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It is unclear which fraction of cosmic rays above an energy of 1 PeV is accelerated by the observed Galactic PeVatron population. These sources' gamma-ray data is typically degenerate between hadronic and leptonic emission scenarios, which hinders their straightforward association with the cosmic ray population. Here, we aimed to distinguish between leptonic and hadronic particle acceleration scenarios for the PeVatron candidate HESS J1646-458, associated with the star cluster Westerlund 1 (Wd 1). We first studied the diffuse X-ray emission from Wd 1 to better understand if its origin is of thermal or nonthermal nature. In addition, we searched for X-ray synchrotron emission from the associated PeVatron candidate HESS J1646-458 to put new constraints on the magnetic field strength and the leptonic particle population of this source. We used data from eROSITA on board the SRG orbital platform to spectrally analyze the diffuse emission from Wd 1 and HESS J1646-458. For Wd 1, we compared a purely thermal model and a model with a thermal and a nonthermal component. Next, we analyzed the spectra of four annuli around Wd 1 which coincide with HESS J1646-458 to search for synchrotron radiation. We find that eROSITA data cannot distinguish between thermal and nonthermal source scenarios for the diffuse emission from Wd 1 itself. In the case of HESS J1646-458, we find no evidence of synchrotron emission. We estimated an upper confidence bound of the synchrotron flux up to 40' around Wd 1 of 1.9e-3 keV-1 cm-2 s-1, which we used to study the spectral energy distribution of the source. From this, we obtained an upper 1 sigma bound on the magnetic field strength of HESS J1646-458 of 7 uG. This is compatible with a previous estimate in the literature for a fully leptonic source scenario. A purely leptonic emission scenario, a hadronic, and a hybrid one are compatible with our results., Comment: To be published in Astronomy & Astrophysics, 18 pages, 10 figures

The Surface Photometry and Accurate Rotation Curves (SPARC) database has provided the community with mass models for 175 nearby galaxies, allowing different research teams to test different dark matter models, galaxy evolution models, and modified gravity theories. Extensive tests, however, are hampered by the somewhat heterogeneous nature of the HI rotation curves and the limited sample size of SPARC. To overcome these limitations, we are working on BIG-SPARC, a new database that consists of about 4000 galaxies with HI datacubes from public telescope archives (APERTIF, ASKAP, ATCA, GMRT, MeerKAT, VLA, and WSRT) and near infrared photometry from WISE. For these galaxies, we will provide homogeneously derived HI rotation curves, surface brightness profiles, and mass models. BIG-SPARC is expected to increase the size of its predecessor by a factor of more than 20. This is a necessary step to prepare for the additional order of magnitude increase in sample size expected from ongoing and future HI surveys with the Square Kilometre Array (SKA) and its pathfinders, Comment: To be published in the proceedings of IAU Symposium 392 "Neutral Hydrogen in and around Galaxies in the SKA Era", 6 pages, 3 figures, 1 table

Particle accelerators with superconducting magnets operating at cryogenic temperatures use a beam screen (BS) liner that extracts heat generated by the circulating bunched charge particle beam before it can reach the magnets. The BS surface, commonly made of high-conductivity copper, provides a low impedance for beam stability reasons, low secondary electron yield (SEY)to mitigate the electron-cloud (EC) effect, and low electron-stimulated desorption yield (ESD) to limit the dynamic pressure rise due to EC. Rare-earth barium copper oxide (REBCO) high-temperature superconductors (HTs) recently reached technical maturity, are produced as coated conductor tapes (REBCO-CCs), and will be considered for application in future colliders to decrease the BS impedance and enable operation at around 50 K, consequently relaxing the cryogenic requirements. Aside from HTS properties, industry-grade REBCO-CCs also need qualification for EC and dynamic vacuum compatibility under accelerator-like conditions. Hence, we report the SEY and ESD measured at cryogenic temperatures of 12 K under low-energy electron irradiation of 0 - 1.4 keV. We also verify the sample compositions and morphologies using XPS, SEM, and EDS methods. The energy and dose dependencies of ESD are comparable to those of technical-grade metals and one sample reached SEY$_{MAX}=$1.2 after electron conditioning.

