Large neighborhood search for the most strings with few bad columns problem

In this work, we consider the following NP-hard combinatorial optimization problem from computational biology. Given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that differ maximally in a pre-defined number of positions. First of all, we introduce an integer linear programming model for this problem. Second, two variants of a rather simple greedy strategy are proposed. Finally, a large neighborhood search algorithm is presented. A comprehensive experimental comparison among the proposed techniques shows, first, that larger neighborhood search generally outperforms both greedy strategies. Second, while large neighborhood search shows to be competitive with the stand-alone application of CPLEX for small- and medium-sized problem instances, it outperforms CPLEX in the context of larger instances.Peer ReviewedPostprint (author's final draft

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Cross-Sender Bit-Mixing Coding

Scheduling to avoid packet collisions is a long-standing challenge in networking, and has become even trickier in wireless networks with multiple senders and multiple receivers. In fact, researchers have proved that even {\em perfect} scheduling can only achieve $\mathbf{R} = O(\frac{1}{\ln N})$. Here $N$ is the number of nodes in the network, and $\mathbf{R}$ is the {\em medium utilization rate}. Ideally, one would hope to achieve $\mathbf{R} = \Theta(1)$, while avoiding all the complexities in scheduling. To this end, this paper proposes {\em cross-sender bit-mixing coding} ({\em BMC}), which does not rely on scheduling. Instead, users transmit simultaneously on suitably-chosen slots, and the amount of overlap in different user's slots is controlled via coding. We prove that in all possible network topologies, using BMC enables us to achieve $\mathbf{R}=\Theta(1)$. We also prove that the space and time complexities of BMC encoding/decoding are all low-order polynomials.Comment: Published in the International Conference on Information Processing in Sensor Networks (IPSN), 201

Statistical data mining for symbol associations in genomic databases

A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test is proposed to assess the significance of a group of symbols when found in several genesets of a given database. Applied to symbol pairs, the thresholded p-values of the test define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections did correspond to already known interactions. On more specific selections of C2, many previously unkown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence

Clustered Integer 3SUM via Additive Combinatorics

We present a collection of new results on problems related to 3SUM, including: 1. The first truly subquadratic algorithm for $\ \ \ \ \$ 1a. computing the (min,+) convolution for monotone increasing sequences with integer values bounded by $O(n)$, $\ \ \ \ \$1b. solving 3SUM for monotone sets in 2D with integer coordinates bounded by $O(n)$, and $\ \ \ \ \$1c. preprocessing a binary string for histogram indexing (also called jumbled indexing). The running time is: $O(n^{(9+\sqrt{177})/12}\,\textrm{polylog}\,n)=O(n^{1.859})$ with randomization, or $O(n^{1.864})$ deterministically. This greatly improves the previous $n^2/2^{\Omega(\sqrt{\log n})}$ time bound obtained from Williams' recent result on all-pairs shortest paths [STOC'14], and answers an open question raised by several researchers studying the histogram indexing problem. 2. The first algorithm for histogram indexing for any constant alphabet size that achieves truly subquadratic preprocessing time and truly sublinear query time. 3. A truly subquadratic algorithm for integer 3SUM in the case when the given set can be partitioned into $n^{1-\delta}$ clusters each covered by an interval of length $n$, for any constant $\delta>0$. 4. An algorithm to preprocess any set of $n$ integers so that subsequently 3SUM on any given subset can be solved in $O(n^{13/7}\,\textrm{polylog}\,n)$ time. All these results are obtained by a surprising new technique, based on the Balog--Szemer\'edi--Gowers Theorem from additive combinatorics

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible