"... In PAGE 8: ... TESTING THE EFFECTS OF DIFFERENT CMC TECHNOLOGIES In these studies, we focused on two common CMC technologies: the Palace and the ChatNet. As outlined in Table1 , the Palace and ChatNet, two popular CMC applications, can be contrasted across the attributes of Modality and Spatiality. The Palace is a multimedia two-dimensional chat program, while ChatNet is a text-only chat program.... ..."

"... In PAGE 5: ... Each rewriting rule reduces the size of the AST, thus ensuring termination. Table1 summarizes the rules, which we describe below in greater detail. Many of these rules gen- eralize.... In PAGE 5: ... Many rules have several symmetric cases. For example, the rst rule in Table1 re- moves an empty else-branch from an if-then-else statement|there is a symmetric rule for removing an empty then-branch by negating the predicate. 4.... In PAGE 5: ... 4.3 if-then-else Rewriting Rules The rst transformation shown in Table1 changes an if-then-else statement into an if-then state- ment when the else branch is empty. This trans- formation is always legal.... In PAGE 5: ... 4.4 Loop Rewriting Rules The third rule in Table1 removes useless continue statements. If the program point after a continue statement is equivalent to the program point before the continue statement, then that continue can be removed.... In PAGE 5: ... 4.5 Short Circuit Evaluation Rewriting Rules The sixth rule shown in Table1 recovers a short- circuit Or conditional. Short-circuit Or apos;s exist when two adjacent conditionals guard the same statement list and failure of either will cause a branch to equivalent locations.... In PAGE 5: ... Short-circuit Or apos;s exist when two adjacent conditionals guard the same statement list and failure of either will cause a branch to equivalent locations. The last transformation in Table1 recovers short- circuit And expressions. This transformation is ap- plicable whenever a simple if statement represents the entire body of another.... ..."

"... In PAGE 2: ... We also show that the satisfiability problem of modal formulas with modal depth bounded by 1 in K4, KD4, and S4 is NP-complete; the satisfiability problem of sets of Horn modal clauses with modal depth bounded by 1 in K, K4, KD4, and S4 is PTIME-complete. In Table1 , we summarize the complexity of the basic modal logics under the mentioned restrictions. There, mdepth stands for modal depth ; PS-cp, NP-cp, and PT-cp respectively stand for PSPACE-complete, NP-complete, and PTIME-complete.... ..."

"... In PAGE 6: ... Tables 1 through 3 detail the complete set of Canonical Abstract Components along with examples. As the dozen abstract tools in Table1 illustrate, interactive functions are distinguished from the perspective of users in interaction with a user interface. In this they are distinguished from Abstract Interaction Objects (AIO), another intermediate abstraction sometimes employed in user interface design and deve lopment [21].... ..."

"... In PAGE 13: ... Here, q = 3 seems to be reasonable. Table2 shows the results of the rst test series. Myers algorithm, LSS1, and LSS2 achieve a ltration ef- ciency 95% for mismatch ratios up to 25%, 42:5%, and 35%, respectively.... ..."

"... In PAGE 15: ...These broad patterns are robust across sub-samples. Table3 shows that the same equation structure holds, with well-defined coefficients, for men and women, the young and the old, and graduates and non-graduates. To this point in the estimation, income has been assumed to enter linearly in the equations.... In PAGE 29: ...Table3 : OLS Life Satisfaction Equations with Disability as Independent Variable for Sub-Samples Male Female Age lt;40 Age gt;=40 Non-graduates Graduates Disabled; able to do day-to-day activities -0.415 (0.... ..."

