No abstract

Large Language Models (LLMs) have a great potential to serve as readily available and costefficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. To develop a more pedagogically informed CITS capable of teaching complex concepts, we construct a BIlingual PEDagogicallyinformed Tutoring Dataset (BIPED) of oneon-one, human-to-human English tutoring interactions. Through post-hoc analysis of the tutoring interactions, we come up with a lexicon of dialogue acts (34 tutor acts and 9 student acts), which we use to further annotate the collected dataset. Based on a two-step framework of first predicting the appropriate tutor act then generating the corresponding response, we implemented two CITS models using GPT-4 and SOLAR-KO, respectively. We experimentally demonstrate that the implemented models not only replicate the style of human teachers but also employ diverse and contextually appropriate pedagogical strategies. * We confine the utilization of data to a subset that has been fully annotated, despite a larger volume of collected data.

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.

No abstract

Processing and properties of a dome-shaped piezoelectric transformer with a composition of 0.03Pb(Sb0.5Nb0.5)O3-0.03Pb(Mn1/3Nb2/3)O3-0.465PbTiO3-0.475PbZrO3 have been investigated. A dome-shaped sample was fabricated by powder injection molding. The dimension of the dome-shaped sample was a 28 mm in diameter and 2.1mm in thickness with a curvature radius of 18 mm. Finite element modeling for the complicated piezoelectric transformer was applied to simulate vibration mode in the sample. The high power characteristics of a dome-shaped piezoelectric transformer were examined by the lighting test for a 55W PL lamp. The 55W PL lamp was successfully driven by the dome-shaped piezoelectric transformer with sustaining efficiency higher than 98%. The transformer with ring/dot area ratio of 2.1 exhibited the maximum properties in terms of output power, efficiency and temperature stability.