PPT-Scalable Visual Instance Mining with Threads of Features

Wei Zhang Hongzhi Li ChongWah Ngo ShihFu Chang City Univeristy of Hong Kong Columbia University 1 Visual Instance Mining visual instance a specific visual entity object car apple flower. Methods. There are three major components of a class definition.. 1. Instance . variables . (also called . fields in the API documentation).. 2. Constructors. .. 3. Methods. .. The following notes will show how to write code for a user designed class, dealing with each of those three parts in order. . Readings. . Silberschatz. et al : Chapter 4. Motivation. Sometimes a program needs to do multiple tasks concurrently. Example: Word processor . Tasks include: Display graphics, respond to keystrokes from the user, and perform spelling and grammar checking. Instructor Notes. This lecture deals with how work groups are scheduled for execution on the compute units of devices. Also explain the effects of divergence of work items within a group and its negative effect on performance. Thomas Plagemann. Slides from Otto J. Anshus, Tore Larsen. (University of Tromsø). , . Kai Li. (. Princeton University. ). Overview. Intro to threads. Concurrency. Race conditions & critical regions. Commutativity. Rule: Designing Scalable Software for Multicore Processors. Austin T. Clements, M. . Frans. . Kaashoek. , . Nickolai. . Zeldovich. , Robert T. Morris, and Eddie Kohler. MIT CSAIL and Harvard University. Thomas Plagemann. Slides from Otto J. Anshus, Tore Larsen. (University of Tromsø). , . Kai Li. (. Princeton University. ). Overview. Intro to threads. Concurrency. Race conditions & critical regions. 1. Nearest Neighbor Learning. Classify based on local similarity. Ranges from simple . nearest neighbor . to case-based and analogical reasoning. Use local information near the current query instance to decide the classification of that instance. Matt Welsh, David Culler, and Eric Brewer. Computer Science Division. University of California, Berkeley. Presented By:. Linh Nguyen. Agenda . Motivations and . background. Concurrencies. SEDA. Goals. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. Outline. The importance of instance selection. Rough set theory. Fuzzy-rough sets. Fuzzy-rough instance selection. Experimentation. Conclusion. Knowledge discovery. The problem of too much data. Requires storage. By William . Stallings. Operating Systems:. Internals and Design Principles. Operating Systems:. Internals and Design Principles. The basic idea is that the several components in any complex system will perform particular subfunctions that contribute to the overall function.. Larry Peterson. In collaboration with . Arizona. , Akamai. ,. . Internet2. , NSF. , North Carolina, . Open Networking Lab, Princeton. (and several pilot sites). S3. DropBox. GenBank. iPlant. Data Management Challenge. and memory layout. l. ist.next. l. ist.prev. l. ist.next. l. ist.prev. l. ist.next. l. ist.prev. fox. fox. fox. Linked lists in Linux. fox. fox. fox. list {. .. next. . . .. prev. }. Node;. COS 418: Distributed Systems. Lecture . 14. Wyatt Lloyd. Consistency Hierarchy. Linearizability. Sequential Consistency. Causal+ Consistency. Eventual Consistency. e.g., RAFT. e.g., Bayou. e.g., Dynamo.