This paper describes a study of the effect of a human interactive robot placed in an urban transit space. The underlying hypothesis is that it is possible to create interesting new living spaces and induce value in terms of experiences, information or economics, by putting socially interactive mobile agents into public urban transit area. To investigate the hypothesis, an experiment was carried out at a bus terminal serving both as a transit space and a shopping mall, where an autonomous robot were to detect and follow random people. The people that were followed were asked to fill out a questionnaire for quantitative analysis of the experiment. In addition video documentation of the experiment was used in the evaluation. The results showed that people were generally positive towards having mobile robots in this type of environment where shopping is combined with transit. However, it also showed harder than expected to start interaction with commuters due to their determination and speed towards their goal. Further it was demonstrated that it was possible to track and follow people, who were not beforehand informed on the experiment. The evaluation indicated, that the distance to initiate interaction was shorter than initially expected, but complies with the distance for normal human to human interaction.

We present the Populus toolkit for exploring the dynamics of mass action systems under different assumptions.Comment: In Proceedings HSB 2013, arXiv:1308.572

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Brownian Motion in Cellular Receptors Ouri Maler In microbiology, chemical receptors on cellular membranes play a key role in a number of important reactions, defining when cells perform given roles and switch behaviors. These receptors are not static; their movements across the cellular membrane have been noted for their similarity to random Brownian motion. At the same time, some of these receptors only fire when clustered. We wish to establish a mathematical model for this phenomenon and check the model's validity by comparing its results to experimental data. In part 1, we will be taking a closer look at the phenomenon being studied. In part 2, we will examine the proposed mathematical model, involving a combination of Brownian motion and asymmetrical obstacles. In part 3, we will explain in greater detail the goals we are striving toward with this project. In part 4, we will present the programs and methods used to simulate the model and analyze the resulting data. In part 5, we will look at our results to date. Finally, in part 6, we will explain our preliminary conclusions and our outlook from here on out.