Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking.However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library called Rmagine allows a robot to simulate sensor data for arbitrary range sensors directly on board via raytracing. Since robots typically only have limited computational resources, the Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like Nvidia Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems

CORRELATION BETWEEN MUSCULAR FUNCTION AND POSTURE - LOWERING THE DEGREE OF PELVIC INCLINATION WITH EXERCISE

Muscular balance is one of the most widely discussed topics in sport science over the past years. The publications show a significant discrepancy between the accuracy of the numerous published statements on the theory of muscular balance and the small number of empirical studies. In the present investigation the authors examined both the logical and the empirical extent of the theory of muscular balance. Examination of the plausibility and the stringency of the argument showed that the theory of muscular balance lacks a scientific basis. However the empirical section disclosed a number of correlations between muscle function and body posture. Within a ten week training period the students´ average pelvic tilt was lowered significantly by a suitable program

Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping

This paper adapts the non-parametric dynamic time warping (DTW) technique in an application to examine the temporal alignment and similarity across economic time series. DTW has important advantages over existing measures in economics as it alleviates concerns regarding a pre-defined fixed temporal alignment of series. For example, in contrast to current methods, DTW can capture alternations between leading and lagging relationships of series. We illustrate DTW in a study of US states’ business cycles around the Great Recession, and find considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession

Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms

Liesel is a probabilistic programming framework focusing on but not limited to semi-parametric regression. It comprises a graph-based model building library, a Markov chain Monte Carlo (MCMC) library with support for modular inference algorithms combining multiple kernels (both implemented in Python), and an R interface (RLiesel) for the configuration of semi-parametric regression models. Each component can be used independently of the others, e.g. the MCMC library also works with third-party model implementations. Our goal with Liesel is to facilitate a new research workflow in computational statistics: In a first step, the researcher develops a model graph with pre-implemented and well-tested building blocks as a base model, e.g. using RLiesel. Then, the graph can be manipulated to incorporate new research ideas, before the MCMC library can be used to run and analyze a default or user-defined MCMC procedure. The researcher has the option to combine powerful MCMC algorithms such as the No U-Turn Sampler (NUTS) with self-written kernels. Various tools for chain post-processing and diagnostics are also provided. Considering all its components, Liesel enables efficient and reliable statistical research on complex models and estimation algorithms. It depends on JAX as a numerical computing library. This way, it can benefit from the latest machine learning technology such as automatic differentiation, just-in-time (JIT) compilation, and the use of high-performance computing devices such as tensor processing units (TPUs)

MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting

Triangle mesh maps have proven to be a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed that allow robots to accurately localize themselves to perform typical tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on a variety of target architectures. We demonstrate the robustness of our localization approach with datasets from agriculture, drones, and automotive domains

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in literature that is able to solve it in real-time. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation

ddml: Double/debiased machine learning in Stata

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.Comment: The package can be installed from https://github.com/aahrens1/ddml

A variance partitioning multi-level model for forest inventory data with a fixed plot design

Forest inventories are often carried out with a particular design, consisting of a multi-level structure of observation plots spread over a larger domain and a fixed plot design of exact observation locations within these plots. Consequently, the resulting data are collected intensively within plots of equal size but with much less intensity at larger spatial scales. The resulting data are likely to be spatially correlated both within and between plots, with spatial effects extending over two different areas. However, a Gaussian process model with a standard covariance structure is generally unable to capture dependence at both fine and coarse scales of variation as well as for their interaction. In this paper, we develop a computationally feasible multi-level spatial model that accounts for dependence at multiple scales. We use a data-driven approach to determine the weight of each spatial process in the model to partition the variability of the measurements. We use simulated and German small tree inventory data to evaluate the model’s performance.Supplementary material to this paper is provided online

Intertemporal Similarity of Economic Time Series

This paper adapts the non-parametric Dynamic Time Warping (DTW) technique in an application to examine the temporal alignment and similarity across economic time series. DTW has important advantages over existing measures in economics as it alleviates concerns regarding a pre-defined fixed temporal alignment of series. For example, in contrast to current methods, DTW can capture alternations between leading and lagging relationships of series. We illustrate DTW in a study of US states’ business cycles around the Great Recession, and find considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession

Assembly of a Parts List of the Human Mitotic Cell Cycle Machinery

The set of proteins required for mitotic division remains poorly characterized. Here, an extensive series of correlation analyses of human and mouse transcriptomics data were performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, 701 cell cycle-associated genes were defined and while it was shown that many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterized. Among these, 27 were screened for subcellular localization revealing many to be nuclear localized and at least three to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins