Three problems with the connectivist conception of learning

Connectivism, which has been argued to be a new learning theory, has emerged in the field of online learning during the last decade. On the World Wide Web at least, connectivism promises to establish learning spaces similar to those that Ivan Illich imagined in ‘Deschooling Society’, through so‐called massive online open courses (MOOCs). In this paper, we critically examine the theoretical postulates of connectivism and identify three important psychological and epistemological problems, namely the lack of a solution to the learning paradox, the underconceptualization of interaction and the inability to explain concept development. Some of the theoretical deficiencies in question may explain certain learning problems experienced by participants in MOOCs. The paper concludes that, although MOOCs are a worthwhile experience and ought to be continued, connectivism as a learning theory has significant theoretical problems and should be profoundly revised if it is to explain and foster learning in such environments.     

Asymmetric Intermolecular Mizoroki-Heck Reaction: From Phosphine/Phosphinite-Nitrogen to Phosphite-Nitrogen Ligands

This review discusses the latest developments in ligand design for the Pd-catalyzed asymmetric intermolecular Mizoroki-Heck reaction, from the successful phosphine/phosphinite-nitrogen ligands to the recently reported phosphite-nitrogen ligands. The presence of a biaryl phosphite group offers several advantages as a ligand scaffold for this process.     

Unsupervised joint face alignment with gradient correlation coefficient

This work proposes an unsupervised joint alignment framework, referred to as “Gradient Correlation Congealing,” which aligns an image ensemble by maximizing a sum of gradient correlation coefficient function defined over all images. We, respectively, develop two different formulations to optimize the objective function regarding the role of “template.” While most existing face alignment methods suffer from outliers, e.g., occlusions, the proposed algorithms are able to align faces undergoing partial occlusions. Moreover, our algorithms can cope with nonuniform illumination changes (even extremely difficult ones), and also, they do not require any predefined templates. We test the novel approaches against four typical joint alignment methods including Least-Squares Congealing, Learned-Miller Congealing, Lucas–Kanade entropy Congealing, and RASL using three challenging face databases: AR, Yale B, and LFW. Experimental results prove the efficiency of our approaches under different conditions, especially when faces are partially occluded, and the proposed algorithms perform much better than all considered methods.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

On the improvement of network resource utilization efficiency for the establishment of static dependable connections in SRLG-constrained WDM translucent networks

In this paper, we investigate the problem of establishing static connections with fault-tolerant requirements, also known as dependable connections, taking into account quality of transmission constraints. To the best of our knowledge, this is the first study that tackles the aforementioned problem under shared risk link group (SRLG) constraints in translucent WDM optical mesh networks where typically a set of strategically localized network nodes are equipped with regeneration capability to overcome physical-layer impairment effects. A novel cross-layer heuristic approach is introduced to solve the problem for an heterogeneous networked scenario relying on a cost-effective two-stage protection procedure which combines the well-known path protection and partial path protection schemes in order to ensure instantaneous recovery from any SRLG-failure event. The proposed heuristic integrates a generic auxiliary graph model that incorporates various network heterogeneity factors such as the number of transceivers at each network node, the number of wavelengths on each fiber link, and the regeneration capability of each node, represented by different edges in the constructed graph. Moreover, the integrated auxiliary graph can be applied efficiently to model either single- or mixed-line-rate translucent WDM optical networks wherein different modulation formats are employed in order to support the transmission at different line rates. Our solution approach aims at maximizing the total number of accommodated requests by reducing network resource consumption through the simultaneous use of the backup–backup and primary–backup multiplexing techniques. We, here, present extended versions of these two techniques that generalize the sharing concept to some other important node resources—specifically, regeneration equipments which constitute the major cost factor in optical transport networks—in addition to link resources (i.e., wavelength channels). As far as we know, this is the first attempt to deploy simultaneously generalized versions of the backup–backup and primary–backup multiplexing techniques when considering static traffic patterns without compromising the 100 % fault-recoverability guarantee. The performances of the proposed heuristic are evaluated and discussed through extensive numerical experiments carried out on different network topologies. Significant improvements are demonstrated, either in terms of network blocking performance or in terms of resource utilization efficiency, in comparison with previously proposed approaches.     

Effects of carob bean gum on performance,nutrient digestibility and Salmonella enterica var. Enteritidis colonisation in chickens

Salmonella is presently one of the microorganisms of higher concern for food safety in poultry products. The present study examined the effect of feeding galactomannans from carob bean gum on nutrient digestibility and performance in chickens, and on the prevalence of Salmonella enterica var. Enteritidis in challenged animals. Four experiments were performed with either broiler or leghorn chickens, challenged with 10⁶ CFU (colony-forming units) of S. Enteritidis at 1 day of life, and feeding carob bean gum at different concentrations (25, 50 or 100 g/kg, depending on the experiment), alone or in combination with β-mannanase, cellulase or α-galactosidase at 8.3 U/g; or feeding D-mannose at 25 g/kg, or depolymerized carob bean gum or guar gum at 100 mg/kg. Trials lasted 3 or 4 weeks. Body weight and feed intake were determined and feed conversion ratio calculated (feed:gain). Faeces were collected during the last week on trial for evaluation of nutrient balance (energy, lipids and protein), using chromium oxide as inert marker. Viscosity of the ileal content was also determined at the end of the second experiment. Salmonella presence in caeca was determined two and 3 weeks after challenge. Performance and nutritive value of diets were impaired in birds fed carob bean gum, with higher effect at higher inclusion rates. D-mannose impaired performance variables only whereas depolymerized gums did not affect bird performance or nutritive value of the diets. Of the enzymes tested, only β-mannanase significantly decreased the viscosity of the intestinal contents of birds fed carob bean gum and partly counteracted the impairment in bird performance and the reduction in the nutritive value of the diets. The number of Salmonella-positive birds varied among experiments and was lower in the third week post-challenge compared to the second week post-challenge. However, the reduction in the number of Salmonella-positive birds was more constant and marked when carob bean gum was present in the diet. The inclusion of carob bean gum in the diet of chickens at the high concentrations used in the present experiment reduced the presence of Salmonella in challenged birds, but it also impaired performance and nutrient digestibility. These impairments were partially counteracted by the addition of β-mannanase to the diet. Carob bean gum might be used to reduce the incidence of Salmonella in chickens, while its negative effects on performance and nutrient digestibility could be counteracted by β-mannanase.     

Renewable polybenzoxazines based in diphenolic acid

The reaction of mixtures of renewable diphenolic acid (DPA) and its methylesterbenzoxazine derivative (MDP-Bz) has been studied. The DPA was introduced to lower the high temperature needed to complete the curing of the pure benzoxazine. In this way, samples with different DPA/MDP-Bz ratio (0, 2, 5, 10 and 25% of DPA) were investigated. Moreover, high performance flame retardant thermosetting resins with phosphorus were prepared through the mixture of MDP-Bz and a DPA-phosphazene derivative (DPA-PPZ). The curing behavior of these materials was studied by differential scanning calorimetry (DSC). Finally, the properties of the materials were evaluated by termogravimetric analysis (TGA), dynamic mechanical analysis (DMTA), tensile measurements, limiting oxygen index (LOI) and UL-94 Burn Test.     

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0% from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0% baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.