Acoustic levitation enables mid-air displays using physical particles to create 3D visuals, but stability limits the achievable animation complexity. Stability depends on factors including the acoustic solver, particle count, motion speed, and path geometry. This paper analyzes these factors, characterizing their effects, identifying constraints, and allowing particles to successfully follow the paths. We then propose Acoustic Actor-Critic (AAC), a closed-loop motion planning system that maximizes stability for multi-particle trajectories with minimal changes to the intended visual content. This follows a plan-detect-repair strategy: i) the Actor plans trajectories under the established constraints; ii) the Critic evaluates their stability and detects instabilities; iii) the Repair modules trigger localized repairs upon unstable path segments. Results showed that AAC can automatically refine and repair multi-particle trajectories, reducing failures from 21% to 6% across 100 paths. Our findings enable creators to produce more stable levitation paths, while AAC automatically refines trajectories with minimal deviation from the original animations.

Figure 1: The research involved: (i) a design futuring workshop on acoustophoresis, (ii) the development and feasibility assessment of two SONARIOS (i.e., scenarios), and (iii) the synthesis of three strong concepts, along with key tensions and responsibilities in shaping the future of acoustophoresis applications.

Acoustic levitation is an emerging technique that has found application in contactless assembly and dynamic displays. It uses precise phase control in an ultrasound transducer array to manage the positions and movements of multiple particles. Yet, maintaining stable mid-air particles is challenging, with unexpected drops disrupting the intended motion and position. Here, we present StableLev, a data-driven pipeline for the detection and amendment of instabilities in multi-particle levitation. We first curate a hybrid levitation dataset, blending optimized simulations with labels based on actual trajectory outcomes. We then design an AutoEncoder to detect anomalies in the simulated data, correlating closely with observed particle drops. Finally, we reconstruct the acoustic field at anomaly regions to improve particle stability and experimentally demonstrate successful dynamic levitation for trajectories within our dataset. Our work provides new insights into multi-particle levitation and enhances its robustness, which will be valuable in a wide range of applications.

Data physicalisation is a technique that encodes data through the geometric and material properties of an artefact, allowing users to engage with data in a more immersive and multi-sensory way. However, current methods of data physicalisation are limited in terms of their reconfigurability and the types of materials that can be used. Acoustophoresis—a method of suspending and manipulating materials using sound waves—offers a promising solution to these challenges. In this paper, we present DataLev, a design space and platform for creating reconfigurable, multimodal data physicalisations with enriched materiality using acoustophoresis. We demonstrate the capabilities of DataLev through eight examples and evaluate its performance in terms of reconfigurability and materiality. Our work offers a new approach to data physicalisation, enabling designers to create more dynamic, engaging, and expressive artefacts.

Human multimodal sentiment analysis is a challenging task that devotes to extract and integrate information from multiple resources, such as language, acoustic and visual information. Recently, multimodal adaptation gate (MAG), an attachment to transformer-based pre-trained language representation models, such as BERT and XLNet, has shown state-of-the-art performance on multimodal sentiment analysis. MAG only uses a 1-layer network to fuse multimodal information directly, and does not pay attention to relationships among different modalities. In this paper, we propose an extended MAG, called MAG+, to reinforce multimodal fusion. MAG+ contains two modules: multi-layer MAGs with modality reinforcement (M3R) and Adaptive Layer Aggregation (ALA). In the MAG with modality reinforcement of M3R, each modality is reinforced by all other modalities via crossmodal attention at first, and then all modalities are fused via MAG. The ALA module leverages the multimodal representations at low and high levels as the final multimodal representation. Similar to MAG, MAG+ is also attached to BERT and XLNet. Experimental results on two widely used datasets demonstrate the efficacy of our proposed MAG+.

People who are to live, study and work abroad will face more challenges in the new cultural environment and suffer more acculturative stress. Virtual Reality (VR), by which an immersive learning environment can be built, may help them adapt to a foreign culture at a lower cost of time and money. In order to work out a design method for culture learning in VR, we have designed a VR application so that learners can experience and learn the typical western festival culture – Christmas culture – in an immersive environment. To evaluate the effectiveness of the VR method, 50 EFL Chinese university students were enrolled in our experiments and randomly assigned to the VR group and the non-VR group, the data was drawn from cultural knowledge questionnaire, behavior test and Intercultural Sensitivity Scale (ISS). The ANCOVA revealed no major effect for group factor on knowledge learning. Similarly, the Mixed ANOVA identified no major effect for group factor on behavior learning and attitude learning. There was no interaction effect between time and group in all experiments. Our results show that the VR method is preferred by most of the participants, but it shows no remarkable advantage over the non-VR method. Moreover, regression analysis between the culture learning and the sense of presence in VR shows that presence has the potential to improve the performance of intercultural interaction engagement. Our findings are of practical value for culture learning in VR.