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Highlights • Graphical requirements for multistationarity in biochemical reaction networks. • Efficient graph rewriting algorithm for checking those conditions. • Faster than symbolic computation methods by several orders of magnitude. • Applied to the model repository BioModels. Graphical abstract Abstract Thomas' necessary conditions for the existence of multiple steady states in gene networks have been proved by Soulé with high generality for dynamical systems defined by differential equations. When applied to (protein) reaction networks however, those conditions do not provide information since they are trivially satisfied as soon as there is a bimolecular or a reversible reaction. Refined graphical requirements have been proposed to deal with such cases. In this paper, we present for the first time a graph rewriting algorithm for checking the refined conditions given by Soliman, and evaluate its practical performance by applying it systematically to the curated branch of the BioModels repository. This algorithm analyzes all reaction networks (of size up to 430 species) in less than 0.05 second per network, and permits to conclude to the absence of multistationarity in 160 networks over 506. The short computation times obtained in this graphical approach are in sharp contrast to the Jacobian-based symbolic computation approach. We also discuss the case of one extra graphical condition by arc rewiring that allows us to conclude on 20 more networks of this benchmark but with a high computational cost. Finally, we study with some details the case of phosphorylation cycles and MAPK signalling models which show the importance of modelling the intermediate complexations with the enzymes in order to correctly analyze the multistationarity capabilities of such biochemical reaction networks. [ABSTRACT FROM AUTHOR]

Abstract This paper develops a new implementation coupling optimisation-based anisotropic mesh adaptivity algorithms to a moving mesh numerical scour model, considering both turbulent suspended and bedload sediment transport. The significant flexibility over mesh structure and resolution, in space and time, that the coupling of these approaches provides makes this framework highly suitable for resolving individual marine structure scales with larger scale ocean dynamics. The use of mesh optimisation addresses the issue of poor mesh quality and/or inappropriate resolution that have compromised existing modelling approaches that apply mesh movement strategies alone, especially in the case of extreme scour. Discontinuous Galerkin finite element-based discretisation methods and a Reynolds Averaged Navier–Stokes-based turbulent modelling approach are used for the hydrodynamic fluid flow. In this work the model is verified in two dimensions for current-dominated scour near a horizontal pipeline. Combined adaptive mesh movement and anisotropic mesh optimisation is found to maintain both the quality and validity of the mesh in response to morphological bed evolution changes, even in the case where it is severely constrained by nearby structures. [ABSTRACT FROM AUTHOR]

Abstract In scalar turbulence it is sometimes the case that the scalar diffusivity is smaller than the viscous diffusivity. The thermally-driven turbulent convection in water is a typical example. In such a case the smallest scale in the problem is the Batchelor scale l b , rather than the Kolmogorov scale l k , as l b = l k / S c 1 / 2 , where Sc is the Schmidt number (or Prandtl number in the case of temperature). In the numerical studies of such scalar turbulence, the conventional approach is to use a single grid for both the velocity and scalar fields. Such single-resolution scheme often over-resolves the velocity field because the resolution requirement for scalar is higher than that for the velocity field, since l b < l k for S c > 1. In this paper we put forward an algorithm that implements the so-called multiple-resolution method with a finite-volume code. In this scheme, the velocity and pressure fields are solved in a coarse grid, while the scalar field is solved in a dense grid. The central idea is to implement the interpolation scheme on the framework of finite-volume to reconstruct the divergence-free velocity from the coarse to the dense grid. We demonstrate our method using a canonical model system of fluid turbulence, the Rayleigh–Bénard convection. We show that, with the tailored mesh design, considerable speed-up for simulating scalar turbulence can be achieved, especially for large Schmidt (Prandtl) numbers. In the same time, sufficient accuracy of the scalar and velocity fields can be achieved by this multiple-resolution scheme. Although our algorithm is demonstrated with a case of an active scalar, it can be readily applied to passive scalar turbulent flows. [ABSTRACT FROM AUTHOR]

Abstract Large computation time is widely considered to be the most important issue in scientific research especially in solving structural evolution problems. Recent developments in this domain have shown that the use of non-incremental schemes through Model Order Reduction led to important results in saving time. Yet, the question arises here how to obtain more time-saving. This paper examines an approach based on a collection of significant modes given by Proper Generalized Decomposition (PGD) solution for different time scales in order to save more computation time. The dictionary of the significant modes allows to construct an accurate solution for different characteristic times and different boundary problems compared to the full solution with a relative error rate less than 5% and with a large time saving of order 50 compared to Finite Element Method (FEM). The ability of the approach with respect to cycle time is discussed. Highlights • A new approach is proposed to decrease computational time for cyclic problems involving different time scales. • The approach is based on the use of pre-computed space–time modes which are further collected in a dictionary. • For a physical time belonging to the range of the dictionary, the solution can be accurately predicted. • For a given cycle time, the dictionary leads to an accurate if it contains the modes associated with this specific time. [ABSTRACT FROM AUTHOR]

Abstract We present a systematic study of the nested sampling algorithm based on the example of the Potts model. This model, which exhibits a first order phase transition for q > 4 , exemplifies a generic numerical challenge in statistical physics: The evaluation of the partition function and thermodynamic observables, which involve high dimensional sums of sharply structured multi-modal density functions. It poses a major challenge to most standard numerical techniques, such as Markov Chain Monte Carlo. In this paper we will demonstrate that nested sampling is particularly suited for such problems and it has a couple of advantages. For calculating the partition function of the Potts model with N sites: a) one run stops after O (N) moves, so it takes O (N 2) operations for the run, b) only a single run is required to compute the partition function along with the assignment of confidence intervals, c) the confidence intervals of the logarithmic partition function decrease with 1 / N and d) a single run allows to compute quantities for all temperatures while the autocorrelation time is very small, irrespective of temperature. Thermodynamic expectation values of observables, which are completely determined by the bond configuration in the representation of Fortuin and Kasteleyn, like the Helmholtz free energy, the internal energy as well as the entropy and heat capacity, can be calculated in the same single run needed for the partition function along with their confidence intervals. In contrast, thermodynamic expectation values of magnetic properties like the magnetization and the magnetic susceptibility require sampling the additional spin degree of freedom. Results and performance are studied in detail and compared with those obtained with multi-canonical sampling. Eventually the implications of the findings on a parallel implementation of nested sampling are outlined. [ABSTRACT FROM AUTHOR]

