Influence of the interaction potential on simulated sputtering and reflection data

The TRIM.SP program which is based on the binary collision approximation was changed to handle not only repulsive interaction potentials, but also potentials with an attractive part. Sputtering yields, average depth and reflection coefficients calculated with four different potentials are compared. Three purely repulsive potentials (Meliere, Kr-C and ZBL) are used and an ab initio pair potential, which is especially calculated for silicon bombardment by silicon. The general trends in the calculated results are similar for all potentials applied, but differences between the repulsive potentials and the ab initio potential occur for the reflection coefficients and the sputtering yield at large angles of incidence.

Biomolecular interaction analysis in functional proteomics

In der funktionellen Proteomforschung werden bekannte Interaktionen eines zellulären Netzwerkes qualitativ untersucht. Diese Veröffentlichung beschreibt verschiedene biomolekulare Interaktionsanalysen, anhand des Modellsystems PKA, die zur detaillierten Charakterisierung von Bindungen herangezogen werden können. Neben den Gleichgewichtsbindungsdaten (generiert aus AlphaScreen, FP, SPR und ITC Messungen) wurden aus ITC Messungen die thermodynamischen Parameter G, H und S ermittelt. Durch Anwendung der BRET2 (Bioluminescence resonance energy transfer) Methode konnten in lebenden Zellen Aussagen über Bindungsereignisse und deren Lokalisation getroffen werden.

Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting for measurement error. From the various specifications, Jöreskog and Yang's (1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

INFO2009 Framework for Critical Friends Interaction

Guidance document detailing the suggested process for the critical friend team interaction

WAIS Seminar:Evaluating the Cognitive Side of Information Interaction

Abstract In this talk, I'll focus on the work we've been doing on evaluating the cognitive side of dealing with information resources and increasingly complex user interfaces. While we can build increasingly powerful user interfaces, they often come at the cost of simple design and ease of use. I'll describe two specific studies: 1) work on the ORCHID project focused on measuring mental workload during tasks using fNIRS (a blood-oxygen-based brain scanner), and 2) a evaluation metric for measuring how much people learn during tasks. Together these provide advances towards understanding the cognitive side of information interaction, in working towards building better tools for users.

Interaction between Public Transportation and Urbanization in the Mexico City Metropolitan Area: an Expansive Model Reaching its Limits

In Metropolitan Area of Mexico City, most of urban displacements happen through semi formal public transportation: small and medium capacity vehicles operated by small private enterprises, through a concession scheme. This kind of public transportation has been playing a major role in the Mexican capital. On one hand, it has been one of the conditions for urbanization to be possible. On the other hand, despite its uncountable deficiencies, public transportation has allowed for a long time the whole population to be able to move within this huge metropolis. However, that important function with regards to integration has now reached its limits in the most recent suburbs of the city, where a new mode of urbanization is taking place, based on massive production of very big social housing gated settlements. Public transportation tends to constitute here a factor of exclusion and households meet with important difficulties for their daily mobility. 

Racial and spatial interaction for neighborhood dynamics in Chicago

We look at at the empirical validity of Schelling’s models for racial residential segregation applied to the case of Chicago. Most of the empirical literature has focused exclusively the single neighborhood model, also known as the tipping point model and neglected a multineighborhood approach or a unified approach. The multi-neighborhood approach introduced spatial interaction across the neighborhoods, in particular we look at spatial interaction across neighborhoods sharing a border. An initial exploration of the data indicates that spatial contiguity might be relevant to properly analyse the so call tipping phenomena of predominately non-Hispanic white neighborhoods to predominantly minority neighborhoods within a decade. We introduce an econometric model that combines an approach to estimate tipping point using threshold effects and a spatial autoregressive model. The estimation results from the model disputes the existence of a tipping point, that is a discontinuous change in the rate of growth of the non-Hispanic white population due to a small increase in the minority share of the neighborhood. In addition we find that racial distance between the neighborhood of interest and it surrounding neighborhoods has an important effect on the dynamics of racial segregation in Chicago.

The language used in early mother-child interaction by very-low-birth-weight children, with a focus on the emergence of grammar. 'El lenguaje utilizado por niños con muy bajo peso al nacer en la interacción temprana entre madre e hijo, con atención especial a la aparición de la gramática'.

Resumen basado en el de la publicación. Resumen en español