CrashCar101: Procedural Generation for Damage Assessment

In this paper, we are interested in addressing the problem of damage assessment for vehicles, such as cars. This task requires not only detecting the location and the extent of the damage but also identifying the damaged part. To train a computer vision system for the semantic part and damage segmentation in images, we need to manually annotate images with costly pixel annotations for both part categories and damage types. To overcome this need, we propose to use synthetic data to train these models. Synthetic data can provide samples with high variability, pixel-accurate annotations, and arbitrarily large training sets without any human intervention. We propose a procedural generation pipeline that damages 3D car models and we obtain synthetic 2D images of damaged cars paired with pixel-accurate annotations for part and damage categories. To validate our idea, we execute our pipeline and render our CrashCar101 dataset. We run experiments on three real datasets for the tasks of part and damage segmentation. For part segmentation, we show that the segmentation models trained on a combination of real data and our synthetic data outperform all models trained only on real data. For damage segmentation, we show the sim2real transfer ability of CrashCar101.Comment: Accepted at WACV 202

The temporal reliability of serum estrogens, progesterone, gonadotropins, SHBG and urinary estrogen and progesterone metabolites in premenopausal women

BACKGROUND: There is little existing research to guide researchers in estimating the minimum number of measurement occasions required to obtain reliable estimates of serum estrogens, progesterone, gonadotropins, sex hormone-binding globulin (SHBG), and urinary estrogen and progesterone metabolites in premenopausal women. METHODS: Using data from a longitudinal study of 34 women with a mean age of 42.3 years (SD = 2.6), we calculated the minimum number of measurement occasions required to obtain reliable estimates of 12 analytes (8 in blood, 4 in urine). Five samples were obtained over 1 year: at baseline, and after 1, 3, 6, and 12 months. We also calculated the percent of true variance accounted for by a single measurement and intraclass correlation coefficients (ICC) between measurement occasions. RESULTS: Only 2 of the 12 analytes we examined, SHBG and estrone sulfate (E(1)S), could be adequately estimated by a single measurement using a minimum reliability standard of having the potential to account for 64% of true variance. Other analytes required from 2 to 12 occasions to account for 81% of the true variance, and 2 to 5 occasions to account for 64% of true variance. ICCs ranged from 0.33 for estradiol (E(2)) to 0.88 for SHBG. Percent of true variance accounted for by single measurements ranged from 29% for luteinizing hormone (LH) to 92% for SHBG. CONCLUSIONS: Experimental designs that take the natural variability of these analytes into account by obtaining measurements on a sufficient number of occasions will be rewarded with increased power and accuracy

Reproductive risk factors for endometrial cancer among Polish women

We conducted a population-based case–control study of reproductive factors in Warsaw and Ló∂ź, Poland, in 551 incident endometrial cancer cases and 1925 controls. The reproductive variable most strongly related to risk was multiparity, with subjects with three or more births having a 70% lower risk than the nulliparous women. The reduced risk was particularly strong below 55 years of age. Subjects with older ages at a first birth were also at reduced risk even after adjustment for number of births. Ages at last birth or intervals since last birth were not strongly related to risk. Spontaneous abortions were unrelated to risk, but induced abortions were associated with slight risk increases (odds ratios=1.28, 95% confidence intervals 0.8–2.1 for 3+ vs no abortions). The absence of effects on risk of later ages at, or short intervals since, a last birth fails to support the view that endometrial cancer is influenced by mechanical clearance of initiated cells. Alternative explanations for reproductive effects should be sought, including alterations in endogenous hormones

Risk factors for endometrial cancer : An umbrella review of the literature

Although many risk factors could have causal association with endometrial cancer, they are also prone to residual confounding or other biases which could lead to over- or underestimation. This umbrella review evaluates the strength and validity of evidence pertaining risk factors for endometrial cancer. Systematic reviews or meta-analyses of observational studies evaluating the association between non-genetic risk factors and risk of developing or dying from endometrial cancer were identified from inception to April 2018 using PubMed, the Cochrane database and manual reference screening. Evidence was graded strong, highly suggestive, suggestive or weak based on statistical significance of random-effects summary estimate, largest study included, number of cases, between-study heterogeneity, 95% prediction intervals, small study effects, excess significance bias and sensitivity analysis with credibility ceilings. We identified 171 meta-analyses investigating associations between 53 risk factors and endometrial cancer incidence and mortality. Risk factors were categorised: anthropometric indices, dietary intake, physical activity, medical conditions, hormonal therapy use, biochemical markers, gynaecological history and smoking. Of 127 meta-analyses including cohort studies, three associations were graded with strong evidence. Body mass index and waist-to-hip ratio were associated with increased cancer risk in premenopausal women (RR per 5 kg/m(2) 1.49; CI 1.39-1.61) and for total endometrial cancer (RR per 0.1unit 1.21; CI 1.13-1.29), respectively. Parity reduced risk of disease (RR 0.66, CI 0.60-0.74). Of many proposed risk factors, only three had strong association without hints of bias. Identification of genuine risk factors associated with endometrial cancer may assist in developing targeted prevention strategies for women at high risk.Peer reviewe

Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling

This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression and the other on Bayesian neural network. Both approaches use a recurrent neural network to predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a novel sampling technique in order to aggregate quantile estimates for link level travel time to yield the multi-link travel time distribution needed for a vehicle to travel from its current position to a specific downstream stop point or transfer site.To motivate the relevance of uncertainty-aware models in the domain, we focus on the connection protection application as a case study: An expert system to determine whether a bus driver should hold and wait for a connecting service, thus ensuring the connection, or break the connection and reduce its own delay. Our results show that the proposed quantile sampling method performs overall best for the 80%, 90% and 95% prediction intervals, both for a 15 min time horizon into the future (𝑡 + 1), but also for the 30 and 45 min time horizon (𝑡+ 2 and 𝑡+ 3), with a constant, but very small underestimation of the uncertainty interval (1–4 pp.). However, we also show, that the Bayesian model still can outperform the DQR for specific cases. Lastly, we demonstrate how a simple decision support system can take advantage of our uncertainty-aware travel time models to prioritize the difference in travel time uncertainty for bus holding at strategic points, thus reducing the introduced delay for the connection protection application

CrashCar101: Procedural Generation for Damage Assessment

In this paper, we are interested in addressing the problem of damage assessment for vehicles, such as cars. This task requires not only detecting the location and the extent of the damage but also identifying the damaged part. To train a computer vision system for the semantic part and damage segmentation in images, we need to manually annotate images with costly pixel annotations for both part categories and damage types. To overcome this need, we propose to use synthetic data to train these models. Synthetic data can provide samples with high variability, pixel-accurate annotations, and arbitrarily large training sets without any human intervention. We propose a procedural generation pipeline that damages 3D car models and we obtain synthetic 2D images of damaged cars paired with pixel-accurate annotations for part and damage categories. To validate our idea, we execute our pipeline and render our CrashCar101 dataset. We run experiments on three real datasets for the tasks of part and damage segmentation. For part segmentation, we show that the segmentation models trained on a combination of real data and our synthetic data outperform all models trained only on real data. For damage segmentation, we show the sim2real transfer ability of CrashCar101