'Possible pairs' presentation slideshows

Sorting

Sorting. CS 4102: Algorithms Spring 2011 Aaron Bloomfield. Reminder: Common Forms of Recurrence Equations. Remember these? Divide and conquer: Like Mergesort T(n) = bT(n/c) + f(n) Solve directly or apply master theorem Chip and conquer: T(n) = T(n-c) + f(n)

872 views • 70 slides

Unit IV Part I

Unit IV Part I. Prepared by :Mr. Badgujar D D SIT, Lonavala. Error Detection and Correction. Whenever bits flow from one point to another , they are subject to unpredictable changes from interferences. This interference can change the shape of the signal. Single-bit error.

778 views • 65 slides

Seminar in Bioinformatics Winter 11/12 An Introduction To System Biology Uri Alon Chapters 3-4

Seminar in Bioinformatics Winter 11/12 An Introduction To System Biology Uri Alon Chapters 3-4 Presented by: Nitsan Chrizman. What's on the menu?. Starter Reminder Main course Network motifs Autoregulation The feed forward loop Desert Summary. let's remind ourselves.

801 views • 63 slides

DNA Sequencing with Longer Reads

DNA Sequencing with Longer Reads. Byung G. Kim Computer Science Dept. Univ. of Mass. Lowell kim@cs.uml.edu. Outline. DNA distribution Counting problem DNA sequencing Reassemble DNA fragments Part of $1,000 genome sequencing. Bioinformatics. What is bioinformatics ? Intersection

651 views • 43 slides

505 views • 36 slides

Appraising Performance

Appraising Performance. Purpose of Performance Appraisal . The main focus of performance appraisal is: improving organisation performance by focusing on individual effort and by developing them The other purpose of a performance appraisal is:

553 views • 36 slides

4.1 (cont.) Probability Models

4.1 (cont.) Probability Models. The Equally Likely Approach (also called the Classical Approach). Assigning Probabilities. If an experiment has N outcomes, then each outcome has probability 1/N of occurring If an event A 1 has n 1 outcomes, then P(A 1 ) = n 1 /N. Dice

408 views • 34 slides

6.1

6.1. Discrete Random Variables. Random Variables. A random variable is a numeric measure of the outcome of a probability experiment Random variables reflect measurements that can change as the experiment is repeated

535 views • 31 slides

6.1

6.1. Discrete Random Variables. Random Variables. A random variable is a numeric measure of the outcome of a probability experiment Random variables reflect measurements that can change as the experiment is repeated

515 views • 30 slides

Chapter 8 Embedding

Chapter 8 Embedding. Introduction. Density Reciprocity Transitivity Clustering Group-external and group-internal ties Krackhardt's graph theoretical dimensions of hierarchy . U nit of analysis. Micro. Actor: People Dyad Triad Subgroups Organizations Collectives/aggregates

492 views • 29 slides

MaxQuant enables high peptide identification rates, individualized p.p.b. -range mass accuracies and proteome-wide pro

MaxQuant enables high peptide identification rates, individualized p.p.b. -range mass accuracies and proteome-wide protein quantification. Yoona Kim University of California, San Diego. UCSD Mass Spectrometry Journal Club 12/03/10. MaxQuant. Outline.

1.34k views • 23 slides

Interpolation

Interpolation. Objectives. To give a definition of Interpolation as it relates to GIS and mapping/surveying To explain How Interpolation Works Discuss Spatial Autocorrelation, Sample Size, and Interpolation Barriers

880 views • 22 slides

Dynamic Programming

Dynamic Programming. What is Dynamic Programming. A method for solving complex problems by breaking them down into simpler sub problems. It is applicable to problems exhibiting the properties of overlapping subproblems which are only slightly smaller

406 views • 20 slides

Computational Linguistic Techniques Applied to Drugname Matching

Computational Linguistic Techniques Applied to Drugname Matching. Bonnie J. Dorr, University of Maryland Greg Kondrak, University of Alberta June 26, 2003. Drugname Matching. String matching to rank similarity between drug names Two classes of string matching

296 views • 20 slides

Fixed vs. Random Effects

Fixed vs. Random Effects. Fixed effect we are interested in the effects of the treatments (or blocks) per se if the experiment were repeated, the levels would be the same conclusions apply to the treatment (or block) levels that were tested treatment (or block) effects sum to zero

1.45k views • 14 slides

The Balance Function: Experimental Studies in STAR

The Balance Function: Experimental Studies in STAR. Marguerite Belt Tonjes, G.D. Westfall, A.M. Vander Molen, the STAR collaboration. STAR. S olenoidal T racker A t R HIC

265 views • 13 slides

Median/Order Statistics Algorithms

Median/Order Statistics Algorithms. Minimum and Maximum Selection in expected linear time Selection in worst-case linear time. Minimum and Maximum. How many comparisons are sufficient to find minimum/maximum? How many comparisons are sufficient to find both minimum AND maximum?

382 views • 13 slides

Biological Sequence Comparison / Database Homology Searching

Biological Sequence Comparison / Database Homology Searching. Aoife McLysaght Summer Intern, Compaq Computer Corporation Ballybrit Business Park, Galway, Ireland. Database Homology Searching.

254 views • 13 slides

Chapter 4: Probability (Cont.)

Chapter 4: Probability (Cont.). In this handout: Venn diagrams Event relations Laws of probability Conditional probability Independence of events. Venn Diagram: representing events graphically.

229 views • 10 slides

nequalities

Takagi Factorization on a GPU using CUDA Gagandeep S. Sachdev , Vishay Vanjani & Mary W. Hall School of Computing, University of Utah. p th col. q th col. p th row. q th row. Jacobi Method. What is Takagi Factorization? . Special form of singular value decomposition (SVD )

85 views • 1 slides