Feasibility of EPC to BPEL Model Transformations Based on Ontology and Patterns

Model-Driven Engineering holds the promise of transforming\ud business models into code automatically. This requires the concept of\ud model transformation. In this paper, we assess the feasibility of model\ud transformations from Event-driven Process Chain models to Business\ud Process Execution Language specifications. To this purpose, we use a\ud framework based on ontological analysis and workflow patterns in order\ud to predict the possibilities/limitations of such a model transformation.\ud The framework is validated by evaluating the transformation of several\ud models, including a real-life case.\ud The framework indicates several limitations for transformation. Eleven\ud guidelines and an approach to apply them provide methodological support\ud to improve the feasibility of model transformation from EPC to\ud BPEL

PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines

Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and the direct information about the ambiguity inherent in the extracted data. We present a new concept for the estimation and propagation of uncertainty involved in image analysis operators. This allows using simple processing operators that are suitable for analyzing large-scale 3D+t microscopy images without compromising the result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it enhance the result quality of various processing operators. All presented concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. Furthermore, the functionality of the proposed approach is validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. Especially, the automated analysis of terabyte-scale microscopy data will benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. The generality of the concept, however, makes it also applicable to practically any other field with processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure

Calibration of quasi-static aberrations in exoplanet direct-imaging instruments with a Zernike phase-mask sensor. II. Concept validation with ZELDA on VLT/SPHERE

Warm or massive gas giant planets, brown dwarfs, and debris disks around nearby stars are now routinely observed by dedicated high-contrast imaging instruments on large, ground-based observatories. These facilities include extreme adaptive optics (ExAO) and state-of-the-art coronagraphy to achieve unprecedented sensitivities for exoplanet detection and spectral characterization. However, differential aberrations between the ExAO sensing path and the science path represent a critical limitation for the detection of giant planets with a contrast lower than a few $10^{-6}$ at very small separations (<0.3\as) from their host star. In our previous work, we proposed a wavefront sensor based on Zernike phase contrast methods to circumvent this issue and measure these quasi-static aberrations at a nanometric level. We present the design, manufacturing and testing of ZELDA, a prototype that was installed on VLT/SPHERE during its reintegration in Chile. Using the internal light source of the instrument, we performed measurements in the presence of Zernike or Fourier modes introduced with the deformable mirror. Our experimental and simulation results are consistent, confirming the ability of our sensor to measure small aberrations (<50 nm rms) with nanometric accuracy. We then corrected the long-lived non-common path aberrations in SPHERE based on ZELDA measurements. We estimated a contrast gain of 10 in the coronagraphic image at 0.2\as, reaching the raw contrast limit set by the coronagraph in the instrument. The simplicity of the design and its phase reconstruction algorithm makes ZELDA an excellent candidate for the on-line measurements of quasi-static aberrations during the observations. The implementation of a ZELDA-based sensing path on the current and future facilities (ELTs, future space missions) could ease the observation of the cold gaseous or massive rocky planets around nearby stars.Comment: 13 pages, 12 figures, A&A accepted on June 3rd, 2016. v2 after language editin

E-learning as a Vehicle for Knowledge Management

Nowadays, companies want to learn from their own experiences and to be able to enhance that experience with best principles and lessons learned from other companies. Companies emphasise the importance of knowledge management, particularly the relationship between knowledge and learning within an organisation. We feel that an e-learning environment may contribute to knowledge management on the one hand and to the learning need in companies on the other hand. In this paper, we report on the challenges in designing and implementing an e-learning environment. We identify the properties from a pedagogical view that should be supported by an e-learning environment. Then, we discuss the challenges in developing a system that includes these properties

A systematic review of recommended modifications of CBT for people with cognitive impairments following brain injury

Due to diverse cognitive, emotional and interpersonal changes that can follow brain injury, psychological therapies often need to be adapted to suit the complex needs of this population. The aims of the study were to synthesise published recommendations for therapy modifications following brain injury from non-progressive traumatic, vascular, or metabolic causes and to determine how often such modifications have been applied to cognitive behavioural therapy (CBT) for post-injury emotional adjustment problems. A systematic review and narrative synthesis of therapy modifications recommended in review articles and reported in intervention studies was undertaken. Database and manual searches identified 688 unique papers of which eight review articles and 16 intervention studies met inclusion criteria. The review articles were thematically analysed and a checklist of commonly recommended modifications composed. The checklist items clustered under themes of: therapeutic education and formulation; attention; communication; memory; and executive functioning. When this checklist was applied to the intervention studies, memory aids and an emphasis on socialising patients to the CBT model were most frequently reported as adaptations. It was concluded that the inconsistent reporting of psychological therapy adaptations for people with brain injury is a barrier to developing effective and replicable therapies. We present a comprehensive account of potential modifications that should be used to guide future research and practice