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What is the diff erent beween complete and incomplete problems?
Wiki User
∙ 12y ago
well for one, the problem that you answered would be considered...complete.
the other one which is not done, would be incomplete.
DUH!
Wiki User
∙ 12y ago
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What did the pharaohs of ancient Egypt do to help the economy?
the pharaohs put people in diffr=erent places to protect economy so it can go down
Dinosaurs are ----------------- animal?
yes
Was the series way side based on the book wayside?
YES YES but there is sum chanjes in the series like Mr.kiddswatter is and Old man and Not a Young man also mis Jewls hair is different and the school outside look is kinda diff,erent Srrey fur bad spelling i a m tires so i rote like dat
How are the exiles of the three speakers in the poems from The Exeter Book similar and di㔵erent?
The exiles in the poems from The Exeter Book share a common experience of loneliness and longing for their homeland. However, their reasons for exile and their emotional responses to their predicament vary. While some exiles may express a sense of resignation, others may convey feelings of despair or hope.
What are traditional clothes in Luxembourg?
They have no national clothing however depending on their religions and their situation its usually just wrap up warm
Why is the Commonwealth Games is such a big shame for India?
because: 1* They aren't ready for the athletes to go to the athletes village as they erent clean and they haven't got they grass down:( 2* because of this the athletes have to stay in hotels and i am only 12 and i think that this is terrible 3* some athletes don't want to go because of security threats and bombers and terririst attacks 4* just basically because everyone else had the same ammount of time and India hasn't finnished 5* i hope this helped
What is the relationship between frequency amplitude and pitch?
Wavelength = (speed of sound) divided by (frequency) Frequency = (speed of sound) divided by (wavelength) Amplitude is not related to frequency or wavelength. A change of amplitude does not cause ... and does not result from ... a change of frequency or wavelength
Write some code to take lists of connections and return families The input is a list of pairs of numbers Each pair indicates a connection eg 2386 means 23 is related to 86?
Write some code to take lists of connections and return families. The input is a list of pairs of numbers. Each pair indicates a connection, e.g. [23,86] means 23 is related to 86. If we had the following input: [50,32] [33,45] [33,46] [5,16] [23,32] [11,33] [86,16] [23,86] [7,21] [5,8] Then your program should produce the following output: 5,8,16,23,32,50,86 7,21 11,33,45,46 (though the families don't have to be in this order, and the lists don't have to be sorted). This example can be visualised as follows: As a practical application, imagine that the connections are peer-to-peer links between comput- ers. Any two computers in the same family are able to communicate with each other, but computers in dierent families cannot. 1
What allowed scientists to come up with the cell theory?
Scientists have been studying living things for over 400 years. At first, they made observations with their unaided eyes. Later, thedevelopment of the microscope allowed scientists to see cells for thefi rst time. Aft er observing many diff erent living things under themicroscope, scientists realized that all living things are made up ofcells. Th is conclusion led scientists to develop thecell theory-an explanation that summarizes the basic characteristics of living things.Th e cell theory states the following:• All living things are composed of one or more cells.• Th e cell is the basic unit of life.• All cells come from pre-existing cells.
What is the project closure analysis?
Closure analysis determines statically which function de¯nitions reach which programpoints. This information is used for many di®erent purposes; e.g., type inference forobject-oriented programming languages [PS91], globalization analysis of functional pro-grams [Ses89], partial evaluation [Bon90], type recovery in Scheme [Shi90], and others.The reason why closure analysis is such a fundamental analysis in di®erent applications isthat it is (sort of) the universal data °ow analysis problem for monomorphic (data °oworiented) analyses for higher-order (functional) languages. As such it may be viewed asthe analogue of path analysis, which is universal for (continuous) ¯rst-order (classical)data °ow analysis problems [Tar81].General closure analysis is, unlike its ¯rst-order counterpart, expensive in the worstcase: the best known algorithms take time £(n3) in the worst case [Hen91a,PS92]. Manytimes it is su±cient, however, to get somewhat coarser information than the exact closureinformation. Coarser here means that the results may indicate that more function def-initions reach a point than a precise closure analysis would really yield; nonetheless theinformation should in practice be only marginally di®erent from the exact analysis.In this note we exhibit a simple, but very e±cient closure analysis based on the binding-time analysis of [Gom90] and the algorithm [Hen91b] for it. (In fact the algorithm hereis substantially simpler than the one in [Hen91b].) The computed abstract value °owinformation1 is coarser than normal closure analysis exactly in the following sense: exactclosure analysis keeps track of uni-directional °ow of (abstract) values from one programpoint to another whereas our simple closure analysis works with the assumption that °owsare reversible; that is, that