Wearable Biomonitoring Platform for the Assessment of Stress and its Impact on Cognitive Performance of Firefighters: An Experimental Study

Background: Stress is a complex process with an impact on health and performance. The use of wearable sensor-based monitoring systems offers interesting opportunities for advanced health care solutions for stress analysis. Considering the stressful nature of firefighting and its importance for the community’s safety, this study was conducted for firefighters. Objectives: A biomonitoring platform was designed, integrating different biomedical systems to enable the acquisition of real time Electrocardiogram (ECG), computation of linear Heart Rate Variability (HRV) features and collection of perceived stress levels. This platform was tested using an experimental protocol, designed to understand the effect of stress on firefighter’s cognitive performance, and whether this effect is related to the autonomic response to stress. Method: The Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. Linear HRV features from the participants were acquired using an wearable ECG. Self-reports were used to assess perceived stress levels. Results: The TSST produced significant changes in some HRV parameters (AVNN, SDNN and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was found after performing the stress event. Conclusion: Current findings suggested that stress compromised cognitive performance and caused a measurable change in autonomic balance. Our wearable biomonitoring platform proved to be a useful tool for stress assessment and quantification. Future studies will implement this biomonitoring platform for the analysis of stress in ecological settings

The Open Data Detector Tracking System

Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments

Machine Learning with ROOT/TMVA

ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow

Machine Learning with ROOT/TMVA

ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow

ACTS GPU Track Reconstruction Demonstrator for HEP

In the future HEP experiments, there will be a significant increase in computing power required for track reconstruction due to the large data size. As track reconstruction is inherently parallelizable, heterogeneous computing with GPU hardware is expected to outperform the conventional CPUs. To achieve better maintainability and high quality of track reconstruction, a host-device compatible event data model and tracking geometry are necessary. However, such a flexible design can be challenging because many GPU APIs restrict the usage of modern C++ features and also have a complicated user interface. To overcome those issues, the ACTS community has launched several R&D projects: traccc as a GPU track reconstruction demonstrator, detray as a GPU geometry builder, and vecmem as a GPU memory management tool. The event data model of traccc is designed using the vecmem library, which provides an easy user interface to host and device memory allocation through C++ standard containers. For a realistic detector design, traccc utilizes the detray library which applies compile-time polymorphism in its detector description. A detray detector can be shared between the host and the device, as the detector subcomponents are serialized in a vecmem-based container. Within traccc, tracking algorithms including hit clusterization and seed finding have been ported to multiple GPU APIs. In this presentation, we highlight the recent progress in traccc and present benchmarking results of the tracking algorithms

A Common Tracking Software Project

Experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C++If you use this software, please cite it using these metadata

A Common Tracking Software Project

Experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C++If you use this software, please cite it using these metadata

acts-project/acts: v13.0.0

Experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C+

acts-project/acts: v22.0.1

Experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C+

acts-project/acts: v17.0.0

Experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C+