A Systematic Review for Transformer-based Long-term Series Forecasting

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.Comment: Accepted to IEEE INFOCOM 202

Federated Learning-Empowered AI-Generated Content in Wireless Networks

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations, e.g., the centralized training in the pre-training, fine-tuning and inference processes, especially their implementations in wireless networks with privacy preservation. Federated learning (FL), as a collaborative learning framework where the model training is distributed to cooperative data owners without the need for data sharing, can be leveraged to simultaneously improve learning efficiency and achieve privacy protection for AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content. Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results show that our scheme achieves advantages in effectively reducing the communication cost and training latency and privacy protection. Finally, we highlight several major research directions and open issues for the convergence of FL and AIGC.Comment: 8 pages, 3 figures and 2 tables. Submitted to IEEE Networ

Author Correction:3D-printed liquid metal polymer composites as NIR-responsive 4D printing soft robot

Correction to: Nature Communications https://doi.org/10.1038/s41467-023-43667-4, published online 28 November 2023

A proposed disease classification system for duck viral hepatitis

The nomenclature of duck viral hepatitis (DVH) was historically not a problem. However, 14 hepatotropic viruses among 10 different genera are associated with the same disease name, DVH. Therefore, the disease name increasingly lacks clarity and may no longer fit the scientific description of the disease. Because one disease should not be attributed to 10 genera of viruses, this almost certainly causes misunderstanding regarding the disease-virus relationship. Herein, we revisited the problem and proposed an update to DVH disease classification. This classification is based on the nomenclature of human viral hepatitis and the key principle of Koch's postulates (“one microbe and one disease”). In total, 10 types of disease names have been proposed. These names were literately matched with hepatitis-related viruses. We envision that this intuitive nomenclature system will facilitate scientific communication and consistent interpretation in this field, especially in the Asian veterinary community, where these diseases are most commonly reported

Mechanism of herpesvirus UL24 protein regulating viral immune escape and virulence

Herpesviruses have evolved a series of abilities involved in the process of host infection that are conducive to virus survival and adaptation to the host, such as immune escape, latent infection, and induction of programmed cell death for sustainable infection. The herpesvirus gene UL24 encodes a highly conserved core protein that plays an important role in effective viral infection. The UL24 protein can inhibit the innate immune response of the host by acting on multiple immune signaling pathways during virus infection, and it also plays a key role in the proliferation and pathogenicity of the virus in the later stage of infection. This article reviews the mechanism by which the UL24 protein mediates herpesvirus immune escape and its effects on viral proliferation and virulence by influencing syncytial formation, DNA damage and the cell cycle. Reviewing these studies will enhance our understanding of the pathogenesis of herpesvirus infection and provide evidence for new strategies to combat against viral infection

Circulating Monocytes Act as a Common Trigger for the Calcification Paradox of Osteoporosis and Carotid Atherosclerosis via TGFB1-SP1 and TNFSF10-NFKB1 Axis

BackgroundOsteoporosis often occurs with carotid atherosclerosis and causes contradictory calcification across tissue in the same patient, which is called the “calcification paradox”. Circulating monocytes may be responsible for this unbalanced ectopic calcification. Here, we aimed to show how CD14+ monocytes contribute to the pathophysiology of coexisting postmenopausal osteoporosis and carotid atherosclerosis.MethodsWe comprehensively analyzed osteoporosis data from the mRNA array dataset GSE56814 and the scRNA-seq dataset GSM4423510. Carotid atherosclerosis data were obtained from the GSE23746 mRNA dataset and GSM4705591 scRNA-seq dataset. First, osteoblast and vascular SMC lineages were annotated based on their functional expression using gene set enrichment analysis and AUCell scoring. Next, pseudotime analysis was applied to draw their differentiated trajectory and identify the key gene expression changes in crossroads. Then, ligand–receptor interactions between CD14+ monocytes and osteoblast and vascular smooth muscle cell (SMC) lineages were annotated with iTALK. Finally, we selected calcification paradox-related expression in circulating monocytes with LASSO analysis.ResultsFirst, we found a large proportion of delayed premature osteoblasts in osteoporosis and osteogenic SMCs in atherosclerosis. Second, CD14+ monocytes interacted with the intermediate cells of the premature osteoblast and osteogenic SMC lineage by delivering TGFB1 and TNFSF10. This interaction served as a trigger activating the transcription factors (TF) SP1 and NFKB1 to upregulate the inflammatory response and cell senescence and led to a retarded premature state in the osteoblast lineage and osteogenic transition in the SMC lineage. Then, 76.49% of common monocyte markers were upregulated in the circulating monocytes between the two diseases, which were related to chemotaxis and inflammatory responses. Finally, we identified 7 calcification paradox-related genes on circulating monocytes, which were upregulated in aging cells and downregulated in DNA repair cells, indicating that the aging monocytes contributed to the development of the two diseases.ConclusionsOur work provides a perspective for understanding the triggering roles of CD14+ monocytes in the development of the calcification paradox in osteoporosis- and atherosclerosis-related cells based on combined scRNA and mRNA data. This study provided us with an elucidation of the mechanisms underlying the calcification paradox and could help in developing preventive and therapeutic strategies

Privacy-Preserved pseudonym scheme for fog computing supported internet of vehicles

As a promising branch of Internet of Things, Internet of Vehicles (IoV) is envisioned to serve as an essential data sensing and processing platform for intelligent transportation systems. In this paper, we aim to address location privacy issues in IoV. In traditional pseudonym systems, the pseudonym management is carried out by a centralized way resulting in big latency and high cost. Therefore, we present a new paradigm named Fog computing supported IoV (F-IoV) to exploit resources at the network edge for effective pseudonym management. By utilizing abundant edge resources, a privacy-preserved pseudonym (P 3 ) scheme is proposed in F-IoV. The pseudonym management in this scheme is shifted to specialized fogs at the network edge named pseudonym fogs, which are composed of roadside infrastructures and deployed in close proximity of vehicles. P 3 scheme has following advantages: 1) context-aware pseudonym changing; 2) timely pseudonym distribution; and 3) reduced pseudonym management overhead. Moreover, a hierarchical architecture for P 3 scheme is introduced in F-IoV. Enabled by the architecture, a context-aware pseudonym changing game and secure pseudonym management communication protocols are proposed. The security analysis shows that P 3 scheme provides secure communication and privacy preservation for vehicles. Numerical results indicate that P 3 scheme effectively enhances location privacy and reduces communication overhead for the vehicles