Search

We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requiring extensive manual editing. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes without the need for costly reprocessing. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy. This approach not only preserves structural and textural details of map data but also maintains compatibility with current geospatial standards, ensuring robust performance across diverse datasets. The results demonstrate significant improvements in 3D model fidelity, making this method highly applicable for maritime situational awareness and the dynamic display of auxiliary information., Comment: Preprint of SPIE Sensor + Imaging 2024 conference paper

The tracking-by-detection paradigm is the mainstream in multi-object tracking, associating tracks to the predictions of an object detector. Although exhibiting uncertainty through a confidence score, these predictions do not capture the entire variability of the inference process. For safety and security critical applications like autonomous driving, surveillance, etc., knowing this predictive uncertainty is essential though. Therefore, we introduce, for the first time, a fast way to obtain the empirical predictive distribution during object detection and incorporate that knowledge in multi-object tracking. Our mechanism can easily be integrated into state-of-the-art trackers, enabling them to fully exploit the uncertainty in the detections. Additionally, novel association methods are introduced that leverage the proposed mechanism. We demonstrate the effectiveness of our contribution on a variety of benchmarks, such as MOT17, MOT20, DanceTrack, and KITTI., Comment: Accepted for the ECCV 2024 Workshop on Uncertainty Quantification for Computer Vision

Functional approaches are the only first principle QCD setup that allow for direct computations at finite density. Predictive power and quantitative reliability of the respective results can only be obtained within a systematic expansion scheme with controlled systematic error estimates. Here we set up such a scheme within the functional renormalisation group (fRG) approach to QCD, aiming for full apparent convergence. In the current work we test this setup, using correlation functions and observables in 2+1 flavour vacuum QCD as a natural benchmark case. While the current work includes many evolutionary improvements collected over the past two decades, we also report on three novel important developments: (i) A comprehensive systematic error analysis based on the modular nature of the fRG approach. (ii) The introduction of a fully automated computational framework, allowing for unprecedented access and improvement of the fRG approach to QCD. (iii) The inclusion of the full effective potential of the chiral order parameter. This also gives access to all-order scattering events of pions and to the full momentum dependence of correlation functions, which is a first application of the automated computational framework (ii). The results compare very well to other state-of-the-art results both from functional approaches and lattice simulations, and provide data on general multi-scattering events of pions and the sigma mode for the first time.

Object pose estimation is essential to many industrial applications involving robotic manipulation, navigation, and augmented reality. Current generalizable object pose estimators, i.e., approaches that do not need to be trained per object, rely on accurate 3D models. Predominantly, CAD models are used, which can be hard to obtain in practice. At the same time, it is often possible to acquire images of an object. Naturally, this leads to the question whether 3D models reconstructed from images are sufficient to facilitate accurate object pose estimation. We aim to answer this question by proposing a novel benchmark for measuring the impact of 3D reconstruction quality on pose estimation accuracy. Our benchmark provides calibrated images for object reconstruction registered with the test images of the YCB-V dataset for pose evaluation under the BOP benchmark format. Detailed experiments with multiple state-of-the-art 3D reconstruction and object pose estimation approaches show that the geometry produced by modern reconstruction methods is often sufficient for accurate pose estimation. Our experiments lead to interesting observations: (1) Standard metrics for measuring 3D reconstruction quality are not necessarily indicative of pose estimation accuracy, which shows the need for dedicated benchmarks such as ours. (2) Classical, non-learning-based approaches can perform on par with modern learning-based reconstruction techniques and can even offer a better reconstruction time-pose accuracy tradeoff. (3) There is still a sizable gap between performance with reconstructed and with CAD models. To foster research on closing this gap, our benchmark is publicly available at https://github.com/VarunBurde/reconstruction_pose_benchmark}.

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework., Comment: https://wild-gaussians.github.io/

We develop a constructive model of homotopy type theory in a Quillen model category that classically presents the usual homotopy theory of spaces. Our model is based on presheaves over the cartesian cube category, a well-behaved Eilenberg-Zilber category. The key innovation is an additional equivariance condition in the specification of the cubical Kan fibrations, which can be described as the pullback of an interval-based class of uniform fibrations in the category of symmetric sequences of cubical sets. The main technical results in the development of our model have been formalized in a computer proof assistant.

Novel view synthesis is an important problem with many applications, including AR/VR, gaming, and simulations for robotics. With the recent rapid development of Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) methods, it is becoming difficult to keep track of the current state of the art (SoTA) due to methods using different evaluation protocols, codebases being difficult to install and use, and methods not generalizing well to novel 3D scenes. Our experiments support this claim by showing that tiny differences in evaluation protocols of various methods can lead to inconsistent reported metrics. To address these issues, we propose a framework called NerfBaselines, which simplifies the installation of various methods, provides consistent benchmarking tools, and ensures reproducibility. We validate our implementation experimentally by reproducing numbers reported in the original papers. To further improve the accessibility, we release a web platform where commonly used methods are compared on standard benchmarks. Web: https://jkulhanek.com/nerfbaselines, Comment: Web: https://jkulhanek.com/nerfbaselines

Visual localization techniques rely upon some underlying scene representation to localize against. These representations can be explicit such as 3D SFM map or implicit, such as a neural network that learns to encode the scene. The former requires sparse feature extractors and matchers to build the scene representation. The latter might lack geometric grounding not capturing the 3D structure of the scene well enough. This paper proposes to jointly learn the scene representation along with a 3D dense feature field and a 2D feature extractor whose outputs are embedded in the same metric space. Through a contrastive framework we align this volumetric field with the image-based extractor and regularize the latter with a ranking loss from learned surface information. We learn the underlying geometry of the scene with an implicit field through volumetric rendering and design our feature field to leverage intermediate geometric information encoded in the implicit field. The resulting features are discriminative and robust to viewpoint change while maintaining rich encoded information. Visual localization is then achieved by aligning the image-based features and the rendered volumetric features. We show the effectiveness of our approach on real-world scenes, demonstrating that our approach outperforms prior and concurrent work on leveraging implicit scene representations for localization., Comment: Published in 3DV24 (highlight)

In homotopy type theory, a natural number type is freely generated by an element and an endomorphism. Similarly, an integer type is freely generated by an element and an automorphism. Using only dependent sums, identity types, extensional dependent products, and a type of two elements with large elimination, we construct a natural number type from an integer type. As a corollary, homotopy type theory with only $\Sigma$, $\mathsf{Id}$, $\Pi$, and finite colimits with descent (and no universes) admits a natural number type. This improves and simplifies a result by Rose., Comment: 9 pages. To appear in LiCS 2024

The Orion Molecular Cloud complex, one of the nearest (D = 406 pc) and most extensively studied massive star-forming regions, is ideal for constraining the physics of stellar feedback, but its ~12 deg diameter on the sky requires a dedicated approach to mapping ionized gas structures within and around the nebula. The Sloan Digital Sky Survey (SDSS-V) Local Volume Mapper (LVM) is a new optical integral field unit (IFU) that will map the ionized gas within the Milky Way and Local Group galaxies, covering 4300 deg^2 of the sky with the new LVM Instrument. We showcase optical emission line maps from LVM covering 12 deg^2 inside of the Orion belt region, with 195,000 individual spectra combined to produce images at 0.07 pc (35.3") resolution. This is the largest IFU map made (to date) of the Milky Way, and contains well-known nebulae (the Horsehead Nebula, Flame Nebula, IC 434, and IC 432), as well as ionized interfaces with the neighboring dense Orion B molecular cloud. We resolve the ionization structure of each nebula, and map the increase in both the [SII]/Ha and [NII]/Ha line ratios at the outskirts of nebulae and along the ionization front with Orion B. [OIII] line emission is only spatially resolved within the center of the Flame Nebula and IC 434, and our ~0.1 pc scale line ratio diagrams show how variations in these diagnostics are lost as we move from the resolved to the integrated view of each nebula. We detect ionized gas emission associated with the dusty bow wave driven ahead of the star sigma Orionis, where the stellar wind interacts with the ambient interstellar medium. The Horsehead Nebula is seen as a dark occlusion of the bright surrounding photo-disassociation region. This small glimpse into Orion only hints at the rich science that will be enabled by the LVM., Comment: 13 pages, 10 figures, accepted for publication in A&A

We present the Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM). The LVM is an integral-field spectroscopic survey of the Milky Way, Magellanic Clouds, and of a sample of local volume galaxies, connecting resolved pc-scale individual sources of feedback to kpc-scale ionized interstellar medium (ISM) properties. The 4-year survey covers the southern Milky Way disk at spatial resolutions of 0.05 to 1 pc, the Magellanic Clouds at 10 pc resolution, and nearby large galaxies at larger scales totaling $>4300$ square degrees of sky, and more than 55M spectra. It utilizes a new facility of alt-alt mounted siderostats feeding 16 cm refractive telescopes, lenslet-coupled fiber-optics, and spectrographs covering 3600-9800A at R ~ 4000. The ultra-wide field IFU has a diameter of 0.5 degrees with 1801 hexagonally packed fibers of 35.3 arcsec apertures. The siderostats allow for a completely stationary fiber system, avoiding instability of the line spread function seen in traditional fiber feeds. Scientifically, LVM resolves the regions where energy, momentum, and chemical elements are injected into the ISM at the scale of gas clouds, while simultaneously charting where energy is being dissipated (via cooling, shocks, turbulence, bulk flows, etc.) to global scales. This combined local and global view enables us to constrain physical processes regulating how stellar feedback operates and couples to galactic kinematics and disk-scale structures, such as the bar and spiral arms, as well as gas in- and out-flows., Comment: 29 pages, 12 figures, accepted for publication in The Astronomical Journal

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and compact surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing marching tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to, or even outperforms, neural implicit methods in both quality and speed., Comment: Project page: https://niujinshuchong.github.io/gaussian-opacity-fields

Pose refinement is an interesting and practically relevant research direction. Pose refinement can be used to (1) obtain a more accurate pose estimate from an initial prior (e.g., from retrieval), (2) as pre-processing, i.e., to provide a better starting point to a more expensive pose estimator, (3) as post-processing of a more accurate localizer. Existing approaches focus on learning features / scene representations for the pose refinement task. This involves training an implicit scene representation or learning features while optimizing a camera pose-based loss. A natural question is whether training specific features / representations is truly necessary or whether similar results can be already achieved with more generic features. In this work, we present a simple approach that combines pre-trained features with a particle filter and a renderable representation of the scene. Despite its simplicity, it achieves state-of-the-art results, demonstrating that one can easily build a pose refiner without the need for specific training. The code is at https://github.com/ga1i13o/mcloc_poseref, Comment: Accepted to CVPR2024 (Highlight)

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.

In this paper, we consider subsets of an attractor of an iterated function system in which each point is associated with an allowable word from an $S$-gap shift. The main result shows that bounds for the box dimension and Hausdorff dimension of a subfractal induced by an $S$-gap shift are given by the zeros of the upper and lower topological pressure functions., Comment: 13 pages

Both the Haldane spin chain and a topologically dimerized chain feature topologically protected edge states that are expected to be robust against some kind of noise. To elucidate whether it might be feasible to create such edge states in dimerized chains in a controlled manner in solid states environments, e.g. as spin chains on surfaces, as has already been successfully achieved with the Haldane chain, we investigate their robustness with respect to long-range coupling, anisotropies and finite chain length. The theoretical investigation is based on an alternating Heisenberg spin chain with spin-1/2, which is investigated using exact diagonalization. We find that dimerized chains and Haldane chains have robustness against long-range coupling and anisotropies. In particular, dimerized spin chains are significantly more robust than Haldane chains, Comment: 12 pages, 15 figures

