Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment

Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.Comment: BMVC Newcastle 201

Representation of the Consumer Interest in the Federal Government

We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices

Evaluation train induced ground vibration boom with Vibtrain

The Swedish Transport Administration is planning new high-speed railway lines that will connect Stockholm, Gothenburg, and Malmö, with operating speeds from 250 to 320 km/h. However, at such high speeds in soft soil areas, can result in significant amplification of ground vibrations, a "ground vibration boom." This phenomenon was first observed in Sweden in 1997, leading to extensive research, during which the NGI developed the VibTrain tool for train-induced ground vibration analysis. There are still various challenges associated with train speeds exceeding 200 km/h on soft ground. To achieve optimized and sustainable ground improvement, a better understanding of its dynamic and cyclic behavior, as well as the validation of design tools and construction techniques, is needed. This paper presents a revival and re-evaluation of VibTrain for assessing the risk of the ground vibration boom for planning of new lines. The tool was compared with previous analyses of the Ledsgård case and extended with a parametric study of both the load model HSLM-A and ground improvement using lime cement stabilization. Additionally, VibTrain was compared with the results from Tyrens (2016) for the Järna location in East link. The analysis indicates that VibTrain is well-suited for initial assessments of the risk associated with the ground vibration boom. However, for the detailed design of soil improvement, more sophisticated calculation models are required. Results indicate importance of the train load description and of well characterized dynamic properties of track and subsoil. Validation of numerical models with field test at speeds above 300 km/h are recommended.Evaluation train induced ground vibration boom with VibtrainpublishedVersio

Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment

An Overview of Mechanical Properties and Material Modeling of Polylactide (PLA) for Medical Applications

Abstract This article provides an overview of the connection between the microstructural state and the mechanical response of various bioresorbable polylactide (PLA) devices for medical applications. PLLA is currently the most commonly used material for bioresorbable stents and sutures, and its use is increasing in many other medical applications. The nonlinear mechanical response of PLLA, due in part to its low glass transition temperature (T g ≈ 60°C), is highly sensitive to the molecular weight and molecular orientation field, the degree of crystallinity, and the physical aging time. These microstructural parameters can be tailored for specific applications using different resin formulations and processing conditions. The stressstrain, deformation, and degradation response of a bioresorbable medical device is also strongly dependent on the time history of applied loads and boundary conditions. All of these factors can be incorporated into a suitable constitutive model that captures the multiple physics that are involved in the device response. Currently developed constitutive models already provide powerful computations simulation tools, and more progress in this area is expected to occur in the coming years