We consider the method of mappings for performing shape optimization for unsteady fluid-structure interaction (FSI) problems. In this work, we focus on the numerical implementation. We model the optimization problem such that it takes several theoretical results into account, such as regularity requirements on the transformations and a differential geometrical point of view on the manifold of shapes. Moreover, we discretize the problem such that we can compute exact discrete gradients. This allows for the use of general purpose optimization solvers. We focus on an FSI benchmark problem to validate our numerical implementation. The method is used to optimize parts of the outer boundary and the interface. The numerical simulations build on FEniCS, dolfin-adjoint and IPOPT. Moreover, as an additional theoretical result, we show that for a linear special case the adjoint attains the same structure as the forward problem but reverses the temporal flow of information.

The eROSITA telescope array aboard the Spektrum Roentgen Gamma (SRG) satellite began surveying the sky in December 2019, with the aim of producing all-sky X-ray source lists and sky maps of an unprecedented depth. Here we present catalogues of both point-like and extended sources using the data acquired in the first six months of survey operations (eRASS1; completed June 2020) over the half sky whose proprietary data rights lie with the German eROSITA Consortium. We describe the observation process, the data analysis pipelines, and the characteristics of the X-ray sources. With nearly 930000 entries detected in the most sensitive 0.2-2.3 keV energy range, the eRASS1 main catalogue presented here increases the number of known X-ray sources in the published literature by more than 60%, and provides a comprehensive inventory of all classes of X-ray celestial objects, covering a wide range of physical processes. A smaller catalogue of 5466 sources detected in the less sensitive but harder 2.3-5 keV band is the result of the first true imaging survey of the entire sky above 2 keV. We show that the number counts of X-ray sources in eRASS1 are consistent with those derived over narrower fields by past X-ray surveys of a similar depth, and we explore the number counts variation as a function of the location in the sky. Adopting a uniform all-sky flux limit (at 50% completeness) of F_{0.5-2 keV} > 5 \times 10^{-14}$ erg\,s$^{-1}$\,cm$^{-2}$, we estimate that the eROSITA all-sky survey resolves into individual sources about 20% of the cosmic X-ray background in the 1-2 keV range. The catalogues presented here form part of the first data release (DR1) of the SRG/eROSITA all-sky survey. Beyond the X-ray catalogues, DR1 contains all detected and calibrated event files, source products (light curves and spectra), and all-sky maps. Illustrative examples of these are provided., Comment: 39 pages, 23 figures. Accepted for publication in A&A. Accompanying eROSITA-DE Data Release 1

The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorded microphone signal. To obtain precise echo estimates, the parameters of the echo canceler, i.e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals. For this a sophisticated adaptation control is required to deal with high-power double-talk and rapidly track time-varying acoustic environments which are often faced with portable devices. In this paper, we address this problem by end-to-end deep learning. In particular, we suggest to infer the step-size for a least mean squares frequency-domain adaptive filter update by a Deep Neural Network (DNN). Two different step-size inference approaches are investigated. On the one hand broadband approaches, which use a single DNN to jointly infer step-sizes for all frequency bands, and on the other hand narrowband methods, which exploit individual DNNs per frequency band. The discussion of benefits and disadvantages of both approaches leads to a novel hybrid approach which shows improved echo cancellation while requiring only small DNN architectures. Furthermore, we investigate the effect of different loss functions, signal feature vectors, and DNN output layer architectures on the echo cancellation performance from which we obtain valuable insights into the general design and functionality of DNN-based adaptation control algorithms., Comment: This article has been submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing

Patient specific brain mesh generation from MRI can be a time consuming task and require manual corrections, e.g., for meshing the ventricular system or defining subdomains. To address this issue, we consider an image registration approach. The idea is to use the registration of an input magnetic resonance image (MRI) to a respective target in order to obtain a new mesh from a template mesh. To obtain the transformation, we solve an optimization problem that is constrained by a linear hyperbolic transport equation. We use a higher-order discontinuous Galerkin finite element method for discretization and motivate the numerical upwind scheme and its limitations from the continuous weak space--time formulation of the transport equation. We present a numerical implementation that builds on the finite element packages FEniCS and dolfin-adjoint. To demonstrate the efficacy of the proposed approach, numerical results for the registration of an input to a target MRI of two distinct individuals are presented. Moreover, it is shown that the registration transforms a manually crafted input mesh into a new mesh for the target subject whilst preserving mesh quality. Challenges of the algorithm are discussed.

Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene. In its simplest form, the delay-and-sum beamformer (DSB) introduces a time delay to all channels to align the desired signal components for constructive superposition. Recent investigations of neural spatiospectral filtering revealed that these filters can be characterized by a beampattern similar to one of traditional beamformers, which shows that artificial neural networks can learn and explicitly represent spatial structure. Using the Complex-valued Spatial Autoencoder (COSPA) as an exemplary neural spatiospectral filter for multichannel speech enhancement, we investigate where and how such networks represent spatial information. We show via clustering that for COSPA the spatial information is represented by the features generated by a gated recurrent unit (GRU) layer that has access to all channels simultaneously and that these features are not source -- but only direction of arrival-dependent., Comment: Accepted to the 31st European Signal Processing Conference (EUSIPCO 2023), Helsinki, Finland. 5 pages, 3 figures

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.

Mesh degeneration is a bottleneck for fluid-structure interaction (FSI) simulations and for shape optimization via the method of mappings. In both cases, an appropriate mesh motion technique is required. The choice is typically based on heuristics, e.g., the solution operators of partial differential equations (PDE), such as the Laplace or biharmonic equation. Especially the latter, which shows good numerical performance for large displacements, is expensive. Moreover, from a continuous perspective, choosing the mesh motion technique is to a certain extent arbitrary and has no influence on the physically relevant quantities. Therefore, we consider approaches inspired by machine learning. We present a hybrid PDE-NN approach, where the neural network (NN) serves as parameterization of a coefficient in a second order nonlinear PDE. We ensure existence of solutions for the nonlinear PDE by the choice of the neural network architecture. Moreover, we present an approach where a neural network corrects the harmonic extension such that the boundary displacement is not changed. In order to avoid technical difficulties in coupling finite element and machine learning software, we work with a splitting of the monolithic FSI system into three smaller subsystems. This allows to solve the mesh motion equation in a separate step. We assess the quality of the learned mesh motion technique by applying it to a FSI benchmark problem. In addition, we discuss generalizability and computational cost of the learned mesh motion operators.

We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AEC and beamforming filters by maximizing the statistical independence of a non-Gaussian source of interest and a stationary Gaussian background modeling interfering signals and residual echo. Double talk-robust and fast-converging parameter updates are derived from a global maximum-likelihood objective function resulting in a computationally efficient Newton-type update rule. Evaluation with simulated acoustic data confirms the benefit of the proposed joint AEC and beamforming filter estimation in comparison to updating both filters individually., Comment: Accepted for International Workshop on Acoustic Signal Enhancement (IWAENC 2022)

In recent years, a plethora of methods combining deep neural networks and partial differential equations have been developed. A widely known and popular example are physics-informed neural networks. They solve forward and inverse problems involving partial differential equations in terms of a neural network training problem. We apply physics-informed neural networks as well as the finite element method to estimate the diffusion coefficient governing the long term, i.e. over days, spread of molecules in the human brain from a novel magnetic resonance imaging technique. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural network faces challenges with noisy measurements in our application. Our numerical results demonstrate that the residual of the partial differential equation after training needs to be small in order to obtain accurate recovery of the diffusion coefficient. To achieve this, we apply several strategies such as tuning the weights and the norms used in the loss function as well as residual based adaptive refinement and exchange of residual training points. We find that the diffusion coefficient estimated with PINNs from magnetic resonance images becomes consistent with results from a finite element based approach when the residuum after training becomes small. The observations presented in this work are an important first step towards solving inverse problems on observations from large cohorts of patients in a semi-automated fashion with physics-informed neural networks.

