Geometry-Based Next Frame Prediction from Monocular Video

We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along with the current video image and the camera trajectory, can then be used to compute the next frame. Unlike prior next-frame prediction approaches, we take advantage of the scene geometry and use the predicted depth for generating the next frame prediction. Our approach can produce rich next frame predictions which include depth information attached to each pixel. Another novel aspect of our approach is that it predicts depth from a sequence of images (e.g. in a video), rather than from a single still image. We evaluate the proposed approach on the KITTI dataset, a standard dataset for benchmarking tasks relevant to autonomous driving. The proposed method produces results which are visually and numerically superior to existing methods that directly predict the next frame. We show that the accuracy of depth prediction improves as more prior frames are considered.Comment: To appear in 2017 IEEE Intelligent Vehicles Symposiu

Zur Implementierung des DFU in der Praxis – Der fremdsprachliche Sachfachunterricht an der Deutschen Schule San José (Costa Rica)

In dem folgenden Beitrag geht es um das an der Deutschen Schule San José (Costa Rica) 2009 erfolgreich implementierte Konzept für „Deutsch als Fachunterricht“ (DFU). Im Mittelpunkt stehen die konkrete inhaltliche und methodische Ausgestaltung von DFU, erste erfolgversprechende Ansätze zur Unterrichtsentwicklung und Qualitätssicherung sowie der Einbezug neuer Medien

Zur Implementierung des DFU in der Praxis – Der fremdsprachliche Sachfachunterricht an der Deutschen Schule San José (Costa Rica)

In dem folgenden Beitrag geht es um das an der Deutschen Schule San José (Costa Rica) 2009 erfolgreich implementierte Konzept für „Deutsch als Fachunterricht“ (DFU). Im Mittelpunkt stehen die konkrete inhaltliche und methodische Ausgestaltung von DFU, erste erfolgversprechende Ansätze zur Unterrichtsentwicklung und Qualitätssicherung sowie der Einbezug neuer Medien.The following article deals with the successful implementation of a concept for „Content and Language Integrated Learning in German“ (CLILiG) at the German Humboldt-School in San José (Costa Rica) in 2009. It focusses on the concrete organisation of subjects taught in German with regards to content and method, first approaches to classroom-development and quality management as well as the integration of new media

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed.Comment: 8 pages, Appeared at KDD 2017, August 13--17, 2017, Halifax, NS, Canad

The implications of geopolitical, socioeconomic, and regulatory constraints on European bioenergy imports and associated greenhouse gas emissions to 2050

Modern sustainable bioenergy can contribute toward mid-century European energy decarbonization targets by replacing fossil fuels. Fulfilling this role would require access to increased volumes of bioenergy, with extra-EU imports projected to play an important part. Access to this resource on the international marketplace is not governed by Europe's economic competitiveness alone. This study investigates geopolitical, socioeconomic, and regulatory considerations that can influence Europe's bioenergy imports but that are so far underexplored. The effect of these constraints on European import volumes, sourcing regions, mitigation potential, and their implications for European and global emissions is projected to the year 2050 using a global integrated assessment model. The projections show that Europe can significantly increase imports from 1.5 EJ year−1 in 2020 to 8.1 EJ year−1 by 2050 whilst remaining compliant with Renewables Energy Directive recast II (RED II) greenhouse gas (GHG) criteria. Under these conditions, bioenergy could provide annual GHG mitigation of 0.44 GtCO2eq. in 2050. However, achieving this would require a structural diversification of trading partners from the present. Furthermore, socioeconomic and logistical concerns may limit the feasibility of some of the projected major sourcing regions, including Africa and South America. Failure to overcome these challenges within supplying regions could limit European imports by 60%, reducing annual mitigation to 0.16 GtCO2eq. in 2050. From a global perspective, regions with a comparatively carbon-intense energy system offer an alternative destination for globally traded biomass that could increase the mitigative potential of bioenergy

Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training

ABSTRACT: BACKGROUND: Manual body weight supported treadmill training and robot-aided treadmill training are frequently used techniques for the gait rehabilitation of individuals after stroke and spinal cord injury. Current evidence suggests that robot-aided gait training may be improved by making robotic behavior more patient-cooperative. In this study, we have investigated the immediate effects of patient-cooperative versus non-cooperative robot-aided gait training on individuals with incomplete spinal cord injury (iSCI). METHODS: Eleven patients with iSCI participated in a single training session with the gait rehabilitation robot Lokomat. The patients were exposed to four different training modes in random order: During both non-cooperative position control and compliant impedance control, fixed timing of movements was provided. During two variants of the patient-cooperative path control approach, free timing of movements was enabled and the robot provided only spatial guidance. The two variants of the path control approach differed in the amount of additional support, which was either individually adjusted or exaggerated. Joint angles and torques of the robot as well as muscle activity and heart rate of the patients were recorded. Kinematic variability, interaction torques, heart rate and muscle activity were compared between the different conditions. RESULTS: Patients showed more spatial and temporal kinematic variability, reduced interaction torques, a higher increase of heart rate and more muscle activity in the patient-cooperative path control mode with individually adjusted support than in the non-cooperative position control mode. In the compliant impedance control mode, spatial kinematic variability was increased and interaction torques were reduced, but temporal kinematic variability, heart rate and muscle activity were not significantly higher than in the position control mode. CONCLUSIONS: Patient-cooperative robot-aided gait training with free timing of movements made individuals with iSCI participate more actively and with larger kinematic variability than non-cooperative, position-controlled robot-aided gait training

Status and Prospects of Top-Quark Physics

The top quark is the heaviest elementary particle observed to date. Its large mass of about 173 GeV/c^2 makes the top quark act differently than other elementary fermions, as it decays before it hadronises, passing its spin information on to its decay products. In addition, the top quark plays an important role in higher-order loop corrections to standard model processes, which makes the top quark mass a crucial parameter for precision tests of the electroweak theory. The top quark is also a powerful probe for new phenomena beyond the standard model. During the time of discovery at the Tevatron in 1995 only a few properties of the top quark could be measured. In recent years, since the start of Tevatron Run II, the field of top-quark physics has changed and entered a precision era. This report summarises the latest measurements and studies of top-quark properties and gives prospects for future measurements at the Large Hadron Collider (LHC).Comment: 76 pages, 35 figures, submitted to Progress in Particle and Nuclear Physic

Measurement of the photon+b+b-jet production differential cross section in ppˉp\bar{p} collisions at \sqrt{s}=1.96~\TeV

We present measurements of the differential cross section dsigma/dpT_gamma for the inclusive production of a photon in association with a b-quark jet for photons with rapidities |y_gamma|< 1.0 and 30<pT_gamma <300 GeV, as well as for photons with 1.5<|y_gamma|< 2.5 and 30< pT_gamma <200 GeV, where pT_gamma is the photon transverse momentum. The b-quark jets are required to have pT>15 GeV and rapidity |y_jet| < 1.5. The results are based on data corresponding to an integrated luminosity of 8.7 fb^-1, recorded with the D0 detector at the Fermilab Tevatron $p\bar{p}$ Collider at sqrt(s)=1.96 TeV. The measured cross sections are compared with next-to-leading order perturbative QCD calculations using different sets of parton distribution functions as well as to predictions based on the kT-factorization QCD approach, and those from the Sherpa and Pythia Monte Carlo event generators.Comment: 10 pages, 9 figures, submitted to Phys. Lett.