Local Subspace-Based Outlier Detection using Global Neighbourhoods

Outlier detection in high-dimensional data is a challenging yet important task, as it has applications in, e.g., fraud detection and quality control. State-of-the-art density-based algorithms perform well because they 1) take the local neighbourhoods of data points into account and 2) consider feature subspaces. In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components. We therefore introduce GLOSS, an algorithm that performs local subspace outlier detection using global neighbourhoods. Experiments on synthetic data demonstrate that GLOSS more accurately detects local outliers in mixed data than its competitors. Moreover, experiments on real-world data show that our approach identifies relevant outliers overlooked by existing methods, confirming that one should keep an eye on the global perspective even when doing local outlier detection.Comment: Short version accepted at IEEE BigData 201

A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

New Extremely Metal-Poor Stars in the Galactic Halo

We present a detailed abundance analysis based on high resolution and high signal-to-noise spectra of eight extremely metal poor (EMP) stars with [Fe/H] < -3.5$dex, four of which are new. Only stars with 4900 < Teff< 5650 K are included. Two stars of the eight are outliers in each of several abundance ratios. The most metal poor star in this sample, HE1424-0241, has [Fe/H] ~ -4 dex and is thus among the most metal poor stars known in the Galaxy. It has highly anomalous abundance ratios unlike those of any other known EMP giant, with very low Si, Ca and Ti relative to Fe, and enhanced Mn and Co, again relative to Fe. Only (low) upper limits for C and N can be derived from the non-detection of the CH and NH molecular bands. HE0132$-$2429, another sample star, has excesses of N and Sc with respect to Fe. The strong outliers in abundance ratios among the Fe-peak elements in these C-normal stars, not found at somewhat higher metallicities, are definitely real. They suggest that at such low metallicities we are beginning to see the anticipated and long sought stochastic effects of individual supernova events contributing to the Fe-peak material within a single star. A detailed comparison of the results of the analysis procedures adopted by our 0Z project compared to those of the First Stars VLT Large Project finds a systematic difference for [Fe/H] of ~0.3 dex, our values always being higher.Comment: Accepted to the Ap

Maximally Divergent Intervals for Anomaly Detection

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.Comment: ICML Workshop on Anomaly Detectio

Chemical Raman Enhancement of Organic Adsorbates on Metal Surfaces

Using a combination of first-principles theory and experiments, we provide a quantitative explanation for chemical contributions to surface-enhanced Raman spectroscopy for a well-studied organic molecule, benzene thiol, chemisorbed on planar Au(111) surfaces. With density functional theory calculations of the static Raman tensor, we demonstrate and quantify a strong mode-dependent modification of benzene thiol Raman spectra by Au substrates. Raman active modes with the largest enhancements result from stronger contributions from Au to their electron-vibron coupling, as quantified through a deformation potential, a well-defined property of each vibrational mode. A straightforward and general analysis is introduced that allows extraction of chemical enhancement from experiments for specific vibrational modes; measured values are in excellent agreement with our calculations.Comment: 5 pages, 4 figures and Supplementary material included as ancillary fil