Consider always-halting non-deterministic TMs. Recall that such an NTM accepts an input x when at least one computation of M accepts x ; moreover, we assume that no computation of the NTM loops on any input. Now, let M be such an NTM and define M be the NTM obtained from M by exchanging the states q_acc and q_rej (leaving everything else and the transition function unchanged). Consider the claim : L(M)=L(M). Is it true or false? If you think it is true, prove it. If you think it is false, disprove it by giving a counterexample (which you must explain).

Our primary purpouse as electricians is to do wich of the following core concepts? a: install electrical components in a way they can be upgraded b: install electrical equiptment in a way that reduces heat c: install electrical systems in a safe manner d: only b and c

Need asap write a short paper describing the history and differences between six sigma, waterfall, agile, and scrum models. understanding these models can give you a good idea of how diverse and interesting it development projects can be. describe what the rationale for them is and describe their key features. describe the history behind their development. at least 400 words

In any one-minute interval, the number of requests for a popular web page is a poisson random variable with expected value 240 requests. (a) a web server has a capacity of requests per minute. if the number of requests in a one-minute interval is greater than the server is overloaded. use the central limit theorem to estimate the smallest value of for which the probability of overload is less than 0.035. note that your answer must be an integer. also, since this is a discrete random variable, don't forget to use "continuity correction". = 268 (b) now assume that the server's capacity in any one-second interval is ⌊/60⌋, where ⌊⌋ is the largest integer ≤ (this is called the floor function.) for the value of derived in part (a), what is the probability of overload in a one-second interval? this time, don't approximate via the clt, but compute the probability exactly.