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Lukas Neumann

Publications and source records attributed to Lukas Neumann.

54 records · Page 3Linked to original sources
arXiv2022

A CO isotopologue Line Atlas within the Whirlpool galaxy Survey (CLAWS)

We present the CO isotopologue Line Atlas within the Whirpool galaxy Survey (CLAWS) based on an IRAM 30-m large programme which provides a benchmark study of numerous, faint CO isotopologues in the mm-wavelength regime across the full disc of M51 (NGC 5194). The survey's core goal is to use the low-J CO isotopologue lines to constrain CO excitation and chemistry, and therefrom the local physical conditions of the gas. In this survey paper, we describe the CLAWS observing and data reduction strategies. We map the J=1-0 and 2-1 transitions of the CO isotopologues $^{12}$CO,$^{13}$CO, C$^{18}$O and C$^{17}$O, as well as several supplementary lines within the 1 mm and 3 mm window (CN(1-0), CS(2-1), CH$_3$OH(2-1), N$_2$H$^+$(1-0), HC$_3$N(10-9)) at ~1 kpc resolution. A total observation time of 149 h offers unprecedented sensitivity. We use these data to explore several CO isotopologue line ratios in detail, study their radial (and azimuthal) trends and investigate whether changes in line ratios stem from changes in ISM properties such as gas temperatures, densities or chemical abundances. For example, we find negative radial trends for the $^{13}$CO}/$^{12}$CO, C$^{18}$O/$^{12}$CO and C$^{18}$O/$^{13}$CO line ratios in their J=1-0 transitions. We also find variations with local environment, such as higher $^{12}$CO(2-1)/(1-0) or $^{13}$CO/$^{12}$CO(1-0) line ratios in interarm regions compared to spiral arm regions. We propose that these aforementioned variations of CO line ratios are most likely due to a variation of the optical depth, while abundance variations due to selective nucleosynthesis on a galaxy-wide scale could also play a role. We also study the CO spectral line energy distribution (SLED) using archival JCMT $^{12}$CO(3-2) data and find a variation of the SLED shape with local environmental parameters further underlying changes in optical depth, gas temperatures or densities.

astro-ph.GA↗
arXiv2021

Calibrating Self-supervised Monocular Depth Estimation

In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal. Whilst the networks achieve good performance, the often over-looked detail is that due to the inherent ambiguity of monocular vision they predict depth up to an unknown scaling factor. The scaling factor is then typically obtained from the LiDAR ground truth at test time, which severely limits practical applications of these methods. In this paper, we show that incorporating prior information about the camera configuration and the environment, we can remove the scale ambiguity and predict depth directly, still using the self-supervised formulation and not relying on any additional sensors.

cs.CV↗
arXiv2021

Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views

We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects. Because the LiDAR point clouds are partial, directly fitting bounding boxes to the point clouds is meaningless. Instead, we suggest that obtaining good results requires sharing information between \emph{all} objects in the dataset jointly, over multiple frames. We then make three improvements to the baseline. First, we address ambiguities in predicting the object rotations via direct optimization in this space while still backpropagating rotation prediction through the model. Second, we explicitly model outliers and task the network with learning their typical patterns, thus better discounting them. Third, we enforce temporal consistency when video data is available. With these contributions, our method significantly outperforms previous work despite the fact that those methods use significantly more complex pipelines, 3D models and additional human-annotated external sources of prior information.

cs.CV↗
arXiv2021

Real Time Monocular Vehicle Velocity Estimation using Synthetic Data

Vision is one of the primary sensing modalities in autonomous driving. In this paper we look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. Contrary to prior methods that train end-to-end deep networks that estimate the vehicles' velocity from the video pixels, we propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity from the tracked bounding boxes. Surprisingly, we find that this still achieves state-of-the-art estimation performance with the significant benefit of separating perception from dynamics estimation via a clean, interpretable and verifiable interface which allows us distill the statistics which are crucial for velocity estimation. We show that the latter can be used to easily generate synthetic training data in the space of bounding boxes and use this to improve the performance of our method further.

cs.CV↗
arXiv2020

Ultra-low switching current density in all-amorphous W-Hf / CoFeB / TaOx films

We study current-induced deterministic magnetization switching and domain wall motion via polar Kerr microscopy in all-amorphous W$_{66}$Hf$_{34}$/CoFeB/TaO$_\text{x}$ with perpendicular magnetic anisotropy and large spin Hall angle. Investigations of magnetization switching as a function of in-plane assist field and current pulse-width yield switching current densities as low as $3\times 10^{9}$ A/m$^2$. We accredit this low switching current density to a low depinning current density, which was obtained from measurements of domain wall displacements upon current injection. This correlation is verified by investigations of a Ta/CoFeB/MgO/Ta reference sample, which showed critical current densities of at least one order of magnitude larger, respectively.

