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Circuits in a graph are interesting structures and identifying them is of an important relevance for many applications. However, enumerating circuits is known to be a difficult problem, since their number can grow exponentially. In this paper, we propose fast parallel approaches for enumerating elementary circuits of directed graphs based on graphics processing unit (GPU). Our algorithms are based on a massive exploration of the graph in a breadth-first search strategy. Algorithm V-FEC explores the graph starting from different vertices simultaneously. To further reduce the search space, we present T-FEC, another algorithm that uses triplets as an initial set to start exploring. To the best of our knowledge, those are the first parallel GPU-based algorithms for finding all circuits of a given graph. In addition, they find circuits of a given length and circuits with a specific vertex or edge. The evaluation results show that the proposed approaches achieve up to 190x speed-up over Johnson's algorithm, one of the most efficient sequential algorithms for finding circuits. [ABSTRACT FROM AUTHOR]

The problem of finding bipartite (Tanner) graphs with given degree sequences that have large girth and few short cycles is of great interest in many applications including construction of good low-density parity-check (LDPC) codes. In this paper, we prove that for given integers $\alpha $ , $\beta $ , and $\gamma $ , and degree sequences $\pi $ and $\pi '$ , the problem of determining whether there exists a simple bipartite graph with degree sequences $(\pi, \pi ')$ that has at most $\alpha $ ($\beta $ and $\gamma $) cycles of length four (six and eight, respectively) is NP-complete. This is proved by a two-step polynomial-time reduction from the 3-Partition Problem. On the other hand, using connections to linear hypergraphs, we prove that given the degree sequence $\pi $ , a polynomial time algorithm can be devised to determine whether there exists a bipartite graph whose degree sequence on one side of the bipartition is $\pi $ and has a girth of at least six. In addition to these complexity results, we devise a quasi-polynomial time algorithm that can construct a bipartite graph with given (irregular) degree sequences and a girth that increases logarithmically with the size of the graph. Compared to the well-known progressive-edge-growth (PEG) algorithm, the proposed method, in some cases, results in Tanner graphs with larger girth, particularly for scenarios where the degree sequences of the graph are strictly enforced. [ABSTRACT FROM AUTHOR]

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases. [ABSTRACT FROM AUTHOR]

In this paper, we present solution algorithms for the cycle hub location problem (CHLP), which seeks to locate p hub facilities that are connected by means of a cycle, and to assign non-hub nodes to hubs so as to minimize the total cost of routing flows through the network. This problem is useful in modeling applications in transportation and telecommunications systems, where large setup costs on the links and reliability requirements make cycle topologies a prominent network architecture. We present a branch-and-cut algorithm that uses a flow-based formulation and two families of mixed-dicut inequalities as a lower bounding procedure at nodes of the enumeration tree. We also introduce a metaheuristic based on greedy randomized adaptive search procedure to obtain initial upper bounds for the exact algorithm and to obtain feasible solutions for large-scale instances of the CHLP. Numerical results on a set of benchmark instances with up to 100 nodes and 8 hubs confirm the efficiency of the proposed solution algorithms. [ABSTRACT FROM AUTHOR]

Identification of rhythmic gene expression from metabolic cycles to circadian rhythms is crucial for understanding the gene regulatory networks and functions of these biological processes. Recently, two algorithms, JTK_CYCLE and ARSER, have been developed to estimate periodicity of rhythmic gene expression. JTK_CYCLE performs well for long or less noisy time series, while ARSER performs well for detecting a single rhythmic category. However, observing gene expression at high temporal resolution is not always feasible, and many scientists are interested in exploring both ultradian and circadian rhythmic categories simultaneously. In this paper, a new algorithm, named autoregressive Bayesian spectral regression (ABSR), is proposed. It estimates the period of time-course experimental data and classifies gene expression profiles into multiple rhythmic categories simultaneously. Through the simulation studies, it is shown that ABSR substantially improves the accuracy of periodicity estimation and clustering of rhythmic categories as compared to JTK_CYCLE and ARSER for the data with low temporal resolution. Moreover, ABSR is insensitive to rhythmic patterns. This new scheme is applied to existing time-course mouse liver data to estimate period of rhythms and classify the genes into ultradian, circadian, and arrhythmic categories. It is observed that 49.2% of the circadian profiles detected by JTK_CYCLE with 1-hour resolution are also detected by ABSR with only 4-hour resolution. [ABSTRACT FROM AUTHOR]

In this study alinearized Heffron-Phillipsmodel of a single machine power system installed with a unified power flow controller (UPFC) has been presented. The selection of the output feedback parameters for the UPFC controllers is converted to an optimization problem which is solved by cuckoo optimization algorithm (COA). COA, as a new evolutionary optimization algorithm, is used in multiple applications. This optimization algorithm has a strong ability to find the most optimistic results for dynamic stability improvement. The effectiveness of the proposed controller for damping low frequency oscillations is tested and results compared with imperialist competitive algorithm (ICA). The controllers are tested to variations in system loading. There sulks analysis reveals that COA minimized cost function and improved dynamic stability, better than ICA. In addition, the potential and superiority of the proposed method over the ICA is demonstrated. The simulation results analysis shows that the designed COA based output feedback UPFC damping controller has an excellent capability in damping low frequency oscillations and enhance rapidly and greatly the dynamic stability of the power systems. [ABSTRACT FROM AUTHOR]

In test security analyses, answer copying, collusion, and the use of a shared brain dump site can be detected by checking similarity between item response strings. The similarity, however, can possibly be contaminated by aberrant data resulted from careless responding or rapid guessing. For example, some test-takers may answer by repeating a pattern (e.g., ABCDABCDABCD or AAAAAAAA). It is crucial for researchers or practitioners to remove these kinds of repeating patterns before conducting any similarity analysis. In this study, a new algorithm for finding repeating patterns is proposed. The application of this algorithm on real and simulated data showed satisfying results; the simulated repeating patterns were identified 100% by the new algorithm with a negligible false positive rate. Although illustrated only in the context of similarity analysis, the proposed algorithm has broad applications: for example, identifying unmotivated test-takers for school intervention or cleaning data before estimating item parameters under an item response theory model. [ABSTRACT FROM AUTHOR]

We introduce a new solution concept, called periodicity, for selecting optimal strategies in strategic form games. This periodicity solution concept yields new insight into nontrivial games. In mixed strategy strategic form games, periodic solutions yield values for the utility function of each player that are equal to the Nash equilibrium ones. In contrast to the Nash strategies, here the payoffs of each player are robust against what the opponent plays. Sometimes, periodicity strategies yield higher utilities, and sometimes the Nash strategies do, but often the utilities of these two strategies coincide. We formally define and study periodic strategies in two player perfect information strategic form games with pure strategies and we prove that every nontrivial finite game has at least one periodic strategy, with nontrivial meaning nondegenerate payoffs. In some classes of games where mixed strategies are used, we identify quantitative features. Particularly interesting are the implications for collective action games, since there the collective action strategy can be incorporated in a purely noncooperative context. Moreover, we address the periodicity issue when the players have a continuum set of strategies available. [ABSTRACT FROM AUTHOR]