of which state we are actually in. Although it is not explicitly represented in Table2 , the generation of the successor belief states works with fully instantiated op- erators. Figure 3 shows a pictorial view of the generation process.... In PAGE 34: ... Instead of rejecting a graduate school applicant with only a no answer, we suggest steps that might be taken by the applicant in the future to increase his/her chance of being admitted the next time around. Table2 . An example customer database.... In PAGE 73: ... The template is composed of one while loop: while there is an ball that is not at its goal location, move to the ball (if necessary), pick up the ball, move to goal location of the object, and drop the ball. Learning Templates: the DISTILL Algorithm The DISTILL algorithm, shown in Table2 , learns templates from sequences of example plans, incrementally adapting the template with each new plan. One benefit of online learning is that it allows a learner with access to a planner to acquire templates on the fly in the course of its regular activity.... ..."

"... In PAGE 27: ... The collected data will also be stored in the VBE management system and will be updated periodically (Figure 10). Select criteria Member registration trust questionnaire Trustworthiness assessment New criteria questionnaire Criteria Information Formulate equations and Generate data based on existing data Generate new criteria using expert support Base criteria Use A p p l y Apply Store data Apply Store data Use Use Store data Specific criteria Store criteria Output to the user Use Use Compare Collect data Use Use Figure 10: Base and Specific Trust Criteria and the Collection of Information For the purpose of this study some base trust criteria were identified as presented in Table2 and further described in annex TM-2. The identification of base trust criteria was based on trust perspective defined in trust perspective pentagon (Figure 9).... In PAGE 29: ... Figure 11 shows the described stepwise approach for defining different elements of trust down to the level of criteria and metrics. Table2 and Annex TM-2 present identified and suggested basic trust criteria that can be used as starting point for generating specific criteria for a trust purpose application. Figure 11: Generating Criteria and Metrics using for Measuring Trustworthiness of other VBE Members Analysis of relations among criteria In order to perform the assessment of VBE members trustworthiness more efficiently, inter- relations among the criteria must be studied and well understood.... ..."

"... In PAGE 8: ... The naive algorithm takes over 6 iterations to reach the same degree of fit that the inverse compositional algorithm reaches in 3. 5 Discussion We have presented a framework (see Table1 ) for gradient descent image alignment. Algorithms can either be additive or compositional, and either forwards or inverse.... ..."

"... In PAGE 15: ...method respectively. Table1 illustrates a comparison between speed performance and the Root Mean Square Error (RMSE) of the two methods. Here, the running time in table 1 is the time cost from data loading to result output.... In PAGE 15: ... As many tiles in the terrain model are described by Grid- based models, and the time performance of constructing multi-resolution Grid models is better than that of constructing multi-resolution TIN models, constructing multi-resolution terrain models based on the new method has better speed performance. Table1 also shows the RMSEs of the models generated by the new method are less than those of the models generated by the quadric error metrics based iterative edge contraction algorithm. For example, the RMSEs of Level_3 generated by the iterative edge contraction algorithm and the new method are Figure 14.... ..."

"... In PAGE 10: ... We restrict arithmetic extensions on constants #28c#29, attributes #28x#29, or aggregate functions #28f#28x#29#29 which can be combined by addition, di#0Berence, multiplication, or division. Based on a base aggregation constraint #1E : A #12 B, where #12 is the comparison predicate, A and B are constants, attributes, or aggre- gate functions depending on the kind of base aggregation constraint, we present three classes of extensions in Table8 . The symbol #05 represents the operator:... ..."

"... In PAGE 4: ... 4 Finite Field Arithmetic We construct the finite extension field Fp12 as a tower of finite extensions: Quadratic on top of a cubic on top of a quadratic. The quadratic/cubic non- residues and reduction polynomials are detailed in Table1 . The multiplication and squaring algorithms chosen to implement field arithmetic are listed in Ta- ble 2.... ..."

"... In PAGE 4: ... 4 Finite Field Arithmetic We construct the flnite extension fleld Fp12 as a tower of flnite extensions: Quadratic on top of a cubic on top of a quadratic. The quadratic/cubic non- residues and reduction polynomials are detailed in Table1 . The multiplication and squaring algorithms chosen to implement fleld arithmetic are listed in Ta- ble 2.... ..."