PPT-Sharing Features Between Objects and Their Attributes

Sung Ju Hwang 1 Fei Sha 2 and Kristen Grauman 1 1 University of Texas at Austin 2 University of Southern California Problem Experimental results ConclusionFuture Work. UML CLASS DIAGRAM RELATIONSHIPS. 215. Identify the information system’s purpose. Identify the information system’s actors and features. Identify Use Cases and create a Use Case Diagram. Identify Objects and their Classes and create a Class Diagram. Data. Data topics. Types of attributes. Data quality issues. Transformations. Visualization. Types of datasets. Preprocessing. Summary statistics. What is data?. Collection of data objects and their attributes. Synchronization. Atomicity. Specifying critical sections. Memory visibility. One thread’s modification seen by the other. Visibility. T1: X = 5; print(X); will result in 5. . T1: X = 5; T2: print(X); may or may not print 5. . Sung . Ju. Hwang. 1. , . Fei. Sha. 2. and Kristen Grauman. 1. 1 University . of Texas at . Austin, 2 University of Southern California. Problem. Sharing features between sub/. superclasses. A single visual instance can have multiple labels at different levels of semantic granularity... Get with your partner and take turns taking one or two counters from the pile of 18. . How many counters do you each have?. You should each have 9 counters.. What multiplication fact can we make from this?. Why you should care about Object Orientation (OO). Iñaki. . Serraller. Vizcaino. Overview. Objectives. OO Vs. Procedural. Concepts. Objects. Classes. Inheritance. Polymorphism. Features. When to Use OO?. Housing Styles. Focus on the visible house features . to . identify the concept.. (Not a name for the style, but the features that make it an example of that style). Yes. No. Develop a hypothesis:. What attributes are similar between . Attribute Detection. Kylie McCarty, Abdullah Jamal . (kyliemccarty@knights.ucf.edu, a_jamal@knights.ucf.edu). University of Central Florida. II. Datasets. I. Problem . III. Our Method . . Method 1: SVM. Vagelis. . Hristidis. Eduardo Ruiz. 1. Collaborative Adaptive Data Sharing - FIU. Motivation. Many application domains where . users . collaborate and share domain-specific information. .. Disaster Management. Bangpeng. Yao, . Xiaoye. Jiang, . Aditya. . Khosla. ,. Andy Lai Lin, . Leonidas. . Guibas. , and Li . Fei-Fei. 1. Stanford University. 2. Action Classification in Still Images. Low level feature. Neeraj Kumar, Alexander C. Berg, Peter N. Belumeur, and Shree K. Nayar. Presented by Gregory Teodoro. Attribute Classification. Early research focused on gender and ethnicity.. Done on small datasets. Dr. Bjarne Berg. COMERIT. What We’ll Cover. Introduction. BW 7.5 on HANA Vs. BW 7.5 Edition for HANA. New Modeling Features in BW 7.5. Advanced DataStore Objects (ADSO) and Composite Providers. Wizards and New Query . Learn . About You.. Luke K. McDowell. U.S. Naval Academy. http://www.usna.edu/Users/cs/lmcdowel. . Joint work with:. MIDN Josh King, USNA. David Aha, NRL. Bio. 1993-1997: Princeton University. B.S.E., Electrical Engineering. CADS: A Collaborative Adaptive Data Sharing Platform Vagelis Hristidis Eduardo Ruiz 1 Collaborative Adaptive Data Sharing - FIU Motivation Many application domains where users collaborate and share domain-specific information