During the COVID-19 pandemic, a major driver of new surges has been the emergence of new variants. When a new variant emerges in one or more countries, other nations monitor its spread in preparation for its potential arrival. The impact of the new variant and the timings of epidemic peaks in a country highly depend on when the variant arrives. The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence. Can we predict when a variant existing elsewhere will arrive in a given region? To address this question, we propose a variant-dynamics-informed Graph Neural Network (GNN) approach. First, we derive the dynamics of variant prevalence across pairs of regions (countries) that apply to a large class of epidemic models. The dynamics motivate the introduction of certain features in the GNN. We demonstrate that our proposed dynamics-informed GNN outperforms all the baselines, including the currently pervasive framework of Physics-Informed Neural Networks (PINNs). To advance research in this area, we introduce a benchmarking tool to assess a user-defined model's prediction performance across 87 countries and 36 variants.

Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR) at a macroscopic level (i.e., directly from sEMG signals). At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information. However, due to complexities of sEMG decomposition and added computational overhead, HGR at microscopic level is less explored than its aforementioned macroscopic-level, DNN-based counterparts. In this regard, we propose the HYDRA-HGR framework, which is a hybrid model for HGR that simultaneously extracts a set of temporal and spatial features through its two independent Vision Transformer (ViT)-based parallel architectures (the so called Macro and Micro paths). The Macro Path is trained directly on the pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p values of the extracted Motor Unit Action Potentials (MUAPs) of each source. Extracted features at macroscopic and microscopic levels are then coupled via a Fully Connected (FC) fusion layer for final gesture classification. We evaluate the proposed hybrid HYDRA-HGR framework through a recently released HD-sEMG dataset, and show that it significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR framework achieves average accuracy of 94.86% for the 250 ms window size, which is 5.52 % and 8.22 % higher than that of the Macro and Micro paths, respectively.

Given the recent surge of significant interest in implementing intelligent hand gesture recognition methods in human-machine interface systems, a wide variety of Deep Neural Networks (DNNs) have been proposed in the literature. In this paper, we introduce a novel and compact Spiking Neural Network (SNN) model for hand gesture recognition using High-Density surface Electromyogram (HD-sEMG) signals. Capitalizing on their ability to extract spatiotemporal features of HD-sEMG signals along with their proven strength in imitating human brain's neural activity using event-driven data processing, we used SNNs as the main building block of our proposed hand gesture recognition model. We show that our proposed model can efficiently differentiate 14 hand movements by considering each sample of the HD-sEMG data as a single time step for the SNN architecture. Moreover, we show that the proposed SNN model does not require huge pre-processing, spike encoding and feature extraction tasks and works effectively on Min-Max normalized continuous-value sEMG signals. We evaluate our SNN model using a 5-fold cross-validation scheme and categorize different participants based on the range of classification accuracy we obtained for them. The following results are acquired by segmenting HD-sEMG signals into windows of size 62.5ms with no overlap. The proposed method led to 6 out of 19 subjects achieving average classification accuracy of ≥ 80% with maximum accuracy of 98% associated with 3rdsession of the sEMG dataset as the test set.