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See detailHey, lass uns das zusammen machen!
Feist, Peter; Dusdal, Jennifer UL

Article for general public (2020)

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See detailEmpowering Large Chemical Knowledge Bases for Exposomics: PubChemLite Meets MetFrag
Schymanski, Emma UL; Kondic, Todor UL; Neumann, Steffen et al

E-print/Working paper (2020)

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See detailSeparation of Concerns Within Robotic Systems Through Proactive Computing
Frantz, Alexandre; Zampunieris, Denis UL

in Proceeding of the 4th IEEE International Conference on Robotic Computing (2020, November)

In this short paper, we first introduce a possible new model for designing and implementing software in robotic systems. This model is based on proactive scenarios, coded through dynamic sets of condition ... [more ▼]

In this short paper, we first introduce a possible new model for designing and implementing software in robotic systems. This model is based on proactive scenarios, coded through dynamic sets of condition-action rules. Each scenario embeds the required rules and can be assembled dynamically with others, allowing the proactive system to achieve a unique objective or behavior and instruct the robot accordingly. Furthermore, a scenario is not aware of the existence of the other scenarios. In fact, it only contains information about a predefined central scenario, which oversees global decision making. In addition, each scenario knows where to enter its suggestions, thus allowing for a high degree in terms of separating concerns and modularity of code. Consequently, allowing easier development, testing and optimization of each scenario independently, possible reuse in different robots, and finally, a faster achievement of robust and scalable robotics software. We then show how to apply this programming model and its functionalities during runtime, by a proof of concept consisting of a virtual robot deployed in the Webots™ simulator. This simulator is controlled with four proactive scenarios (plus the central one), in charge of three different objectives. [less ▲]

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See detailMood induced changes in the cortical processing of food images in bulimia nervosa
Lutz, Annika UL; Dierolf, Angelika; van Dyck, Zoé UL et al

in Addictive Behaviors (2020)

Background Negative mood often triggers binge eating in bulimia nervosa (BN). We investigated motivational salience as a possible underlying mechanism using event-related potentials (ERPs) as indicators ... [more ▼]

Background Negative mood often triggers binge eating in bulimia nervosa (BN). We investigated motivational salience as a possible underlying mechanism using event-related potentials (ERPs) as indicators of motivated attention allocation (P300) and sustained processing (LPP). Methods We collected ERPs (P300: 350–400 ms; LPP: 600–1000 ms) from 21 women with full-syndrome or partially remitted BN and 21 healthy women (HC), matched for age and body mass index. Idiosyncratic negative and neutral situations were used to induce corresponding mood states (counterbalanced), before participants viewed images of high- and low-calorie foods and neutral objects, and provided ratings for pleasantness and desire to eat. Results P300 was larger for foods than objects; LPP was largest for high-calorie foods, followed by low-calorie foods, then objects. The BN group showed an increased desire to eat high-calorie foods under negative mood and stronger mood induction effects on ERPs than the HC group, with generally reduced P300 and a small increase in LPP for high-calorie foods. Effects were limited to circumscribed electrode positions. Exploratory analyses showed clearer effects when comparing high vs. low emotional eaters. Conclusion We argue that negative mood decreased the availability of cognitive resources (decreased P300) in BN, thereby facilitating disinhibition and food cravings (increased desire-to-eat ratings). Increased sustained processing might be linked to emotional eating tendencies rather than BN pathology per se, and reflect approach motivation, conflict, or regulatory processes. Negative mood appears to induce complex changes in food image processing, whose understanding may contribute to the development of tailored interventions in the future. [less ▲]

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