Search

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems. [ABSTRACT FROM AUTHOR]

In mathematical modeling of economic problems, it is common to assume that economic laws influence the process and analyze the type of solution obtained. Sometimes these laws have unknown parameters and they worth studying if these parameters can be determined. In this paper, a structured system is considered and the identifiability is analyzed. In case the parameters can be uniquely identified, an algorithm for obtaining the model parameters is presented and, finally, the existence of a balanced growth solution is studied. [ABSTRACT FROM AUTHOR]

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times. [ABSTRACT FROM AUTHOR]

This paper proposes the new transformation, namely the contractive homothetic transformation, proving that the smooth degree of the series will be improved by using this transformation. Not only the difference information will be decreased, but also the error of class ration will be reduced. At the same time, the smooth degree of the class approaches will be greatly advanced after being processed by this transformation, whether the series is monotone descending or monotone ascending. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm which, for a given compact orientable irreducible boundary irreducible 3-manifold M, verifies whether M contains an essential orientable surface (possibly, with boundary), whose genus is at most N. The algorithm is based on Haken’s theory of normal surfaces, and on a trick suggested by Jaco and consisting in estimating the mean length of boundary curves in an unknown essential surface of a given genus in the given manifold. [ABSTRACT FROM AUTHOR]

Consideration was given to control of a complex object whose motion obeys a multivariable nonlinear nonstationary mathematical model. Rigid constraints were imposed on the object’s dynamic precision. The paper considered computer-aided generation of the current equations of object motion with regard for the actuators which differ from subsystem to subsystem. The object is controlled adaptively with regard for the computer-based realization. The algorithms of control system operation that maintain the guaranteed precision of object motion were constructed. Conditions for problem solvability were formulated. The freeflying space robot was discussed by way of example. [ABSTRACT FROM AUTHOR]

This paper addresses a novel sentence reduction algorithm based on a decision tree model in which semantic information is used to enhance the accuracy of sentence reduction. The proposed algorithm is able to deal with the changeable order problem in sentence reduction. Furthermore, the use of decision list to solve the fragment problem in sentence reduction based decision tree model is also discussed. Our experimental results show an improvement when compared with earlier methods. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment. [ABSTRACT FROM AUTHOR]

This paper proposes a new reliability model, circular consecutive 2-out-of-(r,r)-from-(n,n):F model. In this model, the system consists of a square grid of side n (containing of n2 components) such that the system fails if and only if there is at least one square of side r which includes among them at least two failed components. For i.i.d. case an algorithm is given for computing the reliability of circular system. The reliability function can be expressed by the number of 0–1 matrices having no two or more 0s at any square of side r. [ABSTRACT FROM AUTHOR]

The active appearance model (AAM) is a powerful tool for modeling images of deformable objects and has been successfully used in a variety of alignment, tracking, and recognition applications. AAM uses subspace-based deformable models to represent the images of a certain object class. In general, fitting such complicated models to previously unseen images using standard optimization techniques is a computationally complex task because the gradient matrix has to be numerically computed at every iteration. The critical feature of AAM is a fast convergence scheme which assumes that the gradient matrix is fixed Around the optimal coefficients for all images. Our work in this paper starts with the observation that such a fixed gradient matrix inevitably specializes to a certain region in the texture space, and the fixed gradient matrix is not a good estimate of the actual gradient as the target texture moves away from this region. Hence, we propose an adaptive AAM algorithm that linearly adapts the gradient matrix according to the composition of the target image's texture to obtain a better estimate for the actual gradient. We show that the adaptive AAM significantly outperforms the basic AAM, especially in images that are particularly challenging for the basic algorithm. In terms of speed and accuracy, the idea of a linearly adaptive gradient matrix presented in this paper provides an interesting compromise between a standard optimization technique that recomputes the gradient at every iteration and the fixed gradient matrix approach of the basic AAM. [ABSTRACT FROM AUTHOR]

In this paper, we study the fault diagnosis problem for distributed discrete event systems. The model assumes that the system is composed of distributed components that are modeled in labeled Petri nets and interact with each other via sets of common resources (places). Further, a component's own access to a common resource is an observable event. Based on the diagnoser approach proposed by Sampath et al, a distributed fault diagnosis algorithm with communication is presented. The distributed algorithm assumes that the local diagnosis process can exchange messages upon the occurrence of observable events. We prove the distribute diagnosis algorithm is correct in the sense that it recovers the same diagnostic information as the centralized diagnosis algorithm. And then, the OBDD (Ordered Binary Decision Diagrams) is introduced to manage the state explosion problem in state estimation of the system. [ABSTRACT FROM AUTHOR]

This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a new approach to model coupling that probably forms the methodological basis of a new generation of Integrated Assessment models. This approach respects the knowledge and expertise that is embodied in existing models and encourages their gradual evolution. Modularity is the guiding principle. Our approach is distinguished by the way modules are coupled which is based on an interplay of a job control module, a numerical coupling module, and a couple of stand-alone functional modules. The numerical coupling module - the core component - serves to treat the feedbacks between the functional modules. A first implemented example that couples an economic and a climate module by means of a two-phase meta-optimization is presented here. The algorithm and mathematical structure behind are discussed as well as important features such as convergence behavior and reliability. [ABSTRACT FROM AUTHOR]

Modeling of large lakes or oceans for environmental studies or deformable blood vessels for mini-invasive surgery involve dynamical free boundaries governed by their interaction with their immediate environment. The object of this paper is to further Shape Analysis techniques in the formulation and analysis of dynamical free boundary problems defined over a time-varying domain (noncylindrical problems). The generic example of a potential incompressible fluid flow with a pressure condition (Bernouilli type) on the dynamical free boundary is presented along with a discrete time algorithm to follow the evolution of the time-space differential equation for the normal component of the velocity field on the time-varying free boundary. These ideas and constructions can be extended to more complex and realistic models. [ABSTRACT FROM AUTHOR]

BACKGROUND: Most existing models/algorithms for simulating goal-directed human motions were designed to generate a single "realistic" motion for a given input scenario. OBJECTIVE: This study presents a novel reach motion generation algorithm utilizing multiple posture memories. The algorithm aims to compute and visualize a set of human reach motions that approximates the full range of physically and physiologically feasible human motions for a given input scenario. METHODS: The algorithm utilizes posture memories constructed specifically for an individual worker using a probabilistic posture generation and registration process. The posture memories relate a hand position to the set of postures that place the individual's hand in its vicinity. When given an input scenario, the algorithm first generates different hand paths connecting the starting and ending hand positions specified in the scenario. Then, for each hand path, the algorithm produces different "feasible" motions by selecting and connecting multiple postures stored in the posture memories; the postures corresponding to the hand positions along the hand path are utilized. CONCLUSIONS: The proposed algorithm helps understand the impacts of workplace design on the range of feasible human motion behaviors, and, thereby, contributes to the computer-aided ergonomics design of work tasks and workplaces. [ABSTRACT FROM AUTHOR]

Accurate breath detection is crucial in sleep and respiratory physiology research and in several clinical settings. However, this process is technically challenging due to measurement and physiological artifacts and other factors such as variable leaks in the breathing circuit. Recently developed techniques to quantify the multiple causes of obstructive sleep apnea, require intermittent changes in airway pressure applied to a breathing mask. This presents an additional unique challenge for breath detection. Traditional algorithms often require drift correction. However, this is an empirical operation potentially prone to human error. This paper presents a new algorithm for breath detection during variable mask pressures in awake and sleeping humans based on physiological landmarks detected in the airflow or epiglottic pressure signal (Pepi). The algorithms were validated using simulated data from a mathematical model and against the standard visual detection approach in 4 healthy individuals and 6 patients with sleep apnea during variable mask pressure conditions. Using the flow signal, the algorithm correctly identified 97.6% of breaths with a mean difference±SD in the onsets of respiratory phase compared to expert visual detection of 23±89ms for inspiration and 6±56ms for expiration during wakefulness and 10±74ms for inspiration and 3±28 ms for expiration with variable mask pressures during sleep. Using the Pepi signal, the algorithm correctly identified 89% of the breaths with accuracy of 31±156ms for inspiration and 9±147ms for expiration compared to expert visual detection during variable mask pressures asleep. The algorithm had excellent performance in response to baseline drifts and noise during variable mask pressure conditions. This new algorithm can be used for accurate breath detection including during variable mask pressure conditions which represents a major advance over existing time-consuming manual approaches. [ABSTRACT FROM AUTHOR]

The schlieren method of measuring far-field focal spots offers many advantages at the Shenguang III laser facility such as low cost and automatic laser-path collimation. However, current methods of far-field focal spot measurement often suffer from low precision and efficiency when the final focal spot is merged manually, thereby reducing the accuracy of reconstruction. In this paper, we introduce an improved schlieren method to construct the high dynamic-range image of far-field focal spots and improve the reconstruction accuracy and efficiency. First, a detection method based on weak light beam sampling and magnification imaging was designed; images of the main and side lobes of the focused laser irradiance in the far field were obtained using two scientific CCD cameras. Second, using a self-correlation template matching algorithm, a circle the same size as the schlieren ball was dug from the main lobe cutting image and used to change the relative region of the main lobe cutting image within a 100×100 pixel region. The position that had the largest correlation coefficient between the side lobe cutting image and the main lobe cutting image when a circle was dug was identified as the best matching point. Finally, the least squares method was used to fit the center of the side lobe schlieren small ball, and the error was less than 1 pixel. The experimental results show that this method enables the accurate, high-dynamic-range measurement of a far-field focal spot and automatic image reconstruction. Because the best matching point is obtained through image processing rather than traditional reconstruction methods based on manual splicing, this method is less sensitive to the efficiency of focal-spot reconstruction and thus offers better experimental precision. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to propose, then analyze, and later test a spectral gradient algorithm for solving a convex minimization problem. The considered problem covers the matrix ℓ2,1-norm regularized least squares which is widely used in multi-task learning for capturing the joint feature among each task. To solve the problem, we firstly minimize a quadratic approximated model of the objective function to derive a search direction at current iteration. We show that this direction descends automatically and reduces to the original spectral gradient direction if the regularized term is removed. Secondly, we incorporate a nonmonotone line search along this direction to improve the algorithm’s numerical performance. Furthermore, we show that the proposed algorithm converges to a critical point under some mild conditions. The attractive feature of the proposed algorithm is that it is easily performable and only requires the gradient of the smooth function and the objective function’s values at each and every step. Finally, we operate some experiments on synthetic data, which verifies that the proposed algorithm works quite well and performs better than the compared ones. [ABSTRACT FROM AUTHOR]

The Schwarz alternating method can be used to solve linear elliptic boundary value problems on domains which consist of two or more overlapping subdomains. The solution is approx- imated by an infinite sequence of functions which result from solving a sequence of elliptic boundary value problems in each of the subdomains. This paper considers four Schwarz alternating methods for the N-dimensional, steady, viscous, incompressible Navier­Stokes equations, N ≤ 4. It is shown that the Schwarz sequences converge to the true solution provided that the Reynolds number is sufficiently small. [ABSTRACT FROM AUTHOR]

Structured matrices are encountered in various guises in many mathematical models which describe problems of the real world. In fact, they are the algebraic translation of the specific properties at the basis of the physical problem. The analysis and the exploitation of matrix structures is the first fundamental step in the design of highly efficient algorithms for the solution of complex computational problems. [ABSTRACT FROM AUTHOR]

Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm. [ABSTRACT FROM AUTHOR]

The Student's-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Student's-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Student's-t mixture model centroids. Then, we fit the Student's-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD) algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms. [ABSTRACT FROM AUTHOR]

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/. [ABSTRACT FROM AUTHOR]

The majority of next-generation sequencing short-reads can be properly aligned by leading aligners at high speed. However, the alignment quality can still be further improved, since usually not all reads can be correctly aligned to large genomes, such as the human genome, even for simulated data. Moreover, even slight improvements in this area are important but challenging, and usually require significantly more computational endeavor. In this paper, we present CUSHAW3, an open-source parallelized, sensitive and accurate short-read aligner for both base-space and color-space sequences. In this aligner, we have investigated a hybrid seeding approach to improve alignment quality, which incorporates three different seed types, i.e. maximal exact match seeds, exact-match k-mer seeds and variable-length seeds, into the alignment pipeline. Furthermore, three techniques: weighted seed-pairing heuristic, paired-end alignment pair ranking and read mate rescuing have been conceived to facilitate accurate paired-end alignment. For base-space alignment, we have compared CUSHAW3 to Novoalign, CUSHAW2, BWA-MEM, Bowtie2 and GEM, by aligning both simulated and real reads to the human genome. The results show that CUSHAW3 consistently outperforms CUSHAW2, BWA-MEM, Bowtie2 and GEM in terms of single-end and paired-end alignment. Furthermore, our aligner has demonstrated better paired-end alignment performance than Novoalign for short-reads with high error rates. For color-space alignment, CUSHAW3 is consistently one of the best aligners compared to SHRiMP2 and BFAST. The source code of CUSHAW3 and all simulated data are available at http://cushaw3.sourceforge.net. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class. [ABSTRACT FROM AUTHOR]

Purchasing managers face a complex problem when determining order quantities for multiple items in the presence of quantity discounts and resource limitations. In this paper, the problem is formulated as a mathematical programming model. An efficient solution algorithm is developed utilizing the Lagrangian relaxation approach. Extensive computational experiments are performed and the results are presented. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finding minimal length tree networks on the unit sphere Φ of a given point set (V) where distance is measured along great circular arcs. The related problems of finding a Steiner Minimal Tree SMT (V) and of finding a Minimum Spanning Tree MST(V) are treated through a simplicial decomposition technique based on computing the Delaunay Triangulation DT (V) and the Voronoi Diagram VD(V) of the given point set. O (N log N) algorithms for computing DT(V), VD(V), and WST(V) as well as an O(N log N) heuristic for finding a sub-optimal SMT(V) solution are presented, together with experimental results for randomly distributed points on Φ. [ABSTRACT FROM AUTHOR]

Some methods for synthesizing a protocol specification from a given service specification have been proposed using various computational models. However, the existing methods cannot treat service specifications including both complex control flows and system variables. In this paper, we propose a method for synthesizing a protocol specification automatically from a pair consisting of a service specification and a resource allocation. In our method, service specifications are described in an extended model of Petri nets, which can treat system variables. We have developed a pair consisting of a synthesis system and an execution system, and applied them to CSCW (Computer Supported Cooperative Work). © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 81(8): 18–26, 1998 [ABSTRACT FROM AUTHOR]

This paper introduces the concept of radical-free pseudonormalized Φ-functions, which allows one to describe constraints on minimum and maximum allowable distances between two-dimensional φ-objects. Translations and rotations of φ-objects in a two-dimensional Euclidean space are allowable. The theorem on the existence of a radical-free pseudonormalized Φ-function for a pair of arbitrary-shaped φ-objects whose frontiers are formed by the union of line segments and circular arcs is formulated. An efficient algorithm is proposed to derive pseudonormalized Φ-functions. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS²O), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS²O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for designing a robust discrete-time adaptive controller for stabilizing linear time-invariant (LTI) continuous-time dynamic systems. Such systems may be unstable and noninversely stable in the worst case. A reduced-order model is considered to design the adaptive controller. The control design is based on the discretization of the system with the use of a multirate sampling device with fast-sampled control signal. A suitable on-line adaptation of the multirate gains guarantees the stability of the inverse of the discretized estimated model, which is used to parameterize the adaptive controller. A dead zone is included in the parameters estimation algorithm for robustness purposes under the presence of unmodeled dynamics in the controlled dynamic system. The adaptive controller guarantees the boundedness of the system measured signal for all time. Some examples illustrate the efficacy of this control strategy. [ABSTRACT FROM AUTHOR]

Academic prestige is difficult to quantify in objective terms. Network theory offers the opportunity to use a mathematical formalism to model both the prestige associated with an academic and the relationships between academic colleagues. Early attempts using this line of reasoning have focused on intellectual genealogy as constituted by supervisor student networks. The process of examination is critical in many areas of study but has not played a part in existing models. A network theoretical “social” model is proposed as a tool to explore and understand the dynamics of prestige in the academic hierarchy. It is observed that such a model naturally gives rise to the idea that the prestige associated with a node in the graph (the prestige of an individual academic) can be viewed as a dynamic quantity that evolves with time based on both local and non-local changes in the properties in the network. The toy model studied here includes both supervisor-student and examiner-student relationships. This gives an insight into some of the key features of academic genealogies and naturally leads to a proposed model for “prestige propagation” on academic networks. This propagation is not solely directed forward in time (from teacher to progeny) but sometimes also flows in the other direction. As collaborators do well, this reflects well on those with whom they choose to collaborate and those that taught them. Furthermore, prestige as a quantity continues to be dynamic even after the end of a relationship or career. Given that time ordering of relationships on the network are implicit but that measures such as betweenness are independent of this implicit time dependence: the success of a PhD student later in their career can improve the prestige of their doctoral supervisor. Thus, prestige can be interpreted to have dynamics that flow both forward and backward in time. [ABSTRACT FROM AUTHOR]

Community detection algorithms have been widely used to study the organization of complex networks like the brain. These techniques provide a partition of brain regions (or nodes) into clusters (or communities), where nodes within a community are densely interconnected with one another. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies: clusters embedded within clusters of other clusters. To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across levels of the hierarchy. We demonstrate that this method is sensitive to topological inhomogeneities of the graph’s hierarchy by providing a local measure of community stability and inter-scale reliability across topological scales. We compare the brain’s structural and functional network architectures, and we demonstrate that structural graphs display a more prominent hierarchical community organization than functional graphs. Finally, we build an explicitly multimodal multiplex graph that combines both structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results demonstrate the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data. [ABSTRACT FROM AUTHOR]

The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures. [ABSTRACT FROM AUTHOR]

Plasmids are extrachromosomal DNA elements of microorganisms encoding beneficial genetic information. They were thought to be equally distributed to daughter cells during cell division. Here we use mathematical modeling to investigate the evolutionary stability of plasmid segregation for high-copy plasmids—plasmids that are present in up to several hundred copies per cell—carrying antibiotic resistance genes. Evolutionary stable strategies (ESS) are determined by numerical analysis of a plasmid-load structured population model. The theory predicts that the evolutionary stable segregation strategy of a cell depends on the plasmid copy number: For low and medium plasmid load, both daughters receive in average an equal share of plasmids, while in case of high plasmid load, one daughter obtains distinctively and systematically more plasmids. These findings are in good agreement with recent experimental results. We discuss the interpretation and practical consequences. [ABSTRACT FROM AUTHOR]

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe (), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries. [ABSTRACT FROM AUTHOR]

Time series segmentation aims to identify segment boundary points in a time series, and to determine the dynamical properties corresponding to each segment. To segment time series data, this article presents a Bayesian change-point model in which the data within segments follows an autoregressive moving average (ARMA) model. A prior distribution is defined for the number of change-points, their positions, segment means and error terms. To quantify uncertainty about the location of change-points, the resulting posterior probability distributions are sampled using the Generalized Gibbs sampler Markov chain Monte Carlo technique. This methodology is illustrated by applying it to simulated data and to real data known as the well-log time series data. This well-log data records the measurements of nuclear magnetic response of underground rocks during the drilling of a well. Our approach has high sensitivity, and detects a larger number of change-points than have been identified by comparable methods in the existing literature. [ABSTRACT FROM AUTHOR]

Many link prediction methods have been developed to infer unobserved links or predict missing links based on the observed network structure that is always incomplete and subject to interfering noise. Thus, the performance of existing methods is usually limited in that their computation depends only on input graph structures, and they do not consider external information. The effects of social influence and homophily suggest that both network structure and node attribute information should help to resolve the task of link prediction. This work proposes SASNMF, a link prediction unified framework based on non-negative matrix factorization that considers not only graph structure but also the internal and external auxiliary information, which refers to both the node attributes and the structural latent feature information extracted from the network. Furthermore, three different combinations of internal and external information are proposed and input into the framework to solve the link prediction problem. Extensive experimental results on thirteen real networks, five node attribute networks and eight non-attribute networks show that the proposed framework has competitive performance compared with benchmark methods and state-of-the-art methods, indicating the superiority of the presented algorithm. [ABSTRACT FROM AUTHOR]

We generalize the reduction mechanism for linear programming problems and semidefinite programming problems from Braun et al. (Inapproximability of combinatorial problems via small LPs and SDPs, 2015) in two ways (1) relaxing the requirement of affineness, and (2) extending to fractional optimization problems. As applications we provide several new LP-hardness and SDP-hardness results, e.g., for the problem, the problem, and the problem and show how to extend ad-hoc reductions between Sherali-Adams relaxations to reductions between LPs. [ABSTRACT FROM AUTHOR]

Organizational processes vary. Practitioners have developed simple frameworks to differentiate them. Surprisingly, the academic literature on process management does not–it typically strives for one method to manage all processes. We draw on organizational information-processing theory to systematically develop a new, theoretically motivated classification model for organizational processes. We validate this model using survey data from 141 process practitioners of a global corporation. We derive three distinct types of processes and demonstrate that an understanding of process variety based on process dimensions can differentiate processes better than existing frameworks used in practice. Our findings can enable process managers to make informed decisions and serve as a basis for contingent process management. [ABSTRACT FROM AUTHOR]

The design of new quantum algorithms has proven to be an extremely difficult task. This paper considers a different approach to this task by studying the problem of quantum state generation. We motivate this problem by showing that the entire class of statistical zero knowledge, which contains natural candidates for efficient quantum algorithms such as graph isomorphism and lattice problems, can be reduced to the problem of quantum state generation. To study quantum state generation, we define a paradigm which we call adiabatic state generation (ASG) and which is based on adiabatic quantum computation. The ASG paradigm is not meant to replace the standard quantum circuit model or to improve on it in terms of computational complexity. Rather, our goal is to provide a natural theoretical framework, in which quantum state generation algorithms could be designed. The new paradigm seems interesting due to its intriguing links to a variety of different areas: the analysis of spectral gaps and ground-states of Hamiltonians in physics, rapidly mixing Markov chains, adiabatic computation, and approximate counting. To initiate the study of ASG, we prove several general lemmas that can serve as tools when using this paradigm. We demonstrate the application of the paradigm by using it to turn a variety of (classical) approximate counting algorithms into efficient quantum state generators of nontrivial quantum states, including, for example, the uniform superposition over all perfect matchings in a bipartite graph. [ABSTRACT FROM AUTHOR]

All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset. Based on these deduction rules, we construct a condensed representation of all frequent itemsets, by removing those itemsets for which the support can be derived, resulting in the so called Non-Derivable Itemsets (NDI) representation. We also present connections between our proposal and recent other proposals for condensed representations of frequent itemsets. Experiments on real-life datasets show the effectiveness of the NDI representation, making the search for frequent non-derivable itemsets a useful and tractable alternative to mining all frequent itemsets. [ABSTRACT FROM AUTHOR]

In this paper we will discuss various aspects of computable/constructive analysis, namely semantics, proofs and computations. We will present some of the problems and solutions of exact real arithmetic varying from concrete implementations, representation and algorithms to various models for real computation. We then put these models in a uniform framework using realisability, which opens the door to the use of type theoretic and coalgebraic constructions both in computing and reasoning about these computations. We will indicate that it is often natural to use constructive logic to reason about these computations. [ABSTRACT FROM AUTHOR]

Let S be a set of n points in ℝ². Given an integer 1 ≤ k ≤ n, we wish to find a maximally separated subset I ⊆ S of size k; this is a subset for which the minimum among the (2k) pairwise distances between its points is as large as possible. The decision problem associated with this problem is to determine whether there exists I ⊆ S, ∣I∣ = k, so that all (2k) pairwise distances in I are at least 2. This problem can also be formulated in terms of disk-intersection graphs: Let D be the set of unit disks centered at the points of S. The disk-intersection graph G of D has as edges all pairs of disks with nonempty intersection. Any set I with the above properties is then the set of centers of disks that form an independent set in the graph G. This problem is known to be NP-complete if k is part of the input. In this paper we first present a linear-time e-approximation algorithm for any constant k. Next we give exact algorithms for the cases k = 3 and k = 4 that run in time O(n4/3polylog(n)). We also present a simpler nO(√k)-time exact algorithm (as compared with the recent algorithm in [J. Alber and J. Fiala, J. Algorithms, 52 (2004), pp. 134-151]) for arbitrary values of k. [ABSTRACT FROM AUTHOR]

Almost all of the most successful quantum algorithms discovered to date exploit the ability of the Fourier transform to recover subgroup structures of functions, especially periodicity. The fact that Fourier transforms can also be used to capture shift structure has received far less attention in the context of quantum computation. In this paper, we present three examples of ‘unknown shift’ problems that can be solved efficiently on a quantum computer using the quantum Fourier transform. For one of these problems, the shifted Legendre symbol problem, we give evidence that the problem is hard to solve classically, by showing a reduction from breaking algebraically homomorphic cryptosystems. We also define the hidden coset problem, which generalizes the hidden shift problem and the hidden subgroup problem. This framework provides a unified way of viewing the ability of the Fourier transform to capture subgroup and shift structure. [ABSTRACT FROM AUTHOR]

In the last few years coalition formation algorithms have been proposed as a possible way of modeling autonomous agent cooperation in multi-agent systems. This work is based on a previously proposed coalition formation model founded on game theory for a class of task-oriented problems that guarantees an optimum task allocation and a stable profit division. In this paper we study two properties of the model that are very important for application in real-life scenarios: robustness and tolerance to an agent's misbehavior. First, we study the robustness of this model as regards the effect the agent's failure has on the resultant profits of the coalition formation. Secondly, we also study the coalition formation model in the presence of misbehaving agents. Agents have some kind of execution autonomy, and they can deceive or mislead each other when they reveal their information, if they believe this will give them more profits. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, by using a so-called fixed steplength strategy, we propose a BFGS method without use of line searches for unconstrained optimization. Under mild conditions, we show the global and superlinear convergence of the proposed method even when the objective function is nonconvex. [ABSTRACT FROM AUTHOR]

There are two main paradigms in combining different classification algorithms: Classifier Selection and Classifier Fusion. The first one selects a single model for classifying a new instance, while the latter combines the decisions of all models. The work presented in this paper stands in between these two paradigms aiming to tackle the disadvantages and benefit from the advantages of both. In particular, this paper proposes the use of statistical procedures for the selection of the best subgroup among different classification algorithms and the subsequent fusion of the decision of the models in this subgroup with simple methods like Weighted Voting. Extensive experimental results show that the proposed approach, Selective Fusion, improves over simple selection and fusion methods, leading to performance comparable with the state-of-the-art heterogeneous classifier combination method of Stacking, without the additional computational cost and learning problems of meta-training. [ABSTRACT FROM AUTHOR]

Top-down induction of decision trees is a simple and powerful method of pattern classification. In a decision tree, each node partitions the available patterns into two or more sets. New nodes are created to handle each of the resulting partitions and the process continues. A node is considered terminal if it satisfies some stopping criteria (for example, purity, i.e., all patterns at the node are from a single class). Decision trees may be univariate, linear multivariate, or nonlinear multivariate depending on whether a single attribute, a linear function of all the attributes, or a non- linear function of all the attributes is used for the partitioning at each node of the decision tree. Though nonlinear multivariate decision trees are the most powerful, they are more susceptible to the risks of overfitting. In this paper, we propose to perform model selection at each decision node to build omnivariate decision trees. The model selection is done using a novel classifiability measure that captures the possible sources of misclassification with relative ease and is able to accurately reflect the complexity of the subproblem at each node. The proposed approach is fast and does not suffer from as high a computational burden as that incurred by typical model selection algorithms. Empirical results over 26 data sets indicate that our approach is faster and achieves better classification accuracy compared to statistical model select algorithms. [ABSTRACT FROM AUTHOR]