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With the development of the times, rumors spread rapidly on the Internet. Firstly, this paper establishes a reaction-diffusion system with Allee effect to describe the rumor spreading process and derives the necessary conditions for the emergence of Turing bifurcation. Next, a parameter identification approach utilizing optimal control theory is shown. Ultimately, the impact of the magnitude of the certain parameters in the objective function on parameter identification is examined through numerous parameter identifications in continuous space and various complex networks. Additionally, the convergence rates and error magnitudes of different algorithms for parameter identification are studied across different spatial structures. • The necessary conditions for the emergence of Turing bifurcation are investigated. • This paper applies optimal control theory to achieve parameter identification. • The effects of the three algorithms and topologies on parameter identification are studied. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly popular especially for scientific research and business fields where large volume of datasets need to be used, but it involves data security and privacy concerns. This paper mitigates such concerns by tracking and logging the history of shared data (i.e., provenance records) while preserving data privacy. This is a challenging problem in the data sharing scenario in this paper because the environment is decentralized and internal logs are not accessible publicly due to privacy concerns. We present ProvNet, a decentralized data sharing platform that can detect malicious users and provide secure provenance records using the newly proposed networked blockchain without disclosing raw data contents. Valid sharing records are collected and stored in the blocknet and misbehavior is detected with the stored provenance records according to our accountable protocols. We give a proof-of-concept implementation, and evaluation results show that the overhead is acceptable. • A networked blockchain, blocknet, stores data sharing records bi-directionally. • Using MinHash to check datasets' similarities, thus protect data ownership. • Chameleon Hash is leveraged to add future pointers without changing previous hashes. • A provenance graph built from blocknet, provides users' trackability on shared data. • Prototye performance is acceptable, an indexing system designed for optimization. [ABSTRACT FROM AUTHOR]

• Formulate a non-cooperative game with both UEs and MECs as players. • Prove the existence of and convergence to a Nash equilibrium. • Develop a set of algorithms to find the Nash equilibrium. • Demonstrate numerical examples of non-cooperative games with and without MECs' participation. In a newly emerged fog computing environment, various user equipments (UE) enhance their computing power and extend their battery lifetime by computation offloading to mobile edge cloud (MEC) servers. Such an environment is distributed and competitive in nature. In this paper, we take a game theoretical approach to computation offloading optimization in a fog computing environment. Such an approach captures and characterizes the nature of a competitive environment. The main contributions of the paper can be summarized as follows. First, we formulate a non-cooperative game with both UEs and MECs as players. Each UE attempts to minimize the execution time of its tasks with an energy constraint. Each MEC attempts to minimize the product of its power consumption for computation and execution time for allocated tasks. Second, we develop a heuristic algorithm for a UE to determine its "heuristically" best response to the current situation, an algorithm for an MEC to determine its best response to the current situation, and an iterative algorithm to find the Nash equilibrium. Third, we prove that our iterative algorithm converges to a Nash equilibrium. We demonstrate numerical examples of our non-cooperative games with and without MECs' participation. We observe that our iterative algorithm always quickly converges to a Nash equilibrium. The uniqueness of our non-cooperative games is that the strategy set of a player can be discrete and the payoff function of a player can be obtained by a heuristic algorithm for combinatorial optimization. To the best of the author's knowledge, there has been no such investigation of non-cooperative games based on combinatorial optimization for computation offloading optimization in a fog computing environment. [ABSTRACT FROM AUTHOR]

The paper presents a new method of optimization by the Layered Algorithm (LA). The proposed algorithm reduces the initial area to the sub-area containing the optimum. The proposed technique is based on the classification of the optimized function values (data sampled by sensors). The classification method uses a two-dimensional three-state Cellular Automata (CA). The CA classifies all area points ascribed to the CA cells based on their values. Specification of the categorization layers to the data gives a possibility to identify the different levels areas. Consequently, after analysis, a sub-area containing the optimum can be designated. In this paper, the proposed algorithm is applied to find the location of the airborne contaminant source by analyzing the concentration of released substances reported by mobile sensors distributed over the domain of interest. The Gaussian dispersion model simulation of the contaminant dispersion in the urbanized area is applied to generate the data used to verify the efficiency of the proposed Layered Algorithm. The LA successfully estimates the sub-area of the considered domain where the contamination source is located, taking to account data from sensors solely. • Optimization method reducing the initial area to the sub-area containing the optimum. • Verification of the Layered Algorithm (LA) with Selected Benchmark Functions. • Analysis of the LA efficiency to localize the airborne contaminant source. • LA results assessment by the measures: classification and accuracy error, relevance. [ABSTRACT FROM AUTHOR]

• Efficient parallel computations for solving time consuming problems of multiple global optimization. • A general computational scheme of global optimization methods. • Parallel cooperative and competitive computational schemes for computer systems with shared and distributed memory. • Drastic reduction of the required calculations provided by the complete reuse of the calculated search information. This paper proposes an efficient approach for the parallel solution of computationally time consuming problems of multiple global optimization, in which minimized functions can be multiextremal and calculating function values may require huge amounts of computations. The proposed approach is based on the information-statistical theory of global optimization, within which a general computational scheme of global optimization methods is proposed. In the paper, this general scheme is expanded by the possible reuse of search information obtained in the process of computations when solving multiple global optimization problems. Within the framework of the proposed generalized scheme, parallel algorithms are proposed for computational systems with shared and distributed memory. Results of computational experiments demonstrated that the proposed approach can significantly reduce the computational complexity of solving multiple global optimization problems. [ABSTRACT FROM AUTHOR]

