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We theoretically investigate twisted structures where each layer is composedof a strongly correlated material. In particular, we study a twisted t-J modelof cuprate multilayers within the slave-boson mean field theory. This treatmentencompasses the Mott physics at small doping and self consistently generatesd-wave pairing. Furthermore, including the correct inter-layer tunneling formfactor consistent with the symmetry of the Cu $d_{x^2-y^2}$ orbital proves tobe crucial for the phase diagram. We find spontaneous time reversal (T)breaking around twist angle of $45^\circ$, although only in a narrow window oftwist angles. Moreover, the gap obtained is small and the Chern numbervanishes, implying a non-topological superconductor. At smaller twist angles,driving an interlayer current however can lead to a gapped topological phase.The energy-phase relation of the interlayer Josephson junction displays notabledouble-Cooper-pair tunneling which dominates around $45^o$. The twist angledependence of the Josephson critical current and the Shapiro steps areconsistent with recent experiments. Utilizing the moir\'e structure as a probeof correlation physics, in particular of the pair density wave state, isdiscussed.
Astrocytes play a central role in inducing concerted phase synchronizedneural-wave patterns inside the brain. In this article, we demonstrate thatinjected radio-frequency signal in underlying heavy metal layer of spin-orbittorque oscillator neurons mimic the neuron phase synchronization effectrealized by glial cells. Potential application of such phase coupling effectsis illustrated in the context of a temporal "binding problem". We also presentthe design of a coupled neuron-synapse-astrocyte network enabled by compactneuromimetic devices by combining the concepts of local spike-timing dependentplasticity and astrocyte induced neural phase synchrony.
We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchicalrepresentation that can recover fine geometry detail, while being able toperform global operations such as shape completion. Our model represents acomplex 3D shape with a hi erarchy of latent grids, which can be decoded intodifferent levels of detail and also achieve better accuracy. For shapecompletion, we propose latent grid dropout to simulate partial data in thelatent space and therefore defer the completing functionality to the decoderside. This along with our multires design significantly improves the shapecompletion quality under decoder-only latent optimization. To the best of ourknowledge, MDIF is the first deep implicit function model that can at the sametime (1) represent different levels of detail and allow progressive decoding;(2) support both encoder-decoder inference and decoder-only latentoptimization, and fulfill multiple applications; (3) perform detaileddecoder-only shape completion. Experiments demonstrate its superior performanceagainst prior art in various 3D reconstruction tasks.

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