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Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]

Clutter modeling is used in diverse Synthetic Aperture Radar (SAR) image processing fields, e.g., speckle suppression, detection, classification, and recognition. Therefore, accurate formulation of SAR clutter models is crucial to achieve effective performance in such applications. We present a survey on parametric and semi-parametric distributions in characterizing SAR ground clutter statistics, and we compare estimators for their parameters (when explicitly available) with a theoretical assessment of their computational burden. Furthermore, we discuss how to assess these models with the $\widetilde {k}_{3} \sim \widetilde {k}_{2}$ diagram. We also discuss how to analyze the homogeneity of SAR clutter data using clutter models’ coefficient of variation ( $C_{v}$ ) to justify their effectiveness in portraying scene heterogeneity. Experiments are made on simulated data and SAR images to assess the goodness-of-fit, deviation of estimated $C_{v}$ from the observed $C_{v}$ , and computational complexity for the state-of-the-art clutter models, thereby assessing their effectiveness in characterizing SAR clutter amplitude statistics. The MATLAB code that implements these tools is available at https://github.com/dkmahapatra1/SAR-Clutter-Modelling.git

In binary optimization problems, where the goal is to find the input $\boldsymbol {x}$ that minimizes a given objective function, Grover adaptive search (GAS) is a well-known quantum algorithm that provides quadratic speedup when compared with the brute-force approaches of classical computers. GAS calls a renowned quantum search algorithm, Grover search (GS), as a subroutine to search for inputs with function values less than the threshold value. If such an input is found, the threshold value is updated with the function value and the search targets are narrowed. To search efficiently for a solution, an appropriate number of queries must be selected to amplify the desired state of the GS in each step. This paper discusses this issue and proposes a new method for selecting the number of queries and provides proof that our method achieves quadratic speedup as well as the original GAS. Furthermore, the simulation results for 40-bit problems confirm that our method allows an optimal solution with 22% fewer queries than the standard method, thus offering the possibility of significantly reduced computation times for combinatorial optimization problems.

Gossip algorithms are often considered suitable for wireless sensor networks (WSNs) because of their simplicity, fault tolerance, and adaptability to network changes. They are based on the idea of distributed information dissemination, where each node in the network periodically sends its information to randomly selected neighbors, leading to a rapid spread of information throughout the network. This approach helps reduce the communication overhead and ensures robustness against node failures. They have been commonly employed in WSNs owing to their low communication overheads and scalability. The time required for every node in the network to converge to the average of its initial value is called the average time. The average time is defined in terms of the second-largest eigenvalue of a stochastic matrix. Thus, estimating and analyzing the average time required for large-scale WSNs is computationally complex. This study derives explicit expressions of average time for WSNs and studies the effect of various network parameters such as communication link failures, topology changes, long-range links, network dimension, node transmission range, and network size. Our theoretical expressions substantially reduced the computational complexity of computing the average time to $O\left(n^{-3}\right)$. Furthermore, numerical results reveal that the long-range links and node transmission range of WSNs can significantly reduce average time, energy consumption, and absolute error for gossip algorithms.

The new coding standard Versatile Video Coding (VVC) introduces additional encoding techniques based on the existing video coding standard, such as the difference in block partition structures. While these new technologies bring about enhanced encoding performance, they also result in a significant increase in encoding time complexity. In the paper, we propose a decision algorithm to partition fast CUs, which is based on CNN networks and FSVM. Initially, the algorithm utilizes depth information formed by combining inter-frame correlations as input to our trained CNN model, predicting the optimal depth for encoding.Following that, the second algorithm based on FSVM is introduced. The F-Score method is employed to extract appropriate features for constructing FSVM. Within the predicted depth range from the initial algorithm, the CU partitioning mode is predicted, leading to an additional reduction in encoding complexity. Our experimental results demonstrate that the proposed algorithm can save 53.55% of encoding time, with a marginal increase of only 1.47% in BDBR.It achieves a favorable balance between video quality and encoding speed.

This paper proposes the Set-Membership sign-NLMS (SM-sign-NLMS) adaptive filter, which combines the ability for data censoring (offered by Set-Membership schemes) with robustness against impulsive noise (provided by signed schemes). The algorithm can present a much lower steady-state probability of update than the standard SM-NLMS algorithm when impulsive noise is present in the system. It is derived from a local deterministic optimization problem modulated by a minimum disturbance cost function combined with a bounded error criterion. Several stochastic models are proposed in order to extract insights and a time-variant step size extension of the algorithm. The first of them, based on energy conservation arguments, leads to a fixed-point analytic equation whose solution predicts the asymptotic performance of the algorithm. Further, a transient analysis based on a statistical decoupling of the radial and (discrete) angular distributions of the input vector is derived. Based on such an analysis, an efficient time-variant step-size version of the algorithm is proposed. Additionally, such an analysis is also utilized to obtain a fixed-point formula whose solution describes the asymptotic performance when the unknown plant that the filter intends to match varies according to a first-order Markovian model. Lastly, a novel stochastic model is advanced for the description of the algorithm learning behavior under a deficient-length scenario for a white input signal, which provides some insights about the asymptotic performance of the algorithm. The findings are confirmed by extensive simulations.

A decomposed dynamic dispatching and preventive maintenance (D-DDPM) framework is presented to overcome the computational challenges of solving large DDPM problems. Stochastic deterioration of machine health is explicitly considered. By efficient allocation of individual machine’s capacity under different machine health, the D-DDPM framework decomposes the global DDPM problems into single-machine DDPM problems while ensuring throughput optimality. Intelligent machine agents are then designed to solve the single-machine DDPM problems efficiently and to perform real-time decisions for individual machines. After the decomposition, the overall computational complexity grows only linearly with the number of machines and the proposed framework could be adopted in large systems. In the numerical study, the D-DDPM framework is near-optimal in small systems that have optimal DDPM solutions. In which, the computation time is reduced by 90.72% and the average cycle time is within 6% of optimal. For large systems without optimal DDPM solutions, numerical results show that the D-DDPM maintains throughput optimality and reduces average cycle time by up to 90% against other control policies. [ABSTRACT FROM AUTHOR]

The universality check of Büchi automata is a foundational problem in automata-based formal verification, closely related to the complementation problem, and is known to be computationally difficult, more concretely: PSPACE-complete. This article introduces a novel approach for creating labelled datasets of Büchi automata concerning their universality. We start with small automata, where the universality check can still be algorithmically performed within a reasonable timeframe, and then apply transformations that provably preserve (non-)universality while increasing their size. This approach enables the generation of large datasets of labelled Büchi automata without the need for an explicit and computationally intensive universality check. We subsequently employ these generated datasets to train Graph Neural Networks (GNNs) for the purpose of classifying automata with respect to their universality resp. non-universality. The classification results presented in this article indicate that such a network can learn patterns related to the behaviour of Büchi automata that facilitate the recognition of universality. Additionally, our results on randomly generated automata, which were not constructed using the aforementioned transformation techniques and classified algorithmically, demonstrate the network’s potential in classifying Büchi automata with respect to universality, extending its applicability beyond cases generated using a specific technique.

Driver Monitoring Systems (DMS) represent a promising approach for enhancing driver safety within vehicular technologies. This research explores the integration of neuromorphic event camera technology into DMS, offering faster and more localized detection of changes due to motion or lighting in an imaged scene. When applied to the observation of a human subject event camera provides a new level of sensing capabilities over conventional imaging systems. The study focuses on the application of DMS by incorporating the event cameras, augmented by submanifold sparse neural network models (SSNN) to reduce computational complexity. To validate the effectiveness of proposed machine learning pipeline built on event data we have opted the Driver Distraction as a critical use case. The SSNN model is trained on synthetic event data generated from the publicly available Drive&Act and Driver Monitoring Dataset (DMD) using a video-to-event conversion algorithm (V2E). The proposed approach yields comparable performance with state-of-the-art approaches, achieving an accuracy of 86.25% on the Drive&Act dataset and 80% on comprehensive DMD dataset while significantly reducing computational complexity. In addition, to demonstrate the generalization of our results the network is also evaluated using locally acquired event dataset gathered from a commercially available neuromorphic event sensor.

A transformer can capture long-term dependencies through an attention mechanism, and hence, can be applied to various vision tasks. However, its secondary computational complexity is a major obstacle in vision tasks that require accurate predictions. To address this limitation, this study introduces linear-window attention (LWA), a new attention model for a vision transformer. The transformer computes self-attention that is restricted to nonoverlapping local windows and represented as a linear dot product of kernel feature mappings. Furthermore, the computational complexity of each window is reduced to linear from quadratic using the constraint property of matrix products. In addition, we applied the LWA to feature matching to construct a coarse-to-fine-level detector-free feature matching method, called transformer with linear-window attention for feature matching TRLWAM. At the coarse level, we extracted the dense pixel-level matches, and at the fine level, we obtained the final matching results via multi-head multilayer perceptron refinement. We demonstrated the effectiveness of LWA through Replace experiments. The results showed that the TRLWAM could extract dense matches from low-texture or repetitive pattern regions in indoor environments, and exhibited excellent results with a low computational cost for MegaDepth and HPatches datasets. We believe the proposed LWA can provide new conceptions for transformer applications in visual tasks.

Conventional coherent chaos-based communication systems require synchronization of chaotic signals, which is still practically unattainable in a noisy environment. Moreover, in non-coherent schemes, a part of the bit duration is spent sending non-information-bearing reference samples, which deteriorates the Bit Error Rate performance (BER) of these systems. To tackle these problems, this paper designs an $M$ -ary Deep Learning Chaos Shift Keying $(M$ -ary DLCSK) system. The proposed receiver uses a Convolutional Neural Network (CNN)-based classifier that recovers $M$ -ary modulated data. The trained NN model grasps different chaotic maps, estimates channels, and classifies the received signals effectively. Moreover, we consider a Transfer Learning (TL) framework that enhances the noise performance and classification results. Due to the generalization capabilities of TL, the trained NN can work in different Signal-to-Noise Ratio (SNR) conditions without the need for re-training. We compare the BER performance, complexity, and bandwidth efficiency of the $M$ -ary DLCSK receiver with existing receivers. The results demonstrate that the $M$ -ary DLCSK receiver is the first practical system that achieves the theoretical BER performance of the coherent CSK systems under Rayleigh fading channels. Moreover, the proposed system provides a considerable performance advantage compared to the existing DL-based receivers under Rayleigh fading channels. For example, the BER performance of 8-ary DLCSK shows a gain of 0.1 over the Long Short-Term Memory (LSTM)-aided DNN systems at the target $E_{b}/N_{0}=14dB$ . These features make $M$ -ary DLCSK an attractive candidate for several applications, such as Massive Multiple-Input Multiple Output (MIMO), Vehicle-to-everything (V2X), Quantum, and optical communication systems.

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models.

Zernike radial polynomials (ZRP) play a significant role in application areas such as optics design, imaging systems, and image processing systems. Currently, there are two kinds of numerical schemes for computing the ZRP automatically with computer programs: one is based on the definition in which the factorial operations may lead to the overflow problem and the high order derivatives are troublesome, and the other is based on recursion which is either unstable or with high computational complexity. In this paper, our emphasis is focused on exploring the balanced binary tree (BBT) schemes for computing the ZRP: firstly an elegant formulae for computation is established; secondly the recursive and iterative algorithms based-on BBT are proposed; thirdly the computational complexity of the algorithms are analyzed rigorously; finally the performance of BBT schemes by testing the running time is verified and validated. Theoretical analysis shows that the computational complexity of balanced binary tree recursive algorithm (BBRTA) and iterative algorithm are exponential and quadratic respectively, which coincides with the running time test very well. Experiments show that the time consumption is about 1~10 microseconds with different computation platforms for the balanced binary tree iterative algorithm (BBTIA), which is stable and efficient for real-time applications. In the sense of STEM education, the connection of the BBT and ZRP exhibits the beauty and applications of discrete mathematical structure behind the engineering problem, which is worthy of introducing to the college students, computer programmers and optics engineers.

The problem of query answering over incomplete attributed graph data is a challenging field of database management systems and artificial intelligence. When there are rules on data structure expressed in the form of the ontology, the theoretical complexity of finding exact solution satisfying ontology constraints increases. Logic-based methods use theoretical constructions to obtain efficient rewritings of the original queries with respect to ontology and find an answer to the rewriting query over incomplete data. However, there is an opportunity to use faster machine learning methods to label all the data and query over the “most probable” data model without taking into account the ontology. This research paper investigates the effectiveness and trustworthiness of both mentioned approaches for answering ontology-mediated queries on graph databases that integrate an ontology with a covering axiom, which states that every node belongs to either of two classes. The first approach involves finding precise answers through logical reasoning and rewriting the problem into a datalog program, while the second approach employs a trained graph neural network to label data in a binary classification problem and leverages SQL for query answering. We conduct an in-depth analysis of the time performance of these approaches and evaluate the impact of training set selection on their ability of correct query answering. By comparing these approaches across various experiments, we provide insights into their strengths and limitations for answering ontology-mediated queries containing a Boolean conjunctive query. In particular, we showed the importance of logic-based approaches for ontology with a covering axiom and the inability of machine learning methods to find answers for ontology-mediated queries in large networks.

The broad application area and accompanying challenges make machine learning-based recognition of handwritten scripts a demanding field. Individuals’ writing practices and inherent variations in the size, shape, and tilt of characters may increase the difficulty level. Deep convolutional neural network (DCNN) models have been successful in solving pattern recognition problems, but at the expense of a considerable number of trainable parameters and heavy computational loads. The proposed work addresses these problems by using the shifted window (SWIN) transformer method to recognize handwritten Devanagari numerals for the first time. In the presented model, the SWIN transformer is finely tuned to withstand popular DCNN models, such as VGG-16Net, ResNet-50, and DenseNet-121, in terms of recognition accuracy, space requirement, and computational complexity. The model successfully attained a recognition accuracy of 99.20% with only 0.218 million trainable parameters and 0.0912 giga floating-point operations per second (FLOPs). This indicates the validity and soundness of the proposed model for recognizing handwritten Devanagari numerals.

Large-scale quantum information processing requires the use of quantum error-correcting codes to mitigate the effects of noise in quantum devices. Topological error-correcting codes, such as surface codes, are promising candidates, as they can be implemented using only local interactions in a 2-D array of physical qubits. Procedures, such as defect braiding and lattice surgery, can then be used to realize a fault-tolerant universal set of gates on the logical space of such topological codes. However, error correction also introduces a significant overhead in computation time, the number of physical qubits, and the number of physical gates. While optimizing fault-tolerant circuits to minimize this overhead is critical, the computational complexity of such optimization problems remains unknown. This ambiguity leaves room for doubt surrounding the most effective methods for compiling fault-tolerant circuits for a large-scale quantum computer. In this article, we show that the optimization of a special subset of braided quantum circuits is NP-hard by a polynomial-time reduction of the optimization problem into a specific problem called PlanarRectilinear3SAT.

Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with $1.8-100\times $ faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD. [ABSTRACT FROM AUTHOR]

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine. [ABSTRACT FROM AUTHOR]

The tridiagonal solver is an important kernel and is widely supported in mainstream numerical libraries. While parallel algorithms have been studied for many-core architectures, the performance of current algorithms and implementations is still hindered by input size sensitivity and cross-platform portability. In this paper, we propose a novel algorithm WM-pGE for the batched solution of diagonally dominant tridiagonal systems. The algorithm balances the key design objectives, including computation complexity, memory complexity, parallelism, and input size sensitivity, better than existing algorithms. Moreover, an elegant formulation is presented to show the implementation and cross-platform optimization without loss of efficiency and generality, by extracting the platform-dependent works into only four vector operators. The results from our batched tridiagonal experiments show that the proposed algorithm outperforms the prior work PCR-pThomas by 25% and 12% on NVIDIA Tesla V100 in single and double precision, respectively. On Intel KNL, our method achieves a 10% improvement in performance over PCR-pThomas in double precision. [ABSTRACT FROM AUTHOR]

In this article, event-triggered (ET) observers and ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ controllers are investigated for physically interconnected nonholonomic mechanical agents in harsh conditions, such as skidding, slipping, and dead-zone disturbances. The agents’ models are presented by strict-feedback nonlinear large-scale systems, but unlike the existing studies, unknown dynamics with feedback outputs are considered, and assumptions of polynomial-type nonlinearities for physical interconnection functions are relaxed. Initially, the observer of unmeasurable states via the outputs is designed. Then, ET augmented controllers are established to transform the physically interconnected system into isolated subsystems connected by a communication network. By utilizing the local information of each agent and adaptive dynamic programming (ADP), ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ optimal control laws and disturbance rejection laws are derived. The parameters of the ET observers and controllers are synchronously updated online by event-triggering mechanisms; thus, it reduces computational complexity and communication. It is guaranteed that the states in the closed dynamics are $L_{2}$ -gain bounded and the Zeno behavior is avoided. Additionally, the convergence of cost functions to the near-optimal values is accelerated by a concurrent learning technique, avoiding dependence on the online examination of the persistence of excitation conditions. Finally, the control performance of a nonholonomic multirobot system in a comparative simulation study shows that the proposed observers and controllers are effective. [ABSTRACT FROM AUTHOR]

The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the support vector machine (SVM) or multilayer perceptron (MLP). DD’s main concept is that the algorithm can recognize this class after learning, if the output’s logical expression contains the corresponding class’s logical relationship among inputs (and $\backslash $ or $\backslash $ not). Experiments and main results: DD, a white-box ML algorithm, showed excellent system identification performance for the black-box system. Second, it was verified by nine real-world applications that DD brought better generalization capability relative to the MLP architecture that imitated neurons’ cell body (Cell body Net) for regression. Third, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than the cell body net for classification. The number of modules can effectively adjust DD’s logical expression capacity, which avoids overfitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forwardpropagation. The main contribution of this article is the basic ML algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at https://github.com/liugang1234567/Gang-neuron. [ABSTRACT FROM AUTHOR]

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Visual surveillance for autonomous power facility inspection is considered to bethe prominent field of study in the power industry. This research field completely focuses on either object detection or image fusion which lacks the overall consideration. By considering this, a single end-to-end object detection method by incorporating the image fusion named Fast Detection Fusion Network (FDFNet) is proposed in this paper to output qualitative fused images with detection results. The parameters in the FDFnet are greatly reduced by sharing the feature extraction network between image fusion and object detection tasks, due to which a huge reduction in computational complexity is achieved. On this basis, the object detection algorithm performance on various types of power facility images is compared and analyzed. For experimentation purposes, an IR (infrared) and VIS (visible) image acquisition system has also been designed. In addition, a dataset named CVPower with different sets of images for power facility fusion detection is constructed for this research field. Experimental results demonstrate that the proposed method can achieve the mAP of not less than 70%, process 2 frames per second, and produce high qualitative fused images. [ABSTRACT FROM AUTHOR]

This article presents a learning-based controller to solve the cooperative formation control problem for multiagent system (MAS) with collision avoidance. First, the consensus problem of first-order MAS is mostly solved by linear matrix inequality (LMI) without consideration of energy loss. To overcome these difficulties, an adaptive dynamic programming (ADP) technique is fit to solve the consensus problem and similar formation control problem for second-order MAS by the establishment of a performance index function. Besides, we introduce the generalized policy iteration (GPI) algorithm as a kind of ADP technique without the problem of low convergence speed and high computational complexity. Combined with previous works, it can be found that our proposed structure can be extended to high-order cases based on the structure of local neighborhood formation error and algorithm. Afterward, the convergence analysis, optimality analysis, and stability analysis are given. Neural networks (NNs) are also implemented to approximate the iterative control policies and value functions, respectively. Moreover, we realize that many collisions may occur in the formation control problem. Inspired by the idea of the artificial potential field (APF) technique, the concept of the repulsive force field is introduced based on our proposed learning-based structure to avoid collisions simply and efficiently. Finally, a simulation is provided to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

We propose a novel semi-supervised learning (SSL) assumption—the high separation probability (HSP) assumption—that is complementary to the common low density separation (LDS) assumption, and a highly accurate and efficient SSL algorithm, the transductive minimax probability machine (TMPM). Under the HSP assumption, a preferred classifier should ensure that with high probability the samples are far away from the decision boundary. Similarly, the LDS assumption states that the decision boundary should lie in a low-density region. A remarkable property of HSP is that it provides an elegant theoretical framework for analyzing the generalization performance of SSL, while it remains difficult for the LDS assumption to have a quantitative criterion on how it fits the data. Moreover, it has the same paradigm as the minimax probability machine (MPM) and yields an intractable mixed-integer optimization for solving the transductive label assignment problem. Consequently, we propose TMPM with a label-switching strategy and iterative manner to formalize our approximate solution. Theoretically, we analyze the computational complexity, convergence property, and generalization error bound of TMPM. Empirically, we show that TMPM achieves competitive performance on a wide range of datasets, while being almost one order of magnitude faster than existing SSL approaches. [ABSTRACT FROM AUTHOR]

In order to improve the flexibility of the correntropy that is usually in the form of a single kernel, this brief proposes a novel combinatoric maximum correntropy criterion (C-MCC), where the combinatoric correntropy (C-C) consists of two different single-type kernels. As a new form of correntropy, the C-C has a more generalized form than the traditional single-kernel correntropy, and the corresponding properties and proofs of the C-C are given in this brief. It is noticed that the combined kernel can become a single kernel by changing the combined parameters. Moreover, the adaptive filtering algorithm based on C-MCC is derived and the theoretical performance analysis of the C-MCC algorithm is also provided. Finally, the experimental simulations show that the proposed algorithm has better convergence speed and steady-state performance than MCC-type and other algorithms, and the proposed method of the theoretical analysis of C-MCC agrees well with simulation results. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of estimating diffraction attenuation from the approximation of terrain using a multiple bridged knife-edge model. This model can be considered as a generalization of the well-known multiple knife-edge (MKE) one where spaces between knife-edges are bridged by reflecting surfaces. A series-based-standard solution is presented in the literature but suffers from its very high computational complexity. We, thus, propose to generalize the recursive Vogler algorithm developed for the MKE model to tackle this problem. To reduce complexity, the proposed algorithm exploits the recursive form to avoid repeated calculations of the existing solution. Moreover, we provide a complexity analysis of both the standard algorithm and the proposed algorithm based on the number of computed integrals. Both theoretical and numerical results show that our proposed algorithm is faster than the original one while having identical accuracy, hence proving the effectiveness of our solution. [ABSTRACT FROM AUTHOR]

This paper presents a new objective quality metric that was adapted to the complex characteristics of immersive video (IV) which is prone to errors caused by processing and compression of multiple input views and virtual view synthesis. The proposed metric, IV-PSNR, contains two techniques that allow for the evaluation of quality loss for typical immersive video distortions: corresponding pixel shift and global component difference. The performed experiments compared the proposal with 31 state-of-the-art quality metrics, showing their performance in the assessment of quality in immersive video coding and processing, and in other applications, using commonly used image quality assessment databases– TID2013 and CVIQ. As presented, IV-PSNR outperforms other metrics in immersive video applications and still can be efficiently used in the evaluation of different images and videos. Moreover, basing the metric on the calculation of PSNR allowed the computational complexity to remain low. Publicly available, efficient implementation of IV-PSNR software was provided by the authors of this paper and is used by ISO/IEC MPEG for evaluation and research on the upcoming MPEG Immersive video (MIV) coding standard. [ABSTRACT FROM AUTHOR]

Non-Line-of-Sight (NLOS) imaging reconstructs occluded scenes based on indirect diffuse reflections. The computational complexity and memory consumption of existing NLOS reconstruction algorithms make them challenging to be implemented in real-time. This paper presents a fast and memory-efficient phasor field-diffraction-based NLOS reconstruction algorithm. In the proposed algorithm, the radial property of the Rayleigh Sommerfeld diffraction (RSD) kernels along with the linear property of Fourier transform are utilized to reconstruct the Fourier domain representations of RSD kernels using a set of kernel bases. Moreover, memory consumption is further reduced by sampling the kernel bases in a radius direction and constructing them during the run-time. According to the analysis, the memory efficiency can be improved by as much as $\mathbf {220}\times$ 220 × . Experimental results show that compared with the original RSD algorithm, the reconstruction time of the proposed algorithm is significantly reduced with little impact on the final imaging quality. [ABSTRACT FROM AUTHOR]

Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs —approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power. [ABSTRACT FROM AUTHOR]

Capturing the directions of light by light field cameras powers next-generation immersive multimedia applications. A critical problem in taking advantage of the rich visual information in light field images is depth estimation. Conventional light field depth estimation methods build a cost volume that measures the photo-consistency of pixels refocused to a range of depths, and the highest consistency indicates the correct depth. This strategy works well in most regions but usually generates blurry edges in the estimated depth map due to occlusions. Recent work shows that integrating occlusion models to light field depth estimation can largely reduce blurry edges. However, existing occlusion handling methods rely on complex edge-aided processing and post-refinement, and this reliance limits the resultant depth accuracy and impacts on the computational performance. In this paper, we propose a novel occlusion-aware vote cost (OAVC) which is able to accurately preserve edges in the depth map. Instead of using photo-consistency as an indicator of the correct depth, we construct a novel cost from a new perspective that counts the number of refocused pixels whose deviations from the central-view pixel are less than a small threshold, and utilizes that number to select the correct depth. The pixels from occluders are thus excluded in determining the correct depth. Without the use of any explicit occlusion handling methods, the proposed method can inherently preserve edges and produces high-quality depth estimates. Experimental results show that the proposed OAVC outperforms state-of-the-art light field depth estimation methods in terms of depth estimation accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

Heatmap regression has become the mainstream methodology for deep learning-based semantic landmark localization, including in facial landmark localization and human pose estimation. Though heatmap regression is robust to large variations in pose, illumination, and occlusion in unconstrained settings, it usually suffers from a sub-pixel localization problem. Specifically, considering that the activation point indices in heatmaps are always integers, quantization error thus appears when using heatmaps as the representation of numerical coordinates. Previous methods to overcome the sub-pixel localization problem usually rely on high-resolution heatmaps. As a result, there is always a trade-off between achieving localization accuracy and computational cost, where the computational complexity of heatmap regression depends on the heatmap resolution in a quadratic manner. In this paper, we formally analyze the quantization error of vanilla heatmap regression and propose a simple yet effective quantization system to address the sub-pixel localization problem. The proposed quantization system induced by the randomized rounding operation 1) encodes the fractional part of numerical coordinates into the ground truth heatmap using a probabilistic approach during training; and 2) decodes the predicted numerical coordinates from a set of activation points during testing. We prove that the proposed quantization system for heatmap regression is unbiased and lossless. Experimental results on popular facial landmark localization datasets (WFLW, 300W, COFW, and AFLW) and human pose estimation datasets (MPII and COCO) demonstrate the effectiveness of the proposed method for efficient and accurate semantic landmark localization. Code is available at http://github.com/baoshengyu/H3R. [ABSTRACT FROM AUTHOR]

Efficient lattice-based cryptosystems operate with polynomial rings with the Number Theoretic Transform (NTT) to reduce the computational complexity of polynomial multiplication. NTT has therefore become a major arithmetic component (thus computational bottleneck) in various cryptographic constructions like hash functions, key-encapsulation mechanisms, digital signatures, and homomorphic encryption. Although there exist several hardware designs in prior work for NTT, they all are isolated design instances fixed for specific NTT parameters or parallelization level. This article provides an extensive study of flexible design methods for NTT implementation. To that end, we evaluate three cases: (1) parametric hardware design, (2) high-level synthesis (HLS) design approach, and (3) design for software implementation compiled on soft-core processors, where all are targeted on reconfigurable hardware devices. We evaluate the designs that implement multiple NTT parameters and/or processing elements, demonstrate the design details for each case, and provide a fair comparison with each other and prior work. On a Xilinx Virtex-7 FPGA, compared to HLS and processor-based methods, the results show that the parametric hardware design is on average $4.4\times$ 4. 4 × and $73.9\times$ 73. 9 × smaller and $22.5\times$ 22. 5 × and $19.3\times$ 19. 3 × faster, respectively. Surprisingly, HLS tools can yield less efficient solutions than processor-based approaches in some cases. [ABSTRACT FROM AUTHOR]

As the complexity of the multicore system grows, the large workload diversity results in serious thermal problems. In a practical way, the number of placed thermal sensors is usually limited due to the manufacturing cost. In recent years, the compressive sensing (CS) theory is proven as an efficient way to reconstruct the original signal by using fewer sampling data. However, due to the high computational complexity during the signal reconstruction, the CS theory is not appropriate to apply to the real-time temperature monitoring in the current multicore system. In this article, we propose a grid-based sensor placement approach to placement the number-limited thermal sensors on the target multicore system. On the other hand, we adopt the matrix inversion bypass (MIB) property to reduce the computational complexity of two widely used signal reconstruction approaches in CS theory [i.e., the orthogonal matching pursuit (OMP) and stagewise OMP (StOMP)]. Due to the characteristic of random sampling in CS theory, the complexity of thermal sensor placement for multicore systems can be reduced significantly. In addition, the proposed MIB-based temperature reconstruction method helps to satisfy the requirement of real-time temperature estimation. The experimental results show that the proposed approach can reduce 57%–93% average full-system temperature reconstruction error compared with the previous non-CS-based approaches. Besides, we can also reduce 22%–41% computing latency compared with the current CS-based reconstruction algorithm. Due to the MIB-based operation, we can bypass the matrix inversion operation for temperature reconstruction. Therefore, the hardware overhead of the temperature reconstruction unit can be reduced significantly. Compared with the conventional approaches, we can reduce 24%–87% area overhead and improve 50%–220% hardware efficiency. [ABSTRACT FROM AUTHOR]

The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the primary input leads in combinational circuits to their reliability. Oriented to the input vector, a subcircuit-based traversal method marks the critical input leads of each gate in a circuit. Gate-sensitive attributes and a reverse recursive algorithm quantify the effect of each RCPI on circuit reliability under the input vector. A parallel calculation method based on subcircuits with only one primary output reduces the computational complexity to accelerate the calculation process. Similarity-based clustering avoids unnecessary calculations, and a self-adaptive strategy is used to check convergence. Experimental results on benchmark circuits show that the average accuracy of this approach is 0.9634 with Monte Carlo (MC) as the reference and it is 3445 times faster than the MC on average while its average memory cost is 1.67 greater than the MC model. Although the fitness of the worst input vector obtained by other reference methods is 1.09 times better than that of this approach on average, this approach is approximately 21 times faster than that reference method on average. [ABSTRACT FROM AUTHOR]

The central challenge in massive machine-type communications (mMTC) is to connect a large number of uncoordinated devices through a limited spectrum. The typical mMTC communication pattern is sporadic, with short packets. This could be exploited in grant-free random access in which the activity detection, channel estimation, and data recovery are formulated as a sparse recovery problem and solved via compressed sensing algorithms. This approach results in new challenges in terms of high computational complexity and latency. We present how data-driven methods can be applied in grant-free random access and demonstrate the performance gains. Variations of neural networks for the problem are discussed, as well as future challenges and potential directions. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple-input multiple-output (mMIMO) systems are an alternative topology for mMIMO deployment, wherein a large number of access points are distributed over the coverage area to jointly serve users. Linear precoding methods such as zero-forcing are sufficient to achieve near-optimal performance in mMIMO systems. However, a key challenge in implementing these precoders can be a channel matrix inversion operation, which results in significant computational complexity in systems with large-scale antenna arrays. Hence, instead of direct matrix inversion, we examine several iterative methods to calculate the precoding matrix in a cell-free mMIMO system. We investigate their computational complexity and convergence rate in the presence of small- and large-scale fading and spatial correlation between antennas. Notably, we demonstrate that some iterative methods previously proposed for conventional (co-located) mMIMO do not always converge for cell-free mMIMO. Our main focus is the hyper-power iterative inversion method, which can be applied to both matrix inverses and pseudoinverses with guaranteed convergence; its factorized version also reduces its computational complexity. Although the hyper-power method does not reduce the complexity compared to direct matrix inversion, it converges quickly with high accuracy and strong numerical stability, and is conducive to parallel computation. These qualities make it a good candidate for matrix inversion in cell-free mMIMO system precoders. [ABSTRACT FROM AUTHOR]

In this paper, a joint design of sparse code multiple access (SCMA) codebook and peak-to-average power ratio (PAPR) reduction based on the partial transmit sequence (PTS) technique is proposed for the downlink orthogonal frequency division multiplexing (OFDM) scheme. A benchmark scheme is firstly devised based on a direct extension of the conventional PTS-based OFDM scheme. Based on the characteristics of the near-optimal SCMA codebook designs reported in our previous work, a PTS-based SCMA-OFDM design is proposed by carefully determining the rotation angles of the SCMA codebooks, according to the adopted signal sub-constellations. A receiver which does not need the side information is further devised. Considering the phase ambiguity issue of the receiver, the PTS angles have been carefully refined. When compared to both the original scheme that does not consider PAPR reduction techniques and the benchmark scheme, the proposed scheme achieves a significantly enhanced PAPR performance with approximately the same error performance. In addition, a comparable PAPR performance, an improved error performance, and a lower computational complexity can be achieved when compared to the clipping-based SCMA-OFDM scheme reported in the previous literature. [ABSTRACT FROM AUTHOR]

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes. [ABSTRACT FROM AUTHOR]

Radar-embedded communication (REC) is a new type of covert communication whereby communication signals are embedded in radar backscattered echoes. Conventional REC waveform design strategies generally involve transforming the radar sampling sequence into a square matrix that can be eigenvalue decomposed; however, it may lose some original characteristics of the radar signal during this process. In this paper, we propose two REC waveform design methods based on singular value decomposition (SVD), namely, SVD-NDP (non-dominant-processing waveform based on SVD) and SVD-SNDP (shaped-non-dominant-processing waveform based on SVD), and derive the similarities between the radar signal and the proposed waveforms. We then simulate the anti-detection performance, anti-interception performance, and communication reliability of the proposed waveforms and analyze the degrees of freedom and computational complexity of their design. The results show that, compared with conventional REC waveforms, the proposed waveforms could improve communication reliability without sacrificing low probability of intercept (LPI) performance and at least double the design degrees of freedom. SVD-NDP and SVD-SNDP algorithms can reduce computational complexity by more than 30% compared with conventional waveform design algorithms based on eigenvalue decomposition when the number of communication waveforms (symbols) is set to 4 (2 bits), and the proportion of the dominant subspace is set to 50%. [ABSTRACT FROM AUTHOR]

The next-generation wireless network is expected to be highly dense with a large number of heterogeneous Internet of Things (IoT) devices, working in co-operation with various Small Cell Access Points (SAPs) and Fog Servers (FSs) underlying cellular 5G infrastructure, altogether. The densely deployed 5G architecture is expected to fulfill the IoT's growing data demand in a feasible computational complexity. In this work, the resource provisioning mechanism in a heterogeneous 5G network is formulated to maximize the overall benefit of SAPs by selecting the most appropriate FSs for offloading the set of IoT tasks, as an NP-hard problem. Moreover, a Restricted Three-sided Matching with Size and Cyclic (R-TMSC) preference model is implemented to obtain a stable solution among IoTs, SAPs, and FSs underlying cellular 5G networks. Furthermore, the effectiveness of the proposed heuristic is shown through theoretical, experimental, and real-world data analysis. [ABSTRACT FROM AUTHOR]

A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed decoder initializes $L$ $(L\geq 1)$ active decoding paths with $L$ random codeword permutations sampled from the full symmetry group of the codes. The path extension in the permutation domain is carried out until the first constituent RM code of order 1 is visited. Conventional path extension of the successive-cancellation list decoder is then utilized in the information bit domain. The simulation results show that for a RM code of length 512 with 46 information bits, by running 20 parallel permuted FHT-FSCL decoders with $L=4$ , we reduce 72% of the computational complexity, 22% of the decoding latency, and 84% of the memory consumption of the state-of-the-art simplified successive-cancellation decoder that uses 512 permutations sampled from the full symmetry group of the code, with similar error-correction performance at the target frame error rate of $10^{-4}$. [ABSTRACT FROM AUTHOR]

A novel recursive list decoding (RLD) algorithm for Reed-Muller (RM) codes based on successive permutations (SP) of the codeword is presented. A low-complexity SP scheme applied to a subset of the symmetry group of RM codes is first proposed to carefully select a good codeword permutation on the fly. Then, the proposed SP technique is integrated into an improved RLD algorithm that initializes different decoding paths with random codeword permutations, which are sampled from the full symmetry group of RM codes. Finally, efficient latency and complexity reduction schemes are introduced that virtually preserve the error-correction performance of the proposed decoder. Simulation results demonstrate that at the target frame error rate of 10−3 for the RM code of length 256 with 163 information bits, the proposed decoder reduces 6% of the computational complexity and 22% of the decoding latency of the state-of-the-art semi-parallel simplified successive-cancellation decoder with fast Hadamard transform (SSC-FHT) that uses 96 permutations from the full symmetry group of RM codes, while relatively maintaining the error-correction performance and memory consumption of the semi-parallel permuted SSC-FHT decoder. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new linear-equation ordered-statistics decoding (LE-OSD). Unlike the OSD, LE-OSD uses high reliable parity bits rather than information bits to recover codeword estimates, which is equivalent to solving a system of linear equations (SLE). Only test error patterns (TEPs) that create feasible SLEs, referred to as the valid TEPs, are used to obtain codeword estimates. We introduce several constraints on the Hamming weight of TEPs to limit the overall decoding complexity. Furthermore, we analyze the block error rate (BLER) and the computational complexity of the proposed approach. It is shown that LE-OSD has a similar performance to OSD in terms of BLER, which can asymptotically approach Maximum-likelihood (ML) performance with proper parameter selections. Simulation results demonstrate that the LE-OSD has a significantly reduced complexity compared to OSD, especially for low-rate codes, that usually require high decoding order in OSD. Nevertheless, the complexity reduction can also be observed for high-rate codes. In addition, we further improve LE-OSD by applying the decoding stopping condition and the TEP discarding condition. As shown by simulations, the improved LE-OSD has a considerably reduced complexity while maintaining the BLER performance, compared to the latest OSD approaches from literature. [ABSTRACT FROM AUTHOR]

This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low-complexity schemes for the unsourced setting, while also accounting for inherent randomness. Under joint message-passing decoding, the proposed scheme offers good performance at a low computational complexity. Findings are supported by numerical simulations, and results are compared to existing alternatives. [ABSTRACT FROM AUTHOR]

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks. [ABSTRACT FROM AUTHOR]

Consider the compressed sensing setup where the support $\mathbf {s}^{\ast}$ of an $m$ -sparse $d$ -dimensional signal $\mathbf {x}$ is to be recovered from $n$ linear measurements with a given algorithm. Suppose that the measurements are such that the algorithm does not guarantee perfect support recovery and that true features may be missed. Can they efficiently be retrieved? We address this question through a simple error-correction module referred to as LiRE. LiRE takes as input an estimate $\mathbf {s}_{\text {in}}$ of the true support $\mathbf {s}^{\ast}$ , and outputs a refined support estimate $\mathbf {s}_{\text {out}}$. We establish sufficient conditions under which LiRE is guaranteed to recover the entire support, that is $\mathbf {s}_{\text {out}}\supseteq \mathbf {s}^{\ast} $. These conditions imply, for instance, that in high dimension LiRE can correct a sublinear in $m$ number of errors made by Orthogonal Matching Pursuit (OMP). The computational complexity of LiRE is ${\mathcal{O}}(m n d)$. Experimental results with random Gaussian design matrices show that LiRE substantially reduces the number of measurements needed for perfect support recovery via Compressive Sampling Matching Pursuit, Basis Pursuit (BP), and OMP. Interestingly, adding LiRE to OMP yields a support recovery procedure that is more accurate and significantly faster than BP. This observation carries over in the noisy measurement setup where the combination of LiRE and OMP is faster and more accurate than LASSO. Finally, as a standalone support recovery algorithm with a random initialization, experiments show that LiRE’s support recovery performance lies between OMP and BP. These results show that LiRE can be used generically, on top of any suboptimal baseline support recovery algorithm, to improve support recovery or to operate with a smaller number of measurements, at the cost of a relatively small computational overhead. Alternatively, LiRE may be used as a standalone support recovery algorithm that is competitive with respect to OMP. [ABSTRACT FROM AUTHOR]

In this brief, a q-Rényi kernel functioned Kalman filter (qRKFKF) is proposed based on the q-Rényi kernel function which is to provide better flexibility and performance in non-Gaussian environment. The concrete realization of the proposed qRKFKF are created and analyzed in detail, and its performance is presented and discussed. Three examples are carried out to verify the performance of the proposed qRKFKF for land vehicle navigation via numerical simulations: large outliers in non-Gaussian noise, alpha-stable noise and diverse noise distributions that combines Gaussian and Laplace noises. Compared with Kalman filter (KF), Maximum Correntropy Kalman Filter (MCKF) and minimum error entropy KF (MEE-KF), the qRKFKF is superior to others for combating non-Gaussian noises. [ABSTRACT FROM AUTHOR]

The aim of this article is to perform a rapid electromagnetic (EM) analysis of large perfectly electric conducting (PEC) platforms with local variations. Such problems often require many-query simulations to navigate complex parameter spaces and thus quickly become computationally expensive. The proposed work starts with a stationary-variable domain decomposition, where the computational domain is decomposed into large fixed parts and small portions with local variations. Subsequently, we introduce a separable and compressible platform Green’s function at the outer surface of those variable subdomains in an upfront offline calculation. Once obtained, the online computational complexity does not depend on the size of the in situ platform. With the validation of numerical results, we conclude that the proposed work has the potential to greatly speed up the online evaluation of local designs and optimizations compared to the brute-force computation. [ABSTRACT FROM AUTHOR]

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g., least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of $n$ n components, HSDMPG attains an $\epsilon$ ε -optimization-error within $\mathcal {O} \left(\kappa \log ^{\zeta +1}\left(\frac{1}{\epsilon }\right)\frac{1}{\epsilon }\bigwedge n\log ^{\zeta }\left(\frac{1}{\epsilon }\right)\right)$ O κ log ζ + 1 1 ε 1 ε ⋀ n log ζ 1 ε stochastic gradient evaluations, where $\kappa$ κ is condition number, $\zeta =1$ ζ = 1 for quadratic loss and $\zeta =2$ ζ = 2 for generic loss. For large-scale problems, our complexity outperforms those of SVRG-type algorithms with/without dependence on data size. Particularly, when $\epsilon =\mathcal {O}(1/\sqrt{n})$ ε = O (1 / n) which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively $\mathcal {O} (n^{0.5}\log ^{2}(n))$ O (n 0. 5 log 2 (n)) and $\mathcal {O} (n^{0.5}\log ^{3}(n))$ O (n 0. 5 log 3 (n)) , which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend HSDMPG to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of HSDMPG. [ABSTRACT FROM AUTHOR]