Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually counted database (Robbins & Hynek, 2012, https://doi.org/10.1029/2011JE003966) of >384,000 craters on Mars >1 km in diameter exists. But because crater size scales as a power law, the number of impact craters in the size range 10 m to 1 km is in the tens of millions, a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the You Only Look Once v3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System daytime infrared data set. This training data set contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground truth data set. We applied our algorithm to the Thermal Emission Imaging System global mosaic between ±65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages.

Background Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening. Methods Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples. Results Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers. Conclusions In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.

No abstract

The assessment of content quality (CQ) in social media adds a layer of complexity over traditional information quality assessment frameworks. Challenges arise in accurately evaluating the quality of content that has been created by users from different backgrounds, for different domains and consumed by users with different requirements. This paper presents a comprehensive review of 19 existing CQ assessment related frameworks for social media in addition to proposing directions for framework improvements.

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression.

No abstract

No abstract

Background: Accurate and detailed measurement of a dancer's training volume is a key requirement to understanding the relationship between a dancer's pain and training volume. Currently, no system capable of quantifying a dancer's training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy.Results: Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations. Conclusion: The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers' pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activities Key PointsDeep learning models were shown to have acceptable accuracy when applied to recognised ballet-specific jumping and leg lifting tasks in a population of 23 dancers. A system of multiple sensors (six per dancer) was shown to have the greatest accuracy; however, the optimal single sensor model also performed with acceptable accuracy. The inclusion of all six sensors yielded the highest degree of accuracy: however, fewer sensors still provided an acceptable degree of accuracy. For real-world application, minimal sensors are required to reduce athlete burden. The method demonstrated for model development is highly translatable for future developments in other lower limb dominant sporting activities.