arXiv subjects

Ruiyu Zhang

Publications and source records attributed to Ruiyu Zhang.

At least 19 recordsLinked to original sources

arXiv2026

ASKAP discovery of a pair of large radio bubbles: on the origin of odd radio circles

We report the serendipitous discovery of a large, low-surface-brightness radio bubble in 944 MHz continuum data from the ASKAP Evolutionary Map of the Universe (EMU) survey. The structure, centred on the elliptical galaxy LEDA 217397 at a redshift of $z=0.040$, spans $\sim$8.4 arcmin, corresponding to a projected diameter of $\sim$399 kpc, and consists of two partly overlapping shells both with radii of $\sim$114 kpc. The integrated flux density of the bubble is $56.8\pm2.9$ mJy at 944 MHz, implying a rest-frame 1.4 GHz luminosity of $\sim$ $1.4\times10^{23}$ W Hz$^{-1}$. Combining the EMU measurement with MWA GLEAM-X data at 88--185 MHz, we derive a steep integrated spectral index of $\alpha=-1.04\pm0.04$, and a two-frequency spectral-index map suggesting a possible exterior flattening. Spectral Energy Distribution (SED) fitting indicates a massive ($\log M_{\ast}/M\odot = 10.97\pm0.09$), quiescent (SFR=$0.025\pm0.083\,M\odot$ yr$^{-1}$) early-type host with no mid-infrared AGN signature and no overdense environment. We compare the bubble with odd radio circles (ORCs) and large radio shells, and discuss three scenarios for its origin: a starburst-driven wind, a merger-driven shock, and AGN jet-inflated bubbles. The starburst wind is disfavoured on energetic grounds ($\gtrsim$$10^{59}$ erg required versus $\sim$$10^{8}$ yr electron lifetimes), and neither a halo-scale merger shock nor a spherical nuclear blast wave can explain the unusually regular, double-shell geometry; a bipolar nuclear outburst -- a relic AGN jet episode, possibly triggered by a supermassive-black-hole merger -- provides the most natural explanation, with later shocks possibly re-energising the plasma. Deeper broad-band radio, polarimetric, spectroscopic and X-ray observations are needed to confirm its nature.

astro-ph.GA↗

arXiv2026

Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder

Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time. This dependency limits cross-lingual voice cloning, since in-the-wild reference audio is often untranscribed. In this technical report, we present Confucius4-TTS, a multilingual zero-shot TTS system that supports 14 languages and performs both intra-lingual and cross-lingual reference cloning without requiring transcripts of audio prompts. Confucius4-TTS follows a two-stage architecture, consisting of text-to-semantic (T2S) and semantic-to-acoustic (S2A) modules. The LLM-based T2S module uses a learnable speaker encoder to extract timbre features from self-supervised speech representations, and the conditional flow-matching S2A module converts the predicted semantic tokens into mel-spectrograms. The same model also supports continuation cloning when a reference transcript is available. Confucius4-TTS is trained on large-scale multilingual speech data. It achieves high intelligibility and speaker similarity on public benchmarks. On the CV3-Eval cross-lingual benchmark, Confucius4-TTS obtains an average WER of 3.73% across six directions. On our internal cross-lingual set, it achieves the best average overall rank in human evaluation among recent open-source and commercial systems. We release code, model checkpoints, and demos at https://github.com/netease-youdao/Confucius4-TTS.

cs.SD↗

arXiv2026

Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy

Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi-objective evolutionary algorithm (ABM+MOEA) independently re-implement how an outcome metric is extracted from simulation trajectories. Unlike deliberate analytical choices, MAD operates at the level of pipeline architecture: each stage is internally coherent, and the inconsistency becomes visible only when cross-stage outputs are compared. Code inspection of EpidemiOptim, a JAIR-published epidemic policy toolbox, reveals three structurally independent aggregation paths in peer-reviewed code. A faithful replication of this structure produces champion disagreement in 64.2% of independent runs (n=500, 95% CI: [59.9%, 68.3%]). In a 300-seed policy-flip experiment, divergent aggregation causes the optimizer to recommend the wrong champion in 83% of replications, with a mean welfare gap of 2.19 units and a Gini inequality gap of 0.050 units. In a follow-up inference audit, 3 of 249 flipped seeds cross the significance boundary itself. A complementary enterprise follow-up produces the predicted null under near-commensurable rankings (rho = 0.991), while a public upstream rerun of the Lake Problem DPS workflow shows that the archived published-path recommendation reaches joint-threshold success 0.401 whereas a shared contract-path rule reaches 0.552. We introduce the metric contract - a single shared callable enforced at dispatch time across all pipeline stages - as the remedy. Framed as standard engineering discipline applied to the cross-stage metric interface, the contract eliminates divergence by construction with approximately 3% runtime overhead.

cs.MA↗

arXiv2026

critband: A Python Package for Critical Bandwidth Analysis of Multimodal Distributions

Multimodal density estimation is a fundamental problem in scientific computing. Determining the number of modes in a distribution is a core numerical challenge with applications across ecology, economics, genomics, and astronomy. While the R ecosystem provides mature tools through the multimode package, the Python ecosystem has lacked an equivalent cohesive implementation. We present critband, a Python package for critical bandwidth bimodality detection based on Silverman's kernel density approach. The package implements critical bandwidth search with a robust bracketed mode-count solver and FFT-accelerated KDE, and provides additional features including k-mode detection, component decomposition, bimodality strength quantification, and excess mass estimation. Validation against twelve benchmark cases spanning separation regimes, unequal variances, unequal weights, and small sample sizes shows stable estimates for clearly separated cases and expected instability for boundary cases. Performance benchmarks show critband is typically 3-10 times faster per case than R's modetest() in the tested setup.

cs.MS↗

arXiv2026

Enhancing Citizen-Government Communication with AI: Evaluating the Impact of AI-Assisted Interactions on Communication Quality and Satisfaction

This study integrates critical AI scholarship with relational communication theories to explain how AI language modifications shape the quality of government-citizen communication. Distinguishing between informational-cognitive quality (clarity, ease of response) and expressive-constitutive quality (politeness, respectfulness, feeling heard, trust, urgency, empathy), we hypothesize that AI yields uncontested benefits for the former but contested effects for the latter, potentially enhancing relational markers while muting authentic emotional cues. Using a vignette-based survey with 220 citizens and 214 civil servants in China, we assess perceptions across five interaction contexts: service requests, policy inquiries, complaints, suggestions, and emergencies. Results from paired t-tests and mixed-effects regressions support the claim that AI enhances both informational-cognitive and expressive-constitutive quality from the perspectives of citizens and civil servants, with significant improvements in clarity, politeness, satisfaction, trust, and empathy, but provide no consistent evidence of urgency or empathy signals. These findings suggest that concerns over algorithmic emotional flattening may be overstated or context-specific; they offer theoretical insights into AI-mediated public interactions and practical implications for fostering trust and efficiency in digital governance.

cs.CY↗

arXiv2026

The Double-Episode Jet Genesis of the eROSITA and Fermi Bubbles

The Fermi and eROSITA bubbles are giant gamma-ray and X-ray lobes in the Milky Way, extending up to $\sim$50° and ~$\sim$80° in galactic latitude, respectively, yet their origins remain debated. Using three-dimensional magnetohydrodynamic simulations, we investigate a scenario in which two temporally separated episodes of active galactic nucleus (AGN) jets launched from the Galactic center produce the bubbles, with each structure bounded by a forward shock. Our simulations reveal that the first jet pair, launched 15 Myr ago, forms the outer eROSITA bubbles (extending to $\sim$18 kpc), while the second, launched 5 Myr ago, creates the nested Fermi bubbles ($\sim$10 kpc height). This model broadly reproduces the observed elongated morphology, multi-band X-ray surface brightness distribution, O VIII/O VII line ratios, radio ridge structures, and gamma-ray emissions of the bubbles. Cosmic-ray electrons are accelerated \textit{in situ} at the shock fronts, explaining the sharp edges and nearly uniform gamma-ray surface brightness distribution of Fermi bubbles. The results suggest that the eROSITA and Fermi bubbles encode a time-resolved record of episodic AGN activity in the Galactic center, providing a physically motivated framework for interpreting their multi-wavelength properties.

astro-ph.HE↗

arXiv2026

SimulatorCoder: DNN Accelerator Simulator Code Generation and Optimization via Large Language Models

This paper presents SimulatorCoder, an agent powered by large language models (LLMs), designed to generate and optimize deep neural network (DNN) accelerator simulators based on natural language descriptions. By integrating domain-specific prompt engineering including In-Context Learning (ICL), Chain-of-Thought (CoT) reasoning, and a multi-round feedback-verification flow, SimulatorCoder systematically transforms high-level functional requirements into efficient, executable, and architecture-aligned simulator code. Experiments based on the customized SCALE-Sim benchmark demonstrate that structured prompting and feedback mechanisms substantially improve both code generation accuracy and simulator performance. The resulting simulators not only maintain cycle-level fidelity with less than 1% error compared to manually implemented counterparts, but also consistently achieve lower simulation runtimes, highlighting the effectiveness of LLM-based methods in accelerating simulator development. Our code is available at https://github.com/xiayuhuan/SimulatorCoder.

cs.AR↗

arXiv2026

PEARL: Prototype-Enhanced Alignment for Label-Efficient Representation Learning with Deployment-Driven Insights from Digital Governance Communication Systems

In many deployed systems, new text inputs are handled by retrieving similar past cases, for example when routing and responding to citizen messages in digital governance platforms. When these systems fail, the problem is often not the language model itself, but that the nearest neighbors in the embedding space correspond to the wrong cases. Modern machine learning systems increasingly rely on fixed, high-dimensional embeddings produced by large pretrained models and sentence encoders. In real-world deployments, labels are scarce, domains shift over time, and retraining the base encoder is expensive or infeasible. As a result, downstream performance depends heavily on embedding geometry. Yet raw embeddings are often poorly aligned with the local neighborhood structure required by nearest-neighbor retrieval, similarity search, and lightweight classifiers that operate directly on embeddings. We propose PEARL (Prototype-Enhanced Aligned Representation Learning), a label-efficient approach that uses limited supervision to softly align embeddings toward class prototypes. The method reshapes local neighborhood geometry while preserving dimensionality and avoiding aggressive projection or collapse. Its aim is to bridge the gap between purely unsupervised post-processing, which offers limited and inconsistent gains, and fully supervised projections that require substantial labeled data. We evaluate PEARL under controlled label regimes ranging from extreme label scarcity to higher-label settings. In the label-scarce condition, PEARL substantially improves local neighborhood quality, yielding 25.7% gains over raw embeddings and more than 21.1% gains relative to strong unsupervised post-processing, precisely in the regime where similarity-based systems are most brittle.

cs.LG↗

arXiv2026

Simulating the Formation of the Young "Fermi Bubbles" in the Circinus Galaxy

The Fermi and eROSITA bubbles in the Milky Way represent an archetypal case of galactic nucleus feedback, yet their origin remains highly debated. Here we use hydrodynamic simulations to investigate the formation of the "Fermi bubbles" in the nearby Circinus galaxy, a pair of kpc-scaled elliptical bubbles seen in both radio and X-ray observations. We find that a pair of active galactic nucleus (AGN) jets drive forward shocks in the circumgalactic medium, and after evolving for ~0.95 Myr, the shock-delineated bubble pair roughly matches the observed Circinus bubbles in size and morphology. Our mock X-ray image and spectrum reproduce the observed edge-brightened X-ray surface brightness distribution and spectrum quite well, and suggest that non-thermal emissions from the jet ejecta also contribute substantially to radio and X-ray emissions from the inner "hotspot" region. We further show that AGN winds tend to produce more spherical bubbles with a wider base near the galactic plane, inconsistent with observations. The hotspot emissions and the misalignment between the galaxy rotational axis and the bubble's axis argue against a starburst wind origin. Our study thus corroborates the AGN jet-shock model for the origin of both the Circinus bubbles and the Fermi bubbles, and suggests that AGN jet feedback may be a common origin of extended gaseous bubbles in regular disk galaxies, potentially playing an important role in their evolution.

astro-ph.HE↗

arXiv2025

HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

HEAS is a Python framework that connects agent-based simulation, evolutionary search, and scenario-based evaluation in a single reproducible pipeline. It is designed for researchers who study systems where local interactions produce system-level outcomes-ecosystems, organizations, markets, or regulatory environments-and who need to search over candidate strategies and compare them across uncertain scenarios. HEAS combines three modules: a hierarchy runtime for composing simulations from reusable process layers, an evolutionary tuner for single- or multi-objective search backed by DEAP, and a game module for evaluating strategies across scenario ensembles. Its central design principle is the "metric contract": the same outcome function is shared by optimization, evaluation, and validation, so that different parts of an analysis cannot silently rank strategies by different quantities.

cs.MA↗

arXiv2025

Structural Equation-VAE: Disentangled Latent Representations for Tabular Data

Learning interpretable latent representations from tabular data remains a challenge in deep generative modeling. We introduce SE-VAE (Structural Equation-Variational Autoencoder), a novel architecture that embeds measurement structure directly into the design of a variational autoencoder. Inspired by structural equation modeling, SE-VAE aligns latent subspaces with known indicator groupings and introduces a global nuisance latent to isolate construct-specific confounding variation. This modular architecture enables disentanglement through design rather than through statistical regularizers alone. We evaluate SE-VAE on a suite of simulated tabular datasets and benchmark its performance against a series of leading baselines using standard disentanglement metrics. SE-VAE consistently outperforms alternatives in factor recovery, interpretability, and robustness to nuisance variation. Ablation results reveal that architectural structure, rather than regularization strength, is the key driver of performance. SE-VAE offers a principled framework for white-box generative modeling in scientific and social domains where latent constructs are theory-driven and measurement validity is essential.

cs.LG↗

arXiv2025

Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities

We examine whether higher eco-efficiency encourages local governments to co-produce environmental solutions with citizens. Using Chinese provincial data and advanced textual analysis, we find that high eco-efficiency strongly predicts more collaborative responses to environmental complaints. Causal inference suggests that crossing a threshold of eco-efficiency increases co-production probabilities by about 24 percentage points, indicating eco-efficiency's potential as a catalyst for participatory environmental governance.

econ.GN↗

arXiv2025

Formation of giant radio sources in galaxy clusters

The number of observed giant radio sources (GRSs) has increased significantly in recent years, yet their formation mechanisms remain elusive. The discovery of giant radio galaxies within galaxy clusters has further intensified the ongoing debates. We utilize magnetohydrodynamic simulations to investigate the formation of GRSs in cluster environments. To avoid confounding the effects of power and total energy injection, we hold the energy of jet outbursts fixed and study the effect of power by varying the active duration of the jets. Furthermore, we examine the roles of magnetic, thermal, and kinetic energy components by adjusting their fractions in the jets. Additionally, we calculate radio emission for comparison with observations in the radio power-linear size diagram (P-D diagram). Finally, we also study the energy transport processes of different jets. We find the "lower power-larger bubble" effect: lower-power jets tend to produce larger radio sources with fixed total jet energy. Regarding different energy components, jets dominated by toroidal magnetic field energy generate larger radio sources than kinetic and thermal energy-dominated jets. Conversely, strong poloidal magnetic fields hinder radio lobe growth. When injecting $2.06 \times 10^{59}$ erg into a $10^{14}$ solar mass halo, only jets with powers of approximately $10^{-4}$ to $10^{-3}$ Eddington luminosity efficiently traverse the observational region in the P-D diagram. Our findings suggest that energetic, long-lasting (low-power), continuous jets endowed with significant toroidal magnetic fields facilitate the formation of GRSs in cluster environments. However, although the jets with significantly lower power can generate substantially larger radio sources, their faintness may render them unobservable.

astro-ph.GA↗

arXiv2025

Soundwave: Less is More for Speech-Text Alignment in LLMs

Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth. We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency. We propose Soundwave, which utilizes an efficient training strategy and a novel architecture to address these issues. Results show that Soundwave outperforms the advanced Qwen2-Audio in speech translation and AIR-Bench speech tasks, using only one-fiftieth of the training data. Further analysis shows that Soundwave still retains its intelligence during conversation. The project is available at https://github.com/FreedomIntelligence/Soundwave.

cs.CL↗

arXiv2025

Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis

Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embedding fluctuations caused by unbalanced training data. BERT, which features transformer-based architecture and contextual embeddings, offers greater semantic consistency, making it suitable for analyses in which stability is crucial. This study compares Word2Vec and BERT using 20 years of People's Daily articles to evaluate their performance in semantic representations across different timeframes. The results indicate that BERT outperforms Word2Vec in maintaining semantic stability and still recognizes subtle semantic variations. These findings support BERT's use in text analysis tasks that require stability, where semantic changes are not assumed, offering a more reliable foundation than static alternatives.

cs.CL↗

arXiv2023

Asymmetric eROSITA bubbles as the evidence of a circumgalactic medium wind

The eROSITA bubbles are detected via the instrument with the same name. The northern bubble shows noticeable asymmetric features, including distortion to the west and enhancement in the eastern edge, while the southern counterpart is significantly dimmer. Their origins are debated. Here, we performed hydrodynamic simulations showing that asymmetric eROSITA bubbles favor a dynamic, circumgalactic medium wind model, but disfavor other mechanisms such as a non-axisymmetric halo gas or a tilted nuclear outflow. The wind from the east by north direction in Galactic coordinates blows across the northern halo with a velocity of about 200 km s$^{-1}$, and part of it enters the southern halo. This creates a dynamic halo medium and redistributes both density and metallicity within. This naturally explains the asymmetric bubbles in both the morphology and surface brightness. Our results suggest that our Galaxy is accreting low-abundance circumgalactic medium from one side while providing outflow feedback.

astro-ph.GA↗

arXiv2022

The formation of the stripped envelope type II b Supernova progenitors: Rotation, Metallicity and Overshooting

Type IIb supernovae are believed to originate from core-collapse progenitors having kept only a very thin hydrogen envelope. We aim to explore how some physical factors, such as rotation, metallicity, overshooting, and the initial orbital period in binaries, significantly affect the Roche lobe overflow and the formation of type IIb supernovae. It is found that binaries are the main channel that capable of producing type typeIIb supernovae progenitors in the mass range for initial masses below 20 $M_{\odot}$. The formation of type IIb supernova progenitors is extremely sensitive to the initial orbital period. A less massive hydrogen indicates smaller radius and a higher effective temperatures, and vice versa. Binary systems with initial periods between 300 and 720 days produce type IIb progenitors that are a red supergiant. Those with an initial period between 50 and 300 days produce yellow supergiant progenitors and those with initial periods shorter than 50 days, blue supergiant progenitors. Both rapid rotation and larger overshooting can enlarge the carbon-oxygen core mass and lead to higher core temperature and lower central density at the pre-collapse phase. They are also beneficial to surface nitrogen enrichment but restrict the efficiency of the first dredge-up. SN IIb progenitors with low metallicity have smaller hydrogen envelope masses and radii than the high metallicity counterparts. Ultra-stripped binary models have systematically higher core mass fraction $\rm ^{12}C$ left, which has important influence on the compactness of type IIb progenitors.

astro-ph.SR↗

arXiv2021

Close binary evolution based on Gaia DR2: the origin of late WC-type Wolf-Rayet stars with low luminosity

The observed late-type WC Wolf-Rayet stars (WC7-9) with low luminosity below $\rm \log L/L_{\odot} < 5.4$ in the HR diagram cannot be reproduced satisfactorily by the evolutionary track of single stars. The mass transfer due to Roche lobe overflow drastically modifies the internal structure and surface compositions of two components. Therefore, binaries provide a very promising evolutionary channel to produce these WC stars.

astro-ph.SR↗