PPT-Type Inference with Run-time Logs

Ravi Chugh Motivation Dynamic Languages Dynamicallytyped languages Enable rapid prototyping Facilitate interlanguage development Staticallytyped languages Prevent certain runtime errors Enable optimized execution. . A School Leader’s Guide for Improvement. 1. Georgia Department of Education . Dr. John D. Barge, State School Superintendent . All Rights Reserved. The Purpose of this Module is to…. p. rovide school leaders an opportunity to strengthen their understanding of low inference feedback.. Kathleen Fisher. cs242. Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. The truth, the whole truth, and nothing but the truth.. What is inference?. What you know + what you read = inference. Uses facts, logic, or reasoning to come to an assumption or conclusion. Asks: “What conclusions can you draw based on what is happening . Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!.  . 2. Invariants. Sai . Vallurupalli. What are query logs useful for?. In Social Sciences, Medical & Health, Advertising & Marketing, Law Enforcement etc. . Understanding Search Behavior – Trends and Hot Trends. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. Daniel R. Schlegel and Stuart C. Shapiro. Department of Computer Science and Engineering. University at Buffalo, The State University of New York. Buffalo, New York, USA. <. drschleg,shapiro. >@buffalo.edu. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Parallel Programs. Harish Patil, . Cristiano Pereira. , Mack Stallcup, Gregory Lueck, James Cownie. Intel Corporation. CGO 2010, Toronto, Canada. 1. Non-Determinism. Program execution is not repeatable across runs. Ravi Chugh. Motivation: Dynamic Languages. Dynamically-typed languages. Enable rapid prototyping. Facilitate inter-language development. Statically-typed languages. Prevent certain run-time errors. Enable optimized execution. Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. Conservative program analysis.