Outlier detection in complex categorical data by modelling the feature value couplings

This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing methods on data sets with many noisy features

Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings

© 2016 IEEE. Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data. CUFS quantifies the outlierness (or relevance) of features by learning and integrating both the feature value couplings and feature couplings. Such value-To-feature couplings capture intrinsic data characteristics and distinguish relevant features from those noisy/redundant features. CUFS is further instantiated into a parameter-free Dense Subgraph-based Feature Selection method, called DSFS. We prove that DSFS retains a 2-Approximation feature subset to the optimal subset. Extensive evaluation results on 15 real-world data sets show that DSFS obtains an average 48% feature reduction rate, and enables three different types of pattern-based outlier detection methods to achieve substantially better AUC improvements and/or perform orders of magnitude faster than on the original feature set. Compared to its feature selection contender, on average, all three DSFS-based detectors achieve more than 20% AUC improvement

Normal families and fixed points of iterates

Let F be a family of holomorphic functions and let K be a constant less than 4. Suppose that for all f in F the second iterate of f does not have fixed points for which the modulus of the multiplier is greater than K. We show that then F is normal. This is deduced from a result about the multipliers of iterated polynomials.Comment: 5 page

Crystallization of Arabidopsis thaliana acetohydroxyacid synthase in complex with the sulfonylurea herbicide chlorimuron ethyl

Acetohydroxyacid synthase (AHAS; EC 2.2.1.6) catalyses the formation of 2-acetolactate and 2-aceto-2-hydroxybutyrate as the first step in the biosynthesis of the branched-chain amino acids valine, leucine and isoleucine. The enzyme is inhibited by a wide range of substituted sulfonylureas and imidazolinones and many of these compounds are used as commercial herbicides. Here, the crystallization and preliminary X-ray diffraction analysis of the catalytic subunit of Arabidopsis thaliana AHAS in complex with the sulfonylurea herbicide chlorimuron ethyl are reported. This is the first report of the structure of any plant protein in complex with a commercial herbicide. Crystals diffract to 3.0 Angstrom resolution, have unit-cell parameters a = b = 179.92, c = 185.82 Angstrom and belong to space group P6(4)22. Preliminary analysis indicates that there is one monomer in the asymmetric unit and that these are arranged as pairs of dimers in the crystal. The dimers form a very open hexagonal lattice, with a high solvent content of 81%

Ordered Carboxylates on TiO (110) Formed at Aqueous Interfaces

As models for probing the interactions between TiO2 surfaces and the dye molecules employed in dye-sensitized solar cells, carboxylic acids are an important class of molecules. In this work we present a scanning tunneling microscopy (STM) and low energy electron diffraction (LEED) study of three small carboxylic acids (formic, acetic, and benzoic) that were reacted with the TiO2(110) surface via a dipping procedure. The three molecules display quite different adsorption behavior, illustrating the different inter-adsorbate interactions that can occur. After exposure to a 10 mM solution, formic acid forms a rather disordered formate overlayer with two distinct binding geometries. Acetic acid forms a well-ordered (2 × 1) acetate overlayer similar to that observed following deposition from vapor. Benzoic acid forms a (2 × 2) overlayer which is stabilized by intermolecular interactions between the phenyl groups