PPT-The Kernel Abstraction Main Points

Process concept A process is an OS abstraction for executing a program with limited privileges Dualmode operation user vs kernel Kernelmode execute with complete privileges Usermode execute with fewer privileges. Debugging as Engineering. Much of your time in this course will be spent debugging. In industry, 50% of software dev is debugging. Even more for kernel development. How do you reduce time spent debugging?. Main points. New Jim Crow – Main points. In 2005, 4/5 of all drug arrests were for . posession. .. 80% of the growth in arrests in the 1990s was for marijuana. The prison population went from 300,000 in 1980 to over two million in 2000.. 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 Debugging as Engineering. Much of your time in this course will be spent debugging. In industry, 50% of software dev is debugging. Even more for kernel development. How do you reduce time spent debugging?. Presented by:. Nacer Khalil. Table of content. Introduction. Definition of robustness. Robust Kernel Density Estimation. Nonparametric . Contamination . Models. Scaled project Kernel Density Estimator. Machine Learning. March 25, 2010. Last Time. Basics of the Support Vector Machines. Review: Max . Margin. How can we pick which is best?. Maximize the size of the margin.. 3. Are these really . “equally valid”?. Machine Learning. March 25, 2010. Last Time. Recap of . the Support Vector Machines. Kernel Methods. Points that are . not. linearly separable in 2 dimension, might be linearly separable in 3. . Kernel Methods. Marc Spicker. Visual Computing Group. University . of Konstanz, Germany. Motivation. Stippling:. Illustration technique creating the appearance of shading by placing only dots.. 1. Too few dots?. Too many dots?. Main Points: We can glorify God by practicing the following principles…. Acknowledge God vs.1-2. Eve said “with God’s . help. ”. Acts 12:21-24. Deuteronomy 4:39-40. Challenge: Do we . acknowledge . 3/6/15. Multiple linear regression. What are you predicting?. Data type. Continuous. Dimensionality. 1. What are you predicting it from?. Data type. Continuous. Dimensionality. p. How many data points do you have?. Way of classifying information from abstract (general) to concrete (specific) . . Level 4:. Abstractions. Level 3: . Noun Classes, broad group names with little specification. Level 2:. Noun Categories, more definite groups. Lecture 08(c) – Predicate Abstraction and SLAM . Eran. . Yahav. . Previously . Typestate. Verification. 2. Today. Predicate Abstraction (via SLAM). Acks. Slides cannibalized from . Ball&Rajamani. Slide . 1. PHY Abstraction for MU-MIMO. Date:. 2010-07-11. Authors: . Name. Affiliations. Address. Phone. email. Riichi Kudo. NTT Corporation. Hikarino-oka Yokosuka-shi Japan. +81-46-859- 3140. kudo.riichi@lab.ntt.co.jp. Slide . 1. PHY Abstraction for MU-MIMO. Date:. 2010-07-11. Authors: . Name. Affiliations. Address. Phone. email. Riichi Kudo. NTT Corporation. Hikarino-oka Yokosuka-shi Japan. +81-46-859- 3140. kudo.riichi@lab.ntt.co.jp.