Our comprehensive empirical evaluation demonstrates that our approach consistently achieves strong results, outperforming recent state-of-the-art techniques and confirming its effectiveness for few-shot learning in various modality contexts.
Multiview clustering successfully exploits the diverse and complementary data points from multiple views, thereby improving clustering effectiveness. By utilizing a min-max formulation and a gradient descent algorithm, the SimpleMKKM algorithm, a representative algorithm in the MVC family, aims to decrease its resulting objective function. The new optimization, combined with the innovative min-max formulation, accounts for the empirically observed superiority. We propose a novel approach by integrating SimpleMKKM's min-max learning methodology into late fusion MVC (LF-MVC). A tri-level max-min-max optimization procedure must be employed for the perturbation matrices, weight coefficients, and the clustering partition matrix. We present a two-stage alternative optimization strategy tailored to solve the intricate max-min-max optimization problem. Beyond that, we theoretically evaluate the clustering algorithm's generalizability, as we explore its performance in handling various datasets. Extensive experiments were carried out to evaluate the proposed algorithm's performance, encompassing clustering accuracy (ACC), processing time, convergence rate, the evolution of the learned consensus clustering matrix, the influence of sample size, and analysis of the learned kernel weight. The results of the experiments highlight that the proposed algorithm exhibits a substantial improvement in computational efficiency and clustering accuracy compared to current leading-edge LF-MVC algorithms. Publicly accessible at https://xinwangliu.github.io/Under-Review is the codebase for this undertaking.
This article introduces a stochastic recurrent encoder-decoder neural network (SREDNN), which integrates latent random variables into its recurrent components, for the first time to address generative multi-step probabilistic wind power predictions (MPWPPs). The encoder-decoder framework, employing the SREDNN, empowers the stochastic recurrent model to incorporate exogenous covariates, thereby improving MPWPP metrics. The SREDNN is structured around five core elements: the prior network, the inference network, the generative network, the encoder recurrent network, and the decoder recurrent network. The SREDNN possesses two crucial advantages over conventional RNN-based methods. Integration with respect to the latent random variable generates an infinite Gaussian mixture model (IGMM) as the observation model, substantially bolstering the expressive capability of the wind power distribution. Finally, the SREDNN's hidden states undergo stochastic updates, producing a continuous mixture of IGMM models that fully characterize the wind power distribution and empower the SREDNN to model complex patterns across wind speed and wind power series. To validate the benefits and efficacy of SREDNN for MPWPP, computational investigations were undertaken using a dataset from a commercial wind farm featuring 25 wind turbines (WTs), along with two openly accessible WT datasets. Compared to benchmark models, the SREDNN, according to experimental results, exhibits a lower negative form of the continuously ranked probability score (CRPS), superior prediction interval sharpness, and comparable prediction interval reliability. The results demonstrably highlight the positive impact of considering latent random variables in the application of SREDNN.
Outdoor computer vision systems are often susceptible to performance degradation, particularly when confronted with rain streaks that negatively affect image clarity. Henceforth, the elimination of rain from a visual representation holds significant importance in the field. For the challenging task of single-image deraining, this article proposes a novel deep architecture—the Rain Convolutional Dictionary Network (RCDNet). This architecture is built upon the inherent characteristics of rain streaks and possesses clear interpretability. The first step is to create a rain convolutional dictionary (RCD) model for portraying rain streaks. Then, a proximal gradient descent technique is used to construct an iterative algorithm using only basic operators for tackling the model. Unfolding the design, we subsequently create the RCDNet, where every network component has a distinct physical manifestation, explicitly connected to a particular algorithm step. The remarkable interpretability simplifies visualizing and analyzing the network's inner workings, exposing the rationale for its success in inference. Considering the domain gap that arises in real-world scenarios, we have designed a novel dynamic RCDNet architecture. This network dynamically infers rain kernels specific to input rainy images, thereby reducing the parameter space for estimating the rain layer using a minimal number of rain maps. This leads to superior generalization performance in the context of inconsistent rain types between training and test data. An interpretable network trained end-to-end automatically extracts all associated rain kernels and proximal operators, faithfully characterizing the attributes of both rainy and clear regions, hence naturally leading to enhanced deraining capabilities. Our method's superiority, evident in both visual and quantitative assessments, is supported by extensive experimentation across a range of representative synthetic and real datasets. This is especially true concerning its robust generalization across diverse testing scenarios and the excellent interpretability of all its modules, contrasting it favorably with current leading single image derainers. The code is situated at.
The current surge of interest in brain-inspired architectures, alongside the evolution of nonlinear dynamic electronic devices and circuits, has empowered energy-efficient hardware implementations of numerous key neurobiological systems and features. The control of various rhythmic motor actions in animals is mediated by a neural system known as the central pattern generator (CPG). A central pattern generator (CPG) is capable of generating spontaneous, coordinated, rhythmic output signals, a capability that would, in theory, be achievable through a network of coupled oscillators, without any feedback loop necessary. Synchronized locomotion in bio-inspired robotics is achieved through the control of limb movements using this approach. Thus, the fabrication of a small and energy-efficient hardware infrastructure for neuromorphic CPGs would provide a significant advantage within bio-inspired robotics research. We demonstrate in this work that four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators can create spatiotemporal patterns that correspond to the fundamental quadruped gaits. Gait patterns' phase relationships are determined by four adjustable bias voltages (or coupling strengths), yielding a programmable network architecture. The intricate problem of gait selection and interleg dynamic coordination is thus reduced to choosing only four control parameters. To achieve this objective, we begin by establishing a dynamic model for the VO2 memristive nanodevice, followed by analytical and bifurcation studies of a single oscillator, culminating in numerical simulations that reveal the behavior of coupled oscillators. The application of the proposed model to VO2 memristors reveals an intriguing similarity between VO2 memristor oscillators and conductance-based biological neuron models like the Morris-Lecar (ML) model. This work fosters and directs future investigation into the implementation of neuromorphic memristor circuits, which model neurobiological processes.
Graph neural networks (GNNs) are indispensable in handling diverse graph-related challenges. Most graph neural networks, however, are designed with the assumption of homophily, preventing their straightforward adaptation to heterophilic situations. Heterophily settings introduce diverse characteristics and classification labels among connected nodes. Real-world graphs, moreover, frequently emerge from deeply interconnected latent variables, while existing Graph Neural Networks (GNNs) tend to overlook this intricate structure, instead characterizing heterogeneous node relationships as simple binary homogenous edges. We present a novel relation-based frequency-adaptive graph neural network (RFA-GNN) in this article, which tackles both heterophily and heterogeneity within a unified structure. Employing a decomposition technique, RFA-GNN separates the input graph into multiple relation graphs, with each representing a latent relationship. Bioactive cement Significantly, our work presents a detailed theoretical analysis based on spectral signal processing. human biology This analysis suggests a relation-sensitive, frequency-adaptive method for choosing signals of varying frequencies within the respective relational spaces during the message-passing process. check details The results of extensive experiments on both synthetic and real-world data sets highlight the effectiveness of RFA-GNN, particularly in the contexts of heterophily and heterogeneity. The codebase for this project, readily available to the public, is hosted at https://github.com/LirongWu/RFA-GNN.
Arbitrary image stylization by neural networks is trending; video stylization is an exciting further development of this approach. In contrast to their success with still images, image stylization techniques frequently produce unsatisfactory video outcomes, plagued by noticeable flickering issues. Our investigation in this article meticulously explores the root causes of these flickering effects. Comparative analysis of neural style transfer techniques shows the ill-conditioning of feature migration modules in current leading learning systems, potentially causing a mismatch between input content and generated frames at the channel level. While traditional methods frequently employ additional optical flow constraints or regularization modules to rectify misalignment, our approach directly focuses on upholding temporal continuity by synchronizing each output frame with the input frame.