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The Marching Cubes paper by Bill Lorensen and Harvey Cline, "Marching Cubes: A High Resolution 3D Surface Construction Algorithm," was published at SIGGRAPH 1987.1 According to Google Scholar, their paper has 15,667 citations (as of January 17, 2020), the most highly cited paper in computer graphics. Sadly, while writing this article Bill Lorensen passed away on December 12, 2019. Origins Department Editor Chris Johnson contributed the text in italics. EARLY [ABSTRACT FROM AUTHOR]

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

This paper deals with stabilization of an ideal transformer model (ITM) circuit, used as an interfacing algorithm for hardware-in-the-loop simulations. The ITM allows for the distribution of simulation model computations over multiple processors or can be used in power hardware-in-the-loop simulations. When specific impedance conditions of the partitioned circuit are not met, this interfacing algorithm becomes unstable. This paper first presents a new theoretical approach for representing the ITM interfacing algorithm that is based on delay differential equations. With a stability analysis of the obtained time-delay system, a stabilization method is proposed, which increases the stability boundaries of the ITM algorithm. Simulation examples, including the scenario of a two-level inverter system, are provided. [ABSTRACT FROM AUTHOR]

Screen content coding plays an important role in many applications. Conventional reference picture management (RPM) strategies developed for natural content may not work well for screen content. This is because many regions in screen content remain static for a long time, causing a lot of repetitive contents to stay in the decoded picture buffer. The repetitive contents are not conducive to inter prediction, but still occupy valuable memory. This paper proposes a diversity-based RPM scheme for screen content coding. The concept of diversity is introduced for the reference picture set (RPS) to help formulate the RPM problem. By maximizing the diversity of RPS, more potentially better predictions are provided. Better compression performance can then be achieved. Meanwhile, the proposed scheme is nonnormative and compatible with existing video coding standards, such as High Efficiency Video Coding. The experimental results show that, for low delay screen content coding, the bit saving of the proposed scheme is 4.9% on average and up to 13.7%, without increasing encoding time. [ABSTRACT FROM AUTHOR]

The Internet of vehicles (IoV) is a large information interaction network that collects information on vehicles, roads and pedestrians. One of the important uses of vehicle networks is to meet the entertainment needs of driving users through communication between vehicles and roadside units (RSUs). Due to the limited storage space of RSUs, determining the content cached in each RSU is a key challenge. With the development of 5G and video editing technology, short video systems have become increasingly popular. Current widely used cache update methods, such as partial file precaching and content popularity- and user interest-based determination, are inefficient for such systems. To solve this problem, this paper proposes a QoE-driven edge caching method for the IoV based on deep reinforcement learning. First, a class-based user interest model is established. Compared with the traditional file popularity- and user interest distribution-based cache update methods, the proposed method is more suitable for systems with a large number of small files. Second, a quality of experience (QoE)-driven RSU cache model is established based on the proposed class-based user interest model. Third, a deep reinforcement learning method is designed to address the QoE-driven RSU cache update issue effectively. The experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In social networks, individuals’ decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: $(i)$ (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; $(ii)$ (i i) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a method to improve the traditional mixture of deformable part models (MDPM) method from the learning perspective. First, an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number, size, and location. The algorithm imposes two additional regularization terms in addition to the standard hinge loss function. The first term focuses on automatic part selection and the second term focuses on automatic part placement. Second, this paper introduces an improved MDPM training framework. The framework applies a learned transformation to normalize the prediction score from each individual deformable part model (DPM) into a pseudo probability such that the partition of the entire object appearance feature space becomes less sensitive to the prior distributions of different DPMs. Finally, the two proposed improvements are combined and formulated under the expectation-maximization framework. We evaluate our method mainly using the PASCAL VOC2007 and VOC2010 detection benchmarks and show that the proposed learning algorithms could increase the detection mean AP score by 2.4% and 0.9%, respectively, on these two data sets when using the proposed part selection method and the training algorithm. We also present further in-depth analysis of the proposed algorithm in the experiments. [ABSTRACT FROM PUBLISHER]

Future quadratic programming (FQP) is an interesting and challenging topic due to its unknown future information and time-dependent feature. In this paper, a continuous-time zeroing neurodynamics (CTZN) model for quadratic programming is first obtained via zeroing neurodynamics (ZN) method. Then, a general four-step Zhang et al. discretization formula is presented and adopted to discretize the above CTZN model, and thus the general four-step discrete-time ZN (DTZN) algorithm for the FQP is developed. For comparison, a three-step DTZN algorithm and a one-step DTZN algorithm for the FQP are also presented. It is worth noting that there is an important parameter termed stepsize in the DTZN algorithms, which is closely related to their stability. If the value of stepsize is outside its effective interval, the DTZN algorithms are impossible to achieve convergence in terms of residual errors, which leads to failure of the FQP problem solving. By utilizing bilinear transformation and Routh stability criterion, the effective stepsize interval of the general four-step DTZN algorithm is confirmed via theoretical proof. Besides, numerical results substantiate the effectiveness and superiority of the general four-step DTZN algorithm as well as the accuracy of the effective stepsize interval. Finally, the general four-step DTZN algorithm is applied to fulfill the path-tracking control of different robot manipulators, with the effectiveness and superiority further validated. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model’s expressive power. While similar models have been proposed in the literature, they only exploit the Beta prior as a proxy to ensure a valid Bernoulli parameter in a Bayesian setting; in practice it reduces to a uniform or uninformative prior. Besides, estimation in these models has focused on costly Bayesian inference. In this paper, we propose a simple yet very efficient majorization-minimization algorithm for maximum a posteriori estimation. Our approach leverages the Beta prior whose parameters can be tuned to improve performance in matrix completion tasks. Experiments conducted on three public binary datasets show that our approach offers an excellent trade-off between prediction performance, computational complexity, and interpretability. [ABSTRACT FROM AUTHOR]

The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. We study two practical scenarios: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is connected (to the CSP) or standalone, which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models. [ABSTRACT FROM AUTHOR]

Online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, “bad actors” are increasingly exploiting these platforms, which is a key challenge for their administrators, businesses and society in general. The spread of fake news is a classical example of the abuse of social networks by these bad actors. While some have advocated for stricter policies to control the spread of misinformation in social networks, this often happens in detriment of their democratic and organic structure. In this paper, we aim to limit the influence of a target group in a social network via the removal of a few users/links. We formulate the influence limitation problem in a data-driven fashion, by taking into account past propagation traces. More specifically, our algorithms find critical edges to be removed in order to decrease the influence of a target group based on past data. The idea is to control the diffusion processes while minimizing the amount of disturbance in the network structure. Moreover, we consider two types of constraints over edge removals, a budget constraint and also a, more general, set of matroid constraints. These problems lead to interesting challenges in terms of algorithm design. For instance, we are able to show that influence limitation is APX-hard and propose deterministic and probabilistic approximation algorithms for the budgeted and the matroid version of the problem, respectively. Experiments show that the proposed approaches outperform several baselines. [ABSTRACT FROM AUTHOR]

This paper concerns the deployment problem of wireless sensor networks (WSNs) with mobile robotic sensor nodes for spatiotemporal monitoring. The proposed approach, deep reinforced learning tree (DRLT), utilizes deep reinforcement learning (DRL) to improve the efficiency of searching the most informative sampling locations. The parameterized sampling locations in an infinite horizon space are modeled according to their spatiotemporal correlations and subject to various constraints, including field estimation error and information gain. And the model-based information gain can be calculated efficiently over an infinite horizon. In this manner, the effectiveness of the sampling locations is learned through DRLT during the exploration by the robotic sensors. Then DRLT can instruct the robotic sensors to avoid unnecessary sampling locations in future iterations. Also, it is proved in this paper that the proposed algorithm is capable of searching for the near-optimal sampling locations effectively and guaranteeing a minimum field estimation error. Simulation based on national oceanic and atmospheric administration (NOAA) datasets is presented, which demonstrates the significant enhancements made by the proposed algorithm. Compared with the traditional approaches, such as the information theory-based greedy approach or random sampling, the proposed method shows a superior performance with regard to both estimation error and planning efficiency. [ABSTRACT FROM AUTHOR]

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks. [ABSTRACT FROM AUTHOR]

Cross-validation (CV) is a widely adopted approach for selecting the optimal model. However, the computation of empirical cross-validation error (CVE) has high complexity due to multiple times of learner training. In this paper, we develop a novel approximation theory of CVE and present an approximate approach to CV based on the Bouligand influence function (BIF) for kernel-based algorithms. We first represent the BIF and higher order BIFs in Taylor expansions, and approximate CV via the Taylor expansions. We then derive an upper bound of the discrepancy between the original and approximate CV. Furthermore, we provide a novel computing method to calculate the BIF for general distribution, and evaluate BIF criterion for sample distribution to approximate CV. The proposed approximate CV requires training on the full data set only once and is suitable for a wide variety of kernel-based algorithms. Experimental results demonstrate that the proposed approximate CV is sound and effective. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new methodology to formulate storage behavior in medium- and long-term power system models that use a load duration curve. Traditionally in such models, the chronological information among individual hours is lost; information that is necessary to adequately model the operation of a storage facility. Therefore, these models are not fully capable of optimizing the actual operation of storage units, and often use pre-determined data or some sort of peak-shaving algorithm. In a rapidly changing power system, the proper characterization of storage behavior and its optimization becomes an increasingly important issue. This paper proposes a methodology to tackle the shortcomings of existing models. In particular, we employ the so-called system states framework to recover some of the chronological information within the load duration curve. This allows us to introduce a novel formulation for storage in a system states model. In a case study, we show that our method can lead to computational time reductions of over 90% while accurately replicating hourly behavior of storage levels. [ABSTRACT FROM PUBLISHER]

Electromagnetic devices containing permanent magnet (PM) are common and typical electromagnetic devices that have advantages of low-power consumption, small size, high sensitivity, and its optimization problem has aroused widespread concern. Calculation accuracy of electromagnetic properties and efficiency of optimization processes are two important factors that affect the optimization effect of electromagnetic devices. A fast calculation method was proposed in this paper that aimed at electromagnetic devices with PM characteristics of strong non-linearity and low solving precision. The response surface method was used to derive the basis function, and the error between actual measured values and calculated values was modified by the Kriging method. On the basis of the calculation model, the distributed collaborative strategy was adopted to fulfill the parallel execution of the fast computation. Simultaneously, the updating strategy of particles was improved, and the robustness of the multi-objective particle swarm optimization algorithm in solving the optimization problem of electromagnetic devices with PM was enhanced. Effectiveness of the method proposed in this paper was verified by the case study of a specific type of electromagnetic relay with PM. [ABSTRACT FROM AUTHOR]

The papers in this special section are focused on one aspect of the current technological wave. As we all know, nature-inspired (NI) computing and algorithms play an ever increasing role in problem solving by way of optimization, machine intelligence, data mining, and resource management. Nature has evolved over millions of years under a variety of harsh and severe environments and, thus, provides a rich source of inspiration for designing algorithms and approaches to tackle real-world challenging EMC and Signal & Power Integrity (SI/PI) problems. [ABSTRACT FROM AUTHOR]

Mining interrelated data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space data, very limited exploration has been made on fuzzy clustering of relational data that involve several object types. In this paper, we propose a new fuzzy clustering approach for multitype relational data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multitype relational data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm, which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multitype relational data with two special structures, namely, star-structure and extended star-structure. Experimental studies are conducted on benchmark document datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones. [ABSTRACT FROM PUBLISHER]

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with rotational invariance. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A “direct” approach to the simulation, where one first generates a dense $n \times n$ matrix from the ensemble, requires at least $\mathcal {O}(n^{2})$ resource in space and time. The HD algorithm overcomes this $\mathcal {O}(n^{2})$ bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are $\mathcal {O}(nT)$ and $\mathcal {O}(nT^{2})$ , respectively, with $T$ being the number of iterations. When $T \ll n$ , which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems. [ABSTRACT FROM AUTHOR]

Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]

This paper considers multirobot task allocation problems where the estimated costs for performing tasks are interrelated, and the overall team objective need not be a standard sum-of-costs (or utilities) model, enabling straightforward treatment of the additional costs incurred by resource contention. In the model we introduce, a team may choose one of a set of shared resources to perform a task (e.g., several routes to reach a destination), and interference is modeled when multiple robots use the same resource. We show that the general problem is NP-hard, and investigate specialized subinstances with particular cost structures. For the general problem, we describe an exact algorithm which finds an optimal assignment in a reasonable time on small instances. Aiming at larger problems, we turn two particular subinstances, introducing an two algorithms that find assignments quickly even for problems of considerable size, the first being optimal, the second being an approximation algorithm but also producing high-quality solutions with bounded suboptimality. [ABSTRACT FROM PUBLISHER]

Graph pattern matching has been extensively investigated on general graphs without time information over decades. Nevertheless, few studies focus on temporal graphs, where a relationship between two vertices takes place at a specific moment and lingers for some time. In this paper, we propose a new notion so-called time-respecting flow graph, in which all paths are time-respecting (i.e., a sequence of contacts with non-decreasing time), and one vertex is distinguished as the root, from which other vertices can be reached via a time-respecting path. Based on this, we explore the problem of time-respecting flow graph pattern matching on temporal graphs. This problem motivates important applications in epidemiology, information diffusion, crime detection, etc. To address it, we present one baseline algorithm as well as two optimized algorithms that utilize several efficient matching strategies and topological sort based technique to boost efficiency. Extensive experimental evaluation using both real and synthetic data sets demonstrates the effectiveness and efficiency of our proposed algorithms. Compared with baseline method, our optimized algorithms could achieve up to three orders of magnitude speedup. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

In this paper, a coupled interaction matrix is proposed to describe the interactions between line outages and the load shed at buses. The coupled interaction matrix is effectively estimated by the Expectation Maximization algorithm. A highly probabilistic coupled interaction model is further proposed to efficiently generate cascades with both line outages and the load shed based on the coupled interaction matrix and the distribution of initial outages. To mitigate cascading failures, critical links are identified based on the coupled interaction matrix by calculating a comprehensive severity index that considers the consequences of both line outages and the load shed. Simulation results on the IEEE 300-bus system verify the effectiveness of the proposed approach. The cascades generated from the coupled interaction model match the statistics of the original cascades very well. The identified critical links based on the comprehensive severity index enable a proper tradeoff between reducing line outages and reducing the load shed, leading to a better mitigation effect than only considering either line outages or the load shed. [ABSTRACT FROM AUTHOR]

Many recent studies shed light on the vulnerability of networks against large-scale natural disasters. The corresponding network failures, called regional failures, are manifested at failing multiple network elements that are physically close to each other. The recovery mechanisms of current backbone networks protect failures listed as Shared Risk Link Groups (SRLGs). We aim to design an algorithm for the routing engines, which can generate a reasonable list of SRLGs based on the limited geometric information available. As a first step towards this direction, in this paper, we propose a limited geographic information failure model for the network topology that enables efficient algorithms to compute the set of links that are expected to be close to each other. More precisely, we work with (1) relative node positions without knowing the real distances, (2) an area in the map defines the route of each physical cable, and (3) a regional failure is a circular disk with k = 0,1, . . . nodes in its interior. We describe an efficient algorithm for listing SRLGs based on our limited geographic information failure model and show that under realistic assumptions, the obtained list of SRLGs is short, having approximately 1.2 n and 2.2n elements for k = 0 and k = 1, respectively, where n is the number of nodes of the network. [ABSTRACT FROM AUTHOR]

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, the wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation-based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation-based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN-based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. The experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance. [ABSTRACT FROM AUTHOR]

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given. [ABSTRACT FROM AUTHOR]

Connected component labeling (CCL) is a fundamental image processing problem that has been studied in many platforms, including GPUs. A common approach to CCL performance analysis is studying the total processing time as a function of abstract image features, like the number of connected components or the fraction of foreground pixels, and input data usually includes synthetic images and segmented video datasets. In this paper, we develop on these ideas and propose an evaluation methodology for GPU CCL algorithms based on synthetic image patterns, addressing the nonexistence of a standard and reliable benchmark in the literature. Our methodology, applied on two important algorithms from existing literature, uncovers their data dependency with great detail, and allows us to model their processing time in three real-world video data sets as functions of abstract, high-level image concepts. We also apply our methodology for studying the memory and performance requirements of two strategies for computing connected component properties: an existing memory-hungry approach, and a new memory-preserving strategy. [ABSTRACT FROM AUTHOR]

For a hybrid ac/dc power system incorporating voltage source converter-based multi-terminal direct current (VSC-MTDC) grids, it is essential to solve the power flow problems efficiently for reliable operation. This paper presents an efficient ac/dc power flow algorithm considering the complementary constraints of VSC's reactive power limit and ac node voltage. It can be distinguished from the existing works in terms of both modeling and algorithm aspects. First, the VSC model is formulated, in which the interactions between the reactive power limit and the ac node voltage of VSC are viewed as complementary relations. The complementary relations are expressed using a smooth Fischer-Burmeister function. Then, power flow models of the ac grid and dc grid with a constant Jacobian matrix are formulated based on the VSC model, respectively. To efficiently solve the power flow of an ac/dc grid, the structure characteristic of the Jacobian matrix of ac/dc power flow computation is analyzed. It shows that the Jacobian matrix can be approximated as a block-upper triangular one. Based on this characteristic, a decoupled ac/dc power flow computation algorithm is provided. Test results on two hybrid ac/dc systems under different scenarios validate the correctness, convergence, and efficiency of the proposed power flow algorithm. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require high-dimensional input data. Aiming to overcome this limitation and fully unlock the potentials of LSTM models, in this paper we propose to perform algorithm and hardware co-design towards high-performance energy-efficient LSTM networks. At algorithm level, we propose to develop fully decomposed hierarchical Tucker (FDHT) structure-based LSTM, namely FDHT-LSTM, which enjoys ultra-low model complexity while still achieving high accuracy. In order to fully reap such attractive algorithmic benefit, we further develop the corresponding customized hardware architecture to support the efficient execution of the proposed FDHT-LSTM model. With the delicate design of memory access scheme, the complicated matrix transformation can be efficiently supported by the underlying hardware without any access conflict in an on-the-fly way. Our evaluation results show that both the proposed ultra-compact FDHT-LSTM models and the corresponding hardware accelerator achieve very high performance. Compared with the state-of-the-art compressed LSTM models, FDHT-LSTM enjoys both order-of-magnitude reduction (more than $1000 \times$ 1000 × ) in model size and significant accuracy improvement (0.6% to 12.7%) across different video recognition datasets. Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieve $2.5\times$ 2. 5 × , $1.46\times$ 1. 46 × and $2.41\times$ 2. 41 × increase in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload. For LSTM-UCF workload, our proposed design also outperforms TIE with $1.9\times$ 1. 9 × higher throughput, $1.83\times$ 1. 83 × higher energy efficiency and comparable area efficiency. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness [ABSTRACT FROM AUTHOR]

Classification with a sparsity constraint on the solution plays a central role in many high dimensional signal processing applications. In some cases, the features can be grouped together, so that entire subsets of features can be selected or discarded. In many applications, however, this can be too restrictive. In this paper, we are interested in a less restrictive form of structured sparse feature selection: We assume that while features can be grouped according to some notion of similarity, not all features in a group need be selected for the task at hand. The Sparse Group Lasso (SGL) was proposed to solve problems of this form. The main contributions of this paper are a new procedure called Sparse Overlapping Group (SOG) lasso, an extension to the SGL to overlapping groups and theoretical sample complexity bounds for the same. We establish model selection error bounds that specializes to many other cases. We experimentally validate our proposed method on both real and toy datasets. [ABSTRACT FROM AUTHOR]

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, and various computational models are presented for solving them under the hypothesis of short-time invariance. To eliminate the large lagging error in the solution of the inherently dynamic nonlinear optimization problem, the only way is to estimate the future unknown information by using the present and previous data during the solving process, which is termed the future dynamic nonlinear optimization (FDNO) problem. In this paper, to suppress noises and improve the accuracy in solving FDNO problems, a novel noise-tolerant neural (NTN) algorithm based on zeroing neural dynamics is proposed and investigated. In addition, for reducing algorithm complexity, the quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is employed to eliminate the intensively computational burden for matrix inversion, termed NTN-BFGS algorithm. Moreover, theoretical analyses are conducted, which show that the proposed algorithms are able to globally converge to a tiny error bound with or without the pollution of noises. Finally, numerical experiments are conducted to validate the superiority of the proposed NTN and NTN-BFGS algorithms for the online solution of FDNO problems. [ABSTRACT FROM AUTHOR]

This paper presents a closed-form noniterative transformation of the Maxwell-Garnett mixing rule for biphased mixtures to the triple-pole Debye relaxation formula. For the first time, it is formally proven that such a transformation is complete for conductive constituent materials. In other words, the Maxwell-Garnett representation of any biphased mixture of any conductive materials always has its formal equivalent in the Debye form with three poles at most. For specific aspect ratios of ellipsoidal inclusions, the number of poles reduces to one or two, which is formally proven herein, while in previous studies, a single-pole Debye model was arbitrarily assumed. The proposed transformation provides Debye parameters as an explicit function of a mixture composition, which is competitive to alternative techniques based on laborious curve-fitting algorithms. The newly proposed approach is of particular importance to time-domain modeling of dilute mixtures, where the Maxwell-Garnett mixing rule is usually approximated with available dispersive models. Computational examples given in this paper show advantages of the presented method over previous Maxwell-Garnett to Debye conversion algorithms, in terms of accuracy, robustness, and computational cost. [ABSTRACT FROM AUTHOR]

Suppose that ${ { y}}= \lvert \text {A} { x}_{0}\rvert +\eta $ where ${ x}_{0}\in {\mathbb R} ^{{d}}$ is the target signal and $\eta \in {\mathbb R}^{{m}}$ is a noise vector. The aim of phase retrieval is to estimate ${x}_{0}$ from ${ { y}}$. A popular model for estimating ${ x}_{0}$ is the nonlinear least squares ${\widehat { {x}}}:={\mathrm{ argmin}}_{ x} \| \lvert \text {A} { x}\rvert - { { y}}\|_{2}$. One has already developed many efficient algorithms for solving the model, such as the seminal error reduction algorithm. In this paper, we present the estimation performance of the model with proving that $\| {\widehat { {x}}}- { x}_{0}\|\lesssim {\|\eta \|_{2}}/{\sqrt {{m}}}$ under the assumption of A being a Gaussian random matrix. We also prove the reconstruction error ${\|\eta \|_{2}}/{\sqrt {{m}}}$ is sharp. For the case where ${ x}_{0}$ is sparse, we study the estimation performance of both the nonlinear Lasso of phase retrieval and its unconstrained version. Our results are non-asymptotic, and we do not assume any distribution on the noise $\eta $. To the best of our knowledge, our results represent the first theoretical guarantee for the nonlinear least squares and for the nonlinear Lasso of phase retrieval. [ABSTRACT FROM AUTHOR]

This paper considers the networked system optimization problem by cooperatively finding an approximately optimal solution to the overall network convex cost function, which is the sum of the individual cost functions of nodes (agents) in a networked system. A new distributed gradient descent algorithm is proposed based on the distributed estimation of the sum of gradients of individual cost functions using a consensus-type coordination algorithm. The proposed algorithm can address unknown directed communication topologies and only requires limited communications and information exchanges among nodes in the networked system. Under the assumption that the communication graph is strongly connected, the convergence of the proposed algorithm is rigorously analyzed. Simulation examples are presented to demonstrate the applicability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the fast advances in Internet technologies, social networks have become a major platform for social interaction, lifestyle demonstration, and message dissemination. Effective community detection in social networks helps to assess public sentiment, identify community leaders, and produce personalized recommendation. While different community detection approaches have been proposed in the literature, the trust model based detection schemes model user interactions as trust transfer, which helps to capture the implicit relation in the network. Unfortunately, trust model based detection schemes face a cold start problem, i.e., they cannot accurately model newly joined users as these users have few interactions for a duration after joining the network. In this paper, we propose TLCDA, a novel trust model based community detection algorithm. By enhancing the traditional trust computation with inter-node relation strength and similarity in social networks, TLCDA detects communities through coarse-grained K-Mediods clustering. Our evaluation on real social networks shows that the communities detected by TLCDA exhibit superior preference cohesion while satisfying the topology cohesion. [ABSTRACT FROM AUTHOR]

Connected vehicles will change the modes of future transportation management and organization, especially at an intersection without traffic light. Centralized coordination methods globally coordinate vehicles approaching the intersection from all sections by considering their states altogether. However, they need substantial computation resources since they own a centralized controller to optimize the trajectories for all approaching vehicles in real-time. In this paper, we propose a centralized coordination scheme of automated vehicles at an intersection without traffic signals using reinforcement learning (RL) to address low computation efficiency suffered by current centralized coordination methods. We first propose an RL training algorithm, model accelerated proximal policy optimization (MA-PPO), which incorporates a prior model into proximal policy optimization (PPO) algorithm to accelerate the learning process in terms of sample efficiency. Then we present the design of state, action and reward to formulate centralized coordination as an RL problem. Finally, we train a coordinate policy in a simulation setting and compare computing time and traffic efficiency with a coordination scheme based on model predictive control (MPC) method. Results show that our method spends only 1/400 of the computing time of MPC and increase the efficiency of the intersection by 4.5 times. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern. [ABSTRACT FROM AUTHOR]

This paper investigates resource provisioning in cyber-physical systems (CPSs) by developing a new definition of anelasticity. A flat semi-dormant multicontroller (FSDMC) model is established on a special type of CPS platform named arbitrated networked control system with dual communication channels. A novel, quantitative, and formal definition of anelasticity for the FSDMC is proposed. A new finite capacity M/M/c queuing system with ${N}$ -policy and asynchronous multiple working vacations of partial servers is established, and the FSDMC is modeled as a quasi-birth-and-death process to obtain the stationary probability distribution of the system. Based on the queueing model, we quantify various performance indices of the system to build a nonlinear cost-performance ratio (CPR) function. An optimization model is presented to minimize the CPR. A particle swarm optimization (PSO) algorithm is used to find the optimum solution of the optimization model and obtain the optimal configuration values of the system parameters under stability condition. By changing the system parameters, the sensitivity of the system performance indices and the CPR are analyzed, respectively. The unexpected workload varies randomly over time. Thus, an M/M/1/K queue is constructed in a Markovian environment by employing a three-state, irreducible Markov process. In this queue, the conditional average queue length and the probabilities of the three-state process are calculated. Then, the anelasticity value of the system is precisely determined. When the average arrival rate exceeds the average service rate in the queueing system, an optimal CPR unchanged adaptive algorithm based on PSO is designed to dynamically adjust the controller service rate. Extensive numerical results show the usefulness and effectiveness of the proposed techniques and exhibit that the system can maintain elastic invariance in adaptive adjustment parameters. [ABSTRACT FROM AUTHOR]

In recent years there has been a noticeable increase in the number of available Big Data infrastructures. This fact has promoted the adaptation of traditional machine learning techniques to be capable of addressing large scale problems in distributed environments. Kernel methods like support vector machines (SVMs) suffer from scalability problems due to their nonparametric nature and the complexity of their training procedures. In this paper, we propose a new and efficient distributed implementation of a training procedure for nonlinear semiparametric (budgeted) SVMs called distributed iterative reweighted least squares (IRWLS). This algorithm uses $ {k}$ -means to select the centroids of the semiparametric model and a new distributed algorithmic implementation of the IRWLS optimization procedure to find the weights of the model. We have implemented the proposed algorithm in Apache Spark and we have benchmarked it against other state-of-the-art methods, either full SVM ($ {p}$ -pack SVM) or budgeted (budgeted stochastic gradient descent). Experimental results show that the proposed algorithm achieves higher accuracy while controlling the size of the final model, and also offers high performance in terms of run time and efficiency, when processing very large datasets (the computation time grows linear with the number of training patterns). [ABSTRACT FROM AUTHOR]

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation. [ABSTRACT FROM AUTHOR]

In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the $m$ -waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations. [ABSTRACT FROM AUTHOR]

Subgraph queries are routinely used to search for entities in richly attributed graphs e.g., social networks and knowledge graphs. With little knowledge of underlying data, users often need to rewrite queries multiple times to reach desirable answers. Why-questions are studied to clarify missing or unexpected query results. This paper makes a first step to answer Why-questions for entity search in attributed graphs. We consider three common types of Why-questions: Why-not, Why, and Why-rank, which suggest query manipulations that are responsible for user-specified missing, unexpected, and undesirably ranked entities, respectively. (1) We approach a general query rewriting paradigm that suggests to identify desired entities that are specified by Why-questions. We introduce measures that characterize good query rewrites by incorporating both query editing cost and answer closeness. (2) While computing optimal query rewrites is intractable, we develop feasible algorithms, from approximation to fast heuristics, and provide query rewrites with (near) optimality guarantees whenever possible, for Why, Why-not and Why-rank questions. We further show that our results remain intact for Why questions that (1) request a single query rewrite to clarify multiple types of entities, and (2) variants such as Why-empty and Why-so-many, by providing the matching algorithms. Using real-world graphs, we experimentally verify that our algorithms are effective and feasible for large graphs. Our case study also verifies their application in e.g., knowledge exploration. [ABSTRACT FROM AUTHOR]

Intelligent metasurfaces to create smart electromagnetic environments have attracted a surge of interest. The multiscale property of metasurfaces results in a huge computational challenge, further hindering their engineering development. Our previous paper has developed a fast finite difference time domain (FDTD) algorithm for patchlike metasurfaces analysis using the surface susceptibility model (SSM). This study extends that to efficient fishnetlike metasurfaces analysis using the surface porosity model (SPM), a useful complement to SSM. We introduce the SPM to FDTD, replacing brute force simulations, to accelerate fishnetlike metasurfaces analysis. At last, we conduct a series of numerical experiments, demonstrating that the proposed FDTD-SPM algorithm greatly reduces the number of computational meshing lattices and the consumed time. [ABSTRACT FROM AUTHOR]

Bayesian filtering aims at estimating sequentially a hidden process from an observed one. In particular, sequential Monte Carlo (SMC) techniques propagate in time weighted trajectories which represent the posterior probability density function (pdf) of the hidden process given the available observations. On the other hand, conditional Monte Carlo (CMC) is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable. In this paper, we show that up to some adaptations, one can make use of the time recursive nature of SMC algorithms in order to propose natural temporal CMC estimators of some point estimates of the hidden process, which outperform the associated crude Monte Carlo (MC) estimator whatever the number of samples. We next show that our Bayesian CMC estimators can be computed exactly, or approximated efficiently, in some hidden Markov chain (HMC) models; in some jump Markov state-space systems (JMSS); as well as in multitarget filtering. Finally our algorithms are validated via simulations. [ABSTRACT FROM PUBLISHER]

This paper describes the first reliable Newton algorithm for the sequential computation of the set of dominant poles of scalar and multivariable transfer functions of infinite systems. This dominant pole algorithm incorporates a deflation procedure, which is derived from the partial fraction expansion concept of analytical functions of the complex frequency s and prevents the repeated convergence to previously found poles. The pole residues (scalars or matrices), which are needed in this expansion, are accurately computed by a Legendre-Gauss integral solver scheme for both scalar and multivariable systems. This algorithm is effectively applied to the modal model reduction of multivariable transfer functions for two test systems of considerable complexity and containing many distributed parameter transmission lines. [ABSTRACT FROM PUBLISHER]