In order to achieve complete classification, we proactively developed three critical elements: a comprehensive examination of existing attributes, a suitable leveraging of representative features, and a differentiated merging of multi-domain characteristics. Based on our present comprehension, these three building blocks are being introduced for the initial time, offering a new outlook on configuring HSI-tuned models. Consequently, a complete HSI classification model (HSIC-FM) is introduced to address the limitations of incomplete data. A comprehensive local-to-global geographical representation is achieved through the presentation of a recurrent transformer for Element 1, which meticulously extracts both short-term specifics and long-term semantic information. Thereafter, a feature reuse strategy, mimicking Element 2, is created to effectively and efficiently re-employ useful information for a more accurate classification using a smaller set of annotations. By the end of the process, a discriminant optimization is devised according to the framework of Element 3, to distinctly combine multi-domain characteristics for the purpose of containing the influence of individual domains. The proposed method's effectiveness is demonstrably superior to the state-of-the-art, including CNNs, FCNs, RNNs, GCNs, and transformer-based models, as evidenced by extensive experiments across four datasets—ranging from small to large in scale. The performance gains are particularly impressive, achieving an accuracy increase of over 9% with only five training samples per class. selleck products The HSIC-FM code will become available at the following URL: https://github.com/jqyang22/HSIC-FM in the coming days.
Interpretations and applications following HSI's mixed noise pollution are substantially disturbed. This technical review commences with a noise analysis across various noisy hyperspectral images (HSIs), subsequently extracting key insights to inform the development of effective HSI denoising algorithms. Afterwards, an overarching HSI restoration framework is formulated to drive optimization. Later, a comprehensive review is presented of existing HSI denoising methods, progressing from model-based solutions (nonlocal means, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), through data-driven methods (2-D and 3-D convolutional neural networks, hybrid architectures, and unsupervised learning), to eventually encompass model-data-driven strategies. The favorable and unfavorable aspects of each HSI denoising strategy are delineated and compared. To evaluate HSI denoising methods, we present findings from simulated and real experiments using various noisy hyperspectral images. The efficiency of execution and the classification results of the denoised hyperspectral images (HSIs) are shown using these HSI denoising approaches. This technical review, in its final analysis, presents prospective future methods for tackling HSI denoising challenges. The HSI denoising dataset's location is the cited URL: https//qzhang95.github.io.
A significant category of delayed neural networks (NNs) is explored in this article, characterized by extended memristors that comply with the Stanford model. This model, a widely used and popular one, accurately describes the switching behavior of real nonvolatile memristor devices, deployed in nanotechnology applications. The article's investigation of delayed neural networks with Stanford memristors uses the Lyapunov method to determine complete stability (CS) focusing on the convergence of trajectories among multiple equilibrium points (EPs). The established conditions for CS are dependable and withstand changes in the interconnections, holding true for all values of concentrated delay. Besides this, numerical validation, through linear matrix inequalities (LMI), or analytical confirmation, via the concept of Lyapunov diagonally stable (LDS) matrices, is attainable. The conditions in place cause the transient capacitor voltages and NN power to be nullified at the conclusion. Subsequently, this yields improvements in terms of power usage. In spite of this fact, nonvolatile memristors maintain the results of computations in keeping with the in-memory computing concept. resistance to antibiotics Numerical simulations demonstrate and confirm the validity of the results. From a methodological viewpoint, the article encounters new difficulties in establishing CS, as NNs, thanks to non-volatile memristors, exhibit a continuous range of non-isolated excitation potentials. The physical properties of memristors restrict the state variables to particular intervals, thus requiring a differential variational inequality approach for modeling the neural network's dynamics.
The optimal consensus problem in general linear multi-agent systems (MASs) is scrutinized in this article, employing a dynamic event-triggered approach. An improved cost function, dealing with interaction-related aspects, is introduced here. The second approach involves a dynamic, event-activated architecture, engineered by designing a novel distributed dynamic triggering function and a new consensus protocol tailored to event triggers, in a distributed manner. Subsequently, the adjusted interaction cost function can be minimized through the implementation of distributed control laws, thereby circumventing the challenge of the optimal consensus problem, which necessitates the acquisition of all agents' information to determine the interaction cost function. CMV infection Consequently, sufficient conditions are obtained to uphold optimality. The developed optimal consensus gain matrices are found to be a function of only the selected triggering parameters and the desired modified interaction-related cost function, independently of the system dynamics, initial states, or network characteristics in the controller design Also considered is the tradeoff between peak consensus performance and event-driven behavior. To confirm the efficacy of the devised distributed event-triggered optimal controller, a simulation example is presented.
Visible-infrared object detection systems leverage the differences in visible and infrared data to boost performance. Existing methods, while frequently employing local intramodality information for feature enhancement, often fail to consider the impactful latent interactions embedded within long-range dependencies across diverse modalities. This deficiency frequently leads to unsatisfactory detection outcomes in intricate scenes. We present a long-range attention fusion network (LRAF-Net) with enhanced features to tackle these problems, improving detection outcomes by combining long-range dependencies of the enhanced visible and infrared features. Deep features from visible and infrared images are extracted using a two-stream CSPDarknet53 network, complemented by a novel data augmentation method. This method uses asymmetric complementary masks to diminish the bias towards a single modality. To refine intramodality feature representation, we propose a cross-feature enhancement (CFE) module, drawing upon the variation between visible and infrared image data. Following this, we present a long-range dependence fusion (LDF) module, which combines the improved features using the positional encoding of multi-modal data. Finally, the merged characteristics are directed to a detection head to produce the ultimate detection outcomes. Public datasets, such as VEDAI, FLIR, and LLVIP, demonstrate the proposed method's superior performance compared to existing techniques in experimental evaluations.
The process of tensor completion involves recovering a tensor from a sampled set of its elements, frequently relying on the low-rank nature of the tensor itself. A low tubal rank, among several tensor rank definitions, effectively captures the intrinsic low-rank structure of a tensor. Some recently suggested low-tubal-rank tensor completion algorithms, despite exhibiting promising performance, rely on second-order statistics to assess error residuals. This approach may prove inadequate when dealing with the presence of significant outliers within the observed data entries. This paper proposes a new objective function for completing low-tubal-rank tensors. Correntropy is used as the error measure to reduce the influence of outliers. We optimize the proposed objective with a half-quadratic minimization procedure, converting the optimization into a weighted low-tubal-rank tensor factorization problem. Thereafter, we outline two uncomplicated and productive algorithms for attaining the solution, encompassing discussions of their convergence and computational complexity. Across various synthetic and real datasets, the numerical results showcase the robust and superior performance of the proposed algorithms.
Real-life applications benefit from the broad implementation of recommender systems, which facilitate the discovery of pertinent information. Recent years have witnessed a rise in research on reinforcement learning (RL)-based recommender systems, which are notable for their interactive nature and autonomous learning ability. From an empirical perspective, reinforcement learning-driven recommendation systems typically demonstrate advantages over their supervised learning counterparts. Nonetheless, the application of reinforcement learning to recommender systems encounters a multitude of difficulties. A reference, containing the challenges and appropriate solutions, is necessary for researchers and practitioners engaged in the development and application of RL-based recommender systems. For this purpose, we first offer a comprehensive examination, alongside comparisons and summaries, of reinforcement learning approaches in four prevalent recommendation scenarios: interactive, conversational, sequential, and explainable recommendations. Additionally, we thoroughly examine the difficulties and corresponding remedies, leveraging existing literature. Lastly, addressing open challenges and limitations in reinforcement learning for recommender systems, we delineate potential research directions.
Domain generalization is a crucial, yet often overlooked, problem that deep learning struggles with in unknown environments.