arXiv subjects

Yuchen Lian

Publications and source records attributed to Yuchen Lian.

7 recordsLinked to original sources

arXiv2026

A Possible Mechanism to Explain the Prograde Equatorial Jet of a Jupiter-like Gaseous Giant

Gaseous giants are characterized by their deep atmospheres, which lack clear boundaries with their interiors; therefore, their internal states could directly influence atmospheric dynamics. So far, most modeling studies have considered deep convection as the primary mechanism by which the interior influences atmospheric dynamics. In this work, we propose another possible mechanism that might crucially determine the appearance of gaseous giants' atmospheric cloud-top jet winds, tracing them to a typical hydromagnetic wave (the so-called equatorial Magnetic-Archimedes-Coriolis wave) generated within the stably stratified, strongly magnetized helium rain layer. The associated thermal perturbations can propagate upward through the convective molecular hydrogen envelope, eventually affecting the atmospheric thermal structure - the zonal inhomogeneities that are conducive to the formation of the eastward atmospheric equatorial jet (super-rotation). Our results have important implications for understanding the equatorial dynamics of gaseous giants. This mechanism could also help explain the equatorial westward jets (sub-rotation) observed on Uranus and Neptune, which lack the helium rain layers.

astro-ph.EP↗

arXiv2025

Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents

Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith & Culbertson, 2020). Multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human result. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith and Culbertson (2020)'s findings that highlight the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.

cs.CL↗

arXiv2024

NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution.

cs.CL↗

arXiv2023

Jupiter's equatorial quasi-quadrennial oscillation forced by internal thermal forcing

Observations have shown that there exists downward propagation of alternating westward/eastward jets in Jupiter's equatorial stratosphere, with a quasi-period between four and six years. This phenomenon is generally called the quasi-quadrennial oscillation (QQO). Here, we simulate the QQO by injecting isotropic small-scale thermal disturbances into a three-dimensional general circulation model of Jupiter. It is found that the internal thermal disturbance is able to excite a wealth of waves that generate the equatorial QQO and multiple jet streams at middle and high latitudes of both hemispheres. The dominant wave mode in generating the QQO-like oscillation is that with a zonal wavenumber of 10. Inhomogeneous evolution of potential vorticity favors the emergence of the off-equatorial zonal jets. The off-equatorial jets migrate to the equator, strengthen the deep equatorial jets, and result in the prolonging of the QQO-like oscillations.

astro-ph.EP↗

arXiv2023

Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off

Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. A common explanation is the lack of appropriate cognitive biases in these learners. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. We investigate this latter account focusing on the word-order/case-marking trade-off, a widely attested language universal that has proven particularly hard to simulate. We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a miniature language via supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding specific biases in the agents. We see this as an essential step towards the investigation of language universals with neural learners.

cs.CL↗

arXiv2022

Influences of internal forcing on atmospheric circulations of irradiated giant planets

Close-in giant planets with strong stellar irradiation show atmospheric circulation patterns with strong equatorial jets and global-scale stationary waves. So far, almost all modeling works on atmospheric circulations of such giant planets have mainly considered external radiation alone, without taking into account the role of internal heat fluxes or just treating it in very simplified ways. Here, we study atmospheric circulations of strongly irradiated giant planets by considering the effect of internal forcing, which is characterized by small-scale stochastic interior thermal perturbations, using a three-dimensional atmospheric general circulation model. We show that the perturbation-excited waves can largely modify atmospheric circulation patterns in the presence of relatively strong internal forcing. Specifically, our simulations demonstrate three circulation regimes: superrotation regime, midlatitude-jet regime, and quasi-periodic oscillation regime, depending on the relative importance of external and internal forcings. It is also found that strong internal forcing can cause noticeable modifications of the thermal phase curves.

astro-ph.EP↗

arXiv2021

The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning

Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language.

cs.CL↗