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Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. 

The global COVID-19 pandemic forced all large in-person events to pivot to virtual or online platforms. IEEEVR2020 coincided with rising concerns and restrictions on travel and large gatherings, becoming one of the first academic conferences to rapidly adapt its programming to a completely virtual format. The global pandemic provided an impetus to re-examine the possibility of holding social interactions in virtual worlds. This article aims to: (1) revisit the issues of virtual conferences noted in earlier studies, focusing specifically on academic conferences, (2) introduce new survey and observational data from the recent IEEEVR2020 conference, and (3) present insights and future directions for virtual conferences during and after the COVID-19 pandemic. Findings from a field observation during the conference and a post-conference survey point to complex relationships among users, media platforms selected, and social constraints during the virtual conference.