We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF). While the AEC removes the early echo component, the MVDR BF and PF suppress the residual echo and background noise. As key innovation, we suggest to use a single deep neural network (DNN) to jointly control the adaptation of the various algorithmic components. This allows for rapid convergence and high steady-state performance in the presence of high-level interfering double-talk. End-to-end training of the DNN using a time-domain speech extraction loss function avoids the design of individual control strategies., Comment: Accepted for European Signal Processing Conference (EUSIPCO) 2022, Belgrade, Serbia

We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of the network are optimized in an end-to-end fashion by minimizing the average normalized system distance of the adaptive filter. This avoids the need of explicit signal power spectral density estimation as required for model-based adaptation control and further auxiliary mechanisms to deal with model inaccuracies. The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by high-level, non-white and non-stationary additive noise signals, abrupt environment changes and additional model inaccuracies., Comment: Accepted for IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022, Singapore, Singapore

We introduce a novel algorithm for online estimation of acoustic impulse responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental structure of AIRs. The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates. We discuss various approaches to locally approximate the AIR manifold by affine subspaces which are assumed to be tangential hyperplanes to the manifold. The validity of these model assumptions is verified for simulated data. Subsequently, we describe how the learned models can be used to improve online AIR estimates by projecting them onto an adaptively estimated subspace. The parameters determining the subspace are learned from training samples in a local neighbourhood to the current AIR estimate. This allows the system identification algorithm to benefit from preceding estimates in the acoustic scene. To assess the proximity of training data AIRs to the current AIR estimate, we introduce a probabilistic extension of the Euclidean distance which improves the performance for applications with correlated excitation signals. Furthermore, we describe how model imperfections can be tackled by a soft projection of the AIR estimates. The proposed algorithm exhibits significantly faster convergence properties in comparison to a high-performance state-of-the-art algorithm. Furthermore, we show an improved steady-state performance for speech-excited system identification scenarios suffering from high-level interfering noise and nonunique solutions.

The Literacy Design Collaborative (LDC) was created to support teachers in implementing college and career readiness standards in order to teach literacy skills throughout the content areas. Teachers work collaboratively with coaches to further develop their expertise and design standards-driven, literacy-rich writing assignments within their existing curriculum across all content areas. This annual report reflects the second year of implementation in 18 Cohort 1 schools, which began implementation in the 2016-2017, and the first year of implementation for 35 Cohort 2 schools, which commenced at the beginning of 2017-2018. Teacher, teacher leader, and administrator participants reported positive attitudes toward LDC. All measures of satisfaction or improvement were rated positively by more than half of the respondents. Participants across all groups perceived a positive impact on student outcomes. Approximately three-quarters of teachers agreed that LDC helped improve students'reading skills, literacy-performance, writing, and content knowledge. Analysis of student outcomes indicated that there were positive impacts of LDC on student learning, but the differences were not statistically significant.

The Literacy Design Collaborative (LDC) was created to support teachers in implementing college and career readiness standards in order to teach literacy skills throughout the content areas. Teachers work collaboratively with coaches to further develop their expertise and design standards-driven, literacy-rich writing assignments within their existing curriculum across all content areas. This annual report reflects the second year of implementation in 14 Cohort 1 schools, which began implementation during 2016-2017, and the first year of implementation for 31 Cohort 2 schools, which commenced at the beginning of the 2017-2018 school year. Findings from multiple sources including participant surveys and analyses of student outcomes reveal positive results for the LDC intervention. Teacher, teacher leader, and administrator participants reported positive attitudes toward LDC. Participants across all groups perceived a positive impact on student outcomes. Additionally, analysis of student outcomes confirmed participants' positive views. Our quasi-experimental design study revealed that LDC had statistically significant effects on Cohort 2 middle school students' English language arts (ELA) performance, with an effect size of 0.15 for students receiving LDC instruction in all three core content areas: ELA, science, and social studies/history.

