Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-collection Bias

Friction plays a key role in manipulating objects. Most of what we do with our hands, and most of what robots do with their grippers, is based on the ability to control frictional forces. This paper aims to better understand the variability and predictability of planar friction. In particular, we focus on the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to create a data-driven footprint of planar friction. We show in this paper how we can explain a significant fraction of the observed unconventional phenomena, e.g., stochasticity and multi-modality, by combining the effects of material non-homogeneity, anisotropy of friction and biases due to data collection dynamics, hinting that the variability is explainable but inevitable in practice. We introduce an anisotropic friction model and conduct simulation experiments comparing with more standard isotropic friction models. The anisotropic friction between object and supporting surface results in convergence of initial condition during the automated data collection. Numerical results confirm that the anisotropic friction model explains the bias in the dataset and the apparent stochasticity in the outcome of a push. The fact that the data collection process itself can originate biases in the collected datasets, resulting in deterioration of trained models, calls attention to the data collection dynamics.Comment: 8 pages, 13 figure

Perspectives of Imaging of Single Protein Molecules with the Present Design of the European XFEL. - Part I - X-ray Source, Beamlime Optics and Instrument Simulations

The Single Particles, Clusters and Biomolecules (SPB) instrument at the European XFEL is located behind the SASE1 undulator, and aims to support imaging and structure determination of biological specimen between about 0.1 micrometer and 1 micrometer size. The instrument is designed to work at photon energies from 3 keV up to 16 keV. This wide operation range is a cause for challenges to the focusing optics. In particular, a long propagation distance of about 900 m between x-ray source and sample leads to a large lateral photon beam size at the optics. The beam divergence is the most important parameter for the optical system, and is largest for the lowest photon energies and for the shortest pulse duration (corresponding to the lowest charge). Due to the large divergence of nominal X-ray pulses with duration shorter than 10 fs, one suffers diffraction from mirror aperture, leading to a 100-fold decrease in fluence at photon energies around 4 keV, which are ideal for imaging of single biomolecules. The nominal SASE1 output power is about 50 GW. This is very far from the level required for single biomolecule imaging, even assuming perfect beamline and focusing efficiency. Here we demonstrate that the parameters of the accelerator complex and of the SASE1 undulator offer an opportunity to optimize the SPB beamline for single biomolecule imaging with minimal additional costs and time. Start to end simulations from the electron injector at the beginning of the accelerator complex up to the generation of diffraction data indicate that one can achieve diffraction without diffraction with about 0.5 photons per Shannon pixel at near-atomic resolution with 1e13 photons in a 4 fs pulse at 4 keV photon energy and in a 100 nm focus, corresponding to a fluence of 1e23 ph/cm^2. This result is exemplified using the RNA Pol II molecule as a case study

Component-based simulation for a reconfiguration study of transitic systems

This paper is organized as follows. Part A presents the context of reconfiguring transitic systems and the main idea in implementing the decision step. It comprises sections 1 to 3. Section 3 presents an example that illustrates the concepts presented in the next sections. Parts B and C express the models and principles used to simulate transitic systems, the result of which will be helpful for choosing the new configuration. Part B focuses mainly on models. It comprises sections 4 to 6. Part C focuses mainly on simulation principles. It comprises sections 7 to 10