PPT-Dec 19, 2019 IITB: Compression

1 Data Compression A survey Madhu Sudan Harvard Basic problem Given Want and st Basic correctness Prefixfree correctness not a prefix of Ensures can compress arbitrarily long sequences of . W n upper berth s in use lo wer berths ca nn be co nverte d to a double 5 and D1 06 a l ar ge su te s consisting o f t o b oom t wo b th oom s with bath and sh a lo unge a a ar e ba con Th e su s a suit bl e for shar ng co up le o amilie le as e n 1. Effects of External Compression. Improved Venous and Lymphatic . Circulation. Limits the Shape and Size of Tissue. 2. Clinical Indications for the Use of External Compression. Edema. Causes of . Edema. Smash the information to bits. Overview. The primary goal of image compression is to minimize the memory footprint of image data so that storage and transmission times are minimized. . Storage capacity can be limited, as is the case with digital cameras. Introduction. Compression is the reduction in size of data in order to save space or transmission time. . Compression is the process of reducing the size of a file by encoding its data information more efficiently . Petter Svärd, . Benoit . Hudzia. , Johan Tordsson and Erik Elmroth. Umeå University, . Dept. . of. . Computing. Science. VEE 2011, Newport Beach, CA, USA. Live migration. “Transfer . a VM from one host to . Heap’s law . (empirically observed and postulated):. Size of the vocabulary (distinct terms) in a corpus. with total number of term occurrences . n. , and constants . ,  ( < 1),. classically 20, 0.5 (. Jinseok. Choi and Brian L. Evans. Wireless Networking & Communication Group. The University of Texas as . Austin. Collaboration with Robert . W. Heath, Jr. , . and . Jeonghun. Park. 1. 2. Traditional Radio Access Network. 27 Actions 29 - Sep 6 - Oct 13 - Oct 20 - Oct 27 - Oct 3 - Nov 10 - Nov 17 - Nov 24 - Nov 1 - Dec 8 - Dec 15 - Dec 22 - Dec 29 - Dec 5 - Jan 12 - Jan 19 - Jan 26 - Jan 2 - Feb 9 - Feb 16 - Feb 23 - Fe Tekin. . Bicer. , . Jian. Yin, David Chiu, . Gagan. . Agrawal. . and Karen . Schuchardt. Ohio State University. Washington State University. Pacific Northwest National Laboratories. 1. †. †. Shuochao Yao, Yiwen Xu, Daniel Calzada. Network Compression and Speedup. 1. Source: . http://isca2016.eecs.umich.edu/. wp. -content/uploads/2016/07/4A-1.pdf. Network Compression and Speedup. 2. Why smaller models?. 2-D Warping and Block Matching. Shinjini. . Kundu. Anand. . Kamat. . Tarcar. EE398A Final Project. 1. EE398A - Compression of Light Fields using 2-D Warping and Block Matching. Outline. Motivation and . Gennady Pekhimenko. § . Vivek Seshadri. §. Onur Mutlu. . §. . Michael A. Kozuch. †. Phillip B. Gibbons. † . Todd C. Mowry. . §. § . Carnegie Mellon University . † . Intel Labs Pittsburgh. is a . lossy compression. method used to . compress images. using . fractals. . The method is best suited for photographs of natural scenes (. trees. , . mountains. , . ferns. , . clouds. ). The fractal compression technique relies on the fact that in certain images, parts of the image resemble other parts of the same image.. Future silicon trackers:. 4D tracking. Nicolò. . Cartiglia. INFN - Italy. 2. N. Cartiglia, INFN. - KEK - Dec 12 2019 . Seminar will be set to 1 hour, so, 50 + 10 mins talk will be matched.. Most of audience will be experimental physicists..