Use of Heart Rate Index to Predict Oxygen Uptake – A Validation Study

International Journal of Exercise Science 13(7): 1705-1717, 2020. An equation that uses heart rate index (HRI) defined as HR/HRrest to predict oxygen uptake (VO2) in METs (e.g., METs = 6 × HRI ‒ 5) has been developed retrospectively from aggregate data of 60 published studies. However, the prediction error of this model as used by an individual has not been established. Therefore, the purpose of this study was to examine the predictive validity of the HRI equation by comparing submaximal and maximal VO2 predicted by the equation (VO2-Pred) with that measured by indirect calorimetry (VO2-Meas). Sixty healthy adults (age 20.5 ± 2.4 yr., body mass 69.4 ± 13.4 kg, height 1.7 ± 0.1 m) underwent a VO2max test and an experimental trial consisting of a 15-min resting measurement and three successive 10-min treadmill exercise bouts performed at 40%, 60% and 80% of VO2max. VO2 and HR were recorded during both the submaximal and maximal exercises and used to obtain VO2-Pred and VO2-Meas for each intensity and for VO2max. Validation was carried out by paired t-test, regression analysis, and Bland-Altman plots. A modest but significant (p \u3c 0.05) correlation was observed between VO2-Meas and VO2-Pred at 40% (r = 0.58), 60% (r = 0.53), and 80% of VO2max (r = 0.56) and at VO2max (r = 0.50). No differences between VO2-Pred and VO2-Meas were found at 40% (5.53 ± 1.21 vs. 5.28 ± 0.98 METs, respectively) of VO2max, but VO2-Pred was higher (p \u3c 0.05) than VO2-Meas at 60% (8.42 ± 1.77 vs. 7.96 ± 1.39 METs, respectively) and 80% (10.79 ± 2.13 vs. 10.29 ± 1.81 METs, respectively) of VO2max. In contrast, VO2-Pred was lower (p \u3c 0.05) than VO2-Meas at VO2max (12.32 ± 2.30 vs. 13.38 ± 2.24 METs, respectively). Standard errors of the estimate were 0.81, 1.20, 1.54, and 1.97 METs at 40%, 60%, 80% of VO2max and at VO2max, respectively. These results suggest that further investigation aimed to establish the accuracy of using HRI to predict VO2 is warranted

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 page

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. The Exa.TrkX tracking pipeline clusters detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-like tracking detector), has been demonstrated on various detectors, including the DUNE LArTPC and the CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure -- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis