Titlebook: Learning Discrete Mathematics with ISETL; Nancy Baxter,Ed Dubinsky,Gary Levin Book 1989 Springer-Verlag New York Inc. 1989 Combinatorics.M

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convert

Propositional Calculus, that the student should have a mental model of .. This should take the form of a representation of . that can be combined by logical connectives to form Boolean expressions corresponding to logical statements in English. When the variables are replaced by specific Boolean values (. or .), then the

DEMN

負(fù)擔(dān)

Functions,ay think of a function as nothing more than an expression or formula that sits, statically, on a piece of paper or on the blackboard. This is not a very powerful notion, and it is essential that the student develop a dynamic view of a function as a process that takes an object of some sort and trans

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HUMID

Mathematical Induction,alizing that understanding this very important method of proof requires, at the very least, that students work a variety of problems that go beyond the usual proof that a given finite series has a certain closed form. In addition to presenting problems arising from a wide range of situations, we pro

分散

Relations and Graphs, various representations of a relation by a map, and a Boolean matrix, as well as graphically by an arrow diagram, graph, digraph, or Hasse diagram when possible. One of our goals is to help the student to be able to go back and forth between the various representations.

CURB

ost clinical PET systems: (1) the true data formation process may be modeled accurately to include the Poisson nature of the observation process and factors such as attenuation, scatter, detector efficiency and randoms; and (2) an . statistical model for the image may be employed to model the genera

flaggy

Nancy Baxter,Ed Dubinsky,Gary Levination model from high resolution?(HR) to low resolution?(LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selectio

Discrete

Nancy Baxter,Ed Dubinsky,Gary Levinincluding but not limited to shape analysis, action recognition, subspace clustering and motion segmentation. For these problems, the features usually lie in a very high-dimensional Grassmann manifold and hence an appropriate dimensionality reduction technique is called for in order to curtail the c