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It has been recently shown that a small-scale dynamo (SSD) instability could be possible in solar-like low magnetic Prandtl number Pm plasmas. It has been proposed that the presence of SSD can potentially have a significant impact on the dynamics of the large-scale dynamo (LSD) in the stellar convection zones. Studying these two dynamos, SSD and LSD, together in a global magnetoconvection model requires high-resolution simulations and large amounts of computational resources. Starting from a well-studied global convective dynamo model that produces cyclic magnetic fields, we systematically increased the resolution and lowered the diffusivities to enter the regime of Reynolds numbers that allow for the excitation of SSD on top of the LSD. We studied how the properties of convection, generated differential rotation profiles, and LSD solutions change with the presence of SSD. We performed convective dynamo simulations in a spherical wedge with the Pencil Code. The resolutions of the models were increased in 4 steps by a total factor of 16 to achieve maximal fluid and magnetic Reynolds numbers of over 500. We found that the differential rotation is strongly quenched by the presence of the LSD and SSD. Even though the small-scale magnetic field only mildly decreases increasing Re, the large-scale field strength decreases significantly. We do not find the SSD dynamo significantly quenching the convective flows as claimed recently by other authors; in contrast, the convective flows first grow and then saturate for increasing Re. Furthermore, the angular momentum transport is highly affected by the presence of small-scale magnetic fields, which are mostly generated by LSD. These fields not only change the Reynolds stresses, but also generate dynamically important Maxwell stresses. The LSD evolution in terms of its pattern and field distribution is rather independent of the increase in Rm., Comment: 17 pages, 18 figures with an Appendix with 3 pages and 3 figures, submitted to A&A

Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown. A common method used to explain the concepts learned by DNNs is Feature Visualization (FV), which generates a synthetic input signal that maximally activates a particular neuron in the network. In this paper, we investigate the vulnerability of this approach to adversarial model manipulations and introduce a novel method for manipulating FV without significantly impacting the model's decision-making process. The key distinction of our proposed approach is that it does not alter the model architecture. We evaluate the effectiveness of our method on several neural network models and demonstrate its capabilities to hide the functionality of arbitrarily chosen neurons by masking the original explanations of neurons with chosen target explanations during model auditing.

Coronal magnetic fields evolve quasi statically over long time scales and dynamically over short time scales. As of now there exists no regular measurements of coronal magnetic fields, and therefore generating the coronal magnetic field evolution using the observations of the magnetic field at the photosphere is of fundamental requirement to understand the origin of the transient phenomena from the solar active regions. Using the magnetofriction (MF) approach, we aim to simulate the coronal field evolution in the solar active region 11429. The MF method is implemented in open source \PC along with a driver module to drive the initial field with different boundary conditions prescribed from observed vector magnetic fields at the photosphere. In order to work with vector potential and the observations, we prescribe three types of bottom boundary drivers with varying free-magnetic energy. The MF simulation reproduces the magnetic structure, which better matches to the sigmoidal morphology exhibited by AIA images at the pre-eruptive time. We found that the already sheared field further driven by the sheared magnetic field, will maintain and further build the highly sheared coronal magnetic configuration, as seen in AR 11429. Data-driven MF simulation is a viable tool to generate the coronal magnetic field evolution, capturing the formation of the twisted flux rope and its eruption., Comment: 15 pages, 5 figures, to appear in Research in Astronomy and Astrophysics

We consider a kinetic model of an N-species gas mixture modeled with quantum Bhatnagar-Gross-Krook (BGK) collision operators. The collision operators consist of a relaxation to a Maxwell distribution in the classical case, a Fermi distribution for fermions and a Bose-Einstein distribution for bosons. In this paper we present a numerical method for simulating this model, which uses an Implicit-Explicit (IMEX) scheme to minimize a certain potential function. This is motivated by theoretical considerations coming from entropy minimization. We show that theoretical properties such as conservation of mass, total momentum and total energy as well as positivity of the distribution functions are preserved by the numerical method presented in this paper, and illustrate its usefulness and effectiveness with numerical examples, Comment: arXiv admin note: text overlap with arXiv:2202.05652

Zeeman-Doppler imaging (ZDI) is used to reconstruct the surface magnetic field of late-type stars from high resolution spectropolarimetric observations. The results are usually described in terms of characteristics of the field topology, i.e. poloidality vs. toroidality and axi-symmetry vs. non-axisymmetry in addition to the field strength. We want to test how well these characteristics are preserved when applying the ZDI method on simulated data, i.e. how accurately the field topology is preserved and to what extent stellar parameters influence the reconstruction. We use published magnetic field data from direct numerical MHD simulations. These have variable rotation rates, and hence represent different levels of activity, of an otherwise Sun-like setup. Our ZDI reconstruction is based on spherical harmonics expansion. By comparing the original values to those of the reconstructed images, we study the ability to reconstruct the surface magnetic field in terms of various characteristics of the field. The main large-scale features are reasonably well recovered, but the strength of the recovered magnetic field is just a fraction of the original input. The quality of the reconstruction shows clear correlations with the data quality. Furthermore, there are some spurious dependencies between stellar parameters and the characteristics of the field. Our study uncovers some limits of ZDI. Firstly, the recovered field strength will generally be lower than the "real" value as smaller structures with opposite polarities will be blurred in the inversion. Secondly, the axisymmetry is overestimated. The poloidality vs. toroidality is better recovered. The reconstruction works better for a stronger field and faster rotation velocity. Still, the ZDI method works surprisingly well even for a weaker field and slow rotation, provided the data has a high signal-to-noise and good rotation phase coverage., Comment: 12 pages, 21 figures, A&A, in press, abstract shortened

Magnetic fields on small scales are ubiquitous in the universe. Though they can often be observed in detail, their generation mechanisms are not fully understood. One possibility is the so-called small-scale dynamo (SSD). Prevailing numerical evidence, however, appears to indicate that an SSD is unlikely to exist at very low magnetic Prandtl numbers ($Pr_M$) such as are present in the Sun and other cool stars. We have performed high-resolution simulations of isothermal forced turbulence employing the lowest $Pr_M$ values so far achieved. Contrary to earlier findings, the SSD turns out to be not only possible for $Pr_M$ down to 0.0031, but even becomes increasingly easier to excite for $Pr_M$ below $\simeq\,$0.05. We relate this behaviour to the known hydrodynamic phenomenon referred to as the bottleneck effect. Extrapolating our results to solar values of $Pr_M$ indicates that an SSD would be possible under such conditions., Comment: 17 pages, 5 figures + Supplementary Material (9 pages & 5 figures). This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://www.nature.com/articles/s41550-023-01975-1.pdf

We implement sympathetic cooling of highly charged ions (HCI) by fully enclosing a linear Paul trap within a superconducting radio-frequency resonator. A quantization magnetic field applied while cooling down into the superconducting state remains present in the trap for centuries and external electromagnetic fluctuations are greatly suppressed. A magnetic field decay rate at the 10$^{-10}$ s$^{-1}$ level is found using trapped Doppler-cooled Be$^+$ ions as hyperfine-structure (hfs) qubits. Ramsey interferometry and spin-echo measurements on magnetically-sensitive hfs transitions yield coherence times of >400 ms, showing excellent passive shielding at frequencies down to DC. For sympathetic cooling of HCI, we extract them from an electron beam ion trap (EBIT) and co-crystallize one together with Doppler-cooled Be$^+$ ions. By subsequently ejecting all but one Be$^+$ ions, we prepare single HCI for quantum logic spectroscopy towards frequency metrology and qubit operations with a great variety of HCI species., Comment: 16 pages, 19 figures. The following article has been submitted to Review of Scientific Instruments. After it is published, it will be found at https://rsi.aip.org/

We review the state of the art of three dimensional numerical simulations of solar and stellar dynamos. We summarize fundamental constraints of numerical modelling and the techniques to alleviate these restrictions. Brief summary of the relevant observations that the simulations seek to capture is given. We survey the current progress of simulations of solar convection and the resulting large-scale dynamo. We continue to studies that model the Sun at different ages and to studies of stars of different masses and evolutionary stages. Both simulations and observations indicate that rotation, measured by the Rossby number which is the ratio of rotation period and convective turnover time, is a key ingredient in setting the overall level and characteristics of magnetic activity. Finally, efforts to understand global 3D simulations in terms of mean-field dynamo theory are discussed., Comment: 70 pages, 9 figures, submitted to Space Science Reviews

Backdoors pose a serious threat to machine learning, as they can compromise the integrity of security-critical systems, such as self-driving cars. While different defenses have been proposed to address this threat, they all rely on the assumption that the hardware on which the learning models are executed during inference is trusted. In this paper, we challenge this assumption and introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning. Outside of the accelerator, neither the learning model nor the software is manipulated, so that current defenses fail. To make this attack practical, we overcome two challenges: First, as memory on a hardware accelerator is severely limited, we introduce the concept of a minimal backdoor that deviates as little as possible from the original model and is activated by replacing a few model parameters only. Second, we develop a configurable hardware trojan that can be provisioned with the backdoor and performs a replacement only when the specific target model is processed. We demonstrate the practical feasibility of our attack by implanting our hardware trojan into the Xilinx Vitis AI DPU, a commercial machine-learning accelerator. We configure the trojan with a minimal backdoor for a traffic-sign recognition system. The backdoor replaces only 30 (0.069%) model parameters, yet it reliably manipulates the recognition once the input contains a backdoor trigger. Our attack expands the hardware circuit of the accelerator by 0.24% and induces no run-time overhead, rendering a detection hardly possible. Given the complex and highly distributed manufacturing process of current hardware, our work points to a new threat in machine learning that is inaccessible to current security mechanisms and calls for hardware to be manufactured only in fully trusted environments.

In this paper a hyperbolic system of partial differential equations for two-phase mixture flows with $N$ components is studied. It is derived from a more complicated model involving diffusion and exchange terms. Important features of the model are the assumption of isothermal flow, the use of a phase field function to distinguish the phases and a mixture equation of state involving the phase field function as well as an affine relation between partial densities and partial pressures in the liquid phase. This complicates the analysis. A complete solution of the Riemann initial value problem is given. Some interesting examples are suggested as bench marks for numerical schemes.

In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have successfully captured biomarkers and allowed discrimination between Parkinson's disease (PD) and healthy controls (HC) or differential diagnosis (DD). However, to the best of our knowledge, a comprehensive evaluation of assessments with a multi-modal smart device system has still been lacking. In a prospective study exploring PD, we used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. This study aims to assess the effect of multi-modal vs. single-modal data on PD vs. HC and PD vs. DD classification, as well as on PD group clustering for subgroup identification. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.

Sensors from smart consumer devices have demonstrated high potential to serve as digital biomarkers in the identification of movement disorders in recent years. With the usage of broadly available smartwatches we have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD). In total, 504 participants, including PD patients, differential diagnoses (DD) and healthy controls (HC), were captured with a comprehensive system utilizing two smartwatches and two smartphones. To the best of our knowledge, this study provided the largest PD sample size of two-hand synchronous smartwatch measurements. To establish a future easy-to use home-based assessment system in PD screening, we systematically evaluated the performance of the system based on a significantly reduced set of assessments with only one-sided measures and assessed, whether we can maintain classification accuracy.

Global hydrodynamic simulations of internal solar dynamics have focused on replicating the conditions for solar-like differential rotation and meridional circulation using the results of helioseismic inversions as a constraint. Inferences of meridional circulation, however, have provided controversial results showing the possibility of one, two, or multiple cells along the radius. To resolve this controversy and develop a more robust understanding of global flow regimes in the solar interior, we apply a "forward-modeling" approach to the analysis of helioseismic signatures of meridional circulation profiles obtained from numerical simulations. We employ the global acoustic modeling code GALE to simulate the propagation of acoustic waves through regimes of mean mass flows generated by global hydrodynamic and magnetohydrodynamic models: EULAG, the Pencil Code, and the Rayleigh code. These models are used to create synthetic dopplergram data products, used as inputs for local time-distance helioseismology techniques. Helioseismic travel-time signals from solutions obtained through global numerical simulations are compared directly with inferences from solar observations, in order to set additional constraints on global model parameters in a direct way. We show that even though these models are able to replicate solar-like differential rotation, the resulting rotationally-constrained convection develops a multi-cell global meridional circulation profile that is measurably inconsistent with local time-distance inferences of solar observations. However, we find that the development of rotationally-unconstrained convection close to the model surface is able to maintain solar-like differential rotation, while having a significant impact on the helioseismic travel-time signal, replicating solar observations within one standard deviation of the error due to noise., Comment: 13 pages, 7 figures

In this article we focus on kinetic equations for gas mixtures since in applications one often has to deal with mixtures instead of a single gas. In particular we consider an approximation of the Boltzmann equation, the Bathnagar-Gross-Krook (BGK) equation. This equation is used in many applications because it is very efficient in numerical simulations. In this article, we recall a general BGK equation for gas mixtures which has free parameters. Specific choices of these free parameters lead to special cases in the literature. For this model, we provide an overview concerning modelling, theoretical results and numerics.

We consider a kinetic description of a multi-species gas mixture modeled with Bhatnagar-Gross-Krook (BGK) collision operators, in which the collision frequency varies not only in time and space but also with the microscopic velocity. In this model, the Maxwellians typically used in standard BGK operators are replaced by a generalization of such target functions, which are defined by a variational procedure \cite{arXiv:2101.09047}. In this paper we present a numerical method for simulating this model, which uses an Implicit-Explicit (IMEX) scheme to minimize a certain potential function, mimicking the Lagrange functional that appears in the theoretical derivation. We show that theoretical properties such as conservation of mass, total momentum and total energy as well as positivity of the distribution functions are preserved by the numerical method, and illustrate its usefulness and effectiveness with numerical examples.