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Bill mccurry: so it's a year later. what do you know now that you didn't know a year ago? gary boxall petersfield photographic: well how much value for money i got in doing this radio advertising. the year before we did the advertising and i had a little do through the newspaper, which actually cost me three grand. this cost me 800 pounds. so i had the run up for week and then saturday they were there for half a day, and then the follow on afterwards because they were talking about the day and talking about the food and so on and so forth. so it was certainly good value for money. bill mccurry: so the year before you spent 3, 000 in the newspaper. you spent 800 and your business went, what? up? down? gary boxall petersfield photographic: up, double. bill mccurry: for less money. gary boxall petersfield photographic: yes and video slot machines. You are no longer permitted to take the face-up discard having touched the stock and card games gin. References 1. support pricing & negotiating strategies. by: franoise tourniaire, the association of support professionals 2. trends in fee based support. by: the association of support professionals. 3. discounting practices in the software industry - a study of discounting use, monitoring, and control. by: jim geisman, marketshare. 4. benchmarks: oem contracts softletter december, 2004 vol. 20, no. 26 5. by: jim geisman, marketshare. 6. pricing. by: blois, keith, gijsbrechts, els, campo, katia, oxford textbook of marketing, 2000. 7. provided by georg mueller, ph.d. of the strategic pricing group. 8. competing with gray markets. by: antia, kersi d., bergen, mark, dutta, shantanu, mit sloan management review, fall2004, vol. 46, issue 1. 9. when gray markets can help. mit sloan management review, fall2004, vol. 46 issue 1, p66, 2p. 10. new rules on the road to revenue recognition. softletter oct, 2004 vol. 20, no. 23. additional reading 1. power pricing. by: robert j. dolan & hermann simon. 2. pricing making profitable decisions. by kent b. moore. 3. the strategy and tactics of pricing. by: thomas nagle & r. holden. 4. strategic bundling of products and prices: a new synthesis for marketing. by: tellis, gerard j., journal of marketing, 00222429, jan2002, vol. 66, issue 1 5. bundling-new products, new markets, low risk. by: eppen, gary d., sloan management review, summer 91, vol. 32 issue 4 and video slot machine. To allow direct comparison with the results of rensink et al. 1997 ; , exactly the same experimental procedures and stimuli were used: an original image a and modified image a' were displayed in the sequence a, a, a', a', . with gray blank fields appearing between successive images7 fig. 1 ; . in this "standard" condition, images were displayed for 240 msec and blanks for 80 msec, with blank fields being medium gray. all experiments used the same set of 48 colour images of real-world scenes. each image was 27 wide and 18 high. the images and the changes made to them were exactly those used by rensink et al. 1997 ; . in that study, the presence, colour, or location of a single object or region was changed in each image. all three types of change occurred equally often, with the different types roughly equated for areas and locations in the image. changes were also divided according to the amount of interest in the items being changed. interest was defined operationally via an independent experiment in which five observers were asked to view each image and provide a brief verbal description of it. central interests cis ; were objects or areas mentioned by most observers; marginal interests mis ; were objects or areas mentioned by none. for more details, see rensink et al., 1997. ; a macintosh computer presented the displays, controlled the experiments, and collected the data. ten nave observers participated in each experimental condition. observers were informed of the types of changes possible. they were asked to press a key when they saw the change, and then to verbally describe it. in the standard conditions, each trial consisted of a 3-sec gray field containing a white rectangle, followed by a 1-sec delay, followed by a display sequence that continued until the observer responded or until 60 sec had passed fig. 1 ; . a set of six practice trials familiarized observers with the protocol. the order of the images was randomized for each observer. as might be expected from the use of large changes, virtually all the changes were easily seen once noticed: identification error rates were low, averaging less than 2.0% across all experiments. data analysis primarily consisted of two-tailed t-tests, with the 48 measurements generating six scores for each observer, each score being the average response time for each type of change. anovas were used to determine any systematic variations caused by particular stimulus factors. to compensate for. George co. on hwy. 63 south, 3, 265 sq. ft. 2 bathrooms. excellent location for developing truckstop, restaurant, offices. lots of storage space. paved parking for 50 + vehicles in front. located on 3.02 acres. great investment. 0, 000. view pix at evelynbrown and video card slot. Pre-kindergarten curriculum guidelines: the student imitates the special language in storybooks and story dialogue, and uses it in retelling and dramatic play such as "once upon a time." ; . area: novice reading print awareness. Attack: you may attack with any or all of your creatures. if you don't attack, skip ahead to "play creatures." ; you can attack only with your creatures, and they can attack only your opponent. if you want to kill one of your opponent's creatures, you'll have to attack and hope he blocks with it. attacking with a creature taps it. defence: when attacked, your opponent may block with some, all, or none of his creatures. each creature can block only a single attacker, but more than one creature can gang up to block the same attacker. blocking doesn't tap creatures, but tapped creatures can't block so a creature that attacked during your opponent's last turn can't block this turn ; . damage dealing: each attacking creature that wasn't blocked deals its power in damage to your opponent. if this reduces his life total to 0 or less, you win. a creature that's blocked deals its damage to whatever blocked it and vice versa. if a creature has more than one blocker, you can distribute its damage among them as you like. for example, if your hurloon minotaur 2 3 ; is blocked by three mons's goblin raiders 1 ; , you can divide its 2 damage between two of the raiders, killing them both, but the three raiders will kill the minotaur in return. play creatures: you can play as many creatures as you can afford. remember, you have to pay a creature's casting cost to play it, which requires tapping your lands for mana. discard: if you have more than seven cards in your hand at the end of your turn, discard down to seven. game setup during a game, it's easy to build up quite a few cards in play. for this reason, you'll want to find a dry, flat surface to use as a playing area. you'll also need something to help you keep track of your life total, such as spare change or a pencil and paper. each player begins the game with a deck of at least forty cards, which compose his or her library. before play begins, each player shuffles his or her deck and offers it to the opponent, who may shuffle and or cut it. next, one player decides who will play first and who will draw first; whoever takes the first turn of the game doesn't draw a card that turn. if you and your opponent have just finished a game, the player who lost that game decides who plays first; if the game was a draw, whoever decided who went first that game does so again. if this is your first game in a series, determine randomly who decides. now, play a few games to get a feel for what you've learned. the point of these games is to become familiar with how lands and mana work and with the basics of creature combat. whether you win a game isn't very important. in your first few games, if you draw a card that's not a basic land or a creature, set it aside and draw another card. when you play a creature, pay attention only to its power and toughness; for now, ignore its text box. expanding the game the first few games you just played were probably rather simple. during your turn, you drew a card, played a land if you had any, attacked if you wanted to, and played creatures if you had any. during your opponent's turn, you could do nothing but block. creatures themselves didn't do anything other than attack or block. these are the basic moves of magic. in a normal game of magic, you can assist your creatures in battle by playing spells that make them bigger, help them evade other creatures, and so on. spells may also hinder or kill your opponent's creatures, and some spells even let you affect your opponent directly. the remainder of this section adds to the basics you've already learned. it looks at other types of spells, more detailed turn and attack sequences, and some basic creature abilities. once you're comfortable with the contents of this section, you should be able to play quite a few games of magic without having to refer to section ii. other types of spells so far, you've only played spells that generated creatures: summon spells and artifact creature spells. as you played your first few games, you probably set aside spells of most of the other types. if you haven't played yet, take a moment to examine the spells in your deck. ; those other spells are divided into two categories and online video slots. Of conflict checks over a whole run and dividing this by the total number of evaluations of that run. we use this to determine the average amount of work performed by the algorithms per fitness evaluation. furthermore, we may use conflict checks to measure the average number of conflict checks per run. this enables us to make a comparison with a complete and sound algorithm, as for these algorithms the aes measure has no meaning. for comparing all implemented eas we rank our performance measures. the most important one in evaluating algorithm performance is the success rate -- after all, we ultimately want to solve problems. the second measure is the aes, in case of comparable sr figures we prefer the faster ea. the mean error at termination is only used as a third measure as it might suggest preferences between eas that fail. besides the real performance measures sr, me, and aes-- that tell something about how an algorithm finishes its runs -- we also report statistics reflecting algorithm behaviour during its runs. for this purpose, we use the champions error ce ; measure, being the number of constraints violations in the best individual found up to a given time during a run5 . time is interpreted by the number of fitness evaluations and we record the ce every 1000 evaluations. for each value of p in model e we draw a graph showing the average ce over all runs for that p at every sample time instance i.e., after every 1000 fitness evaluations ; . b. experimental setup our test suite consists of 250 solvable problem instances: 25 instances for 10 values of p in model e 20, p, 2 ; that is, we set the number of variables n and the domain size d to 20, and k at 2. the values for p are chosen from the set . for each p we generate 25 solvable instances. to achieve this we repeatedly generate an instance, then verify if it is solvable using a sound and complete algorithm simple backtracking ; and discard it if it not solvable. this process becomes more difficult when p is increased as more instances become unsolvable, but also because of the phase transition that occurs around 0.33, hereafter the chance that a csp instance is solvable is too small, cf. table xi. on the other hand, instances generated using a value for p lower than 0.24 have many solutions and we found that they are easy to solve quickly using a simple method such as a greedy algorithm that tries to assign a value to a variable without violating any constraints and then moves on to the next variable without ever backtracking. in section vi we discussed the different models that exist to create and analyse binary csps. there we argued for using model e, as it does not suffer from certain flaws found in the other models. however, the parameters of those models can still be calculated for instances generated using model e. the first two parameters, the number of variables. 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