arXiv subjects

Chun-Yen Chen

Publications and source records attributed to Chun-Yen Chen.

6 recordsLinked to original sources

arXiv2026

Cool-Core Destruction in Merging Clusters with AGN Feedback and Radiative Cooling

The origin of cool-core (CC) and non-cool-core (NCC) dichotomy of galaxy clusters remains uncertain. Previous simulations have found that cluster mergers are effective in destroying CCs but fail to prevent overcooling in cluster cores when radiative cooling is included. Feedback from active galactic nuclei (AGN) is a promising mechanism for balancing cooling in CCs; however, the role of AGN feedback in CC/NCC transitions remains elusive. In this work, we perform three-dimensional binary cluster merger simulations incorporating AGN feedback and radiative cooling, aiming to investigate the heating effects from mergers and AGN feedback on CC destruction. We vary the mass ratio and impact parameter to examine the entropy evolution of different merger scenarios. We find that AGN feedback is essential in regulating the merging clusters, and that CC destruction depends on the merger parameters. Our results suggest three scenarios regarding CC/NCC transitions: (1) CCs are preserved in minor mergers or mergers that do not trigger sufficient heating, in which cases AGN feedback is crucial for preventing the cooling catastrophe; (2) CCs are transformed into NCCs by major mergers during the first core passage, and AGN feedback is subdominant; (3) in major mergers with a large impact parameter, mergers and AGN feedback operate in concert to destroy the CCs.

astro-ph.CO↗

arXiv2024

Can the symmetric Fermi and eROSITA bubbles be produced by tilted jets?

The Fermi Gamma-Ray Space Telescope reveals two large bubbles in the Galaxy, extending nearly symmetrically $\sim50^{\circ}$ above and below the Galactic center (GC). Previous simulations of bubble formation invoking active galactic nucleus (AGN) jets have assumed that the jets are vertical to the Galactic disk; however, in general, the jet orientation does not necessarily correlate with the rotational axis of the Galactic disk. Using three-dimensional special relativistic hydrodynamic simulations including cosmic rays (CRs) and thermal gas, we show that the dense clumpy gas within the Galactic disk disrupts jet collimation ("failed jets" hereafter), which causes the failed jets to form hot bubbles. Subsequent buoyancy in the stratified atmosphere renders them vertical to form the symmetric Fermi and eROSITA bubbles (collectively, Galactic bubbles). We find that (1) despite the relativistic jets emanated from the GC are at various angles $\le45^{\circ}$ with respect to the rotational axis of the Galaxy, the Galactic bubbles nonetheless appear aligned with the axis; (2) the edge of the eROSITA bubbles corresponds to a forward shock driven by the hot bubbles; (3) followed by the forward shock is a tangling contact discontinuity corresponding to the edge of the Fermi bubbles; (4) assuming a leptonic model we find that the observed gamma-ray bubbles and microwave haze can be reproduced with a best-fit CR power-law spectral index of 2.4; The agreements between the simulated and the observed multi-wavelength features suggest that forming the Galactic bubbles by oblique AGN failed jets is a plausible scenario.

astro-ph.HE↗

arXiv2022

Using Chatbots to Teach Languages

This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice partners. Our system can adapt to users' language proficiency on the fly. We also provide automatic grammar error feedback to help users learn from their mistakes. According to our first adopters, our system is entertaining and useful. Furthermore, we will provide the learning technology community a large-scale conversation dataset on language learning and grammar correction. Our next step is to make our system more adaptive to user profile information by using reinforcement learning algorithms.

cs.CL↗

arXiv2021

Low-Frequency 1/f Noise Characteristics of Ultra-Thin AlO$_{x}$-Based Resistive Switching Memory Devices with Magneto-Resistive Responses

Low-frequency 1/f voltage noise has been employed to probe stochastic charge dynamics in AlO$_{x}$-based non-volatile resistive memory devices exhibiting both resistive switching (RS) and magneto-resistive (MR) effects. A 1/f$^γ$ noise power spectral density is observed in a wide range of applied voltage biases. By analyzing the experimental data within the framework of Hooge's empirical relation, we found that the Hooge's parameter $α$ and the exponent $γ$ exhibit a distinct variation upon the resistance transition from the low resistance state (LRS) to the high resistance state (HRS), providing strong evidence that the electron trapping/de-trapping process, along with the electric field-driven oxygen vacancy migration in the AlO$_x$ barrier, plays an essential role in the charge transport dynamics of AlO$_x$-based RS memory devices.

cond-mat.mes-hall↗

arXiv2019

Gunrock: A Social Bot for Complex and Engaging Long Conversations

Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users' engagement (e.g., ratings, number of turns). Additionally, users' backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.

cs.CL↗

arXiv2017

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training.

cs.DC↗