Repetition-free longest common subsequence of random sequences

A repetition free Longest Common Subsequence (LCS) of two sequences x and y is an LCS of x and y where each symbol may appear at most once. Let R denote the length of a repetition free LCS of two sequences of n symbols each one chosen randomly, uniformly, and independently over a k-ary alphabet. We study the asymptotic, in n and k, behavior of R and establish that there are three distinct regimes, depending on the relative speed of growth of n and k. For each regime we establish the limiting behavior of R. In fact, we do more, since we actually establish tail bounds for large deviations of R from its limiting behavior. Our study is motivated by the so called exemplar model proposed by Sankoff (1999) and the related similarity measure introduced by Adi et al. (2007). A natural question that arises in this context, which as we show is related to long standing open problems in the area of probabilistic combinatorics, is to understand the asymptotic, in n and k, behavior of parameter R.Comment: 15 pages, 1 figur

Construct, Merge, Solve and Adapt: Application to the repetition-free longest common subsequence problem

In this paper we present the application of a recently proposed, general, algorithm for combinatorial optimization to the repetition-free longest common subsequence problem. The applied algorithm, which is labelled Construct, Merge, Solve & Adapt, generates sub-instances based on merging the solution components found in randomly constructed solutions. These sub-instances are subsequently solved by means of an exact solver. Moreover, the considered sub-instances are dynamically changing due to adding new solution components at each iteration, and removing existing solution components on the basis of indicators about their usefulness. The results of applying this algorithm to the repetition-free longest common subsequence problem show that the algorithm generally outperforms competing approaches from the literature. Moreover, they show that the algorithm is competitive with CPLEX for small and medium size problem instances, whereas it outperforms CPLEX for larger problem instances.Peer ReviewedPostprint (author's final draft

A comprehensive comparison of metaheuristics for the repetition-free longest common subsequence problem

This paper deals with an NP-hard string problem from the bio-informatics field: the repetition-free longest common subsequence problem. This problem has enjoyed an increasing interest in recent years, which has resulted in the application of several pure as well as hybrid metaheuristics. However, the literature lacks a comprehensive comparison between those approaches. Moreover, it has been shown that general purpose integer linear programming solvers are very efficient for solving many of the problem instances that were used so far in the literature. Therefore, in this work we extend the available benchmark set, adding larger instances to which integer linear programming solvers cannot be applied anymore. Moreover, we provide a comprehensive comparison of the approaches found in the literature. Based on the results we propose a hybrid between two of the best methods which turns out to inherit the complementary strengths of both methods.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

Exact algorithms for the repetition-bounded longest common subsequence problem

In this paper, we study exact, exponential-time algorithms for a variant of the classic Longest Common Subsequence problem called the Repetition-Bounded Longest Common Subsequence problem (or RBLCS, for short): Let an alphabet S be a finite set of symbols and an occurrence constraint Cocc be a function Cocc: S → N, assigning an upper bound on the number of occurrences of each symbol in S. Given two sequences X and Y over the alphabet S and an occurrence constraint Cocc, the goal of RBLCS is to find a longest common subsequence of X and Y such that each symbol s ∈ S appears at most Cocc(s) times in the obtained subsequence. The special case where Cocc(s) = 1 for every symbol s ∈ S is known as the Repetition-Free Longest Common Subsequence problem (RFLCS) and has been studied previously; e.g., in [1], Adi et al. presented a simple (exponential-time) exact algorithm for RFLCS. However, they did not analyze its time complexity in detail, and to the best of our knowledge, there are no previous results on the running times of any exact algorithms for this problem. Without loss of generality, we will assume that |X| ≤ |Y | and |X| = n. In this paper, we first propose a simpler algorithm for RFLCS based on the strategy used in [1] and show explicitly that its running time is O(1.44225n). Next, we provide a dynamic programming (DP) based algorithm for RBLCS and prove that its running time is O(1.44225n) for any occurrence constraint Cocc, and even less in certain special cases. In particular, for RFLCS, our DP-based algorithm runs in O(1.41422n) time, which is faster than the previous one. Furthermore, we prove NP-hardness and APX-hardness results for RBLCS on restricted instances

Built-In Test Sequence Generation for Synchronous Sequential Circuits Based on Loading and Expansion of Test Subsequences

We describe an on-chip test generation scheme for synchronous sequential circuits that allows at-speed testing of such circuits. The proposed scheme is based on loading of (short) input sequences into an on-chip memory, and expansion of these sequences on-chip into test sequences. Complete coverage of modeled faults is achieved by basing the selection of the loaded sequences on a deterministic test sequence T 0, and ensuring that every fault detected by T 0 is detected by the expanded version of at least one loaded sequence. Experimental results presented for benchmark circuits show that the length of the sequence that needs to be stored at any time is on the average 10 % of the length of T 0, and that the total length of all the loaded sequences is on the average 46 % of the length of T 0. 1

Synchronization Strings: Codes for Insertions and Deletions Approaching the Singleton Bound

We introduce synchronization strings as a novel way of efficiently dealing with synchronization errors, i.e., insertions and deletions. Synchronization errors are strictly more general and much harder to deal with than commonly considered half-errors, i.e., symbol corruptions and erasures. For every $\epsilon >0$, synchronization strings allow to index a sequence with an $\epsilon^{-O(1)}$ size alphabet such that one can efficiently transform $k$ synchronization errors into $(1+\epsilon)k$ half-errors. This powerful new technique has many applications. In this paper, we focus on designing insdel codes, i.e., error correcting block codes (ECCs) for insertion deletion channels. While ECCs for both half-errors and synchronization errors have been intensely studied, the later has largely resisted progress. Indeed, it took until 1999 for the first insdel codes with constant rate, constant distance, and constant alphabet size to be constructed by Schulman and Zuckerman. Insdel codes for asymptotically large or small noise rates were given in 2016 by Guruswami et al. but these codes are still polynomially far from the optimal rate-distance tradeoff. This makes the understanding of insdel codes up to this work equivalent to what was known for regular ECCs after Forney introduced concatenated codes in his doctoral thesis 50 years ago. A direct application of our synchronization strings based indexing method gives a simple black-box construction which transforms any ECC into an equally efficient insdel code with a slightly larger alphabet size. This instantly transfers much of the highly developed understanding for regular ECCs over large constant alphabets into the realm of insdel codes. Most notably, we obtain efficient insdel codes which get arbitrarily close to the optimal rate-distance tradeoff given by the Singleton bound for the complete noise spectrum

The user attribution problem and the challenge of persistent surveillance of user activity in complex networks

In telecommunication networks, the user attribution problem refers to the challenge faced in recognizing communication traffic as belonging to a given user when information needed to identify the user is missing. This problem becomes more difficult to tackle as users move across many mobile networks (complex networks) owned and operated by different providers. The traditional approach of using the source IP address as a tracking identifier does not work when used to identify mobile users. Recent efforts to address this problem by exclusively relying on web browsing behavior to identify users, brought to light the challenges of solutions which try to link up multiple user sessions together when these approaches rely exclusively on the frequency of web sites visited by the user. This study has tackled this problem by utilizing behavior based identification while accounting for time and the sequential order of web visits by a user. Hierarchical Temporal Memories (HTM) were used to classify historical navigational patterns for different users. This approach enables linking multiple user sessions together forgoing the need for a tracking identifier such as the source IP address. Results are promising. HTMs outperform traditional Markov chains based approaches and can provide high levels of identification accuracy