Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable.
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A shared space area is a low-speed urban area in which pedestrians, cyclists, and vehicles share the road, often relying on informal interaction rules and greatly expanding freedom of movement for pedestrians and cyclists. While shared space has the potential to improve pedestrian priority in urban areas, it presents unique challenges for pedestrian-AV interaction due to the absence of a clear right of way. The current study applied Virtual Reality (VR) experiments to investigate pedestrian-AV interaction in a shared space, with a particular focus on the impact of external human-machine interfaces (eHMIs) on pedestrian crossing behavior. Fifty-three participants took part in the VR experiment and three eHMI conditions were investigated: no eHMI, eHMI with a pedestrian sign on the windshield, and eHMI with a projected zebra crossing on the road. Data collected via VR and questionnaires were used for objective and subjective measures to understand pedestrian-AV interaction. The study revealed that the presence of eHMI had an impact on participants' gazing behavior but not on their crossing decisions. Additionally, participants had a positive user experience with the current VR setting and expressed a high level of trust and perceived safety during their interaction with the AV. These findings highlight the potential of utilizing VR to explore and understand pedestrian-AV interactions.
This paper mainly conducts further research to alleviate the issue of limit cycling behavior in training generative adversarial networks (GANs) through the proposed predictive centripetal acceleration algorithm (PCAA). Specifically, we first derive the upper and lower bounds on the last-iterate convergence rates of PCAA for the general bilinear game, with the upper bound notably improving upon previous results. Then, we combine PCAA with the adaptive moment estimation algorithm (Adam) to propose PCAA-Adam, a practical approach for training GANs. Finally, we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games, multivariate Gaussian distributions, and the CelebA dataset, respectively.
Characterizing shapes of high-dimensional objects via Ricci curvatures plays a critical role in many research areas in mathematics and physics. However, even though several discretizations of Ricci curvatures for discrete combinatorial objects such as networks have been proposed and studied by mathematicians, the computational complexity aspects of these discretizations have escaped the attention of theoretical computer scientists to a large extent. In this paper, we study one such discretization, namely the Ollivier-Ricci curvature, from the perspective of efficient computation by fine-grained reductions and local query-based algorithms. Our main contributions are the following. (a) We relate our curvature computation problem to minimum weight perfect matching problem on complete bipartite graphs via fine-grained reduction. (b) We formalize the computational aspects of the curvature computation problems in suitable frameworks so that they can be studied by researchers in local algorithms. (c) We provide the first known lower and upper bounds on queries for query-based algorithms for the curvature computation problems in our local algorithms framework. En route, we also illustrate a localized version of our fine-grained reduction. We believe that our results bring forth an intriguing set of research questions, motivated both in theory and practice, regarding designing efficient algorithms for curvatures of objects.
While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provide guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicate adherence assessment by recommending activity to occur in at least 10 minute bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1,000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance to the PAG in the Iowa adult population. The model accurately accounts for the 10 minute bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day to day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey.
A shared space area is a low-speed urban area in which pedestrians, cyclists, and vehicles share the road, often relying on informal interaction rules and greatly expanding freedom of movement for pedestrians and cyclists. While shared space has the potential to improve pedestrian priority in urban areas, it presents unique challenges for pedestrian-AV interaction due to the absence of a clear right of way. The current study applied Virtual Reality (VR) experiments to investigate pedestrian-AV interaction in a shared space, with a particular focus on the impact of external human-machine interfaces (eHMIs) on pedestrian crossing behavior. Fifty-three participants took part in the VR experiment and three eHMI conditions were investigated: no eHMI, eHMI with a pedestrian sign on the windshield, and eHMI with a projected zebra crossing on the road. Data collected via VR and questionnaires were used for objective and subjective measures to understand pedestrian-AV interaction. The study revealed that the presence of eHMI had an impact on participants' gazing behavior but not on their crossing decisions. Additionally, participants had a positive user experience with the current VR setting and expressed a high level of trust and perceived safety during their interaction with the AV. These findings highlight the potential of utilizing VR to explore and understand pedestrian-AV interactions.
During the process of robot-assisted ultrasound(US) puncture, it is important to estimate the location of the puncture from the 2D US images. To this end, the calibration of the US image becomes an important issue. In this paper, we proposed a depth camera-based US calibration method, where an easy-to-deploy device is designed for the calibration. With this device, the coordinates of the puncture needle tip are collected respectively in US image and in the depth camera, upon which a correspondence matrix is built for calibration. Finally, a number of experiments are conducted to validate the effectiveness of our calibration method.
This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.
An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement of the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets including Indian Pines, Pavia Centre, and Pavia University.
In this work, we analyze the relation between reparametrizations of gradient flow and the induced implicit bias on general linear models, which encompass various basic classification and regression tasks. In particular, we aim at understanding the influence of the model parameters - reparametrization, loss, and link function - on the convergence behavior of gradient flow. Our results provide user-friendly conditions under which the implicit bias can be well-described and convergence of the flow is guaranteed. We furthermore show how to use these insights for designing reparametrization functions that lead to specific implicit biases like $\ell_p$- or trigonometric regularizers.
In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances from five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey on VLP. We hope that this survey can shed light on future research in the VLP field.
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