Accurate robot simulation through system identification

Robot simulators are useful tools for developing robot behaviours. They provide a fast and efficient means to test robot control code at the convenience of the office desk. In all but the simplest cases though, due to the complexities of the physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. In this paper we present a novel method to create a robot simulator using real sensor data. Logged sensor data is used to construct a mathematically explicit model(in the form of a NARMAX polynomial) of the robot’s environment. The advantage of such a transparent model — in contrast to opaque modelling methods such as artificial neural networks — is that it can be analysed to characterise the modelled system, using established mathematical methods In this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator

Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data

Even though deep neural networks (DNNs) achieve state-of-the-art results for a number of problems involving genomic data, getting DNNs to explain their decision-making process has been a major challenge due to their black-box nature. One way to get DNNs to explain their reasoning for prediction is via attribution methods which are assumed to highlight the parts of the input that contribute to the prediction the most. Given the existence of numerous attribution methods and a lack of quantitative results on the fidelity of those methods, selection of an attribution method for sequence-based tasks has been mostly done qualitatively. In this work, we take a step towards identifying the most faithful attribution method by proposing a computational approach that utilizes point mutations. Providing quantitative results on seven popular attribution methods, we find Layerwise Relevance Propagation (LRP) to be the most appropriate one for translation initiation, with LRP identifying two important biological features for translation: the integrity of Kozak sequence as well as the detrimental effects of premature stop codons.Comment: Accepted for publication at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Workshop on Learning Meaningful Representations of Life (LMRL

Initial state of Heavy-Ion Collisions: Isotropization and thermalization

I discuss how local thermal equilibrium and hydrodynamical flow are reached in heavy-ion collisions in the weak coupling limit.Comment: 8 pages, 5 figs, proceedings of the Quark Matter 201

Towards small x resummed DIS phenomenology

We report on recent progress towards quantitative phenomenology of small x resummation of deep-inelastic structure functions. We compute small x resummed K-factors with realistic PDFs and estimate their impact in the HERA kinematical region. These K-factors, which match smoothly to the fixed order NLO results, approximately reproduce the effect of a small x resummed PDF analysis. Typical corrections are found to be of the same order as the NNLO ones, that is, a few percent, but with opposite sign. These results imply that resummation corrections could be relevant for a global PDF analysis, especially with the very precise combined HERA dataset.Comment: 7 pages, 8 figures, proceedings of 17th International Workshop on Deep Inelastic Scattering (DIS 2009), Madrid, 26-30 Apr 200

Staggered versus overlap fermions: a study in the Schwinger model with Nf=0,1,2N_f=0,1,2

We study the scalar condensate and the topological susceptibility for a continuous range of quark masses in the Schwinger model with $N_f=0,1,2$ dynamical flavors, using both the overlap and the staggered discretization. At finite lattice spacing the differences between the two formulations become rather dramatic near the chiral limit, but they get severely reduced, at the coupling considered, after a few smearing steps.Comment: 15 pages, 7 figures, v2: 1 ref corrected, minor change

Adversarial Inpainting of Medical Image Modalities

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201