61a lecture 13
play

61A Lecture 13 Wednesday, October 2 Announcements 2 Announcements - PowerPoint PPT Presentation

Jan 28, 2023 •294 likes •1.62k views

61A Lecture 13 Wednesday, October 2 Announcements 2 Announcements Homework 3 deadline extended to Wednesday 10/2 @ 11:59pm. 2 Announcements Homework 3 deadline extended to Wednesday 10/2 @ 11:59pm. Optional Hog strategy contest due

  1. Representing Strings: the ASCII Standard American Standard Code for Information Interchange 8 rows: 3 bits 9

  2. Representing Strings: the ASCII Standard American Standard Code for Information Interchange 8 rows: 3 bits 16 columns: 4 bits 9

  3. Representing Strings: the ASCII Standard American Standard Code for Information Interchange 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code 9

  4. Representing Strings: the ASCII Standard American Standard Code for Information Interchange 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code • Rows indexed 2-5 are a useful 6-bit (64 element) subset 9

  5. Representing Strings: the ASCII Standard American Standard Code for Information Interchange 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code • Rows indexed 2-5 are a useful 6-bit (64 element) subset • Control characters were designed for transmission 9

  6. Representing Strings: the ASCII Standard American Standard Code for Information Interchange "Line feed" (\n) 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code • Rows indexed 2-5 are a useful 6-bit (64 element) subset • Control characters were designed for transmission 9

  7. Representing Strings: the ASCII Standard American Standard Code for Information Interchange "Bell" (\a) "Line feed" (\n) 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code • Rows indexed 2-5 are a useful 6-bit (64 element) subset • Control characters were designed for transmission 9

  8. Representing Strings: the ASCII Standard American Standard Code for Information Interchange "Bell" (\a) "Line feed" (\n) 8 rows: 3 bits 16 columns: 4 bits • Layout was chosen to support sorting by character code • Rows indexed 2-5 are a useful 6-bit (64 element) subset • Control characters were designed for transmission (Demo) 9

  9. Representing Strings: the Unicode Standard 10

  10. Representing Strings: the Unicode Standard http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  11. Representing Strings: the Unicode Standard • 109,000 characters http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  12. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  13. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  14. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  15. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg 10

  16. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X 10

  17. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X U+263a WHITE SMILING FACE 10

  18. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X U+263a WHITE SMILING FACE U+2639 WHITE FROWNING FACE 10

  19. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X ' ☺ ' U+263a WHITE SMILING FACE U+2639 WHITE FROWNING FACE 10

  20. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X ' ☺ ' ' ☹ ' U+263a WHITE SMILING FACE U+2639 WHITE FROWNING FACE 10

  21. Representing Strings: the Unicode Standard • 109,000 characters • 93 scripts (organized) • Enumeration of character properties, such as case • Supports bidirectional display order • A canonical name for every character http://ian-albert.com/unicode_chart/unichart-chinese.jpg U+0058 LATIN CAPITAL LETTER X ' ☺ ' ' ☹ ' U+263a WHITE SMILING FACE U+2639 WHITE FROWNING FACE (Demo) 10

  22. Representing Strings: UTF-8 Encoding 11

  23. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) 11

  24. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers 11

  25. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes 11

  26. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 11

  27. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. bytes 11

  28. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. bytes integers 11

  29. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 bytes integers 11

  30. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 11

  31. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 00000010 2 11

  32. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 00000010 2 00000011 3 11

  33. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 00000010 2 00000011 3 Variable-length encoding: integers vary in the number of bytes required to encode them. 11

  34. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 00000010 2 00000011 3 Variable-length encoding: integers vary in the number of bytes required to encode them. In Python: string length is measured in characters, bytes length in bytes. 11

  35. Representing Strings: UTF-8 Encoding UTF (UCS (Universal Character Set) Transformation Format) Unicode: Correspondence between characters and integers UTF-8: Correspondence between those integers and bytes A byte is 8 bits and can encode any integer 0-255. 00000000 0 00000001 1 bytes integers 00000010 2 00000011 3 Variable-length encoding: integers vary in the number of bytes required to encode them. In Python: string length is measured in characters, bytes length in bytes. (Demo) 11

  36. Sequence Processing

  37. Sequence Processing 13

  38. Sequence Processing Consider two problems: 13

  39. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers. 13

  40. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. 13

  41. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. 13

  42. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 13

  43. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. 13

  44. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 13

  45. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. 13

  46. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. filter even: 13

  47. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. filter even: 13

  48. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. filter even: 0, 2, 8, 34, . 13

  49. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. filter even: 0, 2, 8, 34, . accumulate sum: 13

  50. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate naturals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. map fib: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. filter even: 0, 2, 8, 34, . accumulate sum: ., ., ., ., =44 13

  51. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. 14

  52. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. 14

  53. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 14

  54. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' 14

  55. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 14

  56. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 14

  57. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 'University', 'California', 'Berkeley' 14

  58. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 'University', 'California', 'Berkeley' map first: 14

  59. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 'University', 'California', 'Berkeley' map first: 'U', 'C', 'B' 14

  60. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 'University', 'California', 'Berkeley' map first: 'U', 'C', 'B' accumulate tuple: 14

  61. Sequence Processing Consider two problems:  Sum the even members of the first n Fibonacci numbers.  List the letters in the acronym for a name, which includes the first letter of each capitalized word. enumerate words: 'University', 'of', 'California', 'Berkeley' filter capitalized: 'University', 'California', 'Berkeley' map first: 'U', 'C', 'B' accumulate tuple: ( 'U', 'C', 'B' ) 14

  62. Mapping a Function over a Sequence Apply a function to each element of the sequence 15

  63. Mapping a Function over a Sequence Apply a function to each element of the sequence >>> alternates = (-1, 2, -3, 4, -5) 15

  64. Mapping a Function over a Sequence Apply a function to each element of the sequence >>> alternates = (-1, 2, -3, 4, -5) >>> tuple(map(abs, alternates)) 15

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then reviewed the final point of lecture #3: Hennigs logical approach to tree inference is valid as logic, but we have a hard time using it because our data

660 views • 7 slides
Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

TU/e Algorithms (2IL15) Lecture 13 Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13 Part I of the course: Optimization Problems we want to find valid solution minimizing cost (or maximizing

904 views • 20 slides
In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken Birman LEAVE NO TRACE BEHIND Spring, 2020 HTTP://WWW.CS.CORNELL.EDU/COURSES/CS5412/2020SP 1 THE PRIVACY PUZZLE FOR I O T We have sensors

1.04k views • 65 slides
Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface Removal Sort polygons in depth. Use the farthest vertex in each polygon as the sorting key. Efficient Painter - binary space partition (BSP)

563 views • 6 slides
Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 - Resource-sensitive algebraic theories Lecture 3 - Interacting Hopf monoids and graphical linear algebra Lecture 4 - Signal Flow Graphs and recurrence

1.2k views • 61 slides
Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A Threatens Privacy Threatens Try to achieve Lecture 2B: Network threats Lecture 3A: Attacks on Lecture 6A: Security Protocols Web servers, malware

381 views • 25 slides
Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

ISDS & Digital Business Skills Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture actually do? What happens before a lecture? What happens during a lecture? What does the button do?

527 views • 18 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

1.74k views • 60 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

850 views • 46 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

839 views • 58 slides
Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Overview Practical experimental course lectures are only introductory Lecture 1 - theoretical principles classic approaches l h current trends in leading research. Lecture 2 - candidate research methods advantages and

499 views • 15 slides
Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture Chapter 3.1 to 3.4 (Please read lecture topic material before and after each lecture on that topic) What is Artificial Intelligence? Thought

636 views • 31 slides
Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is More Than Half Done! If you have comments they are very welcome y y Lectures Lecture notes Weekly Homework Midterm

795 views • 42 slides
Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma chine e Lea earn rning Paradigm Observe set of examples: training data Infer something about process that generated that data Use

847 views • 36 slides
Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4 Basics of Feedback Control Robert Babu ska Today: Steady-state response. Delft Center for Systems and Control Faculty of Mechanical

388 views • 7 slides
Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students will be able to: Discuss the different stages in the history of psycholinguistics Identify the scholars in the field of psycholinguistics

589 views • 28 slides
ISAs and Y86-64 Samira Khan Agenda ISA vs Microarchitecture ISA Tradeoffs Y86-64 ISA

ISAs and Y86-64 Samira Khan Agenda ISA vs Microarchitecture ISA Tradeoffs Y86-64 ISA

ISAs and Y86-64 Samira Khan Agenda ISA vs Microarchitecture ISA Tradeoffs Y86-64 ISA Y86-64 Format Y86-64 Encoding/Decoding LEVE VELS OF TR TRANSFORMATI TION ISA Agreed upon interface between software and hardware

731 views • 49 slides
ECEN 5682 Theory and Practice of Error Control Codes Convolutional Codes Peter Mathys

ECEN 5682 Theory and Practice of Error Control Codes Convolutional Codes Peter Mathys

Convolutional Codes ECEN 5682 Theory and Practice of Error Control Codes Convolutional Codes Peter Mathys University of Colorado Spring 2007 Peter Mathys ECEN 5682 Theory and Practice of Error Control Codes Basic Definitions, Convolutional

823 views • 56 slides
CS4405 JPEG Transform Coding JPEG Compression Workflow RGB Optional Chroma Subsample

CS4405 JPEG Transform Coding JPEG Compression Workflow RGB Optional Chroma Subsample

CS4405 JPEG Transform Coding JPEG Compression Workflow RGB Optional Chroma Subsample YCbCr Chroma Sub-sampling 4:2:0 Reduces input data by 50% R G B Y U V Chroma Sub-sampling

445 views • 22 slides
Enc Encoding ding, F , Fas ast and Slo t and Slow: w: Low-Latency Video Processing Using

Enc Encoding ding, F , Fas ast and Slo t and Slow: w: Low-Latency Video Processing Using

Enc Encoding ding, F , Fas ast and Slo t and Slow: w: Low-Latency Video Processing Using Thousands of Tiny Threads Presenter: Wen-Fu Lee Outline Vision & Goals mu: Supercomputing as a Service Fine-grained Parallel Video

649 views • 50 slides
RIDESHARE TO TRANSIT PILOT CONGESTION MANAGEMENT PROGRAM TECHNICAL ADVISORY COMMITTEE (TAC)

RIDESHARE TO TRANSIT PILOT CONGESTION MANAGEMENT PROGRAM TECHNICAL ADVISORY COMMITTEE (TAC)

RIDESHARE TO TRANSIT PILOT CONGESTION MANAGEMENT PROGRAM TECHNICAL ADVISORY COMMITTEE (TAC) MEETING APRIL 16, 2020 RIDESHARE TO TRANSIT BACKGROUND Transportation Fund for Clean Air (TFCA) $4 surcharge fee on vehicles registered in the

213 views • 9 slides
Policy 101 Vision & Context Project Development Funding Vision & Context CS is

Policy 101 Vision & Context Project Development Funding Vision & Context CS is

Policy 101 Vision & Context Project Development Funding Vision & Context CS is fundamentally about a commitment to equity Needs of vulnerable road user Context is important Project Development Achieving network of

751 views • 64 slides
designing the transit marketplace Sid Banerjee CNTS Workshop, July 2019 Operations Research,

designing the transit marketplace Sid Banerjee CNTS Workshop, July 2019 Operations Research,

designing the transit marketplace Sid Banerjee CNTS Workshop, July 2019 Operations Research, Cornell ridesharing platforms critical components of modern urban transit crucible for real-time decision making/OR/EconCS 1/22 research in

618 views • 27 slides
MEETING OF THE RTA BOARD OF DIRECTORS SEPTEMBER 10, 2020 Welcome! Meeting Starts at 9 a.m.

MEETING OF THE RTA BOARD OF DIRECTORS SEPTEMBER 10, 2020 Welcome! Meeting Starts at 9 a.m.

RTAChicago.org MEETING OF THE RTA BOARD OF DIRECTORS SEPTEMBER 10, 2020 Welcome! Meeting Starts at 9 a.m. Meeting Agenda @ RTAChicago.org/about-us/boardmeetings RTA BOARD OF DIRECTORS MEETING 1. CALL TO ORDER Please feel free to listen to

910 views • 63 slides

More recommend