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The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

Classification of wheat after harvest is a challenging task. Several techniques are prevalent for measuring the geometrical parameters of the wheat and for scrutinizing the wheat seeds. Parameters used for the classifier includes area, perimeter, compactness, length of kernel, width of kernel, asymmetry coefficient, length of kernel groove. Neural network pattern recognizer is used for the classification of the wheat seeds in this paper. The database for the Wheat seed is downloaded from the UCI machine learning repository. Confusion matrix and ROC is computed as the performance measure. Results show that neural network pattern recognizer is an apt tool for classification of wheat seeds with an overall accuracy of 96.7%.

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles., Comment: The manuscript has been accepted for publication as a regular paper in the IEEE Transactions on Signal Processing

Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification. Theoretical results and extensive experiments validate the effectiveness of our approach.

Alzheimer's disease (AD) is a neurodegenerative disease of the human brain that affects neurotransmitters, tissue, and neurons that impair the senses, memories, and behaviors. Still, now there is no remedy for Alzheimer's disease. Even so, prescribed drugs can help reduce the development of the disease. That's why Alzheimer's early detection is very essential for treatment, and further research. Very limited numbers of trained samples and the higher volume of feature descriptions are the major difficulties in early diagnosis of Alzheimer's disease using different classification strategies. In this article, we proposed and related Alzheimer's disease early diagnostic method using Mild Cognitive Impairment (MCI), Structural Magnetic Resonance (sMR) imaging for AD-discrimination and healthy control participants (HC) with Import Vector Machine (IVM), Regularized Extreme Learning Machine (RELM) and a Support vector machine (SVM).The greedy score-based strategy for choosing essential function vectors is used. Furthermore, a discriminatory, kernel-based method is taken to treat dynamic data transformations. For volume sMR scan image data from Alzheimer's disease neuroimaging initiative (ADNI) repositories, we compare the performance of these classification models. An ADNI datasets experimental study reveals that RELM can greatly enhance the accuracy for classification of AD from MCIs as well as HC individuals along with feature selection methodology.

In this paper we present a new classification method based on Dictionary Learning (DL). The main contribution consists of a kernel version of incoherent DL, derived from its standard linear counterpart. We also propose an improvement of the AK-SVD algorithm concerning the representation update. Our algorithms are tested on several popular databases of classification problems.

In recent years, forward initial margin has attracted the attention of practitioners and academics. From a computational point of view, it poses a challenging problem as it requires the implementation of a nested Monte Carlo simulation. Abundant literature has been published on approximation methods aiming to reduce the dimensionality of the problem, the most popular ones being the family of regression methods. This paper describes the mathematical foundations, on which these regression approximation methods lie. Mathematical rigor is introduced to show that, in essence, all methods are just different variations of approximating the conditional expectation function. In short, all methods are performing orthogonal projections on Hilbert spaces, while simply choosing a different functional form to numerically estimate the conditional expectation. The most popular methods in the literature so far are covered here. These are polynomial approximations, Kernel regressions and Neural Networks.

Accurate and robust state of charge (SOC) estimation of lithium-ion batteries (LIBs) is very important to prolong battery life and prevent catastrophic failures. However, the accuracy of SOC estimation is seriously affected by unknown noise, uncertain interference and temperature. In this paper, a novel model fusion method is presented to achieve precise SOC estimation in the case of non-Gaussian noise and outliers. A state-space model of battery system is first developed to conduct SOC estimate. Then, a novel robust kernel Takagi Sugeno (T-S) fuzzy method to minimize the mean and variance of model error is developed to characterize the electrical performance of battery. This modeling strategy uses local nonlinear modeling mechanism, which the nonlinear relationship between data can be well represented, thus it can obtain a superior modeling performance. Finally, a multi-innovation unscented Kalman filter (UKF) algorithm considering the historical state information is designed to incorporate with the robust fuzzy model to filter out the noise in the observation and update the SOC estimation. Additional stability analysis shows the convergence of the proposed multi-innovation UKF algorithm. Experiments and verifications show that the presented estimation architecture is effective and has better modeling ability compared with several common methods.

Semisupervised learning (SSL) has been extensively studied in related literature. Despite its success, many existing learning algorithms for semisupervised problems require specific distributional assumptions, such as ``cluster assumption'' and ``low-density assumption,'' and thus, it is often hard to verify them in practice. We are interested in quantifying the effect of SSL based on kernel methods under a misspecified setting. The misspecified setting means that the target function is not contained in a hypothesis space under which some specific learning algorithm works. Practically, this assumption is mild and standard for various kernel-based approaches. Under this misspecified setting, this article makes an attempt to provide a theoretical justification on when and how the unlabeled data can be exploited to improve inference of a learning task. Our theoretical justification is indicated from the viewpoint of the asymptotic variance of our proposed two-step estimation. It is shown that the proposed pointwise nonparametric estimator has a smaller asymptotic variance than the supervised estimator using the labeled data alone. Several simulated experiments are implemented to support our theoretical results.

By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual learning method based on transfer learning of combined mid-level features is proposed for hyperspectral classification. Three-layer homogenous superpixels are computed on the image formed by PCA, which is used for computing mid-level features. The three mid-level features include: 1) the sparse reconstructed feature; 2) combined mean feature; and 3) uniqueness. The sparse reconstruction feature is obtained by a joint sparse representation model under the constraint of three-scale superpixels' boundaries and regions. The combined mean features are computed with average values of spectra in multilayer superpixels, and the uniqueness is obtained by the superposed manifold ranking values of multilayer superpixels. Next, three kernels of samples in different feature spaces are computed for mutual learning by minimizing the divergence. Then, a combined kernel is constructed to optimize the sample distance measurement and applied by employing SVM training to build classifiers. Experiments are performed on real hyperspectral datasets, and the corresponding results demonstrated that the proposed method can perform significantly better than several state-of-the-art competitive algorithms based on MKL and deep learning.

Background Pulse diagnosis (wrist pulse signal) is a well-known traditional technique used for a health examination. It has the potential to detect cardiac and non-cardiac diseases. Objective A study was conducted to investigate human emotions using wrist pulse signal assessment. The aim was to categorize anxiety, boredom, physical pain, and reference state by processing and analysis of acquired signals. Method A protocol was designed to induce emotions. Data were acquired from 24 healthy volunteers. Signals were processed and further analyzed using paired t-test and Analysis of Variance (ANOVA). Machine learning algorithms, Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF), and Support Vector Machine using kernel Gaussian radial basis function (RBF-SVM) were used to evaluate significant features and classify the emotions. Results Computing significant plus ranked features performed better over randomly selected features for pairwise emotion classification. Here, the QDF classifier outperforms LDF. Additionally, ANOVA validated the effectiveness of statistically prominent features to classify emotional states. Ratio_Pulse_Strength, total_power, Spectral Entropy, and meancd5 came out as the four most significant features to classify the emotion “Anxiety”, “Boredom”, “Pain”, and “Reference” with positive prediction rate of 100%, 73%, 100%, and 86% respectively using RBF-SVM in the user-independent model. Conclusion Previously, WPS has been used mainly to detect physical abnormality in the human body. The results endorse the potential of user-independent human emotion detection using a wrist pulse signal. The present work was focusing on a few emotional states. Results are encouraging and may be well applied to many more states.

Asymptotic properties of classical kernel estimators for the spectral density are studied in the locally stationary framework. In particular, it is shown that for a locally stationary process standard spectral density estimators consistently estimate the time-averaged spectral density. This result is complemented by some illustrative examples and applications including HAC-inference in the multiple linear regression model, a simple visual tool for the detection of unconditional heteroskedasticity and a test for covariance stationarity.

Kernel-based models have a strong generalization ability, but most of them, including SVM, are vulnerable to the curse of kernelization. Moreover, their predictive performances are sensitive to the hyperparameters tuning, which highly demands computational resources. These problems render kernel methods problematic when dealing with large-scale datasets. To this end, we first formulate the optimization problem in a kernel-based learning setting as a posterior inference problem, and then develop a rich family of Recurrent Neural Network-based variational inference techniques. Unlike existing literature, which stops at the variational distribution and uses it as the surrogate for the true posterior distribution, here we further leverage Stein Variational Gradient Descent to further bring the variational distribution closer to the true posterior, we refer to this step as Stein Refinement. Putting these altogether, we arrive at a robust and efficient variational learning method for multiclass kernel machines with extremely accurate approximation. Moreover, our formulation enables efficient learning of kernel parameters and hyperparameters which robustifies the proposed method against data uncertainties. The extensive experimental results show that our method, without tuning any parameter, obtains comparable performance to LIBSVM, a well-known implementation of SVM, and outperforms other baselines while being able to seamlessly scale with large-scale datasets.

Binary diffing is a process to discover the patched content between two binary programs. Previous research on binary diffing approach it as a function matching problem to formulate an initial 1:1 mapping between functions, and later a sequence matching ratio is computed to classify two functions being an exact match, a partial match or no-match. The accuracy of existing techniques is best only when detecting exact matches and they are not efficient in detecting partially changed functions; especially those with minor patch. These drawbacks are due to two major challenges (i) In 1:1 mapping phase, using a strict policy to match function features (ii) In classification phase, considering an assembly snippet as a normal text and using sequence matching for similarity comparison. An instruction has a unique structure i.e. mnemonics and registers have specific position in an instruction and also have semantic relationship, which makes assembly code different from general text. Sequence matching performs best for general text but it fail to detect structural and semantic changes at an instruction level thus, its use for classification produces many false results. In this research, we have addressed the aforementioned underlying challenges by proposing a two-fold solution. For 1:1 mapping phase, we have proposed computationally inexpensive features, which are compared with a distance based selection criteria to map the similar functions and filter unmatched functions. For classification phase, we have proposed a Siamese classification embedding neural network where each branch is an attention based distributed learning neural networkthat learn the semantic similarity among assembly instructions, learn to highlight the changes at an instruction level and a final stage fully connected layer learn to accurately classify two 1:1 mapped function either an exact or a partial match. We have used x86 kernel binaries for training and achieved 99% classification accuracy; which is higher than existing binary diffing techniques and tools.

The measurement error evaluation of Power Metering Equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially in extreme environmental stresses. However, actual measurement error prediction is often disturbed by environmental noise and insufficient feature information. To address this problem, an Improved Local Outlier Factor (ILOF) method is first proposed to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on boxplot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely Kernel Support Vector Regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

This work examines the impact of multiple vaccination strategies on the dynamics of COVID-19 in a population using the Atangana-Baleanu derivative. The existence and uniqueness of solution of the model is proven using Banach’s fixed point theorem. Local and global asymptotic stability of the equilibria of the model is also proven (under some conditions). Conditions for the existence of a unique or multiple equilibria are also derived and the model is shown to undergo backward bifurcation under certain scenarios. Using available data for the Pfizer, Moderna and Janssen vaccination programme for the city of Texas, United States of America from March 13, 2021 to June 29, 2021, the model is fitted using the three data sets. The three vaccination rates ν 1 , ν 2 and ν 3 corresponding to each vaccine as well as the effective contact rate for COVID-19 transmission, β , are estimated. Simulations of the model under different vaccination strategies are carried out. The results show that the three vaccination strategies not only cause significant reduction in the new asymptomatic and vaccinated symptomatic cases but also cause great decrease in the total number of vaccinated symptomatic individuals with severe COVID-19 illness.

Multiple kernel clustering methods have been quite successful recently especially concerning the multi-view clustering of complex datasets. These methods simultaneously learn a multiple kernel metric while clustering in an unsupervised setting. With the motivation that some minimal supervision can potentially increase their effectiveness, we propose a Multiple Kernel Transfer Clustering (MKTC) method that can be described in terms of two tasks: a source task, where the multiple kernel metric is learned, and a target task where the multiple kernel metric is transferred to partition a dataset. In the source task, we create a weakly supervised multi-instance subset of the dataset, where a set of data instances are together provided some labels. We put forth a Multiple Kernel Multi-Instance $k$ -Means (MKMIKM) method to simultaneously cluster the multi-instance subset while also learning a multiple kernel metric under weak supervision. In the target task, MKTC transfers the multiple kernel metric learned by MKMIKM to perform unsupervised single-instance clustering of the entire dataset in a single step. The advantage of using a multi-instance setup for the source task is that it requires reduced labeling effort to guide the learning of the multiple kernel metric. Our formulations lead to a significantly lower computational cost in comparison to the state-of-the-art multiple kernel clustering algorithms, making them more applicable to larger datasets. Experiments over benchmark computer vision datasets suggest that MKTC can achieve significant improvements in clustering performance in comparison to the state-of-the-art unsupervised multiple-kernel clustering methods and other transfer clustering methods.

How to evaluate Pareto front approximations generated by multi/many-objective optimizers is a critical issue in the field of multiobjective optimization. Currently, there exist two types of comprehensive quality indicators (i.e., volume-based and distance-based indicators). Distance-based indicators, such as Inverted Generational Distance (IGD), are usually computed by summing up the distance of each reference point to its nearest solution. Their high computational efficiency leads to their prevalence in many-objective optimization. However, in the existing distance-based indicators, the distributions of the solution sets are usually neglected, leading to their lacks of ability to well distinguish between different solution sets. This phenomenon may become even more severe in high-dimensional space. To address such an issue, a kernel-based indicator (KBI) is proposed as a comprehensive indicator. Different from other distance-based indicators, a kernel-based maximum mean discrepancy is adopted in KBI for directly measuring the difference that can characterize the convergence, spread and uniformity of two sets, i.e., the solution set and reference set, by embedding them in Reproducing Kernel Hilbert Space (RKHS). As a result, KBI not only reflects the distance between the solution set and the reference set, but also can reflect the distribution of the solution set itself. In addition, to maintain the desirable weak Pareto compliance property of KBI, a nondominated set reconstruction approach is also proposed to shift the original solution set. The detailed theoretical and experimental analysis of KBI is provided in this paper. The properties of KBI have also been analyzed by the optimal μ-distribution.

Recently, the polynomial-based radial basis function neural networks (P-RBFNNs) have been successfully applied to regression tasks. However, the redundant and non-linear partitioned data easily interfere with accurate partitioning of clusters completed in P-RBFNNs, affecting the regression performance of this existing model. Because the squared error is used as the cost function of the learning method, P-RBFNNs are sensitive to noise interference. In order to cope with these problems, this study introduces a double iterative learning-based polynomial based-RBFNNs (DP-RBFNNs) modeling that focuses on the formation of architectures to improve the accuracy of regression performance as well as enhance the robustness through double iterative learning as follows: a) support vector-based Gaussian kernel fuzzy c-means (SV-GKFCM) as a kind of the support vector-based kernel fuzzy clustering are designed to determine connections (weights) between the input and hidden layers of the proposed model. SV-GKFCM helps effectively reduce the number of redundant data to re-modify the partitioning of clusters in the DP-RBFNNs. In addition, the cluster centers can be accurately updated from the non-linear partitioned data with the aid of Gaussian kernel distance in SV-GKFCM; b) least absolute shrinkage deviations (LASD) as a robust estimation are designed to update connection weights between the hidden and output layers. The SAE (sum of absolute error) function in the LASD method is used as a cost function to reduce the noise interference in the procedure of weight estimation as well as enhance the robustness of the DP-RBFNNs. The superiority of the proposed model is demonstrated through the experimental results.

In this paper, we stabilize a discrete-time Markov process evolving on a compact subset of $\mathbb{R}^d$ to an arbitrary target distribution that has an $L^\infty$ density and does not necessarily have a connected support on the state space. We address this problem by stabilizing the corresponding Kolmogorov forward equation, the \textit{mean-field model} of the system, using a density-dependent transition kernel as the control parameter. Our main application of interest is controlling the distribution of a multi-agent system in which each agent evolves according to this discrete-time Markov process. To prevent agent state transitions at the equilibrium distribution, which would potentially waste energy, we show that the Markov process can be constructed in such a way that the operator that pushes forward measures is the identity at the target distribution. In order to achieve this, the transition kernel is defined as a function of the current agent distribution, resulting in a nonlinear Markov process. Moreover, we design the transition kernel to be \textit{decentralized} in the sense that it depends only on the local density measured by each agent. We prove the existence of such

Nowadays, how to select the kernel function and their parameters for ensuring high-performance indicators in fault diagnosis applications remains as two open research issues. This paper provides a comprehensive literature survey of kernel-preprocessing methods in condition monitoring tasks, with emphasis on the procedures for selecting their parameters. Accordingly, twenty kernel optimization criteria and sixteen kernel functions are analyzed. A kernel evaluation framework is further provided for helping in the selection and adjustment of kernel functions. The proposal is validated via a KPCA-based monitoring scheme and two well-known benchmark processes.

The development of industrial cyber-physical system (ICPS) provides a tight connection between the digital model and the industrial physical plant, which enables to use the data-driven methods to reflect the state of process running in the real world. However, due to the noisy and harsh industrial environment, the collected data are often corrupted to some extent. If the corrupted data are not detected in time, the data-driven model will inevitably degenerate and induce a poor process monitoring performance. In addition, the nonlinear characteristics between process variables due to the high complexities in physical plant bring challenges to the data-driven methods. In this article, a robust kernel dictionary learning method, which can overcome the negative influence of outliers and simultaneously extracts the nonlinear characteristics of industrial process, is proposed to address the above problems in ICPS. Our extensive experiments demonstrate that the proposed method has achieved significantly better and stable performance to deal with outlier detection and process monitoring in ICPS.

In the paper, a novel kernel recursive least-squares (KRLS) algorithm named random Fourier feature kernel recursive maximum mixture correntropy (RFF-RMMC) algorithm is proposed, which improves the prediction efficiency and robustness of the KRLS algorithm. Random Fourier feature (RFF) method as well as maximum mixture correntropy criterion (MMCC) are combined and applied into KRLS algorithm afterwards. Using RFF to approximate the kernel function in KRLS with a fixed cost can greatly reduce the computational complexity and simultaneously improve the prediction efficiency. In addition, the MMCC maintains the robustness like the maximum correntropy criterion (MCC). More importantly, it can enhance the accuracy of the similarity measurement between predicted and true values by more flexible parameter settings, and then make up for the loss of prediction accuracy caused by RFF to a certain extent. The performance of the RFF-RMMC algorithm for online time series prediction is verified by the simulation results based on three datasets.

This paper investigates the stochastic stability analysis and stabilization problems for discrete-time Takagi-Sugeno fuzzy semi-Markov jump systems with upper-bounded sojourn time. The fuzzy rules can be different for different system modes. Consequently, the membership functions for fuzzy rules are dependent on the system modes. Allowing for the fact that semi-Markov kernel (SMK) are difficult to fully obtain in practice, the elements in the SMK of the underlying systems are deemed to be partly known, which is more general than both semi-Markov jump systems with completely available semi-Markov kernel and Markov jump systems with unknown transition probabilities. Afterwards, the stability and stabilization conditions are established by part of the known SMK information and then by all the known SMK information. In the end, the validity and the superiority of our proposed theoretical results are exemplified via a single-link robot arm and a truck-trailer model.

This paper concentrates on the $l_{2}-l_{\infty }$ filtering problem of discrete-time fuzzy semi-Markov jump nonlinear system. The random jumps among the studied system are governed by the discrete-time semi-Markov process. Therefore, the storage characteristics of the transition probability between systems are fully considered. To analyze $\sigma$ -mean-square stability of the filtering error system, a filter, which is mode-dependent, based on the discrete-time fuzzy semi-Markov jump model is constructed to estimate the state of the system. Thereafter, based on the Lyapunov stability theory and the discrete-time semi-Markov kernel concept, a set of criteria to ensure the are proposed. In addition, by using Takagi-Sugeno fuzzy model, the nonlinear problem is effectively solved. Ultimately, an illustrative example of a tunnel diode circuit and a numerical example effectively and practicability reveal the developed filtering strategy.

This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we proposed a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM network. The proposed method is called LSKELM-RFE algorithm, which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of LSKELM network output weights, and finally removes a least important gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.

Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling stage or/and the online monitoring stage of statistical process monitoring. We provide a fast incremental nonlinear matrix completion (FINLMC) method for missing data imputation, which enables us to use kernel methods such as kernel principal component analysis (KPCA) to monitor nonlinear multivariate processes even when there are missing data. We also provide theoretical analysis for the effectiveness of the proposed method. Experiments show that the proposed method can reduce the false alarm rate and improve the fault detection rate in nonlinear processing monitoring with missing data. The proposed method FINLMC can also be used to solve missing data in other problems such as classification and process control.

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary ``on the fly.'' This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data.

The tremendous growth in deep learning (DL) applications has created an exponential demand for computing power, which leads to the rise of AI-specific hardware. Targeted towards accelerating computation-intensive deep learning applications, AI hardware, including but not limited to GPGPU, TPU, ASICs, etc., have been adopted ubiquitously. As a result, domainspecific CAD tools play more and more important roles and have been deeply involved in both the design and compilation stages of modern AI hardware. Recently, ISPD 2020 contest introduced a special challenge targeting at the physical mapping of neural network workloads onto the largest commercial deep learning accelerator, CS-1 Wafer-Scale Engine (WSE). In this paper, we proposed CU.POKer, a high-performance engine fullycustomized for WSE’s DNN workload placement challenge. A provably optimal placeable kernel candidate searching scheme and a data-flow-aware placement tool are developed accordingly to ensure the state-of-the-art quality on the real industrial benchmarks. Experimental results on ISPD 2020 contest evaluation suites demonstrated the superiority of our proposed framework over not only the state-of-the-art (SOTA) placer but also the conventional heuristics used in general floorplanning.

The least-square support vector machine (LS-SVM) has been deeply studied in the machine-learning field and widely applied on a great deal of occasions. A disadvantage is that it is less efficient in dealing with the non-Gaussian noise. In this article, a novel probabilistic LS-SVM is proposed to enhance the modeling reliability even data contaminated by the non-Gaussian noise. The stochastic effect of noise on the kernel function and the regularization parameter is first analyzed and estimated. On the basis of this, a new objective function is constructed under a probabilistic sense. A probabilistic inference method is then developed to construct the distribution of the model parameter, including distribution estimation of both the kernel function and the regularization parameter from data. Using this distribution information, a solving strategy is then developed for this new objective function. Different from the original LS-SVM that uses a deterministic scenario approach to gain the model, the proposed method builds the distribution relation between the model and noise and makes use of this distribution information in the process of modeling; thus, it is more robust for modeling of noise data. The effectiveness of the proposed probabilistic LS-SVM is demonstrated by using both artificial and real cases.

In this article, a general approach for directed graph clustering and two new density-based clustering objectives are presented. First, using an equivalence between the clustering objective functions and a trace maximization expression, the directed graph clustering objectives are converted into the corresponding weighted kernel k-means problems. Then, a nonspectral algorithm, which covers both the direction and weight information of the directed graphs, is thus proposed. Next, with Rayleigh's quotient, the upper and lower bounds of clustering objectives are obtained. After that, we introduce a new definition of weak links to characterize the effectiveness of clustering. Finally, illustrative examples are given to demonstrate effectiveness of the results. This article provides a glance at the potential connection between density-based and pattern-based clustering. Compared with other approaches for directed graph clustering, the method proposed in this article naturally avoids the loss of the nonsymmetric edge data because there is no need for any additional symmetrization.

In this paper, we present generalized Jensen-Mercer inequality for a generalized h-convex function on fractal sets. We proved Hermite-Hadamard-Mercer local fractional integral inequalities via integral operators pertaining Mittag-Leffler kernel. Also, we drive two new local fractional integral identities for differentiable functions. By employing these integral identities, we derive some new Hermite-Mercer type inequalities for generalized h-convex function in local fractional calculus settings. Finally, we give some examples to emphasize the applications of derived results. These results will be a significant addition to Jensen-type inequalities in the literature.

This article presents the design of an event-trigger-based networked robust dissipative tracking control systems. An event-triggered mechanism (ETM) is proposed by introducing the probability density distribution of communication delay. Thus, it can yield less conservative results. The tracking error system is transferred by a distributed communication delay system wherein the delay probability density is viewed as the kernel of the distributed delay. The threshold of the proposed ETM is designed as a dynamic parameter to adapt the transmission of the control signal. By applying the Lyapunov method, sufficient conditions are achieved to ensure the stability of the tracking error system with strictly dissipative performance, which includes $H_{\infty }$ performance and passive performance. Based on the dissipativity analysis condition, a co-design method with both tracking control and the ETM is derived in a united framework. Experiments on networked dc motor tracking system are provided to show the effectiveness of the presented method.

A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy saving and its prerequisite is a time-series prediction of carbon efficiency. The existing carbon efficiency models usually have a complex structure, leading to a time-consuming training process. In addition, a complete retraining process will be encountered if the models are inaccurate or data change. Analyzing the complex characteristics of the sintering process, we develop an original prediction framework, that is, a weighted kernel-based fuzzy C-means (WKFCM)-based broad learning model (BLM), to achieve fast and effective carbon efficiency modeling. First, sintering parameters affecting carbon efficiency are determined, following the sintering process mechanism. Next, WKFCM clustering is first presented for the identification of multiple operating conditions to better reflect the system dynamics of this process. Then, the BLM is built under each operating condition. Finally, a nearest neighbor criterion is used to determine which BLM is invoked for the time-series prediction of carbon efficiency. Experimental results using actual run data exhibit that, compared with other prediction models, the developed model can more accurately and efficiently achieve the time-series prediction of carbon efficiency. Furthermore, the developed model can also be used for the efficient and effective modeling of other industrial processes due to its flexible structure.

Positron emission tomography (PET) imaging is used to track biochemical processes in the human body. PET image quality is limited by noise and several methods have been implemented to improve the quality. Kernel-based image reconstruction is among the methods implemented to increase PET image quality and commonly uses a Gaussian kernel to include spatial correlations from image priors into the forward projection model of PET. Unfortunately, the Gaussian kernel tends to smooth details in the reconstructed image. To reduce noise without losing contrast details, a different kernel is needed. Wavelet kernels can be more efficient than the Gaussian kernel in reducing noise while keeping contrast details 1 by better separating signal from noise and thus it does not over smooth peak values in the final reconstructed images. In this work, we evaluate a wavelet kernel for kernel-based PET image reconstruction. For this goal, a wavelet kernel approach has been tested using simulated brain data, physical phantom data and patient data. Reconstruction results are presented and discussed in detail comparing the wavelet kernel method with the Gaussian kernel method. Our results suggest that a wavelet kernel performs better in contrast recovery for phantoms and also results in higher signal-to-noise ratio (SNR) for real patient data.

This work proposes a novel data-driven robust model predictive control (DDRMPC) framework for automatic control of greenhouse in-door climate. The framework integrates dynamic control models of greenhouse temperature, humidity, and CO₂ concentration level with data-driven robust optimization models that accurately and rigorously capture uncertainty in weather forecast error. Data-driven uncertainty sets for ambient temperature, solar radiation, and humidity are constructed from historical data by leveraging a machine learning approach, namely, support vector clustering with weighted generalized intersection kernel. A training-calibration procedure that tunes the size of uncertainty sets is implemented to ensure that data-driven uncertainty sets attain an appropriate performance guarantee. In order to solve the optimization problem in DDRMPC, an affine disturbance feedback policy is utilized to obtain tractable approximations of optimal control. A case study of controlling temperature, humidity, and CO₂ concentration of a semiclosed greenhouse in New York City is presented. The results show that the DDRMPC approach ends up with 14% and 4% lower total cost than rule-based control and robust model predictive control with L₁-norm-based uncertainty set, respectively. The constraint violation probability, which is the percentage of time that the greenhouse system states violate the constraint throughout the whole growing period, for DDRMPC is only 0.39%. Hence, the proposed DDRMPC framework can prevent the greenhouse climate from becoming harmful to plants and fruits. In conclusion, the proposed DDRMPC approach can improve the greenhouse climate control performance and reduce cost compared with other control strategies.

While various biomimetic robotic haptic sensors are often utilized to measure multifarious physical interactions to identify material properties under human exploratory procedures (EPs), traditional methods are unable to fuse well multimodal measurements under EPs. In order to solve this problem, an innovative hybrid joint group kernel sparse coding model (HJGKSC) for material recognition under EPs is proposed. First, a series of feature representations according to the characteristics of different measures are introduced. Second, we propose an innovative hybrid fusion framework based on statistical learning with kernel sparsity to effectively fuse the measurements from robotic EPs. Third, a matched optimization algorithm is constructed to deal with the innate optimization problem in the proposed framework. Finally, we develop an experiment based on three different classification levels (coarse, medium, fine) with the public dataset containing 184 material classes to verify the proposed method and compare with some other similar kernel methods. Experimental results show that the accuracy of coarse, medium and fine classification of the proposed hybrid fusion framework is 98.4%, 96.2% and 98.4%, respectively. The framework can be extended easily to other fusion tasks with multiple measurements.

This paper develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location where noisy gradients of the cost function are evaluated. Classical passive stochastic gradient algorithms use a kernel that approximates a Dirac delta to weigh the gradients based on how far they are evaluated from the desired point. In this paper we construct a multi-kernel passive stochastic gradient algorithm. The algorithm performs substantially better in high dimensional problems and incorporates variance reduction. We analyze the weak convergence of the multi-kernel algorithm and its rate of convergence. In numerical examples, we study the multi-kernel version of the passive least mean squares (LMS) algorithm for transfer learning to compare the performance with the classical passive version.

Touch modality identification has attracted increasing attention due to its importance in human-robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods.

This paper proposes a customized kernel-based Fuzzy C-Means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based Fuzzy C-Means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and Spearman's rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

Fully Bayesian inference in a vector autoregression with stochastic volatility (VAR-SV) typically relies on simulations from a multi-step Markov chain Monte Carlo (MCMC) algorithm. Carriero et al. (2019) propose a new, faster, “triangular” algorithm (TA) to replace the systemwide algorithm (SWA) in the most time-consuming step of the VAR-SV’s standard MCMC algorithm. This paper analytically shows that the TA and SWA generally sample from different distributions, thereby disproving a central claim of Carriero et al. (2019). Replacing the SWA with the TA thus results in an ad hoc change to the MCMC algorithm’s transition kernel, leaving a priori unknown the formal relationship between the model’s posterior and simulations from the MCMC algorithm using the TA.

Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this study. By setting hierarchical prior distributions over the model weights and the parameters of the automatic relevance determination kernel function, respectively, a Gaussian approximation method is designed to approximate the intractable exact marginal likelihood by using the law of the total expectation and the total covariance. Furthermore, in this study, the joint posterior distribution over the model weights and the kernel parameters is estimated by combining the Gibbs sampling with a Laplace approximation. Thus, feature selection is performed by examining the posterior over the kernel parameters. To verify the performance of the proposed method, a series of benchmark datasets and two practical industrial datasets are employed. The experimental results demonstrate that the proposed method not only produces higher prediction accuracy than other methods but also performs better in feature selection, especially in industrial case.

A mathematical model describing the HIV/AIDS transmission dynamics in the existence of an aware community using fractional differential operator having Mittag–Leffler kernel is presented and investigated in this paper. By using the fixed point theorem, the existence and uniqueness conditions of the model are obtained. We have used a novel technique known as the iterative Laplace transform approach to obtain the approximate solution of the mathematical model of HIV/AIDS based on the Atagana-Baleanu operator.We investigate the necessary conditions for the disease control in order to determine the role of unaware infective in the spread of HIV/AIDS. The numerical simulations and plots are demonstrated for different values of fractional order. Moreover, we have compared the obtained numerical results based on the Atagana-Baleanu operator with the values obtained using the Caputo operator for the suggested model. We believe that Atangana-Baleanue fractional derivative and the suggested algorithm are expected to be used in future to formulate and analyse many generalised fractional models.

Kernel size, one of the traits that determine wheat yield, is controlled by multiple quantitative trait loci. Polish wheat (Triticum polonicum) has elongated and plump kernel and is a valuable material for breeding high-yielding wheat cultivars. However, genes or loci determining kernel length (KL) in Polish wheat are unknown. We identified and validated a major KL gene, KL-PW, at the P1 locus in Polish wheat. KL-PW is VRT-A2, which encodes a MIKC-type MADS-box protein (MADS55). An insertion/deletion mutation in intron 1 of VRT-A2PW led to an alternatively spliced transcript, VRT-A2PW2. Quantitative PCR analysis showed that VRT-A2PW was more highly expressed in developing seeds than was VRT-A2Ailanmai. Brassinosteroid (BR) sensitivity experiment and the expression of BR-related genes indicated that VRT-A2PW functions as a positive regulator of BR responses. VRT-A2PW significantly increased KL of wheat. These findings not only reveal the molecular basis of KL-PW in controlling KL, but also provide a valuable genetic resource for increasing kernel size in wheat.

This paper is concerned with the mode-dependent sliding mode control (SMC) for uncertain discrete-time semi-Markov switching systems (S-MSSs). The switching is governed by a semi-Markov chain with a discrete-time semi-Markov kernel (SMK). First, a sliding mode surface (SMS) is constructed to be related to the current switching mode. Second, by means of the upper bound of the sojourn time (ST), some feasible conditions are derived to guarantee σ-error mean square stability for the corresponding S-MSSs. Third, an appropriate SMC law is established to drive the state onto the specified sliding domain. Finally, an F-404 aircraft model validates the proposed control algorithm.

Loop closure detection (LCD) is of significant importance in simultaneous localization and mapping. It represents the robot's ability to recognize whether the current surrounding corresponds to a previously observed one. In this paper, we conduct this task in a two-step strategy: candidate frame selection and loop closure verification. The first step aims to search semantically similar images for the query one using features obtained by Key.Net with HardNet. Instead of adopting the traditional Bag-of-Words strategy, we utilize the aggregated selective match kernel to calculate the similarity between images. Subsequently, based on the potential property of motion field in the LCD scene, we propose a novel feature matching method, i.e., exploiting the smoothness prior and learning the motion field for an image pair in a reproducing kernel Hilbert space (RKHS), to implement loop closure verification. Concretely, we formulate the learning problem into a Bayesian framework with latent variables indicating the true/false correspondences and a mixture model accounting for the distribution of data. Furthermore, we propose a locality-driven mechanism to enhance the local relevance of motion vectors and term the algorithm as locality-driven accurate motion field learning (LAL). To satisfy the requirement of efficiency in the LCD task, we use a sparse approximation and search a suboptimal solution for the motion field in the RKHS, termed as LAL*. Extensive experiments are conducted on public datasets for feature matching and LCD tasks. The quantitative results demonstrate the effectiveness of our method over the current state-of-the-art, meanwhile showing its potential for long-term visual localization. The codes of LAL and LAL* are publicly available at https://github.com/KN-Zhang/LAL.

In nonparametric models, numerous penalization methods using a nonparametric series estimator have been developed for model selection and estimation. However, a penalization has been poorly understood combined with kernel smoothing. This can be attributed to the intrinsic technical and computational difficulties, which leads to different treatments from the developments of the penalized series estimators. Kernel smoothing is a popular and useful nonparametric estimation method; thus it is desirable to establish theoretical and computational analyses for penalized kernel smoothing. In this paper, we develop a novel penalized kernel quantile regression that has nice theoretical and computational properties in varying coefficient models. We show that the proposed method consistently identifies the partially linear structure of the varying coefficient model even when the number of covariates is allowed to increase with a sample size. We develop an efficient algorithm using the alternating direction method of multipliers with a computational convergence guarantee. We derive the plug-in bandwidth selection using high-dimensional kernel regression theory and the penalty parameter is selected by the proposed Bayesian Information Criterion. Our developments require novel high-dimensional kernel regression and computational analyses. The various simulations and real data analyses conducted in this study demonstrate the effectiveness and numerically verify the proposed method.