arXiv subjects

Yiting Huang

Publications and source records attributed to Yiting Huang.

3 recordsLinked to original sources

arXiv2026

Imaging an obstructed Wannier orbital

In conventional insulators, electronic charge is localized in Wannier orbitals centered on the atoms. In an obstructed atomic insulator (OAI), the Wannier orbitals sit instead in the empty voids between atoms. These displaced orbitals give rise to an analog of the topological bulk-boundary correspondence, in which a boundary mode occurs only where the lattice termination severs an orbital. However, experimentally identifying OAIs has remained challenging, because the Wannier orbital of a dispersive electronic band is not an energy eigenstate and cannot be measured directly. Here we engineer an acoustic metamaterial hosting an isolated flat band, in which the Wannier orbitals become energy eigenstates. By measuring the amplitude and phase of the acoustic pressure field, we map the orbital's $f$-like structure and locate its Wannier center in a void between lattice sites---providing the first phase-resolved image of an obstructed Wannier orbital. By altering the lattice termination, we observe the predicted boundary states. Such acoustic flat-band metamaterials offer a direct probe of single-particle ingredients such as quantum geometry, which govern correlated phenomena in electronic flat bands, including proposed routes to unconventional superconductivity.

cond-mat.str-el↗

arXiv2026

Rapidly prototyping kagome flat band physics with acoustic metamaterials

Flat bands with vanishing group velocity and quenched kinetic energy provide fertile ground for correlated states and exotic phenomena such as unconventional superconductivity. Yet, engineering and characterizing flat bands in quantum materials is time-consuming and costly, limiting the exploration of the vast design space of possible flat band systems. Acoustic metamaterials offer an accessible alternative: they can be easily simulated, cheaply fabricated, and quickly measured. Here we present a complete workflow to rapidly prototype flat band systems using acoustic metamaterials. Our design directly implements a tight-binding model using air cavities as lattice sites connected by channels that control hopping, allowing it to generalize to diverse lattice geometries. Using the kagome lattice as a proof-of-concept, we demonstrate excellent agreement between tight-binding theory, finite-element simulations, and experimental measurements. We then design a family of extended kagome lattices whose added sites cancel successively longer-range hopping, flattening the flat band. With fabrication and measurement requiring hours rather than months and a total cost orders of magnitude lower than quantum materials, our approach enables rapid iteration through candidate lattices, facilitating the discovery of new flat band physics in quantum materials.

cond-mat.str-el↗

arXiv2026

Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention

The proliferation of social media platforms and online communities has inadvertently catalyzed the spread of cyberbullying, hate speech, and other forms of online toxicity, making the effective governance of such harm a critical societal and computational challenge. While significant strides have been made in automating content moderation, existing research predominantly treats cyberbullying governance as passive, isolated detection at the post level. This reductionist view overlooks the continuous behavioral dynamics of users, the structural diffusion of toxic events, and the critical need for proactive mitigation. To bridge these gaps, this paper proposes a unified full-lifecycle governance framework that shifts the paradigm of cyberbullying governance from isolated static detection toward integrated, continuous, and proactive moderation. Drawing on cyberbullying research and adjacent fields, we systematically synthesize the state-of-the-art literature across four interconnected stages: (1) Content Identification, (2) User and Behavior Modeling, (3) Diffusion Dynamics and Early Warning, and (4) Intervention and Governance. Furthermore, we review available datasets and evaluation practices, and discuss emerging challenges including multimodality, explainability, algorithmic fairness, and the dual-use risks of generative AI, providing a roadmap for future research toward a safer and more resilient digital ecosystem.

cs.AI↗