Urban skylines from Schelling model

We propose a metapopulation version of the Schelling model where two kinds of agents relocate themselves, with unconstrained destination, if their local fitness is lower than a tolerance threshold. We show that, for small values of the latter, the population redistributes highly heterogeneously among the available places. The system thus stabilizes on these heterogeneous skylines after a long quasi-stationary transient period, during which the population remains in a well mixed phase. Varying the tolerance passing from large to small values, we identify three possible global regimes: microscopic clusters with local coexistence of both kinds of agents, macroscopic clusters with local coexistence (soft segregation), macroscopic clusters with local segregation but homogeneous densities (hard segregation). The model is studied numerically and complemented with an analytical study in the limit of extremely large node capacity.Comment: 16 pages, 10 figure

The Kinematics of Thick Disks in External Galaxies

We present kinematic measurements of the thick and thin disks in two edge-on galaxies. We have derived stellar rotation curves at and above the galaxies' midplanes using Ca II triplet features measured with the GMOS spectrograph on Gemini North. In one galaxy, FGC 1415, the kinematics above the plane show clear rotation that lags that of the midplane by ~20-50%, similar to the behavior seen in the Milky Way. However, the kinematics of the second galaxy, FGC 227, are quite different. The rotation above the plane is extremely slow, showing <25% of the rotation speed of the stars at the midplane. We decompose the observed rotation curves into a superposition of thick and thin disk kinematics, using 2-dimensional fits to the galaxy images to determine the fraction of thick disk stars at each position. We find that the thick disk of FGC 1415 rotates at 30-40% of the rotation speed of the thin disk. In contrast, the thick disk of FGC 227 is very likely counter-rotating, if it is rotating at all. These observations are consistent with the velocity dispersion profiles we measure for each galaxy. The detection of counter-rotating thick disks conclusively rules out models where the thick disk forms either during monolithic collapse or from vertical heating of a previous thin disk. Instead, the data strongly support models where the thick disk forms from direct accretion of stars from infalling satellites.Comment: 13 pages, 10 figures. Accepted for publication in Ap

The DEEP2 Redshift Survey: Lyman Alpha Emitters in the Spectroscopic Database

We present the first results of a search for Lyman-alpha emitters (LAEs) in the DEEP2 spectroscopic database that uses a search technique that is different from but complementary to traditional narrowband imaging surveys. We have visually inspected ~20% of the available DEEP2 spectroscopic data and have found nine high-quality LAEs with clearly asymmetric line profiles and an additional ten objects of lower quality, some of which may also be LAEs. Our survey is most sensitive to LAEs at z=4.4-4.9 and that is indeed where all but one of our high-quality objects are found. We find the number density of our spectroscopically-discovered LAEs to be consistent with those found in narrowband imaging searches. The combined, averaged spectrum of our nine high-quality objects is well fit by a two-component model, with a second, lower-amplitude component redshifted by ~420 km/s with respect to the primary Lyman-alpha line, consistent with large-scale outflows from these objects. We conclude by discussing the advantages and future prospects of blank-sky spectroscopic surveys for high-z LAEs.Comment: Accepted for publication in Ap

Coping with new Challenges in Clustering and Biomedical Imaging

The last years have seen a tremendous increase of data acquisition in different scientific fields such as molecular biology, bioinformatics or biomedicine. Therefore, novel methods are needed for automatic data processing and analysis of this large amount of data. Data mining is the process of applying methods like clustering or classification to large databases in order to uncover hidden patterns. Clustering is the task of partitioning points of a data set into distinct groups in order to minimize the intra cluster similarity and to maximize the inter cluster similarity. In contrast to unsupervised learning like clustering, the classification problem is known as supervised learning that aims at the prediction of group membership of data objects on the basis of rules learned from a training set where the group membership is known. Specialized methods have been proposed for hierarchical and partitioning clustering. However, these methods suffer from several drawbacks. In the first part of this work, new clustering methods are proposed that cope with problems from conventional clustering algorithms. ITCH (Information-Theoretic Cluster Hierarchies) is a hierarchical clustering method that is based on a hierarchical variant of the Minimum Description Length (MDL) principle which finds hierarchies of clusters without requiring input parameters. As ITCH may converge only to a local optimum we propose GACH (Genetic Algorithm for Finding Cluster Hierarchies) that combines the benefits from genetic algorithms with information-theory. In this way the search space is explored more effectively. Furthermore, we propose INTEGRATE a novel clustering method for data with mixed numerical and categorical attributes. Supported by the MDL principle our method integrates the information provided by heterogeneous numerical and categorical attributes and thus naturally balances the influence of both sources of information. A competitive evaluation illustrates that INTEGRATE is more effective than existing clustering methods for mixed type data. Besides clustering methods for single data objects we provide a solution for clustering different data sets that are represented by their skylines. The skyline operator is a well-established database primitive for finding database objects which minimize two or more attributes with an unknown weighting between these attributes. In this thesis, we define a similarity measure, called SkyDist, for comparing skylines of different data sets that can directly be integrated into different data mining tasks such as clustering or classification. The experiments show that SkyDist in combination with different clustering algorithms can give useful insights into many applications. In the second part, we focus on the analysis of high resolution magnetic resonance images (MRI) that are clinically relevant and may allow for an early detection and diagnosis of several diseases. In particular, we propose a framework for the classification of Alzheimer's disease in MR images combining the data mining steps of feature selection, clustering and classification. As a result, a set of highly selective features discriminating patients with Alzheimer and healthy people has been identified. However, the analysis of the high dimensional MR images is extremely time-consuming. Therefore we developed JGrid, a scalable distributed computing solution designed to allow for a large scale analysis of MRI and thus an optimized prediction of diagnosis. In another study we apply efficient algorithms for motif discovery to task-fMRI scans in order to identify patterns in the brain that are characteristic for patients with somatoform pain disorder. We find groups of brain compartments that occur frequently within the brain networks and discriminate well among healthy and diseased people

High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

Metal-poor, Strongly Star-Forming Galaxies in the DEEP2 Survey: The Relationship between Stellar Mass, Temperature-based Metallicity, and Star Formation Rate

We report on the discovery of 28 $z\approx0.8$ metal-poor galaxies in DEEP2. These galaxies were selected for their detection of the weak [OIII]$\lambda$4363 emission line, which provides a "direct" measure of the gas-phase metallicity. A primary goal for identifying these rare galaxies is to examine whether the fundamental metallicity relation (FMR) between stellar mass, gas metallicity, and star formation rate (SFR) holds for low stellar mass and high SFR galaxies. The FMR suggests that higher SFR galaxies have lower metallicity (at fixed stellar mass). To test this trend, we combine spectroscopic measurements of metallicity and dust-corrected SFRs, with stellar mass estimates from modeling the optical photometry. We find that these galaxies are $1.05\pm0.61$ dex above the z~1 stellar mass-SFR relation, and $0.23\pm0.23$ dex below the local mass-metallicity relation. Relative to the FMR, the latter offset is reduced to 0.01 dex, but significant dispersion remains (0.29 dex with 0.16 dex due to measurement uncertainties). This dispersion suggests that gas accretion, star formation and chemical enrichment have not reached equilibrium in these galaxies. This is evident by their short stellar mass doubling timescale of $\approx100^{+310}_{-75}$ Myr that suggests stochastic star formation. Combining our sample with other z~1 metal-poor galaxies, we find a weak positive SFR-metallicity dependence (at fixed stellar mass) that is significant at 94.4% confidence. We interpret this positive correlation as recent star formation that has enriched the gas, but has not had time to drive the metal-enriched gas out with feedback mechanisms.Comment: Resubmitted to ApJ on March 6, 2015. Revised to discuss selection biases and methodologies, and address the former by including more metal-rich galaxies with robust non-detections of [OIII]4363. Primary results on FMR analyses are unchanged. Additional figures are included to illustrate selection biases; previous figures have been revised to improve presentatio

Energy-Efficient β

As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings