Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: long-tailed classification. We propose TMLC, a Trusted Multi-view Long-tailed Classification framework, which makes contributions on two critical aspects: opinion aggregation and pseudo-data generation. Specifically, inspired by Social Identity Theory, we design a group consensus opinion aggregation mechanism that guides decision-making toward the direction favored by the majority of the group. In terms of pseudo-data generation, we introduce a novel distance metric to adapt SMOTE for multi-view scenarios and develop an uncertainty-guided data generation module that produces high-quality pseudo-data, effectively mitigating the adverse effects of class imbalance. Extensive experiments on long-tailed multi-view datasets demonstrate that our model is capable of achieving superior performance.

Information theory has inspired numerous advancements in multi-view learning. Most multi-view methods incorporating information-theoretic principles rely an assumption called multi-view redundancy which states that common information between views is necessary and sufficient for down-stream tasks. This assumption emphasizes the importance of common information for prediction, but inherently ignores the potential of unique information in each view that could be predictive to the task. In this paper, we propose a comprehensive information-theoretic multi-view learning framework named CIML, which discards the assumption of multi-view redundancy. Specifically, CIML considers the potential predictive capabilities of both common and unique information based on information theory. First, the common representation learning maximizes Gács-Körner common information to extract shared features and then compresses this information to learn task-relevant representations based on the Information Bottleneck (IB). For unique representation learning, IB is employed to achieve the most compressed unique representation for each view while simultaneously minimizing the mutual information between unique and common representations, as well as among different unique representations. Importantly, we theoretically prove that the learned joint representation is predictively sufficient for the downstream task. Extensive experimental results have demonstrated the superiority of our model over several state-of-art methods. The code is released on CIML.

Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster's combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hierarchical framework includes a two-phase aggregation process: the intra-view and inter-view aggregation hierarchies. In the intra aggregation, we assume that each view is comprised of common information shared with other views, as well as its specific information. We then aggregate both the common and specific information. This aggregation phase is useful to eliminate the feature noise inherent to view itself, thereby improving the view quality. In the inter-view aggregation, we design an attention mechanism at the evidence level to facilitate opinion aggregation from different views. To the best of our knowledge, this is one of the pioneering efforts to formulate a hierarchical aggregation framework in the trusted multi-view learning domain. Extensive experiments show that our model outperforms some state-of-art trust-related baselines. One can access the source code onhttps://github.com/lshi91/GTMC-HOA.

In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph for clustering. However, it is worth noting that these graph learning methods encounter two significant limitations. Firstly, they often rely on Euclidean distance to measure similarity when constructing the adaptive neighbor graph, which proves inadequate in capturing the intrinsic structure among data points in practice, particularly for high-dimensional data. Secondly, most of these methods focus solely on consensus graph, ignoring unique information from each view. Although a few graph-based studies have considered using specific information as well, the modelling approach employed does not exclude the noise impact from the common or specific components. To this end, we propose a novel tensor-based multi-view graph learning framework that simultaneously considers consistency and specificity, while effectively eliminating the influence of noise. Specifically, we calculate similarity using pseudo-Stiefel manifold distance to preserve the intrinsic properties of data. By making an assumption that the learned neighbor graph of each view comprises a consistent part, a specific part, and a noise part, we formulate a new tensor-based target graph learning paradigm for noise-free graph fusion. Owing to the benefits of tensor singular value decomposition (t-SVD) in uncovering high-order correlations, this model is capable of achieving a comprehensive understanding of the target graph. Furthermore, we derive an algorithm to address the optimization problem. Experiments on six datasets have demonstrated the superiority of our method. We have released the source code onhttps://github.com/lshi91/CSTGL-Code.

In this work we present a full-duplex (FD) underwater acoustic (UWA) communication system capable of simultaneously transmitting and receiving acoustic signals in the same frequency bandwidth with a two-element FD transducer. The key challenge of implementing an FD system is to cancel the strong self-interference (SI) from the near-end transmitter. By using advanced adaptive filtering algorithms providing high accuracy channel estimates, a high level of SI cancellation can be achieved when the far-end signal is absent. However, the SI channel estimation performance is limited in FD scenarios since the far-end signal acts as an interference. In this paper, we propose an FD UWA communication system which alternates between the SI cancellation and far-end data demodulation. Advanced adaptive filters with high tracking performance are used for SI cancellation. An adaptive Rake combiner with multipath interference cancellation is implemented to improve the demodulation performance in time-varying multipath channels. The performance of the FD UWA system is evaluated in lake experiments and numerical simulations. The proposed adaptive Rake combiner with multipath interference cancellation significantly outperforms the conventional Rake combiner and an adaptive decision feedback equalizer in the experiments. With the adaptive Rake combiner, the detection performance of the proposed FD UWA system is comparable with that of the half-duplex system.

Large language models (LLMs) are expected to offer structured Markdown responses for the sake of readability in web chatbots (e.g., ChatGPT). Although there are a myriad of metrics to evaluate LLMs, they fail to evaluate the readability from the view of output content structure. To this end, we focus on an overlooked yet important metric --- Markdown Awareness, which directly impacts the readability and structure of the content generated by these language models. In this paper, we introduce MDEval, a comprehensive benchmark to assess Markdown Awareness for LLMs, by constructing a dataset with 20K instances covering 10 subjects in English and Chinese. Unlike traditional model-based evaluations, MDEval provides excellent interpretability by combining model-based generation tasks and statistical methods. Our results demonstrate that MDEval achieves a Spearman correlation of 0.791 and an accuracy of 84.1% with human, outperforming existing methods by a large margin. Extensive experimental results also show that through fine-tuning over our proposed dataset, less performant open-source models are able to achieve comparable performance to GPT-4o in terms of Markdown Awareness. To ensure reproducibility and transparency, MDEval is open sourced at https://github.com/SWUFE-DB-Group/MDEval-Benchmark.

