'Higher dimensions' presentation slideshows

2.47k views • 111 slides

Modeling & Analyzing Massive Terrain Data Sets

Modeling & Analyzing Massive Terrain Data Sets. Pankaj K. Agarwal Duke University. STREAM Project. S calable T echniques for hi- R esolution E levation data A nalysis & M odeling In collaboration with: Lars Arge, Helena Mitasova Students: Andrew Danner,Thomas Molhave,Ke Yi.

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The Heckscher-Ohlin Model: Features, Flaws, and Fixes II: What's Not to Like about the H-O Model?

The Heckscher-Ohlin Model: Features, Flaws, and Fixes II: What's Not to Like about the H-O Model?. Alan V. Deardorff University of Michigan. Themes of the 3 Lectures, Again. The HO Model is largely well behaved in 2 dimensions, even when you include trade costs

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Whole Genome Assembly Microarray analysis

Whole Genome Assembly Microarray analysis. Mate-pairs allow you to merge islands (contigs) into super-contigs. Mate Pairs. Make clones of truly predictable length. EX: 3 sets can be used: 2Kb, 10Kb and 50Kb. The variance in these lengths should be small.

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Product Formulas for Measures and Applications to Analysis and Geometry Peter Jones

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How to do Bayes-Optimal Classification with Massive Datasets: Large-scale Quasar Discovery

How to do Bayes-Optimal Classification with Massive Datasets: Large-scale Quasar Discovery. Alexander Gray Georgia Institute of Technology College of Computing Joint work with Gordon Richards (Princeton), Robert Nichol (Portsmouth ICG), Robert Brunner (UIUC/NCSA), Andrew Moore (CMU).

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Observers and Kalman Filters

Observers and Kalman Filters. CS 393R: Autonomous Robots. Slides Courtesy of Benjamin Kuipers. Good Afternoon Colleagues. Are there any questions?. Stochastic Models of an Uncertain World. Actions are uncertain. Observations are uncertain. ? i ~ N (0, ? i ) are random variables.

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Renormalization in Classical Effective Field Theory (CLEFT)

Renormalization in Classical Effective Field Theory (CLEFT). Barak Kol Hebrew University - Jerusalem Jun 2009, Crete. Outline Definition & Domain of applicability Review of results (caged, EIH) Standing puzzles Renormalization (in progress). Based on BK and M. Smolkin

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Searches for very high frequency gravitational waves

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Leech Lattices

Leech Lattices. Elicia Williams April 24, 2008. Background Brief Definition Lattices 2-Dimensions 3-Dimensions 24-Dimensions Higher Dimensions Applications. Leech: the man. John Leech Born: July 21, 1926 Died: September 28, 1992

834 views • 33 slides

Higher Dimensions

Higher Dimensions. Let's say we use a pencil to mark a point on paper. x is this point. We pick a direction and move the pencil along this direction. . x. We are not drawing a line yet, just move the pencil to a new position. And then mark another point there. . x.

479 views • 33 slides

Higgs branch localization of 3d theories

Higgs branch localization of 3d theories. Harish-Chandra Research Institute. Masa zumi Honda . Ref.: arXiv:1312.3627 [ hep-th ]. Based on collaboration with. Masashi Fujitsuka (SOKENDAI) & Yutaka Yoshida (KEK ? KIAS ). Progress in the synthesis of integrabilities

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12.5 Chain Rules for functions of several variables

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Probing the scalar sector with ZZH

Probing the scalar sector with ZZH. Debajyoti Choudhury, Anindya Datta, Katri Huitu, hep-ph/0302141, to appear in Nucl.Phys.B. outline: Introduction Models: higher representations radion (nonSUSY/SUSY) Conclusions. Introduction. Assume that EWSB due to Higgs mechanism

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Convex Hull Algorithms for Dynamic Data

Convex Hull Algorithms for Dynamic Data. Kanat Tangwongsan Joint work with Guy Blelloch and Umut Acar (TTI-C). The Convex Hull Problem. Output: CH ( S). Extensively studied: Graham Scan, Gift-Wrapping, Incremental Hull, Overmars, Quick-Hull, Ultimate Hull. Matched Lower-bound already!.

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Online convex optimization Gradient descent without a gradient

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SVMs in a Nutshell

SVMs in a Nutshell. What is an SVM?. Support Vector Machine More accurately called support vector classifier Separates training data into two classes so that they are maximally apart. Simpler version. Suppose the data is linearly separable Then we could draw a line between the two classes.

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Genetic Algorithms -- System Modeling and Function Finding

Genetic Algorithms -- System Modeling and Function Finding. Jack Perdue CPSC 689-608 March 21, 2002. Problem Statement.

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STA4006 Categorical Data Analysis September, 2004

STA4006 Categorical Data Analysis September, 2004. Instructor: Xin-Yuan Song (LSB114) Teaching Assistant: Yan-Hui Wang (LSB 130) Assessment Scheme Exercise 10% Mid-term examination 40%

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System Architecture for On-Chip Networks

System Architecture for On-Chip Networks. Jose Duato, Partha Kandu, Manolis Katevenis, Chita Das, Sudhakar Yalamanchili, John Lockwood, Ani Vaidya. Summary. Societal Impact Lower NRE costs (integration and verification): lower barrier to entry

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