Ömer Eğecioğlu
University of California, Santa Barbara
133 Papers
1K Citations
Ömer Eğecioğlu is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Fibonacci cube & Fibonacci number. The author has an hindex of 22, co-authored 132 publications. Previous affiliations of Ömer Eğecioğlu include Kansas State University & University of California.
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Papers
Asynchronous spiking neural P systems
TL;DR: It is proved that asynchronous systems, with extended rules, and where each neuron is either bounded or unbounded, are not computationally complete and the configuration reachability, membership, emptiness, infiniteness, and disjointness problems are shown to be decidable.
169
Anonymizing weighted social network graphs
Sudipto Das,Ömer Eğecioğlu,Amr El Abbadi +2 more
- 01 Mar 2010
TL;DR: This paper builds a linear programming (LP) model which preserves properties of the graph that are expressible as linear functions of the edge weights, and experimentally evaluates the proposed techniques using real social network data sets.
A new approach to sequence comparison: normalized sequence alignment.
TL;DR: Normalized Local Alignment (NLA) as mentioned in this paper is based on fractional programming and its running time is O(n2log n) compared to the standard Smith-Waterman algorithm.
96
Minimum-energy Broadcast in Simple Graphs with Limited Node Power
Ömer Eğecioğlu,Teofilo F. Gonzalez +1 more
- 01 Jan 2007
TL;DR: This paper shows that the weighted graph minimum-energy broadcast problem is NP-hard in metric space when transmissions are restricted to a given set of power levels by means of an upper bound d on the allowed transmission radius.
91
Dimensionality reduction and similarity computation by inner-product approximations
TL;DR: This work develops dynamic techniques for efficient and accurate approximation of similarity evaluations between high-dimensional vectors based on inner-product approximations and develops a dynamic model to compute the universal coefficients for data sets whose distribution is not known.