Communication plan for the European Energy Efficiency Platform

The European Commission Joint Research Centre (JRC) and in particular the Directorate C - Energy, Transport and Climate is providing support to the implementation of the European Energy Efficiency directive with special focus on contrasting the lack of comprehensive and coherent data in all the areas of energy efficiency. For this purpose, it has designed and developed the web platform E3P – European Energy Efficiency Platform, as a tool to facilitate knowledge exchange and to meet the needs of the online community of experts. In order to be fully exploited, the European Energy Efficiency Platform needs to be known, in the community of energy efficiency experts, but also in the community of energy efficiency stakeholders, and ultimately to the policy makers. The present report outlines a communication strategy for the E3P web platform, specifying which are the communication goals, what are the right means and how to use them to achieve these goals. A set of promotional activities are planned for a specific period of time and for defined targets of users of the platform. These activities can be developed in the next project steps, e.g. from the first 2018 semester. This strategy also delineates the mix of media as well as the content and clarifies which kind of messages the E3P web platform would like to communicate and how to do it.JRC.C.2-Energy Efficiency and Renewable

SoccER: Computer graphics meets sports analytics for soccer event recognition

Automatic event detection from images or wearable sensors is a fundamental step towards the development of advanced sport analytics and broadcasting software. However, the collection and annotation of large scale sport datasets is hindered by technical obstacles, cost of data acquisition and annotation, and commercial interests. In this paper, we present the Soccer Event Recognition (SoccER) data generator, which builds upon an existing, high quality open source game engine to enable synthetic data generation. The software generates detailed spatio-temporal data from simulated soccer games, along with fine-grained, automatically generated event ground truth. The SoccER software suite includes also a complete event detection system entirely developed and tested on a synthetic dataset including 500 minutes of game, and more than 1 million events. We close the paper by discussing avenues for future research in sports event recognition enabled by the use of synthetic data

PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning

Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or isOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common isOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples

Faster-LTN: a neuro-symbolic, end-to-end object detection architecture

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. This architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture.Comment: accepted for presentation at ICANN 202

CCDC6 (coiled-coil domain containing 6)

CCDC6 gene product is a pro-apoptotic protein substrate of ATM whose loss or inactivation enhances tumor progression. In primary tumors the impaired function of CCDC6 protein has been ascribed to CCDC6 rearrangements, to somatic mutations and to CCDC6 different levels in several neoplasia. The CCDC6 turnover is regulated in a cell cycle dependent manner upon post-translational modification events. The impairment of CCDC6 turnover may affect cells behaviour and drug response

Testing accuracy and repeatability of UAV blocks oriented with gnss-supported aerial triangulation

UAV Photogrammetry today already enjoys a largely automated and efficient data processing pipeline. However, the goal of dispensing with Ground Control Points looks closer, as dual-frequency GNSS receivers are put on board. This paper reports on the accuracy in object space obtained by GNSS-supported orientation of four photogrammetric blocks, acquired by a senseFly eBee RTK and all flown according to the same flight plan at 80 m above ground over a test field. Differential corrections were sent to the eBee from a nearby ground station. Block orientation has been performed with three software packages: PhotoScan, Pix4D and MicMac. The influence on the checkpoint errors of the precision given to the projection centers has been studied: in most cases, values in Z are critical. Without GCP, the RTK solution consistently achieves a RMSE of about 2-3 cm on the horizontal coordinates of checkpoints. In elevation, the RMSE varies from flight to flight, from 2 to 10 cm. Using at least one GCP, with all packages and all test flights, the geocoding accuracy of GNSS-supported orientation is almost as good as that of a traditional GCP orientation in XY and only slightly worse in Z

Total Anatomical Reconstruction during Robot-assisted Radical Prostatectomy: Implications on Early Recovery of Urinary Continence

Background: The introduction of robotics revolutionized prostate cancer surgery because the magnified three-dimensional vision system and wristed instruments allow microsurgery to be performed. The advantages of robotic surgery could lead to improved continence outcomes in terms of early recovery compared with the traditional surgical methods. Objective: To describe the total anatomical reconstruction (TAR) technique during robot-assisted radical prostatectomy (RARP). Primary endpoint: evaluation of the continence rate at different time points. Secondary endpoint: evaluation of urine leakage and anastomosis stenosis rates related to the technique. Design, setting, and participants: June, 2013 to November, 2014; prospective consecutive series of patients with localized prostate cancer (cT1-3, cN0, cM0). Surgical procedure: RARP with TAR was performed in all cases. Lymph node dissection was performed if the risk of lymph nodal metastasis was over 5%, according to the Briganti updated nomogram. Measurements: Preoperative, intraoperative, postoperative, and pathological variables were analyzed. Enrolled patients were arbitrarily divided into three groups according to a time criterion. The relationships between the learning curve and the trend of the above-mentioned variables were analyzed using LOESS analysis. Continence was rigorously analyzed preoperatively and at 24h, 1 wk, 4 wk, 12 wk, and 24 wk after catheter removal. Results and limitations: In total, 252 patients were analyzed. The continence rates immediately after catheter removal and at 1 wk, 4 wk, 12 wk, and 24 wk after RARP were 71.8%, 77.8%, 89.3%, 94.4%, and 98.0%, respectively. Multivariate analysis revealed that the nerve sparing technique, D'Amico risk groups, lymph node dissection, and prostate volume were involved in the early recovery of urinary continence. One ileal perforation requiring reoperation was recorded. The transfusion rate was 0.8%. Thirty-one (12.3%) postoperative complications were recorded up to 6 mo after surgery. Among these, eight acute urinary retentions (3.2%) and three urine leakages (1.2%) were recorded. There was a lack of randomization and comparison with other techniques. Both anatomical dissection of the prostatic apex and TAR were used. The results may not be generalized to low-volume centers. Conclusions: The TAR technique showed promising results in the early recovery of urinary continence, as well as watertight anastomosis and a low rate of urine leakage. The oncologic results were not affected. Comparative studies are needed to support the quality of reported results