Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...
Advanced wave engineering demands robust, efficient, and new-degree-of-freedom manipulations in wave intrinsic properties like amplitude, phase, polarization, and even spatial structure. To tackle this, this thesis advances photonic wave manipulation by sy ...
Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...
Additive manufacturing of 316L/CuCrZr multi-material metallic structures has recently attracted significant attention, due to the ideal combination of structural and thermal/electrical properties. In this work, a unique multi-phase microstructure was produ ...
A pressure-induced triclinic-to-monoclinic phase transition has been caught 'in the act' over a wider series of high-pressure synchrotron diffraction experiments conducted on a large, photoluminescent organo-gold(I) compound. Here, we describe the mechanis ...
In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. ...
