arXiv subjects

Minghao Huang

Publications and source records attributed to Minghao Huang.

4 recordsLinked to original sources

arXiv2026

Shallow lunar fractures dominate the long-lasting seismic coda

Lunar seismic records are characterized by long-lasting spindle-shaped coda, which has long been considered to be caused by strong velocity contrasts in a continuous-medium lunar crust; however, simulated waveforms could not well match the observation even with unrealistically strong velocity contrasts, demonstrating its mechanism still unsolved. Here we propose a novel model of coda wave generation with random fractures in the lunar crust, where vacuum fractures prevent seismic wave propagation as insulating boundaries, causing strong persistent reverberation in terms of total reflection among fractures. Our model can easily reproduce the typical characteristics of observed Apollo data even using a limited number of simplified fractures in homogeneous matrix, with envelope correlations over 0.9, much higher than previous studies. Numerical simulations suggest that fractures in the uppermost ~1 km of the crust may dominate lunar seismic scattering. This model enables us to investigate the shallow lunar fracture system using seismic data and to suppress seismic coda for detecting critical phases reflected from deep interiors.

physics.geo-ph↗

arXiv2026

Macrofacet Theory for Gaussian Process Statistical Surfaces

We present macrofacet theory to extend microfacet theory from the micro-space to the macro-space. This is achieved by transforming surfaces into volumetric representations that preserve microfacet characteristics. Therefore, we formulate a macroscopic microfacet model using a classic exponential participating medium. Meanwhile, we observe that traditional microfacet models are equivalent to Gaussian processes by definition but ignore the correlation along the geometric normal of the macro-surface. We extend microfacet theory to address this limitation. Our formulation represents Gaussian process implicit surfaces in a statistical manner, which we refer to as Gaussian process statistical surfaces. As a result, our approach converts Gaussian process statistical surfaces into classic exponential media to render surfaces, volumes and in-betweens without realizations. This enables efficient rendering and improves performance compared to realization-based approaches, while theoretically bridging microfacet models and Gaussian processes. Moreover, our approach is easy to implement.

cs.GR↗

arXiv2026

The atomic structure of work: a micro-action instrument reveals two-pole AI occupational exposure and its decade-scale polar inversion

Research on artificial intelligence and work assigns each occupation a single exposure score. We build an instrument to see what those scores average over: a decomposition of 1,961 O*NET work activities into 15,817 atomic micro-actions by a consensus multi-agent LLM pipeline, clustered from text alone into seven semantic classes. Projecting exposure indicators onto these classes reveals two extreme poles, tool-mediated physical execution and planning-and-design, separated by a gap far larger than random partitions of the same data produce (permutation $P < 10^{-4}$; Cliff's $δ= 0.80$ under our tech-risk index and $0.90$ under GPT-4 task ratings). The poles flank a broad central band that carries most work and is only weakly more compressed than chance. The poles are stable across clustering resolution, sentence encoder (under a common partition), and indicator, yet which pole is most exposed has inverted since 2013: the two extremes swap identity between the Frey-Osborne computerisation era and the LLM era, and at the occupation level an occupation's 2013 automatability declines as its linguistic content rises ($ρ= -0.40$, $n = 618$). We release the instrument and its outputs. The durable object for forecasting is the structure of work itself, not any era's exposure ranking.

cs.CY↗

arXiv2026

Bounded by Risk, Not Capability: Quantifying AI Occupational Substitution Rates via a Tech-Risk Dual-Factor Model

The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly measure theoretical "exposure" to AI capabilities, ignoring critical frictions of real-world commercial adoption: liability, compliance, and physical safety. We argue occupations are not eradicated instantaneously, but gradually encroached upon via atomic actions. We introduce a Tech-Risk Dual-Factor Model to re-evaluate this. By deconstructing 923 occupations into 2,087 Detailed Work Activities (DWAs), we utilize a multi-agent LLM ensemble to score both technical feasibility and business risk. Through variance-based Human-in-the-Loop (HITL) validation with an expert panel, we demonstrate a profound cognitive gap: isolated algorithmic probabilities fail to encapsulate the "institutional premium" imposed by experts bounded by professional liability. Applying a strictly algorithmic baseline via mathematical bottleneck aggregation, we calculate Relative Occupational Automation Indices ($OAI$) for the U.S. labor market. Our findings challenge the traditional Routine-Biased Technological Change (RBTC) hypothesis. Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure ($OAI \approx 0.70$). Conversely, unstructured physical trades and high-stakes caretaking roles exhibit absolute resilience, quantifying a profound "Cognitive Risk Asymmetry." We hypothesize the emergent necessity of a "Compliance Premium," indicating wage resilience increasingly tied to risk-absorption capacity. We frame these findings as a cross-sectional diagnostic of systemic vulnerability, establishing a foundation for subsequent Computable General Equilibrium (CGE) econometric modeling involving dynamic wage elasticity and structural labor reallocation.

cs.CY↗