Anomaly detection using local kernel density estimation and context-based regression

Current local density-based anomaly detection methods are limited in that the local density estimation and the neighbourhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighbourhood density estimation to increase the robustness to changes in the neighborhood size. We further propose a local kernel regression estimator and a hierarchical strategy for combining information from the multiple scale neighbourhoods to refine anomaly factors of samples. We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local density estimation in the visual feature space and kernel-based saliency score propagation in the image enable the assignment of similar saliency values to homogeneous object regions. Experimental results on several benchmark datasets demonstrate that our anomaly detection methods overall outperform several state-of-the-art anomaly detection methods. The effectiveness of our image saliency detection method is validated by comparison with several state-of-the-art saliency detection methods

ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections

The Low-Velocity, Rapidly Fading Type Ia Supernova 2002es

SN 2002es is a peculiar subluminous Type Ia supernova (SN Ia) with a combination of observed characteristics never before seen in a SN Ia. At maximum light, SN 2002es shares spectroscopic properties with the underluminous SN 1991bg subclass of SNe Ia, but with substantially lower expansion velocities (~6000 km/s) more typical of the SN 2002cx subclass. Photometrically, SN 2002es differs from both SN 1991bg-like and SN 2002cx-like supernovae. Although at maximum light it is subluminous (M_B=-17.78 mag), SN 2002es has a relatively broad light curve (Dm15(B)=1.28 +/- 0.04 mag), making it a significant outlier in the light-curve width vs. luminosity relationship. We estimate a 56Ni mass of 0.17 +/- 0.05 M_sun synthesized in the explosion, relatively low for a SN Ia. One month after maximum light, we find an unexpected plummet in the bolometric luminosity. The late-time decay of the light curves is inconsistent with our estimated 56Ni mass, indicating that either the light curve was not completely powered by 56Ni decay or the ejecta became optically thin to gamma-rays within a month after maximum light. The host galaxy is classified as an S0 galaxy with little to no star formation, indicating the progenitor of SN 2002es is likely from an old stellar population. We also present a less extensive dataset for SN 1999bh, an object which shares similar observed properties. Both objects were found as part of the Lick Observatory Supernova Search, allowing us to estimate that these objects should account for ~2.5% of SNe Ia within a fixed volume. We find that current theoretical models are unable to explain the observed of characteristics of SN 2002es.Comment: 19 pages, 15 figures, Submitted to Ap