62,690 research outputs found
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
In this paper, we present a compressed data structure for moving object
trajectories in a road network, which are represented as sequences of road
edges. Unlike existing compression methods for trajectories in a network, our
method supports pattern matching and decompression from an arbitrary position
while retaining a high compressibility with theoretical guarantees.
Specifically, our method is based on FM-index, a fast and compact data
structure for pattern matching. To enhance the compression, we incorporate the
sparsity of road networks into the data structure. In particular, we present
the novel concepts of relative movement labeling and PseudoRank, each
contributing to significant reductions in data size and query processing time.
Our theoretical analysis and experimental studies reveal the advantages of our
proposed method as compared to existing trajectory compression methods and
FM-index variants
Kinematics of Magnetic Bright Features in the Solar Photosphere
Convective flows are known as the prime means of transporting magnetic fields
on the solar surface. Thus, small magnetic structures are good tracers of the
turbulent flows. We study the migration and dispersal of magnetic bright
features (MBFs) in intergranular areas observed at high spatial resolution with
Sunrise/IMaX. We describe the flux dispersal of individual MBFs as a diffusion
process whose parameters are computed for various areas in the quiet Sun and
the vicinity of active regions from seeing-free data. We find that magnetic
concentrations are best described as random walkers close to network areas
(diffusion index, gamma=1.0), travelers with constant speeds over a
supergranule (gamma=1.9-2.0), and decelerating movers in the vicinity of flux
emergence and/or within active regions (gamma=1.4-1.5). The three types of
regions host MBFs with mean diffusion coefficients of 130 km^2/s, 80-90 km^2/s,
and 25-70 km^2/s, respectively. The MBFs in these three types of regions are
found to display a distinct kinematic behavior at a confidence level in excess
of 95%.Comment: 8 pages, 4 figure
Migration of Ca II H bright points in the internetwork
The migration of magnetic bright point-like features (MBP) in the lower solar
atmosphere reflects the dispersal of magnetic flux as well as the horizontal
flows of the atmospheric layer they are embedded in. We analyse trajectories of
the proper motion of intrinsically magnetic, isolated internetwork Ca II H MBPs
(mean lifetime 461 +- 9 s) to obtain their diffusivity behaviour. We use
seeing-free high spatial and temporal resolution image sequences of quiet-Sun,
disc-centre observations obtained in the Ca II H 3968 {\AA} passband of the
Sunrise Filter Imager (SuFI) onboard the Sunrise balloon-borne solar
observatory. Small MBPs in the internetwork are automatically tracked. The
trajectory of each MBP is then calculated and described by a diffusion index
({\gamma}) and a diffusion coefficient (D). We further explore the distribution
of the diffusion indices with the help of a Monte Carlo simulation. We find
{\gamma} = 1.69 +- 0.08 and D = 257 +- 32 km^2/s averaged over all MBPs.
Trajectories of most MBPs are classified as super-diffusive, i.e., {\gamma} >
1, with the determined {\gamma} being to our knowledge the largest obtained so
far. A direct correlation between D and time-scale ({\tau}) determined from
trajectories of all MBPs is also obtained. We discuss a simple scenario to
explain the diffusivity of the observed, relatively short-lived MBPs while they
migrate within a small area in a supergranule (i.e., an internetwork area). We
show that the scatter in the {\gamma} values obtained for individual MBPs is
due to their limited lifetimes. The super-diffusive MBPs can be well-described
as random walkers (due to granular evolution and intergranular turbu- lence)
superposed on a large systematic (background) velocity, caused by granular,
mesogranular and supergranular flows.Comment: 10 pages, 7 figures, 3 table
The complex network of global cargo ship movements
Transportation networks play a crucial role in human mobility, the exchange
of goods, and the spread of invasive species. With 90% of world trade carried
by sea, the global network of merchant ships provides one of the most important
modes of transportation. Here we use information about the itineraries of
16,363 cargo ships during the year 2007 to construct a network of links between
ports. We show that the network has several features which set it apart from
other transportation networks. In particular, most ships can be classified in
three categories: bulk dry carriers, container ships and oil tankers. These
three categories do not only differ in the ships' physical characteristics, but
also in their mobility patterns and networks. Container ships follow regularly
repeating paths whereas bulk dry carriers and oil tankers move less predictably
between ports. The network of all ship movements possesses a heavy-tailed
distribution for the connectivity of ports and for the loads transported on the
links with systematic differences between ship types. The data analyzed in this
paper improve current assumptions based on gravity models of ship movements, an
important step towards understanding patterns of global trade and bioinvasion.Comment: 7 figures Accepted for publication by Journal of the Royal Society
Interface (2010) For supplementary information, see
http://www.icbm.de/~blasius/publications.htm
Collective signal processing in cluster chemotaxis: roles of adaptation, amplification, and co-attraction in collective guidance
Single eukaryotic cells commonly sense and follow chemical gradients,
performing chemotaxis. Recent experiments and theories, however, show that even
when single cells do not chemotax, clusters of cells may, if their interactions
are regulated by the chemoattractant. We study this general mechanism of
"collective guidance" computationally with models that integrate stochastic
dynamics for individual cells with biochemical reactions within the cells, and
diffusion of chemical signals between the cells. We show that if clusters of
cells use the well-known local excitation, global inhibition (LEGI) mechanism
to sense chemoattractant gradients, the speed of the cell cluster becomes
non-monotonic in the cluster's size - clusters either larger or smaller than an
optimal size will have lower speed. We argue that the cell cluster speed is a
crucial readout of how the cluster processes chemotactic signal; both
amplification and adaptation will alter the behavior of cluster speed as a
function of size. We also show that, contrary to the assumptions of earlier
theories, collective guidance does not require persistent cell-cell contacts
and strong short range adhesion to function. If cell-cell adhesion is absent,
and the cluster cohesion is instead provided by a co-attraction mechanism, e.g.
chemotaxis toward a secreted molecule, collective guidance may still function.
However, new behaviors, such as cluster rotation, may also appear in this case.
Together, the combination of co-attraction and adaptation allows for collective
guidance that is robust to varying chemoattractant concentrations while not
requiring strong cell-cell adhesion.Comment: This article extends some results previously presented in
arXiv:1506.0669
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
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