Neural Information Processing Systems http://nips.cc/

Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The vast majority of existing anomaly diagnosis methods offer limited theoretical insights, especially for anomaly localization, which is a vital but largely unexplored area. The aim of this contribution is to study the learning process of a Transformer when applied to MTS by revealing connections to statistical time series methods. Based on these theoretical insights, we propose the Attention Low-Rank Transformer (ALoRa-T) model, which applies low-rank regularization to self-attention, and we introduce the Attention Low-Rank score, effectively capturing the temporal characteristics of anomalies. Finally, to enable anomaly localization, we propose the ALoRa-Loc method, a novel approach that associates anomalies to specific variables by quantifying interrelationships among time series. Extensive experiments and real data analysis, show that the proposed methodology significantly outperforms state-of-the-art methods in both detection and localization tasks.

A supporter of Maduro holds a painting of him in Caracas. A supporter of Maduro holds a painting of him in Caracas. Minutes after Donald Trump announced a "large-scale strike" against Venezuela early on Saturday morning, false and misleading AI-generated images began flooding social media. There were fake photos of Nicolás Maduro being escorted off a plane by US law enforcement agents, images of jubilant Venezuelans pouring into the streets of Caracas and videos of missiles raining down on the city - all fake. The fabricated content intermixed with real videos and photos of US aircraft flying over the Venezuelan capital and explosions lighting up the dark sky.

Neural Information Processing Systems http://nips.cc/

Figure 1 provides an overview of ADBench.