In this paper, we introduce the problem of rewriting finite formal languages using syntactic macros such that the rewriting is minimal in size. We present polynomial-time algorithms to solve variants of this problem and show their correctness. To demonstrate the practical relevance of the proposed problems and the feasibility and effectiveness of our algorithms in practice, we apply these to biomedical ontologies authored in OWL. We find that such rewritings can significantly reduce the size of ontologies by capturing repeated expressions with macros. In addition to offering valuable assistance in enhancing ontology quality and comprehension, the presented approach introduces a systematic way of analysing and evaluating features of rewriting systems (including syntactic macros, templates, or other forms of rewriting rules) in terms of their influence on computational problems., Comment: Extended paper (including supplementary material) accepted at The 38th Annual AAAI Conference on Artificial Intelligence

As capturing devices become common, 3D scans of interior spaces are acquired on a daily basis. Through scene comparison over time, information about objects in the scene and their changes is inferred. This information is important for robots and AR and VR devices, in order to operate in an immersive virtual experience. We thus propose an unsupervised object discovery method that identifies added, moved, or removed objects without any prior knowledge of what objects exist in the scene. We model this problem as a combination of a 3D change detection and a 2D segmentation task. Our algorithm leverages generic 2D segmentation masks to refine an initial but incomplete set of 3D change detections. The initial changes, acquired through render-and-compare likely correspond to movable objects. The incomplete detections are refined through graph optimization, distilling the information of the 2D segmentation masks in the 3D space. Experiments on the 3Rscan dataset prove that our method outperforms competitive baselines, with SoTA results.

The correctness and precision of particle physics simulation software, such as Geant4, is expected to yield results that closely align with real-world observations or well-established theoretical predictions. Notably, the accuracy of these simulated outcomes is contingent upon the software's capacity to encapsulate detailed attributes, including its prowess in generating or incorporating complex geometrical constructs. While the imperatives of precision and accuracy are essential in these simulations, the need to manually code highly detailed geometries emerges as a salient bottleneck in developing software-driven physics simulations. This research proposes Blender-to-Geant4 (B2G4), a modular data workflow that utilizes Blender to create 3D scenes, which can be exported as geometry input for Geant4. B2G4 offers a range of tools to streamline the creation of simulation scenes with multiple complex geometries and realistic material properties. Here, we demonstrate the use of B2G4 in a muon scattering tomography application to image the interior of a sealed steel structure. The modularity of B2G4 paves the way for the designed scenes and tools to be embedded not only in Geant4, but in other scientific applications or simulation software., Comment: submitted for review at the Muographers 2023 conference. Initial version. 6 pages, 4 figures

The chiral crossover of QCD at finite temperature and vanishing baryon density turns into a second order phase transition if lighter than physical quark masses are considered. If this transition occurs sufficiently close to the physical point, its universal critical behaviour would largely control the physics of the QCD phase transition. We quantify the size of this region in QCD using functional approaches, both Dyson-Schwinger equations and the functional renormalisation group. The latter allows us to study both critical and non-critical effects on an equal footing, facilitating a precise determination of the scaling regime. We find that the physical point is far away from the critical region. Importantly, we show that the physics of the chiral crossover is dominated by soft modes even far beyond the critical region. While scaling functions determine all thermodynamic properties of the system in the critical region, the order parameter potential is the relevant quantity away from it. We compute this potential in QCD using the functional renormalisation group and Dyson-Schwinger equations and provide a simple parametrisation for phenomenological applications., Comment: 7+8 pages, 5+4 figures

Internet-wide scans are an important tool to evaluate the deployment of services. To enable large-scale application layer scans, a fast, stateless port scan (e.g., using ZMap) is often performed ahead of time to collect responsive targets. It is a common expectation that port scans on the entire IPv4 address space provide a relatively unbiased view as they cover the complete address space. Previous work, however, has found prefixes where all addresses share particular properties. In IPv6, aliased prefixes and fully responsive prefixes, i.e., prefixes where all addresses are responsive, are a well-known phenomenon. However, there is no such in-depth analysis for prefixes with these responsiveness patterns in IPv4. This paper delves into the underlying factors of this phenomenon in the context of IPv4 and evaluates port scans on a total of 161 ports (142 TCP & 19 UDP ports) from three different vantage points. To account for packet loss and other scanning artifacts, we propose the notion of a new category of prefixes, which we call highly responsive prefixes (HRPs). Our findings show that the share of HRPs can make up 70 % of responsive addresses on selected ports. Regarding specific ports, we observe that CDNs contribute to the largest fraction of HRPs on TCP/80 and TCP/443, while TCP proxies emerge as the primary cause of HRPs on other ports. Our analysis also reveals that application layer handshakes to targets outside HRPs are, depending on the chosen service, up to three times more likely to be successful compared to handshakes with targets located in HRPs. To improve future scanning campaigns conducted by the research community, we make our study's data publicly available and provide a tool for detecting HRPs. Furthermore, we propose an approach for a more efficient, ethical, and sustainable application layer target selection.

Covariance matrices of random vectors contain information that is crucial for modelling. Certain structures and patterns of the covariances (or correlations) may be used to justify parametric models, e.g., autoregressive models. Until now, there have been only few approaches for testing such covariance structures systematically and in a unified way. In the present paper, we propose such a unified testing procedure, and we will exemplify the approach with a large variety of covariance structure models. This includes common structures such as diagonal matrices, Toeplitz matrices, and compound symmetry but also the more involved autoregressive matrices. We propose hypothesis tests for these structures, and we use bootstrap techniques for better small-sample approximation. The structures of the proposed tests invite for adaptations to other covariance patterns by choosing the hypothesis matrix appropriately. We prove their correctness for large sample sizes. The proposed methods require only weak assumptions. With the help of a simulation study, we assess the small sample properties of the tests. We also analyze a real data set to illustrate the application of the procedure.

A widely used formulation for null hypotheses in the analysis of multivariate $d$-dimensional data is $\mathcal{H}_0: \boldsymbol{H} \boldsymbol{\theta} =\boldsymbol{y}$ with $\boldsymbol{H}$ $\in\mathbb{R}^{m\times d}$, $\boldsymbol{\theta}$ $\in \mathbb{R}^d$ and $\boldsymbol{y}\in\mathbb{R}^m$, where $m\leq d$. Here the unknown parameter vector $\boldsymbol{\theta}$ can, for example, be the expectation vector $\boldsymbol{\mu}$, a vector $\boldsymbol{\beta} $ containing regression coefficients or a quantile vector $\boldsymbol{q}$. Also, the vector of nonparametric relative effects $\boldsymbol{p}$ or an upper triangular vectorized covariance matrix $\textbf{v}$ are useful choices. However, even without multiplying the hypothesis with a scalar $\gamma\neq 0$, there is a multitude of possibilities to formulate the same null hypothesis with different hypothesis matrices $\boldsymbol{H}$ and corresponding vectors $\boldsymbol{y}$. Although it is a well-known fact that in case of $\boldsymbol{y}=\boldsymbol{0}$ there exists a unique projection matrix $\boldsymbol{P}$ with $\boldsymbol{H}\boldsymbol{\theta}=\boldsymbol{0}\Leftrightarrow \boldsymbol{P}\boldsymbol{\theta}=\boldsymbol{0}$, for $\boldsymbol{y}\neq \boldsymbol{0}$ such a projection matrix does not necessarily exist. Moreover, since such hypotheses are often investigated using a quadratic form as the test statistic, the corresponding projection matrices often contain zero rows; so, they are not even effective from a computational aspect. In this manuscript, we show that for the Wald-type-statistic (WTS), which is one of the most frequently used quadratic forms, the choice of the concrete hypothesis matrix does not affect the test decision. Moreover, some simulations are conducted to investigate the possible influence of the hypothesis matrix on the computation time.

The Internet Engineering Task Force is standardizing new DNS resource records, namely SVCB and HTTPS. Both records inform clients about endpoint and service properties such as supported application layer protocols, IP address hints or Encrypted Client Hello (ECH) information. Therefore, they allow clients to reduce required DNS queries and potential retries during connection establishment and thus help to improve the quality of experience and privacy of the client. The latter is achieved by reducing visible meta-data, which is further improved with encrypted DNS and ECH. The standardization is in its final stages and companies announced support, e.g., Cloudflare and Apple. Therefore, we provide the first large-scale overview of actual record deployment by analyzing more than 400 M domains. We find 3.96 k SVCB and 10.5 M HTTPS records. As of March 2023, Cloudflare hosts and serves most domains, and most records only contain Application-Layer Protocol Negotiation (ALPN) and IP address hints. Besides Cloudflare, we see adoption by a variety of authoritative name servers and hosting providers indicating increased adoption in the near future. Lastly, we can verify the correctness of records for more than 93 % of domains based on three application layer scans., Comment: Presented at the 8th International Workshop on Traffic Measurements for Cybersecurity (WTMC 2023)

To keep up with increasing demands on quality of experience, assessing and understanding the performance of network connections is crucial for web service providers. While different measures, like TCP options, alternative transport layer protocols like QUIC, or the hosting of services in CDNs, are expected to improve connection performance, no studies are quantifying such impacts on connections on the Internet. This paper introduces an active Internet measurement approach to assess the impacts of mentioned measures on connection performance. We conduct downloads from public web servers considering different vantage points, extract performance indicators like throughput, RTT, and retransmission rate, and survey speed-ups due to TCP option usage. Further, we compare the performance of QUIC-based downloads to TCP-based downloads considering different option configurations. Next to significant throughput improvements due to TCP option usage, in particular TCP window scaling, and QUIC, our study shows significantly increased performance for connections to domains hosted by different giant CDNs., Comment: Presented at the ACM/IRTF Applied Networking Research Workshop 2023 (ANRW23)

QCD at large density reveals a rich phase structure, ranging from a potential critical end point and inhomogeneous phases or moat regimes to color superconducting ones with competing order effects. Resolving this region in the phase diagram of QCD with functional approaches requires a great deal of quantitative reliability, already for a qualitative access. In the present work, we systematically extend the functional renormalisation group approach to low energy QCD by setting up a fully self-consistent approximation scheme in a low energy effective quark-meson theory. In this approximation, all pointlike multi-scattering events of the mesonic pion and the sigma mode are taken into account in terms of an effective potential as well as all higher quark-antiquark-mesonic scattering orders. As a first application we compute the phase structure of QCD including its low temperature - large chemical potential part. The quantitative reliability of the approximation and systematic extensions are also discussed.

The diversity of QUIC implementations poses challenges for Internet measurements and the analysis of the QUIC ecosystem. While all implementations follow the same specification and there is general interoperability, differences in performance, functionality, but also security (e.g., due to bugs) can be expected. Therefore, knowledge about the implementation of an endpoint on the Internet can help researchers, operators, and users to better analyze connections, performance, and security. In this work, we improved the detection rate of QUIC scans to find more deployments and provide an approach to effectively identify QUIC server libraries based on CONNECTION CLOSE frames and transport parameter orders. We performed Internet-wide scans and identified at least one deployment for 18 QUIC libraries. In total, we can identify the libraries with 8.0 M IPv4 and 2.5 M IPv6 addresses. We provide a comprehensive view of the landscape of competing QUIC libraries., Comment: preprint

The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding.