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This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature. In Aceso 1, we adopt an active learning paradigm, which helps to minimize the cost of manual labeling and to optimize the quality of summarization with limited labeled data. An UMLS2Vec model is proposed to learn a vector representation of medical concepts in UMLS 2, and we fuse the embedding of medical knowledge with textual features in summarization. The evaluation shows that our approach is better on identifying PICO sentences against state-of-the-art studies and outperforms baseline methods on producing high-quality evidence summaries.The material attribute of an object's surface is critical to enable robots to perform dexterous manipulations or actively interact with their surrounding objects. Tactile sensing has shown great advantages in capturing material properties of an object's surface. However, the conventional classification method based on tactile information may not be suitable to estimate or infer material properties, particularly during interacting with unfamiliar objects in unstructured environments. Moreover, it is difficult to intuitively obtain material properties from tactile data as the tactile signals about material properties are typically dynamic time sequences. In this article, a visual-tactile cross-modal learning framework is proposed for robotic material perception. In particular, we address visual-tactile cross-modal learning in the lifelong learning setting, which is beneficial to incrementally improve the ability of robotic cross-modal material perception. To this end, we proposed a novel lifelong cross-modal learning model. Experimental results on the three publicly available data sets demonstrate the effectiveness of the proposed method.Modeling image sets or videos as linear subspaces is quite popular for classification problems in machine learning. However, affine subspace modeling has not been explored much. In this article, we address the image sets classification problem by modeling them as affine subspaces. Affine subspaces are linear subspaces shifted from origin by an offset. The collection of the same dimensional affine subspaces of RD is known as affine Grassmann manifold (AGM) or affine Grassmannian that is a smooth and noncompact manifold. The non-Euclidean geometry of AGM and the nonunique representation of an affine subspace in AGM make the classification task in AGM difficult. In this article, we propose a novel affine subspace-based kernel that maps the points in AGM to a finite-dimensional Hilbert space. For this, we embed the AGM in a higher dimensional Grassmann manifold (GM) by embedding the offset vector in the Stiefel coordinates. The projection distance between two points in AGM is the measure of similarity obtained by the kernel function. The obtained kernel-gram matrix is further diagonalized to generate low-dimensional features in the Euclidean space corresponding to the points in AGM. Distance-preserving constraint along with sparsity constraint is used for minimum residual error classification by keeping the locally Euclidean structure of AGM in mind. Experimentation performed over four data sets for gait, object, hand, and body gesture recognition shows promising results compared with state-of-the-art techniques.Ensemble classifiers using clustering have significantly improved classification and prediction accuracies of many systems. read more These types of ensemble approaches create multiple clusters to train the base classifiers. However, the problem with this is that each class might have many clusters and each cluster might have different number of samples, so an ensemble decision based on large number of clusters and different number of samples per class within a cluster produces biased and inaccurate results. Therefore, in this article, we propose a novel methodology to create an appropriate number of strong data clusters for each class and then balance them. Furthermore, an ensemble framework is proposed with base classifiers trained on strong and balanced data clusters. The proposed approach is implemented and evaluated on 24 benchmark data sets from the University of California Irvine (UCI) machine learning repository. An analysis of results using the proposed approach and the existing state-of-the-art ensemble classifier approaches is conducted and presented. A significance test is conducted to further validate the efficacy of the results and a detailed analysis is presented.To achieve plant-wide operational optimization and dynamic adjustment of operational index for an industrial process, knowledge-based methods have been widely employed over the past years. However, the extraction of knowledge base is a bottleneck for most existing approaches. To address this problem, we propose a novel framework based on the generative adversarial networks (GANs), termed as decision-making GAN (DMGAN), which directly learns from operational data and performs human-level decision making of the operational indices for plant-wide operation. In the proposed DMGAN, two adversarial criteria and three cycle consistency criteria are incorporated to encourage efficient posterior inference. To improve the generalization power of a generator with an increasing complexity of the industrial processes, a reinforced U-Net (RU-Net) is presented that improves the traditional U-Net by providing a more general combinator, a building block design, and drop-level regularization. In this article, we also propose three quantitative metrics for assessing the plant-wide operation performance.