"... In PAGE 16: ... This technique can be used in tness proportional, tournament, or rank-based selection. Table2 lists the known linear combinatoric MOEA techniques. Table 2: Linear Fitness Combination Approach Description Application Objectives (#) Chromosome GA [137] (1991) Hybrid GA implemen- tation; Incorporates a schedule builder and evaluator Laboratory re- source scheduling Not stated Permutation task ordering GA-based learning system [79] (1992) Structured popula- tions; Parameterized mating only within overlapping demes; Parallelized Machine learn- ing (route planning and vehicle control) (5) Distance; Re- quired time; Path deviation; Collision monitor activa- tions; Emergency monitor activations \Action Chain; quot; Genes are lists of actions for robotic task GA [80] (1993) Linear normalized tness and weighted penalties 3-D structure con- formational search (2) Match penalty; Energy penalty Binary string; Genes are ro- tation angles GA [14] (1994) Specialized crossover; GA population se- lected from training database; One, some, or all GA population members replace least t database members Adaptive image segmentation (5) Edge-border co- incidence, Bound- ary consistency, Pixel classi cation, Object overlap, Object contrast Binary string; Genes are t- ness, image conditions, and parameters; EVOPs operate only on parameters Multi-Niche Crowding (MNC) GA [150, 21] (1995,1997) Fitness obtained by summing individual rank in each objec- tive; Phenotypic-based crowding; Integrated with ow-transport simulation code Groundwater pol- lution contanimant monitoring; Also tested on multi- modal, dynamic function (3) Cost; Contan- imant removal; Contanimant leak- age Variable length integer string; Genes are geographic nodes GA [125] (1995) Each solution apos;s tness based on how \well quot; it ts its race apos;s ideal None (2) Numeric op- timization (one objective is always \race quot; ideal) Implies binary string GA [5] (1995) Repair procedure encodes valid chromo- somes; Presents unique bit string representa- tion of ow-network paths Computer Aided Process Planning (2) Cost; Quality Binary string; Chromosome is an encoded ow network GA [103] (1995) Standard GA Pot core trans- former design (2) Device area; Magnetic ux density Binary string GA [18] (1995) Crowding-based se- lection; GA deceptive problem Food distribution center management (2) Quality loss; Storage utilization Binary string; Genes are clus- ter capacity and time utilized GA [75] (1997) Steady-state GA; Re- sults appear to use only two criteria Selective laser sin- tering build cylin- der packing (3) Part over- lap; Packing \tightness quot;; Part containment in cylinder List of lists; Permutation in- teger ordering in one dimen- sion; integers in others... In PAGE 16: ... Table 2 lists the known linear combinatoric MOEA techniques. Table2 : Linear Fitness Combination Approach Description Application Objectives (#) Chromosome GA [137] (1991) Hybrid GA implemen- tation; Incorporates a schedule builder and evaluator Laboratory re- source scheduling Not stated Permutation task ordering GA-based learning system [79] (1992) Structured popula- tions; Parameterized mating only within overlapping demes; Parallelized Machine learn- ing (route planning and vehicle control) (5) Distance; Re- quired time; Path deviation; Collision monitor activa- tions; Emergency monitor activations \Action Chain; quot; Genes are lists of actions for robotic task GA [80] (1993) Linear normalized tness and weighted penalties 3-D structure con- formational search (2) Match penalty; Energy penalty Binary string; Genes are ro- tation angles GA [14] (1994) Specialized crossover; GA population se- lected from training database; One, some, or all GA population members replace least t database members Adaptive image segmentation (5) Edge-border co- incidence, Bound- ary consistency, Pixel classi cation, Object overlap, Object contrast Binary string; Genes are t- ness, image conditions, and parameters; EVOPs operate only on parameters Multi-Niche Crowding (MNC) GA [150, 21] (1995,1997) Fitness obtained by summing individual rank in each objec- tive; Phenotypic-based crowding; Integrated with ow-transport simulation code Groundwater pol- lution contanimant monitoring; Also tested on multi- modal, dynamic function (3) Cost; Contan- imant removal; Contanimant leak- age Variable length integer string; Genes are geographic nodes GA [125] (1995) Each solution apos;s tness based on how \well quot; it ts its race apos;s ideal None (2) Numeric op- timization (one objective is always \race quot; ideal) Implies binary string GA [5] (1995) Repair procedure encodes valid chromo- somes; Presents unique bit string representa- tion of ow-network paths Computer Aided Process Planning (2) Cost; Quality Binary string; Chromosome is an encoded ow network GA [103] (1995) Standard GA Pot core trans- former design (2) Device area; Magnetic ux density Binary string GA [18] (1995) Crowding-based se- lection; GA deceptive problem Food distribution center management (2) Quality loss; Storage utilization Binary string; Genes are clus- ter capacity and time utilized GA [75] (1997) Steady-state GA; Re- sults appear to use only two criteria Selective laser sin- tering build cylin- der packing (3) Part over- lap; Packing \tightness quot;; Part containment in cylinder List of lists; Permutation in- teger ordering in one dimen- sion; integers in others... In PAGE 35: ... This is probably due to its simplicity. Table2 re ects its application to many real-world problems, although often incorporated with \vari- ations on a theme. quot; The basic weighted sum MOEA is both easy to understand and implement; the technique is also computationally e cient.... In PAGE 70: ...MOP TEST FUNCTIONS 10.3 MOEA Experimental Methodology Table2 0: Possible Multiobjective NP -Complete Functions NP -Complete Problem Examples 0/1 Knapsack - Bin Packing Max pro t; Min weight (Multiple Knapsacks [167]) Coloring Min # colors, # of each color Layout Min space, overlap, costs Maximum Independent Set (Clique) Max set size; Min geometry Scheduling Min time, missed deadlines, waiting time, resources used Set/Vertex Covering Min total cost, over-covering Traveling Salesperson Min energy, time, and/or distance; Max expansion Vehicle Routing Min time, energy, and/or geometry NP -Complete Problem Combinations Vehicle scheduling and routing 10.3.... ..."

"... In PAGE 8: ... In order to decrease random variations, we have determined the averages of 250 independent runs with different patterns. The results are shown in Table2 . The random sequences are more stringently filtered since they show little resemblance with the structure in popular melodies.... ..."