On Physical Scales of Dark Matter Halos

It is common practice to describe formal size and mass scales of dark matter halos as spherical overdensities with respect to an evolving density threshold. Here, we critically investigate the evolutionary effects of several such commonly used definitions and compare them to the halo evolution within fixed physical scales as well as to the evolution of other intrinsic physical properties of dark matter halos. It is shown that, in general, the traditional way of characterizing sizes and masses of halos dramatically overpredicts the degree of evolution in the last 10 Gyr, especially for low-mass halos. This pseudo-evolution leads to the illusion of growth even though there are no major changes within fixed physical scales. Such formal size definitions also serve as proxies for the virialized region of a halo in the literature. In general, those spherical overdensity scales do not coincide with the virialized region. A physically more precise nomenclature would be to simply characterize them by their very definition instead of calling such formal size and mass definitions 'virial'. In general, we find a discrepancy between the evolution of the underlying physical structure of dark matter halos seen in cosmological structure formation simulations and pseudo-evolving formal virial quantities. We question the importance of the role of formal virial quantities currently ubiquitously used in descriptions, models and relations that involve properties of dark matter structures. Concepts and relations based on pseudo-evolving formal virial quantities do not properly reflect the actual evolution of dark matter halos and lead to an inaccurate picture of the physical evolution of our universe.Comment: 17 pages, 14 figures, 1 table, ApJ accepte

Evolution of Parton Distributions

I present a highly efficient method for evolving parton distributions in perturbative QCD. The method allows evolving the parton distribution functions according to any of the commonly-used truncations of the evolution equations (which differ in their treatment of higher-order terms). I also give formul\ae\ for computing crossing functions within the method.Comment: 28 pages, TeX, "draft" notice delete

Managing evolution and change in web-based teaching and learning environments

The state of the art in information technology and educational technologies is evolving constantly. Courses taught are subject to constant change from organisational and subject-specific reasons. Evolution and change affect educators and developers of computer-based teaching and learning environments alike – both often being unprepared to respond effectively. A large number of educational systems are designed and developed without change and evolution in mind. We will present our approach to the design and maintenance of these systems in rapidly evolving environments and illustrate the consequences of evolution and change for these systems and for the educators and developers responsible for their implementation and deployment. We discuss various factors of change, illustrated by a Web-based virtual course, with the objective of raising an awareness of this issue of evolution and change in computer-supported teaching and learning environments. This discussion leads towards the establishment of a development and management framework for teaching and learning systems

Dynamic System Adaptation by Constraint Orchestration

For Paradigm models, evolution is just-in-time specified coordination conducted by a special reusable component McPal. Evolution can be treated consistently and on-the-fly through Paradigm's constraint orchestration, also for originally unforeseen evolution. UML-like diagrams visually supplement such migration, as is illustrated for the case of a critical section solution evolving into a pipeline architecture.Comment: 19 page

Dynamic Computation of Network Statistics via Updating Schema

In this paper we derive an updating scheme for calculating some important network statistics such as degree, clustering coefficient, etc., aiming at reduce the amount of computation needed to track the evolving behavior of large networks; and more importantly, to provide efficient methods for potential use of modeling the evolution of networks. Using the updating scheme, the network statistics can be computed and updated easily and much faster than re-calculating each time for large evolving networks. The update formula can also be used to determine which edge/node will lead to the extremal change of network statistics, providing a way of predicting or designing evolution rule of networks.Comment: 17 pages, 6 figure