In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

Federated Learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the cloud, Federated Learning coordinates a group of devices to train a shared model in a distributed manner with the training data located on the devices. However, unbalanced data distribution and heterogeneous hardware configurations across different devices badly hurts the performance of collaborative model and severely impacts the overall training progress. Thus, a framework that can well balance the model accuracy and the training progress is urgently required. In this paper, we propose PAGroup, a privacy-aware grouping framework for high-performance Federated Learning. PAGroup intelligently divides the participating clients into different groups through carefully analyzing the privacy requirement of the training data and the solid social relationship of the participating clients. After that, PAGroup conducts data shaping and capability-ware training in order to improve the model performance while accelerating the overall training process. We evaluate the performance of PAGroup with both simulation and hardware testbed. The evaluation results show that PAGroup improves model accuracy up to 21%. Meanwhile, it decreases 81% communication overhead, 29% computation cost and 84% wall-clock time at best comparing with the baselines. • Specific private data that required by a learned model can be exchanged daily in certain social community. • PAGroup can share private date within task-specific groups organized according to strongly connected social community. • Training devices in a group can be viewed as a "super" client to compatibility to existing optimization algorithms. • The impact of local data scale, diversity, and label distribution to the model performance has been theoretically proved. [ABSTRACT FROM AUTHOR]

The second-order cone (SOC) is a class of simple convex cones and optimizing over them can be done more efficiently than with semidefinite programming. It is interesting both in theory and in practice to investigate which convex cones admit a representation using SOCs, given that they have a strong expressive ability. In this paper, we prove constructively that the cone of sums of nonnegative circuits (SONC) admits a SOC representation. Based on this, we give a new algorithm for unconstrained polynomial optimization via SOC programming. We also provide a hybrid numeric-symbolic scheme which combines the numerical procedure with a rounding-projection algorithm to obtain exact nonnegativity certificates. Numerical experiments demonstrate the efficiency of our algorithm for polynomials with fairly large degree and number of variables. [ABSTRACT FROM AUTHOR]

This paper describes a unified view of parallel evolutionary algorithms for multi-objective optimization problems. The parallel optimization algorithms are detailed from both design and implementation aspects. The proposed taxonomy is based on three hierarchical parallel models. Moreover, various parallel architectures are taken into account. The performance assessment issue of parallel multi-objective evolutionary algorithms (MOEA) is also presented. This work can be extended to any population-based metaheuristics such as particle swarm and scatter search. • The main highlights of this paper is the development of a unified view of parallel multi-objective evolutionary algorithms in terms of design and implementation. • The parallel models have been analyzed according to different parallel architectures. • The different parallel models have been illustrated. • The paper is also a guideline to researchers and engineers to design and implement efficient and effective parallel evolutionary algorithms for multi-objective optimization. [ABSTRACT FROM AUTHOR]

In this paper we show the first polynomial-time algorithm for the problem of minimizing submodular functions on the product of diamonds of finite size. This submodular function minimization problem is reduced to the membership problem for an associated polyhedron, which is equivalent to the optimization problem over the polyhedron, based on the ellipsoid method. The latter optimization problem is a generalization of the weighted fractional matroid matching problem. We give a combinatorial polynomial-time algorithm for this optimization problem by extending the result by Gijswijt and Pap (2013) [9]. [ABSTRACT FROM AUTHOR]

The study of greedy approximation in the context of convex optimization is becoming a promising research direction as greedy algorithms are actively being employed to construct sparse minimizers for convex functions with respect to given sets of elements. In this paper we propose a unified way of analyzing a certain kind of greedy-type algorithms for the minimization of convex functions on Banach spaces. Specifically, we define the class of Weak Biorthogonal Greedy Algorithms for convex optimization that contains a wide range of greedy algorithms. We analyze the introduced class of algorithms and establish the properties of convergence, rate of convergence, and numerical stability, which is understood in the sense that the steps of the algorithm are allowed to be performed not precisely but with controlled computational inaccuracies. We show that the following well-known algorithms for convex optimization — the Weak Chebyshev Greedy Algorithm (co) and the Weak Greedy Algorithm with Free Relaxation (co) — belong to this class, and introduce a new algorithm — the Rescaled Weak Relaxed Greedy Algorithm (co). Presented numerical experiments demonstrate the practical performance of the aforementioned greedy algorithms in the setting of convex minimization as compared to optimization with regularization, which is the conventional approach of constructing sparse minimizers. [ABSTRACT FROM AUTHOR]

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction. [ABSTRACT FROM AUTHOR]

