976 research outputs found
The Center for the Study of Ethics in Society at Twenty-Five
Center for the Study of Ethics in Society: Celebrating 25 Years - Presented November 15, 2010
Deep learning to represent sub-grid processes in climate models
The representation of nonlinear sub-grid processes, especially clouds, has
been a major source of uncertainty in climate models for decades.
Cloud-resolving models better represent many of these processes and can now be
run globally but only for short-term simulations of at most a few years because
of computational limitations. Here we demonstrate that deep learning can be
used to capture many advantages of cloud-resolving modeling at a fraction of
the computational cost. We train a deep neural network to represent all
atmospheric sub-grid processes in a climate model by learning from a
multi-scale model in which convection is treated explicitly. The trained neural
network then replaces the traditional sub-grid parameterizations in a global
general circulation model in which it freely interacts with the resolved
dynamics and the surface-flux scheme. The prognostic multi-year simulations are
stable and closely reproduce not only the mean climate of the cloud-resolving
simulation but also key aspects of variability, including precipitation
extremes and the equatorial wave spectrum. Furthermore, the neural network
approximately conserves energy despite not being explicitly instructed to.
Finally, we show that the neural network parameterization generalizes to new
surface forcing patterns but struggles to cope with temperatures far outside
its training manifold. Our results show the feasibility of using deep learning
for climate model parameterization. In a broader context, we anticipate that
data-driven Earth System Model development could play a key role in reducing
climate prediction uncertainty in the coming decade.Comment: View official PNAS version at https://doi.org/10.1073/pnas.181028611
Using Sequential Two-Part Focus Groups As A Supplemental Instrument For Student Course Evaluations
This paper describes the use of a sequential two-part focus group program designed to be a supplemental instrument for eliciting student suggestions to improve the teaching/learning process. The sequential two-part focus group program provides students with two opportunities to provide feedback and have that feedback both noted and mirrored back to them. Many of the student comments and suggestions elicited during the focus group sessions were positive and constructive. Moreover, use of the sequential two-part focus group program resulted in statistically significant improvement in student evaluations in all but one of the seven major sections of the Educational Testing Service’s SIR II, the primary instrument utilized to obtain the student course evaluations. The “Overall Evaluation” of the course, wherein students are asked to, “Rate the quality of instruction in this course as it contributed to your learning,” improved most significantly. 
Safety, the Preface Paradox and Possible Worlds Semantics
This paper contains an argument to the effect that possible worlds semantics renders
semantic knowledge impossible, no matter what ontological interpretation is given
to possible worlds. The essential contention made is that possible worlds semantic
knowledge is unsafe and this is shown by a parallel with the preface paradox
Everolimus plus exemestane in postmenopausal patients with HR(+) breast cancer: BOLERO-2 final progression-free survival analysis.
