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Graph query autocompletion (gQAC) generates a small list of ranked query suggestions during the graph query formulation process in a visual environment. The current state-of-the-art of gQAC provides suggestions that are formed by adding subgraph increments to arbitrary places of an existing (partial) user query. However, according to the research results on human-computer interaction (HCI), humans can only interact with a small number of recent software artifacts in hand. Hence, many of such suggestions could be irrelevant. In this paper, we present the GFocus framework that exploits a novel notion of user focus of graph query formulation (or simply focus). Intuitively, the focus is the subgraph that a user is working on. We formulate locality principles inspired by the HCI research to automatically identify and maintain the focus. We propose novel monotone submodular ranking functions for generating popular and comprehensive query suggestions only at the focus. In particular, the query suggestions of GFocus have high result counts (when they are used as queries) and maximally cover the possible suggestions at the focus. We propose efficient algorithms and an index for ranking the suggestions. Our results show that GFocus saves 12-32 percent more mouse clicks and is 35× more efficient than the state-of-the-art competitor. [ABSTRACT FROM AUTHOR]

Reed-Muller (RM) codes are among the oldest, simplest and perhaps most ubiquitous family of codes. They are used in many areas of coding theory in both electrical engineering and computer science. Yet, many of their important properties are still under investigation. This paper covers some of the recent developments regarding the weight enumerator and the capacity-achieving properties of RM codes, as well as some of the algorithmic developments. In particular, the paper discusses the recent connections established between RM codes, thresholds of Boolean functions, polarization theory, hypercontractivity, and the techniques of approximating low weight codewords using lower degree polynomials (when codewords are viewed as evaluation vectors of degree r polynomials in m variables). It then overviews some of the algorithms for decoding RM codes. It covers both algorithms with provable performance guarantees for every block length, as well as algorithms with state-of-the-art performances in practical regimes, which do not perform as well for large block length. Finally, the paper concludes with a few open problems. [ABSTRACT FROM AUTHOR]

In modern SoCs embedded memories should be protected by ECC against field failures to achieve acceptable reliability. They should also be repaired after fabrication to achieve acceptable fabrication yield. In technologies affected by high defect densities, conventional repair induces very high costs. To reduce it, we can use ECC-based repair, consisting in using the ECC for fixing words comprising a single faulty cell and self-repair to fix all other faulty words. However, as we show in this paper, for high defect densities the diagnosis circuitry required for ECC-based repair may induce very large hardware cost. To fix this issue, we introduce a new family of memory test algorithms that exhibit a property we termed as “single-read double-fault detection”. This approach gains interest in ultimate CMOS and post-CMOS technologies, where the defect densities are expected to increase significantly, and/or in very-low power design, as very-low voltage sharply increases defect densities. [ABSTRACT FROM PUBLISHER]

Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA, based on the Karhunen-Loéve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ and the latest synthetic SISR quality dataset (i.e., QADS) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics. The dataset and the code will be made available at https://github.com/Zhentao-Liu/RealSRQ-KLTSRQA. [ABSTRACT FROM AUTHOR]

To save energy and alleviate interference, connected dominating set (CDS) was proposed to serve as a virtual backbone of wireless sensor networks (WSNs). Because sensor nodes may fail due to accidental damages or energy depletion, it is desirable to construct a fault tolerant virtual backbone with high redundancy in both coverage and connectivity. This can be modeled as a $k$ -connected $m$ -fold dominating set (abbreviated as $(k,m)$ -CDS) problem. A node set $C\subseteq V(G)$ is a $(k,m)$ -CDS of graph $G$ if every node in $V(G)\backslash C$ is adjacent with at least $m$ nodes in $C$ and the subgraph of $G$ induced by $C$ is $k$ -connected. Constant approximation algorithm is known for $(3,m)$ -CDS in unit disk graph, which models homogeneous WSNs. In this paper, we present the first performance guaranteed approximation algorithm for $(3,m)$ -CDS in a heterogeneous WSN. In fact, our performance ratio is valid for any topology. The performance ratio is at most $\gamma $ , where $\gamma =\alpha +8+2\ln (2\alpha -6)$ for $\alpha \geq 4$ and $\gamma =3\alpha +2\ln 2$ for $\alpha <4$ , and $\alpha $ is the performance ratio for the minimum $(2,m)$ -CDS problem. Using currently best known value of $\alpha $ , the performance ratio is $\ln \delta +o(\ln \delta )$ , where $\delta $ is the maximum degree of the graph, which is asymptotically best possible in view of the non-approximability of the problem. Applying our algorithm on a unit disk graph, the performance ratio is less than 27, improving previous ratio 62.3 by a large amount for the $(3,m)$ -CDS problem on a unit disk graph. [ABSTRACT FROM PUBLISHER]

Set queries are fundamental operations in computer networks. This paper addresses the fundamental problem of designing a probabilistic data structure that can quickly process set queries using a small amount of memory. We propose a shifting bloom filter (ShBF) framework for representing and querying sets. We demonstrate the effectiveness of ShBF using three types of popular set queries: membership, association, and multiplicity queries. The key novelty of ShBF is on encoding the auxiliary information of a set element in a location offset. In contrast, prior BF-based set data structures allocate additional memory to store auxiliary information. We further extend our shifting framework from BF-based data structures to sketch-based data structures, which are widely used to store multiplicities of items. We conducted experiments using real-world network traces, and results show that ShBF significantly advances the state-of-the-art on all three types of set queries. [ABSTRACT FROM PUBLISHER]

In many applications, top-k dominating query is an important operation to return k tuples with the highest domination scores in a potentially huge data space. It is analyzed that the existing algorithms have their performance problems when performed on massive data. This paper proposes a novel table-scan-based TDTS algorithm to efficiently compute top-k dominating results. TDTS first presorts the table for early termination. The early termination checking is proposed in this paper, along with the theoretical analysis of scan depth. The pruning operation for tuples is devised in this paper. The theoretical pruning effect shows that the number of tuples maintained in TDTS can be reduced substantially. The extensive experimental results, conducted on synthetic and real-life data sets, show that TDTS outperforms the existing algorithms significantly. [ABSTRACT FROM PUBLISHER]

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$ d c , which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index. [ABSTRACT FROM AUTHOR]

Recent advances in noiseless non-adaptive group testing have led to a precise asymptotic characterization of the number of tests required for high-probability recovery in the sublinear regime $k = n^{\theta }$ (with $\theta \in (0,1)$), with $n$ individuals among which $k$ are infected. However, the required number of tests may increase substantially under real-world practical constraints, notably including bounds on the maximum number $\Delta $ of tests an individual can be placed in, or the maximum number $\Gamma $ of individuals in a given test. While previous works have given recovery guarantees for these settings, significant gaps remain between the achievability and converse bounds. In this paper, we substantially or completely close several of the most prominent gaps. In the case of $\Delta $ -divisible items, we show that the definite defectives (DD) algorithm coupled with a random regular design is asymptotically optimal in dense scaling regimes, and optimal to within a factor of e more generally; we establish this by strengthening both the best known achievability and converse bounds. In the case of $\Gamma $ -sized tests, we provide a comprehensive analysis of the regime $\Gamma = \Theta (1)$ , and again establish a precise threshold proving the asymptotic optimality of SCOMP (a slight refinement of DD) equipped with a tailored pooling scheme. Finally, for each of these two settings, we provide near-optimal adaptive algorithms based on sequential splitting, and provably demonstrate gaps between the performance of optimal adaptive and non-adaptive algorithms. [ABSTRACT FROM AUTHOR]

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed. [ABSTRACT FROM AUTHOR]

The simplex growing algorithm (SGA) has been widely used for finding endmembers. It can be considered as a sequential version of the well-known endmember finding algorithm, N-finder algorithm (N-FINDR), which finds endmembers one at a time by growing simplexes. However, one of the major hurdles for N-FINDR and SGA is the calculation of simplex volume (SV) which poses a great challenge in designing any algorithm using SV as a criterion for finding endmembers. This paper develops an orthogonal projection (OP)-based SGA (OP-SGA) which essentially resolves this computational issue. It converts the issue of calculating SV to calculating the OP on previously found simplexes without computing matrix determinants. Most importantly, a recursive Kalman filter-like OP-SGA, to be called recursive OP-SGA (ROP-SGA), can be also derived to ease computation. By virtue of ROP-SGA, several advantages and benefits in computational savings and hardware implementation can be gained for which N-FINDR and SGA do not have. [ABSTRACT FROM AUTHOR]

Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error/MSEI together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when λ = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overtitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter λ in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. [ABSTRACT FROM AUTHOR]

This paper presents two symbolic model checking algorithms for the verification of analog/mixed-signal circuits. The first model checker utilizes binary decision diagrams while the second is a bounded model checker that uses a satisfiability modulo theory solver. Both methods have been implemented, and preliminary results are promising. [ABSTRACT FROM AUTHOR]

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in RAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of RAM. Some revised encoding schemes, such as small variance learning for RAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both, pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function. [ABSTRACT FROM AUTHOR]

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there has been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik–Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this article, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of the computation of square roots over finite extension fields. We propose two novel algorithms for computing square roots over even field extensions of the form \BBF{q^2}, with q = p^n, p an odd prime and n \geq 1. Both algorithms have an associate computational cost roughly equivalent to one exponentiation in \BBF{q^2}. The first algorithm is devoted to the case when q \equiv 1\, mod\, 4, whereas the second one handles the case when q \equiv 3\, mod\,4. Numerical comparisons show that the two algorithms presented in this paper are competitive and in some cases more efficient than the square root methods previously known. [ABSTRACT FROM AUTHOR]

Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors. With respect to efficiency, DG2 reuses the sample points that are generated for detecting interactions and saves up to half of the computational resources on fully separable functions. We mathematically show that the new sampling technique achieves the lower bound with respect to the number of function evaluations. Unlike its predecessor, DG2 checks all possible pairs of variables for interactions and has the capacity to identify overlapping components of an objective function. On the accuracy aspect, DG2 outperforms the state-of-the-art decomposition methods on the latest large-scale continuous optimization benchmark suites. DG2 also performs reliably in the presence of imbalance among contribution of components in an objective function. Another major advantage of DG2 is the automatic calculation of its threshold parameter ( $\epsilon $ ), which makes it parameter-free. Finally, the experimental results show that when DG2 is used within a cooperative co-evolutionary framework, it can generate competitive results as compared to several state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency. [ABSTRACT FROM AUTHOR]

In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately $k$ -sparse vector $x \in \mathbb {C} ^{n}$ given access to $y= |\Phi x|$ , where $|v|$ denotes the vector obtained from taking the absolute value of $v\in \mathbb {C} ^{n}$ coordinate-wise. In this paper we present sublinear-time algorithms for a few for-each variants of the compressive phase retrieval problem which are akin to the variants considered for the classical compressive sensing problem in theoretical computer science. Our algorithms use pure combinatorial techniques and near-optimal number of measurements. [ABSTRACT FROM AUTHOR]

The paper proposes an improved difference-of-syndromes decoding algorithm for decoding quadratic residue codes up to half the minimum distance. Therein, soft-decision information is not essential, but it can be used to speed up the computations. The improvement over the prior art is demonstrated by simulations. Moreover, the proposed algorithm is also compared with the classic Berlekamp-Massey algorithm by taking two BCH codes as examples. [ABSTRACT FROM AUTHOR]

Dr. Wenjing Lou is currently the W. C. English Endowed Professor of Computer Science at Virginia Tech and a Fellow of the IEEE. Her research interests span many topics in the fields of wireless networks and cybersecurity. On wireless networks, her interests lie in distributed algorithms and lightweight protocols for wireless networks. Her previous works include algorithm and protocol design for ad hoc networks, multihop cooperative communications, opportunistic routing, and network coding. She has recently collaborated with other colleagues on interference cancellation, MIMO networks, and dynamic spectrum sharing, and 5G wireless communications. On cybersecurity, she has worked on many security and privacy topics in networked information systems, including anonymous authentication, encryption-based information access control and sharing, search over encrypted data, and confidential data computation. More recently, she has been working on block-chain and decentralized consensus protocols, securing 5G/nextG mobile networks, and adversarial machine learning and trustworthy AI systems. Her research goal is to develop innovative solutions to address fundamental research challenges in emerging technologies and critical infrastructures. [ABSTRACT FROM AUTHOR]

Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes $s$ s and $t$ t , and an area $\mathcal {A}$ A on road network graph $\mathcal {G}$ G , is there a “good” route (e.g., short enough path) $P$ P from $s$ s to $t$ t that crosses $\mathcal {A}$ A in $\mathcal {G}$ G ? In a taxi-sharing system, $s$ s and $t$ t can be a taxi's current and destined locations, and $\mathcal {A}$ A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a $\rho$ ρ -route, which is an $s$ s - $t$ t path that passes $\mathcal {A}$ A , with a length of at most $(1+\rho)$ (1 + ρ) times the shortest distance between $s$ s and $t$ t . The existence of a $\rho$ ρ -route implies that a service or task located at $\mathcal {A}$ A can be found for a given moving object $m$ m , and that $m$ m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms. [ABSTRACT FROM AUTHOR]

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$ -learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion. [ABSTRACT FROM AUTHOR]

This paper introduces m -inductiveness over a set of nodes S in sequential circuits. The m -inductive property can be used for equivalence-checking or improved sequential optimization. It allows the behavior of many next state functions (not in S ) to be changed while maintaining correctness at the primary outputs of a circuit. As such, it creates flexibility that can be used for sequential optimization. It is shown that the number of nodes in S is reduced as m, the parameter for the inductiveness, increases. We provide an algorithm for finding a minimal set S , as well as one for using m -inductiveness in optimization. We give examples of such optimized circuits and show that m -inductive-based optimization can result in significant area reduction when applied to industrial designs. [ABSTRACT FROM AUTHOR]

This paper presents a video retargeting method considering both spatial and temporal coherence for resizing videos. Our algorithm is based on a novel matching-area-based temporal energy adjustment that allows per-frame seam carving to remove the optimal pixels to achieve spatially and temporally continuous resized videos. The temporal energy adjustment allows the seam to track the object it previously carved, and avoid carving the seam on different objects in two consecutive frames to achieve both spatial and temporal coherence. Our method outperforms other state-of-the-art retargeting systems, as demonstrated in the results and widely supported by the conducted user study. [ABSTRACT FROM AUTHOR]

A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated. [ABSTRACT FROM AUTHOR]

Manifold learning is a hot topic in the field of computer science, particularly since nonlinear dimensionality reduction based on manifold learning was proposed in Science in 2000. The work has achieved great success. The main purpose of current manifold-learning approaches is to search for independent intrinsic variables underlying high dimensional inputs which lie on a low dimensional manifold. In this paper, a new manifold is built up in the training step of the process, on which the input training samples are set to be close to each other if the values of their intrinsic variables are close to each other. Then, the process of dimensionality reduction is transformed into a procedure of preserving the continuity of the intrinsic variables. By utilizing the new manifold, the dynamic tracking of a human who can move and rotate freely is achieved. From the theoretical point of view, it is the first approach to transfer the manifold-learning framework to dynamic tracking. From the application point of view, a new and low dimensional feature for visual tracking is obtained and successfully applied to the real-time tracking of a free-moving object from a dynamic vision system. Experimental results from a dynamic tracking system which is mounted on a dynamic robot validate the effectiveness of the new algorithm. [ABSTRACT FROM AUTHOR]

By the ordered tree inclusion we will check whether a pattern tree P can be included in a target tree T, where the order of siblings in both P and T matters. This problem has many applications in practice, such as retrieval of documents, data mining, and RNA structure matching. In this paper, we propose an efficient algorithm for this problem. Its time complexity is bounded by ${\text{O}(\vert }T\vert \cdot \;min\{h_{P}{,\;\vert \text{leaves(}}P)\vert \})$O(|T|·min{hP,|leaves(P)|}), with ${\text{O}(\vert }T\vert + \vert P\vert)$O(|T|+|P|) space being used, where hP (hT) represents the height of P (resp., T) and leaves (P) stands for the set of the leaves of P. Up to now the best algorithm for this problem needs $\Theta (\vert T\vert \cdot \vert leaves(P)\vert)$Θ(|T|·|leaves(P)|) time and ${\text{O}(\vert }P\vert + \vert T\vert)$O(|P|+|T|) space. Extensive experiments have been done, which show that the new algorithm can perform much better than the existing ones in practice. [ABSTRACT FROM AUTHOR]

This paper introduces a general framework for describing dynamic neural networks—the layered digital dynamic network (LDDN). This framework allows the development of two general algorithms for computing the gradients and Jacobians for these dynamic networks: backpropagation-through-time (BPTT) and real-time recurrent learning (RTRL). The structure of the LDDN framework enables an efficient implementation of both algorithms for arbitrary dynamic networks. This paper demonstrates that the BPTT algorithm is more efficient for gradient calculations, but the RTRL algorithm is more efficient for Jacobian calculations. [ABSTRACT FROM AUTHOR]

In dealing with large data sets, the reduced support vector machine (RSVM) was proposed for the practical objective to overcome some computational difficulties as well as to reduce the model complexity. In this paper, we study the RSVM from the viewpoint of sampling design, its robustness, and the spectral analysis of the reduced kernel. We consider the nonlinear separating surface as a mixture of kernels. Instead of a full model, the RSVM uses a reduced mixture with kernels sampled from certain candidate set. Our main results center on two major themes. One is the robustness of the random subset mixture model. The other is the spectral analysis of the reduced kernel. The robustness is judged by a few criteria as follows: 1) model variation measure; 2) model bias (deviation) between the reduced model and the full model; and 3) test power in distinguishing the reduced model from the full one. For the spectral analysis, we compare the eigenstructures of the full kernel matrix and the approximation kernel matrix. The approximation kernels are generated by uniform random subsets. The small discrepancies between them indicate that the approximation kernels can retain most of the relevant information for learning tasks in the full kernel. We focus on some statistical theory of the reduced set method mainly in the context of the RSVM. The use of a uniform random subset is not limited to the RSVM. This approach can act as a supplemental algorithm on top of a basic optimization algorithm, wherein the actual optimization takes place on the subset-approximated data. The statistical properties discussed in this paper are still valid. [ABSTRACT FROM AUTHOR]

As the demand for high-quality Internet increases, emerging network applications are spurring the need for faster, feature-rich, and cost-effective routers. Multifield packet classification in routers has been a computation-intensive data path function for software implementation. Therefore, solutions for packet classification based on hardware design, such as Ternary Content Addressable Memory (TCAM), are necessary to sustain gigabit line processing rate. Traditionally, TCAMs have been designed for storing prefixes. However, multifield packet classification usually involves two fields of arbitrary ranges that are TCP/IP layer 4 source and destination ports. Storing ranges in TCAMs relies on decomposing each individual range into multiple prefixes, which leads to range-to-prefix blowout. To reduce the total number of prefixes needed to represent all ranges, this paper proposes a 2-level TCAM architecture and two range-to-prefix conversion schemes. In the first proposed scheme, designed for disjoint ranges, the maximum number of entries needed in TCAM is 2m - 1 for m disjoint ranges. In the second proposed scheme, designed for contiguous ranges, only m TCAM entries are needed. In a general case of n arbitrary ranges, all ranges can first be converted into disjoint ranges or contiguous ranges and then the proposed algorithms can be applied. As a result, only 4n - 3 TCAM entries are needed for the disjoint ranges and only 2n + 1 TCAM entries are needed for contiguous ranges. This paper also proposes insertion and deletion algorithms to accommodate incremental changes to the range sets. The experiments made show that the proposed range-to-prefix conversion schemes perform better than the existing schemes in terms of the number of required TCAM entries and execution time for range update operations. [ABSTRACT FROM AUTHOR]

Most weak memory consistency models are incapable of supporting a solution to mutual exclusion using only read and write operations to shared variables. Processor Consistency-Goodman's version (PC-G) is an exception. Ahamad et al. showed that Peterson's mutual exclusion algorithm is correct for PC-G, but Lamport's bakery algorithm is not. This paper derives a lower bound on the number of and type of (single or multiwriter) variables that a mutual exclusion algorithm must use in order to be correct for PC-G. Specifically, any such solution for n processes must use at least one multiwriter variable and n single-writer variables. Peterson's algorithm for two processes uses one multiwriter and two single-writer variables, and therefore establishes that this bound is tight for two processes. This paper presents a new n-process algorithm for mutual exclusion that is correct for PC-G and achieves the bound for any n. While Peterson's algorithm is fair, this extension to arbitrary n is not fair. Six known algorithms that use the same number and type of variables are shown to fail to guarantee mutual exclusion when the memory consistency model is only PC-G, as opposed to the Sequential Consistency model for which they were designed. A corollary of our investigation is that, in contrast to Sequential Consistency, multiwriter variables cannot be implemented from single-writer variables in a PC-G system. [ABSTRACT FROM AUTHOR]

Input-queued switches are used extensively in the design of high-speed routers. As switch speeds and sizes increase, the design of the switch scheduler becomes a primary challenge, because the time interval for the matching computations needed for determining switch configurations becomes very small. Possible alternatives in scheduler design include increasing the scheduling interval by using envelopes [19], and using a frame-based scheduler that guarantees fixed rates between input-output pairs. However, both these alternatives have significant jitter drawbacks: the jitter increases with the envelope size in the first alternative, and previously-known methods do not guarantee tight jitter bounds in the second. In this paper, we propose a hybrid approach to switch scheduling. Traffic with tight jitter constraints is first scheduled using a frame-based scheduler that achieves low jitter bounds. Jitter-insensitive traffic is later scheduled using an envelope-based scheduler. The main contribution of this paper is a scheduler design for generating low-jitter schedules. The scheduler uses a rate matrix decomposition designed for low jitter and different from the minimum-bandwidth Birkhoff-Von Neumann (BV) decomposition. In addition to generating low-jitter schedules, this decomposition in the worst case yields fewer switch configuration matrices (O(n)) than the BV decomposition (O(n²)). and so requires far less high-speed switch memory. We develop an efficient algorithm for decomposing the rate matrix and for scheduling the permutation matrices. We prove that our low-jitter algorithm has an O(log n) factor bound on its bandwidth consumption in comparison to the minimum-bandwidth BV decomposition. Experimentally, we find that the bandwidth increase in practice is much lower than the theoretical bound. We also prove several related performance bounds for our scheduler. Finally, we propose a practical algorithm for bandwidth-guaranteed algorithm, and show how our findings could even be extended to systems with large tuning time. [ABSTRACT FROM AUTHOR]

This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF). The paper first introduces the concept of significance for the hidden neurons and then uses it in the learning algorithm to realize parsimonious networks. The growing and pruning strategy of GGAP-RBF is based on linking the required learning accuracy with the significance of the nearest or intentionally added new neuron. Significance of a neuron is a measure of the average information content of that neuron. The GGAP-RBF algorithm can be used for any arbitrary sampling density for training samples and is derived from a rigorous statistical point of view. Simulation re- suits for bench mark problems in the function approximation area show that the GGAP-RBF outperforms several other sequential learning algorithms in terms of learning speed, network size and generalization performance regardless of the sampling density function of the training data. [ABSTRACT FROM AUTHOR]

Systems subjected to imperfect fault-coverage may fail even prior to the exhaustion of spares due to uncovered component failures. This paper presents optimal cost-effective design policies for k-out-of-n:G subsystems subjected to imperfect fault-coverage. It is assumed that there exists a k-out-of-n: G subsystem in a nonseries-parallel system and, except for this subsystem, the redundancy configurations of all other subsystems are fixed. This paper also presents optimal design polices which maximize overall system reliability. As a special case, results are presented for k-out-of-n:G systems subjected to imperfect fault-coverage. Examples then demonstrate how to apply the main results of this paper to find the optimal configurations of all subsystems simultaneously. In this paper, we show that the optimal n which maximizes system reliability is always less than or equal to the it which maximizes the reliability of the subsystem itself. Similarly, if the failure cost is the same, then the optimal n which minimizes the average system cost is always less than or equal to the n which minimizes the average cost of the subsystem. It is also shown that if the subsystem being analyzed is in series with the rest of the system, then the optimal n which maximizes subsystem reliability can also maximize the system reliability. The computational procedure of the proposed algorithms is illustrated through the examples. [ABSTRACT FROM AUTHOR]

Wearable computing has recently gained much popularity as an ambitious vision for future personalized mobile systems. Its aim is intelligent, environment aware systems unobtrusively embedded into the mobile environments of their users. With the combination of complex processing requirements, the necessity of placing sensors and input/output modules at different locations on the user's body, and stringent limits on size, weight, and battery capacity, the design of such systems is an inherently challenging problem. This paper demonstrates how systematic design and quantitative analysis can be applied to wearable architectures. We first present a model that allows various factors influencing the design of a wearable system to be incorporated into formal cost metrics. In particular, we show how to consistently incorporate specific wearable factors such as device placement requirements, ergonomics, and dynamic workload profiles into the model. We then discuss how efficient estimation algorithms can be extended and applied to the evaluation of different architectures with respect to our cost metrics. Finally, we discuss quantitative results from a proof-of-concept case study showing the trade offs between different architectures for a given wearable scenario. Summarized, this paper demonstrates how the description and the design of wearable systems can be put on a systematic, formal basis allowing us to treat them similar as conventional embedded systems. [ABSTRACT FROM AUTHOR]

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its Insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, to overcome the above problems, we first propose two variants, FCMS1 and FCMS2, of FCM_S to aim at simplifying its computation and then extend them, including FCM_S, to corresponding robust kernelized versions KFCM_S, KFCMS1 and KFCMS2 by the kernel methods. Our main motives of using the kernel methods consist in: inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data; enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective. [ABSTRACT FROM AUTHOR]

Recent years have seen a proliferation of research in "Problems on Sequences" which has benefited from the interplay between information theory and computer science-each of these two fields has had an impact upon the other in providing design paradigms and in providing ways of obtaining performance bounds. Because of the continued expansion of this research interest, it was deemed to be an opportune time for this special issue, devoted to this interface. Analysis of algorithms, a field consists of powerful techniques for analyzing the asymptotic performance of algorithms on sequences. Complex analysis is a prime tool in the analysis of algorithms "toolbox."

The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GM N²), where G is the number of generations, M is the number of objectives, and N is the population size. The N² factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN log[supm-1] N), much faster than the O(GM N²) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(M N²) algorithms. Furthermore, the archives currently used in multiobjective EAs (usually based on lists) are slow and can be improved by using orthogonal range-searching data... [ABSTRACT FROM AUTHOR]

In this paper we study the problem of characterizing and exploiting the complete flexibility of a special logic architecture, called P-circuits, which realize a Boolean function by projecting it onto overlapping subsets given by a generalized Shannon decomposition. P-circuits are used to restructure logic by pushing some signals towards the outputs. The algorithms proposed so far for exploiting the structural flexibility of P-circuits do not guarantee to find the best implementation, because they cast the problem as the minimization of an incompletely specified function. Instead, here we show that to explore all solutions we must set up the problem as the minimization of a Boolean relation, because there are don’t care conditions that cannot be expressed by single cubes. Finally we report the results obtained using a minimizer of Boolean relations, which improve in a major way with respect to the previous literature. [ABSTRACT FROM PUBLISHER]

Tree comparison metrics are an important tool for the study of phylogenetic trees. Path-difference distances measure the dissimilarity between two phylogenetic trees (on the same set of taxa) by comparing their path-length vectors. Various norms can be applied to this distance. Three important examples are the $l_{1}\text{-},\;l_{2}\text{-}$l1-,l2-, and $l_{{\infty }}$l∞-norms. The previous best algorithms for computing path-difference distances all have $O(n^{2})$O(n2) running time. In this paper, we show how to compute the $l_{1}$l1-norm path-difference distance in $O(n\;{\log}^{2}\;n)$O(nlog2n) time and how to compute the $l_{2}$l2- and $l_{{\infty }}$l∞-norm path-difference distances in $O(n\;{\log}\;n)$O(nlogn) time. By extending the presented algorithms, we also show that the $l_{p}$lp-norm path-difference distance can be computed in $O(pn\;{\log}^{2}\;n)$O(pnlog2n) time for any positive integer $p$p. In addition, when the integer $p$p is even, we show that the distance can be computed in $O(p^{2}n\;{\log}\;n)$O(p2nlogn) time as well. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) promises unprecedentedly flexible network management but it is susceptible to forwarding faults. Such faults originate from data-plane rules with missing faults and priority faults. Yet existing fault detection ignores priority faults, because they are not discovered on commercial switches until recently. In this paper, we present RuleScope, a more comprehensive solution for inspecting SDN forwarding. RuleScope offers a series of accurate and efficient algorithms for detecting and troubleshooting rule faults. They inspect forwarding behavior using customized probe packets to exercise data-plane rules. The detection algorithm exposes not only missing faults but also priority faults and the troubleshooting algorithm uncover actual forwarding states of data-plane flow tables. Both of them help track real-time forwarding status and benefit reliable network monitoring. Furthermore, toward fast inspection of dynamic networks, we propose incremental algorithms for rapidly evolving network policies to amortize detection and troubleshooting overhead without sacrificing accuracy. Experiments with our prototype on the Ryu SDN controller and Pica8 P-3297 switch show that the RuleScope achieves accurate fault detection on 320-entry flow tables with a cost of 1500+ probe packets within 16 s. [ABSTRACT FROM AUTHOR]

Algorithms determine which calculations computers use to solve problems and are one of the central pillars of computer science. As algorithms improve, they enable scientists to tackle larger problems and explore new domains and new scientific techniques ,. Bold claims have been made about the pace of algorithmic progress. For example, the President’s Council of Advisors on Science and Technology (PCAST), a body of senior scientists that advise the U.S. President, wrote in 2010 that “in many areas, performance gains due to improvements in algorithms have vastly exceeded even the dramatic performance gains due to increased processor speed”. However, this conclusion was supported based on data from progress in linear solvers , which is just a single example. With no guarantee that linear solvers are representative of algorithms in general, it is unclear how broadly conclusions, such as PCAST’s, should be interpreted. Is progress faster in most algorithms? Just some? How much on average? [ABSTRACT FROM AUTHOR]

Network virtualization allows multiple heterogeneous virtual networks (VNs) to coexist on a shared infrastructure. Efficient mapping of virtual nodes and virtual links of a VN request onto substrate network resources, also known as the VN embedding problem, is the first step toward enabling such multiplicity. Since this problem is known to be \cal NP-hard, previous research focused on designing heuristic-based algorithms that had clear separation between the node mapping and the link mapping phases. In this paper, we present ViNEYard—a collection of VN embedding algorithms that leverage better coordination between the two phases. We formulate the VN embedding problem as a mixed integer program through substrate network augmentation. We then relax the integer constraints to obtain a linear program and devise two online VN embedding algorithms D-ViNE and R-ViNE using deterministic and randomized rounding techniques, respectively. We also present a generalized window-based VN embedding algorithm (WiNE) to evaluate the effect of lookahead on VN embedding. Our simulation experiments on a large mix of VN requests show that the proposed algorithms increase the acceptance ratio and the revenue while decreasing the cost incurred by the substrate network in the long run. [ABSTRACT FROM PUBLISHER]