Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

End-to-End Learning of Video Super-Resolution with Motion Compensation

Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow. We rather propose an operation for motion compensation that performs warping from low to high resolution directly. We show that with this network configuration, video super-resolution can benefit from optical flow and we obtain state-of-the-art results on the popular test sets. We also show that the processing of whole images rather than independent patches is responsible for a large increase in accuracy.Comment: Accepted to GCPR201

Motion Deblurring in the Wild

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions

The Effect of the Pairing Interaction on the Energies of Isobar Analog Resonances in 112−124^{112-124}Sb and Isospin Admixture in 100−124^{100-124}Sn Isotopes

In the present study, the effect of the pairing interaction and the isovector correlation between nucleons on the properties of the isobar analog resonances (IAR) in $^{112-124}$Sb isotopes and the isospin admixture in $^{100-124}$Sn isotopes is investigated within the framework of the quasiparticle random phase approximation (QRPA). The form of the interaction strength parameter is related to the shell model potential by restoring the isotopic invariance of the nuclear part of the total Hamiltonian. In this respect, the isospin admixtures in the $^{100-124}$Sn isotopes are calculated, and the dependence of the differential cross section and the volume integral $J_{F}$ for the Sn($^{3}$He,t)Sb reactions at E($^{3}$He)$=200$ MeV occurring by the excitation of IAR on mass number A is examined. Our results show that the calculated value for the isospin mixing in the $^{100}$Sn isotope is in good agreement with Colo et al.'s estimates $(4-5$, and the obtained values for the volume integral change within the error range of the value reported by Fujiwara et al. (53$\pm$5 MeV fm$^{3}$). Moreover, it is concluded that although the differential cross section of the isobar analog resonance for the ($^{3}$He,t) reactions is not sensitive to pairing correlations between nucleons, a considerable effect on the isospin admixtures in $N\approx Z$ isotopes can be seen with the presence of these correlations.Comment: 16 pages, 5 EPS figures and 2 tables, Late

Ascites as an initial presentation of spontaneously ruptured hydatid cyst

We describe the diagnosis of a 77-year-old woman admitted toour outpatient department with a 3-month history of abdominalbloating and distension. Abdominal computed tomographyrevealed a large cystic lesion in the posterior segment of the righthepatic lobe, with a separated germinal layer and widespreadascites with dense internal echoes and septal appearance. Theresult of a serum Echinococcus indirect haemagglutination testwas positive and findings were indicative of the spontaneousrupture of a hydatid cyst into the peritoneal cavity withouttrauma. Ascites is rarely seen in the course of hydatid disease,but can result from cyst rupture into the peritoneal cavity. Thisshould be considered in the differential diagnosis of ascites,especially in areas such as Turkey, where hydatid disease inendemic

A review of inorganic photoelectrode developments and reactor scale-up challenges for solar hydrogen production

Green hydrogen, produced using solar energy, is a promising means of reducing greenhouse gas emissions. Photoelectrochemical (PEC) water splitting devices can produce hydrogen using sunlight and integrate the distinct functions of photovoltaics and electrolyzers in a single device. There is flexibility in the degree of integration between these electrical and chemical energy generating components, and so a plethora of archetypal PEC device designs has emerged. Although some materials have effectively been ruled out for use in commercial PEC devices, many principles of material design and synthesis have been learned. Here, the fundamental requirements of PEC materials, the top performances of the most widely studied inorganic photoelectrode materials, and reactor structures reported for unassisted solar water splitting are revisited. The main phenomena limiting the performance of up‐scaled PEC devices are discussed, showing that engineering must be considered in parallel with material development for the future piloting of PEC water splitting systems. To establish the future commercial viability of this technology, more accurate techno‐economic analyses should be carried out using data from larger scale demonstrations, and hence more durable and efficient PEC systems need to be developed that meet the challenges imposed from both material and engineering perspectives

Sizing solar-based mini-grids for growing electricity demand: insights from rural India

Mini-grids are a critical way to meet electricity access goals according to current and projected electricity demand of communities and so appropriately sizing them is essential to ensure their financial viability. However, estimation of demand for communities awaiting electricity access is uncertain and growth in demand along with the associated cost implications is rarely considered during estimation of mini-grid sizing. Using a case study of two rural communities in India, we assess the implications of demand growth on financial costs and performance of a mini-grid system consisting of solar photovoltaic (PV) panels and battery storage using two different system sizing approaches. We show a cost-saving potential of up to 12% when mini-grids are sized using a multi-stage approach where mini-grids gradually expand in several stages, rather than a single-stage optimisation approach. We perform a sensitivity analysis of the cost of the two sizing approaches by varying six key parameters: demand growth rate, logistics cost, system re-sizing frequency, likelihood of blackouts, solar PV and battery cost, and degradation rate. Of these, we find that system costs are most sensitive to variations in demand growth rates and cost decreases in solar PV and batteries. Our study shows that demand growth scenarios and choice of mini-grid sizing approaches have important financial and operational implications for the design of systems for rural electrification

Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset