HI Observations of the starburst galaxy NGC 2146

NGC 2146 is a peculiar spiral galaxy which is currently undergoing a major burst of star formation and is immersed in a extended HI structure that has morphological and kinematical resemblence to a strong tidal interaction. This paper reports aperture synthesis observations carried out in the 21cm line with the Very Large Array (VLA - The National Radio Astronomy Observatory (NRAO) is operated by Associated Universities, Inc. under cooperative agreement with the National Science Foundation.) of two fields positioned to optimally cover the HI streams to the north and south of the galaxy, along with a 300 ft total power spectral mapping program to recover the low surface brightness extended emission. The observations reveal elongated streams of neutral hydrogen towards both the north and the south of the optical galaxy extending out up to 6 Holmberg radii. The streams are not in the principle plane of rotation of the galaxy, but instead are suggestive of a tidal interaction between NGC 2146 and a LSB companion that was destroyed by the encounter and remains undetected at optical wavelengths. Part of the southern stream is turning back to fall into the main galaxy, where it will create a long-lived warp in the HI disk of NGC 2146. Analysis of the trajectory of the outlying gas suggests that the closest encounter took place about 0.8 billion years ago and that infall of debris will continue for a similar time span.Comment: To be published in A&

Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS

Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot cope with the data stream problems. In fact, large-scale data are mostly generated by the non-stationary data stream where its pattern evolves over time. To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving algorithm is distributed over the worker nodes in the cloud to learn large-scale data stream. Scalable PANFIS framework incorporates the active learning (AL) strategy and two model fusion methods. The AL accelerates the distributed learning process to generate an initial evolving large-scale data stream model (initial model), whereas the two model fusion methods aggregate an initial model to generate the final model. The final model represents the update of current large-scale data knowledge which can be used to infer future data. Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms. The results indicate that Scalable PANFIS with AL improves the training time to be almost two times faster than Scalable PANFIS without AL. The results also show both rule merging and the voting mechanisms yield similar accuracy in general among Scalable PANFIS algorithms and they are generally better than Spark-based algorithms. In terms of running time, the Scalable PANFIS training time outperforms all Spark-based algorithms when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS

A long-standing assumption common in algorithm design is that any part of the input is accessible at any time for unit cost. However, as we work with increasingly large data sets, or as we build smaller devices, we must revisit this assumption. In this thesis, I present some of my work on graph algorithms designed for circumstances where traditional assumptions about inputs do not apply. 1. Classical graph algorithms require direct access to the input graph and this is not feasible when the graph is too large to fit in memory. For computation on massive graphs we consider the dynamic streaming graph model. Given an input graph defined by as a stream of edge insertions and deletions, our goal is to approximate properties of this graph using space that is sublinear in the size of the stream. In this thesis, I present algorithms for approximating vertex connectivity, hypergraph edge connectivity, maximum coverage, unique coverage, and temporal connectivity in graph streams. 2. In certain applications the input graph is not explicitly represented, but its edges may be discovered via queries which require costly computation or measurement. I present two open-source systems which solve real-world problems via graph algorithms which may access their inputs only through costly edge queries. M ESH is a memory manager which compacts memory efficiently by finding an approximate graph matching subject to stringent time and edge query restrictions. PathCache is an efficiently scalable network measurement platform that outperforms the current state of the art

Efficient and Error-bounded Spatiotemporal Quantile Monitoring in Edge Computing Environments

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM

An incremental interval Type-2 neural fuzzy Classifier

© 2015 IEEE. Most real world classification problems involve a high degree of uncertainty, unsolved by a traditional type-1 fuzzy classifier. In this paper, a novel interval type-2 classifier, namely Evolving Type-2 Classifier (eT2Class), is proposed. The eT2Class features a flexible working principle built upon a fully sequential and local working principle. This learning notion allows eT2Class to automatically grow, adapt, prune, recall its knowledge from data streams in the single-pass learning fashion, while employing loosely coupled fuzzy sub-models. In addition, eT2Class introduces a generalized interval type-2 fuzzy neural network architecture, where a multivariate Gaussian function with uncertain non-diagonal covariance matrixes constructs the rule premise, while the rule consequent is crafted by a local non-linear Chebyshev polynomial. The efficacy of eT2Class is numerically validated by numerical studies with four data streams characterizing non-stationary behaviors, where eT2Class demonstrates the most encouraging learning performance in achieving a tradeoff between accuracy and complexity

Influence of Spatial Distribution of Roughness Elements on Turbulent Flow Past a Biofouled Surface

No abstract available

Incidence Geometries and the Pass Complexity of Semi-Streaming Set Cover

Set cover, over a universe of size $n$, may be modelled as a data-streaming problem, where the $m$ sets that comprise the instance are to be read one by one. A semi-streaming algorithm is allowed only $O(n\, \mathrm{poly}\{\log n, \log m\})$ space to process this stream. For each $p \ge 1$, we give a very simple deterministic algorithm that makes $p$ passes over the input stream and returns an appropriately certified $(p+1)n^{1/(p+1)}$-approximation to the optimum set cover. More importantly, we proceed to show that this approximation factor is essentially tight, by showing that a factor better than $0.99\,n^{1/(p+1)}/(p+1)^2$ is unachievable for a $p$-pass semi-streaming algorithm, even allowing randomisation. In particular, this implies that achieving a $\Theta(\log n)$-approximation requires $\Omega(\log n/\log\log n)$ passes, which is tight up to the $\log\log n$ factor. These results extend to a relaxation of the set cover problem where we are allowed to leave an $\varepsilon$ fraction of the universe uncovered: the tight bounds on the best approximation factor achievable in $p$ passes turn out to be $\Theta_p(\min\{n^{1/(p+1)}, \varepsilon^{-1/p}\})$. Our lower bounds are based on a construction of a family of high-rank incidence geometries, which may be thought of as vast generalisations of affine planes. This construction, based on algebraic techniques, appears flexible enough to find other applications and is therefore interesting in its own right.Comment: 20 page

Hide and seek between Andromeda's halo, disk, and giant stream

Photometry in B, V (down to V ~ 26 mag) is presented for two 23' x 23' fields of the Andromeda galaxy (M31) that were observed with the blue channel camera of the Large Binocular Telescope during the Science Demonstration Time. Each field covers an area of about 5.1kpc x 5.1kpc at the distance of M31 ((m-M)o ~ 24.4 mag), sampling, respectively, a northeast region close to the M31 giant stream (field S2), and an eastern portion of the halo in the direction of the galaxy minor axis (field H1). The stream field spans a region that includes Andromeda's disk and the giant stream, and this is reflected in the complexity of the color magnitude diagram of the field. One corner of the halo field also includes a portion of the giant stream. Even though these demonstration time data were obtained under non-optimal observing conditions the B photometry, acquired in time-series mode, allowed us to identify 274 variable stars (among which 96 are bona fide and 31 are candidate RR Lyrae stars, 71 are Cepheids, and 16 are binary systems) by applying the image subtraction technique to selected portions of the observed fields. Differential flux light curves were obtained for the vast majority of these variables. Our sample includes mainly pulsating stars which populate the instability strip from the Classical Cepheids down to the RR Lyrae stars, thus tracing the different stellar generations in these regions of M31 down to the horizontal branch of the oldest (t ~ 10 Gyr) component.Comment: 59 pages, 26 figures, 12 tables, ApJ in pres