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This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics. [ABSTRACT FROM AUTHOR]

The requirements of public administration institutions are increasing and projects becoming progressively challenging. Managing a project is a complex activity, in particular when it involves many people working over long periods of time and many different stakeholders. This increasing complexity requires management practices and tools that assure an efficient use of resources. In this context, a Project Management Office (PMO) can be of great value. In this position paper we discuss several scenarios for PMO implementation in the public administration sector, as a promoter of project success and a key ingredient for a better resources usage. [ABSTRACT FROM AUTHOR]

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user's received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability. • Maximizes the LEO system throughput while ensuring the GEO system service quality. • Builds a PPO algorithm based deep reinforcement learning framework. • Constructs two benchmark schemes: improved FO (IFO) and the DNN-accelerated IFO. • Achieves the highest LEO system throughput with the lowest computational complexity. • Yields the best system stability compared with the benchmarks. [ABSTRACT FROM AUTHOR]

The Diffusion Peterlin Viscoelastic Fluid (DPVF) model describes the movement of specific incompressible polymeric fluids. In this paper, we introduce and evaluate a new low-complexity linear-time filter finite element (FE) method for the DPVF model. In order to avoid the value at time t = − Δ t , the proposed time filter method consists of three steps, including a post-processing step. Firstly, a first-order Euler backward nonlinear fully discrete mixed FE scheme is employed to compute the numerical solutions at time t 1 = Δ t. For n ≥ 1 , we obtain the intermediate values ( u ˜ h n + 1 , p ˜ h n + 1 , d ˜ h n + 1) in Step II using a fully implicit backward Euler scheme. At the same time level, we proceed with these intermediate values ( u ˜ h n + 1 , p ˜ h n + 1 , d ˜ h n + 1) using the linear time filters. The linear time filters step does not significantly increase computational complexity. However, it can enhance temporal convergence accuracy from first order to second order for backward Euler time filter (BE time filter), and from second order to three order for BDF2 time filter. We demonstrate the almost unconditional stability of the scheme. Error estimates for the time filter method are derived and presented. Several numerical experiments are conducted to validate the theoretical findings and showcase the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a low-cost, penalty parameter-free, and pressure-robust Stokes solver based on the enriched Galerkin (EG) method with a discontinuous velocity enrichment function. The EG method employs the interior penalty discontinuous Galerkin (IPDG) formulation to weakly impose the continuity of the velocity function. However, despite its advantage of symmetry, the symmetric IPDG formulation requires a lot of computational effort to choose an optimal penalty parameter and compute different trace terms. To reduce such effort, we replace the derivatives of the velocity function with its weak derivatives computed by the geometric data of elements. Therefore, our modified EG (mEG) method is a penalty parameter-free numerical scheme that has reduced computational complexity and conserves the optimal convergence orders. Moreover, we achieve pressure robustness for the mEG method by employing a velocity reconstruction operator on the load vector on the right-hand side of the discrete system. The theoretical results are confirmed through numerical experiments with two- and three-dimensional examples. [ABSTRACT FROM AUTHOR]

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites' attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics. [ABSTRACT FROM AUTHOR]

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by taking full use of the special structure of coefficient matrix arising from complex Helmholtz equations, two types of optimized PU (OPU) methods are discussed theoretically and experimentally. Specifically, the convergence factors of these two OPU methods are less than 0.172, and the optimal result of first OPU method can reach 0.0396 with σ 1 ≥ σ 2 > 0 , which is currently the best theoretical result in the literatures. Moreover, the second OPU method has better computational advantages compared with the first OPU method as it avoids the inverse computation of W at each step, indicating the less CPU time will be costed by the second OPU method. In addition, the optimal parameters involved in our algorithms consist of constants, reducing the computational complexity associated with parameter selection. Finally, numerical results are given, not only show the effectiveness of OPU methods, but also confirm the rationality of theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data. • It studies a generalized HMM where observation depends on two-step states. • Two-filter smoothing formula and its generalization are established. • Auxiliary variable sampling technique is applied to draw samples. • It proposes two particle smoothing algorithms by using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]

Abrupt-motion tracking is challenging due to the target's unpredictable action. Although particle filter (PF) is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. This paper proposed a quantum-inspired particle filter for abrupt-motion tracking. We apply the concept of quantum superposition to transform classical particles into quantum particles. Quantum representation and corresponding quantum operations are addressed to utilize quantum particles. The superposition property of quantum particles avoids the concerns of particle impoverishment and sample-size dependency. The proposed diversity-preserving quantum-enhanced particle filter (DQPF) obtains better accuracy and stability with fewer particles. A smaller sample size also helps to reduce computational complexity. Moreover, it has significant advantages for abrupt-motion tracking. The quantum particles are propagated at the prediction stage. They will exist at possible places when abrupt motion occurs, which reduces the tracking delay and enhances the tracking accuracy. This paper conducted experiments compared to state-of-the-art particle filter algorithms. The numerical results demonstrate that the DQPF is not susceptible to motion mode and particle number. Meanwhile, DQPF maintains excellent accuracy and stability. • We transformed classical particles into quantum ones. • We addressed quantum representation and corresponding quantum operations. • The proposed DQPF avoids particle impoverishment and sample-size dependency. • The proposed DQPF has significant advantages for abruptmotion tracking. [ABSTRACT FROM AUTHOR]

In this paper, we consider and analyze the stability of three implicit/explicit (IMEX) schemes for the time-dependent natural convection problem, these considered numerical schemes contain the first order backward Euler scheme, second order Crank-Nicolson IMEX scheme and BDF2-AB2 combination. All numerical schemes deal with the linear terms implicitly and the nonlinear terms explicitly. Then the original nonlinear problem is split into two linearized subproblems with constant coefficient matrixes, which reduces the computational complexity and saves a lot of storage. The biggest feature of this paper is to establish the unconditional stability of three IMEX schemes for incompressible flows, which improves and supplements the published theoretical findings. Finally, some numerical examples are provided to show the performances of the considered numerical schemes. [ABSTRACT FROM AUTHOR]

Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cram é r-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations. [ABSTRACT FROM AUTHOR]

In this paper, some new approaches for stability and stabilizability determination as well as state feedback stabilization controllers of linear time-invariant (LTI) interval systems are proposed. The presented stability conditions are less conservative than those of Kharitonov's theorem, and Gerschgorin's disc theorem methods. Moreover, some of the proposed stability, stabilizability, and feedback stabilization control methods for LTI interval systems are proved to be sufficient and necessary conditions. Compared with some traditional stability analysis and feedback stabilization design methods for LTI interval systems, these new approaches have lower computational complexity because of a special form of parameter vertex matrices developed in this work. Some numerical and practical examples are given to demonstrate the effectiveness and advantages of the proposed methods. • The proposed stability conditions are less conservative compared to traditional methods. • One of the state feedback stabilization control methods is proven to be both a sufficient and necessary condition. • The computational complexity of the proposed strategies is substantially reduced, courtesy of the KCV matrix. [ABSTRACT FROM AUTHOR]

With the wide application of vision in the fields of autonomous driving and medical imaging, the demand for overexposure image correction algorithms is becoming increasingly urgent. However, existing overexposure image correction algorithms can lead to problems such as blurring, color bias, and over-enhancement of the generated images. Optimizing overexposure image quality has a significant impact on improving system performance, accuracy, and safety. In this paper, we propose an overexposure image correction network. First, we built the Detail Enhancement Module (DEM). It adopts global average pooling on each channel of the input feature map. After pooling, an activation function is used for nonlinear mapping to generate a channel attention weight vector. And it is multiplied with the original input feature map to achieve the purpose of enhancing the details of the overexposed image. Second, we construct a context-aware backbone (CAB) to extract features such as color and texture. The linear attention gating mechanism replaces the multi-head attention module in Transformer, and reduces the computational complexity in high-resolution images while maintaining performance by learning linear transformation and attention gating. Finally, we design an attention-guided feature fusion (AGFF) to fuse shallow and deep features. It computes weight vectors for shallow features through an attention module. The calculated result is converted to the same dimension as the input feature by bilinear interpolation, so as to enrich the semantic information and detailed information of the generated image. In addition to designing the network structure, we design a hybrid loss function to improve the quality of the generated image from the spatial and structural aspects, and the exposure function can correct the exposure degree of the generated image. Experiments are conducted on two public datasets and the dataset in this paper. Specifically, the PSNR and SSIM of images generated on the dataset MSEC increased by 1.3813% and 5.56%. The PSNR and SSIM of images generated on the dataset SICE increased by 1.545% and 4.64%. The proposed method can effectively generate clear and high-fidelity images. • An end-to-end overexposure correction algorithm. • The context-aware networks is constructed to extract exposed feature information. • The exposure loss is employed to prevent over-enhancement. • The proposed model aims to generator normally exposed images. [ABSTRACT FROM AUTHOR]

Fiber-reinforced concrete is a typical unidirectional fiber-reinforced composite. For practicality and efficiency, fiber-reinforced concrete usually has the fiber and matrix modeled separately and then the fiber is embedded into the matrix. Therefore, this paper proposes a novel method to realize the topology and fiber orientation optimization for fiber-reinforced concrete structures. The optimization formulation considers two types of discrete variables, i.e., topology and fiber orientation, which are optimized by using the solid isotropic material with penalization method and bi-value coding parameterization method, respectively. To reduce the computational complexity, the topology and fiber orientation are independently updated at each iteration during the optimization process. Numerical examples verify the validity of the proposed method, which provides new ideas for the design of fiber-reinforced concrete structures. • This paper proposes a topology optimization method for fiber-reinforced concrete structures using membrane-embedded model. [ABSTRACT FROM AUTHOR]

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is one of the most classical metaheuristic algorithms that has gained significant attention since its inception. It has some inherent advantages, such as easy implementation, rapid convergence, low computational complexity and so on. However, the drawbacks of being prone to local optimization and insufficient diversity cannot be ignored. Therefore, a new multiple adaptive co-evolved particle swarm algorithm (ACEPSO) with adaptive population grouping strategy, pros-cons coevolution mechanism, new co-evolved mechanism and adaptive mutation strategy is proposed in this paper. Firstly, ACEPSO partitions the overall population into two distinct subpopulations: elite population and common population. The size of the subpopulations undergoes variations at different stages. Secondly, the introduced pros-cons coevolution mechanism effectively improves the exploration ability of PSO. Meanwhile, a new co-evolved mechanism is proposed here aiming to enhance population diversity and balance the exploration and exploitation ability. This mechanism can better transfer information between individuals and promote effective collaboration. Finally, an adaptive mutation strategy is introduced. It improves the population diversity and prevents the algorithm from falling into local optimality productively. To validate the outstanding performance of ACEPSO, this paper compares it with various state-of-the-art metaheuristic algorithms as well as their variants on CEC2017 and CEC2022 test sets. The results exhibit that ACEPSO has a standout comprehensive performance. In addition, ACEPSO is utilized to tackle a set of twelve engineering optimization problems as well as 2D robot path planning problems. On all these complex optimisation problems, ACEPSO obtains the relatively best results. All the above results manifest that ACEPSO has great advantages and competitiveness in solving some of the optimization problems. [ABSTRACT FROM AUTHOR]

This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper , we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times. [ABSTRACT FROM AUTHOR]

In this paper, several local and parallel finite element algorithms are proposed and analyzed for the 2D/3D stationary inductionless incompressible magnetohydrodynamic (MHD) equations. The core concept is to guarantee the charge-conservation property by choosing mixed finite element spaces in H 0 (div , Ω) × L 0 2 (Ω) to approximate (J , ϕ) , meantime combining the idea of domain decomposition method to realize parallel operation. The characteristic of the proposed algorithms is that the computational complexity is greatly reduced while ensuring the accuracy of the numerical simulation. With the local a prior estimate as the technical means of theoretical analysis, we give the error estimates of the algorithms. Finally, several numerical experiments are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the main causes of permanent blindness, especially in middle-aged and elderly people. Early detection of DR is crucial for preventing vision loss. Automated DR grading can assist ophthalmologists to evaluate the severity of diseases and customize exclusive treatment plans. Most of the existing methods grade DR based on lesions such as microaneurysms and hemorrhage, which require pixel-level labeled images. The end-to-end networks require only image-level labels but often obtain relatively low accuracy due to the complexity of DR lesions. This paper proposed a multi-scale end-to-end network taking EfficientNet as the backbone to extract high-quality features and grade DR without introducing the lesion information. The proposed architecture combines the expansion techniques on the depth and width of the convolution layers to obtain a compact model with fewer parameters and computational complexity. The multi-scale features from different of EfficientNets are fused to capture fine details in the fundus images and obtain a more comprehensive and rich representation. The disease-attention module is involved to further focus on the crucial regions of the lesions for the proposed architecture. The method in this paper is verified on two public datasets, Messidor1 and IDRID, and experiments demonstrate that our method achieves high accuracy, precision, recall, and f1-score on the Messidor1 up to 97.5%, 98.6%, 97.1%, and 97.8% respectively, which is far better than CANet, Zoom-in-Net and Semi+Adv. • We adopt incorporation of Multi-scale Features. • Attention Mechanism - Disease Attention Module. • We select EfficientNet as Backbone. • An end-to-end network independence from Additional Lesion Information. [ABSTRACT FROM AUTHOR]

In visual tracking, the Transformer architecture is widely used because it can capture the global dependencies of sequence data without inductive bias. However, the attention mechanism of Transformer will bring ultra-high computational complexity and space occupancy, so that the tracking task cannot meet the real-time requirements. In this paper, we explore a sparsity region-aware attention mechanism. The sparse attention mechanism retains the regions with semantic relevance, and performs fine-grained attention calculation in this region. In the region-aware attention mechanism, a DropKey technique is introduced to reduce model over-fitting and improve the generalization ability of the model. Using region-aware attention as the basic building block, we design a dynamic region-aware Transformer backbone for visual tracking. This backbone network can effectively reduce the computational complexity while exploring global context dependencies. Based on the region-aware Transformer backbone network, this paper proposes a dynamic region-aware Transformer backbone visual tracking algorithm, which uses an optimization based model predictor to fully fuse object appearance and background information, so as to achieve more robust object tracking. The proposed tracker is trained in an end-to-end manner and experimentally evaluated on eight tracking benchmarks. Experimental results show that the algorithm has good tracking performance, especially in the application of unmanned aerial vehicle (UAV) tracking, our proposed tracker achieves an area under curve (AUC) score of 66.5% on the UAV123 dataset. Code is available at https://github.com/YSGFF/RTDiMP. [ABSTRACT FROM AUTHOR]

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far. [ABSTRACT FROM AUTHOR]

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications. • Developing an accurate dynamic model for a boost converter. • Proposing a long-horizon direct power model predictive control strategy. • Limiting the number of calculations and the execution time of the proposed strategy using move-blocking approach. [ABSTRACT FROM AUTHOR]

The integer motion estimation (IME) involves high computational complexity and large amount of computation data due to the variable block sizes, and it is one of the most critical bottlenecks in video coding. The traditional algorithm for integer motion estimation loses search accuracy when simplifying the motion search process, or it becomes complex to pursue search accuracy, which is not conducive to video encoding and transmission. This paper presents a data reuse algorithm based on search window division, block splitting, the arrangement of search points in the Test Zone (TZ) search algorithm and block division. which divides pixel blocks of different sizes into several 8x8 pixel blocks and then reuses the overlapping 8x8 pixel blocks between adjacent search points of the Test Zone (TZ) search algorithm to reduce hardware resource consumption. By comparing the Sum of Absolute Differences (SAD) values of left and top pixel blocks with current pixel block, the integer motion estimation (IME) can be terminated in advance, reducing the computation complexity of integer motion estimation by over 70 %. Furthermore, the paper also presents a hardware architecture based on the data reuse algorithm, which can process 7680 × 4320@60fps videos at an operating clock frequency of 182.99 MHz, with lower resource consumption compared to similar hardware designs. [ABSTRACT FROM AUTHOR]

Motion matching has become a widely adopted technique for generating high-quality interactive animation systems in video games. However, its current implementations suffer from significant computational and memory resource overheads, limiting its scalability in the context of modern video game performance profiles. Our method significantly reduces the computational complexity of approaches to motion synthesis, such as "Learned Motion Matching", while simultaneously improving the compactness of the data that can be stored and the robustness of pose output. As a result, our method enables the efficient execution of motion matching that significantly outperforms other implementations, by 8.5× times in CPU execution cost and at 80% of the memory requirements of "Learned Motion Matching", on contemporary video game hardware, thereby enhancing its practical applicability and scalability in the gaming industry and unlocking the ability to apply on large numbers of animated in-game characters. In this paper, we expand upon our published paper "Learning Robust and Scalable Motion with Lipschitz Continuity and Sparse Mixture of Experts", where we successfully proposed a novel method for learning motion matching that combines a Sparse Mixture of Experts model architecture and a Lipschitz-continuous latent space for representation of poses. We present further details on our method, with extensions to our approach to expert utilization within our neural networks. [Display omitted] • Applies the Sparse Mixture of Experts architecture to animation synthesis for the first time. • Significantly reduce theoretical and practical compute requirements, 8.5 times that of prior art. • Lipschitz-continuous encoding neural networks producing a linearly interpolatable manifold. • SMoE Networks express a higher density of animation data than Feed-Forward Neural Networks. [ABSTRACT FROM AUTHOR]

Abstract When analyzing the computational complexity of well-known puzzles, most papers consider the algorithmic challenge of solving a given instance of (a generalized form of) the puzzle. We take a different approach by analyzing the computational complexity of designing a "good" puzzle. We assume a puzzle maker designs part of an instance, but before publishing it, wants to ensure that the puzzle has a unique solution. Given a puzzle, we introduce the FCP (fewest clues problem) version of the problem: Given an instance to a puzzle, what is the minimum number of clues we must add in order to make the instance uniquely solvable? We analyze this question for the Nikoli puzzles Sudoku, Shakashaka, and Akari. Solving these puzzles is NP -complete, and we show their FCP versions are Σ 2 P -complete. Along the way, we show that the FCP versions of Triangle Partition , Planar 1- in -3 SAT , and Latin Square are all Σ 2 P -complete. We show that even problems in P have difficult FCP versions, sometimes even Σ 2 P -complete, though "closed under cluing" problems are in the (presumably) smaller class NP ; for example, FCP 2 SAT is NP -complete. [ABSTRACT FROM AUTHOR]

During the last few years, many deep learning (DL) methods have been proposed for the classification of hyperspectral images (HSI), and the final results show that these models have great potential. However, most existing deep models only consider the original spectral HSI datacube, excluding other sources of information such as textural and geometrical features. In addition to their computational complexity, most DL approaches cannot effectively consider local nonlinear relationships between features. In order to address these issues, this paper develops a new DL approach called MLKD-CNN, which uses multifeature local kernel descriptors (MLKDs) and convolutional neural networks (CNN) to classify HSIs. This method produces local kernel descriptors (LKDs) of spectral, extended morphological profiles, and Gabor features in the feature generation step. A simple CNN is then used to classify MLKD generated from spatial and spectral features. The proposed MLKD considers local nonlinear relationships of both spectral and spectral features, making them capable of achieving high accuracy levels while reducing the computational burden of deep methods. The experiments on four benchmark HSIs show that the proposed MLKD-CNN outperforms some recent HSI classification methods in terms of accuracy and computation time. [ABSTRACT FROM AUTHOR]

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms. • Multi-detection multi-target tracking using OTHR is studied by dimension descent. • The detection of new-born targets with unknown prior knowledge is considered. • The convergence of the novel multi-target tracking algorithm is analyzed. • The labeled GMPHD is proposed to reduce the computational burden. [ABSTRACT FROM AUTHOR]

Neural material representations are becoming a popular way to represent materials for rendering. They are more expressive than analytic models and occupy less memory than tabulated BTFs. However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained. While this is practical when there is no need to edit the material, it can become very limiting when the fragment of the material used for training is too small or not tileable, which frequently happens when the material has been captured with a gonioreflectometer. In this paper, we propose a novel neural material representation which jointly tackles the problems of BTF compression, tiling, and extrapolation. At test time, our method uses a guidance image as input to condition the neural BTF to the structural features of this input image. Then, the neural BTF can be queried as a regular BTF using UVs, camera, and light vectors. Every component in our framework is purposefully designed to maximize BTF encoding quality at minimal parameter count and computational complexity, achieving competitive compression rates compared with previous work. We demonstrate the results of our method on a variety of synthetic and captured materials, showing its generality and capacity to learn to represent many optical properties. • Neural BTF representations allow for efficient reflectance encoding. • Autoencoder models trained with data augmentation allow for material propagation. • Neural reflectance propagation enables the creation of tileable BTF. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. • Reputation-based control has a fault detection and isolation mechanism. • W-MSR and RCA-T use a fault-resilient control that make microgrids flexible. • The reputation-based algorithm tolerates switching topologies of communication graph. • The W-MSR and RCA-T algorithms do not require computational complexity on DG level. • The performance is evaluated via simulations in addition to theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective online portfolio selection strategy by integrating expert opinions, which are obtained based on mean reversion and trading volume. Existing studies have found that mean reversion and high volume premium exist in the stock market in the short term. Some online portfolio strategies have been proposed that rely upon mean reversion, but it is rare to consider both mean reversion and high volume premium, which is what we will do in this paper. First, a portfolio based on mean reversion and high volume premium is constructed using recent window data. Second, a pool of portfolios, also known as a pool of expert strategies or expert opinions, is established by changing the window size. Finally, the online gradient update algorithm is adopted to integrate a pool of expert strategies, and the MRvol strategy in this paper is proposed. Theoretically, we prove that the regret of MRvol is bounded. Empirically, we use the actual stock price and trading volume data from different markets to test the performance of MRvol. The results show that MRvol performs better than other online strategies in terms of final cumulative wealth and risk-adjusted return metrics in most cases, among which the annualized percentage yield on all datasets is 16%–37%, and the average values of Sharpe ratio, Calmar ratio and Information ratio are 1.113, 0.9216 and 0.0299, respectively. Additionally, MRvol has linear computational time complexity, and the running time of test results is all less than 1 s. Furthermore, it maintains strong robustness under different parameters and can bear reasonable transaction costs. • Mean reversion and high volume premium phenomena are used to design expert advice. • Online gradient update algorithm is applied to establish portfolio strategy MRvol. • It is proved that the MRvol strategy has a theoretical regret bound. • Extensive experiments show that MRvol performs well in multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

In computed tomography (CT) imaging, artifacts caused by metals can seriously interfere with the accurate judgment, evaluation, and analysis of object structures. Deep learning presented new opportunities for minimizing metal artifacts. The currently proposed supervised methods mainly resort to simulated data for network training, which is difficult to be applied in real scenarios. Unsupervised methods do not completely suppress artifacts and are prone to instability in the corrected results. Therefore, this paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The encoder and decoder based on the efficient Transformer achieve the extraction and processing of global image content features and artifact features. By correlating global features through attention mechanisms, vital features are generated to enhance the consistency of recovery information. The efficient Transformer framework reduces the computational complexity of the attention mechanism, allowing efficient processing of standard medical CT images with 512 × 512 pixels. Projection preserving loss uses the metal trace outer projection as a fidelity constraint to overcome the problem that unsupervised networks are difficult to train and prone to spurious information. Experimental results indicate that the proposed method outperforms the original CycleGAN and ADN networks in suppressing metal artifacts while avoiding introducing spurious structures. Specifically, the proposed method improved the PSNR of the correction results by 9.23% and reduced the NMAD by 13.21% compared to the original CycleGAN, and improved the PSNR by 5.17% and reduced the NMAD by 5.57% compared to the ADN. [Display omitted] This paper uses the CycleGAN architecture as the base framework to achieve unsupervised reduction of metal artifacts by introducing an efficient Transformer-based generator and a relative discriminator. The generation model consists of a feature extraction and encoding module and a feature decoding module, which consists of two parts: a forward loop and a backward loop. A relative discriminator is used to effectively discriminate and classify the generated image from the real image to more effectively facilitate artifact correction by the generator. The efficient Transformer-based encode enables the extraction and processing of global image content and artifact features and the correlation of global feature relevance through an attention mechanism. This approach produces stronger features to enhance the consistency of the recovered information. Using metal trace outer projection as a projection preserving loss enables more accurate recovery of image information and prevents the generation of spurious structures, further enhancing the fidelity of the recovered information. • Efficient Transformer Unsupervised MAR network effectively extracts CT features. • MAR based on global features improves the consistency of the recovered information. • Efficient Transformer module optimizes the computational efficiency and memory usage. • Projection preserving loss improves the recovery fidelity of image content. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by introducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol-level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given (n , k) Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when compared to other symbol-level iterative decoders while working at a relatively lower complexity. [ABSTRACT FROM AUTHOR]

This paper presents a distributed filtering problem for three-dimensional angle-only target tracking (AOTT) in sensor (i.e., observer) networks. An instrumental variable-based pseudo-linear information filter (IVIF) is firstly derived on the basis of the designed bias-compensated pseudo-linear information filtering, with the help of summation forms of information quantities and bias compensation in a centralized fusion manner. Then, the distributed IVIF (DIVIF) is put forward by using finite-time average consensus to obtain the arithmetic means of defined information quantities and compensated bias in observer networks, which ensures that the filtering result of every observer is consistent with the centralized one. Finally, the iteratively DIVIF is proposed via gradually approaching the true values of relative distance and the corresponding angles between the target and every observer to get the filtering parameters more and more accurately, in order to achieve higher filtering precision. In addition, the computational complexity of the proposed method is also analyzed. The advantages of filtering precision of the proposed method over the existing pseudo-linear Kalman filter and its variants are demonstrated by an AOTT example in observer networks in terms of iteration steps, different levels of process noises and observer's accuracy. • A distributed filtering problem is presented for angle-only target tracking. • An instrumental variable-based pseudo-linear information filter is derived. • The distributed implementation is designed through finite-time average consensus. • An iterative estimation is adopted to further improve filtering precision. • The computational complexity of the proposed method is analyzed. [ABSTRACT FROM AUTHOR]

Due to the complexity of process operation, industrial process data are often nonlinear and nonstationary, high dimensional, and multivariate with complex interactions between multiple outputs. To address all these issues, this paper proposes a novel industrial predictive model that integrates deep feature extraction and fast online adaptation, and can effectively deal with multiple process outputs. Specifically, a multi-output gradient radial basis function network (MGRBF) with excellent predictive capacity of nonstationary data is first used to provide preliminary prediction of target outputs. This prior quality information is combined with the original process input for deep feature learning and dimensional reduction. Through layer-wise feature extraction by the stacked autoencoder (SAE), deep quality-enhanced features can be obtained, which is further fed into a MGRBF tracker for online prediction. In order to timely capture the fast-changing process characteristics, the first two modules, namely, preliminary MGRBF predictor and SAE feature extractor are frozen after training, while the structure and parameters of the MGRBF tracker are updated online in an efficient manner. Two industrial case studies demonstrate that the proposed adaptive deep MGRBF network outperforms existing state-of-the-art online modeling approaches as well as deep learning models, in terms of both multi-output modeling accuracy and online computational complexity. • A quality-relevant feature extraction strategy integrating MGRBF predictor and SAE feature extractor. • An efficient online adaptation strategy is designed to tune the adaptive part of the deep model. • Evaluation with two industrial case studies. • Outperforming SOTA approaches with better prediction accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

• The dynamic equation of rigid-flexible coupled robotic systems with large beam deflections is modeled for the first time. • A novel minimal-learning-parameter neural network-based fixedtime sliding mode control scheme is proposed. • The fixed time stability of tracking errors is achievable for two angular positions. • The computational complexity of neural networks will be greatly reduced by resorting to minimal-learning-parameter method. The rigid-flexible coupled robotic mechanisms possess several desired properties for real mechanical applications only relying on the elastic deformation of the compliant beams/flexures. In this paper, a comprehensive modeling methodology, based on the virtual work principle, has been developed to characterize the rigid-flexible coupled robotic mechanisms with large deformations. Simultaneously, the neural network fixed-time slide mode trajectory controller is designed to adjust the desired angular positions of rigid-flexible coupled robotic mechanisms. The minimum-learning-parameter-based neural network is adopted to approximate the unknown dynamics of rigid-flexible coupled robotic mechanisms. Additionally, it is shown that the tracking errors enjoy practical fixed-time stability via the developed scheme. Finally, two numerical cases are implemented to verify the efficacy and superiority of the developed minimum-learning-parameter-based neural network-based fixed-time slide mode trajectory control method. [ABSTRACT FROM AUTHOR]

• We combine the domian knowledge of HSI with deep learning. • We develop an efficient unfolding network (EUNet) for HSI SR. • We design a lightweight HSI SR prior network with disentangled spatial-spectral representation. • Our EUNet can achieve comparable results in different scenarios with limited computing resources. Hyperspectral image super-resolution (HSI SR) is dramatically impacted by high spectral dimensionality, insufficient spatial resolution, and limited availability of training samples. Current approaches mainly rely on complex data-driven models to address some of these challenges, and the characteristics of HSI are not fully considered in the model design. In this paper, we propose an efficient unfolding network with disentangled spatial-spectral representation (EUNet) for HSI SR by combining domain knowledge (i.e., spectral correlation, degradation model, and structure prior) with deep learning. Specifically, the optimization process of the super-resolution prior-driven Maximum A Posterior (MAP) framework is unfolded into an interpretable multi-stage network, which inherits the advantages of deep learning-based image super-resolution (e.g., feature extraction in low-resolution space) and explicitly imposes the degradation model constraint. To well incorporate the structure prior of HSI, spatial and spectral feature extraction is disentangled by a variant of depthwise separable convolution, and spectral correlation is embedded by a lightweight spectral attention mechanism, so that the difficulty and computational complexity of feature learning are greatly reduced. Experiments on benchmark datasets with different degradation models demonstrate the feasibility and superiority of the proposed EUNet over other state-of-the-art methods in terms of evaluation metrics and computational complexity. The source code is available at https://github.com/denghong-liu/EUNet. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive direct power control (MP-DPC) with power compensation and circulating current reduction is proposed for modular multilevel converter under unbalanced grid conditions. The main objectives of this paper are to achieve the circulating currents reduction and the power ripples elimination under unbalanced grid-side voltage conditions. In order to realize the aforementioned objectives, a triple-stage MP-DPC method based on a decoupled mathematical model of circulating current and DC-link current components is presented, which results in the elimination of weighting factors. Meanwhile, a power compensation term is embedded in the proposed design so as to eliminate the power ripples. In this sense, the power compensation terms are expressed by grid voltages and their quadrature signals without the complex positive/negative sequences extraction of grid voltage/currents and phase-locked loop. The proposed methodology can not only relieve the effect of circulating currents but also eliminate the active and reactive power ripples while remaining computationally feasible under unbalanced grid conditions. Additionally, a reduced switching frequency approach is introduced to decrease the power loss. Finally, the effectiveness of the proposed MP-DPC method is validated by both simulation and experimental results. • A triple-stage FCS-MPC method is proposed to regulate multiple control objectives of MMC, which results in the elimination of weighting factors. • To enhance the grid-side power quality, the proposed strategy takes power compensation scheme into consideration under unbalanced grid-side conditions. • Compared with the conventional MP-DPC method, the circulating current and switching frequency are restrained in this design while not punishing the computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, an efficient dimension splitting method is applied to the two-dimensional (2D) and three-dimensional (3D) Cahn-Hilliard type equations with significant applications in physical, biological and computer science. The proposed method can greatly reduce the storage requirements and computational complexity in two and three dimensions, and preserve the high precision and mass conservation. The dimension splitting method is based on auxiliary variables for spatial derivative terms and the operator splitting approach. It converts the 2D or 3D problem into a series of one-dimensional (1D) problems which can be solved by using multi-thread. The stability and accuracy of the proposed method are improved by a local stabilizing approach and extrapolation. The analyses of discrete energy and discrete mass conservation property are shown. Numerical examples confirm the advantages of proposed method. And a variety of simulations, such as the phase separations, curvature driven flows, diblock copolymers, tumor growth and volume restoration, are performed with respect to practical applications. • Dimension splitting method for Cahn-Hilliard type equations. • The global stability is achieved by the local stability of solving each sub-problem. • Fourth-order mass conservative scheme based on the compact differencing and extrapolation. • A large number of numerical examples for practical applications. [ABSTRACT FROM AUTHOR]

The concept of mutual-visibility in graphs has been recently introduced. If X is a subset of vertices of a graph G, then vertices u and v are X- visible if there exists a shortest u , v-path P such that V(P) ∩ X ⊆ { u , v}. If every two vertices from X are X- visible, then X is a mutual-visibility set. The mutual-visibility number of G is the cardinality of a largest mutual-visibility set of G. It is known that computing the mutual-visibility number of a graph is NP-complete, whereas it has been shown that there are exact formulas for special graph classes like paths, cycles, blocks, cographs, and grids. In this paper, we study the mutual-visibility in distance-hereditary graphs and show that the mutual-visibility number can be computed in linear time for this class. [ABSTRACT FROM AUTHOR]

Person image generation is a challenging task aimed to transfer the person of the source image from a source pose to a target pose while preserving its style. In this paper, we proposed a Generative Adversarial Network based on Decoupled Semantic Attention Transfer (DSAT-GAN), focusing on that local semantic representations of different image styles and contents cannot be accurately decoupled and transferred. This architecture employs a novel Multi-scale Semantic Mapping Generation Network (Ms-SMGN), driven by two network modules with different semantic attention mechanism, aiming to accurately align and transfer the representations of local semantics at different spatial scales. Then, a channel-separated convolution is applied in the encoding networks instead of the traditional channel fully-connected operation, which reduces computational complexity while realizing channel semantic decoupling. Moreover, a Gram matrix-based global style loss is introduced to further enhance the consistency of high-level semantic between generated and target images. Experiments on Market-1501 and DeepFashion datasets show that DSAT-GAN has superior performance compared with other recent baselines. Additionally, this architecture can be extended to the data enhancement scenes to significantly improve the accuracy of person Re-identification. [Display omitted] • Our model alleviates the loss of semantic features of complex alignment regions. • Encoders learn feature vectors with channel-separated convolution and attention. • Decoders can fuse and decode feature vectors and generate integrated person details. • Global style loss constrain the high-level semantic of generated person images. • The experimental results on the datasets demonstrate the superiority of our model. [ABSTRACT FROM AUTHOR]

The complexity of variants of 3 -SAT and Not-All-Equal 3 -SAT is well studied. However, in contrast, very little is known about the complexity of the problems' quantified counterparts. In the first part of this paper, we show that ∀∃ 3 -SAT is Π 2 P -complete even if (1) each variable appears exactly twice unnegated and exactly twice negated, (2) each clause is a disjunction of exactly three distinct variables, and (3) the number of universal variables is equal to the number of existential variables. Furthermore, we show that the problem remains Π 2 P -complete if (1a) each universal variable appears exactly once unnegated and exactly once negated, (1b) each existential variable appears exactly twice unnegated and exactly twice negated, and (2) and (3) remain unchanged. On the other hand, the problem becomes NP-complete for certain variants in which each universal variable appears exactly once. In the second part of the paper, we establish Π 2 P -completeness for ∀∃ Not-All-Equal 3 -SAT even if (1′) the Boolean formula is linear and monotone, (2′) each universal variable appears exactly once and each existential variable appears exactly three times, and (3′) each clause is a disjunction of exactly three distinct variables that contains at most one universal variable. On the positive side, we uncover variants of ∀∃ Not-All-Equal 3 -SAT that are co-NP-complete or solvable in polynomial time. [ABSTRACT FROM AUTHOR]

Since it was invented, Elliptic Curve Cryptography (ECC) is considered an ideal choice for implementing public key cryptography in resource constrained devices, thanks to its small keys. Scalar point multiplication is the central and the complex operation in the cryptographic computations of ECC that requires a lot of optimizations on execution time and energy consumption especially in low computing power devices such as embedded systems. Thus, many scalar point multiplication algorithms are proposed, each using their own computational techniques and mathematical formulas. In this paper, we have combined the computational techniques with some optimized mathematical formulas and we have implemented them on some Elliptic Curves scalar point multiplication algorithms over finite field. The aim of this work is to identify the most efficient algorithm that combine the best computational technique and the mathematical formulas, and consequently offer less-memory requirements and faster field arithmetic operations. The results show that Montgomery ladder algorithm in co-Z addition with update formula gives better results compared to the other algorithms implemented in this paper. [ABSTRACT FROM AUTHOR]

• VTR-based modeling framework eliminates variable time-delay while building soft sensor. • Variable Time-delay and model parameters are cooperatively estimated by IDE and VB algorithms. • Three novel soft sensors are developed with reconstructed dataset under the VTR framework. • Qualitative process knowledge turns to be quantitative through VTR-based modeling framework. Due to the difference of variable positions brought by process structure, time-delay exists between process variables and quality variables. In this paper, this commonly overlooked problem in data-driven soft sensor modeling is illustrated and solved. The main idea in this paper is to take the variable time-delay (VTD) as a model parameter to reconstruct the dataset and then solve it through optimizing the objective function of models. However, the combination of VTD would lead to an intractable high computational complexity, then it is proposed to use an efficient population-based Integer Differential Evolution (IDE) algorithm to select the optimal VTD values and cooperatively learn model parameters. With the help of IDE algorithm, a Variable Time Reconstruction (VTR) modeling framework is then formulated for soft sensor development. As examples, three types of VTR-based soft sensors are developed under this framework to cope with different cases of data features. The presented numerical and industrial cases demonstrate that the proposed VTR-based model can effectively learn the VTD values, which can reconstruct and recover the original data pattern, and thus significantly help increase the generalization performance of soft sensor models. [ABSTRACT FROM AUTHOR]

Visual tracking is an art of tracking a moving object over video frames using non-stationary cameras, for which feature descriptors of the target are computed and applied to a motion model. In this paper, SURF is used to compute the feature descriptors and Mean-Shift algorithm as a motion model. M ean - shift algorithm is fundamentally a logical approach to track the object on an image frame where the appearance is described by histograms. Mean-Shift tracking can directly be applied to SURF features but there is a big constraint of availability of an adequate number of feature keypoints for a given object. To get over this limitation, the re-projection technique is implemented in this paper for tracking an object in any video recorded from a mobile or stationary camera. Re-projection, online updates the histogram of object template for every frame by superimposing the feature keypoints from upcoming frames to the first frame. The object can be tracked using SURF descriptors without using any secondary information (color, texture, optical flow, gradient, etc.) regarding the object, which makes it computationally inexpensive to be used in real-time systems. Also, the SURF feature is calculated only for the target object that is to be tracked, this further reduces computational complexity. The simulation results prove the effectiveness of the work. [ABSTRACT FROM AUTHOR]

The paper presents necessity of data pre-processing to process the data set through machine learning algorithms, and it filters the raw data to make the data as in compatible format for analysis purpose. The methods such as mean normalization, feature scaling and dimensionality reduction are used for data smoothing and therefore formatted data set gets processed by learning algorithms to predict best possible outcome as per the analysis. Machine learning became famous for such processing purpose and through unsupervised learning we are capable of processing over various data formats. Such algorithms performance is up-to the mark, but as per the availability of inherent parallel processing through quantum machines, computational speedup can be achieved by designing classical equivalent quantum machine learning algorithms. In this paper, we discussed some of the classical unsupervised learning algorithms, and then we propose the equivalent quantum version of algorithms along with the mathematical justification over the complexity analysis and achieved computational speedup and show the betterment by processing such problems over quantum machines. [ABSTRACT FROM AUTHOR]