Reliable and interpretable tumor classification from clinical imaging remains a core challenge. The main difficulties arise from heterogeneous modality quality, limited annotations, and the absence of structured anatomical guidance. We present REACT-KD, a Region-Aware Cross-modal Topological Knowledge Distillation framework that transfers supervision from highfidelity multi-modal sources into a lightweight CT-based student model. The framework employs a dual teacher design. One branch captures structure-function relationships through dualtracer PET/CT, while the other models dose-aware features using synthetically degraded low-dose CT. These branches jointly guide the student model through two complementary objectives. The first achieves semantic alignment through logits distillation, and the second models anatomical topology through region graph distillation. A shared CBAM3D module ensures consistent attention across modalities. To improve reliability in deployment, REACTKD introduces modality dropout during training, which enables robust inference under partial or noisy inputs. As a case study, we applied REACT-KD to hepatocellular carcinoma staging. The framework achieved an average AUC of 93.5 % on an internal PET/CT cohort and maintained$\mathbf{7 6. 6 \%}$to$\mathbf{8 1. 5 \%}$AUC across varying levels of dose degradation in external CT testing. Decision curve analysis further shows that REACT-KD consistently provides the highest net clinical benefit across all thresholds, confirming its value in real-world diagnostic practice. Code is available at: https://github.com/Kinetics-JOJO/REACT-KD.

The latest video coding standard, versatile video coding (VVC), was developed to achieve higher video compression efficiency and support more media applications than its predecessor, high-efficiency video coding (HEVC). To address nontranslational motion, such as rotation and zooming, affine motion compensation has been employed in VVC during interframe prediction. However, the complexity increases significantly due to a large number of linear equation solving steps during affine motion estimation (AME). To address the problem, this paper proposes a fast linear equation solving algorithm and an accompanying pipelined hardware architecture design. To the best of our knowledge, our work is the first attempt to address the hardware architecture design of the linear equation solving algorithm in affine mode. First, an integer-based division-free algorithm (I-DFA) is proposed to achieve fast equation solving. Then, a novel dynamic scaling algorithm is proposed to compensate for integer computation errors due to overflow problems. Finally, a pipelined and interleaved hardware architecture is proposed to minimize the number of iteration clock cycles and improve the throughput. The proposed algorithm achieves average time savings of 5.3% and 5.7% with only 0.03% and 0.07% increase in the Bjøntegaard delta bit rate (BD-BR) under low-delay P (LDP) and random access (RA) configurations, respectively. The proposed hardware architecture can solve 16.7M six-parameter affine systems of linear equations per second under a working frequency of 100MHz, which represents a 21x improvement compared to the existing methods.

Relationships in online social networks often imply social connections in real life. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of pre-labeled relationships or abundant interaction features on relationships. Unfortunately, both relationship feature data and label data are very sparse in real social platforms like WeChat, rendering existing methods inapplicable. In this paper, we present an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task. To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types. LoCEC enforces a three-phase processing, namely local community detection, community classification and relationship classification, to address the sparsity issue of relationship features and relationship labels. Moreover, LoCEC is designed to handle large-scale networks by allowing parallel and distributed processing. We conduct extensive experiments on the real-world WeChat network with hundreds of billions of edges to validate the effectiveness and efficiency of LoCEC.