Toward a Smart EU Energy Policy: Rationale and 22 Recommendations

We are in desperate need of an EU Energy Policy. The facts are that, yes, there is indeed an EU Energy Policy. It is a policy based on a vision, a vision with three components. The policy is aiming for “markets, competition and efficiency”, it is equally focussing on “a sustainable energy economy”, and thirdly, it wants to “secure the EU’s energy supply”. Three objectives, three separate action lines. Balancing the three objectives in an integrated approach is challenging and difficult. To what extent is the market approach consistent with the other two policy packages? What impact does a climate package with tradable emission rights and non-tradable targets for green energy have on the market designs for gas and electricity? Are the necessary investments in new pipes and wires for securing our energy supplies sufficiently coming under the prevailing regulatory framework? Or, to put it differently; are we smart enough in the way in which we are making implementing steps in order to meet our stated objectives? Our paper ends with a proposed new vision and a set of 22 recommendations to the new European Commission.energy policy; climate change; security of energy supply; EU internal marke

Toward a Smart EU Energy Policy: Rationale and 22 Recommendation

QM-AI-10-003-EN-C (print)/QM-AI-10-003-EN-N (online)In the spring of 2007, the European Council agreed on a policy vision with three components: the green component (to promote a sustainable energy economy), the market component (to enhance efficiency and competition) and the security of supply component (to secure the EU’s energy supply). With regard to these three components, distinct implementing paths and action lines were developed. The existence of separate implementing paths entails some coordination issues. Coordination is necessary here to guarantee that the three action lines are integrated into a consistent EU Energy Policy. EU Energy policy needs to get smarter and align the incentives deriving from the three components to produce an integrated vision that moves beyond 2020. 22 policy recommendations can then be formulated for the most relevant energy-related issues which the EU is facing nowadays

L'évolution géodynamique de la chaîne paléozoïque du Tianshan

La chaîne du Tianshan s'étend sur plus de 3000 km en Asie centrale, elle sépare le Tarim au Sud du Juggar et du Kazakhstan au Nord (Fig. 1a). La collision indienne est responsable du haut-relief actuel, mais l'architecture de la chaîne est due à plusieurs événements d'âge Paléozoïque. Classiquement, la chaîne du Tianshan est divisée en Tianshan Nord, Tianshan Central, Tianshan Sud et Bloc de Yili (Fig. 1b). Ce dernier est souvent considéré comme l'extension occidentale du Tianshan Central, mais nos données structurales, géochimiques et paléomagnétiques suggèrent que ces domaines et leurs limites doivent être redéfinis

Energy policy: European, national, regional?

When it comes to energy policy, EU countries go their own way with little regard for other member states. What strategies exist in the EU Commission to coordinate and integrate energy markets? Are these strategies consistent with national plans currently in action? Is it too late to establish a unified energy policy? What can be achieved in a unified energy policy given the considerable differences in resource endowment and political preferences in energy strategies? Can the effectiveness of EU energy policy objectives be enhanced through policy coordination at the regional scale? This Forum seeks to provide answers to these questions

Palaeozoic tectonic evolution of the Tianshan belt, NW China

International audienceThe Chinese Tianshan belt is a major part of the southern Central Asian Orogenic Belt, extending westward to Kyrgyzstan and Kazakhstan. Its Paleozoic tectonic evolution, crucial for understanding the amalgamation of Central Asia, comprises two stages of subduction-collision. The first collisional stage built the Eo-Tianshan Mountains, before a Visean unconformity, in which all structures are verging north. It implied a southward subduction of the Central Tianshan Ocean beneath the Tarim active margin, that induced the Ordovician-Early Devonian Central Tianshan arc, to the south of which the South Tianshan back-arc basin opened. During the Late Devonian, the closure of this ocean led to a collision between Central Tianshan arc and the Kazakhstan-Yili-North Tianshan Block, and subsequently closure of the South Tianhan back-arc basin, producing two suture zones, namely the Central Tianshan and South Tianshan suture zones where ophiolitic mélanges and HP metamorphic rocks were emplaced northward. The second stage included the Late Devonian-Carboniferous southward subduction of North Tianshan Ocean beneath the Eo-Tianshan active margin, underlined by the Yili-North Tianshan arc, leading to the collision between the Kazakhstan-Yili-NTS plate and an inferred Junggar Block at Late Carboniferous-Early Permian time. The North Tianshan Suture Zone underlines likely the last oceanic closure of Central Asia Orogenic Belt; all the oceanic domains were consumed before the Middle Permian. The amalgamated units were affected by a Permian major wrenching, dextral in the Tianshan. The correlation with the Kazakh and Kyrgyz Tianshan is clarified. The Kyrgyz South Tianshan is equivalent to the whole part of Chinese Tianshan (CTS and STS) located to the south of Narat Fault and Main Tianshan Shear Zone; the so-called Middle Tianshan thins out toward the east. The South Tianshan Suture of Kyrgyzstan correlates with the Central Tianshan Suture of Chinese Tianshan. The evolution of this southern domain remains similar from east (Gangou area) to west until the Talas-Ferghana Fault, which reflects the convergence history between the Kazakhstan and Tarim blocks

Robustness evaluation of deep neural networks for endoscopic image analysis:Insights and strategies

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance loss.Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance los

Barrett's lesion detection using a minimal integer-based neural network for embedded systems integration

Embedded processing architectures are often integrated into devices to develop novel functions in a cost-effective medical system. In order to integrate neural networks in medical equipment, these models require specialized optimizations for preparing their integration in a high-efficiency and power-constrained environment. In this paper, we research the feasibility of quantized networks with limited memory for the detection of Barrett’s neoplasia. An Efficientnet-lite1+Deeplabv3 architecture is proposed, which is trained using a quantization-aware training scheme, in order to achieve an 8-bit integer-based model. The performance of the quantized model is comparable with float32 precision models. We show that the quantized model with only 5-MB memory is capable of reaching the same performance scores with 95% Area Under the Curve (AUC), compared to a fullprecision U-Net architecture, which is 10× larger. We have also optimized the segmentation head for efficiency and reduced the output to a resolution of 32×32 pixels. The results show that this resolution captures sufficient segmentation detail to reach a DICE score of 66.51%, which is comparable to the full floating-point model. The proposed lightweight approach also makes the model quite energy-efficient, since it can be real-time executed on a 2-Watt Coral Edge TPU. The obtained low power consumption of the lightweight Barrett’s esophagus neoplasia detection and segmentation system enables the direct integration into standard endoscopic equipment

A model for atomic and molecular interstellar gas: The Meudon PDR code

We present the revised ``Meudon'' model of Photon Dominated Region (PDR code), presently available on the web under the Gnu Public Licence at: http://aristote.obspm.fr/MIS. General organisation of the code is described down to a level that should allow most observers to use it as an interpretation tool with minimal help from our part. Two grids of models, one for low excitation diffuse clouds and one for dense highly illuminated clouds, are discussed, and some new results on PDR modelisation highlighted.Comment: accepted in ApJ sup

The Effect of FRAND Commitments on Patent Remedies

This chapter addresses a special category of cases in which an asserted patent is, or has been declared to be, essential to the implementation of a collaboratively-developed voluntary consensus standard, and the holder of that patent has agreed to license it to implementers of the standard on terms that are fair, reasonable and non-discriminatory (FRAND). In this chapter, we explore how the existence of such a FRAND commitment may affect a patent holder’s entitlement to monetary damages and injunctive relief. In addition to issues of patent law, remedies law and contract law, we consider the effect of competition law on this issue