University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Delta TFIDF: An Improved Feature Space for Sentiment Analysis

Abstract: Mining opinions and sentiment from social networking sites is a popular application for social media systems Common approaches use a machine learning system with a bag of words feature set We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification Delta TFIDF is easy to compute, implement, and understand We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets

Discovery of coherent pulsations from the Ultraluminous X-ray Source NGC 7793 P13

Abstract: We report the detection of coherent pulsations from the ultraluminous X-ray source NGC 7793 P13. The ~0.42s nearly sinusoidal pulsations were initially discovered in broadband X-ray observations using XMM-Newton and NuSTAR taken in 2016. We subsequently also found pulsations in archival XMM-Newton data taken in 2013 and 2014. The significant (>>5 sigma) detection of coherent pulsations demonstrates that the compact object in P13 is a neutron star with an observed peak luminosity of ~1e40 erg/s (assuming isotropy), well above the Eddington limit for a 1.4 M_sun accretor. This makes P13 the second ultraluminous X-ray source known to be powered by an accreting neutron star. The pulse period varies between epochs, with a slow but persistent spin up over the 2013-2016 period. This spin-up indicates a magnetic field of B ~ 1.5e12 G, typical of many accreting pulsars. The most likely explanation for the extreme luminosity is a high degree of beaming, however this is difficult to reconcile with the sinusoidal pulse profile.

Joint Blind Source Separation by Multiset Canonical Correlation Analysis

Abstract: In this paper, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multiset canonical correlation analysis (M-CCA) [J. R. Kettenring, "Canonical analysis of several sets of variables", Biometrika, vol. 58, pp. 433-451, 1971]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task.

Keyframe-based video summarization using Delaunay clustering

Abstract: Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.