Update Method Research Articles

Federated Label-Noise Learning with Local Diversity Product Regularization

Training data in federated learning (FL) frameworks can have label noise, since they must be stored and annotated on clients' devices. If trained over such corrupted data, the models learn the wrong knowledge of label noise, which highly degrades their performance. Although several FL schemes are designed to combat label noise, they suffer performance degradation when the clients' devices only have limited local training samples. To this end, a new scheme called federated label-noise learning (FedLNL) is developed in this paper. The key problem of FedLNL is how to estimate a noise transition matrix (NTM) accurately in the case of limited local training samples. If a gradient-based update method is used to update the local NTM on each client's device, it can generate too large gradients for the local NTM, causing a high estimation error of the local NTM. To tackle this issue, an alternating update method for the local NTM and the local classifier is designed in FedLNL, where the local NTM is updated by a Bayesian inference-based update method. Such an alternating update method makes the loss function of existing NTM-based schemes not applicable to FedLNL. To enable federated optimization of FedLNL, a new regularizer on the parameters of the classifier called local diversity product regularizer is designed for the loss function of FedLNL. The results show that FedLNL improves the test accuracy of a trained model by up to 25.98%, compared with the state-of-the-art FL schemes that tackle label-noise issues.

Abstract 2337: Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly

Abstract Background Optical genome maps (OGM) from Bionano enable the detection of genomic structural and copy number variants that cannot be detected by next-generation sequencing (NGS) technologies and are often missed by conventional cytogenetic techniques. Bionano has developed bioinformatics pipelines for calling structural and copy number variants including the Bionano Solve de novo assembly pipeline for constitutional analysis and the Rare Variant Analysis (RVA) pipeline for low-allele-fraction cancer applications. Both pipelines are computationally intensive and currently take 5-10 hours on the latest generation of the Bionano Saphyr® Compute which is deployed as a four-node compute cluster and requires significant IT resources. Methods To increase throughput and simplify the deployment of the compute resources needed to support the Stratys™ optical genome mapping instrument, Bionano has developed Stratys™ Compute. Stratys Compute improves both compute and analytical performance by adapting compute-intensive stages in the Solve pipeline to run on GPUs and by developing the Guided Assembly pipeline. Stratys Compute is powered by state-of-the-art NVIDIA RTX 6000 Ada generation cards and CUDA-optimized refinement, alignment, and structural variation detection kernels to accelerate OGM analysis. Stratys Compute is a standalone workstation placed adjacent to Stratys instrument. This will minimize the IT footprint and bypass the integration with the customer site’s data center. We have incorporated these optimizations into the new Guided Assembly pipeline, which aims to combine the low-allele fraction detection capability of the RVA pipeline with the whole genome coverage and ability to detect smaller structural variants enabled by the de novo assembly pipeline. The Guided Assembly pipeline uses the reference genome as an initial seed followed by extension, refinement, and structural variant calling. This new analysis method has been evaluated through comparison to previous results from both existing pipelines and standard benchmarking datasets used to estimate structural variant calling performance and is deployed on Stratys Compute for both constitutional and low allele-fraction applications. Results Guided Assembly has been adapted to run on GPU hardware in a simplified compute tower that can be deployed to a lab along with the Stratys instrument without the need for a dedicated server room. We found concordance between the guided assembly results and our previous de novo and RVA pipeline results. We also found increased sensitivity at low allele fractions for detecting insertion variants smaller than 5 kb and larger than 200 kb while finding equivalent performance for other variant types with the updated methods. The accelerated Guided Assembly for constitutional and low-allele-fraction applications will be available to early access customers in Q4 2023 with full commercial release in Q2 2024. Citation Format: Damla Senol Cali, Thomas Anantharaman, Martin Muggli, Samer Al-Saffar, Charles Schoonover, Neil Miller. Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2337.

Development of a time pressure-based model for the simulation of an evacuation in a fire emergency

The study of pedestrian behaviour in a fire emergency is essential for effectively reducing the number of casualties. However, most studies only consider the effect of fire on pedestrian behaviour and ignore the effect of a fire emergency on pedestrian psychology. In this study, we introduce a method of assessing the pedestrian time pressure within a cellular automaton model using the available safe egress time and required safe egress time in fire evacuations. Moreover, a more realistic reflection of the evacuation process in a fire emergency is obtained by introducing Pyrosim to compute the fire floor field and by proposing a sequential update method based on time pressure. To assess the model, we conduct sensitivity analyses on model parameters across various environmental configurations. Numerical simulation results show that fire accelerates the evacuation rate and radiated heat transfer and temperature have the greatest effects on the time pressure of pedestrians in a fire evacuation. The distribution of pedestrians is U-shaped owing to fire avoidance behaviour and in the later stage of evacuation, pedestrians keeping away from heated walls. The trend of time pressure over time has a bimodal distribution as pedestrians initially pass the fire and the environment then becomes untenable as the fire fully develops. In addition, the exit location affects the evacuation process, with a symmetrical positioning improving the evacuation efficiency. We verify the model in a series of physical experiments. The agreement between the numerical simulation results and experimental results demonstrates that the time pressure-based model is suitable for simulating the route choice behaviour and evacuation process in fire emergencies.

