UniNet: Next Term Course Recommendation using Deep Learning

Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance prediction. This is shown to be meaningful across different student GPA levels and course difficultie

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Adjunctive use of an ethyl lauroyl arginate-(LAE-)-containing mouthwash in the nonsurgical therapy of periodontitis: a randomized clinical trial

BACKGROUND: This randomized clinical trial evaluated the clinical and microbiological effects of 0.147% ethyl lauroyl arginate (LAE)-containing mouthwash compared to 0.12% chlorhexidine (CHX) mouthwash in the treatment of periodontitis. METHODS: Subjects were randomized to use 0.147% LAEand 0.12% CHX mouthwash after professional mechanical plaque removal (PMPR) twice daily 1 hour after brushing for 4 weeks. Periodontal pocket depth (PPD), bleeding on probing (FMBS) and dental plaque (FMPS) were measured at baseline, 4 weeks and 3 months. Microbiological samples were taken at baseline, at 4 weeks and 3 months after plaque recording and prior to PPD and BoP measurements. RESULTS: Forty subjects were randomized to treatment. Both therapies resulted in reduced FMPS, FMBS and residual pockets at 4 weeks and 3 months. The differences were not statistically significant. There were no treatment-related adverse events. Total bacterial count and the specific pathogens were reduced at 4 weeks and 3 months by both mouthwashes with no statistical differences between them at neither period of time

Inference without significance: measuring support for hypotheses rather than rejecting them

Despite more than half a century of criticism, significance testing continues to be used commonly by ecologists. Significance tests are widely misused and misunderstood, and even when properly used, they are not very informative for most ecological data. Problems of misuse and misinterpretation include: (i) invalid logic; (ii) rote use; (iii) equating statistical significance with biological importance; (iv) regarding the P-value as the probability that the null hypothesis is true; (v) regarding the P-value as a measure of effect size; and (vi) regarding the P-value as a measure of evidence. Significance tests are poorly suited for inference because they pose the wrong question. In addition, most null hypotheses in ecology are point hypotheses already known to be false, so whether they are rejected or not provides little additional understanding. Ecological data rarely fit the controlled experimental setting for which significance tests were developed. More satisfactory methods of inference assess the degree of support which data provide for hypotheses, measured in terms of information theory (model-based inference), likelihood ratios (likelihood inference) or probability (Bayesian inference). Modern statistical methods allow multiple data sets to be combined into a single likelihood framework, avoiding the loss of information that can occur when data are analyzed in separate steps. Inference based on significance testing is compared with model-based, likelihood and Bayesian inference using data on an endangered porpoise, Phocoena sinus. All of the alternatives lead to greater understanding and improved inference than provided by a P-value and the associated statement of statistical significance