The extensive experimental outcomes corroborate the promising performance of our work, exceeding the performance of current state-of-the-art approaches and validating its effectiveness in few-shot learning across various modality settings.
The diverse and complementary information embedded in various views is adeptly utilized by multiview clustering to achieve better clustering results. As a model MVC algorithm, SimpleMKKM, in its newly proposed form, employs a min-max formulation and a gradient descent algorithm to lessen the resultant objective function. The novel min-max formulation, coupled with the new optimization, is demonstrably responsible for its superior qualities. This article details the integration of the min-max learning paradigm from SimpleMKKM into the late fusion MVC architecture (LF-MVC). Perturbation matrices, weight coefficients, and clustering partition matrices are subject to a tri-level max-min-max optimization problem. A two-part alternative optimization methodology is presented to successfully navigate the complex max-min-max optimization problem. Subsequently, we delve into the theoretical underpinnings of the proposed method's clustering performance, specifically its ability to generalize to novel datasets. For a thorough evaluation of the suggested algorithm, exhaustive experiments were conducted encompassing clustering precision (ACC), execution duration, convergence characteristics, the changes in the learned consensus clustering matrix, clustering using varied sample sets, and an investigation into the learned kernel weights. Comparative analysis of experimental results reveals that the proposed algorithm substantially reduces computation time and improves clustering accuracy when assessed against various state-of-the-art LF-MVC algorithms. Publicly accessible at https://xinwangliu.github.io/Under-Review is the codebase for this undertaking.
Employing latent random variables within its recurrent structure, this paper presents, for the first time, a stochastic recurrent encoder-decoder neural network (SREDNN) aimed at generative multi-step probabilistic wind power predictions (MPWPPs). To enhance MPWPP, the SREDNN enables the encoder-decoder framework's stochastic recurrent model to utilize exogenous covariates. Five components, namely the prior network, the inference network, the generative network, the encoder recurrent network, and the decoder recurrent network, collectively form the SREDNN. The SREDNN possesses two crucial advantages over conventional RNN-based methods. Building on integration of the latent random variable, an infinite Gaussian mixture model (IGMM) is established as the observation model, leading to a significant rise in the expressiveness of wind power distributions. Following this, a stochastic procedure is used to update the internal states of the SREDNN, creating an infinite mixture of IGMM distributions for the ultimate wind power distribution, thereby enabling the SREDNN to model complex relationships between wind speed and power. An assessment of the SREDNN's performance in MPWPP was undertaken through computational experiments based on a dataset of a commercial wind farm with 25 wind turbines (WTs), and two openly accessible datasets of wind turbines. 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.
Rain-induced streaks on images negatively affect the accuracy and efficiency of outdoor computer vision systems. As a result, removing rain from images has become a critical issue in the related field of research. To address the intricate single-image deraining problem, this paper introduces a novel deep architecture, the Rain Convolutional Dictionary Network (RCDNet). Crucially, this network incorporates implicit knowledge about rain streaks and offers a clear and understandable framework. Specifically, we initially develop a rain convolutional dictionary (RCD) model for depicting rain streaks, and then employ the proximal gradient descent method to formulate an iterative algorithm consisting solely of basic operators for addressing the model. The uncoiling process yields the RCDNet, wherein each network component holds a definite physical significance, aligning with each operation of the algorithm. This great interpretability simplifies the visualization and analysis of the network's internal operations, thereby explaining the reasons for its success in the inference stage. Additionally, taking into account the domain gap in real-world scenarios, a new dynamic RCDNet is designed. The network dynamically infers rain kernels tailored to each input rainy image, thereby allowing for a reduced space for estimating the rain layer using only a limited number of rain maps, hence ensuring superior generalization performance across different rain types between training and testing datasets. Employing end-to-end training on such an interpretable network, all pertinent rain kernels and proximal operators are automatically discerned, accurately reflecting the characteristics of both rainy and clear background regions, thus naturally enhancing deraining efficacy. Through comprehensive experiments on representative synthetic and real datasets, the superiority of our method in deraining tasks has been established. The method's strength lies in its well-rounded adaptability to diverse testing scenarios, and in the clear interpretability of its constituent modules, noticeably exceeding the capabilities of existing single image derainers, both visually and in numerical measures. The code is located at.
The increasing attention towards brain-inspired architectures, along with the evolution of nonlinear dynamic electronic devices and circuits, has enabled the realization of energy-efficient hardware representations of critical neurobiological systems and attributes. The control of various rhythmic motor actions in animals is mediated by a neural system known as the central pattern generator (CPG). Central pattern generators (CPGs) have the potential to produce spontaneous, coordinated, and rhythmic output signals, potentially achieved through a system of coupled oscillators that operate independently of any feedback mechanisms. For coordinated limb movement in locomotion, bio-inspired robotics implements this methodology. In this regard, creating a small and energy-efficient hardware platform for neuromorphic central pattern generators promises great value for bio-inspired robotics. This work demonstrates the capability of four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators to produce spatiotemporal patterns that match the fundamental quadruped gaits. Four tunable bias voltages (or coupling strengths) dictate the phase relationships within the gait patterns, resulting in a programmable network. This simplification of gait selection and dynamic interleg coordination reduces the problem to choosing four control parameters. In pursuit of this goal, we initially present a dynamic model of the VO2 memristive nanodevice, subsequently undertaking analytical and bifurcation analyses of a solitary oscillator, and ultimately showcasing the dynamics of interconnected oscillators via comprehensive numerical simulations. Employing the presented model on a VO2 memristor reveals a striking resemblance between VO2 memristor oscillators and conductance-based biological neuron models, including the Morris-Lecar (ML) model. Further research into neuromorphic memristor circuits mimicking neurobiological phenomena can be inspired and guided by this.
Various graph-related tasks have benefited substantially from the important contributions of graph neural networks (GNNs). Nevertheless, the majority of current graph neural networks rely on the principle of homophily, thus rendering them unsuitable for direct application to heterophily scenarios, where interconnected nodes might exhibit differing attributes and classification labels. Furthermore, graphs encountered in real-world scenarios are often shaped by complex latent factors intertwined in intricate ways, yet extant GNNs tend to disregard this crucial aspect, merely labeling heterogeneous relations between nodes as homogenous binary 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. RFA-GNN's first stage involves the separation of the input graph into multiple relation graphs, wherein each one embodies a latent relationship. selleck chemicals llc Significantly, our work presents a detailed theoretical analysis based on spectral signal processing. algal bioengineering Therefore, we propose a relation-driven, frequency-adaptive system for adaptively choosing signals with differing frequencies in each respective relational space during the message-passing operation. Lipid biomarkers Comparative experiments on synthetic and real-world datasets affirm the remarkable efficacy of RFA-GNN in the presence of heterophily and heterogeneity, showing very promising results. The source code is accessible at https://github.com/LirongWu/RFA-GNN.
Image stylization, using neural networks for arbitrary modifications, has achieved significant attention, and video stylization is building on this success with even more interest. Despite the effectiveness of image stylization methods in certain contexts, their application to videos frequently produces problematic results characterized by significant flickering. This article undertakes a comprehensive and detailed analysis of the underlying causes of these flickering appearances. Analyzing typical neural style transfer methods, we find that the feature migration components in current top-performing learning systems are poorly conditioned, potentially causing mismatches between the input content's channels and the generated frames. In contrast to conventional approaches that correct misalignment using supplemental optical flow constraints or regularization layers, our method prioritizes maintaining temporal consistency by aligning each output frame with its corresponding input frame.