RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is clean without anomalies. This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly trained by time series with anomalies. Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and sample robustness when these anomalies exist. Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly robust and outperforms all existing approaches. The code is available at https://github.com/haochenglouis/RobustTSF.Comment: Accepted by the 12th International Conference on Learning Representations (ICLR 2024

Caesium europium(III) polyphosphate, CsEu(PO3)4

Caesium europium polyphosphate, CsEu(PO3)4, was synthesized by a high-temperature solution reaction. Its structure is charaterized by a three-dimensional framework made up of double PO4 spiral chains and EuO8 and CsO11 polyhedra

Onset of chaotic gravitational lensing in non-Kerr rotating black holes with quadrupole mass moment

In the electromagnetic channel, chaotic gravitational lensing is a peculiar phenomenon in strong gravitational lensing. In this work, we analyze the properties and emergence of chaotic gravitational lensing in the Manko-Novikov black hole spacetime. Aiming to understand better the underlying physics, we elaborate on the boundaries of the accessible region in terms of the analyses of the contours of the effective potentials. The latter is associated with the two roots of a quadratic equation. In particular, we explore its interplay with ergoregion, which leads to specific features of the effective potentials, such as the emergence of cuspy edge and the formation of {\it pocket}, that serves as a static constraint on the geodesics. Besides, we investigate the properties of the radial and angular accelerations at the turning points in photons' trajectories. Moreover, the accelerations are analyzed, which is argued to provide a kinematic constraint on the geodesics. It is concluded that the onset of the chaotic lensing is crucially related to both constraints, and as a result, an arbitrarily slight deviation in the incident photon is significantly amplified during the course of evolution through an extensive period, demonstrating the complexity in the highly nonlinear deterministic gravitational system.Comment: 18 pages, 13 figure

An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques

Recent advancements in Intrusion Detection Systems (IDS), integrating Explainable AI (XAI) methodologies, have led to notable improvements in system performance via precise feature selection. However, a thorough understanding of cyber-attacks requires inherently explainable decision-making processes within IDS. In this paper, we present the Interpretable Generalization Mechanism (IG), poised to revolutionize IDS capabilities. IG discerns coherent patterns, making it interpretable in distinguishing between normal and anomalous network traffic. Further, the synthesis of coherent patterns sheds light on intricate intrusion pathways, providing essential insights for cybersecurity forensics. By experiments with real-world datasets NSL-KDD, UNSW-NB15, and UKM-IDS20, IG is accurate even at a low ratio of training-to-test. With 10%-to-90%, IG achieves Precision (PRE)=0.93, Recall (REC)=0.94, and Area Under Curve (AUC)=0.94 in NSL-KDD; PRE=0.98, REC=0.99, and AUC=0.99 in UNSW-NB15; and PRE=0.98, REC=0.98, and AUC=0.99 in UKM-IDS20. Notably, in UNSW-NB15, IG achieves REC=1.0 and at least PRE=0.98 since 40%-to-60%; in UKM-IDS20, IG achieves REC=1.0 and at least PRE=0.88 since 20%-to-80%. Importantly, in UKM-IDS20, IG successfully identifies all three anomalous instances without prior exposure, demonstrating its generalization capabilities. These results and inferences are reproducible. In sum, IG showcases superior generalization by consistently performing well across diverse datasets and training-to-test ratios (from 10%-to-90% to 90%-to-10%), and excels in identifying novel anomalies without prior exposure. Its interpretability is enhanced by coherent evidence that accurately distinguishes both normal and anomalous activities, significantly improving detection accuracy and reducing false alarms, thereby strengthening IDS reliability and trustworthiness