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  1. Phononic waveguides (PnWGs) are devices with rationally designed periodic structures to manipulate mechanical oscillations and to engineer and control the propagation of acoustic waves, thus allowing for frequency and band selection of wave transmission and routing, promising for both classical and quantum transduction on chip-scale platforms with various constituent materials of interest. They can be incorporated into both electromechanical and optomechanical signal transduction schemes. Here, we present an overview of emerging micro/nanoscale PnWGs and offer perspectives for future. We evaluate the typical structural designs, frequency scaling, and phononic band structures of the PnWGs. Material choices, fabrication techniques, and characterization schemes are discussed based on different PnWG designs. For classical transduction schemes, an all-phononic integrated circuit perspective is proposed. Toward emerging quantum applications, the potential of utilizing PnWGs as universal interfaces and transduction channels has been examined. We envision PnWGs with extraordinary propagation properties, such as nonreciprocity and active tunability, can be realized with unconventional design strategies (e.g., inverse design) and advanced materials (e.g., van der Waals layered crystals), opening opportunities in both classical and quantum signal transduction schemes.

     
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    Free, publicly-accessible full text available February 12, 2025
  2. Abstract

    Natural polymers, particularly plant‐derived nanocelluloses, self‐organize into hierarchical structures, enabling mechanical robustness, bright iridescence, emission, and polarized light reflection. These biophotonic properties are facilitated by the assembly of individual components during evaporation, such as cellulose nanocrystals (CNCs), which exhibit a left‐handed helical pitch in a chiral nematic state. This work demonstrates how optically active films with pre‐programmed opposite handedness (left or right) can be constructed via shear‐induced twisted printing with clockwise and counter‐clockwise shearing vectors. The resulting large‐area thin films are transparent yet exhibit pre‐determined mirror‐symmetrical optical activity, enabling the distinction of absorbed and emitted circularly polarized light. This processing method allows for sequential printing of thin and ultrathin films with twisted layered organization and on‐demand helicity. The complex light polarization behavior is due to step‐like changes in linear birefringence within each deposited layer and circular birefringence, different from that of conventional CNC films as revealed with Muller matrix analysis. Furthermore, intercalating an achiral organic dye into printed structures induces circularly polarized luminescence while preserving high transmittance and controlled handedness. These results suggest that twisted sequential printing can facilitate the construction of chiroptical metamaterials with tunable circular polarization, absorption, and emission for optical filters, encryption, photonic coatings, and chiral sensors.

     
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    Free, publicly-accessible full text available May 17, 2025
  3. Free, publicly-accessible full text available November 12, 2024
  4. Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e.g., the context in question and answer; “QA context”) and structured (e.g., knowledge graph for the QA context and scene; “concept graph”) multimodal knowledge. Existing works typically combine a scene graph and a concept graph of the scene by connecting corresponding visual nodes and concept nodes, then incorporate the QA context representation to perform question answering. However, these methods only perform a unidirectional fusion from unstructured knowledge to structured knowledge, limiting their potential to capture joint reasoning over the heterogeneous modalities of knowledge. To perform more expressive reasoning, we propose VQA-GNN, a new VQA model that performs bidirectional fusion between unstructured and structured multimodal knowledge to obtain unified knowledge representations. Specifically, we inter-connect the scene graph and the concept graph through a super node that represents the QA context, and introduce a new multimodal GNN technique to perform inter-modal message passing for reasoning that mitigates representational gaps between modalities. On two challenging VQA tasks (VCR and GQA), our method outperforms strong baseline VQA methods by 3.2% on VCR (Q-AR) and 4.6% on GQA, suggesting its strength in performing concept-level reasoning. Ablation studies further demonstrate the efficacy of the bidirectional fusion and multimodal GNN method in unifying unstructured and structured multimodal knowledge. 
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    Free, publicly-accessible full text available October 4, 2024