2,460 research outputs found

    SOM-VAE: Interpretable Discrete Representation Learning on Time Series

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    High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.Comment: Accepted for publication at the Seventh International Conference on Learning Representations (ICLR 2019

    On the Development of a Generic Multi-Sensor Fusion Framework for Robust Odometry Estimation

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    In this work we review the design choices, the mathematical and software engineering techniques employed in the development of the ROAMFREE sensor fusion library, a general, open-source framework for pose tracking and sensor parameter self-calibration in mobile robotics. In ROAMFREE, a comprehensive logical sensor library allows to abstract from the actual sensor hardware and processing while preserving model accuracy thanks to a rich set of calibration parameters, such as biases, gains, distortion matrices and geometric placement dimensions. The modular formulation of the sensor fusion problem, which is based on state-of-the-art factor graph inference techniques, allows to handle arbitrary number of multi-rate sensors and to adapt to virtually any kind of mobile robot platform, such as Ackerman steering vehicles, quadrotor unmanned aerial vehicles, omni-directional mobile robots. Different solvers are available to target high-rate online pose tracking tasks and offline accurate trajectory smoothing and parameter calibration. The modularity, versatility and out-of-the-box functioning of the resulting framework came at the cost of an increased complexity of the software architecture, with respect to an ad-hoc implementation of a platform dependent sensor fusion algorithm, and required careful design of abstraction layers and decoupling interfaces between solvers, state variables representations and sensor error models. However, we review how a high level, clean, C++/Python API, as long as ROS interface nodes, hide the complexity of sensor fusion tasks to the end user, making ROAMFREE an ideal choice for new, and existing, mobile robot projects

    Financial Services to the Unbanked: The Case of the Mzansi Intervention in South Africa

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    The Mzansi intervention is a major initiative designed to provide banking services to the unbanked South African population. This study investigates the underlying variables that define the choice of a Mzansi account from a consumer perspective. Unlike previous studies, we do not assume that demand for financial services is a given but instead that it is underlain by perceptions and attitudes. Financial attitudes and perceptions are found to exert significant effects on financial choices. In particular, aspirations and forward-looking values are instrumental in facilitating access to finance

    The velocity distribution of nearby stars from Hipparcos data I. The significance of the moving groups

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    We present a three-dimensional reconstruction of the velocity distribution of nearby stars (<~ 100 pc) using a maximum likelihood density estimation technique applied to the two-dimensional tangential velocities of stars. The underlying distribution is modeled as a mixture of Gaussian components. The algorithm reconstructs the error-deconvolved distribution function, even when the individual stars have unique error and missing-data properties. We apply this technique to the tangential velocity measurements from a kinematically unbiased sample of 11,865 main sequence stars observed by the Hipparcos satellite. We explore various methods for validating the complexity of the resulting velocity distribution function, including criteria based on Bayesian model selection and how accurately our reconstruction predicts the radial velocities of a sample of stars from the Geneva-Copenhagen survey (GCS). Using this very conservative external validation test based on the GCS, we find that there is little evidence for structure in the distribution function beyond the moving groups established prior to the Hipparcos mission. This is in sharp contrast with internal tests performed here and in previous analyses, which point consistently to maximal structure in the velocity distribution. We quantify the information content of the radial velocity measurements and find that the mean amount of new information gained from a radial velocity measurement of a single star is significant. This argues for complementary radial velocity surveys to upcoming astrometric surveys

    Sensor fusion for flexible human-portable building-scale mapping

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    This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system's ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment.Lincoln LaboratoryUnited States. Air Force (Contract FA8721-05-C-0002)United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020

    High-level environment representations for mobile robots

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    In most robotic applications we are faced with the problem of building a digital representation of the environment that allows the robot to autonomously complete its tasks. This internal representation can be used by the robot to plan a motion trajectory for its mobile base and/or end-effector. For most man-made environments we do not have a digital representation or it is inaccurate. Thus, the robot must have the capability of building it autonomously. This is done by integrating into an internal data structure incoming sensor measurements. For this purpose, a common solution consists in solving the Simultaneous Localization and Mapping (SLAM) problem. The map obtained by solving a SLAM problem is called ``metric'' and it describes the geometric structure of the environment. A metric map is typically made up of low-level primitives (like points or voxels). This means that even though it represents the shape of the objects in the robot workspace it lacks the information of which object a surface belongs to. Having an object-level representation of the environment has the advantage of augmenting the set of possible tasks that a robot may accomplish. To this end, in this thesis we focus on two aspects. We propose a formalism to represent in a uniform manner 3D scenes consisting of different geometric primitives, including points, lines and planes. Consequently, we derive a local registration and a global optimization algorithm that can exploit this representation for robust estimation. Furthermore, we present a Semantic Mapping system capable of building an \textit{object-based} map that can be used for complex task planning and execution. Our system exploits effective reconstruction and recognition techniques that require no a-priori information about the environment and can be used under general conditions
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