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Huan_etal_PSB04_Final
Huan_etal_PSB04_Final

Quasi-Minuscule Quotients and Reduced Words for Reflections
Quasi-Minuscule Quotients and Reduced Words for Reflections

... However if si has a unique agent, then x = y and w = x −1 si x = t. (c) ⇒ (e) We claim that the map w → −wβ is an order isomorphism between the two intervals. Indeed, for any w ≤ L t, we may obtain a reduced expression for t by prepending terms to any reduced expression for w, so wβ appears along s ...
ACCURATE CLASSIFICATION OF PROTEIN
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... (MI) between two random variables X and Y measures the correlation between X and Y. We define a subgraph X as coherent if it is strongly correlated with every sufficiently large sub-subgraph Y embedded in it. Based on the MI metric, we have designed a search scheme that only reports coherent subgrap ...
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... slight generalization of known results. Here, we also derive some new conditions for sequences, obtained by uniform decimation, to reach their maximum linear complexity. The period of the output sequence generated by an arbitrary clock-controlled LFSR with an irreducible feedback polynomial and an a ...
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... • If (n + 1) is composite, it can be written as the product of two integers a and b such that 2  a  b < n + 1. By the induction hypothesis, both a and b can be written as the product of primes. Therefore, n + 1 = ab can be written as the product of primes. Fall 2002 ...
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... LOG_aLevel® Data Logger Module - Standard card size 1 GB CF Industrial Grade, up to 2 GB cards supplied - In continuous mode at 5 Hz sample rate a 1 GB card has space for 6 months of data IMPORTANT: The file with the level data is ALWAYS called "PEGEL.LOG" and MUST ALWAYS be in the directory of the ...
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... Therefore, the 48th and final payment will be for $6.66. c. If Simon makes monthly payments of $30, we can solve for the length of time required before the account is paid off. I = 2; PV = 305.44; PMT = -30; FV = 0; and then solve for N = 11.4978. With $30 monthly payments, Simon will only need 12 m ...
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... A POMDP model is a tuple hS, A, O, T, Z, R, b0 , γi, where S is the set of states, A is the set of actions, and O is the set of observations. At each step, the agent is in a state s ∈ S, takes an action a ∈ A, and moves from s to an end state s0 ∈ S. To represent uncertainty in the effect of perform ...
Dynamic Programming
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... LCS DP –step 1: Optimal Substructure • Characterize optimal substructure of LCS. • Theorem 15.1: Let X= (= Xm) and Y= (= Yn) and Z= (= Zk) be any LCS of X and Y, – 1. if xm= yn, then zk= xm= yn, and Zk-1 is the LCS of Xm-1 and Yn-1. – 2. if xm yn, then zk  ...
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DEVELOPMENT OF ABRAHAM MODEL CORRELATIONS FOR

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A finite basis for failure semantics
A finite basis for failure semantics

... Syntax of BCCSP BCCSP(A) is a basic process algebra for expressing finite process behavior. Its syntax consists of closed (process) terms p, q that are constructed from a constant 0, a binary operator + called alternative composition, and unary prefix operators a , where a ranges over a nonempty set ...
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Corecursion

In computer science, corecursion is a type of operation that is dual to recursion. Whereas recursion works analytically, starting on data further from a base case and breaking it down into smaller data and repeating until one reaches a base case, corecursion works synthetically, starting from a base case and building it up, iteratively producing data further removed from a base case. Put simply, corecursive algorithms use the data that they themselves produce, bit by bit, as they become available, and needed, to produce further bits of data. A similar but distinct concept is generative recursion which may lack a definite ""direction"" inherent in corecursion and recursion. Where recursion allows programs to operate on arbitrarily complex data, so long as they can be reduced to simple data (base cases), corecursion allows programs to produce arbitrarily complex and potentially infinite data structures, such as streams, so long as it can be produced from simple data (base cases). Where recursion may not terminate, never reaching a base state, corecursion starts from a base state, and thus produces subsequent steps deterministically, though it may proceed indefinitely (and thus not terminate under strict evaluation), or it may consume more than it produces and thus become non-productive. Many functions that are traditionally analyzed as recursive can alternatively, and arguably more naturally, be interpreted as corecursive functions that are terminated at a given stage, for example recurrence relations such as the factorial.Corecursion can produce both finite and infinite data structures as result, and may employ self-referential data structures. Corecursion is often used in conjunction with lazy evaluation, to only produce a finite subset of a potentially infinite structure (rather than trying to produce an entire infinite structure at once). Corecursion is a particularly important concept in functional programming, where corecursion and codata allow total languages to work with infinite data structures.
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