SaferDrive: an NLG-based Behaviour Change Support System for Drivers

Despite the long history of Natural Language Generation (NLG) research, the potential for influencing real world behaviour through automatically generated texts has not received much attention. In this paper, we present SaferDrive, a behaviour change support system that uses NLG and telematic data in order to create weekly textual feedback for automobile drivers, which is delivered through a smartphone application. Usage-based car insurances use sensors to track driver behaviour. Although the data collected by such insurances could provide detailed feedback about the driving style, they are typically withheld from the driver and used only to calculate insurance premiums. SaferDrive instead provides detailed textual feedback about the driving style, with the intent to help drivers improve their driving habits. We evaluate the system with real drivers and report that the textual feedback generated by our system does have a positive influence on driving habits, especially with regard to speeding

The Farthest Known Supernova: Support for an Accelerating Universe and a Glimpse of the Epoch of Deceleration

We present photometric observations of an apparent Type Ia supernova (SN Ia) at a redshift of ~1.7, the farthest SN observed to date. SN 1997ff, was discovered in a repeat observation by the HST of the HDF-), and serendipitously monitored with NICMOS on HST throughout the GTO campaign. The SN type can be determined from the host galaxy type:an evolved, red elliptical lacking enough recent star formation to provide a significant population of core-collapse SNe. The class- ification is further supported by diagnostics available from the observed colors and temporal behavior of the SN, both of which match a typical SN Ia. The photo- metric record of the SN includes a dozen flux measurements in the I, J, and H bands spanning 35 days in the observed frame. The redshift derived from the SN photometry, z=1.7+/-0.1, is in excellent agreement with the redshift estimate of z=1.65+/-0.15 derived from the U_300,B_450,V_606,I_814,J_110,J_125,H_160, H_165,K_s photometry of the galaxy. Optical and near-infrared spectra of the host provide a very tentative spectroscopic redshift of 1.755. Fits to observations of the SN provide constraints for the redshift-distance relation of SNe~Ia and a powerful test of the current accelerating Universe hypothesis. The apparent SN brightness is consistent with that expected in the decelerating phase of the preferred cosmological model, Omega_M~1/3, Omega_Lambda~2/3. It is inconsistent with grey dust or simple luminosity evolution, candidate astro- physical effects which could mimic past evidence for an accelerating Universe from SNe Ia at z~0.5.We consider several sources of possible systematic error including lensing, SN misclassification, selection bias, and calibration errors. Currently, none of these effects appears likely to challenge our conclusions.Comment: Accepted to the Astrophysical Journal 38 pages, 15 figures, Pretty version available at http://icarus.stsci.edu/~stefano/ariess.tar.g

A Real-Time Machine Learning and Visualization Framework for Scientific Workflows

High-performance computing resources are currently widely used in science and engineering areas. Typical post-hoc approaches use persistent storage to save produced data from simulation, thus reading from storage to memory is required for data analysis tasks. For large-scale scientific simulations, such I/O operation will produce significant overhead. In-situ/in-transit approaches bypass I/O by accessing and processing in-memory simulation results directly, which suggests simulations and analysis applications should be more closely coupled. This paper constructs a flexible and extensible framework to connect scientific simulations with multi-steps machine learning processes and in-situ visualization tools, thus providing plugged-in analysis and visualization functionality over complex workflows at real time. A distributed simulation-time clustering method is proposed to detect anomalies from real turbulence flows

Inference of Traffic Regulations at Intersections Based on Trajectory Data

Zahlreiche moderne Lösungen im Bereich Autonomes Fahren greifen auf hochpräzises Kartenmaterial zurück. Neben anderen Informationen muss das Kartenmaterial solche über Verkehrsregeln enthalten. In dieser Arbeit wird eine Offline-Lösung für die Inferenz von Verkehrsregeln an Deutschen Kreuzungen entwickelt. Mithilfe dieser Lösung werden für jeden Fahrstreifen einer Kreuzung Klassifikationsentscheidungen für jede mögliche Zielrichtung, welche von diesem Fahrstreifen aus erreichbar ist, getroffen. Verkehrsregeln werden mithilfe von Hidden-Markov-Models repräsentiert und, basierend auf errechneten Likelihood-Werten, bestimmt. Die Modelle werden mithilfe künstlich erzeugter Trajektorien von Kreuzungsüberquerungen parametrisiert und evaluiert. Unter realen Umständen würden solche Daten opportunistisch und sensorgestützt von einer Fahrzeugflotte über einen längeren Zeitraum hinweg gesammelt werden. In einer Reihe von Experimenten wird eine geeignete Trajektorienrepräsentation festgelegt und der Klassifikationsansatz getestet und verfeinert. Die Klassifikationsperformanz des Ansatzes wird mithilfe eines Kreuzvalidierungsverfahren bestimmt. Mittlere F$_1$-Scores zur Quantifizierung der besten Ergebnisse unter den erzielten Testergebnissen variieren zwischen 0.809 und 0.832. Bezüglich der Verkehrsregeln, welche mithilfe von Vorfahrts- und Stoppschildern, sowie Lichtsignalanlagen kommuniziert werden, werden hohe Klassifikationsleistungen erreicht. Allerdings bestehen Schwierigkeiten bei der Klassifikation im Zusammenhang mit den Verkehrsregeln Vorfahrt achten und Rechts vor Links. Da die initial erzielten Ergebnisse vielversprechend sind, wird empfohlen diesen Ansatz in zukünftigen Arbeiten weiterzuentwickeln und zu verbessern

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom