An improved method for mobility prediction using a Markov model and density estimation

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination

Identifying atypical travel patterns for improved medium-term mobility prediction

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDuring the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays’ order delivers no reliable indication for the next day’s travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework’s real-world applicability. We show that clustering an individual’s historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.University of Exete

Better, worse, or different than expected: on the role of value and identity prediction errors in fear memory reactivation

Although reconsolidation-based interventions constitute a promising new avenue to treating fear and anxieties disorders, the success of the intervention is not guaranteed. The initiation of memory reconsolidation is dependent on whether a mismatch between the experienced and predicted outcome-a prediction error (PE)-occurs during fear memory reactivation. It remains, however, elusive whether any type of PE renders fear memories susceptible to reconsolidation disruption. Here, we investigated whether a value PE, elicited by an outcome that is better or worse than expected, is necessary to make fear memories susceptible to reconsolidation disruption or whether a model-based identity PE, i.e., a PE elicited by an outcome equally aversive but different than expected, would be sufficient. Blocking beta-adrenergic receptors with propranolol HCl after reactivation did, however, not reduce the expression of fear after either type of PE. Instead, we observed intact fear memory expression 24 h after reactivation in the value-, identity- and a no-PE control group. The present results do not corroborate our earlier findings of reconsolidation disruption and point towards challenges that the field is currently facing in observing evidence for memory reconsolidation at all. We provide potential explanations for the unexpected failure of replicating reconsolidation disruption and discuss future directions

Fear expression is suppressed by tyrosine administration

Animal studies have demonstrated that catecholamines regulate several aspects of fear conditioning. In humans, however, pharmacological manipulations of the catecholaminergic system have been scarce, and their primary focus has been to interfering with catecholaminergic activity after fear acquisition or expression had taken place, using L-Dopa, primarily, as catecholaminergic precursor. Here, we sought to determine if putative increases in presynaptic dopamine and norepinephrine by tyrosine administered before conditioning could affect fear expression. Electrodermal activity (EDA) of 46 healthy participants (24 placebo, 22 tyrosine) was measured in a fear instructed task. Results showed that tyrosine abolished fear expression compared to placebo. Importantly, tyrosine did not affect EDA responses to the aversive stimulus (UCS) or alter participants' mood. Therefore, the effect of tyrosine on fear expression cannot be attributed to these factors. Taken together, these findings provide evidence that the catecholaminergic system influences fear expression in humans

Better, worse, or different than expected - On the role of value and identity prediction errors in fear memory reactivation

Although reconsolidation-based interventions constitute a promising new avenue to treating fear and anxieties disorders, the success of the intervention is not guaranteed. The initiation of memory reconsolidation is dependent on whether a mismatch between the experienced and predicted outcome – a prediction error (PE) – occurs during fear memory reactivation. It remains, however, elusive whether any type of PE renders fear memories susceptible to reconsolidation disruption. Here, we investigated whether a value PE, elicited by an outcome that is better or worse than expected, is necessary to make fear memories susceptible to reconsolidation disruption or whether a model-based identity PE, i.e., a PE elicited by an outcome equally aversive but different than expected, would be sufficient. Blocking beta-adrenergic receptors with propranolol HCl after reactivation did, however, not reduce the expression of fear after either type of PE. Instead, we observed intact fear memory expression 24h after reactivation in the value-, identity- and a no-PE control group. The present results do not corroborate our earlier findings of reconsolidation disruption and point towards challenges that the field is currently facing in observing evidence for memory reconsolidation at all. We provide potential explanations for the unexpected failure of replicating reconsolidation disruption and discuss future directions

Times have changed. Using a Pictorial Smartphone App to Collect Time Use Data in Rural Zambia.

One challenge of collecting socioeconomic data, such as data on time use, is recall biases. While time use researchers have continuously developed new methods to make data collection more accurate and easy, these methods are difficult to use in developing countries where study participants may have low literacy levels and no clock -based concepts of time. To contribute to the closing of this research gap, we developed a picture-based smartphone-app called Time-Tracker that allows the recording of data in real time to avoid recall biases. We pilot-tested the app in rural Zambia, collecting 2790 data days. In this paper, we compare the data recorded with the app to data collected with 24-hours-recall-questions. The results confirm the literature on recall biases, suggesting that using the app leads to valid results. We conclude that smartphone-apps using visual tools provide new opportunities for researchers collecting socioeconomic data in developing countries. Acknowledgement : We are especially grateful to all the farm families participating in the study. We are also grateful for the financial support from the Program of Accompanying Research for Agricultural Innovation , which is funded by the German Federal Ministry of Economic Cooperation and Development

Better, worse, or different than expected - On the role of value and identity prediction errors in fear memory reactivation

Although reconsolidation-based interventions constitute a promising new avenue to treating fear and anxieties disorders, the success of the intervention is not guaranteed. The initiation of memory reconsolidation is dependent on whether a mismatch between the experienced and predicted outcome – a prediction error (PE) – occurs during fear memory reactivation. It remains, however, elusive whether any type of PE renders fear memories susceptible to reconsolidation disruption. Here, we investigated whether a value PE, elicited by an outcome that is better or worse than expected, is necessary to make fear memories susceptible to reconsolidation disruption or whether a model-based identity PE, i.e., a PE elicited by an outcome equally aversive but different than expected, would be sufficient. Blocking beta-adrenergic receptors with propranolol HCl after reactivation did, however, not reduce the expression of fear after either type of PE. Instead, we observed intact fear memory expression 24h after reactivation in the value-, identity- and a no-PE control group. The present results do not corroborate our earlier findings of reconsolidation disruption and point towards challenges that the field is currently facing in observing evidence for memory reconsolidation at all. We provide potential explanations for the unexpected failure of replicating reconsolidation disruption and discuss future directions

A Context-Based Design Process for Future Use Cases of Autonomous Driving: Prototyping AutoGym

Autonomous cars are on the horizon, meaning passengers will no longer have to focus on driving leaving them with extra time for other activities, or engagements. However, research has focused primarily on safety related aspects of autonomous driving, overlooking the need to design for this new free time. This raises the question, how do we design new interactive experiences for the future of autonomous cars? In this paper, we present a design process derived from our research-through-design approach to explore possible everyday use cases of autonomous driving from an experience-focused perspective. We report details of the four methods that constituted, and influenced our design process and led to the creation of AutoGym, an exertion interface with context-based interactions suitable for future car-based commuting. The contribution is twofold: Foremost, our design process suggests guidelines on how to design and simulate future use cases of what we assume will constitute the autonomous driving experience. Secondly, we aim to inspire automotive user experience designers to pursue a context-based design approach by leveraging situational features which support experiences that are tailored and unique to autonomous driving