Grid Integration of Robotic Telescopes

Robotic telescopes and grid technology have made significant progress in recent years. Both innovations offer important advantages over conventional technologies, particularly in combination with one another. Here, we introduce robotic telescopes used by the Astrophysical Institute Potsdam as ideal instruments for building a robotic telescope network. We also discuss the grid architecture and protocols facilitating the network integration that is being developed by the German AstroGrid-D project. Finally, we present three user interfaces employed for this purpose.Comment: 4 pages, 5 Figures, refereed proceedings of "Hot-wiring the Transient Universe", June 2007 (Tucson); version 2 including latex geometry package as recommended by arXiv and minor changes as requested by AN except removal of two figure

Galaxy classification: deep learning on the OTELO and COSMOS databases

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sersic index or the concentration index. Methods. We used three classification methods for the OTELO database: 1) u-r color separation , 2) linear discriminant analysis using u-r and a shape parameter classification, and 3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged dataComment: 20 pages, 10 tables, 14 figures, Astronomy and Astrophysics (in press

Goal-oriented hierarchical task networks and its application on interactive narrative planning

Two of the most commonly used AI architectures in digital games are Behavior Tree (BT) and Goal-Oriented Action Planning (GOAP). The BT architecture is script based, highly controllable but barely expandable. On the other hand the GOAP architecture is planner based, barely controllable but highly expandable. This thesis proposes a hybrid AI architecture called Goal-Oriented Hierarchical Task Network (GHTN); combining planner based approach of GOAP with script based approach of BT. GHTN modifies the Hierarchical Task Network (HTN) architecture by replacing its iterative planner with a goal oriented planner, while maintaining the BT-like scripting capabilities of HTN. GHTN's iterative-planner hybrid architecture is suitable to be used for Interactive Narrative Planning. Using GHTN with a previously crafted domain, it is possible to obtain a non-repetitive and continuous narrative flow which can also be directed by external goals. The user is presented with choices that are intelligently chosen to push the narrative towards the goal; then, depending on the answers new choices are generated. The initial state of the world and the goals are specified by a Scenarist who has the knowledge of the domain. The proposed architecture is tested on Interactive Narrative Planning task with an example domain set in the Lala Land universe, and the architecture is tested with several initial world states and goals

Neural Network Models for Stock Selection Based on Fundamental Analysis

Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS

The architecture of the protein domain universe

Understanding the design of the universe of protein structures may provide insights into protein evolution. We study the architecture of the protein domain universe, which has been found to poses peculiar scale-free properties (Dokholyan et al., Proc. Natl. Acad. Sci. USA 99: 14132-14136 (2002)). We examine the origin of these scale-free properties of the graph of protein domain structures (PDUG) and determine that that the PDUG is not modular, i.e. it does not consist of modules with uniform properties. Instead, we find the PDUG to be self-similar at all scales. We further characterize the PDUG architecture by studying the properties of the hub nodes that are responsible for the scale-free connectivity of the PDUG. We introduce a measure of the betweenness centrality of protein domains in the PDUG and find a power-law distribution of the betweenness centrality values. The scale-free distribution of hubs in the protein universe suggests that a set of specific statistical mechanics models, such as the self-organized criticality model, can potentially identify the principal driving forces of molecular evolution. We also find a gatekeeper protein domain, removal of which partitions the largest cluster into two large sub-clusters. We suggest that the loss of such gatekeeper protein domains in the course of evolution is responsible for the creation of new fold families.Comment: 14 pages, 3 figure

A New Theory of Consciousness: The Missing Link - Organization

What is consciousness and what is the missing link between the sensory input and the cortical centre in the brain for consciousness? In the literature there are more than a million pages written about consciousness. The perspectives range from the field of metaphysics to those of quantum mechanics. However, no one today has produced a theory which is universally accepted. Consciousness is “something” which the majority of humans know that they posses, they use it when they want to understand their environment. However, no individual human knows whether other humans also posses consciousness. unless some tests such as she is looking at me, he is talking etc., are performed. We are caught in an intellectual sort of recursive carousel – we need consciousness to understand consciousness. To understand consciousness we have to understand the mechanism of its function, which is to effectively organize sensory inputs from our environment. Consciousness is the outcome of the process of organizing these sensory inputs. This implies that organization is an act which precedes consciousness. Since every activity in nature is to organize/disorganize, what is the element which compels this action? I am proposing that just like energy is the physical element that causes action, there is another physical element I have called it NASCIUM which has the capacity to cause organization. This is the missing link. Understanding the nature of organization, i.e. nascium, will enhance our capability to understand consciousness