27,965 research outputs found
Adversarially Tuned Scene Generation
Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201
Pathophysiologic correlates of exercise intolerance in adults with pulmonary hypertension and congenital heart disease
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Assessments as Teaching and Research Tools in an Environmental Problem-Solving Program for In-Service Teachers
This article discusses the use of a scenario-based assessment tool in two environmental geoscience in-service programs for middle school and high school teachers. This tool served both to guide instructional techniques and as a method to evaluate the success of the instructional approach. In each case, participants were assessed before the workshops to reveal misconceptions that could be addressed in program activities and afterwards to reveal shifts in their understanding of concepts and approaches. The researchers noted that this scenario-based assessment was effective in providing guidance in refining instructional techniques and as a method to evaluate the effectiveness of an instructional program. In addition, participating teachers reported significant changes in their teaching as a result of the program. Educational levels: Graduate or professional, Graduate or professional
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Fault-based regression testing in a reactive environment
Regression testing is the process of retesting software after modification. Regression testing is a major factor contributing to the high cost of software maintenance. To control this cost, regression testing must be accomplished efficiently through effective reuse of test cases and judicious generation of new test cases.Fault-based testing focuses on the detection of particular classes of faults. RELAY is a fault-based testing technique that guarantees the detection of errors caused by any fault in a chosen fault classification. RELAY can be used as a regression testing technique to generate the test cases required to demonstrate that a modification is properly made. In addition, the information related to a test case chosen to detect a potential fault guides in choosing previously-selected test cases that should be reused, for a given modification.This paper presents the concepts behind RELAY and discusses how RELAY could be used as a regression testing technique. It also describes a testing environment that supports reactive regression testing as well as testing throughout the development lifecycle, which is based on integrating the RELAY model with other testing techniques
Gravitational radiation reaction and inspiral waveforms in the adiabatic limit
We describe progress evolving an important limit of binary orbits in general
relativity, that of a stellar mass compact object gradually spiraling into a
much larger, massive black hole. These systems are of great interest for
gravitational wave observations. We have developed tools to compute for the
first time the radiated fluxes of energy and angular momentum, as well as
instantaneous snapshot waveforms, for generic geodesic orbits. For special
classes of orbits, we compute the orbital evolution and waveforms for the
complete inspiral by imposing global conservation of energy and angular
momentum. For fully generic orbits, inspirals and waveforms can be obtained by
augmenting our approach with a prescription for the self force in the adiabatic
limit derived by Mino. The resulting waveforms should be sufficiently accurate
to be used in future gravitational-wave searches.Comment: Accepted for publication in Phys. Rev. Let
Computational investigations of maximum flow algorithms
"April 1995."Includes bibliographical references (p. 55-57).by Ravindra K. Ahuja ... [et al.
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