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Topological Insulators (TIs) are promising platforms for Quantum Technology due to their topologically protected surface states (TSS). Plasmonic excitations in TIs are especially interesting both as a method of characterisation for TI heterostructures, and as potential routes to couple optical and spin signals in low-loss devices. Since the electrical properties of the TI surface are critical, tuning TI surfaces is a vital step in developing TI structures that can be applied in real world plasmonic devices.

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Topologically associating domains (TADs) are essential components of three-dimensional (3D) genome organization and significantly influence gene transcription regulation. However, accurately identifying TADs from sparse chromatin contact maps and exploring the structural and functional elements within TADs remain challenging. To this end, we develop TADGATE, a graph attention auto-encoder that can generate imputed maps from sparse Hi-C contact maps while adaptively preserving or enhancing the underlying topological structures, thereby facilitating TAD identification.

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