Existing multimodal recommenders largely rely on late fusion, where modalities are separately encoded and aligned before fusion. Such designs work well with complete content but become fragile in practice: modalities are often missing, alignment breaks down, and imputation adds complexity with limited benefit. We propose EFMRec, a missing-aware early-fusion framework that follows a relaxed principle of leveraging modalities when available and ignoring them when absent. By exploiting pretrained multimodal models, EFMRec projects available modalities into a shared semantic space and aggregates them through a missing-aware early fusion, producing unified representations without reconstruction or auxiliary losses. These representations are further propagated via a GCN-based architecture and integrated with a collaborative filtering backbone to jointly model multimodal and collaborative signals. Experiments on three benchmarks demonstrate that EFMRec consistently outperforms strong baselines under both full- and missing-modality settings, highlighting its robustness to modality incompleteness.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

The TD-LTE system is envisaged to adopt dynamic time division duplexing (TDD) transmissions for small cells to adapt their communication service to the fast variation of downlink (DL) and uplink (UL) traffic demands. However, different DL/UL directions for the same subframe in adjacent cells will result in new destructive interference components, i.e., eNB-to-eNB and UE-to-UE, with levels that can significantly differ from one subframe to another. In this paper, a feasible UL power control mechanism is proposed to manage eNB-to-eNB interference, where different UL power control parameters are set based on different interference level. We consider the geometric location information and the subframe set selection process about adjacent eNBs when the interference level is estimated. The performance of the proposed scheme is evaluated through system level simulations and it is shown that the scheme can achieve preferable improvement in terms of UL average and 5%-ile packet throughputs compared with the original scheme without power control. Also, the UE-to-UE interference is not worse when the UE transmit power become higher.