arXiv subjects

Yuya Asano

Publications and source records attributed to Yuya Asano.

6 recordsLinked to original sources

arXiv2026

ALMA Observations of DEM L241/LMC P3 in the Large Magellanic Cloud: Evidence for the Formation of Cool Molecular Jets Driven by a Microquasar

We present ALMA observations of DEML 241/LMC P3, the most luminous $\gamma$-ray binary consisting of a compact object and an O star, in CO emission. We have found an one-sided jet-like CO feature of 8 pc length and 1 pc width, which accompanies another weaker CO jet candidate with slightly different orientation. The one-sided CO jet exhibits striking alignment with LMC P3, suggesting that the jet was driven by LMC P3. We have determined kinetic temperature of the CO jet to be significantly high at 33$-$60 K as compared with $\sim$15 K in the nearby non-jet CO cloud whereas no radiative heat source is found. We interpret that the high temperatures are due to shock heating of a microquasar jet driven by the $\gamma$-ray binary, where the compact object has an accretion disk fed by the O star winds. The CO jet matches existing predictions from magneto-hydrodynamical simulations, which show that CO jet can form from the interaction of the microquasar jet and an ambient ISM cloud. These results provide strong evidence that CO jets are a signature sculptured by microquasar jets, lending support for mass accretion in LMC P3 as the $\gamma$-ray origin. The results suggest a second case of CO jets potentially driven by a microquasar along with the CO jets in the microquasar candidate HESS J1023-575 recently identified in the Milky Way. Further, our results suggest the use of sub-mm observations for identifying microquasars, opening a new possible window for their discovery and study.

astro-ph.HE↗

arXiv2026

Optical observations and atomic environment of supernova remnant G25.1-2.3

The supernova remnant (SNR) G25.1-2.3 was identified in the radio band during the Sino-German $\lambda$6 cm survey of the Galactic plane. We present a detailed investigation of the optical, HI, and CO emission towards the G25.1-2.3 to better understand its characteristics and environment. In this study, optical spectroscopic data of the remnant and its environment have been analysed for the first time, providing new insights into their emission properties. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and 1.5-m Russian-Turkish Telescope (RTT150) data show variations across the observed regions, with [SII]/H$\alpha$ ranging from 0.16 to 0.83. We identified shock-heated gas in the northern and southern regions and several photoionized regions around the SNR based on their [SII]/H$\alpha$ ratios derived from spectra. The [SII]$\lambda$6716/$\lambda$6731 ratio observed in the northern region suggests electron densities ($n_{\rm e}$) ranging from 120 to 1030 cm$^{-3}$, whereas the southern regions show higher values, between 490 and 4500 cm$^{-3}$. The variations in the observed H$\alpha$/H$\beta$ ratios indicate significant differences in extinction across the regions. H$\alpha$ images obtained using the 1-m Turkish Telescope (T100) reveal optical emission in the northern and southern, characterized by filamentary and diffuse structures. We newly found a hole-like distribution of HI, whose spatial extent is roughly consistent with the diameter of the SNR. Based on radio data, we examine the evolutionary stage of G25.1-2.3 using the surface brightness-diameter ($\Sigma-D$) relation and the equipartition method.

astro-ph.HE↗

arXiv2025

Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI

General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named entities. We extend correction to tasks that have no prior user data and exhibit linguistic flexibility such as lexical and syntactic variations. We propose a novel context augmentation with a large language model and a ranking strategy that incorporates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks. Our method ranks (1) n-best ASR hypotheses by their lexical and semantic similarity with context and (2) context by phonetic correspondence with ASR hypotheses. Evaluated in home improvement and cooking domains with real-world users, our method improves recall and F1 of correction by 34% and 16%, respectively, while maintaining precision and false positive rate. Users rated .8-1 point (out of 5) higher when our correction method worked properly, with no decrease due to false positives.

cs.CL↗

arXiv2024

What metrics of participation balance predict outcomes of collaborative learning with a robot?

One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question whether we should balance human participation in human-human interactions (HHI) or human-robot interactions (HRI) and whether we should consider robots' participation in collaborative learning involving multiple humans and a robot. This paper examines collaborative learning between a pair of students and a teachable robot that acts as a peer tutee to answer the aforementioned question. Through an exploratory study, we hypothesize which balance metrics in the literature and which portions of dialogues (including vs. excluding robots' participation and human participation in HHI vs. HRI) will better predict learning as a group. We test the hypotheses with another study and replicate them with automatically obtained units of participation to simulate the information available to robots when they adaptively fix imbalances in real-time. Finally, we discuss recommendations on which metrics learning science researchers should choose when trying to understand how to facilitate collaboration.

cs.HC↗

arXiv2023

Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport

While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learning gains and learners' rapport with the agents. Our results show they are not related to learning gains or rapport, regardless of the types of responses the agents should have returned given the correct input from learners without ASR errors. We also discuss the implications for optimal error-recovery policies for teachable agents that can be drawn from these findings.

cs.HC↗

arXiv2022

Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.

cs.CL↗