Energy aware hybrid flow shop scheduling

Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years

Structured Prediction for Object Detection in Deep Neural Networks

Abstract. Deep convolutional neural networks are currently applied to computer vision tasks, especially object detection. Due to the large di-mensionality of the output space, four dimensions per bounding box of an object, classification techniques do not apply easily. We propose to adapt a structured loss function for neural network training which di-rectly maximizes overlap of the prediction with ground truth bounding boxes. We show how this structured loss can be implemented efficiently, and demonstrate bounding box prediction on two of the Pascal VOC 2007 classes

Prognostische Bedeutung pränataler Befunde bei kongenitalen Anomalien der Nieren und Harnwege

Kongenitale Anomalien der Nieren und Harnwege zeigen oftmals bereits pränatal sonographische Auffälligkeiten, doch insbesondere bei der Harntrakterweiterung ist die prognostische Bedeutung pränataler Befunde in Diskussion. Es wurde die Aussagekraft des pränatalen anterior-posterioren Pyelondiameters (APPD) bei Harntrakterweiterungen für verschiedene Zeitpunkte der Schwangerschaft ermittelt. Weiterhin wurde die Übereinstimmung von prä- und postnataler Diagnose bestimmt, sowie die Assoziation von Fehlbildungen des Harntraktes mit anderen Anomalien und die Bedeutung eines pränatal erweiterten Ureters untersucht. Die Analysen wurden anhand der Befunde von 196 Patienten der Universitätsfrauenklinik und Universitätskinderklinik Jena im Zeitraum 01/1990-12/2000 durchgeführt. Es konnte nachgewiesen werden, dass bei einem APPD >= 3 mm vor der 26. Schwangerschaftswoche (SSW) und/oder einem APPD >= 7 mm nach der 26. SSW postnatale Diagnostik notwendig ist, dass bei einem APPD >= 15 mm nach der 35. SSW die Wahrscheinlichkeit einer operativen Therapie deutlich erhöht ist und dass jedes Kind mit einem pränatal erweiterten Ureter einer postnatalen Diagnostik unterzogen werden muss

Illness behaviour and influencing aspects of general practitioners in Germany and their use of the health care system: a qualitative study

Objectives The aim of this study was to explore aspects that play a role when general practitioners (GPs) become ill and thus gain a more comprehensive understanding of the overall illness behaviour of GPs and their use of the healthcare system. Setting Primary care practices in Thuringia, Germany. Participants Convenience sample of 16 GPs. Design Qualitative study design with semistructured interviews and content analysis. Results Using our approach of having participants report their own episodes of illness, we found that self-treatment was practised and accepted by all 16 participants. The widespread use of naturopathy and complementary methods seems to be a special feature of German GPs. Formal use of the healthcare system mainly took place through direct consultation with specialists. Our study revealed various aspects influencing the illness behaviour of the GPs and their use of the healthcare system. Some aspects also apply to lay patients, but it became clear how strongly illness behaviour is influenced by participants’ activities as physicians. Noteworthy and less described aspects are especially the influence of patients and practice staff, the influence of biographical and professional imprint and the attitudes and values of the physicians. Complex inter-relationships were found between illness behaviour and influencing aspects; these are subjected to a dynamic and recursive process. Conclusions The illness behaviour of German GPs seems to be comprehensively influenced by their activities as responsible healthcare providers. The ability to perceive and reflect in this regards should already be actively promoted in studies and further education. Further research is needed for a better understanding of the inter-relationships

Combining semantic and geometric features for object class segmentation of indoor scenes

Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously used in indoor robotics. We investigate strategies for efficient pixelwise object class labeling of indoor scenes that combine both pretrained semantic features transferred from a large color image dataset and geometric features, computed relative to the room structures, including a novel distance-from-wall feature, which encodes the proximity of scene points to a detected major wall of the room. We evaluate our approach on the popular NYU v2 dataset. Several deep learning models are tested, which are designed to exploit different characteristics of the data. This includes feature learning with two different pooling sizes. Our results indicate that combining semantic and geometric features yields significantly improved results for the task of object class segmentation.This research is partially funded by the CSIC project MANIPlus (201350E102), and the project RobInstruct (TIN2014-58178-R).Peer reviewe

Hochleistungsrechnen in Baden-Württemberg - Ausgewählte Aktivitäten im bwGRiD 2012 : Beiträge zu Anwenderprojekten und Infrastruktur im bwGRiD im Jahr 2012

bwGRiD bezeichnet eine einzigartige Kooperation zwischen den Hochschulen des Landes Baden-Württtemberg, die Wissenschaftlern aller Disziplinenen Ressourcen im Bereich des HPCs effizient und hochverfügbar zur Verfügung zu stellt. Der präsentierte 8. bwGRiD-Workshop in Freiburg bot die Chance, einen breiten Überblick zum Stand des Projektes zu verschaffen, Anwender und Administratoren gleichsam zu Wort kommen zu lassen und den Austausch zwischen den Fach-Communities zu befördern

Practical algorithms for multivariate rational approximation

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.1016/j.cpc.2020.107663We present two approaches for computing rational approximations to multivariate functions, motivated by their effectiveness as surrogate models for high-energy physics (HEP) applications. Our first approach builds on the Stieltjes process to efficiently and robustly compute the coefficients of the rational approximation. Our second approach is based on an optimization formulation that allows us to include structural constraints on the rational approximation (in particular, constraints demanding the absence of singularities), resulting in a semi-infinite optimization problem that we solve using an outer approximation approach. We present results for synthetic and real-life HEP data, and we compare the approximation quality of our approaches with that of traditional polynomial approximations.This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357. Support for this work was provided through the SciDAC program funded by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Re search. This work was also supported by the U.S. Department of Energy through grant DE-FG02-05ER25694, and by Fermi Re search Alliance, LLC, United States of America under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of Nuclear Physics, Scientific Discovery through Advanced Computing (SciDAC) program through the FASTMath Institute under Contract No. DE-AC02-05CH11231 at Lawrence Berkeley National Laboratory

Semantic segmentation priors for object discovery

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and service robot applications. In these scenes, semantic segmentation methods have made huge advances in recent years. Such methods can provide useful prior information for object discovery by removing false positives and by delineating object boundaries. We propose a novel method that combines bottom-up object discovery and semantic priors for producing generic object candidates in RGB-D images. We use a deep learning method for semantic segmentation to classify colour and depth superpixels into meaningful categories. Separately for each category, we use saliency to estimate the location and scale of objects, and superpixels to find their precise boundaries. Finally, object candidates of all categories are combined and ranked. We evaluate our approach on the NYU Depth V2 dataset and show that we outperform other state-of-the-art object discovery methods in terms of recall.Peer ReviewedPostprint (author's final draft

IMECE2008-67335 ADVANCED FINITE ELEMENT FORMULATION FOR PIEZOELECTRIC SMART SHELL STRUCTURES UNDER CONSIDERATION OF GEOMETRICAL NONLINEARITY

ABSTRACT