IntroductionEffective treatments for hormone-receptor-positive (HR(+)) breast cancer (BC) following relapse/progression on nonsteroidal aromatase inhibitor (NSAI) therapy are needed. Initial Breast Cancer Trials of OraL EveROlimus-2 (BOLERO-2) trial data demonstrated that everolimus and exemestane significantly prolonged progression-free survival (PFS) versus placebo plus exemestane alone in this patient population.MethodsBOLERO-2 is a phase 3, double-blind, randomized, international trial comparing everolimus (10 mg/day) plus exemestane (25 mg/day) versus placebo plus exemestane in postmenopausal women with HR(+) advanced BC with recurrence/progression during or after NSAIs. The primary endpoint was PFS by local investigator review, and was confirmed by independent central radiology review. Overall survival, response rate, and clinical benefit rate were secondary endpoints.ResultsFinal study results with median 18-month follow-up show that median PFS remained significantly longer with everolimus plus exemestane versus placebo plus exemestane [investigator review: 7.8 versus 3.2 months, respectively; hazard ratio = 0.45 (95% confidence interval 0.38-0.54); log-rank P < 0.0001; central review: 11.0 versus 4.1 months, respectively; hazard ratio = 0.38 (95% confidence interval 0.31-0.48); log-rank P < 0.0001] in the overall population and in all prospectively defined subgroups, including patients with visceral metastases, [corrected] and irrespective of age. The incidence and severity of adverse events were consistent with those reported at the interim analysis and in other everolimus trials.ConclusionThe addition of everolimus to exemestane markedly prolonged PFS in patients with HR(+) advanced BC with disease recurrence/progression following prior NSAIs. These results further support the use of everolimus plus exemestane in this patient population. ClinicalTrials.gov #NCT00863655
Zonally opposing shifts of the intertropical convergence zone in response to climate change
Future changes in the location of the intertropical convergence zone (ITCZ)
due to climate change are of high interest since they could substantially alter
precipitation patterns in the tropics and subtropics. Although models predict a
future narrowing of the ITCZ during the 21st century in response to climate
warming, uncertainties remain large regarding its future position, with most
past work focusing on the zonal-mean ITCZ shifts. Here we use projections from
27 state-of-the-art climate models (CMIP6) to investigate future changes in
ITCZ location as a function of longitude and season, in response to climate
warming. We document a robust zonally opposing response of the ITCZ, with a
northward shift over eastern Africa and the Indian Ocean, and a southward shift
in the eastern Pacific and Atlantic Ocean by 2100, for the SSP3-7.0 scenario.
Using a two-dimensional energetics framework, we find that the revealed ITCZ
response is consistent with future changes in the divergent atmospheric energy
transport over the tropics, and sector-mean shifts of the energy flux equator
(EFE). The changes in the EFE appear to be the result of zonally opposing
imbalances in the hemispheric atmospheric heating over the two sectors,
consisting of increases in atmospheric heating over Eurasia and cooling over
the Southern Ocean, which contrast with atmospheric cooling over the North
Atlantic Ocean due to a model-projected weakening of the Atlantic meridional
overturning circulation
Approximate Bayesian Computation: a nonparametric perspective
Approximate Bayesian Computation is a family of likelihood-free inference
techniques that are well-suited to models defined in terms of a stochastic
generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds
by computing summary statistics s_obs from the data and simulating summary
statistics for different values of the parameter theta. The posterior
distribution is then approximated by an estimator of the conditional density
g(theta|s_obs). In this paper, we derive the asymptotic bias and variance of
the standard estimators of the posterior distribution which are based on
rejection sampling and linear adjustment. Additionally, we introduce an
original estimator of the posterior distribution based on quadratic adjustment
and we show that its bias contains a fewer number of terms than the estimator
with linear adjustment. Although we find that the estimators with adjustment
are not universally superior to the estimator based on rejection sampling, we
find that they can achieve better performance when there is a nearly
homoscedastic relationship between the summary statistics and the parameter of
interest. To make this relationship as homoscedastic as possible, we propose to
use transformations of the summary statistics. In different examples borrowed
from the population genetics and epidemiological literature, we show the
potential of the methods with adjustment and of the transformations of the
summary statistics. Supplemental materials containing the details of the proofs
are available online
ACE: A fast, skillful learned global atmospheric model for climate prediction
Existing ML-based atmospheric models are not suitable for climate prediction,
which requires long-term stability and physical consistency. We present ACE
(AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning
emulator of an existing comprehensive 100-km resolution global atmospheric
model. The formulation of ACE allows evaluation of physical laws such as the
conservation of mass and moisture. The emulator is stable for 100 years, nearly
conserves column moisture without explicit constraints and faithfully
reproduces the reference model's climate, outperforming a challenging baseline
on over 90% of tracked variables. ACE requires nearly 100x less wall clock time
and is 100x more energy efficient than the reference model using typically
available resources. Without fine-tuning, ACE can stably generalize to a
previously unseen historical sea surface temperature dataset.Comment: Accepted at Tackling Climate Change with Machine Learning: workshop
at NeurIPS 202
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