Differential spatial modulation (DSM) is a noncoherent multi-antenna technique that conveys additional information by selecting transmit antennas. Space-time block coded DSM (STBC-DSM) combines DSM with STBC to achieve a transmit diversity order of two. Recently, we proposed a new STBC-DSM scheme that improves spectral efficiency through temporal permutations, termed bandwidth-efficient generalized DSM (BE-GDSM). This paper enhances BE-GDSM in two key aspects: either reducing detection complexity or increasing spectral efficiency. We propose a maximum-likelihood (ML) detector with significantly lower computational complexity for BE-GDSM. Besides, we introduce spatial permutations for STBC-DSM and demonstrate that such permutations require differential encoding. Building on this observation, we propose STBC-DSM with joint spatial and temporal permutations (STBC-DSM-JSTP), which achieves the maximal number of antenna bits among all STBC-DSM schemes. Numerical results show that STBC-DSM-JSTP improves error performance compared with BE-GDSM at the same data rate.

In recent years, artificial intelligence (AI) has emerged as a significant topic, spurring numerous novel and innovative applications leveraging AI technology. As AI relies on large-scale datasets, the collection of such data is crucial for these applications. However, large-scale data acquisition in the physical world can be both expensive and time-consuming. To address this challenge, we introduce a new computational problem called the k-data retriever problem (k-DRP), which aims to minimize the traversal length of the longest walk by a set ofkdata retrievers within a road network. We establish the NP-hardness of k-DRP and propose a constant-ratio approximation algorithm named collective search walk planning (CSP) to tackle this problem. Additionally, we enhance CSP’s efficiency through GPU acceleration and a pruning strategy. Furthermore, we observe that CSP may result in retrievers traversing more duplicate or unnecessary routes. To address these instances, we propose the weight-balance collective search walk planning (WBCSP) algorithm. Through extensive experimental evaluations, we demonstrate that our proposed CSP and WBCSP algorithms surpass other baseline methods in both solution quality and efficiency.

Deep learning on graphs has shown to be powerful in modeling graph-structured data and learning representations for graphs, which involves aggregating information from graph neighborhoods to derive effective node representations. Although graph neural networks (GNNs) can be trained from scratch, pretraining GNNs to learn transferable knowledge for downstream tasks has been used recently to improve performance. However, using pre-training and fine-tuning to model each subtask can sometimes ignore the relationships, conflicts, and constraints between tasks, resulting in worse performance. In this paper, we propose a novel framework called Self-supervised Multi-Task Learning for Generative Graph Convolutional Network (SMT-GGCN) to enhance graph-based neural network models. SMT-GGCN combines self-supervised learning pretext tasks with supervised learning to train Graph Convolutional Networks (GCN) for graph structure understanding. We demonstrate that self-supervised multi-task learning can improve GCN’s generalization ability and enhance feature learning.

This paper describes the process of designing an enterprise application that connects employees, the unique interface challenges, research activities, and subsequent changes to the design. The user-centered design process was driven by three research activities that informed the design in stages. Focus group sessions enabled us to understand requirements, wireframe usability evaluations helped us to validate macro level design decisions, and live prototype testing provided feedback that helped refine the design and validate the interactions. The collective research activities contributed to improved usability for navigation, actions, search, and a hierarchical organization chart. In addition, social networking within the work context was better understood and important concerns were identified. Key changes to the design included a streamlined three-level organization chart, the reduction of icons, and a new layout for search.