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(The University of Massachusetts Amherst)

The University of Massachusetts Amherst (otherwise known as UMass or UMass Amherst or Massachusetts) is a public research and land-grant university in Amherst, Massachusetts and the flagship of the University of Massachusetts system. With more than 1,370 faculty members and more than 26,000 students, UMass Amherst is the largest public university in New England.

More information: http://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst

Example Four photographs (A,B,C,D) produced ten DEMs (ZAB, ZBA, ZAC,…) and a 10x10 precision covariance matrix Reconstruction discovered an asymmetry. DEM pairs (ZAB,ZBA) (ZAC,ZCA),… are partially uncorrelated. See the 2x2 block structure on the diagonal. Reconstruction is model independent – no assumptions about what DEMs are correlated with each other. Covariance matrix has less entries turned on than those required for exact reconstruction. Some photographs produced less noisy models – some data inputs not as good as others. Compressed sensing can recover error signal The L-1 reconstruction can be solved if the covariance matrix is sparse enough. The key is having enough models. Number of equations needed to solve for all the covariance matrix entries: Number of equations available from autonomous difference equations: Therefore, the sparsity requirement becomes easier to fulfil as m becomes large Autonomous Difference Equations Given n DEMs we can construct globally invariant quantities that cancel out ground truth. Since any estimate can be written as But Ground truth cancels out, leaving! Squaring these autonomous differences and average over the predictions results in a linear algebra system for the entries in the precision error covariance matrix. For example, for two models Results in an Under-determined linear system always for covariance matrix entries. For example Autonomous Geometric Precision Error Esti...

Placement Algorithms Exploiting Sharing for Data Center Consolidation Timothy Wood, Jim Cipar, Gabriel Tarasuk-Levin, Peter Desnoyers, Emery Berger, Mark Corner, Prashant Shenoy University of Massachusetts, Amherst Memory is an expensive resource and can be the limiting factor when consolidating virtual machines with low CPU utilization. ESX Server supports page sharing – allowing virtual machines to reduce memory consumption by sharing identical pages. If two VM’s have an identical page in memory, only store a single copy until one makes a write. Matches are found by comparing hashes generated for each page in a VM’s memory. Currently, ESX only monitors sharing within a single host. We must efficiently calculate the sharing potential between VMs across large data centers. Brute force comparison of page hashes is costly in both computation and memory. Using sharing potential can help optimize placement of virtual machines. Sharing reduces memory requirements, increasing consolidation possibilities. Additional memory tracing techniques can help detect and prevent memory hotspots. Finding Similar Virtual Machines Sharing Memory Motivation and Challenges Table from Memory Resource Management in VMware ESX Server, Carl A. Waldspurger, OSDI 2002 Using the potential for memory sharing as a guide for placing VMs can lead to substantial memory savings. Memory requirements and the potential for sharing fluctuate over time, thus the system must monitor memory utilizatio...
Introduction to Biostatistics (Pubhlth 540) Lecture 3: Numerical Summary Measures

Introduction to Biostatistics (Pubhlth 540) Lecture 3: Numerical Summary Measures

Acknowledgement: Thanks to Professor Pagano (Harvard School of Public Health) for lecture material
Compensation for language-specific rules Linguistics 820 John Kingston 25 April 2007

Compensation for language-specific rules Linguistics 820 John Kingston 25 April 2007
Review Exercises Pagano and Gauvreau (2nd Edition) Chapter 2: Problems 2, 9, 10 and 14

Review Exercises Pagano and Gauvreau (2nd Edition) Chapter 2: Problems 2, 9, 10 and 14
Introduction to Biostatistics (PUBHLTH 540) Hypothesis Testing

Introduction to Biostatistics (PUBHLTH 540) Hypothesis Testing

General Idea How unusual is the result? Test statistics Type I error (alpha level) p-value Type II error (beta level) Power=1-beta
Week 1 Currency Systems and Crises

Week 1 Currency Systems and Crises
Types of Foreign Exchange Exposures

Types of Foreign Exchange Exposures
Week 3 The Parities

Week 3 The Parities
Dynamic Systems

Dynamic Systems

Thanks to Derek Harter for having notes on the web. Also see, Port & Van Gelder and Beltrami.
Modeling Behavior Psychology 891C

Modeling Behavior Psychology 891C

Continuous Probability Distributions: The Normal Distribution

The Curricular Process 3 STRUCTURING OF CONTENT 4 INSTRUCTION 5 IMPLEMENTATION 2 SELECTION OF CONTENT 6 ASSESSMENT 1 OBJECTIVE & GOALS
Language Disorders and Linguistic Theory What the Diagnostic Evaluation of Language Disorders (2005) H. Seymour, T. Roeper, and J/deVilliers

Language Disorders and Linguistic Theory What the Diagnostic Evaluation of Language Disorders (2005) H. Seymour, T. Roeper, and J/deVilliers
Tag-probe method: Fitting Z → μ+μ- mass peaks

Tag-probe method: Fitting Z → μ+μ- mass peaks

rec. muon pT (GeV/c) 1. 2. Motivation: 1. Want to use long pT tail of muon from Z to extrapolate to high pT regime where Z’ might live (1TeV - 2TeV) 2. Also include low pT muons (< 30 GeV/c) need loose selection criteria to allow for softer muons so far no hard cuts or isolation on ID probe track CBNT level analysis, using MuidCombined muons so far Release 12 sample Pythia Z → μ+μ- (using ~250K events corresponding to ~150 pb-1)

Hypothesis Testing: Additional Applications In this lesson we consider a series of examples that parallel the situations we discussed for confidence interval estimation. We will also focus on computer analysis for conducting hypothesis tests

Hypothesis Testing: Type II Error and Power

An Introduction to REGRESSION AND CORRELATION
Ecology of Fish: a brief review of evolutionary theory

Ecology of Fish: a brief review of evolutionary theory

Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky, 1973
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