FDA has released a number of documents that could help clarify its expectations for artificial intelligence, machine learning, and cybersecurity. These include Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, published in January 2021; Good Machine Learning Practice for Medical Device Development: Guiding Principles, published in October 2021; and the just-released draft guidance, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. The AI/ML action plan provides a "more tailored regulatory framework for AI/ML," explained Pavlovic. She referred to FDA's 2019 discussion paper, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback, which laid out a "total product lifecycle approach to AI/ML regulations with the understanding that AI/ML products can be iterated much more efficiently and quickly than a typical medical device implant product or something that isn't software based." This is "because there is an opportunity to add additional data to training sets on which the products were originally formulated," she said.

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. This paper presents an approach to segmental sequence alignment that jointly segments and aligns two sequences, generalizing the traditional per-sample alignment. To accomplish this task, we introduce a distance metric between segments based on average pairwise distances and then present a modified pair-HMM (PHMM) that incorporates the proposed distance metric to solve the joint segmentation and alignment task. We also propose a relaxation to our model that improves the computational efficiency of the generic segmental PHMM. Our results demonstrate that this new measure of sequence similarity can lead to improved classification performance, while being resilient to noise, on a variety of sequence retrieval problems, from EEG to motion sequence classification.

Bayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed affine structure from motion (SfM).