Anomaly detection in dynamic graphs is a critical area of research that focuses on identifying abnormal components within evolving graph structures that deviate significantly from typical patterns. Despite advancements in traditional temporal pattern mining and deep learning techniques, a comprehensive benchmarking framework for Dynamic Graph Anomaly Detection (DyGAD) has been lacking. To address this gap, we introduce BAG, the first comprehensive benchmark specifically designed for anomaly detection on dynamic graphs. BAG enables extensive evaluation of 25 leading DyGAD models, covering both classical approaches and advanced Dynamic Graph Neural Networks (DGNNs), across 10 diverse real-world datasets that include both synthetic and naturally occurring anomalies. The framework supports evaluations at both the edge and node levels, offering a robust tool to advance DyGAD research. Our main finding is that Continuous-time Dynamic Graph (CTDG) models demonstrate superior performance and potential in detecting anomalies in dynamic graph edges, compared to Discrete-time Dynamic Graph (DTDG) models. Furthermore, the results reveal that existing methods are less effective at detecting organic anomalies, primarily due to the presence of temporal anomalies and highly imbalanced samples. The proposed BAG benchmark significantly enhances the evaluation of DyGAD methods by improving dataset selection, metric application, and model training. Moreover, BAG supports reproducibility and further exploration in this field by integrating all models, datasets, and evaluation protocols into an open-source repository.

Individual investors are disadvantaged in financial markets, overwhelmed by abundant information and lacking professional analysis.Equity research reports are crucial resources, offering valuable insights.By leveraging these reports, large language models (LLMs) can enhance investors' decision-making and strengthen financial analysis.However, two key challenges limit their effectiveness: (1) the rapid evolution of market events outpaces the slow update cycles of existing knowledge bases, and (2) the long-form, unstructured nature of financial reports hinders timely, context-aware integration by LLMs.To address these challenges, we tackle both data and methodology aspects.We introduce the Event-Enhanced Automated Construction of Financial Knowledge Graph (FinKario), a dataset with over 305,360 entities, 210,328 relational triples, and 19 relation types.FinKario integrates real-time company fundamentals and events through promptdriven extraction guided by institutional templates, providing structured, accessible financial insights for LLMs.We further propose a Two-stage, Graph-based retrieval strategy (FinKario-RAG) to optimize retrieval over evolving, large-scale financial knowledge.Experiments show that FinKario with FinKario-RAG achieves superior trend prediction accuracy, outperforming financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting.

Overcoming image acquisition perspectives and face pose variations is a key problem in unconstrained face recognition tasks. One of the practical approaches is by reconstructing the face with extreme pose into a version that is more easily recognized by the discriminator, such as a frontal face. Often, existing methods attempt to balance the accuracy of downstream tasks with human visual perception, but ignore the differences in propensity between the two. Besides, large-scale datasets of profile-frontal paired face images are absent, which further hinders the training of models. In this work, we investigate a variety of face reconstruction approaches and propose a very simple, but very effective method to match face images across different scenes, named facial representation learning (FRL). The core idea of FRL is to introduce a representation generator in front of a pre-trained face recognition model, which can extract face representations from arbitrary faces that are more suitable for recognition model discrimination. In particular, the representation generator reconstructs the facial representation by minimising identity differences from the frontal face and adds pixel-level and adversarial constraints to cater for discriminator preferences. Extensive benchmark experiments show that the proposed method not only achieves better performance than state-of-the-art methods, but also can further squeeze the inference potential of existing face recognition models.

Factor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations. However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting, where the model can adapt itself to match the current market regime identified based on only point-in-time market information. To tackle this problem, we propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the underlying relationship between the market situation and stock-wise latent factors, so that HireVAE can effectively estimate useful latent factors given only historical market information and subsequently predict accurate stock returns. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods, verifying the potential of such online and adaptive factor model.

This paper considers the problem of face frontalization in the wild, which transforms a face image with profile views into a frontal face. Face frontalization provides an effective solution to the face recognition problem in uncontrolled scenes. However, the existing methods either focus on deep learning techniques as an end-to-end framework or combine other explicit facial prior estimation tasks, such as 3D representation, optical flow estimation and so on, where computation is highly redundant and facial identity cannot be well represented. In this paper, we focus on how to maximise the potential of the model for identity learning and representation, and propose an accurate and lightweight face frontalization approach, named identity-preserving model (IPM). IPM has a well-designed encoder-decoder architecture which restores input face to a frontal counterpart. The encoder is constructed to extract representation from the input face, where a contrastive loss function is applied that encourages representations to form compact clusters, while preserving their relationships across the corpora. Then a cross-domain rectification module is proposed to eliminate the representation differences between the recognition and reconstruction domains, thus improving the accuracy of the reconstructed face. Extensive experiments on benchmark datasets show that the proposed IPM approach not only outperforms the state-of-the-art on public datasets but also can cope with images in the uncontrolled scenes.