This google slide presentation describes a numeracy routine for grade 1 use during the second quarter. The routine involves sequencing various representations of numbers on a clothesline. Printable materials are included.

This google slide presentation describes a numeracy routine for grade 2 use during the second quarter. The routine involves sequencing various representations of numbers on a clothesline. Printable materials are included.

This google slide presentation describes a numeracy routine for kindergarten use during the third quarter. The routine involves sequencing various representations of numbers on a clothesline. Printable materials are included.

This is an instructor's guide for an experiment to measure electrostatic force, using parallel plates made from two washers, insulating perf-board, and aluminum foil. Photos and detailed instructions are provided for experimental setup. SEE RELATED MATERIALS for a Java simulation by the same authors on the topic of capacitance. For an Excel spreadsheet developed specifically to accompany this experiment, see link below: MIT Physics 8.02 Open Courseware: Labs

Together, this course and 8.06 Quantum Physics III cover quantum physics with applications drawn from modern physics. Topics covered in this course include the general formalism of quantum mechanics, harmonic oscillator, quantum mechanics in three-dimensions, angular momentum, spin, and addition of angular momentum.

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.