Highlights • Two indexes are considered, which are number of consumed resources R and distance to boundary I. Minimizing R and minimizing I can reduce blocking probability of network. • Optimizations of R and I are sometimes contradictory, so a RMSA algorithm is proposed, which can make a reasonable tradeoff between these two indexes. • Blocking probability of the proposed algorithm is significantly lowered, comparing with the existing well-performed RMSA algorithms. Abstract Elastic optical network (EON) has been a promising technology for the next generation of core transport networks, because it can support flexible spectrum assignment. Routing, modulation and spectrum assignment (RMSA) is one of the key issues in EON. In this paper, two indexes are considered during the spectrum assignment process, number of consumed resources R and distance to boundary I. Minimizing R means using the least resources to establish a connection while minimizing I implies that remainder idle resources are more lumped after a connection is established. Both of them can reduce blocking probability of network. However, optimizations of R and I are sometimes contradictory, so we propose three policies to make a tradeoff between them, which are R : I , I : R and R + I. The R : I policy takes R as the primary objective while the I : R policy sets I as the chief one; the R + I policy takes a linear combination α R + (1 - α) I as the optimizing objective. Simulations are taken on the NSF-Net and UBN topologies, which show that the R + I policy can get the best performance in most cases. We further compare the R + I policy with two existing well-performed algorithms, the First-Last-Fit algorithm and the Block-Assignment algorithm. Simulation results reveal that blocking probability of the R + I policy is far less than that of the comparative objects. [ABSTRACT FROM AUTHOR]

Highlights • Improving the performance of optical packet switches (OPS) is challenging. • No single wavelength conversion (WC) technology is able to improve packet loss. • A hybrid OPS architecture with limited-range and parametric WCs is proposed. • Proposed OPS reduces packet loss significantly compared to known OPS architectures. Abstract In this paper, we propose and study the performance of two conversion schemes for optical packet switch (OPS) with the objective of reducing packet contention, and hence, improving the overall packet loss ratio (PLR). In both schemes, conversion is performed in two layers. The first layer contains a dedicated wavelength converter (WC) for each input wavelength, while the second conversion layer consists of a dynamic pump-wavelength selection (DPS) of parametric wavelength converters (PWCs) shared in a Feed-Back fashion. The first scheme uses non-circular WCs (NCWCs) with DPS of chained PWCs (called LDPSC, for short). In the LDPSC scheme, an incoming packet is accepted only if it could be assigned to its required output using only the NCWCs in the first layer or more than one PWCs in a chained fashion in the second layer; otherwise it will be dropped. The second scheme uses circular wavelength conversion (CWCs) with DPS of PWCs without chaining (named CDPS, for short). In the CDPS scheme, an incoming packet is accepted only if it can be assigned to its required output using only CWCs in the first layer or only one of the PWCs in the second layer; otherwise it will be dropped. For each of the two schemes, a matching algorithm along with a Dynamic pump conversion (DPC) algorithm is presented. The performances of the proposed schemes are compared to two conventional schemes; namely, the DPSC scheme that uses a shared pool of DPS of chained PWCs, and the LDPS scheme that uses a dedicated NCWC for each input wavelength and a shared pool of DPS of PWCs without chaining. Simulation results show that, the new CDPS scheme achieves the same performance (in terms of PLR) of the LDPS scheme, while reducing the required number of PWCs by more than 44 % and 57 % for conversion distances (that is the maximum number of wavelengths adjacent to any input wavelength on any one of its sides), d = 1 and 2; respectively. Moreover, using conversion distance d = 1 , the new LDPSC scheme reduces the PLR compared to the LDPS scheme by up to 85 % and 22 % for traffic loads, 0.5 and 1; respectively. In addition, for d = 1 , the new LDPSC scheme reduces the PLR compared to the DPSC scheme by up to 99.8 % and 30 % for traffic loads, 0.5 and 1; respectively. [ABSTRACT FROM AUTHOR]

In this paper, we consider the following Kirchhoff type equations (0.1) { − ( a + b ∫ R 3 | ∇ u | 2 ) Δ u + λ V ( x ) u = q ( x ) f ( u ) in R 3 , u ∈ H 1 ( R 3 ) , where a , b , λ > 0 , V ∈ C ( R 3 , R ) is a potential well, q ( x ) is a positive bounded function, f ( s ) is either asymptotically linear or asymptotically 3-linear in s at infinity. Under some other suitable conditions on V , q and f , the existence, nonexistence and concentrating behavior of solutions to problem (0.1) are obtained by using variational methods. We mainly extend the results in J. Sun and T. Wu (2014) [26] , which dealt with Kirchhoff type equations with positive potential well, to Kirchhoff type equations with sign-changing potential well. [ABSTRACT FROM AUTHOR]

This paper deals with improvements of the Trudinger–Moser inequality related to the operator Q V ( u ) : = − Δ n u + V ( x ) | u | n − 2 u , where n ≥ 2 and the potential V : R n → R belongs to a class of nonnegative and continuous functions. Precisely, under suitable assumptions on V we consider the subspace E : = { u ∈ W 1 , n ( R n ) : ∫ R n V ( x ) | u | n d x < ∞ } endowed with the norm ‖ u ‖ : = [ ∫ R n ( | ∇ u | n + V ( x ) | u | n ) d x ] 1 / n and we prove that if ( u k ) is a sequence in E such that ‖ u k ‖ = 1 , u k ⇀ u ≢ 0 in E and 0 < p < p n ( u ) : = β n ( 1 − ‖ u ‖ n ) − 1 / ( n − 1 ) , then ( ⁎ ) sup k ⁡ ∫ R n Ψ ( p | u k | n / ( n − 1 ) ) d x < ∞ , where Ψ ( t ) : = e t − ∑ i = 0 n − 2 t i i ! , β n : = n ω n − 1 1 / ( n − 1 ) and ω n − 1 is the measure of the unit sphere in R n . Furthermore, p n ( u ) is sharp in the sense that there exists a sequence ( u k ) ⊂ E satisfying ‖ u k ‖ = 1 and u k ⇀ u ≢ 0 in E such that the supremum (⁎) is infinite for p ≥ p n ( u ) . As an application of the previous result we prove the following sharp form of the Trudinger–Moser inequality for the subspace E . Considering ℓ ( α ) : = sup { u ∈ E : ‖ u ‖ = 1 } ⁡ ∫ R n Ψ ∘ ν α ( u ) d x , where ν α ( u ) : = β n ( 1 + α ‖ u ‖ n n ) 1 / ( n − 1 ) | u | n / ( n − 1 ) , assuming some conditions of symmetry on V it is established (1) for 0 ≤ α < λ 1 ( V ) we have ℓ ( α ) < ∞ , (2) for α ≥ λ 1 ( V ) , ℓ ( α ) = ∞ and (3) moreover, we prove that for 0 ≤ α < λ 1 ( V ) , an extremal function for ℓ ( α ) exists. Here λ 1 ( V ) denotes the first eigenvalue of Q V ( u ) . [ABSTRACT FROM AUTHOR]

Highlights • Space-time finite element discretizations for constrained FOSLS are studied numerically for time-dependent PDEs. • The considered formulation provides the expected orders of approximation and convergence. • A multilevel algorithm can be used to convert the saddle-point problem into the divergence-free setting to use multigrid. • Two different preconditioning strategies are studied and compared numerically. • Multigrid preconditioners in the div-free space outperform the block-diagonal preconditioners for the saddle point systems. Abstract This paper studies finite element discretizations for three types of time-dependent PDEs, namely heat equation, scalar conservation law and wave equation, which we reformulate as first order systems in a least-squares setting, subject to a space-time conservation constraint (coming from the original PDE). Available piecewise polynomial finite element spaces in (n + 1) -dimensions for functional spaces from the (n + 1) -dimensional de Rham sequence for n = 2 , 3 are used for the implementation of the method. Computational results illustrating the error behavior, iteration counts and performance of block-diagonal and monolithic geometric multigrid preconditioners are presented for the discrete CFOSLS system. The results are obtained from a parallel implementation of the methods for which we report reasonable scalability. [ABSTRACT FROM AUTHOR]

Highlights • Volume of fluid method with high-order representation for moving interface. • Rigorous conservation of mass/volume of the transported VOF field. • Sub-grid resolution for fine interfacial structures that are smaller than mesh size. • Easy-to-code and straightforward algorithm for 3D unstructured-grid. • Verified superior solution quality in comparison with classical VOF methods. Abstract In this paper, we propose a simple and accurate numerical method for capturing moving interfaces on fixed Eulerian grids by coupling the Tangent of Hyperbola Interface Capturing (THINC) method and Level Set (LS) method. The innovative and practically-significant aspects of the proposed method, so-called THINC/LS method, lie in (1) representing the interface with polynomial of high-order (arbitrary order in principle) rather than the plane representation commonly used in the Piecewise Linear Interface Calculation (PLIC) Volume of fluid (VOF) methods, (2) conserving rigorously the mass of the transported VOF field, (3) being able to resolving fine interface structures under mesh resolution, and (4) providing a straightforward and easy-to-code algorithm for 3D implementation. We verified the proposed scheme with the widely used benchmark tests. Numerical results show that this new method can obtain high-order accuracy for interface reconstruction and can produce more accurate results in capturing moving interfaces compared to the classical VOF methods. [ABSTRACT FROM AUTHOR]

Abstract This paper describes a novel partitioned algorithm for fluid–structure interaction (FSI) problems that couples the motion of rigid bodies and incompressible flow. This is the first partitioned algorithm that remains stable and second-order accurate, without sub-time-step iterations, for very light, and even zero-mass, bodies in three dimensions. This new added-mass partitioned (AMP) algorithm extends the previous developments in [1,2] by generalizing the added-damping tensors to account for arbitrary three-dimensional rotations, and by employing a general quadrature for the surface integral over a rigid body to derive the discrete AMP interface condition for the fluid pressure. Stability analyses for two three-dimensional model problems show that the algorithm remains stable for bodies of any mass when applied to the relevant model problems. The resulting AMP algorithm is implemented in parallel using a moving composite grid framework to treat one or more rigid bodies in complex three-dimensional configurations. The new three-dimensional algorithm is verified and validated though several benchmark problems, including the motion of a sphere in a viscous incompressible fluid and the interaction of a bi-leaflet mechanical heart valve and a pulsating fluid. Numerical simulations confirm the predictions of the stability analysis even for complex problems, and show that the AMP algorithm remains stable, without sub-iterations, for light and even zero-mass three-dimensional rigid bodies of general shape. These benchmark problems are further used to examine the parallel performance of the algorithm and to investigate the conditioning of the linear system for the pressure including the newly derived AMP interface conditions. Highlights • A partitioned FSI scheme for incompressible flow and rigid bodies is extended to 3D. • Stability for simple geometries is proved using 3D model problems. • Numerical results confirm stability and accuracy for light bodies of general shapes. • A benchmark for a bi-leaflet mechanical heart valve is designed and conducted. • Refinement studies for the heart valve are provided. [ABSTRACT FROM AUTHOR]

Abstract In the process of vibration suppression of hot galvanized steel strip by using maglev technology, the flexible modal vibration control of steel strip is a challenging problem. Aiming at this problem, this paper proposes a phase compensation control algorithm to control the modal vibration in the maglev steel strip vibration suppression system. The modal vibration suppression process is as follows. First, the model of the steel strip and the mode shape and frequency response curve of the strip are established by using the finite element analysis software. Second, the electromagnet-steel strip model is simplified as a single degree of freedom maglev system. Then, the equivalent damping is obtained according to the theory. And the vibration is suppressed according to the principle of the phase compensator. In addition, the effect of the phase lead angle, compensating field and scale factor on the damping performance of the phase compensator from the zero-pole of the closed-loop system is studied. Finally, the experimental study is carried out on the vibration suppression system of maglev steel strip based on phase compensator. The experimental results show that the phase compensator with appropriate parameters can be selected to reduce the amplitude of the first order bending mode by 89.2%. [ABSTRACT FROM AUTHOR]

Pisot numbers are real algebraic integers bigger than 1, whose other conjugates all have modulus smaller than 1. In this paper we deal with the algorithmic problem of finding the smallest Pisot unit generating a given number field. We first solve this problem in all real fields, then we consider the analogous problem involving the so called complex Pisot numbers and we solve it in all number fields that admit such a generator, in particular this includes all fields without CM. [ABSTRACT FROM AUTHOR]

Highlights • The first study on the problem of scheduling multi-type ONUs in NG-EPON. • Fairness, efficiency and load balancing are considered. • Mathematical model for scheduling multi-type ONUs is established. • Proposed DWBA algorithm performs well in bandwidth utilization, fairness and load balancing. Abstract In the Next Generation Ethernet Passive Optical Network (NG-EPON), Optical Network Units (ONUs) can work on multiple wavelengths simultaneously to achieve higher peak rates. There are three types of ONUs (25G, 50G and 100G ONUs) sharing all the 100 Gb/s bandwidth. Each type of ONUs has a total bandwidth constraint due to ONUs' working wavelength capabilities. In this paper, we investigate the fairness and efficiency of dynamic wavelength and bandwidth allocation (DWBA) algorithms for scheduling multi-type ONUs while considering their bandwidth capacity constraints. Proposed DWBA algorithms can achieve high efficiency, excellent fairness as well as good wavelength load balancing. [ABSTRACT FROM AUTHOR]

Highlights • The first DBA algorithm to support the differentiated QoS and SLA in NG-EPON. • A novel MUMS two-dimensional priority queue model for NG-EPON is designed. • The ONU flexible configuration for multi-type users and services is supported. • The flexibility of the multi-transceiver ONUs has been fully developed and used. Abstract Recently, the IEEE 802.3 Ethernet Working Group has been working to explore new solutions for the next-generation Ethernet passive optical network (NG-EPON). It's desirable for NG-EPON to support the coexistence of multi-type users and multi-type services (MUMS) on a single access platform. Moreover, in the future, ONU may be equipped with multiple transceivers to break through bandwidth limit of a single wavelength and then achieve greater throughput per ONU. In this paper, a two-dimensional priority queue model is designed for buffer differentiation management. Then, a dynamic bandwidth allocation (DBA) algorithm including joint grant sizing and scheduling is presented to carry out differentiated bandwidth allocation among MUMS. By providing different aggregated data rates and setting scalable wavelength resource pools for different users and services, flexible ONU configurations are allowed. By using time-order filling (ToF) and wavelength-order filling (WoF) in combination to realize grant scheduling, the flexibility of multi-transceiver ONUs gets further exploited. Finally, the simulation demonstrates the effectiveness of the proposed algorithm. Not only each type of service/user can get reasonable network access capacity that matches its bandwidth requirements, but the overall network performance also gets improved compared with other DBAs for NG-EPON. [ABSTRACT FROM AUTHOR]

Blind deconvolution algorithms prove to be effective tools for fault identification, being able to extract excitation sources from noisy observations only. In this scenario, the present paper introduces a novel blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm. This approach not only is characterized by a weighting matrix that drives the deconvolution process, but can also be easily adapted to arbitrary criteria. Based on this framework, a novel criterion rooted on the maximization of the cyclostationarity of the excitation – as typically encountered with machine faults – is proposed and compared with other blind deconvolution methods existing in the literature. The comparisons involve both synthesized and real vibration signals, taking into account a gear tooth spall and an outer race bearing fault. The results reveal superior capability to recover impulsive cyclostationary sources with respect to other blind deconvolution methods, even in the presence of impulsive noise or under non-constant speed. [ABSTRACT FROM AUTHOR]

Abstract Sequence comparison is a prerequisite to virtually all comparative genomic analyses. It is often realised by sequence alignment techniques, which are computationally expensive. This has led to increased research into alignment-free techniques, which are based on measures referring to the composition of sequences in terms of their constituent patterns. These measures, such as q -gram distance, are usually computed in time linear with respect to the length of the sequences. In this paper, we focus on the complementary idea: how two sequences can be efficiently compared based on information that does not occur in the sequences. A word is an absent word of some sequence if it does not occur in the sequence. An absent word is minimal if all its proper factors occur in the sequence. Here we present the first linear-time and linear-space algorithm to compare two sequences by considering all their minimal absent words. In the process, we present results of combinatorial interest, and also extend the proposed techniques to compare circular sequences. We also present an algorithm that, given a word x of length n , computes the largest integer for which all factors of x of that length occur in some minimal absent word of x in time and space O (n). Finally, we show that the known asymptotic upper bound on the number of minimal absent words of a word is tight. [ABSTRACT FROM AUTHOR]

One of the main aims of second order cyclostationary (CS2) analysis is the estimation of the full spectral correlation, allowing the identification of different CS2 components in a signal and their characterisation in terms of both spectral frequency f and cyclic frequency α . Unfortunately, traditional estimators of the full spectral correlation (e.g. averaged cyclic periodogram) are highly computationally expensive and hence their application has been quite limited. On the other hand, fast envelope-based CS2 indicators (e.g. cyclic modulation spectrum, CMS) are bound by a cyclic-spectral form of the uncertainty principle, which limits the extent of the cyclic frequency axis α max at approximately the value chosen for the spectral frequency axis resolution Δ f . A recent work has however introduced a ground-breaking approach resulting in a fast algorithm for the calculation of the spectral correlation. This approach is based on the calculation of a series of CMS-like quantities, each scanning a different cyclic-frequency band, given a certain spectral frequency resolution. The superposition of all these quantities allows covering a larger α -band breaking the constraint between maximum cyclic frequency α max and spectral frequency axis resolution Δ f , at a limited computational cost. In this paper a new algorithm for the calculation of the same fast spectral correlation is introduced, resulting in a further computational efficiency gain, and a simplification of the computational procedure. [ABSTRACT FROM AUTHOR]

Wheelset bearings are among the crucial elements of bogie frames used in high-speed trains. Wheelset-bearing fault detection can actively reduce or preclude safety-related accidents and realize condition-based maintenance in high-speed train service. Therefore, it is of great significance to automatically detect wheelset-bearing faults. Sparse representations based on circular-structure dictionary learning (SRCSDL) provide an excellent framework for extracting fault impact trains (FITs) induced by wheelset-bearing faults. However, the performance of SRCSDL on extracting FITs heavily relies on the selection of method-related parameters. A systematic method for selecting such parameters has not been reported in the literature. A novel fault detection method, adaptive SRCSDL (ASRCSDL), is therefore proposed in this paper. The effects of the selection of each SRCSDL parameter on extracting FITs are investigated. It was found that three parameters (the length of single set signals, the number of signal sets, and convergence error) can be fixed according to the characteristics of the SRCSDL algorithm. To adaptively tune the remaining three parameters, main frequency analysis is used to select the number of kernel functions, the number of maximum extreme values is employed to determine the length of the kernel function, and envelope spectra kurtosis-guided self-tuning algorithms are proposed to tune the target sparsity of SRCSDL. The proposed method is then validated using the simulated signals and bench and real-line tests. [ABSTRACT FROM AUTHOR]

This paper presents the design of an XYZ nano-manipulator as well as the model based high precision tracking control of the nano-manipulating system. Aiming at large range and high mechanical bandwidth, the proposed mechanical design employs compound bridge-type amplifiers to increase the workspace without significant drop of stiffness. To further improve the system tracking performance and avoid possible actuator saturations, a robust anti-windup tracking control architecture combining a parallel internal-model based controller and an anti-windup compensator is adopted for the trajectory tracking of the designed nano-manipulating system. As a theoretical extension on a recent result [17] , we further investigate the robust stability condition of the closed-loop system and formulate the optimization design of the anti-windup compensators as a two block H ∞ optimization problem solvable with the Nehari approach. Real time control experiments demonstrate excellent tracking performance and saturation compensation capability with tracking precision error less than 0.28 % , which significantly outperforms relevant algorithms in the literature. [ABSTRACT FROM AUTHOR]

Abstract Identifying patterns in 2D linear projections is important in understanding multi-dimensional datasets. However, local patterns, which are composed of partial data points, are usually obscured by noises and missed in traditional quality measure approaches that measure the whole dataset. In this paper, we propose an interactive interface to explore 2D linear projections with visual patterns on subsets. First, we propose a voting-based algorithm to recommend optimal projection, in which the identified pattern looks the most salient. Specifically, we propose three kinds of point-wise quality metrics of 2D linear projections for outliers, clusterings, and trends, respectively. For each sampled projection, we measure its importance by accumulating the metrics of selected points. The projection with the highest importance is recommended. Second, we design an exploring interface with a scatterplot, a projection trail map, and a control panel. Our interface allows users to explore projections by specifying interested data subsets. At last, we employ three datasets and demonstrate the effectiveness of our approach through three case studies of exploring clusters, outliers, and trends. [ABSTRACT FROM AUTHOR]

Abstract Word embeddings are distributed representations for natural language words, and have been wildly used in many natural language processing tasks. The word embedding space contains local clusters with semantically similar words and meaningful directions, such as the analogy. However, there are different training algorithms and text corpora, which both have a different impact on the generated word embeddings. In this paper, we propose a visual analytics system to visually explore and compare word embeddings trained by different algorithms and corpora. The word embedding spaces are compared from three aspects, i.e., local clusters, semantic directions and diachronic changes, to understand the similarity and differences between word embeddings. [ABSTRACT FROM AUTHOR]

Abstract Animation is a commonly used technique in information visualization for smooth transitions between different views. When observing animations of moving objects, people often need to track several specific objects while identify the major trend of movement simultaneously. In this paper, we propose a novel focus+context grouping technique to facilitate target tracking and trend identification. It divides objects into several groups based on a comprehensive tree cut algorithm and generates a staggering animation in which groups are animated sequentially. A balance between efficiency and accuracy is achieved for an effective animation planning. To evaluate the effectiveness of the proposed technique, a carefully designed user study is conducted. The results indicate that focus+context grouping is effective for users to track targets without losing context (i.e., major trend of movement). Based on the study, we discuss advantages and limitations of the proposed grouping technique and conclude with design implications. [ABSTRACT FROM AUTHOR]

Standard filtering techniques for structural parameter estimation assume that the input force is either known or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, force must therefore also be estimated. In this paper, the input force is considered to be an additional state that is estimated in parallel to the structural parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, an interacting Particle-Kalman filter is used to target systems with correlated noise. Alongside this, a second filter is used to estimate the seismic force acting on the structure. In the proposed algorithm, the parameters and the inputs are estimated as being conditional on each other, thus ensuring stability in the estimation. The proposed algorithm is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system and a five-story building structure. The stability of the proposed filter is also tested by subjecting it to a sufficiently long measurement time history. The estimation results confirm the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new heterogeneous fast multipole method (H-FMM) for 2-D Helmholtz equation in layered media. To illustrate the main algorithm ideas, we focus on the case of two layers in this work. The key compression step in the H-FMM is based on a fact that the multipole expansion for the sources of the free-space Green's function can be used also to compress the far field of the sources of the layered-media or domain Green's function, and a similar result exists for the translation operators for the multipole and local expansions. The mathematical error analysis is shown rigorously by an image representation of the Sommerfeld spectral form of the domain Green's function. As a result, in the H-FMM algorithm, the multipole-to-multipole, multipole-to-local, and local-to-local translation operators are the same as those in the free-space case, allowing easy adaptation of existing free-space FMM. All the spatially variant information of the domain Green's function is collected into the multipole-to-local translations and therefore the FMM becomes heterogeneous. The compressed representation further reduces the cost of evaluating the domain Green's function when computing the local direct interactions. Preliminary numerical experiments are presented to demonstrate the efficiency and accuracy of the algorithm with much improved performance over some existing methods for inhomogeneous media. [ABSTRACT FROM AUTHOR]

In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. However, many approaches do not give an intuitive relation between the raw features and the diagnosis. Therefore, they are difficult for clinicians to interpret. Moreover, most approaches treat the features extracted from the brain (for example, voxelwise gray matter concentration maps from brain MRI) as independent variables and ignore their spatial and anatomical relations. In this paper, we present a new Support Vector Machine (SVM)-based learning method for the classification of Alzheimer's disease (AD), which integrates spatial-anatomical information. In this way, spatial-neighbor features in the same anatomical region are encouraged to have similar weights in the SVM model. Secondly, we introduce a group lasso penalty to induce structure sparsity, which may help clinicians to assess the key regions involved in the disease. For solving this learning problem, we use an accelerated proximal gradient descent approach. We tested our method on the subset of ADNI data selected by Cuingnet et al. (2011) for Alzheimer's disease classification, as well as on an independent larger dataset from ADNI. Good classification performance is obtained for distinguishing cognitive normals (CN) vs. AD, as well as on distinguishing between various sub-types (e.g. CN vs. Mild Cognitive Impairment). The model trained on Cuignet's dataset for AD vs. CN classification was directly used without re-training to the independent larger dataset. Good performance was achieved, demonstrating the generalizability of the proposed methods. For all experiments, the classification results are comparable or better than the state-of-the-art, while the weight map more clearly indicates the key regions related to Alzheimer's disease. [ABSTRACT FROM AUTHOR]

A novel damage imaging of engineering structures and materials methodology using the k-space method with adjoint method is proposed in this paper. The proposed methodology consists of two parts: the wave propagation solution algorithms for elastic solids in k-space and adjoint method-based optimization algorithms for damage imaging. One major advantage of the proposed method is the superior computational efficiency compared to the classical finite element method-based wave propagation and gradient-based optimization algorithms. The k-space formulation for wave propagation in 2D and 3D solids are discussed first. Adjoint method and a modified conjugate gradient method is presented for the inverse problem of damage imaging. Following this, parametric studies for measurement duration, number of sensors, noise effect, and computational efficiency are discussed in detail. Next, demonstration examples for concrete structures with and without reinforcement are presented. Several conclusions and future work are given based on the proposed method. [ABSTRACT FROM AUTHOR]

Estimating attitude using an inexpensive MEMS inertial measurement unit has many applications in smart phones, wearable sensors, rehabilitation medicine and robots. Traditional approaches to attitude estimation from the aerospace world focus on the use of either Euler angles or quaternions. These approaches suffer from disadvantages including singularities and nonlinear models. This paper proposes a method to estimate the direction cosine matrix (DCM) which encapsulates attitude information, instead of Euler angles or quaternions. The DCM does not suffer from singularities and also has linear dynamics. A rigorous DCM estimation algorithm, that incorporates automatic magnetometer bias calibration and satisfaction of an inherent orthonormal property of the DCM, is developed. The validity of the developed algorithm is demonstrated through experimental results with estimation of attitude on a 5-DOF robot. The estimation results are compared with values computed from encoders on the robot as well as with results from previously published algorithms. [ABSTRACT FROM AUTHOR]

A variable step-size median least-mean-square (VS-MLMS) algorithm, for active noise control (ANC) of rail vehicle interior noises is presented in this paper. Firstly, the interior noises of an urban rail vehicle under normal operating conditions are measured and analysed. Results show that the interior noise includes obvious impact noise components, and the sound energy of this noise is mainly concentrated in a low-frequency range below 1000 Hz. Utilising the measured interior noises, the adaptive filter order, median window length, and the optimal step size of the MLMS algorithm are determined. According to the step-size scope, the VS-MLMS algorithm is developed using a Sine step-size adjusting function. The MLMS, VS-MLMS and other algorithms are applied to the measured interior noise signals. Results suggest that the proposed VS-MLMS, which can balance the convergence speed and the steady-state error well, is more suitable for active control of a low-frequency noise with impact noise components. The VS-MLMS algorithm can be directly used in an ANC system of the sample urban rail vehicle, which is helpful to improve the ride comfort of the passengers. [ABSTRACT FROM AUTHOR]

