Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based methods makes it possible to generate satisfactory segmentation results. However, existing methods still face challenges in adapting network parameters to various land cover distributions and enhancing the interaction between spatial and frequency domain features. To address these challenges, we propose the Adaptive Frequency Enhancement Network (AFENet), which integrates two key components: the Adaptive Frequency and Spatial feature Interaction Module (AFSIM) and the Selective feature Fusion Module (SFM). AFSIM dynamically separates and modulates high- and low-frequency features according to the content of the input image. It adaptively generates two masks to separate high- and low-frequency components, therefore providing optimal details and contextual supplementary information for ground object feature representation. SFM selectively fuses global context and local detailed features to enhance the network’s representation capability. Hence, the interactions between frequency and spatial features are further enhanced. Extensive experiments on three publicly available datasets demonstrate that the proposed AFENet outperforms state-of-the-art methods. In addition, we also validate the effectiveness of AFSIM and SFM in managing diverse land cover types and complex scenarios. Our codes are available at https://github.com/oucailab/AFENet.

Aspect-Based Multimodal Sentiment Analysis (ABMSA) aims to infer the users’ sentiment polarities over individual aspects using visual, textual, and acoustic signals. Although psychological studies have shown that personality has a direct impact on people's sentiment orientations, most existing methods disregard the potential personality character while executing ABMSA tasks. To tackle this issue, a novel psychological perspective, the people's personalities are introduced. To the best of our knowledge, this paper is the very first study in this field. Different from current pipelined multi-task sentiment analysis methods, an end-to-end ABMSA method called Personality-coupled mUlti-task leaRning framEwork (PURE) is proposed, which strongly couples personality mining and ABMSA tasks in a unified architecture to avoid error propagation and enhance the overall system robustness. Specifically, an adaptive personality feature extraction method is designed to accurately model the first impression of different people's personalities. Then, a multi-task ABMSA framework is designed to strongly couple the multimodal features of aspects extracted by the interactive attention fusion network with people's personalities. Subsequently, the proposed framework optimizes them parallel via extended Bayesian meta-learning. Finally, compared to the current optimal model, the classification accuracy and macro F1 score of the proposed model have both shown significant improvements on public datasets. In addition, PURE is transferable and can effectively couple personality modeling tasks with any other sentiment analysis methods.

In this paper, we proposed large selective kernel and sparse attention network (LSKSANet) for remote sensing image semantic segmentation. The LSKSANet is a lightweight network that effectively combines convolution with sparse attention mechanisms. Specifically, we design large selective kernel module to decomposing the large kernel into a series of depth-wise convolutions with progressively increasing dilation rates, thereby expanding the receptive field without significantly increasing the computational burden. In addition, we introduce the sparse attention to keep the most useful selfattention values for better feature aggregation. Experimental results on the Vaihingen and Postdam datasets demonstrate the superior performance of the proposed LSKSANet over state-of-the-art methods.

Abstract. This paper presents new lower bounds for the lattice covering densities of simplices by studying the Degree-Diameter Problem for abelian Cayley digraphs. In particular, it proves that the density of any lattice covering of a tetrahedron is at least [Formula: see text] and the density of any lattice covering of a four-dimensional simplex is at least [Formula: see text].

Along with the development of several emerging computing paradigms and information communication technologies, it is said that cyber computing technology is playing an increasingly important role across cyber-related systems and applications. In this article, we focus on cyber-social computing and propose a computational model that integrates large-scale group decision-making (LSGDM) into social recommendations for cyber-enabled online services. As a concrete application example, a graph model is built to describe the LSGDM problem among researchers in scholarly big data environments. Following the basic profiling to describe decision-makers within scholarly networks, measures are defined to evaluate one researcher's academic performance and research outcome and further quantify correlations between them based on their collaboration relationships in a constructed network model. A two-stage large-scale decision-making solution is then proposed for social recommendations: A network partition algorithm is developed based on the identification of experts along with their influence extending to a group of researchers, and a random walk with the restart-based algorithm is improved to calculate the weighted decisions for group decision aggregation and alternative ranking. Experiments using the real-world data demonstrate the usefulness and effectiveness of our proposed model and method, which can provide the target researcher with more reliable recommendations.