cond-mat.mtrl-sci↗
arXiv2020

Monocular Depth Estimation with Self-supervised Instance Adaptation

Recent advances in self-supervised learning havedemonstrated that it is possible to learn accurate monoculardepth reconstruction from raw video data, without using any 3Dground truth for supervision. However, in robotics applications,multiple views of a scene may or may not be available, depend-ing on the actions of the robot, switching between monocularand multi-view reconstruction. To address this mixed setting,we proposed a new approach that extends any off-the-shelfself-supervised monocular depth reconstruction system to usemore than one image at test time. Our method builds on astandard prior learned to perform monocular reconstruction,but uses self-supervision at test time to further improve thereconstruction accuracy when multiple images are available.When used to update the correct components of the model, thisapproach is highly-effective. On the standard KITTI bench-mark, our self-supervised method consistently outperformsall the previous methods with an average 25% reduction inabsolute error for the three common setups (monocular, stereoand monocular+stereo), and comes very close in accuracy whencompared to the fully-supervised state-of-the-art methods.

cs.CV↗
arXiv2019

Analysis of the Block Coordinate Descent Method for Linear Ill-Posed Problems

Block coordinate descent (BCD) methods approach optimization problems by performing gradient steps along alternating subgroups of coordinates. This is in contrast to full gradient descent, where a gradient step updates all coordinates simultaneously. BCD has been demonstrated to accelerate the gradient method in many practical large-scale applications. Despite its success no convergence analysis for inverse problems is known so far. In this paper, we investigate the BCD method for solving linear inverse problems. As main theoretical result, we show that for operators having a particular tensor product form, the BCD method combined with an appropriate stopping criterion yields a convergent regularization method. To illustrate the theory, we perform numerical experiments comparing the BCD and the full gradient descent method for a system of integral equations. We also present numerical tests for a non-linear inverse problem not covered by our theory, namely one-step inversion in multi-spectral X-ray tomography.

math.NA↗
arXiv2018

Influence of the Hall-bar geometry on harmonic Hall voltage measurements of spin-orbit torques

Harmonic Hall voltage measurements are a wide-spread quantitative technique for the measurement of spin-orbit induced effective fields in heavy-metal / ferromagnet heterostructures. In the vicinity of the voltage pickup lines in the Hall bar, the current is inhomogeneous, which leads to a hitherto not quantified reduction of the effective fields and derived quantities, such as the spin Hall angle or the spin Hall conductivity. Here we present a thorough analysis of the influence of the aspect ratio of the voltage pickup lines to current channel widths on the apparent spin Hall angle. Experiments were performed with Hall bars with a broad range of aspect ratios and a substantial reduction of the apparent spin Hall angle is already seen in Hall crosses with an aspect ratio of 1:1. Our experimental results are confirmed by finite-element simulations of the current flow.

cond-mat.mes-hall↗
arXiv2018

Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography

The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional to the number applies sources. Additionally, we introduce a completely new formulation of QPAT as multilinear (MULL) inverse problem which avoids explicitly solving the RTE. The MULL formulation of QPAT is again addressed with stochastic proximal gradient methods. Numerical results for both approaches are presented. Besides the introduction of stochastic proximal gradient algorithms to QPAT, we consider the new MULL formulation of QPAT as main contribution of this paper.

math.NA↗
arXiv2017

Radially symmetric shadow wave solutions to the system of pressureless gas dynamics in arbitrary dimensions

Radially symmetric shadow wave solutions to the system of multidimensional pressureless gas dynamics are introduced, which allow one to capture concentration of mass. The transformation to a one-dimensional system with source terms is performed and physically meaningful boundary conditions at the origin are determined. Entropy conditions are derived and applied to single out physical (nonnegative mass) and dissipative (entropic) solutions. A complete solution to the pseudo-Riemann problem with initial data exhibiting a single shock on a sphere is obtained.

math.AP↗
arXiv2017

Analysis of the Linearized Problem of Quantitative Photoacoustic Tomography

Quantitative image reconstruction in photoacoustic tomography requires the solution of a coupled physics inverse problem involvier light transport and acoustic wave propagation. In this paper we address this issue employing the radiative transfer equation as accurate model for light transport. As main theoretical results, we derive several stability and uniqueness results for the linearized inverse problem. We consider the case of single illumination as well as the case of multiple illuminations assuming full or partial data. The numerical solution of the linearized problem is much less costly than the solution of the non-linear problem. We present numerical simulations supporting the stability results for the linearized problem and demonstrate that the linearized problem already gives accurate quantitative results.

math.AP↗
arXiv2016

Temperature dependence of the spin Hall angle and switching current in the nc-W(O)/CoFeB/MgO system with perpendicular magnetic anisotropy

We investigated the temperature dependence of the switching current for a perpendicularly magnetized CoFeB film deposited on a nanocrystalline tungsten film with large oxygen content: nc-W(O). The spin Hall angle $|Θ_\mathrm{SH}| \approx 0.22$ is independent of temperature, whereas the switching current increases strongly at low temperature. We show that the nc-W(O) is insensitive to annealing. It thus can be a good choice for the integration of spin Hall driven writing of information in magnetic memory or logic devices that require a high-temperature annealing process during fabrication.

cond-mat.mtrl-sci↗
arXiv2016

COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. The images were not collected with text in mind and thus contain a broad variety of text instances. To reflect the diversity of text in natural scenes, we annotate text with (a) location in terms of a bounding box, (b) fine-grained classification into machine printed text and handwritten text, (c) classification into legible and illegible text, (d) script of the text and (e) transcriptions of legible text. The dataset contains over 173k text annotations in over 63k images. We provide a statistical analysis of the accuracy of our annotations. In addition, we present an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on our dataset. While scene text detection and recognition enjoys strong advances in recent years, we identify significant shortcomings motivating future work.

cs.CV↗
arXiv2015

Single-stage reconstruction algorithm for quantitative photoacoustic tomography

The development of efficient and accurate image reconstruction algorithms is one of the cornerstones of computed tomography. Existing algorithms for quantitative photoacoustic tomography currently operate in a two-stage procedure: First an inverse source problem for the acoustic wave propagation is solved, whereas in a second step the optical parameters are estimated from the result of the first step. Such an approach has several drawbacks. In this paper we therefore propose the use of single-stage reconstruction algorithms for quantitative photoacoustic tomography, where the optical parameters are directly reconstructed from the observed acoustical data. In that context we formulate the image reconstruction problem of quantitative photoacoustic tomography as a single nonlinear inverse problem by coupling the radiative transfer equation with the acoustic wave equation. The inverse problem is approached by Tikhonov regularization with a convex penalty in combination with the proximal gradient iteration for minimizing the Tikhonov functional. We present numerical results, where the proposed single-stage algorithm shows an improved reconstruction quality at a similar computational cost.

math.AP↗
arXiv2015

A kinetic reaction model: decay to equilibrium and macroscopic limit

We propose a kinetic relaxation-model to describe a generation-recombination reaction of two species. The decay to equilibrium is studied by two recent methods for proving hypocoercivity of the linearized equations. Exponential decay of small perturbations can be shown for the full nonlinear problem. The macroscopic/fast-reaction limit is derived rigorously employing entropy decay, resulting in a nonlinear diffusion equation for the difference of the position densities.

math.AP↗
arXiv2008

Quantum Fokker-Planck models: the Lindblad and Wigner approaches

In this article we try to bridge the gap between the quantum dynamical semigroup and Wigner function approaches to quantum open systems. In particular we study stationary states and the long time asymptotics for the quantum Fokker-Planck equation. Our new results apply to open quantum systems in a harmonic confinement potential, perturbed by a (large) sub-quadratic term.

math-ph↗
arXiv2006

Quantitative perturbative study of convergence to equilibrium for collisional kinetic models in the torus

For a general class of linear collisional kinetic models in the torus, including in particular the linearized Boltzmann equation for hard spheres, the linearized Landau equation with hard and moderately soft potentials and the semi-classical linearized fermionic and bosonic relaxation models, we prove explicit coercivity estimates on the associated integro-differential operator for some modified Sobolev norms. We deduce existence of classical solutions near equilibrium for the full non-linear models associated, with explicit regularity bounds, and we obtain explicit estimates on the rate of exponential convergence towards equilibrium in this perturbative setting. The proof are based on a linear energy method which combines the coercivity property of the collision operator in the velocity space with transport effects, in order to deduce coercivity estimates in the whole phase space.

math.AP↗