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In Blind Hyperspectral Unmixing, the accuracy of the estimated number of endmembers affects the succeeding steps of extraction of endmember signatures and acquiring their fractional abundances. The characteristics of endmember signature depend on the nature of the material on the ground and share similar characteristics for variants of the same material. In this paper, we introduce a new concept of sub-endmembers to identify similar materials that are variants of a global endmember. Identifying the sub-endmembers will provide a meaningful interpretation of the endmember variability along with increased unmixing accuracy. This paper proposes a new algorithm exploiting both the spatial and spectral information present in hyperspectral data. The hyperspectral data are segmented into homogenous regions (superpixels) based on the Simple Linear Iterative Clustering (SLIC) algorithm, and the mean spectral of each region is accounted for in finding the global endmembers. The difference of eigenvalues-based thresholding method is used to find the number of global and sub-endmembers. The method has been tested on synthetic and real hyperspectral data and has successfully estimated the number of global endmembers as well as sub-endmembers. The method is also compared with other state-of-the-art methods, and better performances are obtained at a reasonably lower computational complexity. [ABSTRACT FROM AUTHOR]

The three-level inverter is extensively preferred in medium-voltage industrial applications over the conventional inverter due to its benefits. The performance of inverter is better when the space vector modulation (SVM) approach is preferred. However, the implementation of conventional SVM approach is more complex. In this paper, a decomposition-based new SVM algorithm for three-level inverter is proposed to reduce computational complexity and implementation time using a low-cost digital controller. Initially, the performance of the proposed SVM algorithm is studied through MATLAB simulation using the conventional switching sequence 0127. Further enhancing the performance, the proposed algorithm is implemented with various advanced discontinuous SVPWM strategies using the advanced switching sequences of 1012–2721 and 0121–7212. The no-load motor current THD and switching loss analysis for ADSVPWM and conventional SVPWM strategies at different modulation indices are presented is this paper for a comparison point of view. The no load motor current THD results obtained through simulation are validated experimentally by implementing the proposed algorithm on real-time interface tool through dSPACE, which is used to control constant V/F-controlled three-level inverter-fed induction motor. Further to show the effectiveness of the proposed technique, the experimental investigation is done on induction motor at loaded condition and the results are presented. [ABSTRACT FROM AUTHOR]

For a simple, undirected, connected graph G , a function h : V (G) → { 0 , 1 , 2 , 3 } which satisfies the following conditions is called a total Roman {3}-dominating function (TR3DF) of G with weight h (V) = ∑ p ∈ V h (p) : (C1) For every vertex u ∈ V if h (u) = 0 , then u has m (m ≥ 1) neighbors such that whose sum is at least 3, and if h (u) = 1 , then u has n (n ≥ 1) neighbors such that whose sum is at least 2. (C2) The subgraph induced by the set of vertices labeled one, two or three has no isolated vertices. For a graph G , the smallest possible weight of a TR3DF of G denoted γ t { R 3 } (G) is known as the total Roman { 3 } -domination number of G. The problem of determining γ t { R 3 } (G) of a graph G is called minimum total Roman {3}-domination problem (MTR3DP). In this paper, we show that the problem of deciding if G has a TR3DF of weight at most l for chordal graphs is NP-complete. We also show that MTR3DP is polynomial time solvable for bounded treewidth graphs, chain graphs and threshold graphs. We design a 3 (ln (Δ − 0. 5) + 1. 5) -approximation algorithm for the MTR3DP and show that the same cannot have (1 − δ) ln | V | ratio approximation algorithm for any δ > 0 unless NP ⊆ DTIME (| V | O (log log | V |)). Next, we show that MTR3DP is APX-complete for graphs with Δ = 4. We also show that the domination and total Roman {3}-domination problems are not equivalent in computational complexity aspects. Finally, we present an integer linear programming formulation for MTR3DP. [ABSTRACT FROM AUTHOR]

Identifying critical nodes in complex networks has gained increasing attention in recent years. However, how to design an algorithm that has low computational complexity but can accurately identify important network nodes is still a challenge. Considering the role of structural holes in shaping communication channels, this paper presents an effective method based on local characteristics to identify critical nodes that play important roles in maintaining network connectivity. Our method considers the connections of a node as well as the connectivity of the neighborhood of the node. Through numerical simulations on various real-world networks, we have demonstrated that the proposed approach outperforms some other well-known heuristic algorithms in identifying vital nodes and leads to faster network collapse in target destruction. [ABSTRACT FROM AUTHOR]

Prolonging the lifetime of wireless sensor networks (WSN) is the biggest challenging issue. In wireless multimedia sensor networks (WMSNs), sensor nodes have limited energy resource and to increase the lifetime of the network, it is necessary to design an effective fast algorithm that aims at reducing the consumed power. This paper proposes an energy-efficient discrete cosine transform (DCT) approximation requiring only 12 additions. Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, but above all, a very low computational complexity and significant compatibility with the exact DCT. Simulation results clearly show that the proposed fast transform algorithm achieves a better trade-off between image quality, computational complexity and energy consumption compared to any existing pruned DCT approximations. Furthermore, it is very suitable for the resource constrained wireless visual sensor networks (WVSNs) requiring low bitrates. [ABSTRACT FROM AUTHOR]

Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or clusters. However, the existing algorithms are with high complexity, which cost much computational time. In this paper, an efficient graph clustering algorithm based on spectral coarsening is proposed, to deal with the large time complexity of the traditional spectral algorithm. We first find the subset most possibly belonged to the same cluster in the original network, and merge them into a single node. The scale of the network will decrease with the network being coarsened. Then, the spectral clustering algorithm is performed on the coarsened network with the maintained advantages and the improved time efficiency. Finally, the experimental results on the multiple datasets demonstrate that the proposed algorithm, compared with the current state-of-the-art methods, has superior performance. [ABSTRACT FROM AUTHOR]

The goal of an outdegree-constrained edge-modification problem is to find a spanning subgraph or supergraph H of an input undirected graph G such that either: (Type I) the number of edges in H is minimized or maximized and H can be oriented to satisfy some specified constraints on the vertices' resulting outdegrees; or: (Type II) among all subgraphs or supergraphs of G that can be constructed by deleting or inserting a fixed number of edges, H admits an orientation optimizing some objective involving the vertices' outdegrees. This paper introduces eight new outdegree-constrained edge-modification problems related to load balancing called (Type I) MIN-DEL-MAX, MIN-INS-MIN, MAX-INS-MAX, and MAX-DEL-MIN and (Type II) p -DEL-MAX, p -INS-MIN, p -INS-MAX, and p -DEL-MIN. In each of the eight problems, the input is a graph and the goal is to delete or insert edges so that the resulting graph has an orientation in which the maximum outdegree (taken over all vertices) is small or the minimum outdegree is large. We first present a framework that provides algorithms for solving all eight problems in polynomial time on unweighted graphs. Next we investigate the inapproximability of the edge-weighted versions of the problems, and design polynomial-time algorithms for six of the problems on edge-weighted trees. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

For the virtual reality 360 ∘ videos, equirectangular projection (ERP) is a commonly used projection format. However, its high resolution brings extraordinary huge computational complexity in encoding. In order to speed up the intra coding process, a fast coding unit (CU) partitioning algorithm based on regional decision tree is proposed in this paper. The frame image is divided into two regions from a statistical point of view, and the earlysplit and pruned decision trees are established using light weight sample attributes for each region. With the help of these decision trees, the CU partitioning process is accelerated. Compared with the original algorithm of HM16.20, the proposed algorithm can reduce the encoding time by 28%, while BD-rate only increases by 0.27%. [ABSTRACT FROM AUTHOR]

In this paper, a new auto-scoring algorithm that automatically evaluates the Berg balance scale (BBS) tasks is proposed. The BBS can be used as an indicator for patients to analyze their rehabilitation status by themselves. In the proposed method, the patient must use a wearable inertial measurement unit (IMU) sensor, and the result of the patient's BBS task execution would be scored automatically by the evaluation algorithm. The proposed evaluation algorithm involves only few computations and has high scoring accuracy. Nonlinear kernel principal component analysis and a small number of linear features were combined to reduce the features from each sensor, and the algorithm model was implemented using a support vector machine (SVM), a machine learning technique with low computational complexity. The effectiveness of the algorithm was evaluated through clinical evaluation of 53 subjects with up to eight IMU sensors. The average accuracy of the proposed algorithm using eight sensors was 93.2%, and that using five sensors was 91.5%. There was a 12.6% and 10.9% increase in accuracy, respectively, compared to a previous study. The training and testing times of the proposed SVM model were over 38 times faster than the multi-layer perceptron model used in a previous study. [ABSTRACT FROM AUTHOR]

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. ∼ 32% less attack traces than correlation power analysis, when the templates are properly built. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scheme to implement hybrid encryption of images using generalized Arnold transformation and matrix nonlinear operations. This scheme consists of two stages. In the first stage, the pixels scrambling encryption is achieved by using generalized Arnold transformation. In the second stage, the pixels sequence encryption is carried out by a nonlinear matrix operation based on the key stream generated by a random matrix. Accordingly, the decryption process is completed by two steps inverse transformations. The hybrid encryption algorithm is one-time pad, and therefore has good anti-attack performance. The algorithm is featured by good confidentiality, low computational complexity, and easy-to-program processing. Moreover, the effectiveness, security and robustness of the algorithm are demonstrated by an image encryption simulation and the encryption performance analysis. Compared with the traditional Arnold scrambling encryption scheme and the chaotic encryption method based on the generalized standard mapping, the superiority of the proposed algorithm is demonstrated. [ABSTRACT FROM AUTHOR]

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in the runtime complexity of programs based on multiset rewriting. The motivation behind this work is the study of the complexity of chemistry-inspired programming models, which recently regained momentum due to their adequacy to large autonomous systems. In these models, data are most of the time left unstructured in a container, formally, a multiset. The program to be applied to this multiset is specified as a set of conditioned rules rewriting the multiset. At run time, these rewrite operations are applied concurrently, until no rule can be applied anymore (the set of elements they need cannot be found in the multiset anymore). A limitation of these models stand in their complexity: each computation step may require a complexity in where n denotes the number of elements in the multiset, and k is the size of the subset of elements needed to trigger a given rule. By analogy with chemistry, such elements can be called reactants. In this paper, we explore the possibility of improving the complexity of searching reactants through a static analysis of the rules' condition. In particular, we give a characterisation of this complexity, by analogy to the subgraph isomorphism problem. Given a rule R, we define its rank rk(R) and its calibre C(R), allowing us to exhibit an algorithm with a complexity in for searching reactants, while showing that and that most of the time. [ABSTRACT FROM AUTHOR]

The equivalence of finite automata and regular expressions dates back to the seminal paper of Kleene on events in nerve nets and finite automata from 1956. In the present paper we tour a fragment of the literature and summarize results on upper and lower bounds on the conversion of finite automata to regular expressions and vice versa. As an interesting special case also one-unambiguous regular expressions, a sort of a deterministic version of a regular expression, are considered. We also briefly recall the known bounds for the removal of spontaneous transitions ( ε-transitions) on non- ε-free nondeter-ministic devices. Moreover, we report on recent results on the average case descriptional complexity bounds for the conversion of regular expressions to finite automata and new developments on the state elimination algorithm that converts finite automata to regular expressions. [ABSTRACT FROM AUTHOR]

This paper discusses a computational treatment of the localization of an affine coordinate ring at a prime ideal and its associated graded algebra with the means of computer algebra. Building on Mora's paper [T. Mora, La queste del Saint : A computational approach to local algebra, Discrete Appl. Math. 33 (1991) 161-190], we present shorter proofs on two of the central statements and expand on the applications touched by Mora: resolutions of ideals, systems of parameters and Hilbert polynomials, as well as dimension and regularity of . All algorithms are implemented in the library graal.lib for the computer algebra system Singular. [ABSTRACT FROM AUTHOR]

At present many algorithms for mining association rules have been proposed, but most of them are only suitable for discovering specific frequent itemsets from characteristic data sets on the appointed environments, namely, these algorithms are not general enough when mining association rules. In this paper, a general framework based on composite granules for mining association rules is proposed, which is a general data mining model without appointed restriction from frequent itemsets, data sets or mining environments and so on. An iterative method is efficiently applied to the general mining framework for discovering frequent itemsets, which adopts repartitioning frequent attributes to iteratively reconstruct the mixed radix information system for reducing a relational database. In order that the framework for discovering frequent itemsets has a generality, in discussing and establishing the general mining framework, this paper introduces a novel conception and data model, namely, a mixed radix information system is applied to describe a relational database, and a composite granules is used to build a specific relationship between an information system and a mixed radix information system, which can hold the same extension and simultaneously exist in two different information systems. The mixed radix information system can help the general framework to reduce information data and improve the performance of the framework for generating frequent itemsets. The composite granules model can create a relationship between an information granule and a digital information granule, and help the framework for computing the support to avoid reading the database repeatedly or using the complex data structure. Finally, a new taxonomy is presented to verify the generality and the high efficiency of the mining framework and all the experiments based on the taxonomy indicate that the general mining framework has the required generality, and the performance of the framework is better than these classical mining frameworks. [ABSTRACT FROM AUTHOR]

The formality of signed relationships has been recently adopted in a lot of complicated systems. The relations among these entities are complicated and multifarious. We cannot indicate these relationships only by positive links, and signed networks have been becoming more and more universal in the study of social networks when community is being significant. In this paper, to identify communities in signed networks, we exploit a new greedy algorithm, taking signs and the density of these links into account. The main idea of the algorithm is the initial procedure of signed modularity and the corresponding update rules. Specially, we employ the 'Asymmetric and Constrained Belief Evolution' procedure to evaluate the optimal number of communities. According to the experimental results, the algorithm is proved to be able to run well. More specifically, the proposed algorithm is very efficient for these networks with medium size, both dense and sparse. [ABSTRACT FROM AUTHOR]

Given a source node and a node set, the node-to-set disjoint paths problem is to find disjoint paths from the source node to all nodes from the node set. In this paper, we propose an improved algorithm for the node-to-set disjoint paths problem on bi-rotator graphs. Our result improves the complexity of previous result from to , where n is the dimension of the input graph. [ABSTRACT FROM AUTHOR]

Motion estimation (ME) accounts for the major part of computational complexity of any video coding standard. The diamond search (DS) algorithm is widely used as a fast search technique to perform motion estimation. In this paper, a novel architecture for the diamond search technique is proposed that efficiently handles memory addressing and reduces hardware complexity. The proposed architecture meets the speed requirements for real-time video processing without compromising the area. The design when implemented in Verilog HDL on Virtex-5 technology and synthesized using Xilinx ISE Design Suite 12.4, gives rise to a critical path delay of 3.25 ns and the equivalent area is calculated to be 3.5K gate equivalent. Working at a frequency of 308 MHz, the proposed design can process 128 CIF frames per second. So, the proposed architecture can be incorporated in a video codec targeted for commercial devices like smart-phones, camcorders and video conferencing system. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic bit frame (window) selection algorithm to address the mismatch in the number of bits in the output of a digital signal processing and the bit range size of the input of the following block. The algorithm is designed to be a flexible tool to tackle general, possibly highly varying input signals in such a way that maintains the maximum signal power without introducing significant data loss. Numerical experiments are presented in the end of the paper to illustrate the behavior of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Chaotic Ant Swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in search space. However, it often loses its effectiveness and advantages when applied to large and complex problems, e.g. those with high dimensions. To resolve the problems of high computational complexity and low solution accuracy existing in CAS, we propose a Disturbance Chaotic Ant Swarm (DCAS) algorithm to significantly improve the performance of the original algorithm. The aim of this paper is achieved by three strategies which include modifying the method of updating ant's best position, neighbor selection method and establishing a self-adaptive disturbance strategy. The global convergence of the DCAS algorithm is proved in this paper. Extensive computational simulations and comparisons are carried out to validate the performance of the DCAS on two sets of benchmark functions with up to 1000 dimensions. The results show clearly that DCAS substantially enhances the performance of the CAS paradigm in terms of computational complexity, global optimality, solution accuracy and algorithm reliability for complex high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

In this paper we consider the computational complexity of deciding the existence of a perfect matching in certain classes of dense k-uniform hypergraphs. It has been known that the perfect matching problem for the classes of hypergraphs H with minimum ((k - 1)-wise) vertex degreeδ(H) at least c|V(H)| is NP-complete for $c < \frac{1}{k}$ and trivial for c ≥ ½, leaving the status of the problem with c in the interval $[\frac{1}{k}, \frac{1}{2})$ widely open. In this paper we show, somehow surprisingly, that ½ is not the threshold for tractability of the perfect matching problem, and prove the existence of an ε > 0 such that the perfect matching problem for the class of hypergraphs H with δ(H) ≥ (½ - ε)|V(H)| is solvable in polynomial time. This seems to be the first polynomial time algorithm for the perfect matching problem on hypergraphs for which the existence problem is nontrivial. In addition, we consider parallel complexity of the problem, which could be also of independent interest. [ABSTRACT FROM AUTHOR]

This paper deals with the use of a minimal model for performing secure computations. The communication is based on a protocol which makes use of disjoint function decomposition and more precisely of minimal ESCT (Exclusive-or Sum of Complex Terms) expressions in order to perform a secure computation. The complexity of this protocol is directly proportional to the size of the ESCT expression in use, which is much smaller in comparison to other proposed minimal models (e.g., ESOP). Moreover, quantum algorithms are discussed that provide significant speedup to the process of producing the ESCT expressions, when compared to conventional ones. Hence, this paper provides a very useful application of the ESCT expressions in the field of cryptographic protocols. [ABSTRACT FROM AUTHOR]

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

We present a self-directed method for image segmentation using a modified top-down region dividing (TDRD) approach. The TDRD-based image segmentation method solves some of the issues with histogram and region growing-based segmentation techniques. The process is efficient and achieves proper results without over segmentation or spatial-structure destruction. In this paper, we examine seven user-defined parameters of the method. These parameters are converted from human inputs to values derived from in-class information created by the algorithm allowing for autonomous image segmentation, without the need of human input or feedback. Our new autonomous implementation also reduces the computational complexity of the algorithm. This reduction will produce significant savings for the total number of computations the algorithm needs to perform image segmentation. Experimental results show that the images using these new derived values yield superior results as compared to other methods, including the original TDRD method. We compare our results visually and numerically based on the within-class standard deviation (WCSD) and the number of connected components (NCC). [ABSTRACT FROM AUTHOR]

Studying blocks behavior during their lifetime in cache can provide useful information to reduce the miss rate and therefore improve processor performance. According to this rationale, the peLIFO replacement algorithm [M. Chaudhuri, Proc. Micro'09, New York, 12-16 December, 2009, pp. 401-412], which learns dynamically the number of cache ways required to satisfy short-term reuses preserving the remaining ways for long-term reuses, has been recently proposed. In this paper, we propose several changes to the original peLIFO policy in order to reduce the implementation complexity involved, and we extend the algorithm to a shared-cache environment considering dynamic information about threads behavior to improve cache efficiency. Experimental results confirm that our simplification techniques reduce the required hardware with a negligible performance penalty, while the best of our thread-aware extension proposals reduces average CPI by 8.7% and 15.2% on average compared to the original peLIFO and LRU respectively for a set of 43 multi-programmed workloads on an 8 MB 16-way set associative shared L2 cache. [ABSTRACT FROM AUTHOR]

Ramanujan Sums (RS) have been found to be very successful in signal processing recently. However, as far as we know, the RS have not been applied to image analysis. In this paper, we propose two novel algorithms for image analysis, including moment invariants and pattern recognition. Our algorithms are invariant to the translation, rotation and scaling of the 2D shapes. The RS are robust to Gaussian white noise and occlusion as well. Our algorithms compare favourably to the dual-tree complex wavelet (DTCWT) moments and the Zernike's moments in terms of correct classification rates for three well-known shape datasets. [ABSTRACT FROM AUTHOR]

Partially observable Markov decision processes (POMDPs) are powerful for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model the real-life situation precisely, due to various reasons such as limited data for learning the model, inability of exact POMDPs to model dynamic situations, etc. In this paper, assuming that the parameters of POMDPs are imprecise but bounded, we formulate the framework of bounded-parameter partially observable Markov decision processes (BPOMDPs). A modified value iteration is proposed as a basic strategy for tackling parameter imprecision in BPOMDPs. In addition, we design the UL-based value iteration algorithm, in which each value backup is based on two sets of vectors called U-set and L-set. We propose four strategies for computing U-set and L-set. We analyze theoretically the computational complexity and the reward loss of the algorithm. The effectiveness and robustness of the algorithm are shown empirically. [ABSTRACT FROM AUTHOR]

The random (2 + p)-SAT model has been proposed [18] to study the possible relation between the 'order' of phase transitions and computational complexity. It was also claimed that there exists pc > 0, such that for p < pc the random (2 + p)-SAT instance behaves like 2-SAT. Later, Achlioptas et al. [3] obtained the first rigorous results that 0.4 ≤ pc ≤ 0.695, the methods they use are the first moment method and the simple Unit-Clause algorithm. In this paper, we try to optimize the local maximality condition of the truth assignments when implementing the first moment method. We prove that the phase transition point of clauses-to-variables ratio r (dependent on p) can be improved. Moreover, we show that the upper bound of pc can be reduced to 0.6846. This fact implies that, for a constant λ < 1, a random (2 + p)-SAT formula with λn 2-clauses and 2.17 n 3-clauses is almost surely unsatisfiable. [ABSTRACT FROM AUTHOR]

This paper examines the computational complexity of determining whether or not an algebra satisfies a certain Mal'Cev condition. First, we define a class of Mal'Cev conditions whose satisfaction can be determined in polynomial time (special cube term satisfying the DCP) when the algebra in question is idempotent and provide an algorithm through which this determination may be made. The aforementioned class notably includes near unanimity terms and edge terms of fixed arity. Second, we define a different class of Mal'Cev conditions whose satisfaction, in general, requires exponential time to determine (Mal'Cev conditions satisfiable by CPB0 operations). [ABSTRACT FROM AUTHOR]

In this paper, an efficient multi-scale image disparity estimation algorithm is proposed which estimates the local displacements needed to align different regions between a pair of images. This phase-based approach is based on fractional dual-tree complex wavelet transform (FrDTCWT). In the proposed FrDTCWT, initially we obtained the fractional Fourier transform of the image. We decomposed the fractional transformed image by dual-tree complex wavelet transform into real and imaginary parts. The complex analytic signal is obtained from the real-valued function which is obtained from this FrDTCWT. The FRDTCWT inherits the excellent mathematical properties of DTCWT and FrFT. Phase and amplitude of the image are computed from the complex analytic signal. The disparity is estimated as the optical flow using the phase difference method. The efficiency of the proposed algorithm is carried out by different experiments on synthetic as well as on realistic image sequences. [ABSTRACT FROM AUTHOR]

The paper sets forth an improved edge-directed image interpolation algorithm with low time complexity. The algorithm partitions images into homogeneous and edge areas by setting the preset threshold value based on the local structure characteristics. Specified algorithms are assigned to interpolate each classified areas respectively. The proposed method implements strategy in three steps to interpolate after setting the preset threshold value. In this way, it can achieve the goals of real-time interpolation and good subjective quality. Furthermore, the interpolated images have much more explicit edge regions and better visual effects using our proposed method than that of using other algorithms. Experimental results demonstrate that the method proposed by the authors is high-performed in image interpolation. [ABSTRACT FROM AUTHOR]

Algorithmic combinatorics on partial words, or sequences of symbols over a finite alphabet that may have some do-not-know symbols or holes, has been developing in the past few years. Applications can be found, for instance, in molecular biology for the sequencing and analysis of DNA, in bio-inspired computing where partial words have been considered for identifying good encodings for DNA computations, and in data compression. In this paper, we focus on two areas of algorithmic combinatorics on partial words, namely, pattern avoidance and subword complexity. We discuss recent contributions as well as a number of open problems. In relation to pattern avoidance, we classify all binary patterns with respect to partial word avoidability, we classify all unary patterns with respect to hole sparsity, and we discuss avoiding abelian powers in partial words. In relation to subword complexity, we generate and count minimal Sturmian partial words, we construct de Bruijn partial words, and we construct partial words with subword complexities not achievable by full words (those without holes). [ABSTRACT FROM AUTHOR]

Let (W, Σ) be a finite Coxeter system, and θ an involution such that θ (Δ) = Δ, where Δ is a basis for the root system Φ associated with W, and $\mathcal{I}_{\theta}=\{w\in W\,|\,\theta(w)=w^{-1}\}$ the set of θ-twisted involutions in W. The elements of $\mathcal{I}_{\theta}$ can be characterized by sequences in Σ which induce an ordering called the Richardson-Spinger Bruhat poset. The main algorithm of this paper computes this poset. Algorithms for finding conjugacy classes, the closure of an element and special cases are also given. A basic analysis of the complexity of the main algorithm and its variations is discussed, as well experience with implementation. [ABSTRACT FROM AUTHOR]

This paper presents a data estimation scheme for wide band multichannel charge sampling filter bank receivers together with a complete system calibration algorithm based on the least mean squared (LMS) algorithm. A unified model has been defined for the receiver containing all first order mismatches, offsets, imperfections, and the LMS algorithm is employed to track these errors. The performance of this technique under noisy channel conditions has been verified. Moreover, a detailed complexity analysis of the calibration algorithm is provided which shows that sinc filter banks have much lower complexity than traditional continuous-time filter banks. [ABSTRACT FROM AUTHOR]

In this paper, we study one dimensional finite linear cellular automata with reflective boundary condition by using matrix algebra built on the field ℤp. We present an algorithm for determining the reversibility of this family of cellular automata. We also answer the reversibility question for some special subfamilies. Finally, we present some examples of this family of cellular automata under the reflective boundary condition. [ABSTRACT FROM AUTHOR]

The hairpin completion is an operation on formal languages which is inspired by the hairpin formation in biochemistry. Hairpin formations occur naturally within DNA-computing. It has been known that the hairpin completion of a regular language is linear context-free, but not regular, in general. However, for some time it is was open whether the regularity of the hairpin completion of a regular language is decidable. In 2009 this decidability problem has been solved positively in [5] by providing a polynomial time algorithm. In this paper we improve the complexity bound by showing that the decision problem is actually NL-complete. This complexity bound holds for both, the one-sided and the two-sided hairpin completions. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of automata algorithms to machine learning. It introduces the first optimization solution for support vector machines used with sequence kernels that is purely based on weighted automata and transducer algorithms, without requiring any specific solver. The algorithms presented apply to a family of kernels covering all those commonly used in text and speech processing or computational biology. We show that these algorithms have significantly better computational complexity than previous ones and report the results of large-scale experiments demonstrating a dramatic reduction of the training time, typically by several orders of magnitude. [ABSTRACT FROM AUTHOR]

A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new cardinality constraint: Ordered Distribute. Given a set of variables, this constraint limits for each value v the number of times v or any value greater than v is taken. It extends the global cardinality constraint, that constrains only the number of times a value v is taken by a set of variables and does not consider at the same time the occurrences of all the values greater than v. We design an algorithm for achieving generalized arc-consistency on Ordered Distribute, with a time complexity linear in the sum of the number of variables and the number of values in the union of their domains. In addition, we give some experiments showing the advantage of this new constraint for problems where values represent levels whose overrunning has to be under control. Finally, we present three extensions of our constraint that can be particularly useful in practice. [ABSTRACT FROM AUTHOR]

In this paper, we obtain an efficient separability criterion for bipartite quantum pure state systems, which is based on the two-order minors of the coefficient matrix corresponding to quantum state. Then, we generalize this criterion to multipartite arbitrarily dimensional pure states. Our criterion is directly built upon coefficient matrices, but not density matrices or observables, so it has the advantage of being computed easily. Indeed, to judge separability for an arbitrary n-partite pure state in a d-dimensional Hilbert space, it only needs at most O(d) times operations of multiplication and comparison. Our criterion can be extended to mixed states. Compared with Yu's criteria, our methods are faster, and can be applied to any quantum state. [ABSTRACT FROM AUTHOR]

Many problems in pattern classification and feature extraction involve dimensionality reduction as a necessary processing. Traditional manifold learning algorithms, such as ISOMAP, LLE, and Laplacian Eigenmap, seek the low-dimensional manifold in an unsupervised way, while the local discriminant analysis methods identify the underlying supervised submanifold structures. In addition, it has been well-known that the intraclass null subspace contains the most discriminative information if the original data exist in a high-dimensional space. In this paper, we seek for the local null space in accordance with the null space LDA (NLDA) approach and reveal that its computational expense mainly depends on the quantity of connected edges in graphs, which may be still unacceptable if a great deal of samples are involved. To address this limitation, an improved local null space algorithm is proposed to employ the penalty subspace to approximate the local discriminant subspace. Compared with the traditional approach, the proposed method can achieve more efficiency so that the overload problem is avoided, while slight discriminant power is lost theoretically. A comparative study on classification shows that the performance of the approximative algorithm is quite close to the genuine one. [ABSTRACT FROM AUTHOR]

Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices defined based on the network structure. Empirical results on the co-purchase network of political books show that the similarity-based method can, to some extent, overcome these two difficulties and give higher accurate classification than the relational neighbors method, especially when the labeled nodes are sparse. Furthermore, we find that when the information of known labeled nodes is sufficient, the indices considering only local information can perform as good as those global indices while having much lower computational complexity. [ABSTRACT FROM AUTHOR]

Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyper-spherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition and Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes the noisy data in the second phase. In the third phase, the normal subclusters are continuously merged to form the larger clusters based on the inter-cluster distance and intra-cluster distance criteria. From the experimental results, the SPSM algorithm is very efficient to handle the noisy data set, and to cluster the data sets of arbitrary shapes of different density. Several examples for color image show the versatility of the proposed method and compare with results described in the literature for the same images. The computational complexity of the SPSM algorithm is O(N2), where N is the number of data points. [ABSTRACT FROM AUTHOR]

Evolutionary prototype selection has shown its effectiveness in the past in the prototype selection domain. It improves in most of the cases the results offered by classical prototype selection algorithms but its computational cost is expensive. In this paper, we analyze the behavior of the evolutionary prototype selection strategy, considering a complexity measure for classification problems based on overlapping. In addition, we have analyzed different k values for the nearest neighbour classifier in this domain of study to see its influence on the results of PS methods. The objective consists of predicting when the evolutionary prototype selection is effective for a particular problem, based on this overlapping measure. [ABSTRACT FROM AUTHOR]

Let L ≥ 1, ε > 0 be real numbers, (M, d) be a finite metric space and (N, ρ) be a metric space. A query to a metric space consists of a pair of points and asks for the distance between these points. We study the number of queries to metric spaces (M, d) and (N, ρ) needed to decide whether (M, d) is L-bilipschitz embeddable into (N, ρ) or ∊-far from being L-bilipschitz embeddable into N, ρ). When (M, d) is ∊-far from being L-bilipschitz embeddable into (N, ρ), we allow an o(1) probability of error (i.e., returning the wrong answer "L-bilipschitz embeddable"). However, no error is allowed when (M, d) is L-bilipschitz embeddable into (N, ρ). That is, algorithms with only one-sided errors are studied in this paper. When |M| ≤ |N| are both finite, we give an upper bound of $O(\sqrt{\frac{\ln |N|}{\epsilon |M|}}\, (|M|^2+|N|^2))$ on the number of queries for determining with one-sided error whether (M, d) is L-bilipschitz embeddable into (N, ρ) or ∊-far from being L-bilipschitz embeddable into (N, ρ). For the special case of finite |M| = |N|, the above upper bound evaluates to $O(|N|^{3/2} \sqrt {{{\ln |N|} \over \epsilon }} )$. We also prove a lower bound of Ω(|N|3/2) for the special case when |M| = |N| are finite and L = 1, which coincides with testing isometry between finite metric spaces. For finite |M| = |N|, the upper and lower bounds thus match up to a multiplicative factor of at most $\sqrt{\frac{\ln |N|}{\epsilon}}$, which depends only sublogarithmically in |N|. We also investigate the case when (N, ρ) is not necessarily finite. Our results are based on techniques developed in an earlier work on testing graph isomorphism. [ABSTRACT FROM AUTHOR]

A convertible authenticated encryption scheme allows a designated receiver to retrieve an authenticated ciphertext and convert the authenticated ciphertext into an ordinary signature. The receiver can prove the dishonesty of the sender to anyone if the sender repudiates his/her signature. Recently, many researchers have proposed convertible authenticated encryption schemes based on cryptological algorithms. In this paper, the authors shall present a new convertible authenticated encryption scheme based on the ElGamal cryptosystem. The proposed scheme is more efficient than Wu-Hsu's scheme in terms of computational complexity. [ABSTRACT FROM AUTHOR]

This paper studies the complexity of determining if a finite algebra generates a variety that satisfies various Maltsev conditions, such as congruence distributivity or modularity. For idempotent algebras we show that there are polynomial time algorithms to test for these conditions but that in general these problems are EXPTIME complete. In addition, we provide sharp bounds in terms of the size of two-generated free algebras on the number of terms needed to witness various Maltsev conditions, such as congruence distributivity. [ABSTRACT FROM AUTHOR]

Recently, several wavelet-based algorithms have been proposed for feature extraction in non-stationary signals such as ECG. These methods, however, have mainly used general purpose (unmatched) wavelet bases such as Daubechies and Quadratic Spline. In this paper, five new matched wavelet bases, with minimum approximation error and maximum coding gain criteria, are designed and applied to ECG signal analysis. To study the effect of using different wavelet bases for this application, two different wavelet-based R peak detection algorithms are implemented: (1) a conventional wavelet-based method; and (2) a modified wavelet-based R peak detection algorithm. Both algorithms are evaluated using the MIT-BIH Arrhythmia database. Experimental results show lower computational complexity (up to 76%) of the proposed R peak detection method compared to the conventional method. They also show considerable decrease in the number of failed detections (up to 55%) for both the conventional and the proposed algorithms when using matched wavelets instead of Quadratic Spline wavelet which, according to the literature, has generated the best detection results among all conventional wavelet bases studied previously for ECG signal analysis. [ABSTRACT FROM AUTHOR]