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Run-Length Encoding (RLE) is one of the most fundamental tools in data compression. However, its compression power drops significantly if there lacks consecutive elements in the sequence. In extreme cases, the output of the encoder may…

Data Structures and Algorithms · Computer Science 2023-12-29 Xutan Peng , Yi Zhang , Dejia Peng , Jiafa Zhu

We propose a new approach for calculating the Lempel-Ziv factorization of a string, based on run length encoding (RLE). We present a conceptually simple off-line algorithm based on a variant of suffix arrays, as well as an on-line algorithm…

Data Structures and Algorithms · Computer Science 2015-03-20 Jun'ichi Yamamoto , Hideo Bannai , Shunsuke Inenaga , Masayuki Takeda

We revisit the problem of finding shortest unique substring (SUS) proposed recently by [6]. We propose an optimal $O(n)$ time and space algorithm that can find an SUS for every location of a string of size $n$. Our algorithm significantly…

Data Structures and Algorithms · Computer Science 2014-01-14 Atalay Mert İleri , M. Oğuzhan Külekci , Bojian Xu

Natural language text corpora are often available as sets of syntactically parsed trees. A wide range of expressive tree queries are possible over such parsed trees that open a new avenue in searching over natural language text. They not…

Databases · Computer Science 2012-08-02 Pirooz Chubak , Davood Rafiei

One of the distinctive features of Information Retrieval systems comparing to Database Management systems, is that they offer better compression for posting lists, resulting in better I/O performance and thus faster query evaluation. In…

Information Retrieval · Computer Science 2009-06-18 Panagiotis Papadakos , Yannis Theoharis , Yannis Marketakis , Nikos Armenatzoglou , Yannis Tzitzikas

We show that both the Lempel Ziv 77- and the 78-factorization of a text of length $n$ on an integer alphabet of size $\sigma$ can be computed in $O(n \lg \lg \sigma)$ time (linear time if we allow randomization) using $O(n \lg \sigma)$ bits…

Data Structures and Algorithms · Computer Science 2016-05-31 Dominik Köppl , Kunihiko Sadakane

Compressed Sensing (CS) seeks to recover an unknown vector with $N$ entries by making far fewer than $N$ measurements; it posits that the number of compressed sensing measurements should be comparable to the information content of the…

Information Theory · Computer Science 2010-04-29 Jeffrey D. Blanchard , Coralia Cartis , Jared Tanner

Many interesting data sets available on the Internet are of a medium size---too big to fit into a personal computer's memory, but not so large that they won't fit comfortably on its hard disk. In the coming years, data sets of this…

Computation · Statistics 2018-05-24 Benjamin S. Baumer

Suffix arrays encode the lexicographical order of all suffixes of a text and are often combined with the Longest Common Prefix array (LCP) to simulate navigational queries on the suffix tree in reduced space. In space-critical applications…

Data Structures and Algorithms · Computer Science 2017-11-02 Nicola Prezza

Motivated by the philosophy and phenomenal success of compressed sensing, the problem of reconstructing a matrix from a sampling of its entries has attracted much attention recently. Such a problem can be viewed as an information-theoretic…

Information Theory · Computer Science 2009-05-15 Zhisu Zhu , Anthony Man-Cho So , Yinyu Ye

This paper shows how to optimize sparse tensor algebraic expressions by introducing temporary tensors, called workspaces, into the resulting loop nests. We develop a new intermediate language for tensor operations called concrete index…

Mathematical Software · Computer Science 2023-10-18 Fredrik Kjolstad , Willow Ahrens , Shoaib Kamil , Saman Amarasinghe

A compressed sensing method consists of a rectangular measurement matrix, $M \in \mathbbm{R}^{m \times N}$ with $m \ll N$, together with an associated recovery algorithm, $\mathcal{A}: \mathbbm{R}^m \rightarrow \mathbbm{R}^N$. Compressed…

Information Theory · Computer Science 2013-02-26 M. A. Iwen

The Binary Jumbled String Matching problem is defined as: Given a string $s$ over $\{a,b\}$ of length $n$ and a query $(x,y)$, with $x,y$ non-negative integers, decide whether $s$ has a substring $t$ with exactly $x$ $a$'s and $y$ $b$'s.…

Data Structures and Algorithms · Computer Science 2013-06-03 Golnaz Badkobeh , Gabriele Fici , Steve Kroon , Zsuzsanna Lipták

Introduced about thirty years ago in the field of Data Compression, the Burrows-Wheeler Transform (BWT) is a string transformation that, besides being a booster of the performance of memoryless compressors, plays a fundamental role in the…

Data Structures and Algorithms · Computer Science 2023-05-09 Raffaele Giancarlo , Giovanni Manzini , Antonio Restivo , Giovanna Rosone , Marinella Sciortino

Compressive-sensing-based uncertainty quantification methods have become a pow- erful tool for problems with limited data. In this work, we use the sliced inverse regression (SIR) method to provide an initial guess for the alternating…

Numerical Analysis · Mathematics 2018-09-11 Xiu Yang , Weixuan Li , Alexandre Tartakovsky

The random access problem for compressed strings is to build a data structure that efficiently supports accessing the character in position $i$ of a string given in compressed form. Given a grammar of size $n$ compressing a string of size…

Data Structures and Algorithms · Computer Science 2015-01-27 Patrick Hagge Cording

The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new…

Machine Learning · Statistics 2024-06-27 Cencheng Shen , Li Chen , Yuexiao Dong , Carey E. Priebe

We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that…

Data Structures and Algorithms · Computer Science 2007-05-23 Philip Bille , Rolf Fagerberg , Inge Li Goertz

Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and…

Computer Vision and Pattern Recognition · Computer Science 2021-04-02 Longguang Wang , Xiaoyu Dong , Yingqian Wang , Xinyi Ying , Zaiping Lin , Wei An , Yulan Guo

In this paper, we consider the problem of efficiently representing a set $S$ of $n$ items out of a universe $U=\{0,...,u-1\}$ while supporting a number of operations on it. Let $G=g_1...g_n$ be the gap stream associated with $S$, $gap$ its…

Data Structures and Algorithms · Computer Science 2015-05-15 Nicola Prezza