Detection of a Cosmic Ray with Measured Energy Well Beyond the Expected Spectral Cutoff due to Cosmic Microwave Radiation

We report the detection of a 51-joule (320 +/- 90 EeV) cosmic ray by the Fly's Eye air shower detector in Utah. This is substantially greater than the energy of any previously reported cosmic ray. A Greisen-Zatsepin-Kuz'min cutoff of the energy spectrum (due to pion photoproduction energy losses) should occur below this energy unless the highest energy cosmic rays have traveled less than about 30 Mpc. The error box for the arrival direction in galactic coordinates is centered on b=9.6 deg, l=163.4 deg. The particle cascade reached a maximum size near a depth of 815 g/cm^2 in the atmosphere, a depth which does not uniquely identify the type of primary particle.Comment: uuencoded compressed postscript, 20 pages, to appear in ApJ (3/1/95

Matter Creation via Vacuum Fluctuations in the Early Universe and Observed Ultra-High Energy Cosmic Ray Events

Cosmic rays of the highest energy, above the Greisen-Zatsepin-Kuzmin cut-off of the spectrum, may originate in decays of superheavy long-living X-particles. These particles may be produced in the early Universe from vacuum fluctuations during or after inflation and may constitute a considerable fraction of Cold Dark Matter. We calculate numerically their abundance for a wide range of models. X-particles are considered to be either bosons or fermions. Particles that are several times heavier than inflaton, m_inflaton \approx 10^{13} GeV, and were produced by this mechanism, can account for the critical mass in the Universe naturally. In some cases induced isocurvature density fluctuations can leave an imprint in anisotropy of cosmic microwave background radiation.Comment: LaTeX, 9 page

Autocatalytic endogenous reflective architecture

The document describes the architecture of the system being developed in the project. The principal contribution of this work is to the engineering of autonomous systems in a general fashion, i.e. independently of the target domain. This work is not “biologically inspired” and contributing to modeling natural intelligences is not an objective of the project. The main challenge set for the system is to adapt in dynamic open-ended environments with insufficient knowledge and limited resources. The system is to perform in real-time and extract knowledge from the domain it operates in. In particular, this means discovering meaningful states in the environment and learning skills by observing intentional agents in the domain. No system facing the complexity of the real world is able to learn effectively and efficiently from scratch. For each domain our system is plunged in, we hand craft a bootstrap code (called the Masterplan) that consists of the necessary initial and minimal knowledge to observe and act in the domain. The system is model-based and model-driven, meaning that its architecture is unified and consists essentially of dynamic hierarchies of models that capture knowledge in an executable form. The system is thus composed of executable models. From this perspective, learning translates into building models, integrating them into the existing hierarchies and revising them continuously. This perpetual rearranging of the internal agency of the system addresses the first objective of the HUMANOBS project: to design an auto-reconfigurable architecture. Learning is based on model building, which in turn is driven by goals and predictions, i.e. the evaluation by the system of the observed phenomenology of the domain. In other words, the system infers what it shall do (specification) and observes ways to reach these goals (implementation). In that regard, this addresses the second objective of the project: to build a system that can auto-generate specifications for skills and behaviors based on their observation. Learned specifications and implementations are highly context-dependent, which raises the challenge of identifying when to reuse (or refrain from reusing) learned models. Specifications and implementations are built hierarchically, which means that the system is able to reuse previously learned skills, however said skills are by design executable in parallel: this raises the challenge of coordinating (or sequentializing) the operation of the models that implement said skills. The architecture has been designed from the onset to solve these issues, and this addresses the last objective of the project: to build behavior generation and coordination mechanisms for the reproduction and reuse of observed skills. The architecture has been designed in a principled way (each part of the architecture is based on the same architectural template), and organizes the cooperation of four continuous processes: Model Acquisition, Model Revision, Compaction (or model compression) and Reaction (reactive behavior in the domain). It is the interplay of said processes that not only ensure the viability of the whole system but also improves its performance. For example, acquiring models only requires a few examples, performs in real-time and is fast, but this process requires another process that revises models also rapidly and in real-time, and this in turn is supported by the reactive behavior of the system that pays attention only to meaningful entities and phenomena – which it has learned previously, the whole cycle having been bootstrapped by the Masterplan. This is in sharp contrast with traditional Machine Learning approaches which ignore the other cognitive processes of a system and thus, left on their own, require enormous quantities of training examples and cannot perform in real-time. A functional prototype has been developed as a proof of concept of the architecture, and the preliminary results reported in this deliverable strongly indicate that the architecture is sound and that our approach towards the engineering of autonomous systems is tractable and promising. 5/80 This prototype has been expanded, generalized and optimized furthermore into the final state of the architecture (with respect to the time frame of the project). The implemented final system satisfies the requirements of the project (although the evaluation results are presented in a separate deliverable) and, in particular, is completely domain-independent. Future developments and related research avenues have been identified and will bring the architecture beyond the requirements of this project. In the main, these developments are aimed at addressing the issues of (a) adding curiosity and imagination to the system's capabilities and, (b) controlling the autonomous bottom-up growth by means of top-down allonomic constraints