Efficient Softmax Reformulation for Homomorphic Encryption via Moment Generating Function

Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. However, evaluating softmax, a core component of transformer architectures, remains particularly challenging in HE due to its multivariate structure, the large dynamic range induced by exponential functions, and the need for accurate division during normalization. In this paper, we propose MGF-softmax, a novel softmax reformulation based on the moment generating function (MGF) that replaces the softmax denominator with its moment-based counterpart. This reformulation substantially reduces multiplicative depth while preserving key properties of softmax and asymptotically converging to the exact softmax as the number of input tokens increases. Extensive experiments on Vision Transformers and large language models show that MGF-softmax provides an efficient and accurate approximation of softmax in encrypted inference. In particular, it achieves inference accuracy close to that of high-depth exact methods, while requiring substantially lower computational cost through reduced multiplicative depth.

SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of marker-less methods based on deep learning. However, acquiring realistic surgical data, with ground truth instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation.

Fast State-of-Health Estimation Method for Lithium-ion Battery using Sparse Identification of Nonlinear Dynamics

Lithium-ion batteries (LIBs) are utilized as a major energy source in various fields because of their high energy density and long lifespan. During repeated charging and discharging, the degradation of LIBs, which reduces their maximum power output and operating time, is a pivotal issue. This degradation can affect not only battery performance but also safety of the system. Therefore, it is essential to accurately estimate the state-of-health (SOH) of the battery in real time. To address this problem, we propose a fast SOH estimation method that utilizes the sparse model identification algorithm (SINDy) for nonlinear dynamics. SINDy can discover the governing equations of target systems with low data assuming that few functions have the dominant characteristic of the system. To decide the state of degradation model, correlation analysis is suggested. Using SINDy and correlation analysis, we can obtain the data-driven SOH model to improve the interpretability of the system. To validate the feasibility of the proposed method, the estimation performance of the SOH and the computation time are evaluated by comparing it with various machine learning algorithms.