Machine Learning Interatomic Potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with high accuracy. While equivariant MLIPs achieve state-of-the-art accuracy, they face significant computational bottlenecks centered around their Tensor-Product layer, which account for up to 75% of training time and cause substantial memory overhead. We present FlashTP, a highly optimized tensor-product library that addresses these inefficiencies through kernel fusion, sparse computation, and path-aggregated execution. FlashTP achieves up to 41.6$\times$ and 60.8$\times$ kernel speedups over e3nn and NVIDIA cuEquivariance, respectively. For SevenNet-l3i5, it delivers 4.2$\times$ and 3.5$\times$ speedup while reducing peak memory usage by 6.3$\times$ and 6.2$\times$ for inference and training, respectively. The code is available at https://github.com/SNU-ARC/flashTP.

This paper proposes a reversible information hiding for supporting hierarchical control of access to embedded data. The proposed method firstly hides the most important information to an image by the reversible manner in which information is enciphered. To a stego image to which once information is hidden, this method embeds different information that is enciphered with a different key. In the proposed method, only one key corresponding to the most important data is managed, and other keys are generated from the managed key through a one-way hash function. The privileged user receives the managed key and he/she extracts and deciphers all hidden data, whereas the unprivileged user receives a key that for the least important data. The proposed method also has tamper detection ability. Simulation results show the effectiveness of the proposed method.