Lc‐Stream: An elastic scheduling strategy with latency constraints in geo‐distributed stream computing environments

SummaryAn effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

Dynamic response of piles embedded in granular soil to lateral impacts

Soil-embedded vehicle barriers, such as W-beam guardrail systems, play a pivotal role in transportation safety, mitigating the risks associated with vehicular collisions with roadside hazards. The efficacy of these barriers greatly depends on the pile-soil system's kinetic energy dissipation capability during vehicular impacts. However, a comprehensive understanding of how soil strength, embedment depth, and impact velocity collectively govern the dynamic behavior of the pile-soil system remains a gap in current research. This study explores the dynamics of lateral impacts on piles embedded in various granular soils. The process of dynamic lateral impact and interaction between the pile and the soil was modeled using the Updated Lagrangian Finite Element Method (UL-FEM). A damage-based element erosion algorithm was incorporated into the model to accommodate severe mesh distortions and element entanglements of the soil material brought by the pile impact. Validation against well-documented large-scale physical impact tests ascertained the model's fidelity. Our findings elucidate the significant differences in resistive forces between piles in strong versus weak granular soils – notably, the former exhibited resistive forces roughly double their weaker counterparts under equivalent embedment depths and varied impact velocities. Intriguingly, a stiff pile in weak soil necessitates nearly double the embedment depth to match the energy dissipation of its strong-soil counterpart. Furthermore, the study discerned consistent depth of rotation point ranges for piles embedded in distinct soil strengths, regardless of embedment depth and impact velocity.

A Review of Crowdsourcing Update Methods for High-Definition Maps

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

Potential Output and Cyclically Adjusted Balance: An Application of the Updated Okun Method to Spain (1987–2022)

ABSTRACT The estimation of potential output is at the heart of the ongoing debate on the reformulation of fiscal rules in the European Union as it is an essential variable for their application. Spain constitutes a striking case, with a small and even positive output gap along with one of the highest unemployment rates in the European Union. To resolve this contradiction, in this article we estimate the potential output of the Spanish economy for the period 1987Q2–2022Q4 by means of the Updated Okun Method. Our results indicate that both the output gap and the cyclically adjusted balance are much higher than what European institutions estimate. Consequently, Spain has much more fiscal space to implement expansionary fiscal policies.

A protocol for distilling animal body water from biological samples and measuring oxygen and hydrogen stable isotopes via cavity ring-down spectroscopy

ABSTRACT The application of stable isotope analysis (SIA) to the fields of ecology and animal biology has rapidly expanded over the past three decades, particularly with regards to water analysis. SIA now provides the opportunity to monitor migration patterns, examine food webs, and assess habitat changes in current and past study systems. While carbon and nitrogen SIA of biological samples have become common, analyses of oxygen or hydrogen are used more sparingly despite their promising utility for tracing water sources and animal metabolism. Common ecological applications of oxygen or hydrogen SIA require injecting enriched isotope tracers. As such, methods for processing and analyzing biological samples are tailored for enriched tracer techniques, which require lower precision than other techniques given the large signal-to-noise ratio of the data. However, instrumentation advancements are creating new opportunities to expand the applications of high-throughput oxygen and hydrogen SIA. To support these applications, we update methods to distill and measure water derived from biological samples with consistent precision equal to, or better than, ± 0.1 ‰ for δ 17O, ± 0.3 ‰ for δ 18O, ± 1 ‰ for δ 2H, ± 2 ‰ for d-excess, and ± 15 per meg for Δ 17O.

Multistrategy Fusion Particle Swarm for Dynamic Economic Dispatch Optimization of Renewable Energy Sources

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching.

Shiga Toxin-Producing Escherichia coli Testing in New York 2011-2022 Reveals Increase in Non-O157 Identifications.

