Primary proton spectrum between 200 TeV and 1000 TeV observed with the Tibet burst detector and air shower array

Since 1996, a hybrid experiment consisting of the emulsion chamber and burst detector array and the Tibet-II air-shower array has been operated at Yangbajing (4300 m above sea level, 606 g/cm^2) in Tibet. This experiment can detect air-shower cores, called as burst events, accompanied by air showers in excess of about 100 TeV. We observed about 4300 burst events accompanied by air showers during 690 days of operation and selected 820 proton-induced events with its primary energy above 200 TeV using a neural network method. Using this data set, we obtained the energy spectrum of primary protons in the energy range from 200 to 1000 TeV. The differential energy spectrum obtained in this energy region can be fitted by a power law with the index of -2.97 $\pm$ 0.06, which is steeper than that obtained by direct measurements at lower energies. We also obtained the energy spectrum of helium nuclei at particle energies around 1000 TeV.Comment: 25 pages, 22 figures, Accepted for publication in Phys. Rev.

Adaptive Model Selection for Digital Linear Classifiers

Adaptive model selection can be defined as the process thanks to which an optimal classifiers h* is automatically selected from a function class H by using only a given set of examples z. Such a process is particularly critic when the number of examples in z is low, because it is impossible the classical splitting of z in training + test + validation. In this work we show that the joined investigation of two bounds of the prediction error of the classifier can be useful to select h* by using z for both model selection and training. Our learning algorithm is a simple kernel-based Perceptron that can be easily implemented in a counter-based digital hardware. Experiments on two real world data sets show the validity of the proposed method

The Smart Library Architecture of an Orientation Portal

artificial neural networks, competences analysis, e-Learning, knowledge management,

Knowledge Representation Requirements for Intelligent Tutoring Systems

Abstract. In this paper, we make a first effort to define requirements for knowledge representation (KR) in an ITS. The requirements concern all stages of an ITS’s life cycle (construction, operation and maintenance), all types of users (experts, engineers, learners) and all its modules (domain knowledge, user model, pedagogical model). We also briefly present and compare various KR formalisms used (or that could be used) in ITSs as far as the specified KR requirements are concerned. It appears that various hybrid approaches to knowledge representation can satisfy the requirements in a greater degree than that of single representations. Another finding is that there is not a hybrid formalism that can satisfy the requirements of all of the modules of an ITS, but each one individually. So, a multi-paradigm representation environment could provide a solution to requirements satisfaction.