6,045 research outputs found
Representations of orbifold groupoids
Orbifold groupoids have been recently widely used to represent both effective
and ineffective orbifolds. We show that every orbifold groupoid can be
faithfully represented on a continuous family of finite dimensional Hilbert
spaces. As a consequence we obtain the result that every orbifold groupoid is
Morita equivalent to the translation groupoid of an action of a bundle of
compact topological groups.Comment: 15 page
Introduction to flavour physics
We give a brief introduction to flavour physics. The first part covers the
flavour structure of the Standard Model, how the Kobayashi-Maskawa mechanism is
tested and provides examples of searches for new physics using flavour
observables, such as meson mixing and rare decays. In the second part we give a
brief overview of the recent flavour anomalies and how the Higgs can act as a
new flavour probe.Comment: 32 pages, 22 figures, the write-up is a combination of lectures given
at ESHEP 2018, SSI 2018 and the US Belle II summer schools, Fig. 1 corrected,
several typographical errors fixe
Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model
Networks arising from social, technological and natural domains exhibit rich
connectivity patterns and nodes in such networks are often labeled with
attributes or features. We address the question of modeling the structure of
networks where nodes have attribute information. We present a Multiplicative
Attribute Graph (MAG) model that considers nodes with categorical attributes
and models the probability of an edge as the product of individual attribute
link formation affinities. We develop a scalable variational expectation
maximization parameter estimation method. Experiments show that MAG model
reliably captures network connectivity as well as provides insights into how
different attributes shape the network structure.Comment: 15 pages, 7 figures, 7 table
Donor Retention in Online Crowdfunding Communities: A Case Study of DonorsChoose.org
Online crowdfunding platforms like DonorsChoose.org and Kickstarter allow
specific projects to get funded by targeted contributions from a large number
of people. Critical for the success of crowdfunding communities is recruitment
and continued engagement of donors. With donor attrition rates above 70%, a
significant challenge for online crowdfunding platforms as well as traditional
offline non-profit organizations is the problem of donor retention.
We present a large-scale study of millions of donors and donations on
DonorsChoose.org, a crowdfunding platform for education projects. Studying an
online crowdfunding platform allows for an unprecedented detailed view of how
people direct their donations. We explore various factors impacting donor
retention which allows us to identify different groups of donors and quantify
their propensity to return for subsequent donations. We find that donors are
more likely to return if they had a positive interaction with the receiver of
the donation. We also show that this includes appropriate and timely
recognition of their support as well as detailed communication of their impact.
Finally, we discuss how our findings could inform steps to improve donor
retention in crowdfunding communities and non-profit organizations.Comment: preprint version of WWW 2015 pape
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Recommending products to consumers means not only understanding their tastes,
but also understanding their level of experience. For example, it would be a
mistake to recommend the iconic film Seven Samurai simply because a user enjoys
other action movies; rather, we might conclude that they will eventually enjoy
it -- once they are ready. The same is true for beers, wines, gourmet foods --
or any products where users have acquired tastes: the `best' products may not
be the most `accessible'. Thus our goal in this paper is to recommend products
that a user will enjoy now, while acknowledging that their tastes may have
changed over time, and may change again in the future. We model how tastes
change due to the very act of consuming more products -- in other words, as
users become more experienced. We develop a latent factor recommendation system
that explicitly accounts for each user's level of experience. We find that such
a model not only leads to better recommendations, but also allows us to study
the role of user experience and expertise on a novel dataset of fifteen million
beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure
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