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

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

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