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Documents authored by Gao, Jia


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Gao, Jia

Document
Optimal Computation of Overabundant Words

Authors: Yannis Almirantis, Panagiotis Charalampopoulos, Jia Gao, Costas S. Iliopoulos, Manal Mohamed, Solon P. Pissis, and Dimitris Polychronopoulos

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)


Abstract
The observed frequency of the longest proper prefix, the longest proper suffix, and the longest infix of a word w in a given sequence x can be used for classifying w as avoided or overabundant. The definitions used for the expectation and deviation of w in this statistical model were described and biologically justified by Brendel et al. (J Biomol Struct Dyn 1986). We have very recently introduced a time-optimal algorithm for computing all avoided words of a given sequence over an integer alphabet (Algorithms Mol Biol 2017). In this article, we extend this study by presenting an O(n)-time and O(n)-space algorithm for computing all overabundant words in a sequence x of length n over an integer alphabet. Our main result is based on a new non-trivial combinatorial property of the suffix tree T of x: the number of distinct factors of x whose longest infix is the label of an explicit node of T is no more than 3n-4. We further show that the presented algorithm is time-optimal by proving that O(n) is a tight upper bound for the number of overabundant words. Finally, we present experimental results, using both synthetic and real data, which justify the effectiveness and efficiency of our approach in practical terms.

Cite as

Yannis Almirantis, Panagiotis Charalampopoulos, Jia Gao, Costas S. Iliopoulos, Manal Mohamed, Solon P. Pissis, and Dimitris Polychronopoulos. Optimal Computation of Overabundant Words. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


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@InProceedings{almirantis_et_al:LIPIcs.WABI.2017.4,
  author =	{Almirantis, Yannis and Charalampopoulos, Panagiotis and Gao, Jia and Iliopoulos, Costas S. and Mohamed, Manal and Pissis, Solon P. and Polychronopoulos, Dimitris},
  title =	{{Optimal Computation of Overabundant Words}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{4:1--4:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.4},
  URN =		{urn:nbn:de:0030-drops-76468},
  doi =		{10.4230/LIPIcs.WABI.2017.4},
  annote =	{Keywords: overabundant words, avoided words, suffix tree, DNA sequence analysis}
}

Gao, Jiawei

Document
On the Fine-Grained Complexity of Least Weight Subsequence in Multitrees and Bounded Treewidth DAGs

Authors: Jiawei Gao

Published in: LIPIcs, Volume 148, 14th International Symposium on Parameterized and Exact Computation (IPEC 2019)


Abstract
This paper introduces a new technique that generalizes previously known fine-grained reductions from linear structures to graphs. Least Weight Subsequence (LWS) [Hirschberg and Larmore, 1987] is a class of highly sequential optimization problems with form F(j) = min_{i < j} [F(i) + c_{i,j}] . They can be solved in quadratic time using dynamic programming, but it is not known whether these problems can be solved faster than n^{2-o(1)} time. Surprisingly, each such problem is subquadratic time reducible to a highly parallel, non-dynamic programming problem [Marvin Künnemann et al., 2017]. In other words, if a "static" problem is faster than quadratic time, so is an LWS problem. For many instances of LWS, the sequential versions are equivalent to their static versions by subquadratic time reductions. The previous result applies to LWS on linear structures, and this paper extends this result to LWS on paths in sparse graphs, the Least Weight Subpath (LWSP) problems. When the graph is a multitree (i.e. a DAG where any pair of vertices can have at most one path) or when the graph is a DAG whose underlying undirected graph has constant treewidth, we show that LWSP on this graph is still subquadratically reducible to their corresponding static problems. For many instances, the graph versions are still equivalent to their static versions. Moreover, this paper shows that if we can decide a property of form Exists x Exists y P(x,y) in subquadratic time, where P is a quickly checkable property on a pair of elements, then on these classes of graphs, we can also in subquadratic time decide whether there exists a pair x,y in the transitive closure of the graph that also satisfy P(x,y).

Cite as

Jiawei Gao. On the Fine-Grained Complexity of Least Weight Subsequence in Multitrees and Bounded Treewidth DAGs. In 14th International Symposium on Parameterized and Exact Computation (IPEC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 148, pp. 16:1-16:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{gao:LIPIcs.IPEC.2019.16,
  author =	{Gao, Jiawei},
  title =	{{On the Fine-Grained Complexity of Least Weight Subsequence in Multitrees and Bounded Treewidth DAGs}},
  booktitle =	{14th International Symposium on Parameterized and Exact Computation (IPEC 2019)},
  pages =	{16:1--16:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-129-0},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{148},
  editor =	{Jansen, Bart M. P. and Telle, Jan Arne},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2019.16},
  URN =		{urn:nbn:de:0030-drops-114778},
  doi =		{10.4230/LIPIcs.IPEC.2019.16},
  annote =	{Keywords: fine-grained complexity, dynamic programming, graph reachability}
}
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