Moon-tracking orbits using motorized tethers for continuous earth–moon payload exchanges

For human colonization of the moon to become reality, an efficient and regular means of exchanging resources between the Earth and the moon must be established. One possibility is to pass and receive payloads at regular intervals between a symmetrically laden motorized momentum-exchange tether orbiting about Earth and a second orbiting about the moon. There are significant challenges associated with this method, among the greatest of which is the development of a system that incorporates the complex motion of the moon into its operational architecture in addition to conducting these exchanges on a per-lunar-orbit basis. One way of achieving this is to use a motorized tether orbiting Earth and tracking the nodes of the moon’s orbit to allow payload exchanges to be undertaken periodically with the arrival of the moon at either of these nodes. Tracking these nodes is achieved by arranging the tether to orbit Earth with a critical inclination, thus rendering its argument of perigee stationary in addition to using the precession effects resulting from an oblate Earth. Using this in conjunction with pre-emptive adjustments to its angle of right ascension, the tether will periodically realign itself with these nodes simultaneously with the arrival of the moon

The evolution of representation in simple cognitive networks

Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether or not they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks---an artificial neural network and a network of hidden Markov gates---to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation, and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system, and should be predictive of an agent's long-term adaptive success.Comment: 36 pages, 10 figures, one Tabl

High temperature mobility of CdTe

The Hall mobility of electrons μH is measured in CdTe in the temperature interval 450-1050°C and defined Cd overpressure in near-intrinsic conditions. The strong decrease of μH above 600°C is reported. The effect is explained within a model of multivalley conduction where both electrons in �1c minimum and in L1c minima participate. The theoretical description is based on the solution of the Boltzmann transport equation within the relaxation time approximation including the polar and acoustic phonon intravalley and intervalley scatterings. The �1c to L1c separation �E=0.29 - 10-4T (eV) for the effective mass in the L valley mL=0.35m0 is found to best fit the experimental data. Such �E is about four times smaller than it is predicted by first-principle calculations. © 2001 American Institute of Physics

The Incremental Garbage Collection of Processes

Key Words and Phrases: garbage collection, multiprocessing systems, processor scheduling. "lazy evaluation, "eager" evaluation. CR Categories: 3.60, 3.80, 4.13, 4.22, 4.32. This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0522. This paper was presented at the AI*PL Conference at Rochester, N.Y. in August, 1977.This paper investigates some problems associated with an argument evaluation order that we call "future" order, which is different from both call-by-name and call-by-value. In call-by-future, each formal parameter of a function is bound to a separate process (called a "future") dedicated to the evaluation of the corresponding argument. This mechanism allows the fully parallel evaluation of arguments to a function, and has been shown to augment the expressive power of a language. We discuss an approach to a problem that arises in this context: futures which were thought to be relevant when they were created become irrelevant through being ignored in the body of the expression where they were bound. The problem of irrelevant processes also appears in multiprocessing problem-solving systems which start several processors working on the same problem but with different methods, and return with the solution which finishes first. This parallel method strategy has the drawback that the processes which are investigating the losing methods must be identified, stopped, and re-assigned to more useful tasks. The solution we propose is that of garbage collection. We propose that the goal structure of the solution plan be explicitly represented in memory as part of the graph memory (like Lisp's heap) so that a garbage collection algorithm can discover which processes are performing useful work, and which can be recycled for a new task. An incremental algorithm for the unified garbage collection of storage and processes is described.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc