The proximity between newspapers and political parties is strongly subjective and difficult to measure. Yet, political tendencies of newspapers can have a significant impact on voters’ opinion‐forming and ought to be known by the public in a transparent and timely manner. This article introduces the Sentiment Political Compass (SPC), a data‐driven framework for analyzing political bias of newspapers toward political parties. Using the SPC, newspapers are embedded in a two‐dimensional space (left‐leaning vs. right‐leaning, libertarian vs. autocratic). To assess the informative value of our framework, we crawled a data set consisting of 180,000 newspaper articles from twenty‐five newspapers during the German Federal Elections over a time period of 18 months and extracted 740,000 political entities enriched with their contextual sentiment. We analyze this dataset on the party‐ and politician‐level as well as considering the temporal dimension and draw insights about the relationship between newspapers and political parties. We provide the data set and our code open‐source at http://www.politicalcompass.de to encourage the application of the SPC to other political landscapes.
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input viewwise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated scene model. However, there has so far been no attempt to quantitatively compare view-based and map-based labelling. Here, we present an experimental framework and comparison which uses real-time height map fusion as an accessible platform for a fair comparison, opening up the route to further systematic research in this area.
While robotics has made significant advances in perception, planning and control in recent decades, the vast majority of tasks easily completed by a human, especially acting in dynamic, unstructured environments, are far from being autonomously performed by a robot. Teleoperation, remotely controlling a slave robot by a human operator, can be a realistic, complementary transition solution that uses the motion intelligence of a human in complex tasks while exploiting the robot's autonomous reliability and precision in less challenging situations. We introduce DE VITO, a seven degree-of-freedom, dual-arm upperlimb exoskeleton that passively measures the pose of a human arm. DE VITO is a lightweight, simplistic and energy-efficient design with a total material cost of at least an order of magnitude less than previous work. Given the estimated human pose, we implement both joint and Cartesian space kinematic control algorithms and present qualitative experimental results on various complex manipulation tasks teleoperating Robot DE NIRO, a research platform for mobile manipulation, that demonstrate the functionality of DE VITO. We provide the CAD models, open-source code and supplementary videos of DE VITO at http://www.imperial.ac.uk/robot-intelligence/robots/de_vito/.
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