This paper presents INRFlow, a mature, frugal, flow-level simulation framework for modelling large-scale networks and computing systems. INRFlow is designed to carry out performance-related studies of interconnection networks for both high performance computing systems and datacentres. It features a completely modular design in which adding new topologies, routings or traffic models requires minimum effort. Moreover, INRFlow includes two different simulation engines: a static engine that is able to scale to tens of millions of nodes and a dynamic one that captures temporal and causal relationships to provide more realistic simulations. We will describe the main aspects of the simulator, including system models, traffic models and the large variety of topologies and routings implemented so far. We conclude the paper with a case study that analyses the scalability of several typical topologies. INRFlow has been used to conduct a variety of studies including evaluation of novel topologies and routings (both in the context of graph theory and optimization), analysis of storage and bandwidth allocation strategies and understanding of interferences between application and storage traffic. • We present our flow-level simulation framework INRFlow. • It is a mature, flexible and efficient tool for simulating large scale systems. • It models network, storage, scheduler and applications. • It has been used extensively for our research in the past. • INRFlow is open source and programmed in C. [ABSTRACT FROM AUTHOR]

Abstract This paper is concerned with the positive definiteness (PDness) problem in covariance matrix estimation. For high-dimensional data, many regularized estimators have been proposed under structural assumptions on the true covariance matrix, including sparsity. They were shown to be asymptotically consistent and rate-optimal in estimating the true covariance matrix and its structure. However, many of them do not take into account the PDness of the estimator and produce a non-PD estimate. To achieve PDness, researchers considered additional regularizations (or constraints) on eigenvalues, which make both the asymptotic analysis and computation much harder. In this paper, we propose a simple modification of the regularized covariance matrix estimator to make it PD while preserving the support. We revisit the idea of linear shrinkage and propose to take a convex combination between the first-stage estimator (the regularized covariance matrix without PDness) and a given form of diagonal matrix. The proposed modification, which we call the FSPD (Fixed Support and Positive Definiteness) estimator, is shown to preserve the asymptotic properties of the first-stage estimator if the shrinkage parameters are carefully selected. It has a closed form expression and its computation is optimization-free, unlike existing PD sparse estimators. In addition, the FSPD is generic in the sense that it can be applied to any non-PD matrix, including the precision matrix. The FSPD estimator is numerically compared with other sparse PD estimators to understand its finite-sample properties as well as its computational gain. It is also applied to two multivariate procedures relying on the covariance matrix estimator – the linear minimax classification problem and the Markowitz portfolio optimization problem – and is shown to improve substantially the performance of both procedures. [ABSTRACT FROM AUTHOR]

As the main media of human communication and understanding of the world, image is one of the important information sources of human intelligence activities. With the development of the times, the demand for image processing technology is increasing day by day. The rapid development of computer technology also provides a platform for the application of image processing. In order to achieve better image processing effect, this paper focuses on the application of artificial intelligence algorithm in image processing. Image segmentation is a technology that decomposes images into regions with different characteristics and extracts useful targets. It can be regarded as a combinatorial optimization problem. It is completely feasible to apply artificial intelligence algorithm to optimization problems. Firstly, this paper introduces the ant colony algorithm in artificial intelligence algorithm, elaborates the basic principle and mathematical model of the ant colony algorithm. Secondly, in order to improve the ability of global search of ant colony algorithm, this paper introduces the crowding degree function of fish into the ant colony algorithm. Finally, the improved ant colony algorithm is used in image segmentation to improve the effect of image segmentation. The simulation results show that it is feasible to use ant colony algorithm in image segmentation. And the optimization improvement of ant colony algorithm is effective. The improved ant colony algorithm applied in image segmentation can significantly improve the segmentation performance. [ABSTRACT FROM AUTHOR]

Abstract With the development of vision and optimization techniques, visual-inertial odometry (VIO) has shown the capability of motion estimating in the GNSS-denied condition. The VIO can provide absolute pitch and roll angles estimating value, but no the absolute azimuth. In the paper, we proposed a VIO aided by compass, which can obtain the azimuth with respect to the north direction in the geographic frame. Moreover, aided by compass, the yaw angle estimating error was reduced to a greater degree, due to the measurement of azimuth. Furthermore, the consistency of the VIO backend estimator is improved as well, while the accuracy of the estimated pose states was also wholly improved. The aiding approach is a tightly-couple information fusion system of camera, IMU and magnetoresistive sensors. The optimization method is based on the pre-integration and bundle adjustment. In the paper, we derived the compass residual model based on the pre-integration model, and then its Jacobian and covariance formation were deduced to solve the nonlinear equations. The compass aided VIO software was implemented based on the Nvidia Jetson Tx2. The system was fully tested based on hardware-in-the-loop simulation and vehicle test in the real physical environment. The pose errors of VIOs with and without compass aiding were compared in the above tests. The simulation results showed that the position was and yaw errors were improved obviously; the compass aided VIO was still consistent, but the pure VIO was consistent not. The consistency character is evaluated by average NEES by Monte-Carlo in simulation. The vehicle test showed that the position error was reduced by 23%; the yaw error was reduced by 21%. As a result, the compass aided VIO not only improved the pose estimated accuracy, especially position and yaw, but also improved the consistency of VIO system. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of collocation points. In this paper, we present a novel and lightweight algorithm to conduct AD for physics-informed operator learning, which we call the trick of Zero Coordinate Shift (ZCS). Instead of making all sampled coordinates as leaf variables, ZCS introduces only one scalar-valued leaf variable for each spatial or temporal dimension, simplifying the wanted derivatives from "many-roots-many-leaves" to "one-root-many-leaves" whereby reverse-mode AD becomes directly utilisable. It has led to an outstanding performance leap by avoiding the duplication of the computational graph along the dimension of functions (physical parameters). ZCS is easy to implement with current deep learning libraries; our own implementation is achieved by extending the DeepXDE package. We carry out a comprehensive benchmark analysis and several case studies, training physics-informed DeepONets to solve partial differential equations (PDEs) without data. The results show that ZCS has persistently reduced GPU memory consumption and wall time for training by an order of magnitude, and such reduction factor scales with the number of functions. As a low-level optimisation technique, ZCS imposes no restrictions on data, physics (PDE) or network architecture and does not compromise training results from any aspect. • We present a novel algorithm to conduct automatic differentiation w.r.t. coordinates for physics-informed operator learning. • Our algorithm can reduce GPU memory and wall time for training physics-informed DeepONets by an order of magnitude. • Our algorithm neither affects training results nor imposes any restrictions on data, physics (PDE) or network architecture. [ABSTRACT FROM AUTHOR]

