6,587 research outputs found
Flows in inkjet-printed aqueous rivulets
We used optical microscopy to investigate flows inside water rivulets that
were inkjet-printed onto different surfaces and under different ambient
conditions. The acquired fluid dynamics videos were submitted to the 2013
Gallery of Fluid Motion.Comment: This article accompanies a fluid dynamics video submitted to the 2013
Gallery of Fluid Motion of the 66th Annual Meeting of the APS Division of
Fluid Dynamic
Woods, Tiger; and Yamaguchi, Kristi.
Contributions by Howard J. Bromberg to Great Lives from History: Asian and Pacific Islander Americans, a collection of short biographical essays
Trump, Donald: Environmental Policy of,
Businessman and US. president Donald John Trump was born in Queens, New York, to Frederick (Fred) Trump and Mary MacLeod. Fred Trump, a real estate developer, brought Donald into the family real estate business. Through his business operations, Trump became a billionaire. Donald also became a television celebrity with the reality show The Apprentice. In one of the most unpredictable elections in American history, Trump became the 45th president of the United States. His administration aggressively promoted development of oil, gas, mineral, and coal resources. In doing so, he revoked numerous environmental protections
Aeterni Patris; Infallibility; O\u27Connor, Flannery; Papal Documents
Encyclopedia entries written by Howard J. Bromberg
The IBMAP approach for Markov networks structure learning
In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum
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