3,596 research outputs found
Spectra of primordial fluctuations in two-perfect-fluid regular bounces
We introduce analytic solutions for a class of two components bouncing
models, where the bounce is triggered by a negative energy density perfect
fluid. The equation of state of the two components are constant in time, but
otherwise unrelated. By numerically integrating regular equations for scalar
cosmological perturbations, we find that the (would be) growing mode of the
Newtonian potential before the bounce never matches with the the growing mode
in the expanding stage. For the particular case of a negative energy density
component with a stiff equation of state we give a detailed analytic study,
which is in complete agreement with the numerical results. We also perform
analytic and numerical calculations for long wavelength tensor perturbations,
obtaining that, in most cases of interest, the tensor spectral index is
independent of the negative energy fluid and given by the spectral index of the
growing mode in the contracting stage. We compare our results with previous
investigations in the literature.Comment: 11 pages, 5 figure
Gravitational wave background in perfect fluid quantum cosmologies
We discuss the gravitational wave background produced by bouncing models
based on a full quantum evolution of a universe filled with a perfect fluid.
Using an ontological interpretation for the background wave function allows us
to solve the mode equations for the tensorial perturbations, and we find the
spectral index as a function of the fluid equation of state.Comment: 12 pages, 5 figures, to appear in Phys. Rev. D (2006
Asymmetric Actor Critic for Image-Based Robot Learning
Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simulator. While several recent works have shown promising results in
transferring policies trained in simulation to the real world, they often do
not fully utilize the advantage of working with a simulator. In this work, we
exploit the full state observability in the simulator to train better policies
which take as input only partial observations (RGBD images). We do this by
employing an actor-critic training algorithm in which the critic is trained on
full states while the actor (or policy) gets rendered images as input. We show
experimentally on a range of simulated tasks that using these asymmetric inputs
significantly improves performance. Finally, we combine this method with domain
randomization and show real robot experiments for several tasks like picking,
pushing, and moving a block. We achieve this simulation to real world transfer
without training on any real world data.Comment: Videos of experiments can be found at http://www.goo.gl/b57WT
Perturbations in the Ekpyrotic Scenarios
With the new cosmological data gathered over the last few years, the
inflationary paradigm has seen its predictions largely unchallenged. A recent
proposal, called the ekpyrotic scenario, was argued to be a viable competitor
as it was claimed that the spectrum of primordial perturbations it produces is
scale invariant. By investigating closely this scenario, we show that the
corresponding spectrum depends explicitly on an arbitrary function of
wavenumber and is therefore itself arbitrary. It can at will be set scale
invariant. We conclude that the scenario is not predictive at this stage.Comment: 4 pages, no figure, uses moriond.sty, to appear in the proceeding of
the Moriond cosmology meeting held at Les Arcs, France (March 16-23, 2002
Certifying sustainability: opportunities and challenges for the cattle supply chain in Brazil
Up to 75% of deforestation in Brazil is associated with cattle ranching. To reduce forest conversion and increase sustainability in the cattle supply chain, government, private sector and civil society support interventions based on combinations of institutions and policies, incentives, and information and technology. In this paper we analyse the observed and expected interactions among the Sustainable Agriculture Network (SAN) Standard for Sustainable Cattle Production Systems certification program and other interventions associated with livestock and deforestation in Amazonia. Semi-structured interviews were conducted with cattle supply chain key actors, who identified the opportunities and barriers to the development and scaling of the SAN cattle program. The SAN cattle program has set a new high standard for sustainability, demonstrated the viability of certifying the cattle supply chain, and created new incentives and markets. However, the program has certified few farms to date. Other interventions are playing a critical role in incentivizing farms towards enhanced sustainability. Interventions that complement progress towards the SAN program include those that help producers to comply with forest laws or provide farmers with access to information and technology to improve their practices. Other interventions may constrain the program, for example by competing with the standards in the marketplace. Greater coordination among interventions may catalyze a more coherent, strategic approach to enhanced sustainability
Analyzing social experiments as implemented: evidence from the HighScope Perry Preschool Program
Social experiments are powerful sources of information about the effectiveness of interventions. In practice, initial randomization plans are almost always compromised. Multiple hypotheses are frequently tested. "Significant" effects are often reported with p-values that do not account for preliminary screening from a large candidate pool of possible effects. This paper develops tools for analyzing data from experiments as they are actually implemented. We apply these tools to analyze the influential HighScope Perry Preschool Program. The Perry program was a social experiment that provided preschool education and home visits to disadvantaged children during their preschool years. It was evaluated by the method of random assignment. Both treatments and controls have been followed from age 3 through age 40. Previous analyses of the Perry data assume that the planned randomization protocol was implemented. In fact, as in many social experiments, the intended randomization protocol was compromised. Accounting for compromised randomization, multiple-hypothesis testing, and small sample sizes, we find statistically significant and economically important program effects for both males and females. We also examine the representativeness of the Perry study. Download appendix
A New Cost-Benefit and Rate of Return Analysis for the Perry Preschool Program: A Summary
This paper summarizes our recent work on the rate of return and cost-benefit ratio of an influential early childhood program.early childhood, rate of return, cost-benefit analysis
Analyzing Social Experiments as Implemented: A Reexamination of the Evidence from the HighScope Perry Preschool Program
Social experiments are powerful sources of information about the effectiveness of interventions. In practice, initial randomization plans are almost always compromised. Multiple hypotheses are frequently tested. "Significant" effects are often reported with p-values that do not account for preliminary screening from a large candidate pool of possible effects. This paper develops tools for analyzing data from experiments as they are actually implemented. We apply these tools to analyze the influential HighScope Perry Preschool Program. The Perry program was a social experiment that provided preschool education and home visits to disadvantaged children during their preschool years. It was evaluated by the method of random assignment. Both treatments and controls have been followed from age 3 through age 40. Previous analyses of the Perry data assume that the planned randomization protocol was implemented. In fact, as in many social experiments, the intended randomization protocol was compromised. Accounting for compromised randomization, multiple-hypothesis testing, and small sample sizes, we find statistically significant and economically important program effects for both males and females. We also examine the representativeness of the Perry study.social experiment, compromised randomization, early childhood intervention, multiple-hypothesis testing
Light Touch Density and Filtering Down: City of Seattle Case Study
Key takeaways:In the City of Seattle, about 12 times as much land is zoned for Single Family (SF) than for Low-Rise Multifamily (LRM).In the mid-1990s, the creation of the LRM zone allowed property owners to use their land more efficiently. As a consequence, many single-family detached homes have been converted to mostly townhomes. This is light-touch density at its best.Since 2000, 18,000 new townhomes units have been built in the LRM zone. As a result, its housing stock increased by about 75% â or about 3% per year. The supply addition in the SF zone from new single-family homes is minimal.The new townhomes are generally starter homes, which has enabled homeownership for lower-income, younger, and more diverse households.Home values in the LRM zone have appreciated at the same rate as home values in the SF zone.Unfortunately, this success is now being derailed by Seattle's Mandatory Housing Affordability (MFA) program.This program will produce a small amount of heavily-subsidized "housing Ferraris" that will be sold to low-income households and destroy the progress LRM zoning has made in expanding broad-based housing affordability
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
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