any abstract value that °ows from point p to point p0 can also°ow (backwards!) from p0 to p.In the practice of partial evaluation this loss of information appears to be insubstantial.Since the simple closure analysis algorithm runs in almost-linear time [Hen91b] and is very¤DIKU Semantics Report D-1931We prefer to call the abstractions of values corresponding to expressions in a program abstract valuesrather than (abstract) closures, since these values represent values other than (run-time) closures, includingpairs, integers, etc.12 BASIC IDEA OF SIMPLE CLOSURE ANALYSIS2e±cient in practice [Hen91c] this appears to be an attractive alternative to computingcomplete closure information.2 Basic idea of simple closure analysisIn the binding-time analysis of [Gom89], on which [Hen91b] is based, we begin by asso-ciating a unique abstract value (also called a token, label or a type variable depending onthe intention of their use) with every (sub)expression in a program, and constraints areextracted that capture the °ow of actual values represented by these abstract values in theprogram. The °ow is, of course, a conservative approximation of the actual °ow of data.It not only makes the standard assumptions that all expressions inside a function de¯ni-tion are actually evaluated and that abstract values can °ow forwards and backwards, asmentioned above, but also that the °ows of di®erent arguments of a function merge insidethe function | the latter is why we refer to this analysis as monomorphic.In the second step, the critical one, these constraints are normalized by a (very small)set of rewriting rules. In the process di®erent abstract values may be identi¯ed, which istantamount to saying that one abstract value could \°ow" to the other, in any direction.At the end of this process a single abstract value represents a whole set of abstract valuesthat have been identi¯ed during normalization. Such a set may contain (abstract valueslabeling) closures as well as other program points, notably application points such as theactual parameters of function applications. This information can be interpreted by sayingthat the closures in the set may reach any of the application points in the same set. Theanalysis is conservative w.r.t. to exact closure analysis in the sense that no other closuresreach the application points, but that some of the reported closures may actually be shownnot to reach some application point in the same set by exact closure analysis. (Of course,\exact" closure analysis is itself a conservative approximation of the actual dynamic °owof run-time values, including concrete closures.)3 Simple closure analysis exempli¯edIn this section we exemplify the steps above by considering a simple example.Consider the following code (fragment), representing Turner's tautology checker andtwo calls to it:taut = fn f => fn n. if n = 0 then f else taut (f true) (n-1) and taut (f false) (n-1)g = fn x => fn y => (x and not y) or (not x or y)h = fn z => ztaut g 2 taut h 13.1 Constraint extractionIn the ¯rst step we associate a distinct abstract value with every occurrence of a subex-pression occurring in this code; for simplicity's sake all occurrences of a variable havethe same abstract value. We shall refer to the abstract value of an (occurrence of an)expression by the special variable ® indexed by the expression; e.g., the abstract valuesof the three function de¯nitions are ®taut; ®g and ®h. On top of these abstract valuesrepresenting (abstract) closures we have the abstract values ®¸f and ®¸n corresponding to3 SIMPLE CLOSURE ANALYSIS EXEMPLIFIED 3the partial applications of taut to one, resp. two arguments. Finally, we have ®¸y corre-sponding to the partial application of g to one argument. These are all the abstract valuesrepresenting (abstract) closures; of course, as mentioned before, every subexpression hasa corresponding (unique) abstract value.In the constraint extraction phase of the binding-time analysis algorithm the con-straints in Figure 1 are generated for the code above. Two kinds of constraints are gener-ated.² ® = ®0: Such an equation between abstract values expresses that one °ows to theother, and vice versa (Remember our basic assumption of bidirectionality of °ow!);e.g., ® could be the abstract value of an actual argument to a function, and ®0 theabstract value of the function's corresponding formal parameter.² ® ! ®0 · ®00 or integer · ®00, etc.: An inequation has an abstract value constructorapplied to n ¸ 0 abstract values on the left-hand side and an abstract value on theright; such an inequality expresses that the right-hand side abstract value \can be"the abstract value on the left (there may be more than one, but not with the sameconstructor; see below).3.2 Constraint normalizationIn the constraint normalization phase we combine de¯nitional information (constraints ofthe second form above) and form equivalence classes of abstract values (for the constraintsof the ¯rst form).For simple closure analysis we need only 2 of the 11 rewriting rules in [Hen91b]. Inparticular, there is no need for an occurs check rule since we don't have to interpret theresult as ¯nite type expressions; and there is no need for a special type Dynamic (or ¤)representing either possible type errors or run-time computable expressions or both, sincewe are interested neither in the former nor in the latter. In particular, the rewriting rulesmanipulating Dynamic can be omitted. We end up with the rewriting rules in Figure 2.As proved in [Hen91b] a rewriting system normalizing constraints with additionalrewriting rules can be implemented in time O(n®(n; n) where ®(n; n)
Properties of e-cash in e-commerce?
2 Properties To purchase items over the Internet, people currently use credit cards as the prevailing form of payment. For years, however, people have asked "Why not use Electronic Cash?" These are the properties that would be necessary for such a scheme: 1. Financial Infrastructure: There is a big di?erence between bits and atoms. People have used atoms, gold, bills, etc. as money. Behind the bits in E-cash, there must be financial infrastruc- ture that the money represented by the instructions in the bits from one account to another. A transaction is an instruction to move money from a consumer's account to a merchant's account. 2. No Double-Spending and Non-forgeability: Bits can be easily duplicated but atoms cannot. So copies of cash should not be spendable. Nor should one be able to forge or create e-cash and spend it. 3. Security: Account information should be kept secure. Transfers should be kept secure. 4. Immediate Verifiability that Payment is OK Online vs. O?ine systems: Every time you receive a payment, you could instantly relay it to the bank to verify it. Or there could be an intrinsic property that lets you know the money is good if a bank is not readily available. 5. Persistence: Atoms stick around better than bits do. If your computer crashes, you should not be bankrupted. A backup of your wallet to record your wealth should not be spendable. 6. Exclusive Ownership 7. Anonymity: There are di?erent types of anonymity, payer, payee, and even bank anonymity. The merchant may accept money without knowing who the payer is. Also you should be able to deposit money without the bank knowing where the transaction comes from. There are issues of money laundering. People can transfer money without the bank knowing. 8. Transferability: A can pass money to B and then to C easily and anonymously. 9. Amounts: It would be nice to support a variety of "coin sizes". 10. Traceable to issuer: We should know who backs each bit of money. E.g., We can tell by inspection that U.S. money is issued by the U.S. treasury. 11. Divisibility and Combination: If you have an instrument worth 1 dollar, you should be able to divide it into two instruments each worth 50 cents. 12. Compatibility with existing systems: An electronic payment system should interface smoothly with existing payment systems. (To what extent a monetary system actually depends on others is an interesting open question; can you have e.g., a Galactic System with no central Government authority?) 13. Efficient for small amounts 14. Scalability 15. Competition between Issuers: Free banking before the Gold Standard
What is multipart pricing?
A Conjoint Approach to Multi-Part Pricing Abstract Multi-part pricing is commonly used by providers of such services as car rentals, prescription drug plans, HMOs and wireless telephony. The general structure of these pricing schemes is a ¯xed access fee, which sometimes entitles users to a certain level of product use; a variable fee for additional use; and still another fee for add-on features that are priced individually and/or as bundles. The authors propose a method using conjoint analysis for multi-part pricing. The method re°ects the two-way dependence between prices and consumption, and incorporates the uncertainty consumers have about their use of a service. The proposed method estimates both choice probabilities and usage levels for each individual as functions of the product features and the di®erent price components. These estimates are then used to evaluate the expected revenues and pro¯ts of alternative plans and pricing schemes. The method is illustrated using data from a conjoint study concerning cellular phone services. Its results are compared with those obtained from using several competing models. The proposed procedure is used to identify the optimal set of features in a base plan, and the pricing of optional features, for a provider of cellular phone services. Each of these methods assumes that a product is sold at a single price, and that a consumer cannot upgrade or add features to a product by paying an additional fee. Another assumption common to these methods is that consumer usage rates do not depend on price. These assumptions are approximately, if not perfectly, satis¯ed for some products, for example such durable goods as washing machines and refrigerators. However, there are also categories of products and services where one or more of these assumptions is not appropriate. For example, some services charge not one price but two prices, and charge additional fees for add-on features. Examples are car rentals, some HMO plans, prescription drug plans, Xerox copying services, memberships to health clubs, museums and zoos, and telephone services (Danaher 2002; Narayanan, Chintagunta and Miravete 2007). Some of these services charge an additional variable fee. For example, institutional users pay a per page charge for copies on a Xerox machine; and members of an HMO pay a deductible for each visit to a doctor or each purchase of a prescription drug. Other services, like car rentals, cellular phone services, museum and health club memberships, charge a ¯xed fee and allow \free" use up to a certain level, beyond which consumers have to pay a usage-based unit rate. This induces a two-way dependence of price and consumption | the price charged by a provider in°uences consumption while the price a consumer pays depends on his or her usage level. Some services allow customers to purchase optional features, such as rollover minutes for cell phone services and extra life insurance for car rentals and air travel. Other services, like HMO and prescription drug plans, do not allow service enhancements but o®er alternative plans with bundles of add-on features. Still other services, such as cable television, allow unlimited use for a monthly fee but allow consumers additional subscriptions to such options as digital channels, high-de¯nition broadcasts, pay- per-view, broadband internet access and IP telephony.