We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed algorithm overcomes the well-known limitations of Kalman filter-based adaptation control in scenarios characterized by abrupt echo path changes. As the key innovation, we suggest to exploit the different statistical properties of the interfering signal components for robustly estimating the adaptation step size. This is achieved by leveraging the postfilter near-end estimate and the estimation error of the Kalman filter. The proposed synergistic scheme allows for rapid reconvergence of the adaptive filter after abrupt echo path changes without compromising the steady state performance achieved by state-of-the-art approaches in static scenarios., Comment: Accepted for European Signal Processing Conference (EUSIPCO), Dublin, Ireland, August 2021

Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidates requires $O(n)$ evaluations of the gain function for an interval with $n$ observations. If each evaluation is computationally demanding (e.g. in high-dimensional models), this can become infeasible. Instead, we propose optimistic search methods with $O(\log n)$ evaluations exploiting specific structure of the gain function. Towards solid understanding of our strategy, we investigate in detail the $p$-dimensional Gaussian changing means setup, including high-dimensional scenarios. For some of our proposals, we prove asymptotic minimax optimality for detecting change points and derive their asymptotic localization rate. These rates (up to a possible log factor) are optimal for the univariate and multivariate scenarios, and are by far the fastest in the literature under the weakest possible detection condition on the signal-to-noise ratio in the high-dimensional scenario. Computationally, our proposed methodology has the worst case complexity of $O(np)$, which can be improved to be sublinear in $n$ if some a-priori knowledge on the length of the shortest segment is available. Our search strategies generalize far beyond the theoretically analyzed setup. We illustrate, as an example, massive computational speedup in change point detection for high-dimensional Gaussian graphical models., Comment: Generalize the univariate theory to Gaussian mean changes of general dimension, including high-dimensional scenarios

Electron-stimulated desorption (ESD) of cryosorbed molecules on surfaces is a process of relevance to fields as varied as vacuum dynamics in accelerators and astrochemistry. While desorption from such molecular systems induced by keV electrons and fast ions has been extensively studied, the sub-keV electron regime is comparatively little known. We measured and quantified electron-stimulated desorption from molecular ice systems (layers of N$_2$, CO, CO$_2$, Ar and H$_2$O/D$_2$O condensed at cryogenic temperatures) in the 150-2000 eV electron energy range. In this regime stopping power is no longer sufficient to explain the electron energy dependence of ESD yields. We introduce the notion of desorption-relevant depth, which characterizes the transition between two energy deposition regimes near the surface. We then apply this notion to the different systems, showing how ESD in the sub-keV regime can for example reveal the differences in species diffusion in crystalline and porous amorphous CO$_2$ ices., Comment: The following article has been accepted by the Journal of Applied Physics. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/

In this paper we present a novel algorithm for improved block-online supervised acoustic system identification in adverse noise scenarios by exploiting prior knowledge about the space of Room Impulse Responses (RIRs). The method is based on the assumption that the variability of the unknown RIRs is controlled by only few physical parameters, describing, e.g., source position movements, and thus is confined to a low-dimensional manifold which is modelled by a union of affine subspaces. The offsets and bases of the affine subspaces are learned in advance from training data by unsupervised clustering followed by Principal Component Analysis. We suggest to denoise the parameter update of any supervised adaptive filter by projecting it onto an optimal affine subspace which is selected based on a novel computationally efficient approximation of the associated evidence. The proposed method significantly improves the system identification performance of state-of-the-art algorithms in adverse noise scenarios.

We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noisy observations by a linear Gaussian Discrete Fourier Transform-domain state space model whose parameters are estimated by an online generalized Expectation-Maximization algorithm. Unlike all other state-of-the-art approaches we suggest to model the covariance matrix of the observation probability density function by a dictionary model. We propose to learn the noise dictionary from training data, which can be gathered either offline or online whenever the system is not excited, while we infer the activations continuously. The proposed algorithm represents a novel machine-learning based approach to noise-robust adaptation control which allows for faster convergence in applications characterized by high-level and non-stationary interfering noise signals and abrupt system changes.