30,207 research outputs found
Cannon-Thurston Maps for Kleinian Groups
We show that Cannon-Thurston maps exist for degenerate free groups without
parabolics, i.e. for handlebody groups. Combining these techniques with earlier
work proving the existence of Cannon-Thurston maps for surface groups, we show
that Cannon-Thurston maps exist for arbitrary finitely generated Kleinian
groups without parabolics, proving conjectures of Thurston and McMullen. We
also show that point pre-images under Cannon-Thurston maps for degenerate free
groups without parabolics correspond to end-points of leaves of an ending
lamination in the Masur domain, whenever a point has more than one pre-image.
This proves a conjecture of Otal. We also prove a similar result for point
pre-images under Cannon-Thurston maps for arbitrary finitely generated Kleinian
groups without parabolics.Comment: 39 pgs 1 fig. Final version incorporating referee comments. To appear
in Forum of Mathematics, P
Identifying Real Estate Opportunities using Machine Learning
The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.Comment: 24 pages, 13 figures, 5 table
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