53,429 research outputs found
Optimal Uncertainty Quantification
We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the
forefront. This framework, which we call \emph{Optimal Uncertainty
Quantification} (OUQ), is based on the observation that, given a set of
assumptions and information about the problem, there exist optimal bounds on
uncertainties: these are obtained as values of well-defined optimization
problems corresponding to extremizing probabilities of failure, or of
deviations, subject to the constraints imposed by the scenarios compatible with
the assumptions and information. In particular, this framework does not
implicitly impose inappropriate assumptions, nor does it repudiate relevant
information. Although OUQ optimization problems are extremely large, we show
that under general conditions they have finite-dimensional reductions. As an
application, we develop \emph{Optimal Concentration Inequalities} (OCI) of
Hoeffding and McDiarmid type. Surprisingly, these results show that
uncertainties in input parameters, which propagate to output uncertainties in
the classical sensitivity analysis paradigm, may fail to do so if the transfer
functions (or probability distributions) are imperfectly known. We show how,
for hierarchical structures, this phenomenon may lead to the non-propagation of
uncertainties or information across scales. In addition, a general algorithmic
framework is developed for OUQ and is tested on the Caltech surrogate model for
hypervelocity impact and on the seismic safety assessment of truss structures,
suggesting the feasibility of the framework for important complex systems. The
introduction of this paper provides both an overview of the paper and a
self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository
Research Papers). See SIAM Review for higher quality figure
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on
Knowledge Discovery and Data Minin
Active Sampling-based Binary Verification of Dynamical Systems
Nonlinear, adaptive, or otherwise complex control techniques are increasingly
relied upon to ensure the safety of systems operating in uncertain
environments. However, the nonlinearity of the resulting closed-loop system
complicates verification that the system does in fact satisfy those
requirements at all possible operating conditions. While analytical proof-based
techniques and finite abstractions can be used to provably verify the
closed-loop system's response at different operating conditions, they often
produce conservative approximations due to restrictive assumptions and are
difficult to construct in many applications. In contrast, popular statistical
verification techniques relax the restrictions and instead rely upon
simulations to construct statistical or probabilistic guarantees. This work
presents a data-driven statistical verification procedure that instead
constructs statistical learning models from simulated training data to separate
the set of possible perturbations into "safe" and "unsafe" subsets. Binary
evaluations of closed-loop system requirement satisfaction at various
realizations of the uncertainties are obtained through temporal logic
robustness metrics, which are then used to construct predictive models of
requirement satisfaction over the full set of possible uncertainties. As the
accuracy of these predictive statistical models is inherently coupled to the
quality of the training data, an active learning algorithm selects additional
sample points in order to maximize the expected change in the data-driven model
and thus, indirectly, minimize the prediction error. Various case studies
demonstrate the closed-loop verification procedure and highlight improvements
in prediction error over both existing analytical and statistical verification
techniques.Comment: 23 page
An improved approach for flight readiness assessment
An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft
Rotorcraft aviation icing research requirements: Research review and recommendations
The status of rotorcraft icing evaluation techniques and ice protection technology was assessed. Recommendations are made for near and long term icing programs that describe the needs of industry. These recommended programs are based on a consensus of the major U.S. helicopter companies. Specific activities currently planned or underway by NASA, FAA and DOD are reviewed to determine relevance to the overall research requirements. New programs, taking advantage of current activities, are recommended to meet the long term needs for rotorcraft icing certification
Effectiveness of organic certification: a study on an italian organic certificator's data.
The aim of this paper is to implemnt risk-based models for the inspection procedures in the organic certification. particularly, the aim is to analyse the the relationship between the type of sanction a farm receives, and the farm's structure and productions, aiming at the definition of potential risk factors
Multilevel Models with Stochastic Volatility for Repeated Cross-Sections: an Application to tribal Art Prices
In this paper we introduce a multilevel specification with stochastic
volatility for repeated cross-sectional data. Modelling the time dynamics in
repeated cross sections requires a suitable adaptation of the multilevel
framework where the individuals/items are modelled at the first level whereas
the time component appears at the second level. We perform maximum likelihood
estimation by means of a nonlinear state space approach combined with
Gauss-Legendre quadrature methods to approximate the likelihood function. We
apply the model to the first database of tribal art items sold in the most
important auction houses worldwide. The model allows to account properly for
the heteroscedastic and autocorrelated volatility observed and has superior
forecasting performance. Also, it provides valuable information on market
trends and on predictability of prices that can be used by art markets
stakeholders
A Quiet Helicopter for Air Taxi Operations
NASA is exploring rotorcraft designs for VTOL air taxi operations, also known as urban air mobility (UAM) or on-demand mobility (ODM) applications. Several concept vehicles have been developed, intended to focus and guide NASA research activities in support of aircraft development for this emerging market. This paper examines a single main-rotor helicopter designed specifically for low-noise air taxi operations. Based on demonstrated technology, the aircraft uses a turboshaft engine with a sound-absorbing installation, and the NOTAR anti-torque system to eliminate tail-rotor noise, consequently the noise and annoyance of the aircraft are dominated by the main rotor. Several design parameters are explored to reduce the noise, including rotor tip speed, blade geometry, and higher-harmonic control. Commensurate with the level of design detail, the noise is calculated for compact loading and thickness sources on the rotating blades. The metric is the reduction of the noise for the helicopter certification conditions (takeoff, flyover, and approach), relative a baseline aircraft with typical (high) tip speed, conventional blade planform, and no higher-harmonic control
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