In this paper, we discussed the VISOLE learning mode,which is a hybrid learning that combined the game-based learning and the traditional learning. Briefly speaking, the VISOLE approach is composed of 3 phases in which students have to (Phase 1) preliminarily acquire some high-level knowledge in specific subject domains through teachers’ scaffolding; (Phase 2) actively participate as game chara...

The capacity to learn new motor sequences is fundamental to adaptive motor behavior. The early phase of motor sequence learning relies on the ventral and anterior striatal circuitry, whereas the late phase relies on the dorsal and posterior striatal circuitry. Early Parkinson's disease (PD) is mainly characterized by dopaminergic denervation of the dorsal and posterior striatum while sparing an...

Abstract This paper reports on the second “Throughput” phase of Tracking Machine Learning (TrackML) challenge Codalab platform. As in first “Accuracy” phase, participants had to solve a difficult experimental problem linked tracking accurately trajectory particles as e.g. created at Large Hadron Collider (LHC): given $$O(10^5)$$ <mml:mro...

This study investigated the neural correlates of second-language lexical acquisition in terms of learning phase and word type. Ten French-speaking participants learned 80 Spanish words-40 cognates, 40 non-cognates-by means of a computer program. The learning process included the early learning phase, which comprised 5 days, and the consolidation phase, which lasted 2 weeks. After each phase, pa...

Phase space reconstruction of financial time series has become an effective approach for financial system analysis. Since the phase space obtained by the traditional method had the large embedding dimension and was susceptible to noise, this paper proposed a novel phase space reconstruction approach based on manifold learning, called manifold learning-based phase space reconstruction (MLPSR), w...

In this paper we investigate techniques for semi-supervised learning that split their unsupervised and supervised components — that is, an initial unsupervised phase is followed by a supervised learning phase. We first analyze the relative value of labeled and unlabeled data. We then present methods that perform “split” semi-supervised learning and show promising empirical results.