10,876 research outputs found
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
HI Observations Towards the Sagittarius Dwarf Spheroidal Galaxy
We have measured the 21-cm line of Galactic HI over more than 50 square
degrees in the direction of the Sagittarius dwarf spheroidal galaxy. The data
show no evidence of HI associated with the dwarf spheroidal which might be
consider analogous to the Magellanic Stream as it is associated in both
position and velocity with the Large Magellanic Cloud. Nor do the HI data show
evidence for any disturbance in the Milky Way disk gas that can be
unambiguously assigned to interaction with the dwarf galaxy. The data shown
here limit the HI mass at the velocity of the Sagittarius dwarf to <7000 solar
masses over some 18 square degrees between Galactic latitudes -13 degrees and
-18 degrees.Comment: 5 pages, 4 figures; accepted for publication in Astronomy &
Astrophysic
Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?
Purpose: Quantitative analysis of CT scans has proven to be a reproducible technique, which might help to understand the pathophysiology of chronic obstructive pulmonary disease (COPD) and combined pulmonary fibrosis and emphysema. The aim of this retrospective study was to find out if the lung function of patients with COPD with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages III or IV and pulmonary emphysema is measurably influenced by high attenuation areas as a correlate of concomitant unspecific fibrotic changes of lung parenchyma.
Patients and methods: Eighty-eight patients with COPD GOLD stage III or IV underwent CT and pulmonary function tests. Quantitative CT analysis was performed to determine low attenuation volume (LAV) and high attenuation volume (HAV), which are considered to be equivalents of fibrotic (HAV) and emphysematous (LAV) changes of lung parenchyma. Both parameters were determined for the whole lung, as well as peripheral and central lung areas only. Multivariate regression analysis was used to correlate HAV with different parameters of lung function.
Results: Unlike LAV, HAV did not show significant correlation with parameters of lung function. Even in patients with a relatively high HAVof more than 10%, in contrast to HAV (p=0.786) only LAV showed a significantly negative correlation with forced expiratory volume in 1 second (r=−0.309, R2=0.096, p=0.003). A severe decrease of DLCO% was associated with both larger HAV (p=0.045) and larger LAV (p=0.001). Residual volume and FVC were not influenced by LAV or HAV.
Conclusion: In patients with COPD GOLD stage III-IV, emphysematous changes of lung parenchyma seem to have such a strong influence on lung function, which is a possible effect of concomitant unspecific fibrosis is overwhelmed
LASAGNE: Locality And Structure Aware Graph Node Embedding
In this work we propose Lasagne, a methodology to learn locality and
structure aware graph node embeddings in an unsupervised way. In particular, we
show that the performance of existing random-walk based approaches depends
strongly on the structural properties of the graph, e.g., the size of the
graph, whether the graph has a flat or upward-sloping Network Community Profile
(NCP), whether the graph is expander-like, whether the classes of interest are
more k-core-like or more peripheral, etc. For larger graphs with flat NCPs that
are strongly expander-like, existing methods lead to random walks that expand
rapidly, touching many dissimilar nodes, thereby leading to lower-quality
vector representations that are less useful for downstream tasks. Rather than
relying on global random walks or neighbors within fixed hop distances, Lasagne
exploits strongly local Approximate Personalized PageRank stationary
distributions to more precisely engineer local information into node
embeddings. This leads, in particular, to more meaningful and more useful
vector representations of nodes in poorly-structured graphs. We show that
Lasagne leads to significant improvement in downstream multi-label
classification for larger graphs with flat NCPs, that it is comparable for
smaller graphs with upward-sloping NCPs, and that is comparable to existing
methods for link prediction tasks
Drude weight fluctuations in many-body localized systems
We numerically investigate the distribution of Drude weights of many-body
states in disordered one-dimensional interacting electron systems across the
transition to a many-body localized phase. Drude weights are proportional to
the spectral curvatures induced by magnetic fluxes in mesoscopic rings. They
offer a method to relate the transition to the many-body localized phase to
transport properties. In the delocalized regime, we find that the Drude weight
distribution at a fixed disorder configuration agrees well with the
random-matrix-theory prediction , although
the distribution width strongly fluctuates between disorder
realizations. A crossover is observed towards a distribution with different
large- asymptotics deep in the many-body localized phase, which however
differs from the commonly expected Cauchy distribution. We show that the
average distribution width , rescaled by ,
being the average level spacing in the middle of the spectrum and
the systems size, is an efficient probe of the many-body localization
transition, as it increases/vanishes exponentially in the delocalized/localized
phase.Comment: 5 pages, 3 figures + 1 page Supplemental Material, 2 figure
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