279 research outputs found
Statistical support for the ATL program
Statistical experimental designs are presented for various numbers of organisms and agar solutions pertinent to the experiment, ""colony growth in zero gravity''. Missions lasting 7 and 30 days are considered. For the designs listed, the statistical analysis of the observations obtained on the space shuttle are outlined
Upscaling the shallow water model with a novel roughness formulation
This study presents a novel roughness formulation
to conceptually account for microtopography
and compares it to four existing roughness models from
literature. The aim is to increase the grid size for computational
efficiency, while capturing subgrid scale effects
with the roughness formulation to prevent the loss
in accuracy associated with coarse grids. All roughness
approaches are implemented in the Hydroinformatics
Modeling System and compared with results of
a high resolution shallow water model in three test
cases: rainfall-runoff on an inclined plane with sinewave
shaped microtopography,
ow over an inclined
plane with random microtopography and rainfall-runoff
in a small natural catchment. Although the high resolution
results can not be reproduced exactly by the coarse
grid model, e.g. local details of
ow processes can not
be resolved, overall good agreement between the upscaled models and the high resolution model has been
achieved. The proposed roughness formulation generally
shows the best agreement of all compared models.
It is further concluded that the accuracy increases with
the number of calibration parameters available, however
the calibration process becomes more difficult. Using
coarser grids results in significant speedup in comparison
with the high resolution simulation. In the presented
test cases the speedup varies from 20 up to 2520,
depending on the size and complexity of the test case
and the difference in cell sizes.The authors thank the Alexander von
Humboldt-Foundation for the Humboldt Research Fellowship
granted to Dr. Dongfang Liang.This is the accepted manuscript. The final version is available at http://link.springer.com/article/10.1007%2Fs12665-015-4726-7
Artificial neural networks for 3D cell shape recognition from confocal images
We present a dual-stage neural network architecture for analyzing fine shape
details from microscopy recordings in 3D. The system, tested on red blood
cells, uses training data from both healthy donors and patients with a
congenital blood disease. Characteristic shape features are revealed from the
spherical harmonics spectrum of each cell and are automatically processed to
create a reproducible and unbiased shape recognition and classification for
diagnostic and theragnostic use.Comment: 17 pages, 8 figure
Model Integration and Coupling in A Hydroinformatics System
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Towards Business-to-IT Alignment in the Cloud
Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes
Reports of the AAAI 2019 spring symposium series
Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates
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