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Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality’s latent representation towards a shared aggregate posterior. This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods. 

We report the P-V-T equation of state measurements of B4C to 50GPa and approximately 2500K in laser-heated diamond anvil cells. We obtain an ambient temperature, third-order Birch–Murnaghan fit to the P-V data that yields a bulk modulus K0 of 221(2) GPa and derivative, (dK/dP)0 of 3.3(1). These were used in fits with both a Mie–Grüneisen– Debye model and a temperature-dependent, Birch– Murnaghan equation of state that includes thermal pressure estimated by thermal expansion (α) and a temperature-dependent bulk modulus (dK0/dT). The ambient pressure thermal expansion coefficient (α0+α1T), Grüneisen γ (V)=γ 0(V/V0)q and volumedependent Debye temperature, were used as input parameters for these fits and found to be sufficient to describe the data in the whole P-T range of this study. 

Today, acoustic metamaterials (AMMs) form a core area of metamaterial research. They offer bespoke wave control, achievable through their rationally designed structure and are at the forefront of metamaterial applications and commercialisation. They find purpose across science and defence sectors in wave filtering, sensing, communications, energy harvesting, thermal emission control, and aeroacoustics, to name but a few. They enjoy success in metropolitan environments with designer audio and noise mitigation falling within their remit; AMM technologies are already penetrating the market across audio and healthcare sectors. The landscape of AMM research is continually expanding, now incorporating several wave regimes under a broader definition that we adopt here. The diversity of AMM research displays how they exist not only to translate electromagnetic phenomena, but also to provide a unique platform for exploring all metamaterial physics, and for solving key societal challenges. The aim of this Roadmap is to present a summary of the state of AMM research and innovation in 2024, with opinions on the challenges and future opportunities from a group of renowned experts, covering key interdisciplinary areas from fundamental acoustics to device implementation. 

Abstract In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion. Graphical abstract