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

Clearing the Air on Radon Testing: The Duty of Real Estate Brokers to Protect Prospective Homebuyers

This Note recommends that the federal government create legislation that will impose a duty on real estate brokers to test homes for radon and to disclose the results to prospective purchasers. Based on a common law negligence theory, such a duty would become part of the current obligation of a real estate broker: (1) to conduct a reasonably diligent and competent search of property for sale; and (2) to disclose to prospective homebuyers all material defects affecting the value or desirability of the home. In his investigation, the broker must use the expertise and knowledge that derive from his training and experience as a professional. Initially, the Note addresses the dilemma of the homebuyer who discovers radon only after occupying the home and who has no formally defined cause of action based on common law precedent or statute. Part II traces the development of a real estate broker\u27s liability in negligence to the recently imposed duty to discover and disclose latent defects. Part III analyzes the duty to discover and disclose latent defects with respect to radon and concludes that real estate brokers should have an affirmative duty to test for radon and to disclose the results to prospective purchasers. Finally, part IV recommends legislation to protect the unwary homebuyer who otherwise would take possession of the home and suffer potential economic loss and exposure to a carcinogenic substance

Preprocessing Solar Images while Preserving their Latent Structure

Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics Observatory, a NASA satellite, collect massive streams of high resolution images of the Sun through multiple wavelength filters. Reconstructing pixel-by-pixel thermal properties based on these images can be framed as an ill-posed inverse problem with Poisson noise, but this reconstruction is computationally expensive and there is disagreement among researchers about what regularization or prior assumptions are most appropriate. This article presents an image segmentation framework for preprocessing such images in order to reduce the data volume while preserving as much thermal information as possible for later downstream analyses. The resulting segmented images reflect thermal properties but do not depend on solving the ill-posed inverse problem. This allows users to avoid the Poisson inverse problem altogether or to tackle it on each of $\sim$10 segments rather than on each of $\sim$10$^7$ pixels, reducing computing time by a factor of $\sim$10$^6$. We employ a parametric class of dissimilarities that can be expressed as cosine dissimilarity functions or Hellinger distances between nonlinearly transformed vectors of multi-passband observations in each pixel. We develop a decision theoretic framework for choosing the dissimilarity that minimizes the expected loss that arises when estimating identifiable thermal properties based on segmented images rather than on a pixel-by-pixel basis. We also examine the efficacy of different dissimilarities for recovering clusters in the underlying thermal properties. The expected losses are computed under scientifically motivated prior distributions. Two simulation studies guide our choices of dissimilarity function. We illustrate our method by segmenting images of a coronal hole observed on 26 February 2015

HOXA10 controls osteoblastogenesis by directly activating bone regulatory and phenotypic genes

HOXA10 is necessary for embryonic patterning of skeletal elements, but its function in bone formation beyond this early developmental stage is unknown. Here we show that HOXA10 contributes to osteogenic lineage determination through activation of Runx2 and directly regulates osteoblastic phenotypic genes. In response to bone morphogenic protein BMP2, Hoxa10 is rapidly induced and functions to activate the Runx2 transcription factor essential for bone formation. A functional element with the Hox core motif was characterized for the bone-related Runx2 P1 promoter. HOXA10 also activates other osteogenic genes, including the alkaline phosphatase, osteocalcin, and bone sialoprotein genes, and temporally associates with these target gene promoters during stages of osteoblast differentiation prior to the recruitment of RUNX2. Exogenous expression and small interfering RNA knockdown studies establish that HOXA10 mediates chromatin hyperacetylation and trimethyl histone K4 (H3K4) methylation of these genes, correlating to active transcription. HOXA10 therefore contributes to early expression of osteogenic genes through chromatin remodeling. Importantly, HOXA10 can induce osteoblast genes in Runx2 null cells, providing evidence for a direct role in mediating osteoblast differentiation independent of RUNX2. We propose that HOXA10 activates RUNX2 in mesenchymal cells, contributing to the onset of osteogenesis, and that HOXA10 subsequently supports bone formation by direct regulation of osteoblast phenotypic genes. <br/

Detecting Unspecified Structure in Low-Count Images

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet

Paying More for the American Dream: A Multi-State Analysis of Higher-Cost Home Purchase Lending

This report demonstrates that African-American and Latino borrowers are paying more than their white counterparts for home purchase loans in six geographic areas: Boston, Charlotte, Chicago, Los Angeles, New York, and Rochester. This review of federal lending data shows dramatic disparities. For example, in New York, African-American borrowers were five times more likely to receive higher-cost home purchase loans than were white borrowers