20,342 research outputs found
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
We detail our ongoing work in Flint, Michigan to detect pipes made of lead
and other hazardous metals. After elevated levels of lead were detected in
residents' drinking water, followed by an increase in blood lead levels in area
children, the state and federal governments directed over $125 million to
replace water service lines, the pipes connecting each home to the water
system. In the absence of accurate records, and with the high cost of
determining buried pipe materials, we put forth a number of predictive and
procedural tools to aid in the search and removal of lead infrastructure.
Alongside these statistical and machine learning approaches, we describe our
interactions with government officials in recommending homes for both
inspection and replacement, with a focus on the statistical model that adapts
to incoming information. Finally, in light of discussions about increased
spending on infrastructure development by the federal government, we explore
how our approach generalizes beyond Flint to other municipalities nationwide.Comment: 10 pages, 10 figures, To appear in KDD 2018, For associated
promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9
Use and Communication of Probabilistic Forecasts
Probabilistic forecasts are becoming more and more available. How should they
be used and communicated? What are the obstacles to their use in practice? I
review experience with five problems where probabilistic forecasting played an
important role. This leads me to identify five types of potential users: Low
Stakes Users, who don't need probabilistic forecasts; General Assessors, who
need an overall idea of the uncertainty in the forecast; Change Assessors, who
need to know if a change is out of line with expectatations; Risk Avoiders, who
wish to limit the risk of an adverse outcome; and Decision Theorists, who
quantify their loss function and perform the decision-theoretic calculations.
This suggests that it is important to interact with users and to consider their
goals. The cognitive research tells us that calibration is important for trust
in probability forecasts, and that it is important to match the verbal
expression with the task. The cognitive load should be minimized, reducing the
probabilistic forecast to a single percentile if appropriate. Probabilities of
adverse events and percentiles of the predictive distribution of quantities of
interest seem often to be the best way to summarize probabilistic forecasts.
Formal decision theory has an important role, but in a limited range of
applications
Robots that can adapt like animals
As robots leave the controlled environments of factories to autonomously
function in more complex, natural environments, they will have to respond to
the inevitable fact that they will become damaged. However, while animals can
quickly adapt to a wide variety of injuries, current robots cannot "think
outside the box" to find a compensatory behavior when damaged: they are limited
to their pre-specified self-sensing abilities, can diagnose only anticipated
failure modes, and require a pre-programmed contingency plan for every type of
potential damage, an impracticality for complex robots. Here we introduce an
intelligent trial and error algorithm that allows robots to adapt to damage in
less than two minutes, without requiring self-diagnosis or pre-specified
contingency plans. Before deployment, a robot exploits a novel algorithm to
create a detailed map of the space of high-performing behaviors: This map
represents the robot's intuitions about what behaviors it can perform and their
value. If the robot is damaged, it uses these intuitions to guide a
trial-and-error learning algorithm that conducts intelligent experiments to
rapidly discover a compensatory behavior that works in spite of the damage.
Experiments reveal successful adaptations for a legged robot injured in five
different ways, including damaged, broken, and missing legs, and for a robotic
arm with joints broken in 14 different ways. This new technique will enable
more robust, effective, autonomous robots, and suggests principles that animals
may use to adapt to injury
The decomposition of disease and disability life expectancies in England 1992-2004
ISBN 978-1-905752-23-2 www.cass.city.ac.uk "This paper originated in an independent report for the Department of Health. Any opinions expressed in this paper are my/our own and not necessarily those of my/our employer or anyone else I/we have discussed them with. In particular, the views expressed may not necessarily reflect the views or policies of the Department of Health. You must not copy this paper or quote it without my/our permission"
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Surplus analysis for variable annuities with a GMDB option
In this paper, we analyze the insurance surplus for a Variable Annuity contract with a Guaranteed Minimum Death Benefit (GMDB) option. Initially, we derive the first two moments of the distribution of the surplus; and subsequently, we develop the whole distribution using a stochastic model which involves an integrated analysis of financial and mortality risk for a portfolio of annuities with GMDB embedded options. We offer a model according which the premium can be modified as per the forecasts of mortality probabilities, interest rate and fund evolution. Moreover, the study enables us to determine the premium that leads to a required probability of insolvency, and so it can be used for an evaluation of the adequacy of solvency. Numerical examples illustrate the results
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