Randomized comparisons among health informatics students identify hypertutorial features as improving web-based instruction.

Hypertutorials optimize five features - presentation, learner control, practice, feedback, and elaborative learning resources. Previous research showed graduate students significantly and overwhelmingly preferred Web-based hypertutorials to conventional Book-on-the-Web statistics or research design lessons. The current report shows that the source of hypertutorials\u27 superiority in student evaluations of instruction lies in their hypertutorial features. Randomized comparisons between the two methodologies were conducted in two successive iterations of a graduate level health informatics research design and evaluation course. The two versions contained the same text and graphics, but differed in the presence or absence of hypertutorial features: Elaborative learning resources, practice, feedback, and amount of learner control. Students gave high evaluations to both Web-based methodologies, but consistently rated the hypertutorial lessons as superior. Significant differences localized in the hypertutorial subscale that measured student responses to hypertutorial features

Regular black holes: A short topic review

The essential singularity in Einstein's gravity can be avoidable if the preconditions of Penrose's theorem can be bypassed, i.e., if the strong energy condition is broken in the vicinity of a black hole center. The singularity mentioned here includes two aspects: (i) the divergence of curvature invariants, and (ii) the incompleteness of geodesics. Both aspects are now taken into account in order to determine whether a black hole contains essential singularities. In this sense, black holes without essential singularities are dubbed regular (non-singular) black holes. The regular black holes have some intriguing phenomena that are different from those of singular black holes, and such phenomena have inspired numerous studies. In this review, we summarize the current topics that are associated with regular black holes.Comment: Major revision, 45 pages, 2 figures, some references have ben adde

Blunt-end vectors generated by polymerase chain reaction (PCR) for direct cloning of blunt-end DNA fragments

Blunt-end cloning is a convenient way to clone polymerase chain reaction (PCR) products generated by proof-reading DNA polymerase. However, it is a time consuming procedure to prepare the linearized blunt-end vector, which usually involves plasmid extraction and restriction enzyme digestion. Moreover, 5’ dephosporylation of the vector is usually required to avoid vector self-ligation. Here, we reported a method for generating linearized blunt-end vector pBSK-blunt by PCR. Vector generated in this way has no 5’-phosphate groups, hence completely avoiding vector self-ligation and yielding almost 100% positive clones.Key words: Blunt-end cloning, phosphorylated DNA fragment, dephosphorylated blunt-end vector

Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization

Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and generalize to new tasks, with which the deep learning models struggle. Towards human-like intelligence, various works on disentangled representation learning have been proposed, and recently some studies on compositional generalization have been presented. However, few works study the relationship between disentanglement and compositional generalization, and the observed results are inconsistent. In this paper, we study several typical disentangled representation learning works in terms of both disentanglement and compositional generalization abilities, and we provide an important insight: vector-based representation (using a vector instead of a scalar to represent a concept) is the key to empower both good disentanglement and strong compositional generalization. This insight also resonates the neuroscience research that the brain encodes information in neuron population activity rather than individual neurons. Motivated by this observation, we further propose a method to reform the scalar-based disentanglement works ($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities. We investigate the impact of the dimensions of vector-based representation and one important question: whether better disentanglement indicates higher compositional generalization. In summary, our study demonstrates that it is possible to achieve both good concept recognition and novel concept composition, contributing an important step towards human-like intelligence.Comment: Preprin

A Study on the Influencing Factors of Teaching Interaction on Deep Learning from the Perspective of Social Cognitive Theory

Based on Social Cognitive Theory SCT a research model is constructed with teaching interaction as the independent variable self-efficacy as the mediating variable and Deep learning as the dependent variable The research uses regression analysis and Bootstrap test to explore the impact of teaching interaction on college students Deep learning and the mediating role of self-efficacy The research results show that teaching interaction positively and significantly affects college students Deep learning and self- efficacy of which material-chemical interaction has the most significant effect on college students Deep learning 0 431 self-efficacy positively affects college students Deep learning 0 255 and play a partial mediating role in teaching interaction and Deep learning Finally the research proposes to build a multi-modal interaction mechanism to promote the realization of Deep learning to create an embodied collaborative learning context to improve the quality of teaching interaction Learn and referenc