Housing price volatility and downsizing in later life

In this paper, we modeled several types of housing transitions of the elderly in two countries -- Britain and the United States. One important form of these transitions involves downsizing of housing consumption, the importance of which among older households is still debated. This downsizing takes multiple forms, including reductions in the number of rooms per dwelling and the value of the home. There is also evidence that this downsizing is greater when house price volatility is greater and that American households try to escape housing price volatility by moving to places that are experience significantly less housing price volatility. Our comparative evidence in suggests that there is less evidence of downsizing in Britain. Our results indicate that housing consumption appears to decline with age in the US, even after controlling for the other demographic and work transitions associated with age that would normally produce such a decline. No such fall in housing consumption is found in Britain, largely because British households are much more likely to stay in their original residence

Housing Price Volatility and Downsizing in Later Life

In this paper, we modeled several types of housing transitions of the elderly in two countries -- Britain and the United States. One important form of these transitions involves downsizing of housing consumption, the importance of which among older households is still debated. This downsizing takes multiple forms, including reductions in the number of rooms per dwelling and the value of the home. There is also evidence that this downsizing is greater when house price volatility is greater and that American households try to escape housing price volatility by moving to places that are experience significantly less housing price volatility. Our comparative evidence in suggests that there is less evidence of downsizing in Britain. Our results indicate that housing consumption appears to decline with age in the US, even after controlling for the other demographic and work transitions associated with age that would normally produce such a decline. No such fall in housing consumption is found in Britain, largely because British households are much more likely to stay in their original residence.

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Learning Behavior Models for Interpreting and Predicting Traffic Situations

In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees

A Survey on Policy Search for Robotics

Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning. We review recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. We classify model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and present a unified view on existing algorithms. Learning a policy is often easier than learning an accurate forward model, and, hence, model-free methods are more frequently used in practice. However, for each sampled trajectory, it is necessary to interact with the * Both authors contributed equally. robot, which can be time consuming and challenging in practice. Modelbased policy search addresses this problem by first learning a simulator of the robot’s dynamics from data. Subsequently, the simulator generates trajectories that are used for policy learning. For both modelfree and model-based policy search methods, we review their respective properties and their applicability to robotic systems

Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications

A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or predict this path. Such algorithms have wide applications in robotics, computer vision and video surveillance. Understanding the human behavior can provide useful information towards the design of these algorithms. Human trajectory forecasting algorithm is an interesting problem because the outcome is influenced by many factors, of which we believe that the destination, geometry of the environment, and the humans in it play a significant role. In addressing this problem, we propose a model to estimate the occupancy behavior of humans based on the geometry and behavioral norms. We also develop a trajectory forecasting algorithm that understands this occupancy and leverages it for trajectory forecasting in previously unseen geometries. The algorithm can be useful in a variety of applications. In this work, we show its utility in three applications, namely person re-identification, camera placement optimization, and human tracking. Experiments were performed with real world data and compared to state-of-the-art methods to assess the quality of the forecasting algorithm and the enhancement in the quality of the applications. Results obtained suggests a significant enhancement in the accuracy of trajectory forecasting and the computer vision applications.Computer Science, Department o