arXiv subjects

Michael Wimmer

Publications and source records attributed to Michael Wimmer.

At least 73 records · Page 4Linked to original sources

arXiv2020

Test Scene Design for Physically Based Rendering

Physically based rendering is a discipline in computer graphics which aims at reproducing certain light and material appearances that occur in the real world. Complex scenes can be difficult to compute for rendering algorithms. This paper introduces a new comprehensive test database of scenes that treat different light setups in conjunction with diverse materials and discusses its design principles. A lot of research is focused on the development of new algorithms that can deal with difficult light conditions and materials efficiently. This database delivers a comprehensive foundation for evaluating existing and newly developed rendering techniques. A final evaluation compares different results of different rendering algorithms for all scenes.

cs.GR↗

arXiv2020

Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

q-fin.GN↗

arXiv2019

Hybrid kernel polynomial method

The kernel polynomial method allows to sample overall spectral properties of a quantum system, while sparse diagonalization provides accurate information about a few important states. We present a method combining these two approaches without loss of performance or accuracy. We apply this hybrid kernel polynomial method to improve the computation of thermodynamic quantities and the construction of perturbative effective models, in a regime where neither of the methods is sufficient on its own. To achieve this we develop a perturbative kernel polynomial method to compute arbitrary order series expansions of expectation values. We demonstrate the efficiency of our approach on three examples: the calculation of supercurrent and inductance in a Josephson junction, the interaction of spin qubits defined in a two dimensional electron gas, and the calculation of the effective band structure in a realistic model of a semiconductor nanowire.

cond-mat.mes-hall↗

arXiv2019

Next steps of quantum transport in Majorana nanowire devices

Majorana zero modes are localized quasiparticles that obey non-Abelian exchange statistics. Braiding Majorana zero modes forms the basis of topologically protected quantum operations which could in principle significantly reduce qubit decoherence and gate control errors in the device level. Therefore, searching for Majorana zero modes in various solid state systems is a major topic in condensed matter physics and quantum computer science. Since the first experimental signature observed in hybrid superconductor-semiconductor nanowire devices, this field has witnessed a dramatic expansion in material science, transport experiments and theory. While making the first topological qubit based on these Majorana nanowires is currently an on-going effort, several related important transport experiments are still being pursued in the near term. These will not only serve as intermediate steps but also show Majorana physics in a more fundamental aspect. In this perspective, we summarize these key Majorana experiments and the potential challenges.

cond-mat.mes-hall↗

arXiv2019

Photorealistic Material Editing Through Direct Image Manipulation

Creating photorealistic materials for light transport algorithms requires carefully fine-tuning a set of material properties to achieve a desired artistic effect. This is typically a lengthy process that involves a trained artist with specialized knowledge. In this work, we present a technique that aims to empower novice and intermediate-level users to synthesize high-quality photorealistic materials by only requiring basic image processing knowledge. In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e.g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image. Our method combines the advantages of a neural network-augmented optimizer and an encoder neural network to produce high-quality output results within 30 seconds. We also demonstrate that it is resilient against poorly-edited target images and propose a simple extension to predict image sequences with a strict time budget of 1-2 seconds per image.

cs.GR↗

arXiv2019

Rendering Point Clouds with Compute Shaders

We propose a compute shader based point cloud rasterizer with up to 10 times higher performance than classic point-based rendering with the GL_POINT primitive. In addition to that, our rasterizer offers 5 byte depth-buffer precision with uniform or customizable distribution, and we show that it is possible to implement a high-quality splatting method that blends together overlapping fragments while still maintaining higher frame-rates than the traditional approach.

cs.GR↗

arXiv2019

A unified numerical approach to semiconductor-superconductor heterostructures

We develop a unified numerical approach for modeling semiconductor-superconductor heterostructures. Our approach takes into account on equal footing important key ingredients: proximity-induced superconductivity, orbital and Zeeman effect of an applied magnetic field, spin-orbit coupling as well as the electrostatic environment. As a model system, we consider indium arsenide (InAs) nanowires with epitaxial aluminum (Al) shell and demonstrate qualitative agreement of the obtained results with the existing experimental data. Finally, we characterize the topological superconducting phase emerging in a finite magnetic field and calculate the corresponding topological phase diagram.

cond-mat.supr-con↗

arXiv2019

Spin-Orbit Protection of Induced Superconductivity in Majorana Nanowires

Spin-orbit interaction (SOI) plays a key role in creating Majorana zero modes in semiconductor nanowires proximity coupled to a superconductor. We track the evolution of the induced superconducting gap in InSb nanowires coupled to a NbTiN superconductor in a large range of magnetic field strengths and orientations. Based on realistic simulations of our devices, we reveal SOI with a strength of 0.15-0.35 eV$\require{mediawiki-texvc}Å$. Our approach identifies the direction of the spin-orbit field, which is strongly affected by the superconductor geometry and electrostatic gates.

cond-mat.mes-hall↗

arXiv2019

Enhanced proximity effect in zigzag-shaped Majorana Josephson junctions

High density superconductor-semiconductor-superconductor junctions have a small induced superconducting gap due to the quasiparticle trajectories with a large momentum parallel to the junction having a very long flight time. Because a large induced gap protects Majorana modes, these long trajectories constrain Majorana devices to a low electron density. We show that a zigzag-shaped geometry eliminates these trajectories, allowing the robust creation of Majorana states with both the induced gap $E_\textrm{gap}$ and the Majorana size $ξ_\textrm{M}$ improved by more than an order of magnitude for realistic parameters. In addition to the improved robustness of Majoranas, this new zigzag geometry is insensitive to the geometric details and the device tuning.

cond-mat.mes-hall↗

arXiv2018

StretchDenoise: Parametric Curve Reconstruction with Guarantees by Separating Connectivity from Residual Uncertainty of Samples

We reconstruct a closed denoised curve from an unstructured and highly noisy 2D point cloud. Our proposed method uses a two- pass approach: Previously recovered manifold connectivity is used for ordering noisy samples along this manifold and express these as residuals in order to enable parametric denoising. This separates recovering low-frequency features from denoising high frequencies, which avoids over-smoothing. The noise probability density functions (PDFs) at samples are either taken from sensor noise models or from estimates of the connectivity recovered in the first pass. The output curve balances the signed distances (inside/outside) to the samples. Additionally, the angles between edges of the polygon representing the connectivity become minimized in the least-square sense. The movement of the polygon's vertices is restricted to their noise extent, i.e., a cut-off distance corresponding to a maximum variance of the PDFs. We approximate the resulting optimization model, which consists of higher-order functions, by a linear model with good correspondence. Our algorithm is parameter-free and operates fast on the local neighborhoods determined by the connectivity. We augment a least-squares solver constrained by a linear system to also handle bounds. This enables us to guarantee stochastic error bounds for sampled curves corrupted by noise, e.g., silhouettes from sensed data, and we improve on the reconstruction error from ground truth. Open source to reproduce figures and tables in this paper is available at: https://github.com/stefango74/stretchdenoise

cs.GR↗

arXiv2018

Gaussian Material Synthesis

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner. Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling. Similarly, expert users experience a significant decrease in the total modeling time when populating a scene with materials. Furthermore, our proposed solution also offers controllable recommendations and a novel latent space variant generation step to enable the real-time fine-tuning of materials without requiring any domain expertise.

cs.LG↗

arXiv2017

Engineering Hybrid Epitaxial InAsSb/Al Nanowire Materials for Stronger Topological Protection

The combination of strong spin-orbit coupling, large $g$-factors, and the coupling to a superconductor can be used to create a topologically protected state in a semiconductor nanowire. Here we report on growth and characterization of hybrid epitaxial InAsSb/Al nanowires, with varying composition and crystal structure. We find the strongest spin-orbit interaction at intermediate compositions in zincblende InAs$_{1-x}$Sb$_{x}$ nanowires, exceeding that of both InAs and InSb materials, confirming recent theoretical studies \cite{winkler2016topological}. We show that the epitaxial InAsSb/Al interfaces allows for a hard induced superconducting gap and 2$e$ transport in Coulomb charging experiments, similar to experiments on InAs/Al and InSb/Al materials, and find measurements consistent with topological phase transitions at low magnetic fields due to large effective $g$-factors. Finally we present a method to grow pure wurtzite InAsSb nanowires which are predicted to exhibit even stronger spin-orbit coupling than the zincblende structure.

cond-mat.mes-hall↗

arXiv2017

$h/e$ superconducting quantum interference through trivial edge states in InAs

Josephson junctions defined in strong spin orbit semiconductors are highly interesting for the search for topological systems. However, next to topological edge states that emerge in a sufficient magnetic field, trivial edge states can also occur. We study the trivial edge states with superconducting quantum interference measurements on non-topological InAs Josephson junctions. We observe a SQUID pattern, an indication of superconducting edge transport. Also, a remarkable $h/e$ SQUID signal is observed that, as we find, stems from crossed Andreev states.

cond-mat.mes-hall↗

arXiv2017

Robust Helical Edge Transport in Quantum Spin Hall Quantum Wells

We show that burying of the Dirac point in semiconductor-based quantum-spin-Hall systems can generate unexpected robustness of edge states to magnetic fields. A detailed ${\bf k\cdot p}$ band-structure analysis reveals that InAs/GaSb and HgTe/CdTe quantum wells exhibit such buried Dirac points. By simulating transport in a disordered system described within an effective model, we further demonstrate that buried Dirac points yield nearly quantized edge conduction out to large magnetic fields, consistent with recent experiments.

cond-mat.mes-hall↗

arXiv2017

Ballistic superconductivity in semiconductor nanowires

Semiconductor nanowires have opened new research avenues in quantum transport owing to their confined geometry and electrostatic tunability. They have offered an exceptional testbed for superconductivity, leading to the realization of hybrid systems combining the macroscopic quantum properties of superconductors with the possibility to control charges down to a single electron. These advances brought semiconductor nanowires to the forefront of efforts to realize topological superconductivity and Majorana modes. A prime challenge to benefit from the topological properties of Majoranas is to reduce the disorder in hybrid nanowire devices. Here, we show ballistic superconductivity in InSb semiconductor nanowires. Our structural and chemical analyses demonstrate a high-quality interface between the nanowire and a NbTiN superconductor which enables ballistic transport. This is manifested by a quantized conductance for normal carriers, a strongly enhanced conductance for Andreev-reflecting carriers, and an induced hard gap with a significantly reduced density of states. These results pave the way for disorder-free Majorana devices.

cond-mat.mes-hall↗

arXiv2017

Orbital contributions to the electron g-factor in semiconductor nanowires

Recent experiments on Majorana fermions in semiconductor nanowires [Albrecht et al., Nat. 531, 206 (2016)] revealed a surprisingly large electronic Landé g-factor, several times larger than the bulk value - contrary to the expectation that confinement reduces the g-factor. Here we assess the role of orbital contributions to the electron g-factor in nanowires and quantum dots. We show that an LS coupling in higher subbands leads to an enhancement of the g-factor of an order of magnitude or more for small effective mass semiconductors. We validate our theoretical finding with simulations of InAs and InSb, showing that the effect persists even if cylindrical symmetry is broken. A huge anisotropy of the enhanced g-factors under magnetic field rotation allows for a straightforward experimental test of this theory.

cond-mat.mes-hall↗

arXiv2017

Spin-orbit interaction in a dual gated InAs/GaSb quantum well

Spin-orbit interaction is investigated in a dual gated InAs/GaSb quantum well. Using an electric field the quantum well can be tuned between a single carrier regime with exclusively electrons as carriers and a two-carriers regime where electrons and holes coexist. Spin-orbit interaction in both regimes manifests itself as a beating in the Shubnikov-de Haas oscillations. In the single carrier regime the linear Dresselhaus strength is characterized by $β=$ 28.5 meV$Å$ and the Rashba coefficient $α$ is tuned from 75 to 53 meV$Å$ by changing the electric field. In the two-carriers regime the spin splitting shows a nonmonotonic behavior with gate voltage, which is consistent with our band structure calculations.

cond-mat.mes-hall↗

arXiv2017

Conductance through a helical state in an InSb nanowire

The motion of an electron and its spin are generally not coupled. However in a one dimensional material with strong spin-orbit interaction (SOI) a helical state may emerge at finite magnetic fields, where electrons of opposite spin will have opposite momentum. The existence of this helical state has applications for spin filtering and Cooper pair splitter devices and is an essential ingredient for realizing topologically protected quantum computing using Majorana zero modes. Here we report electrical conductance measurements of a quantum point contact (QPC) formed in an indium antimonide nanowire as a function of magnetic field. At magnetic fields exceeding 3T, the $2e^2/h$ plateau shows a reentrant conductance feature towards $1e^2/h$ which increases linearly in width with magnetic field before enveloping the $1e^2/h$ plateau. Rotating the external magnetic field either parallel or perpendicular to the spin-orbit field allows us to clearly attribute this experimental signature to SOI. We compare our observations with a model of a QPC incorporating SOI and extract a spin-orbit energy of ~6.5meV, which is significantly stronger than the SO energy obtained by other methods.

cond-mat.mes-hall↗