Temporal graph representation learning has drawn considerable attention in recent years. Most existing works mainly focus on modeling local structural dependencies of temporal graphs. However, underestimating the inherent global structural role information in many real-world temporal graphs inevitably leads to sub-optimal graph representations. To overcome this shortcoming, we propose a novel Role-based Temporal Graph Convolution Network (RTGCN) that fully leverages the global structural role information in temporal graphs. Specifically, RTGCN can effectively capture the static global structural roles by using hypergraph convolution neural networks. To capture the evolution of nodes' structural roles, we further design structural role-based gated recurrent units. Finally, we integrate structural role proximity in our objective function to preserve global structural similarity, further promoting temporal graph representation learning. Experimental results on multiple real-world datasets demonstrate that RTGCN consistently outperforms state-of-the-art temporal graph representation learning methods by significant margins in various temporal link prediction and node classification tasks. Specifically, RTGCN achieves AUC improvement of up to 5.1% for link prediction and F1 improvement of up to 6.2% for new link prediction. In addition, RTGCN achieves AUC improvement up to 4.6% for node classification and 2.7% for structural role classification.

Latent multi-view subspace clustering has been demonstrated to have desirable clustering performance. However, the original latent representation method vertically concatenates the data matrices from multiple views into a single matrix along the direction of dimensionality to recover the latent representation matrix, which may result in an incomplete information recovery. To fully recover the latent space representation, we in this paper propose an Enhanced Latent Multi-view Subspace Clustering (ELMSC) method. The ELMSC method involves constructing an augmented data matrix that enhances the representation of multi-view data. Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information. Meanwhile, the non-block-diagonal entries are composed based on the similarity between different views to capture the consistent information. In addition, we enforce a sparse regularization for the non-diagonal blocks of the augmented self-representation matrix to avoid redundant calculations of consistency information. Finally, a novel iterative algorithm based on the framework of Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem for ELMSC. Particularly, we theoretically analyze the convergence of ELMSC in detail. Extensive experiments on real-world datasets show that our proposed ELMSC is able to achieve higher clustering performance than some state-of-art multi-view clustering methods. Moreover, our experiments show that our method remains effective with randomly chosen parameters, demonstrating ELMSC’s practical potential.

The maximum correntropy criterion (MCC) algorithm depends upon two fundamental parameters, i.e., step-size and kernel width. Previous studies of parameter optimization in the MCC mainly focus on a single parameter (mainly the kernel width), lacking optimization research concerning both parameters. To this end, this letter investigates a novel optimization scheme simultaneously involving step-size and kernel width. The optimization framework is based on making the power of weight error vector undergo the steepest attenuation. Under the premise of maintaining the same evolutionary trend for time-varying step-size and kernel width, we formulate a constrained parameter optimization problem, where the step-size is subject to a kernel width induced constraint. By taking this approach, the original bivariate optimization can be transformed into a univariate optimization problem, which facilitates optimization solving. We further develop an existing reset scheme to make it suitable for kernel width to ensure a good tracking capability. In addition, we investigate the convergence behavior of the optimized algorithm. Simulation results demonstrate that the developed optimization scheme is beneficial for performance improvement, and the resulting algorithm outperforms some state-of-art MCC-based algorithms.

The Euclidean direction search (EDS) algorithm can reduce the complexity by avoiding the matrix inversion operation. However, it may fail to work in impulsive environments. To address this problem, a novel EDS based upon the maximum correntropy criterion (EDS-MCC) algorithm is proposed, which provides computational savings and robustness for combating impulsive noise. Additionally, the EDS-MCC algorithm is analyzed to obtain the theoretical performance by utilizing the energy conservation argument (ECA) and the Taylor expansion method. Simulations are exhibited to show the robustness of the EDS-MCC algorithm and verify the accuracy of the theoretical analysis.

The shrinkage linear complex-valued least mean squares (SL-CLMS) algorithm with a variable step size overcomes the conflicting issue between fast convergence and low steady-state misalignment. To the best of our knowledge, the theoretical performance analysis of the SL-CLMS algorithm has not been presented yet. This letter focuses on the theoretical analysis of the excess mean square error transient and steady-state performance of the SL-CLMS algorithm. Simulation results obtained for identification scenarios show a good match with the analytical results.

As an important category of clustering methods, subspace clustering algorithms have arisen particular attention during the last decade. Most subspace clustering algorithms are designed by first constructing a similarity matrix and then using spectral clustering algorithms to perform clustering. How to learn a suitable representation matrix to construct the similarity matrix is essential to the clustering performance. In most existing algorithms, the representation matrix is solved by norm-minimization, which commonly enforces the error matrix with nuclear norm or sparsity norm. However, these methods may fail to achieve satisfactory performance for real data contaminated by complex noise. To this end, we propose a novel robust subspace clustering method based on the Logarithmic Hyperbolic Cosine Function (LHCF). We theoretically analyze the grouping effect, as well as the convergence behavior, which illustrates that highly correlated samples can be grouped into the same cluster. Experimental results conducted on the Extended Yale B dataset show that the newly proposed algorithm yields better clustering performance compared with some advanced methods.