The host galaxies and black holes of typical z~0.5-1.4 AGNs

We study the stellar and star formation properties of the host galaxies of 58 X-ray-selected AGNs in the GOODS portion of the Chandra Deep Field South (CDF-S) region at z ~ 0.5-1.4. The AGNs are selected such that their rest-frame UV to near-infrared spectral energy distributions (SEDs) are dominated by stellar emission; i.e., they show a prominent 1.6 μm bump, thus minimizing the AGN emission "contamination." This AGN population comprises approximately 50% of the X-ray-selected AGNs at these redshifts. We find that AGNs reside in the most massive galaxies at the redshifts probed here. Their characteristic stellar masses (M_* ~ 7.8 × 10^10 and M_* ~ 1.2 × 10^11 M_☉ at median redshifts of 0.67 and 1.07, respectively) appear to be representative of the X-ray-selected AGN population at these redshifts and are intermediate between those of local type 2 AGNs and high-redshift (z ~ 2) AGNs. The inferred black hole masses (M_BH ~ 2 × 10^8 M_☉) of typical AGNs are similar to those of optically identified quasars at similar redshifts. Since the AGNs in our sample are much less luminous (L_2–10 keV < 10^44 erg s^−1) than quasars, typical AGNs have low Eddington ratios (η ~ 0.01-0.001). This suggests that, at least at intermediate redshifts, the cosmic AGN "downsizing" is due to both a decrease in the characteristic stellar mass of typical host galaxies and less efficient accretion. Finally, there is no strong evidence in AGN host galaxies for either highly suppressed star formation (expected if AGNs played a role in quenching star formation) or elevated star formation when compared to mass-selected (i.e., IRAC-selected) galaxies of similar stellar masses and redshifts.

Mid-infrared spectroscopy of lensed galaxies at 1 < z < 3: The nature of sources near the MIPS confusion limit

We present Spitzer IRS mid-infrared spectra for 15 gravitationally lensed, 24 μm-selected galaxies, and combine the results with four additional very faint galaxies with IRS spectra in the literature. The median intrinsic 24 μm flux density of the sample is 130 μJy, enabling a systematic survey of the spectral properties of the very faint 24 μm sources that dominate the number counts of Spitzer cosmological surveys. Six of the 19 galaxy spectra (32%) show the strong mid-IR continuua expected of AGNs; X-ray detections confirm the presence of AGNs in three of these cases, and reveal AGNs in two other galaxies. These results suggest that nuclear accretion may contribute more flux to faint 24 μm-selected samples than previously assumed. Almost all the spectra show some aromatic (PAH) emission features; the measured aromatic flux ratios do not show evolution from z = 0. In particular, the high signal-to-noise mid-IR spectrum of SMM J163554.2+661225 agrees remarkably well with low-redshift, lower luminosity templates. We compare the rest-frame 8 μm and total infrared luminosities of star-forming galaxies, and find that the behavior of this ratio with total IR luminosity has evolved modestly from z = 2 to z = 0. Since the high aromatic-to-continuum flux ratios in these galaxies rule out a dominant contribution by AGNs, this finding implies systematic evolution in the structure and/or metallicity of infrared sources with redshift. It also has implications for the estimates of star-forming rates inferred from 24 μm measurements, in the sense that at z ~ 2, a given observed frame 24 μm luminosity corresponds to a lower bolometric luminosity than would be inferred from low-redshift templates of similar luminosity at the corresponding rest wavelength.

The star formation and extinction coevolution of UV-selected galaxies over 0.05 < z < 1.2

We use a new stacking technique to obtain mean mid-IR and far-IR to far-UV flux ratios over the rest-frame near-UV, near-IR color-magnitude diagram. We employ COMBO-17 redshifts and COMBO-17 optical, GALEX far- and near-UV, and Spitzer IRAC and MIPS mid-IR photometry. This technique permits us to probe the infrared excess (IRX), the ratio of far-IR to far-UV luminosity, and the specific star formation rate (SSFR) and their coevolution over 2 orders of magnitude of stellar mass and over redshift 0.1 < z < 1.2. We find that the SSFR and the characteristic mass (Script M_0) above which the SSFR drops increase with redshift (downsizing). At any given epoch, the IRX is an increasing function of mass up to Script M_0. Above this mass the IRX falls, suggesting gas exhaustion. In a given mass bin below Script M_0, the IRX increases with time in a fashion consistent with enrichment. We interpret these trends using a simple model with a Schmidt-Kennicutt law and extinction that tracks gas density and enrichment. We find that the average IRX and SSFR follow a galaxy age parameter ξ, which is determined mainly by the galaxy mass and time since formation. We conclude that blue-sequence galaxies have properties which show simple, systematic trends with mass and time such as the steady buildup of heavy elements in the interstellar media of evolving galaxies and the exhaustion of gas in galaxies that are evolving off the blue sequence. The IRX represents a tool for selecting galaxies at various stages of evolution.

Near-infrared and star-forming properties of local luminous infrared galaxies

We use Hubble Space Telescope (HST) NICMOS continuum and Paα observations to study the near-infrared and star formation properties of a representative sample of 30 local (d ~ 35-75 Mpc) luminous infrared galaxies (LIRGs, infrared [8-1000 μm] luminosities of log L_IR = 11-11.9 L_☉). The data provide spatial resolutions of 25-50 pc and cover the central ~3.3-7.1 kpc regions of these galaxies. About half of the LIRGs show compact (~1-2 kpc) Paα emission with a high surface brightness in the form of nuclear emission, rings, and minispirals. The rest of the sample show Paα emission along the disk and the spiral arms extending over scales of 3-7 kpc and larger. About half of the sample contains H II regions with Hα luminosities significantly higher than those observed in normal galaxies. There is a linear empirical relationship between the mid-IR 24 μm and hydrogen recombination (extinction-corrected Paα) luminosity for these LIRGs, and the H II regions in the central part of M51. This relation holds over more than four decades in luminosity, suggesting that the mid-IR emission is a good tracer of the star formation rate (SFR). Analogous to the widely used relation between the SFR and total IR luminosity of R. Kennicutt, we derive an empirical calibration of the SFR in terms of the monochromatic 24 μm luminosity that can be used for luminous, dusty galaxies.

Are long gamma-ray bursts biased tracers of star formation? Clues from the host galaxies of the Swift/BAT6 complete sample of bright LGRBs II. Star formation rates and metallicities at z < 1

Aims. Long gamma-ray bursts (LGRBs) are associated with the deaths of massive stars and might therefore be a potentially powerful tool for tracing cosmic star formation. However, especially at low redshifts (z< 1.5) LGRBs seem to prefer particular types of environment. Our aim is to study the host galaxies of a complete sample of bright LGRBs to investigate the effect of the environment on GRB formation. Methods. We studied host galaxy spectra of the Swift/BAT6 complete sample of 14 z< 1 bright LGRBs. We used the detected nebular emission lines to measure the dust extinction, star formation rate (SFR), and nebular metallicity (Z) of the hosts and supplemented the data set with previously measured stellar masses M_*. The distributions of the obtained properties and their interrelations (e.g. mass-metallicity and SFR-M_* relations) are compared to samples of field star-forming galaxies. Results. We find that LGRB hosts at z< 1 have on average lower SFRs than if they were direct star formation tracers. By directly comparing metallicity distributions of LGRB hosts and star-forming galaxies, we find a good match between the two populations up to 12 +log (O/H)~8.4−8.5, after which the paucity of metal-rich LGRB hosts becomes apparent. The LGRB host galaxies of our complete sample are consistent with the mass-metallicity relation at similar mean redshift and stellar masses. The cutoff against high metallicities (and high masses) can explain the low SFR values of LGRB hosts. We find a hint of an increased incidence of starburst galaxies in the Swift/BAT6 z< 1 sample with respect to that of a field star-forming population. Given that the SFRs are low on average, the latter is ascribed to low stellar masses. Nevertheless, the limits on the completeness and metallicity availability of current surveys, coupled with the limited number of LGRB host galaxies, prevents us from investigating more quantitatively whether the starburst incidence is such as expected after taking into account the high-metallicity aversion of LGRB host galaxies.

Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

The Most Complete Photometric Analysis of 548 CALIFA Galaxies

We present an extensive photometric catalog for 548 CALIFA galaxies observed as of the summer of 2015. CALIFA is currently lacking photometry matching the scale and diversity of its spectroscopy; this work is intended to meet all photometric needs for CALIFA galaxies while also identifying best photometric practices for upcoming integral field spectroscopy surveys such as SAMI and MaNGA. This catalog comprises gri surface brightness profiles derived from Sloan Digital Sky Survey (SDSS) imaging, a variety of non-parametric quantities extracted from these pro files, and parametric models fitted to the i-band pro files (1D) and original galaxy images (2D). To compliment our photometric analysis, we contrast the relative performance of our 1D and 2D modelling approaches. The ability of each measurement to characterize the global properties of galaxies is quantitatively assessed, in the context of constructing the tightest scaling relations. Where possible, we compare our photometry with existing photometrically or spectroscopically obtained measurements from the literature. Close agreement is found with Walcher et al. (2014), the current source of basic photometry and classifications of CALIFA galaxies, while comparisons with spectroscopically derived quantities reveals the effect of CALIFA's limited field of view compared to broadband imaging surveys such as the SDSS. The colour-magnitude diagram, star formation main sequence, and Tully-Fisher relation of CALIFA galaxies are studied, to give a small example of the investigations possible with this rich catalog. We conclude with a discussion of points of concern for ongoing integral field spectroscopy surveys and directions for future expansion and exploitation of this work.

Rise, fragmentation, infringement and fear: emerging urban issues in Chinese clustering in Italy

China is today facing rapid economic development and the long-term implications of China’s rise for European economy, society and culture, are constantly debated but still almost unknown. Moreover, only recently a new volume edited by Kunzmann has clearly pointed out a particular field of research like the EU spatial impact of China’s convergence in the global market. The aim of the present paper is to deal with the spatial issues related to the growing Chinese communities, especially in Italy, that are part of a more general and considerable transformation process of the traditional Chinese enclaves in EU cities: from recognizable “Chinatowns” to new hybrid urban formations where housing, retail, wholesale and even commodity production often tend to match. Key-Concepts like rise, fragmentation, infringement and fear are useful in analysing some of the more controversial socio-economic dynamics of Chinese clusters especially in a traditionally manufactured-based country like Italy, where it’s recognizable a unique paradox of a “double competition” from outside and from inside. This statement poses a serious threat to local economic systems in terms of sustainability and social cohesion, making it necessary to rethink the role and the nature of public action in facing new forms of marginality at urban and regional level.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

MixKMeans: Clustering Question-Answer Archives

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality. Our method, {\it MixKMeans}, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate. This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space. We empirically evaluate our method on a variety of real-world labeled datasets. Our results indicate that our method significantly outperforms state-of-the-art clustering methods for the task of clustering question-answer archives.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

Respiratory virus-associated severe acute respiratory illness (SARI) and viral clustering in Malawian children in a setting with a high prevalence of HIV, malaria and malnutrition

BACKGROUND:  We used four years of paediatric severe acute respiratory illness (SARI) sentinel surveillance in Blantyre, Malawi to identify factors associated with clinical severity and co-viral clustering.

METHODS:  From January 2011 to December 2014, 2363 children aged 3 months to 14 years presenting to hospital with SARI were enrolled. Nasopharyngeal aspirates were tested for influenza and other respiratory viruses. We assessed risk factors for clinical severity and conducted clustering analysis to identify viral clusters in children with co-viral detection.

RESULTS:  Hospital-attended influenza-positive SARI incidence was 2.0 cases per 10,000 children annually; it was highest children aged under 1 year (6.3 cases per 10,000), and HIV-infected children aged 5 to 9 years (6.0 cases per 10,000). 605 (26.8%) SARI cases had warning signs, which were positively associated with HIV infection (adjusted risk ratio [aRR]: 2.4, 95% CI: 1.4, 3.9), RSV infection (aRR: 1.9, 95% CI: 1.3, 3.0) and rainy season (aRR: 2.4, 95% CI: 1.6, 3.8). We identified six co-viral clusters; one cluster was associated with SARI with warning signs.

CONCLUSIONS:  Influenza vaccination may benefit young children and HIV infected children in this setting. Viral clustering may be associated with SARI severity; its assessment should be included in routine SARI surveillance.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.