We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames atdifferent layers, making itsuitable for generating 3D models from images.

Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc.) and often overlook the inherent connections among different object types of graph anomalies. For instance, a money laundering transaction might involve an abnormal account and the broader community it interacts with. To address this, we present UniGAD, the first unified framework for detecting anomalies at node, edge, and graph levels jointly. Specifically, we develop the Maximum Rayleigh Quotient Subgraph Sampler (MRQSampler) that unifies multi-level formats by transferring objects at each level into graph-level tasks on subgraphs.

The current paradigm of evaluating Large Language Models (LLMs) through static benchmarks comes with significant limitations, such as vulnerability to data contamination and a lack of adaptability to the evolving capabilities of LLMs.

We first prove the first inequality using Jensen's inequality, which states that for a real-valued, convex Next, we leverage the property of inequalities to prove the second inequality. However, this method has limited effectiveness in scenarios with severe domain shifts between the source and target domains. Directly taking source risk as target risk is unreliable due to domain distribution shifts between domains. This work was completed while Dapeng ( lhxxhb15@gmail.com) Subsequently, Reverse V alidation performs a reversed adaptation from the pseudo-labeled target to the source and utilizes the source risk in this reversed adaptation task for validation.

Existing approaches can be categorized into two types. The first type involves leveraging labeled source data for target-domain model selection [9,14-16]. The second type designs unsupervised metrics based on priors of the learned target-domain structure and utilizes the metrics for model selection[17,19,18,20].

Traditional IM approaches consider each user/node independently as a potential target customer.