Online Visual Robot Tracking and Identification using Deep LSTM Networks

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar

Location Dependency in Video Prediction

Deep convolutional neural networks are used to address many computer vision problems, including video prediction. The task of video prediction requires analyzing the video frames, temporally and spatially, and constructing a model of how the environment evolves. Convolutional neural networks are spatially invariant, though, which prevents them from modeling location-dependent patterns. In this work, the authors propose location-biased convolutional layers to overcome this limitation. The effectiveness of location bias is evaluated on two architectures: Video Ladder Network (VLN) and Convolutional redictive Gating Pyramid (Conv-PGP). The results indicate that encoding location-dependent features is crucial for the task of video prediction. Our proposed methods significantly outperform spatially invariant models.Comment: International Conference on Artificial Neural Networks. Springer, Cham, 201

Bank ownership and performance in the Middle East and North Africa region

Although both domestic and foreign private banks have gained ground in MENA in recent years, state banks continue to play an important role in many countries. Using a MENA bank-level panel dataset for the period 2001-08, the paper contributes to the empirical literature by documenting recent ownership trends and assessing the role of ownership and bank performance in MENA while accounting for key bank characteristics such as size and balance sheet composition. The paper analyzes headline performance indicators as well as their key drivers and finds that state banks exhibit significantly weaker performance, despite their larger size. This result is mainly driven by a larger holding of government securities, higher costs due to larger staffing numbers, and larger loan loss provisions reflecting weaker asset quality. The results reflect both operational inefficiencies and policy mandates. The paper also provides a detailed performance analysis of foreign and listed banks. Foreign banks are fairly new in MENA, yet perform on par with domestic banks despite their smaller size and higher investment costs. Listed banks exhibit superior performance driven by higher interest margins even in the face of higher costs associated with listing. Taken together, the results do not reject the development role for state banks, but do show that their intervention comes at a cost. As such, there is scope to reduce the share of state banks in some countries and to clarify the mandates, improve the governance, and strengthen the operational efficiency of most state banks in MENA.Banks&Banking Reform,Access to Finance,Debt Markets,Corporate Law,Bankruptcy and Resolution of Financial Distress

Extending a geo-catalogue with matching capabilities

To achieve semantic interoperability, geo-spatial applications need to be equipped with tools able to understand user terminology that is typically different from the one enforced by standards. In this paper we summarize our experience in providing a semantic extension to the geo-catalogue of the Autonomous Province of Trento (PAT) in Italy. The semantic extension is based on the adoption of the S-Match semantic matching tool and on the use of a specifically designed faceted ontology codifying domain specific knowledge. We also briefly report our experience in the integration of the ontology with the geo-spatial ontology GeoWordNet