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The results obtained on several facial and object datasets show that READ outperforms many state-of-the-art methods when using deep features.This article considers a general class of nonautonomous discontinuous ordinary differential equations (ODE). By constructing the Filippov multimap, the fixed-time stability (FTS) problem of discontinuous ODE is transformed into that of differential inclusion (DI). In order to establish the FTS criteria of the zero solution for DI, the generalized Lyapunov function (LF) method is developed. The generalized LF of this article is relaxed to have an indefinite derivative for almost everywhere along the state trajectories of the system. However, the traditional LF is required to possess negative definite or semi-negative definite derivative for everywhere. As a result, several novel sufficient conditions for FTS are given. Moreover, the settling time of FTS is provided. Then, the theoretical results are applied to solve the fixed-time stabilization control problems of ball motion model and neural networks (NNs) with discontinuities. The developed LF method of FTS is extremely significant in the field of control engineering.Multiview clustering (MVC) has recently been the focus of much attention due to its ability to partition data from multiple views via view correlations. However, most MVC methods only learn either interfeature correlations or intercluster correlations, which may lead to unsatisfactory clustering performance. To address this issue, we propose a novel dual-correlated multivariate information bottleneck (DMIB) method for MVC. DMIB is able to explore both interfeature correlations (the relationship among multiple distinct feature representations from different views) and intercluster correlations (the close agreement among clustering results obtained from individual views). For the former, we integrate both view-shared feature correlations discovered by learning a shared discriminative feature subspace and view-specific feature information to fully explore the interfeature correlation. This allows us to attain multiple reliable local clustering results of different views. Following this, we explore the intercluster correlations by learning the shared mutual information over different local clusterings for an improved global partition. By integrating both correlations, we formulate the problem as a unified information maximization function and further design a two-step method for optimization. Moreover, we theoretically prove the convergence of the proposed algorithm, and discuss the relationships between our method and several existing clustering paradigms. The experimental results on multiple datasets demonstrate the superiority of DMIB compared to several state-of-the-art clustering methods.This article is concerned with the multiloop decentralized H∞ fuzzy proportional-integral-derivative-like (PID-like) control problem for discrete-time Takagi-Sugeno fuzzy systems with time-varying delays under dynamical event-triggered mechanisms (ETMs). The sensors of the plant are grouped into several nodes according to their physical distribution. For resource-saving purposes, the signal transmission between each sensor node and the controller is implemented based on the dynamical ETM. Taking the node-based idea into account, a general multiloop decentralized fuzzy PID-like controller is designed with fixed integral windows to reduce the potential accumulation error. The overall decentralized fuzzy PID-like control scheme involves multiple single-loop controllers, each of which is designed to generate the local control law based on the measurements of the corresponding sensor node. These kinds of local controllers are convenient to apply in practice. Sufficient conditions are obtained under which the controlled system is exponentially stable with the prescribed H∞ performance index. The desired controller gains are then characterized by solving an iterative optimization problem. Finally, a simulation example is presented to demonstrate the correctness and effectiveness of the proposed design procedure.An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal correlations. Therefore, new methods that can clearly distinguish emotional states in EEG data are required. In this paper, we propose a new emotion recognition method, named AsEmo. The proposed method extracts effective features boosting classification performance on various emotional states from multi-class EEG data. AsEmo Automatically determines the number of spatial filters needed to extract significant features using the explained variance ratio (EVR) and employs a Subject-independent method for real-time processing of Emotion EEG data. The advantages of this method are as follows (a) it automatically determines the spatial filter coefficients distinguishing emotional states and extracts the best features; (b) it is very robust for real-time analysis of new data using a subject-independent technique that considers subject sets, and not a specific subject; (c) it can be easily applied to both binary-class and multi-class data. Experimental results on real-world EEG emotion recognition tasks demonstrate that AsEmo outperforms other state-of-the-art methods with a 2-8% improvement in terms of classification accuracy.The high capacity of neural networks allows fitting models to data with high precision, but makes generalization to unseen data a challenge. If a domain shift exists, i.e. differences in image statistics between training and test data, care needs to be taken to ensure reliable deployment in real-world scenarios. In digital pathology, domain shift can be manifested in differences between whole-slide images, introduced by for example differences in acquisition pipeline - between medical centers or over time. In order to harness the great potential presented by deep learning in histopathology, and ensure consistent model behavior, we need a deeper understanding of domain shift and its consequences, such that a model's predictions on new data can be trusted. This work focuses on the internal representation learned by trained convolutional neural networks, and shows how this can be used to formulate a novel measure - the representation shift - for quantifying the magnitude of model-specific domain shift. buy Climbazole We perform a study on domain shift in tumor classification of hematoxylin and eosin stained images, by considering different datasets, models, and techniques for preparing data in order to reduce the domain shift.
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