1,251 research outputs found
Analysis, Visualization, and Transformation of Audio Signals Using Dictionary-based Methods
date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +0000date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +000
Cloud/climate sensitivity experiments
A study of the relationships between large-scale cloud fields and large scale circulation patterns is presented. The basic tool is a multi-level numerical model comprising conservation equations for temperature, water vapor and cloud water and appropriate parameterizations for evaporation, condensation, precipitation and radiative feedbacks. Incorporating an equation for cloud water in a large-scale model is somewhat novel and allows the formation and advection of clouds to be treated explicitly. The model is run on a two-dimensional, vertical-horizontal grid with constant winds. It is shown that cloud cover increases with decreased eddy vertical velocity, decreased horizontal advection, decreased atmospheric temperature, increased surface temperature, and decreased precipitation efficiency. The cloud field is found to be well correlated with the relative humidity field except at the highest levels. When radiative feedbacks are incorporated and the temperature increased by increasing CO2 content, cloud amounts decrease at upper-levels or equivalently cloud top height falls. This reduces the temperature response, especially at upper levels, compared with an experiment in which cloud cover is fixed
Similarity as a Window on the Dimensions of Object Representation
Hebart et al. recently analysed 1.5 million human similarity judgments and found that natural objects are described by a small set of interpretable dimensions. Such large-scale analyses offer new opportunities to characterise how people represent their knowledge, but also challenges, including scaling to even larger data sets and integrating accounts of semantic representation
GCIP water and energy budget synthesis (WEBS)
Journal ArticleAs part of the World Climate Research Program's (WCRPs) Global Energy and Water-Cycle Experiment (GEWEX) Continental-scale International Project (GCIP), a preliminary water and energy budget synthesis (WEBS) was developed for the period 1996-1999 from the "best available" observations and models
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
Advances in object recognition flourished in part because of the availability
of high-quality datasets and associated benchmarks. However, these
benchmarks---such as ILSVRC---are relatively task-specific, focusing
predominately on predicting class labels. We introduce a publicly-available
dataset that embodies the task-general capabilities of human perception and
reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ)
is composed of human similarity judgments that supplement the ILSVRC validation
set. The new dataset supports a range of task and performance metrics,
including the evaluation of unsupervised learning algorithms. We demonstrate
two methods of assessment: using the similarity judgments directly and using a
psychological embedding trained on the similarity judgments. This embedding
space contains an order of magnitude more points (i.e., images) than previous
efforts based on human judgments. Scaling to the full 50,000 image set was made
possible through a selective sampling process that used variational Bayesian
inference and model ensembles to sample aspects of the embedding space that
were most uncertain. This methodological innovation not only enables scaling,
but should also improve the quality of solutions by focusing sampling where it
is needed. To demonstrate the utility of ImageNet-HSJ, we used the similarity
ratings and the embedding space to evaluate how well several popular models
conform to human similarity judgments. One finding is that more complex models
that perform better on task-specific benchmarks do not better conform to human
semantic judgments. In addition to the human similarity judgments, pre-trained
psychological embeddings and code for inferring variational embeddings are made
publicly available. Collectively, ImageNet-HSJ assets support the appraisal of
internal representations and the development of more human-like models
Role of boundary and initial conditions for dynamical seasonal predictability
Journal ArticleThe importance of initial state and boundary forcing for atmospheric predictability is explored on global to regional spatial scales and on daily to seasonal time scales. A general circulation model is used to conduct predictability experiments with different combinations of initial and boundary conditions. The experiments are verified under perfect model assumptions as well as against observational data. From initial conditions alone, there is significant instantaneous forecast skill out to 2 months. Different initial conditions show different predictability using the same kind of boundary forcing. Even on seasonal time scales, using observed atmospheric initial conditions leads to a substantial increase in overall skill, especially during periods with weak tropical forcing. The impact of boundary forcing on predictability is detectable after 10 days and leads to measurable instantaneous forecast skill at very long lead times. Over the Northern Hemisphere, it takes roughly 4 weeks for boundary conditions to reach the same effect on predictability as initial conditions. During events with strong tropical forcing, these time scales are somewhat shorter. Over the Southern Hemisphere, there is a strongly enhanced influence of initial conditions during summer. We conclude that the long term memory of initial conditions is important for seasonal forecasting
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