60,591 research outputs found
Discovering conversational topics and emotions associated with Demonetization tweets in India
Social media platforms contain great wealth of information which provides us
opportunities explore hidden patterns or unknown correlations, and understand
people's satisfaction with what they are discussing. As one showcase, in this
paper, we summarize the data set of Twitter messages related to recent
demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights
from Twitter's data. Our proposed system automatically extracts the popular
latent topics in conversations regarding demonetization discussed in Twitter
via the Latent Dirichlet Allocation (LDA) based topic model and also identifies
the correlated topics across different categories. Additionally, it also
discovers people's opinions expressed through their tweets related to the event
under consideration via the emotion analyzer. The system also employs an
intuitive and informative visualization to show the uncovered insight.
Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI),
to select the best LDA models. The obtained LDA results show that the tool can
be effectively used to extract discussion topics and summarize them for further
manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with
arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by
other author
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
Efficient algorithms to discover alterations with complementary functional association in cancer
Recent large cancer studies have measured somatic alterations in an
unprecedented number of tumours. These large datasets allow the identification
of cancer-related sets of genetic alterations by identifying relevant
combinatorial patterns. Among such patterns, mutual exclusivity has been
employed by several recent methods that have shown its effectivenes in
characterizing gene sets associated to cancer. Mutual exclusivity arises
because of the complementarity, at the functional level, of alterations in
genes which are part of a group (e.g., a pathway) performing a given function.
The availability of quantitative target profiles, from genetic perturbations or
from clinical phenotypes, provides additional information that can be leveraged
to improve the identification of cancer related gene sets by discovering groups
with complementary functional associations with such targets.
In this work we study the problem of finding groups of mutually exclusive
alterations associated with a quantitative (functional) target. We propose a
combinatorial formulation for the problem, and prove that the associated
computation problem is computationally hard. We design two algorithms to solve
the problem and implement them in our tool UNCOVER. We provide analytic
evidence of the effectiveness of UNCOVER in finding high-quality solutions and
show experimentally that UNCOVER finds sets of alterations significantly
associated with functional targets in a variety of scenarios. In addition, our
algorithms are much faster than the state-of-the-art, allowing the analysis of
large datasets of thousands of target profiles from cancer cell lines. We show
that on one such dataset from project Achilles our methods identify several
significant gene sets with complementary functional associations with targets.Comment: Accepted at RECOMB 201
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