Shmuel Tomi Klein | Bar-Ilan University (original) (raw)
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Papers by Shmuel Tomi Klein
SN computer science, Mar 17, 2023
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Prague Stringology Conference, 2013
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Prague Stringology Conference, 2018
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Prague Stringology Conference, 2016
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Springer eBooks, 2004
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The Computer Journal, Jan 9, 2021
A major property of flash memory is that a 0-bit can be changed into a 1-bit, but the symmetric t... more A major property of flash memory is that a 0-bit can be changed into a 1-bit, but the symmetric task of switching from a 1-bit to a zero may only be performed in blocks and is therefore often prohibited. This led to the development of rewriting codes using the same storage space more than once, subject to the constraint that 0-bits can be changed into 1-bits, but not vice versa. Context sensitive rewriting codes extend this idea by also incorporating information gathered from surrounding bits. Several new context sensitive rewriting codes are presented and analyzed, some of which are better than the state of the art for sparse input. Empirical simulations show a good match with the theoretical results.
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Journal of Discrete Algorithms, Mar 1, 2017
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Lecture Notes in Computer Science, 2023
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Springer eBooks, 2011
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Prague Stringology Conference, 2015
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Information Processing Letters, 2023
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arXiv (Cornell University), May 17, 2020
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DCC, Mar 1, 2008
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The Computer Journal, Feb 17, 2012
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International ACM SIGIR Conference on Research and Development in Information Retrieval, 1991
In large IR systems, information about word occurrence may be stored as a bit matrix, with rows c... more In large IR systems, information about word occurrence may be stored as a bit matrix, with rows corresponding to different words and columns to documents. Such a matrix is generally very large and very sparse. New methods for compressing such matrices are presented, which exploit possible correlations between rows and between columns. The methods are based on partitioning the matrix into small blocks and predicting the l-bit distribution within a block by means of various bit generation models. Each block is then encoded using Huffman or arithmetic coding. Preliminary experimental results indicate improvements over previous methods.
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Prague Stringology Conference, 2014
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SN computer science, Mar 17, 2023
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Prague Stringology Conference, 2013
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Prague Stringology Conference, 2018
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Prague Stringology Conference, 2016
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Springer eBooks, 2004
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The Computer Journal, Jan 9, 2021
A major property of flash memory is that a 0-bit can be changed into a 1-bit, but the symmetric t... more A major property of flash memory is that a 0-bit can be changed into a 1-bit, but the symmetric task of switching from a 1-bit to a zero may only be performed in blocks and is therefore often prohibited. This led to the development of rewriting codes using the same storage space more than once, subject to the constraint that 0-bits can be changed into 1-bits, but not vice versa. Context sensitive rewriting codes extend this idea by also incorporating information gathered from surrounding bits. Several new context sensitive rewriting codes are presented and analyzed, some of which are better than the state of the art for sparse input. Empirical simulations show a good match with the theoretical results.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Journal of Discrete Algorithms, Mar 1, 2017
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Lecture Notes in Computer Science, 2023
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Springer eBooks, 2011
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Prague Stringology Conference, 2015
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Information Processing Letters, 2023
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arXiv (Cornell University), May 17, 2020
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DCC, Mar 1, 2008
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The Computer Journal, Feb 17, 2012
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International ACM SIGIR Conference on Research and Development in Information Retrieval, 1991
In large IR systems, information about word occurrence may be stored as a bit matrix, with rows c... more In large IR systems, information about word occurrence may be stored as a bit matrix, with rows corresponding to different words and columns to documents. Such a matrix is generally very large and very sparse. New methods for compressing such matrices are presented, which exploit possible correlations between rows and between columns. The methods are based on partitioning the matrix into small blocks and predicting the l-bit distribution within a block by means of various bit generation models. Each block is then encoded using Huffman or arithmetic coding. Preliminary experimental results indicate improvements over previous methods.
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Prague Stringology Conference, 2014
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