The group of causal automorphisms

The group of causal automorphisms on Minkowski space-time is given and its structure is analyzed

Relationships between chlorophyll density and ocean radiance as measured by U2/OCS: Algorithms, examples and comparison

An ocean atmosphere radiative transfer process computation method which is suitable for determining lower boundary ocean albedo and other radiation components from spectral measurements of upwelling radiance taken from a high altitude platform is described. The method was applied to a set of color scanner data taken from slope water of the South Atlantic Bight to determine the influence of cholorophyll-a pigments in the sea on the ratio of upwelling radiance to down welling irradiance as a function of wavelength. The resulting chlorophyll concentrations are compared with measurements made by ships stationed along the flight path

Origin of synchronized traffic flow on highways and its dynamic phase transitions

We study the traffic flow on a highway with ramps through numerical simulations of a hydrodynamic traffic flow model. It is found that the presence of the external vehicle flux through ramps generates a new state of recurring humps (RH). This novel dynamic state is characterized by temporal oscillations of the vehicle density and velocity which are localized near ramps, and found to be the origin of the synchronized traffic flow reported recently [PRL 79, 4030 (1997)]. We also argue that the dynamic phase transitions between the free flow and the RH state can be interpreted as a subcritical Hopf bifurcation.Comment: 4 pages, source TeX file and 4 figures are tarred and compressed via uufile

The q-component static model : modeling social networks

We generalize the static model by assigning a q-component weight on each vertex. We first choose a component $(\mu)$ among the q components at random and a pair of vertices is linked with a color $\mu$ according to their weights of the component $(\mu)$ as in the static model. A (1-f) fraction of the entire edges is connected following this way. The remaining fraction f is added with (q+1)-th color as in the static model but using the maximum weights among the q components each individual has. This model is motivated by social networks. It exhibits similar topological features to real social networks in that: (i) the degree distribution has a highly skewed form, (ii) the diameter is as small as and (iii) the assortativity coefficient r is as positive and large as those in real social networks with r reaching a maximum around $f \approx 0.2$.Comment: 5 pages, 6 figure

Pentaquark Θ+\Theta^+ in nuclear matter and Θ+\Theta^+ hypernuclei

We study the properties of the $\Theta^+$ in nuclear matter and $\Theta^+$ hypernuclei within the quark mean-field (QMF) model, which has been successfully used for the description of ordinary nuclei and $\Lambda$ hypernuclei. With the assumption that the non-strange mesons couple only to the $u$ and $d$ quarks inside baryons, a sizable attractive potential of the $\Theta^+$ in nuclear matter is achieved as a consequence of the cancellation between the attractive scalar potential and the repulsive vector potential. We investigate the $\Theta^+$ single-particle energies in light, medium, and heavy nuclei. More bound states are obtained in $\Theta^+$ hypernuclei in comparison with those in $\Lambda$ hypernuclei.Comment: 16 pages, 5 figure

Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization

Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this paper we propose a measurement-based classification framework that exploits unsupervised learning to accurately categorise network anomalies to specific classes. We introduce the combinatorial use of two-class and multi-class unsupervised Support Vector Machines (SVM)s to first distinguish normal from anomalous traffic and to further classify the latter category to individual groups depending on the nature of the anomaly