Shiga toxin-producing Escherichia coli (STEC) are an important cause of bacterial enteric infection. STEC strains cause serious human gastrointestinal disease, which may result in life-threatening complications such as hemolytic uremic syndrome. They have the potential to impact public health due to diagnostic challenges of identifying non-O157 strains in the clinical laboratory. The Wadsworth Center (WC), the public health laboratory of the New York State Department of Health, has isolated and identified non-O157 STEC for decades. A shift from initially available enzyme immunoassay testing to culture-independent diagnostic tests (CIDTs) has increased the uptake of testing at clinical microbiology laboratories. This testing change has resulted in an increased number of specimen submissions to WC. During a 12-year period between 2011 and 2022, WC received 5037 broths and/or stool specimens for STEC confirmation from clinical microbiology laboratories. Of these, 3992 were positive for Shiga toxin genes (stx1 and/or stx2) by real-time PCR. Furthermore, culture methods were utilized to isolate, identify, and characterize 2925 STEC from these primary specimens. Notably, WC observed a >200% increase in the number of STEC specimens received in 2021-2022 compared with 2011-2012 and an 18% increase in the number of non-O157 STEC identified using the same methodologies. During the past decade, the WC testing algorithm has been updated to manage the increase in specimens received, while also navigating the novel COVID-19 pandemic, which took priority over other testing for a period of time. This report summarizes updated methods for confirmation, surveillance, and outbreak detection of STEC and describes findings that may be related to our algorithm updates and the increased use of CIDTs, which is starting to elucidate the true incidence of non-O157 STEC.

Reviewing the Exposure of Engineering Occupations to Artificial Intelligence

An engineering degree should include learning the current skills required in a variety of jobs in an engineering discipline of choice. However, as new artificial intelligence (AI) technologies are implemented, the skills required by human workers will evolve. This paper builds on previous research which compared the verb/noun pairs in AI patents to those found in occupational descriptions to obtain numerical scores reflecting how exposed various occupations are to AI disruption. Improvements to this method, including a new selection method for identifying AI specific patents and the use of a different verb/noun extraction method, are used in this work. The updated method is then employed to identify tasks associated with engineering professions that are vulnerable to AI displacement. Specifically, it was found that Microsystems, Human Factors, and Electrical Engineering have relatively high exposure scores. The exposure of Electrical Engineering is further analyzed to determine the aspects of the occupation that are potentially most vulnerable to AI displacement. The analysis illustrates that the impact of AI might not undermine an entire task or skill. Rather, this type of emerging technology may displace or alter the nature of a task. This work is still in progress. The exposure scores themselves are not the focus, but they are a first step toward identifying the type of skills and engineering occupations that may be affected by AI technologies. This work has ramifications for engineering education as educators make choices about which skills to teach and which to abandon in the curriculum.

Prediction of bridge structure deformation and strain based on dynamic testing and intelligent algorithms

The field load test is a direct and effective method for evaluating the performance of bridge structures. However, existing bridge field static load tests are costly and inefficient; moreover, they obstruct traffic and cause unavoidable damage to the bridge structure. As an alternative the the static load test, a random model update method based on bridge dynamic load tests and the Bayesian inference is proposed in this paper. The bridge static load test results were predicted with a high accuracy. To speed up the Bayesian method to infer the posterior probability density of the updated parameters, the Gaussian process was used in place of the finite element model, and the Bayesian inference used the Markov chain Monte Carlo method based on the delayed rejection adaptive Metropolis algorithm. First, the parameters to be modified for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The research results reveal that the prediction results of the bridge static load test based on the dynamic load test and Bayesian inference method agree with the actual test results.

Knowledge Verification From Data.

Knowledge verification is an important task in the quality management of knowledge graphs (KGs). Knowledge is a summary of facts and events based on human cognition and experience. Due to the nature of knowledge, most knowledge quality (KQ) management methods are designed by human experts or the characteristics of existing knowledge, which may be limited by human cognition and the quality of existing knowledge. Numerical data contain a wealth of potential information that may be helpful in verifying knowledge, which is rarely explored. However, due to the implicit representation of numerical data to facts as well as the noise in the data, it is challenging to use data to verify the knowledge. Therefore, this article proposes a knowledge verification method, which discovers the correlation and causality from numerical data to validate knowledge and then evaluate the quality of knowledge. Moreover, to address the impact of noise, the method integrates multisource knowledge to jointly evaluate the KQ. Specifically, an iterative update method is designed to update KQ by utilizing the consistency between multisource knowledge while designing knowledge verification factors based on data causality and correlation to manage update process. The method is validated with multiple datasets, and the results demonstrate that the proposed method could evaluate KQ more accurately and has strong robustness to noise in the data.

Efficient implicit constraint handling approaches for constrained optimization problems

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2–12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Herb induced liver injury by Xianling Gubao Tablets: A case assessed for causality using updated RUCAM and integrated evidence chain

ObjectiveA typical case of Xianling Gubao (XLGB) Tablets-induced liver injury was systematically studied in the clinic and the laboratory. MethodsA patient with herb-induced liver injury (HILI) and a history of taking XLGB Tablets before disease onset was engaged as the study subject, and the case was diagnosed according to the updated Roussel Uclaf Causality Assessment Method (RUCAM) and the integrated evidence chain (iEC) method recommended by the Guidelines for Diagnosis and Treatment of Herb-induced Liver Injury (HILI Guidelines). ResultsClinical history, biochemical indexes and imaging tests were used to exclude the influence of fundamental diseases and confusing liver diseases such as viral, alcoholic and autoimmune liver diseases on the diagnosis. Based on an investigation of the patient’s medication history, she was suspected to have HILI caused by XLGB Tablets, as the patient was only taking an oral preparation of XLGB Tablets, and the influence of other drugs on the diagnosis was excluded. This patient with alanine aminotransferase (ALT) ≥ 3 × upper limit of normal (ULN) and a calculated R of 6 was diagnosed with possible acute drug-induced hepatocellular injury. The relationship was considered “highly probable” (score of 9) using the updated RUCAM of 2016. Moreover, the fingerprint similarity between the preparation taken by the patient and a commercially available preparation was 0.99, suggesting that the patient was consuming XLGB Tablets rather than another drug. LC-MS technology and the Agilent Fake TCM-Drugs database were used to investigate the drug, and no chemical additions were found. Examination of the drug for pesticide residues, heavy metals, aflatoxins and other exogenous substances indicated compliance with the content limits of the Chinese Pharmacopoeia. ConclusionIn summary, the final diagnosis of XLGB-induced liver injury reached the clinical diagnosis of HILI and was acute severe hepatocellular injury type by the updated RUCAM and iEC. Therefore, this study provides scientific evidence regarding the causality evaluation of compound preparations of traditional Chinese medicines-induced liver injury.

An implicit Updated Lagrangian Fragile Points Method with a support domain refinement scheme for solving large deformation problems

AbstractEngineering structures may undergo large deformations, but simulating is still challenging for existing numerical methods, for example, the element‐based methods struggle from the mesh distortion and the strong form particle‐based methods exhibit tensile instability. This article aims on presenting a novel numerical method for large deformation simulations, which is named the implicit Updated Lagrangian Fragile Points Method (ULFPM). The point‐based nature of the proposed method ensures immunity to mesh distortion, and its stability is guaranteed by its foundation on weak form formulations. For verifications, the proposed implicit ULFPM has been applied to several large deformation problems. For all the examples, the proposed method is able to provide reliable results with a good agreement with the reference results. Moreover, the implicit ULFPM performs better than the Finite Element Method to some extent for extremely large deformation problems. It is also discussed that the accuracy of the implicit ULFPM is improved significantly by using the support domain refinement scheme. The examples indicate that the proposed method can serve as a reliable tool for predicting the large deformations of structures in engineering practices.

Self-Organized Evolution of the Internal Transport Barrier in Ion-Temperature-Gradient Driven Gyrokinetic Turbulence.

Understanding the self-organization of the most promising internal transport barrier in fusion plasmas needs a long-time nonlinear gyrokinetic global simulation. The neighboring equilibrium update method is proposed, which solves the secularity problem in a perturbative simulation and speeds up the numerical computation by more than 10 times. It is found that the internal transport barrier emerges at the magnetic axis due to inward propagated turbulence avalanche, and its outward expansion is the catastrophe of self-organized structure induced by outward propagated avalanche.

Assessing the validity of the zero-velocity update method for sprinting speeds.

The zero-velocity update (ZUPT) method has become a popular approach to estimate foot kinematics from foot worn inertial measurement units (IMUs) during walking and running. However, the accuracy of the ZUPT method for stride parameters at sprinting speeds remains unknown, specifically when using sensors with characteristics well suited for sprinting (i.e., high accelerometer and gyroscope ranges and sampling rates). Seventeen participants performed 80-meter track sprints while wearing a Blue Trident IMeasureU IMU. Two cameras, at 20 and 70 meters from the start, were used to validate the ZUPT method on a stride-by-stride and on a cumulative distance basis. In particular, the validity of the ZUPT method was assessed for: (1) estimating a single stride length attained near the end of an 80m sprint (i.e., stride at 70m); (2) estimating cumulative distance from ∼20 to ∼70 m; and (3) estimating total distance traveled for an 80-meter track sprint. Individual stride length errors at the 70-meter mark were within -6% to 3%, with a bias of -0.27%. Cumulative distance errors were within -4 to 2%, with biases ranging from -0.85 to -1.22%. The results of this study demonstrate the ZUPT method provides accurate estimates of stride length and cumulative distance traveled for sprinting speeds.