The MoF (Moment of Fluid) method is one of the most accurate approaches among various interface reconstruction algorithms. Alike other second order methods, the MoF method needs to solve an implicit optimization problem to obtain the optimal approximate interface, so an iteration process is inevitable under most circumstances. In order to solve the optimization efficiently, the properties of the objective function are worthy of studying. In 2D problems, the first order derivative has been deduced and applied in the previous researches. In this paper, the high order derivatives of the objective function are deduced on the convex polygon. We show that the n th ( n ≥ 2 ) order derivatives are discontinuous, and the number of the discontinuous points is two times the number of the polygon edge. A rotation algorithm is proposed to successively calculate these discontinuous points, thus the target interval where the optimal solution is located can be determined. Since the high order derivatives of the objective function are continuous in the target interval, the iteration schemes based on high order derivatives can be used to improve the convergence rate. Moreover, when iterating in the target interval, the value of objective function and its derivatives can be directly updated without explicitly solving the volume conservation equation. The direct update makes a further improvement of the efficiency especially when the number of edges of the polygon is increasing. The Halley's method, which is based on the first three order derivatives, is applied as the iteration scheme in this paper and the numerical results indicate that the CPU time is about half of the previous method on the quadrilateral cell and is about one sixth on the decagon cell. [ABSTRACT FROM AUTHOR]

For agricultural tractors fixed ballast cannot provide high-efficiency traction under time-varying resistance in the field. Combining the characteristics of electric tractors with high-weight battery packs, this paper develops an intelligent ballast control system including a battery position adjustment (BPA) mechanism and an active ballasting control method. A tractor traction performance prediction model was developed to predict traction performance parameters and load-transfer in real time. The movement of the battery pack enables load-transfer based on the BPA. To ensure the lowest sliding rate of optimal tractive efficiency, an active ballasting control method based on a particle swarm optimisation algorithm was proposed that enabled control of the optimal battery position. The results of MATLAB/Simulink simulation indicate that the traction resistance after grading and pre-treatment not only reduced the frequency of change in the resistance signal but also retain the original trend. The results of a hardware in the loop test showed that the average tractive efficiency in the mode of active ballasting mode higher than that of the no ballasting mode (increased by 6.6%) and fixed ballasting mode (increased by 4.7%). The mean wheel slip in active ballasting mode was lower than that of the no ballasting mode (decreased by 10.3%) and the fixed ballasting mode (decreased by 4.9%). In addition, the front axle dynamic load distribution ratio in active ballasting mode was always greater than 0.2, which ensured the operational stability of the tractor. This study provides theoretical support and technical reference for optimising the traction performance of electric tractors. • An intelligent ballast control system with active load-transfer is developed. • The concept of batteries instead of ballasts. • Battery position adjustment mechanism (BPA) is designed. • Active ballasting control method based on traction performance prediction model. • The maximum average tractive efficiency is increased by 6.6%. [ABSTRACT FROM AUTHOR]

• The placement of data and the scheduling of request are studied in edge caching. • Network unreliability and limited storage and bandwidth resources are considered. • Two algorithms with different computation complexity and cache efficiency are developed. • The experiments are conducted to evaluate the performance of proposed caching strategies. Edge caching is a prevailing media delivery technology where data is hosted at the edge nodes with computing and storage capability in close proximity to users, in order to expand the backhaul network capacity and enhance users' quality of experience (QoE). The existing work in this area often neglects the fact that large-scale distributed cache networks are not particularly reliable and many edge nodes are prone to failure. In this paper we investigate and develop a novel, cooperative caching mechanism for content placement and request routing. We aim to minimize the content access delay and achieve the optimization in polynomial time, taking into account failures in an unreliable network environment with limited edge storage and bandwidth. We introduce two optimization algorithms: 1) a primal-dual algorithm that is based on the Lagrangian dual decomposition and subgradient method, and 2) a greedy-based approximation algorithm with a proven approximation ratio. Numerical results show that the proposed algorithms outperform other comparative approaches in synthetic and real network environments, and the approximation algorithm is particularly suitable for networking scenarios with sparse node connectivity and resources in short supply. [ABSTRACT FROM AUTHOR]

Management of a portfolio that includes an illiquid asset is an important problem of modern mathematical finance. One of the ways to model illiquidity among others is to build an optimization problem and assume that one of the assets in a portfolio cannot be sold until a certain finite, infinite or random moment of time. This approach arises a certain amount of models that are actively studied at the moment. Working in the Merton's optimal consumption framework with continuous time we consider an optimization problem for a portfolio with an illiquid, a risky and a risk-free asset. Our goal in this paper is to carry out a complete Lie group analysis of PDEs describing value function and investment and consumption strategies for a portfolio with an illiquid asset that is sold in an exogenous random moment of time with a prescribed liquidation time distribution. The problem of such type leads to three dimensional nonlinear Hamilton–Jacobi–Bellman (HJB) equations. Such equations are not only tedious for analytical methods but are also quite challenging form a numeric point of view. To reduce the three-dimensional problem to a two-dimensional one or even to an ODE one usually uses some substitutions, yet the methods used to find such substitutions are rarely discussed by the authors. We find the admitted Lie algebra for a broad class of liquidation time distributions in cases of HARA and log utility functions and formulate corresponding theorems for all these cases. We use found Lie algebras to obtain reductions of the studied equations. Several of similar substitutions were used in other papers before whereas others are new to our knowledge. This method gives us the possibility to provide a complete set of non-equivalent substitutions and reduced equations. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small ( n ≤ 35 ) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating ( r ≥ 0.97 ) also with the expected lesion volume. [ABSTRACT FROM AUTHOR]

A method for experimentally modeling complex connections, such as “Line-Type” connection (LTC) and “Area-Type” connection (ATC), is proposed in this paper. Unlike traditional methods, instead of treating the junction forces as presumed approximate functions, a new strategy proposed in the present paper is to estimate them from experimentally measured accelerations. Along with them, the junction motion information is also estimated from the measured data. Based on this estimated information, the connection’s model is built. In this way, two potential disadvantages of traditional methods in modeling complex connections, i.e. the difficulty in finding appropriate functions for complex connections and the computation burden in identifying too many parameters through optimization process, can be avoided. In the proposed method, the LTC is modeled as a series of independent junction node pairs, and the ATC is modeled as a combination of a presumed Virtual Structure and a series of independent Virtual Node Pairs. Numerical and experimental results with the constructed example have verified the effectiveness of the proposed method in modeling both LTC and ATC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose an optimal control optimization algorithm for reconstructing admittivity distributions (i.e., both conductivity and permittivity) from multi-frequency micro-electrical impedance tomography. A convergent and stable optimal control scheme is shown to be obtainable from multi-frequency data. This opens a door for convergence analysis of optimal control type approaches in imaging from internal data. The results of this paper have potential applicability in cancer imaging, cell culturing and differentiation, food sciences, and biotechnology. [ABSTRACT FROM AUTHOR]

In this paper, we study a maximization and a minimization problem associated with a Poisson boundary value problem. Optimal solutions in a set of rearrangements of a given function define stationary and stable flows of an ideal fluid in two dimensions. The main contribution of this paper is to determine the optimal solutions. At first, we determine a nearly optimal solution which is an approximation of the optimal solution when the problems are in low contrast regime. Secondly, for the high contrast regime, two optimization algorithms are developed. For the minimization problem, we prove that our algorithm converges to the global minimizer regardless of the initializer. The maximization algorithm is capable of deriving all local maximizers including the global one. Numerical experiments lead us to a conjecture about the location of the maximizers in the set of rearrangements of a function. [ABSTRACT FROM AUTHOR]

We introduce a set of benchmark corpora of conversational English speech derived from the Switchboard-I and Fisher datasets. Traditional automatic speech recognition (ASR) research requires considerable computational resources and has slow experimental turnaround times. Our goal is to introduce these new datasets to researchers in the ASR and machine learning communities in order to facilitate the development of novel speech recognition techniques on smaller but still acoustically rich, diverse, and hence interesting corpora. We select these corpora to maximize an acoustic quality criterion while limiting the vocabulary size (from 10 words up to 10,000 words), where both “acoustic quality” and vocabulary size are adeptly measured via various submodular functions. We also survey numerous submodular functions that could be useful to measure both “acoustic quality” and “corpus complexity” and offer guidelines on when and why a scientist may wish use to one vs. another. The corpora selection process itself is naturally performed using various state-of-the-art submodular function optimization procedures, including submodular level-set constrained submodular optimization (SCSC/SCSK), difference-of-submodular (DS) optimization, and unconstrained submodular minimization (SFM), all of which are fully defined herein. While the focus of this paper is on the resultant speech corpora, and the survey of possible objectives, a consequence of the paper is a thorough empirical comparison of the relative merits of these modern submodular optimization procedures. We provide baseline word recognition results on all of the resultant speech corpora for both Gaussian mixture model (GMM) and deep neural network (DNN)-based systems, and we have released all of the corpora definitions and Kaldi training recipes for free in the public domain. [ABSTRACT FROM AUTHOR]

In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes only state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved. [ABSTRACT FROM AUTHOR]

Finite element (FE) model of updating is the process of calibrating model parameters to improve the accuracy of numerical prediction. This goal is usually achieved by solving an optimization problem with the objective function measuring the misfit between simulated responses and experimental data. The sensitivity method is the predominate class of algorithms to FE model updating problems, and has opened a wide range of applications. However, this method often suffers from large errors due to linearization. To address this challenge, this paper proposes to utilize the unscented Kalman inversion (UKI) method to solve the FE model updating problems in a derivative-free manner. As an iterative optimization method, the UKI determines the new iterate using function values at a set of sample points rather than the derivative information. Implementation details, such as handling constraints during the optimization process, are presented in the paper. To validate the proposed UKI, model updating of a pedestrian bridge is conducted using the simulated and experimental data. Both validation examples show that the UKI is a competitive method for solving FE model updating problems. • The UKI method solves FE model updating problems in a derivative-free manner. • The UKI takes the effect of noises into account in a natural way. • Method is proposed to incorporate constraints during iterative process of the UKI. • The UKI is compared with the Levenberg–Marquardt algorithm. • The UKI is applied to updating a pedestrian bridge model using field test data. [ABSTRACT FROM AUTHOR]

The material in this paper has been divided into two main parts. In the first part we describe two optimization problems--one maximization and one minimization--related to a sharp trace inequality that was recently obtained by G. Auchmuty. In both problems the admissible set is the one comprising characteristic functions whose supports have a fixed measure. We prove the maximization to be solvable, whilst the minimization will turn out not to be solvable in general. We will also discuss the case of radial domains. In the second part of the paper, we study approximation and stability results regarding rearrangement optimization problems. First, we show that if a sequence of the generators of rearrangement classes converges, then the corresponding sequence of the optimal solutions will also converge. Second, a stability result regarding the Hausdorff distance between the weak closures of two rearrangement classes is presented. [ABSTRACT FROM AUTHOR]

This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius. [ABSTRACT FROM AUTHOR]

This paper considers the problem of recovering the delays and amplitudes of a weighted superposition of pulses. This problem is motivated by a variety of applications, such as ultrasound and radar. We show that for univariate and bivariate stream of pulses, one can recover the delays and weights to any desired accuracy by solving a tractable convex optimization problem, provided that a pulse-dependent separation condition is satisfied. The main result of this paper states that the recovery is robust to additive noise or model mismatch. [ABSTRACT FROM AUTHOR]

The expensive cost and intermittent availability of renewable energy bring great challenges to its efficient utilization in green data centers. In this paper, we propose a new way to achieve an explicit trade-off between operational cost and carbon emission by dynamic storing off-site renewable energy in distributed data centers. We first formulate a constrained stochastic optimization problem for cost minimization of data centers. Then, by leveraging Lyapunov optimization theory, we design an online Carbon Capped Cost Minimization algorithm (CCCM) to achieve a near-optimal cost with rigorous mathematical proof. Specially, the decisions at each time slot are determined with an efficient iterative algorithm based on the Generalized Benders Decomposition (GBD) technique. Finally, extensive simulations are conducted to show the effectiveness of our algorithm. The results show that our algorithm can save about 6% total costs compared with the algorithm without offsite energy storage. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in using metaheuristic algorithms to solve various complex engineering optimization problems. Most of the real-world problems comprise of more than one objective. Due to the inherent difficulty of such problems and lack of proficiency, researchers in different domains often aggregate multiple objectives and use single-objective optimization algorithms to solve them. However, the aggregation-based methods fail to solve the multi-objective problems (MOPs) effectively. Several multi-objective evolutionary algorithms (MOEAs) have been proposed and are being used to solve such problems in the past few years. In this paper, we propose an Emulous Mechanism-based multi-objective Moth–Flame Optimization (EMMFO) algorithm, where the moth positions are updated based on the pairwise competitions between the moths in each generation. The proposed EMMFO is tested on a diverse set of multi-objective benchmark functions like ZDT, DTLZ, WFG, CEC09 special session test suites and four constrained engineering design problems. The results are compared with various state-of-the-art multi-objective algorithms like NSGAII, SPEA2, PESA2, MOEA/D, MOPSO, MOACO, NSMFO, IEMO, CLPSO-LS, MOEA/D-CRA, PAL-SAPSO, and MORBABC/D. Extensive experimental results demonstrate superior optimization performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fourier analysis based optimization techniques have been developed for numerical schemes on structured grids and result in significant performance improvements. However, there lacks an effective optimization technique for numerical schemes on general unstructured grids, due to the mesh geometry complexity that makes Fourier transformation infeasible. To address this issue, an optimization framework based on machine learning is developed in this paper for numerical schemes on unstructured grids. The optimization of a compact high-order variational reconstruction on triangular grids is performed to illustrate the framework. An artificial neural network (ANN) is used to predict the optimal values of the derivative weights on cell interfaces that are the free parameters of the variational reconstruction, given the local geometry as input. The ANN is trained by minimizing the solution errors of a linear advection equation on a set of generated compact stencils, with a set of sampled sine waves as initial conditions. The developed optimization framework is applicable to general numerical schemes on unstructured grids as the training does not require explicit computation of dispersion or dissipation. Numerical results for inviscid flow problems show that the optimized variational finite volume scheme is remarkably more accurate and efficient than its non-optimized counterpart, demonstrating the effectiveness of the machine learning optimization. • A machine learning optimization framework for numerical schemes on unstructured grids. • A feedforward neural network to predict optimal parameter values of numerical schemes. • Training of the network by minimizing solution errors of a linear advection equation. • A machine learning optimized compact high-order variational reconstruction. • Generalization of the optimized variational reconstruction to nonlinear problems. [ABSTRACT FROM AUTHOR]

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world's largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship entry timings, and ship placements within the lock chamber. By analyzing navigational rules, operational characteristics, and existing problems of TGH, this research introduces an intelligent scheduling system framework. With the incorporation of multiple objectives and constraints, a mixed-integer nonlinear programming(MINLP) model is formulated. Subsequently, a hybrid intelligent algorithm based on simulated annealing and heuristic search is constructed to achieve the optimization of the lock schedule. The algorithm efficacy is validated using the real-world data from TGH. [ABSTRACT FROM AUTHOR]

Software systems rely on events for logging, coordination, handling unusual situations, and more. Monitoring event streams from systems that provide services can ensure the service complies with policies, regulations, and other business rules. Notably, monitoring to detect violations of rules as early as possible is much desired as, for example, the service may reclaim more resources from erring enactments. The primary goal of this paper is to develop techniques for detecting violations of time constraints as early as possible. We formalize a model for events and a language to specify constraints on event timing and data. We develop algorithms to detect violations of individual rules, then use a chase process to detect violations of acyclic sets of rules. We also present optimization techniques to reduce monitoring overhead. Finally, we implement and evaluate our algorithms through experiments to demonstrate our approach is feasible and beneficial. [ABSTRACT FROM AUTHOR]

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM 2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model. • Quantify the uncertainty of data to obtain a more complete physical meaning. • Fuzzy theory strategies more scientifically measure data characteristics. • The combination of trend term and fluctuation term process data more rationally. • The modified optimization algorithm expands the application scope of the model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method based on maximizing a periodogram-type function for parameter estimation of a two-dimensional (2-D) mono-component chirp signal model. The obtained estimators are called approximate least squares estimators (ALSEs). We also put forward a sequential algorithm for parameter estimation of a more general version of the model with multiple number of components. The main focus of the paper has been on developing the large-sample properties of the proposed estimators. Derived theoretical results show that the proposed estimators are strongly consistent and asymptotically normally distributed. Moreover, it is shown that the derived asymptotic distribution is identical to that of the traditional least squares estimators (LSEs). The numerical performance of the ALSEs and sequential ALSEs is demonstrated through extensive simulation studies. The results are positioned parallel to that obtained using the least squares method and 2-D multilag High Order Ambiguity function (2D-ml-HAF) for a comparative study. These simulations make use of the true values as the initial guesses in the optimization algorithm as using a fine grid search for this purpose is practically infeasible. We also provide a practical alternative for modeling of real world signals in an another set of simulation experiments. This involves a combination of two methods- 2D-ml-HAF estimators and genetic algorithm (GA). The results show that the proposed combination is a reasonable scheme to find initial guesses and provides satisfactory performance for varying signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

We introduce in this paper fixed-point proximity-gradient algorithms for solving a class of structured convex optimization problems arising from image restoration. The objective function of such optimization problems is the sum of three convex functions. We study in this paper the scenario where one of the convex functions involved is differentiable with a Lipschitz continuous gradient and another convex function is composed by an affine transformation. We first characterize the solutions of the optimization problem as fixed-points of a mapping defined in terms of the gradient of the differentiable function and the proximity operators of the other two functions. Then, a fixed-point proximity-gradient iterative scheme is developed based on the fixed-point equation which characterizes the solutions. We establish the convergence of the proposed iterative scheme by the notion of averaged nonexpansive operators. Moreover, we obtain that in general the proposed iterative scheme has O ( 1 k ) convergence rate in the ergodic sense and the sense of partial primal–dual gap. Under stronger assumptions on the convex functions involved the proposed iterative scheme will converge linearly. We in particular derive fixed-point proximity-gradient algorithms from the proposed iterative scheme. The quasi-Newton and the overrelaxation strategies are designed to accelerate the algorithms. Numerical experiments for the computerized tomography reconstruction problem demonstrate that the proposed algorithms perform favorably and the quasi-Newton as well as the overrelaxation strategies significantly accelerate the convergence of the algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a systematic comparative study of Analog and RF performances of an underlapped double gate (U-DG) NMOSFET with Gate Stack (GS) for varying straggle lengths. Asymmetric underlap devices (A-U-DG) have been proposed as one of the remedies for reducing Short Channel Effects (SCE's) with the underlap being present towards the source for sub 20 nm devices. However, the Source to Drain (S/D) implant lateral diffusion leads to a variation in the effective underlap length. This paper investigates the impact of variation of straggle length on the Analog and RF parameters of the device. The RF performance is analyzed by considering the intrinsic capacitances (C gd , C gs ), intrinsic resistances (R gd , R gs ), transport delay (τ m ), inductance (L sd ), cutoff frequency (f T ), and the maximum frequency of oscillations (f max ). The circuit performance of the devices are also studied. It is seen that the Analog and RF performances of the devices are improved by optimizing the S/D lateral straggle. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of in situ calibration of a pin hole-mounted microphone array for in-duct acoustic measurements. One approach is to individually measure the frequency response of each microphone, by submitting the probe to be calibrated and a reference microphone to the same pressure field. Although simple, this task may be very time consuming for large microphone arrays and eventually suffer from lack of access to microphones once they are installed on the test bench. An alternative global calibration procedure is thus proposed in this paper. The approach is based on the fact that the acoustic pressure can be expanded onto an analytically known spatial basis. A projection operator is defined allowing the projection of measurements onto the duct modal basis. The main assumption of the method is that the residual resulting from the difference between actual and projected measurements is mainly dominated by calibration errors. An iterative procedure to estimate the calibration factors of each microphone is proposed and validated through an experimental set-up. In addition, it is shown that the proposed scheme allows an optimization of physical parameters such as the sound speed and parameters associated to the test bench itself, such as the duct radius or the termination reflection coefficient. [ABSTRACT FROM AUTHOR]

The paper puts forward a new estimation method of modal parameters for dynamical systems. The problem of parameter estimation has been simplified to optimisation which is carried out using the ant colony system algorithm. The proposed method significantly constrains the solution space, determined on the basis of frequency plots of the receptance FRFs (frequency response functions) for objects presented in the frequency domain. The constantly growing computing power of readily accessible PCs makes this novel approach a viable solution. The combination of deterministic constraints of the solution space with modified ant colony system algorithms produced excellent results for systems in which mode shapes are defined by distinctly different natural frequencies and for those in which natural frequencies are similar. The proposed method is fully autonomous and the user does not need to select a model order. The last section of the paper gives estimation results for two sample frequency plots, conducted with the proposed method and the PolyMAX algorithm. [ABSTRACT FROM AUTHOR]

In this paper we study two types of means of the entries of a nonnegative matrix: the permanental mean , which is defined using permanents, and the scaling mean , which is defined in terms of an optimization problem. We explore relations between these two means, making use of important results by Egorychev and Falikman (the van der Waerden conjecture), Friedland, Sinkhorn, and others. We also define a scaling mean for functions in a much more general context. Our main result is a Law of Large Permanents, a pointwise ergodic theorem for permanental means of dynamically defined matrices that expresses the limit as a functional scaling mean. The concepts introduced in this paper are general enough so to include as particular cases certain classical types of means, as for example symmetric means and Muirhead means. As a corollary, we reobtain a formula of Halász and Székely for the limit of the symmetric means of a stationary random process. [ABSTRACT FROM AUTHOR]

An ability to shape frequency response of a vibrating plate according to precisely defined demands has a very high practical potential. It can be applied to improve acoustic radiation of the plate for required frequencies or enhance acoustic isolation of noise barriers and device casings by using both passive and active control. The proposed method is based on mounting severaladditional ribs and masses (passive and/or active) to the plate surface at locations followed from an optimization process. This paper, Part I, concerns derivation of a mathematical model of the plate with attached elements in the function of their shape and placement. The model is validated by means of simulations and laboratory experiments, and compared with models known from the literature. This paper is followed by a companion paper, Part II, where the optimization process is described. It includes arrangement of passive elements as well as actuators and sensors to improve controllability and observability measures, if active control is concerned. [ABSTRACT FROM AUTHOR]

This work discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package authored at Oak Ridge National Laboratory. Shift has been developed to scale well from laptops to small computing clusters to advanced supercomputers and includes features such as support for multiple geometry and physics engines, hybrid capabilities for variance reduction methods such as the Consistent Adjoint-Driven Importance Sampling methodology, advanced parallel decompositions, and tally methods optimized for scalability on supercomputing architectures. The scaling studies presented in this paper demonstrate good weak and strong scaling behavior for the implemented algorithms. Shift has also been validated and verified against various reactor physics benchmarks, including the Consortium for Advanced Simulation of Light Water Reactors' Virtual Environment for Reactor Analysis criticality test suite and several Westinghouse AP1000 ® problems presented in this paper. These benchmark results compare well to those from other contemporary Monte Carlo codes such as MCNP5 and KENO. [ABSTRACT FROM AUTHOR]

The Biot theory is commonly used for the simulation of the vibro-acoustic behaviour of poroelastic materials. However, it relies on a number of material parameters. These can be hard to characterize and require dedicated measurement setups, yielding a time-consuming and costly characterisation. This paper presents a characterisation method which is able to identify all material parameters using only an impedance tube. The method relies on the assumption that the sample is clamped within the tube, that the shear wave is excited and that the acoustic field is no longer one-dimensional. This paper numerically shows the potential of the developed method. It therefore performs a sensitivity analysis of the quantification parameters, i.e. reflection coefficients and relative pressures, and a parameter estimation using global optimisation methods. A 3-step procedure is developed and validated. It is shown that even in the presence of numerically simulated noise this procedure leads to a robust parameter estimation. [ABSTRACT FROM AUTHOR]

This paper proposes to use gravitational search algorithm (GSA) to design a supplementary sub-synchronous damping controller for the static synchronous series compensator (SSSC) device to damp the torsional oscillations. The IEEE second benchmark model (SBM) which is a series compensated ac system is considered in this study for design and analysis purpose. The design problem is formulated as an optimization problem and GSA is employed to search for the optimal controller parameters. The dynamic performance of the system under study is evaluated at various levels of series compensation with different types of disturbances. Fast Fourier Transform (FFT) analysis and robustness analysis against operating point changes and system uncertainties are also investigated. For specific system studied in the paper, the superiority of the GSA optimization technique over genetic algorithm (GA) optimization technique is also shown by comparing the simulation results and various performance indexes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study the Slater constraint qualification (CQ) for a semi-infinite optimization problem with upper-semicontinuous quasiconvex objective and constraint functions. Then, some Karush–Kuhn–Tucker (KKT) type necessary and sufficient optimality conditions as well as duality results are derived. The final part of the paper is devoted to a linear characterization of optimality and the gap function for considered semi-infinite problem. [ABSTRACT FROM AUTHOR]