Extension of the C-XSC Library with Scalar Products with Selectable Accuracy

The C++ class library C-XSC for scientific computing has been extended with the possibility to compute scalar products with selectable accuracy in version 2.3.0. In previous versions, scalar products have always been computed exactly with the help of the so-called long accumulator. Additionally, optimized floating point computation of matrix and vector operations using BLAS-routines are added in C-XSC version 2.4.0. In this article the algorithms used and their implementations, as well as some potential pitfalls in the compilation, are described in more detail. Additionally, the theoretical background of the employed DotK algorithm and the necessary modifications of the concrete implementation in C-XSC are briefly explained. Run-time tests and numerical examples are presented as well

Acute Effects of Esports on the Cardiovascular System and Energy Expenditure in Amateur Esports Players

INTRODUCTION: Esports is practiced by millions of people worldwide every day. On a professional level, esports has been proven to have a high stress potential and is sometimes considered equivalent to traditional sporting activities. While traditional sports have health-promoting effects through muscle activity and increased energy expenditure, amateur esports could represent a purely sedentary activity, which would carry potentially harmful effects when practiced regularly. Therefore, this study aims to investigate the acute effects of esports on the cardiovascular system and energy expenditure in amateur esports players to show whether esports can be considered as physical strain or mental stress or whether amateur esports has to be seen as purely sedentary behavior. METHODS: Thirty male subjects participated in a 30-min gaming session, playing the soccer simulation game FIFA 20 or the tactical, first-person multiplayer shooter Counter-Strike: Global Offensive. Respiratory and cardiovascular parameters, as well as energy expenditure, blood glucose, lactate, and cortisol, were determined pre-, during, and post-gaming. RESULTS: There were no significant changes in oxygen uptake, carbon dioxide output, energy expenditure, stroke volume, or lactate levels. Heart rate, blood glucose and cortisol decreased through the intervention until reaching their minimum levels 10 min post-gaming (Cortisol(pre): 3.1 ± 2.9 ng/ml, Cortisol(post): 2.2 ± 2.3 ng/ml, p < 0.01; HR(min0.5): 82 ± 11 bpm, HR(post): 74 ± 13 bpm, p < 0.01). CONCLUSION: A 30-min esports intervention does not positively affect energy expenditure or metabolism in amateur esports players. Therefore, it cannot provide the same health-promoting effects as traditional sports participation, but could in the long-term rather cause the same potentially health-damaging effects as purely sedentary behavior. However, it does not trigger a negative stress response in the players. Deliberate physical activity and exercise routines adapted to these demands should therefore be part of the daily life of amateur esports players

Real-Time And Robust 3D Object Detection with Roadside LiDARs

This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. To prove the effectiveness of our proposed modules, we train and evaluate the model on three different vehicle and infrastructure datasets. To show the domain adaptation ability of our detector, we train it on an infrastructure dataset from China and perform transfer learning on a different dataset recorded in Germany. We do several sets of experiments and ablation studies for each module in the detector that show that our model outperforms the baseline by a significant margin, while the inference speed is at 45 Hz (22 ms). We make a significant contribution with our LiDAR-based 3D detector that can be used for smart city applications to provide connected and automated vehicles with a far-reaching view. Vehicles that are connected to the roadside sensors can get information about other vehicles around the corner to improve their path and maneuver planning and to increase road traffic safety.Comment: arXiv admin note: substantial text overlap with arXiv:2204.0013

Homocysteine in cerebrovascular disease: An independent risk factor for subcortical vascular encephalopathy

Hyperhomocysteinemia is a risk factor for obstructive large-vessel disease. Here, we studied plasma concentrations of homocysteine and vitamins in patients suffering from subcortical vascular encephalopathy (SVE), a cerebral small-vessel disease leading to dementia. These results were compared to the homocysteine and vitamin plasma concentrations from patients with cerebral large vessel disease and healthy control subjects. Plasma concentrations of homocysteine, vascular risk factors and vitamin status (B-6, B-12, folate) were determined in 82 patients with subcortical vascular encephalopathy, in 144 patients with cerebral large-vessel disease and in 102 control subjects. Patients with SVE, but not those with cerebral large-vessel disease, exhibited pathologically increased homocysteine concentrations in comparison with control subjects without cerebrovascular disease. Patients with SVE also showed lower vitamin B6 values in comparison to subjects without cerebrovascular disease. Logistic regression analysis showed that homocysteine is associated with the highest risk for SVE (odds ratio 5.7; CI 2.5-12.9) in comparison to other vascular risk factors such as hypertension, age and smoking. These observations indicate that hyperhomocysteinemia is a strong independent risk factor for SVE

Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%

On the design of reliable libraries

Software libraries are an important instrument for achieving reuse of both designs and programs. This goal is supported by several non-functional properties of libraries. Specifically, a library should be flexible by allowing components to be reused in different contexts with possibly changing requirements. However, reuse must not introduce new errors: the library components must remain correct in unknown contexts (robustness). Most common object-oriented libraries focus unilaterally on flexibility, neglecting robustness requirements. This paper investigates the trade-off between flexibility and robustness. We show that the desire for both flexible reuse and robustness can be met. Design patterns is identified as an important tool for structuring large object-oriented class libraries. The concepts discussed in this article have been realized in KARLA, an object-oriented library of algorithms and data structures, that currently contains more than 200 classes, and that is continuously bein..