Direct Observation of Quantum Anomalous Vortex in Fe(Se,Te)
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Publications and source records attributed to Jiang, K..
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Here, we present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time-varying scalar fields, optimizing to preserve spatial gradients, and random-access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.
The objective of this document is to conduct and record heat transfer calculations for a Cold Test Box. The Cold Test Box is constructed of three layers of insulating foam with an inlet and outlet port that allows a spherical object housed inside the box to be temperature-controlled by closed-loop cold air convection from a Cincinnati Sub-Zero Thermal Cooling Unit (TCU).