In this paper we study the ℓ 1 -penalized partial likelihood estimator for the sparse high-dimensional Cox proportional hazards model. In particular, we investigate how the ℓ 1 -penalized partial likelihood estimation recovers the sparsity pattern and the conditions under which the sign support consistency is guaranteed. We establish sign recovery consistency and ℓ ∞ -error bounds for the Lasso partial likelihood estimator under suitable and interpretable conditions, including mutual incoherence conditions. More importantly, we show that the conditions of the incoherence and bounds on the minimal non-zero coefficients are necessary, which provides significant and instructional implications for understanding the Lasso for the Cox model. Numerical studies are presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of segmenting n aligned random sequences of equal length m into a finite number of independent blocks. We propose a penalized maximum likelihood criterion to infer simultaneously the number of points of independence as well as the position of each point. We show how to compute exactly the estimator by means of a dynamic programming algorithm with time complexity O ( m 2 n ) . We also propose another method, called hierarchical algorithm, that provides an approximation to the estimator when the sample size increases and runs in time O { m ln ( m ) n } . Our main theoretical results are the strong consistency of both estimators when the sample size n grows to infinity. We illustrate the convergence of these algorithms through some simulation examples and we apply the method to identify recombination hotspots in real SNPs data. [ABSTRACT FROM AUTHOR]

Uncertainty quantification is an emerging research area aiming at quantifying the variation in engineering system outputs due to uncertain inputs. One approach to study problems in uncertainty quantification is using polynomial chaos expansions. Though, a well-known limitation of polynomial chaos approaches is that their computational cost becomes prohibitive when the dimension of the stochastic space is large. In this paper, we propose a procedure to solve high dimensional stochastic problems with a limited computational budget. The methodology is based on an existing non-intrusive model reduction scheme for polynomial chaos representation, introduced by Raisee et al. [1] , that is further extended by introducing sparse polynomial chaos expansions. Specifically, an optimal stochastic basis is calculated from a coarse scale analysis, using proper orthogonal decomposition and sparse polynomial chaos and is then utilized in the fine scale analysis. This way, the computational expense on both the coarse and fine discretization levels is drastically reduced. Two application examples are considered to validate the proposed method and demonstrate its potential in solving high dimensional uncertainty quantification problems. One analytical stochastic problems is first studied, where up to 20 uncertainties were introduced in order to challenge the proposed method. A more realistic CFD type application is then discussed. It consists of a two dimensional NACA 0012 symmetric profile operating at subsonic flight conditions. It is shown that the proposed reduced order method based on sparse polynomial chaos expansions is able to predict statistical quantities with little loss of information, at a cheaper cost than other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Centroidal Voronoi tessellation (CVT)-based mesh generation is a very effective technique for creating high-quality Voronoi meshes and their dual Delaunay triangulations that often play a crucial role in applications, including ocean and atmospheric simulations using finite volume schemes. In the next generation climate models, the spacing scales change dramatically across the whole sphere and require ultra-high resolution and smooth transitions from coarse to fine grid regions. Thus fast and robust spherical CVT (SCVT) meshing algorithms become highly desirable. In this paper, we first propose a Lloyd-preconditioned limited-memory BFGS method for constructing SCVTs that is also applicable to the construction of CVTs of general domains. This method is then parallelized based on overlapping domain decomposition, enabling excellent scalability on distributed systems. Results of several computational experiments show that the new method could incur computational time costs one order of magnitude smaller compared with some existing methods for generating large-scale highly variable-resolution meshes, while also providing significant improvements in mesh quality. [ABSTRACT FROM AUTHOR]

In this paper an implicit characterization of the complexity classes k - EXP and k - FEXP , for k ≥ 0 , is given, by a type assignment system for a stratified λ -calculus, where types for programs are witnesses of the corresponding complexity class. Types are formulae of Elementary Linear Logic ( ELL ), and the hierarchy of complexity classes k - EXP is characterized by a hierarchy of types. [ABSTRACT FROM AUTHOR]

Abstract It is known that, for systems of initial-value problems, algorithms using adaptive information perform much better in the worst case setting than the algorithms using nonadaptive information. In the latter case, lower and upper complexity bounds significantly depend on the number of equations. However, in contrast with adaptive information, existing lower and upper complexity bounds for nonadaptive information are not asymptotically tight. In this paper, we close the gap in the complexity exponents, showing asymptotically matching bounds for nonadaptive standard information, as well as for a more general class of nonadaptive linear information. [ABSTRACT FROM AUTHOR]

Abstract Doubly stochastic learning algorithms are scalable kernel methods that perform very well in practice. However, their generalization properties are not well understood and their analysis is challenging since the corresponding learning sequence may not be in the hypothesis space induced by the kernel. In this paper, we provide an in-depth theoretical analysis for different variants of doubly stochastic learning algorithms within the setting of nonparametric regression in a reproducing kernel Hilbert space and considering the square loss. Particularly, we derive convergence results on generalization error for the studied algorithms either with or without an explicit penalty term. To the best of our knowledge, the derived results for the unregularized variants are the first of this kind, while the results for the regularized variants improve those in the literature. The novelties in our proof are a sample error bound that requires controlling the trace norm of a cumulative operator, and a refined analysis of bounding initial error. [ABSTRACT FROM AUTHOR]

The wireless sensor networks recently abstract great attention and are used for a wide range of cyber-enabled applications each of which with rigid accuracy requirements. The emergence and existence of coverage holes in WSNs will dramatically degrade the network coverage performance and quality of service. To diminish the negative effects of coverage holes, this paper addresses and studies the confident information coverage hole detection problem (CICHD) based on the proposed novel confident information coverage model (CIC). For solving the CICHD problem, we design two effective heuristic CIC holes detection algorithms including the CHD without considering the nodes residual energy and the other CHDRE taking the nodes’ residual energy into account. In the both proposed algorithms, the sensing field is firstly partitioned into a series of reconstruction grids based on the spatial correlation and correlation range. Then each reconstruction grid will be scanned and detected based on the CIC model to be judged whether it is a hole. Once obtaining the coverage status of every reconstruction grid, the boundary of the coverage hole will be exacted by image processing method. The results of the simulations show that both the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the CHDRE algorithm is more practical and efficient compared to the CHD without considering the energy problem. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A novel distance metric learning (DML) using cost sensitive for CBIR is proposed. • Three kinds of costs of the minority class are adequately discussed. • The method of replacing the kernel function is proposed for CBIR. How to retrieve the desired images quickly and accurately from the large scale image database has become a hot topic in the field of multimedia research. Many content-based image retrieval (CBIR) technologies already exist, but they are not always satisfactory. In many applications, the CBIR model based on machine learning relies heavily on the distance metric between samples. Although the traditional distance metric methods are simple and convenient, it is not always appropriate for CBIR tasks. In this paper, a novel distance metric learning (DML) method based on cost sensitive learning (CSL) is studied, and then it is used in a large margin distribution learning machine (LDM) to replace the traditional kernel functions. The improved LDM also takes into account CSL, and which is called CS-DLDM. Finally, CS-DLDM model is applied to CBIR tasks for implementation classification. We compare the proposed CS-DLDM model with other classifiers based on CSL. The experimental results show that the proposed CS-DLDM model not only has satisfactory classification performance but also the lowest misclassification cost, can effectively avoid the class imbalance of sample. [ABSTRACT FROM AUTHOR]

Highlights • An improved face recognition is proposed using the fusion of global and local structures. • A combination of preprocessing techniques coupled with discriminative feature extractors. • Improved performance under poor illumination, partial occlusion and low resolution. • Our best recognition results using FRGC 2.0.4 indicated more than 28% improvement. In this paper an improved face recognition algorithm under degrading conditions is proposed. The proposed algorithm uses a combination of preprocessing techniques coupled with discriminative feature extractors to obtain the best distinctive features for classification. Preprocessing approach is the fusion of multi-scale Weber and enhanced complex wavelet transform. Combination of multiple feature extraction based on Gabor filters, block-based local phase quantization (LPQ) coupled with principal component analysis (PCA) proved to be very effective to improve correct rate of recognition. We have also used two known classifiers, extreme learning machine (ELM), and sparse classifier (SC), and fused their outputs to obtain best recognition rate. Experimental results show improved performance of proposed algorithm under poor illumination, partial occlusion and low-quality images in uncontrolled conditions. Our best recognition results using second version of face recognition grand challenge (FRGC 2.0.4) which is the most challenging database, indicated more than 28% improvement over previous works. [ABSTRACT FROM AUTHOR]

Highlights • A method for visual object tracking is proposed. • We propose a decomposition model in visual tracking. • The decomposition model is utilized in an integrated generative model and discriminative model tracking framework. • Alternating direction method of multipliers algorithm guarantees the convergence of the decomposition model. This paper presents a multi-task based object tracking algorithm via a collaborative model. Under the framework of particle filtering, we develop a multi-task sparse learning based generative and discriminative classifier model. In the generative model, we propose a histogram based subspace learning method which takes advantage of adaptive templates update. In the discriminative model, we introduce an effective method to compute the confidence value which assigns more weights to the foreground than the background. A decomposition model is employed to take the common features and outliers of each particle into consideration. The alternating direction method of multipliers (ADMM) algorithm guarantees the optimization problem can be solved robustly and accurately. Qualitative and quantitative comparisons with nine state-of-the-art methods demonstrate the effectiveness and efficiency of our method in handling various challenges during tracking. [ABSTRACT FROM AUTHOR]

Video clipping system is very important in many intelligent applications. In order to shorten the time of video and extract the framework of the video, many methods have been proposed. But these methods just considered videos without taking sound into account. As we know, sound is also an important information for image processing. For example, many sport match videos include rich sound of audience and commentator such as NBA. In addition, human pay more attention to some video clips of interest (VCOI) such as scoring time instead of pause. So in this paper, we propose a sound-based video clipping framework toward specific sports scenes. First, we convert sound of sport videos to sonogram. For some aesthetically-pleasing images (APIM) such as slam dunk or jump shot, a set of object patches are selected using BING feature. Then, these object patches are ordered by our active object patches ranking algorithm. After that, ordered object patches and sonogram are fed into CNN respectively to obtain patch-level deep feature. In order to obtain image-level deep representation, deep feature extracted from ordered object patches are aggregated statistically into a deep representation. Finally, probabilistic model is used to select VCOI and APIM. Experiments on some NBA basketball matches have shown the effectiveness of our video clipping framework. [ABSTRACT FROM AUTHOR]

Highlights • We propose an intensity-tracking-guided multi-frame 4D fusion pipeline. • The 4D fusion is for improving the quality of high-dynamic 3D reconstruction. • The fusion combines temporal inter-frame fusion with intra-frame filtering. • The algorithm reduces spatial noise and temporal fluctuations of 3D sequences. • The algorithm is applicable on static, dynamic, or deformable 3D objects. The availability of high-speed 3D video sensors has greatly facilitated 3D shape acquisition of dynamic and deformable objects, but high frame rate 3D reconstruction is always degraded by spatial noise and temporal fluctuations. This paper presents a simple yet powerful dynamic 3D reconstruction improvement algorithm based on intensity video guided multi-frame 4D fusion. Temporal tracking of intensity image points (of moving and deforming objects) allows registration of the corresponding 3D model points, whose 3D noise and fluctuations are then reduced by spatio-temporal multi-frame 4D fusion. We conducted simulated noise tests and real experiments on four 3D objects using a 1000 fps 3D video sensor. The results demonstrate that the proposed algorithm is effective at reducing 3D noise and is robust against intensity noise. It outperforms existing algorithms with good scalability on both stationary and dynamic objects. [ABSTRACT FROM AUTHOR]

Availability of low-cost range sensors and the development of relatively robust algorithms for the extraction of skeleton joint locations have inspired many researchers to develop human activity recognition methods using 3-D data. In this paper, an effective method for the recognition of human activities from the normalized joint trajectories is proposed. We represent the actions as multidimensional signals and introduce a novel method for generating action templates by averaging the samples in a “dynamic time” sense. Then, in order to deal with the variations in speed and style of performing actions, we warp the samples with action templates by an efficient algorithm and employ wavelet filters to extract meaningful spatiotemporal features. The proposed method is also capable of modeling the human-object interactions, by performing the template generation and temporal warping procedure via the joint and object trajectories simultaneously. Experimental evaluations on several challenging datasets demonstrates the effectiveness of our method compared to the state-of-the-arts as well as its robustness against different sources of noise. [ABSTRACT FROM AUTHOR]

Highlights • Information about image feature extraction methods is provided. • A survey on image clustering for various applicative areas is carried out. • Research gaps and challenges in image clustering are discussed. • Image characteristics along with suitable clustering approaches are provided. A huge amount of image data is being collected in real world sectors. Image data analytics provides information about important facts and issues of a particular domain. But, it is challenging to handle voluminous, unstructured and unlabeled image collection. Clustering provides groups of homogeneous unlabeled data. Therefore, it is used quite often to access the interesting data easily and quickly. Image clustering is a process of partitioning image data into clusters on the basis of similarities. Whereas, features extracted from images are used for the computation of similarities among them. In this paper, significant feature extraction approaches and clustering methods applied on the image data from nine important applicative areas are reviewed. Medical, 3D imaging, oceanography, industrial automation, remote sensing, mobile phones, security and traffic control are considered applicative areas. Characteristics of images, suitable clustering approaches for each domain, challenges and future research directions for image clustering are discussed. [ABSTRACT FROM AUTHOR]

Highlights • Study of sampling schemes for data augmentation, taking into account prior knowledge. • Investigations on features generation methods: space coverage and landmark importance. • Analysis of semantic-driven sampling schemes compared to conventional blind sampling. • Results show that semantic-driven samplings provide faster and better convergence. Automatic face landmarking has received a lot of attention in the past decades. It is now mature enough to be implemented in fully autonomous video systems. As cascade-of-regression based algorithms have become state of the art in such systems, two major (and still relevant) sources of interest have slowly faded away: the need for semantic-driven learning beyond ground truth annotation, and full video chain performance i.e. tracking efficiency, which in the case of said methods strongly relates to their robustness towards shape initialization before fitting. In this paper, we investigate how data sampling using face priors can affect their performance in terms of convergence and robustness. We propose new strategies based on said priors to overcome inconsistencies observed during cascade-of-regression learning on purely random sampling-based stages. We will show that simple choices can be easily integrated within regression-based face tracking systems to increase accuracy and robustness. [ABSTRACT FROM AUTHOR]

Highlights • We present a novel framework for soccer video event detection. • We propose an Adaptive Label-Consistent K-SVD algorithm. • The proposed method automatically extracts semantic concepts of each soccer event. • Experiments indicate that our approach outperforms state-of-the-art approaches. In this paper, we formulate the soccer video event detection task as a sparse representation problem by learning a supervised, discriminative and event-oriented dictionary based on learned weighted local features. To this end, we present a novel framework based on two ideas: First, we propose an approach for computing the representativeness of each video frame for each soccer event. Second, we propose an Adaptive Label-Consistent K-SVD (ALC-KSVD) algorithm to learn an event-oriented and discriminative dictionary based on the computed representativeness of frames to transfer video frames to a sparse space. To improve discrimination among frames of different events, we proposed a weighting method to identify local features that are more representative in each event category. Next, the representativeness score of each frame is calculated by aggregating the weighted local features within each frame. The calculated representativeness score of each frame indicates its belonging degree to each event. The representativeness score matrix, being a discriminative term, is combined with the reconstruction error to form an objective function to improve the discrimination ability in the sparse representation during the dictionary learning process. The obtained objective function is efficiently and optimally solved by the K-SVD algorithm. The representativeness score matrix, which is automatically calculated based on the training samples, defines an adaptive correspondence between the dictionary atoms and the labels of the frames. We demonstrate the effectiveness of the proposed framework on the detection and classification of several soccer events based on an extensive experimental investigation that was conducted using a large collection of video data. The experimental results indicate that our approach maintains good classification performance and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • Two interaction mechanisms are allowed: fully-user-supervised (BnS) or machine-assisted (BnQ). • The manifold ranking is integrated with the query-based interactive mechanism. • The foreground-object-confidence update rule is efficient and effective. • The average response time per-interaction is quite short. • The initial bounding box can be imprecise. This paper presents an efficient and effective interactive segmentation scheme for extracting the region of a foreground object in an image. Our goal is to design an interactive segmentation algorithm that unifies the bounding-box-based, seed-based, and query-based interaction mechanisms for pursuing (i) high efficiency in simple interaction mechanism, (ii) few interaction rounds, and (iii) short response time. The proposed algorithm starts with a user-provided bounding box and obtains candidate background superpixels for inferring the foreground object. Our algorithm tolerates imprecise bounding boxes and provides two kinds of interactions for acquiring correct labels from the user. The user can either input the seed/scribble annotations or label the algorithm-queried regions. Our algorithm selects the most uncertain region as a query, and this query-based interaction mechanism reduces the burden of the user on deciding suitable annotation locations. The average response time per-interaction of our algorithm is merely 0.014 s. Our experiments demonstrate that the algorithm achieves an efficient unified scheme for interactive image segmentation. [ABSTRACT FROM AUTHOR]

Highlights • A novel method for video summarization based on keyframe extraction is proposed. • Keyframes are extracted using mean shift with global frame orientation features. • A new algorithm to remove the redundant frames of the video is proposed. • A new verification approach to objectively evaluate the video summary quality is proposed. • The proposed method achieves the state-of-the-art performances on several datasets. Video abstraction is an interesting topic that aims at briefly representing the entire video stream by producing a short summary either statically or dynamically. In this paper, we present an optimal static video summarization method based on keyframe extraction, termed as MSKVS. The proposed MSKVS has three major components: A new feature representation is exploited to describe the visual content of the video, a simple and fast algorithm is proposed to eliminate most similar and redundant frames, and an adaptive mean shift algorithm is used to select the most representative keyframes. We further develop a novel verification technique to measure the amount of information preserved by the produced summary and to make sure that it deserves to present the entire video stream regardless of human opinion impact. We report experimental results on six challenging datasets using different evaluation metrics, showing that MSKVS achieves state-of-the-art performances in a short computation time. [ABSTRACT FROM AUTHOR]

Highlights • We propose a weighted group-based simultaneous sparse coding model (WGSSC). • By introducing the SCESM prior, the sparse coefficients and weights of our model can be solved by AM algorithm. • The weighted nuclear norm minimization model can be seen a special case of our model. • We apply the proposed WGSSC-SCESM denoiser prior to more general IR problems under the ADMM framework. According to the nonlocal self-similarity property of natural images, group-based simultaneous sparse coding (GSSC) model assumes that nonlocal similar patches have similar sparse representations in a given dictionary and have been widely used in various image inverse problems. Inspired by the success of the GSSC mode in image restoration problems, this paper proposes a weighted group-based simultaneous sparse coding (WGSSC) model based on the spherically contoured exponential scale mixture (SCESM) prior for image restoration. Compared with traditional GSSC models, which often characterize the similar sparse coefficients by a common set of zero supports and lack spatial adaption and principled fashion, the proposed model considers the dependent relation and adaptivity of similar sparse coefficient, so it is more rational than the GSSC model. The similar sparse coefficients and scale variables can be jointly estimated by the alternating minimization algorithm with the SCESM prior. Based on the estimated sparse coefficients, we can reconstruct the clear patch group and obtain the global denoised image by averaging these patch groups. We refer this denoised method as WGSSC-SCESM based denoiser prior, which can be effectively plugged into general image restoration problems by the alternating direction method of multipliers (ADMM) techniques. Extensive experiments on various types of image restoration problems, e.g., image denoising, inpainting, deblurring and single image super-resolution, demonstrate that the proposed method outperforms many state-of-the-art restored methods in term of the objective and subjective metrics. [ABSTRACT FROM AUTHOR]