Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content. To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness. These components work together to produce structured, cognitively aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions.

Account risk detection, which aims to identify accounts at forced liquidation risk within financial account-asset bipartite graphs, is crucial for ensuring financial market stability and economic resilience. Although traditional node-classification-based anomaly detection techniques can be applied to this task, these approaches often exhibit two key limitations: (1) insufficient consideration of asset fluctuations, resulting in unsatisfactory accuracy; and (2) scalability challenges, making them unsuitable for large-scale financial graphs. To address these issues, we propose RiskGuard, a novel framework for account risk detection that integrates auxiliary asset prediction and gradient-based sampling. First, we introduce an auxiliary asset prediction paradigm to capture the critical influence of asset fluctuations on account risk. Rather than solely predicting account risk, our unified model employs aTemporal-AttentionNet(TANet) to jointly predict asset fluctuations and account risk. This auxiliary task enables the model to learn fluctuation-aware asset representations, significantly enhancing prediction accuracy. To overcome scalability challenges, we design GLUE, an online graph sampler leveraging gradient entropy. GLUE dynamically adjusts sampling weights based on model gradients and graph structure, prioritizing high-entropy nodes in the neighborhood for improved efficiency. Extensive experiments on five financial datasets demonstrate that RiskGuard outperforms existing techniques in accuracy while achieving high efficiency in processing large financial graphs.

Hypergraphs, which use hyperedges to capture groupwise interactions among different entities, have gained increasing attention recently for their versatility in effectively modeling real-world networks. In this paper, we study the problem of computing hyper-triangles (formed by three fully-connected hyperedges), which is a basic structural unit in hypergraphs. Although existing approaches can be adopted to compute hyper-triangles by exhaustively examining hyperedge combinations, they overlook the structural characteristics distinguishing different hyper-triangle patterns. Consequently, these approaches lack specificity in computing particular hyper-triangle patterns and exhibit low efficiency. In this paper, we unveil a new formation pathway for hyper-triangles, transitioning from hyperedges to hyperwedges before assembling into hyper-triangles, and classify hyper-triangle patterns based on hyperwedges. Leveraging this insight, we introduce a two-step framework to reduce the redundant checking of hyperedge combinations. Under this framework, we propose efficient algorithms for computing a specific pattern of hyper-triangles. Approximate algorithms are also devised to support estimated counting scenarios. Furthermore, we introduce a fine-grained hypergraph clustering coefficient measurement that can reflect diverse properties of hypergraphs based on different hyper-triangle patterns. Extensive experimental evaluations conducted on 11 real-world datasets validate the effectiveness and efficiency of our proposed techniques.

In cardiac arterial interventional therapy, coronary angiograms provides key information to physicians for treatment strategy selection. However, it is hard to extract the information about arteries with CTO lesion in absence of contrast opacification and visualization of the CTO coronary arteries in angiograms. In this paper, we present an algorithm, which can predict the extension direction of artery with CTO and reconstruct the morphology of blocked artery in coronary angiograms. First, our algorithm segment the CTO artery angiogram and extract the artery skeleton. Second, an iterative approach is preformed to reconstruct the skeleton of blocked artery. Finally, our algorithm generates the simulated postoperative angiogram according to the skeleton image. The results demonstrate that automatic morphology reconstructing of CTO coronary artery with high accuracy and reliability is feasible.