2,498 research outputs found
Combining Revealed and Stated Preference Data to Estimate the Nonmarket Value of Ecological Services: An Assessment of the State of the Science
This paper reviews the marketing, transportation, and environmental economics literature on the joint estimation of revealed and stated preference data. The revealed preference and stated preference approaches are first described with a focus on the strengths and weaknesses of each. Recognizing these strengths and weaknesses, the potential gains from combining data are described. A classification system for combined data that emphasizes the type of data combination and the econometric models used is proposed. A methodological review of the literature is pursued based on this classification system. Examples from the environmental economics literature are highlighted. A discussion of the advantages and disadvantages of each type of jointly estimated model is then presented. Suggestions for future research, in particular opportunities for application of these models to environmental quality valuation, are presented.Nonmarket Valuation, Revealed Preference, Stated Preference
Secants of minuscule and cominuscule minimal orbits
We study the geometry of the secant and tangential variety of a cominuscule
and minuscule variety, e.g. a Grassmannian or a spinor variety. Using methods
inspired by statistics we provide an explicit local isomorphism with a product
of an affine space with a variety which is the Zariski closure of the image of
a map defined by generalized determinants. In particular, equations of the
secant or tangential variety correspond to relations among generalized
determinants. We also provide a representation theoretic decomposition of
cubics in the ideal of the secant variety of any Grassmannian
Parameterized Verification of Asynchronous Shared-Memory Systems
We characterize the complexity of the safety verification problem for
parameterized systems consisting of a leader process and arbitrarily many
anonymous and identical contributors. Processes communicate through a shared,
bounded-value register. While each operation on the register is atomic, there
is no synchronization primitive to execute a sequence of operations atomically.
We analyze the complexity of the safety verification problem when processes are
modeled by finite-state machines, pushdown machines, and Turing machines. The
problem is coNP-complete when all processes are finite-state machines, and is
PSPACE-complete when they are pushdown machines. The complexity remains
coNP-complete when each Turing machine is allowed boundedly many interactions
with the register. Our proofs use combinatorial characterizations of
computations in the model, and in case of pushdown-systems, some
language-theoretic constructions of independent interest.Comment: 26 pages, International Conference on Computer Aided Verification
(CAV'13
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition
Most state-of-the-art action feature extractors involve differential
operators, which act as highpass filters and tend to attenuate low frequency
action information. This attenuation introduces bias to the resulting features
and generates ill-conditioned feature matrices. The Gaussian Pyramid has been
used as a feature enhancing technique that encodes scale-invariant
characteristics into the feature space in an attempt to deal with this
attenuation. However, at the core of the Gaussian Pyramid is a convolutional
smoothing operation, which makes it incapable of generating new features at
coarse scales. In order to address this problem, we propose a novel feature
enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks
features extracted using a family of differential filters parameterized with
multiple time skips and encodes shift-invariance into the frequency space. MIFS
compensates for information lost from using differential operators by
recapturing information at coarse scales. This recaptured information allows us
to match actions at different speeds and ranges of motion. We prove that MIFS
enhances the learnability of differential-based features exponentially. The
resulting feature matrices from MIFS have much smaller conditional numbers and
variances than those from conventional methods. Experimental results show
significantly improved performance on challenging action recognition and event
detection tasks. Specifically, our method exceeds the state-of-the-arts on
Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on
HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup
strategy for feature extraction with minimal or no accuracy cost
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
QuizMap: Open social student modeling and adaptive navigation support with TreeMaps
In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg
Enabling high confidence detections of gravitational-wave bursts
With the advanced LIGO and Virgo detectors taking observations the detection
of gravitational waves is expected within the next few years. Extracting
astrophysical information from gravitational wave detections is a well-posed
problem and thoroughly studied when detailed models for the waveforms are
available. However, one motivation for the field of gravitational wave
astronomy is the potential for new discoveries. Recognizing and characterizing
unanticipated signals requires data analysis techniques which do not depend on
theoretical predictions for the gravitational waveform. Past searches for
short-duration un-modeled gravitational wave signals have been hampered by
transient noise artifacts, or "glitches," in the detectors. In some cases, even
high signal-to-noise simulated astrophysical signals have proven difficult to
distinguish from glitches, so that essentially any plausible signal could be
detected with at most 2-3 level confidence. We have put forth the
BayesWave algorithm to differentiate between generic gravitational wave
transients and glitches, and to provide robust waveform reconstruction and
characterization of the astrophysical signals. Here we study BayesWave's
capabilities for rejecting glitches while assigning high confidence to
detection candidates through analytic approximations to the Bayesian evidence.
Analytic results are tested with numerical experiments by adding simulated
gravitational wave transient signals to LIGO data collected between 2009 and
2010 and found to be in good agreement.Comment: 15 pages, 6 figures, submitted to PR
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