Search

We introduce PerCo (SD), a perceptual image compression method based on Stable Diffusion v2.1, targeting the ultra-low bit range. PerCo (SD) serves as an open and competitive alternative to the state-of-the-art method PerCo, which relies on a proprietary variant of GLIDE and remains closed to the public. In this work, we review the theoretical foundations, discuss key engineering decisions in adapting PerCo to the Stable Diffusion ecosystem, and provide a comprehensive comparison, both quantitatively and qualitatively. On the MSCOCO-30k dataset, PerCo (SD) demonstrates improved perceptual characteristics at the cost of higher distortion. We partly attribute this gap to the different model capacities being used (866M vs. 1.4B). We hope our work contributes to a deeper understanding of the underlying mechanisms and paves the way for future advancements in the field. Code and trained models will be released at https://github.com/Nikolai10/PerCo.

Nuclear magnetism underpins areas such as medicine in magnetic resonance imaging (MRI). Hyperpolarization of nuclei enhances the quantity and quality of information that can be determined from these techniques by increasing their signal to noise ratios by orders of magnitude. However, some of these hyperpolarization techniques rely on the use of low to ultralow magnetic fields (ULF) (nTs-mTs), where empirical data on how nuclear magnetism behaves is lacking. The broadband character and ultrasensitive field sensitivity of superconducting quantum interference devices (SQUID) allow for probing nuclear magnetism at these fields, where other magnetometers, such as Faraday coils and flux gates do not. To this end, we designed a hyperpolarization reactor to hyperpolarize [1-$^{13}$C]pyruvate with the technique, signal amplification by reversible exchange in shield enables alignment transfer to heteronuclei (SABRE-SHEATH). Afterwards, we were able to characterize the field sensitivity of our setup by simulating the filled reactor in relation to its placement in our ultralow noise, ULF MRI setup. Using the results of the simulations, we determined that our hyperpolarization setup results in a $^{13}$C polarization of 0.4%, a signal enhancement of ~100 000 000 versus the predicted thermal equilibrium signal at earth field (~50 $\mu$T). This results in a $^{13}$C signal of 7.44$\pm$0.91 pT, which with our ultralow noise setup, opens the possibility for the first direct detection of nuclear magnetism without system perturbation., Comment: 5 pages, 7 figures, ASC 2024, submitted to IEEE TAS

Driving condensed matter systems with periodic electromagnetic fields can result in exotic states not found in equilibrium. Termed Floquet engineering, such periodic driving applied to electronic systems can tailor quantum effects to induce topological band structures and control spin interactions. However, Floquet engineering of magnon band structures in magnetic systems has proven challenging so far. Here, we present a class of Floquet states in a magnetic vortex that arise from nonlinear interactions between the vortex core and microwave magnons. Floquet bands emerge through the periodic oscillation of the core, which can be initiated by either driving the core directly or pumping azimuthal magnon modes. For the latter, the azimuthal modes induce core gyration through nonlinear interactions, which in turn renormalizes the magnon band structure. This represents a self-induced mechanism for Floquet band engineering and offers new avenues to study and control nonlinear magnon dynamics.

This paper delves into the connection between flat and curvilinear magnetization dynamics. For this, we numerically study the evolution of the magnon spectrum of rectangular waveguides upon rolling its cross-section up to a full tube. Magnon spectra are calculated over a wide range of magnetization states using a finite-element dynamic-matrix method, which allows us to trace the evolution of the magnon frequencies and several critical magnetic fields with increasing curvature. By analyzing the parity of the higher-order magnon modes, we find a curvature-induced mode hetero-symmetry that originates from a chiral contribution to the exchange interaction and is related to the Berry phase of magnons in closed loops. Importantly, this curvature-induced parity loss has profound consequences for the linear coupling between different propagating magnons, allowing for hybridization between initially orthogonal modes. In this context, we demonstrate the integral role of edge modes in forming the magnon spectrum in full tubes. Our findings provide new theoretical insights into curvilinear magnetization dynamics and are relevant for interpreting and designing experiments in the field.

With the release of ChatGPT and other large language models (LLMs) the discussion about the intelligence, possibilities, and risks, of current and future models have seen large attention. This discussion included much debated scenarios about the imminent rise of so-called "super-human" AI, i.e., AI systems that are orders of magnitude smarter than humans. In the spirit of Alan Turing, there is no doubt that current state-of-the-art language models already pass his famous test. Moreover, current models outperform humans in several benchmark tests, so that publicly available LLMs have already become versatile companions that connect everyday life, industry and science. Despite their impressive capabilities, LLMs sometimes fail completely at tasks that are thought to be trivial for humans. In other cases, the trustworthiness of LLMs becomes much more elusive and difficult to evaluate. Taking the example of academia, language models are capable of writing convincing research articles on a given topic with only little input. Yet, the lack of trustworthiness in terms of factual consistency or the existence of persistent hallucinations in AI-generated text bodies has led to a range of restrictions for AI-based content in many scientific journals. In view of these observations, the question arises as to whether the same metrics that apply to human intelligence can also be applied to computational methods and has been discussed extensively. In fact, the choice of metrics has already been shown to dramatically influence assessments on potential intelligence emergence. Here, we argue that the intelligence of LLMs should not only be assessed by task-specific statistical metrics, but separately in terms of qualitative and quantitative measures., Comment: 3 pages, 1 figure

Everlasting contact between language communities leads to constant changes in languages over time, and gives rise to language varieties and dialects. However, the communities speaking non-standard language are often overlooked by non-inclusive NLP technologies. Recently, there has been a surge of interest in studying diatopic and diachronic changes in dialect NLP, but there is currently no research exploring the intersection of both. Our work aims to fill this gap by systematically reviewing diachronic and diatopic papers from a unified perspective. In this work, we critically assess nine tasks and datasets across five dialects from three language families (Slavic, Romance, and Germanic) in both spoken and written modalities. The tasks covered are diverse, including corpus construction, dialect distance estimation, and dialect geolocation prediction, among others. Moreover, we outline five open challenges regarding changes in dialect use over time, the reliability of dialect datasets, the importance of speaker characteristics, limited coverage of dialects, and ethical considerations in data collection. We hope that our work sheds light on future research towards inclusive computational methods and datasets for language varieties and dialects., Comment: LChange24 Camera Ready

Solid state quantum emitters are a prime candidate in distributed quantum technologies since they inherently provide a spin-photon interface. An ongoing challenge in the field, however, is the low photon extraction due to the high refractive index of typical host materials. This challenge can be overcome using photonic structures. Here, we report the integration of V2 centers in a cavity-based optical antenna. The structure consists of a silver-coated, 135 nm thin 4H-SiC membrane functioning as a planar cavity with a broadband resonance yielding a theoretical photon collection enhancement factor of 34. The planar geometry allows us to identify over 20 single V2 centers at room temperature with a mean (maximum) count rate enhancement factor of 9 (15). Moreover, we observe 10 V2 centers with a mean absorption linewidth below 80MHz at cryogenic temperatures. These results demonstrate a photon collection enhancement that is robust to the lateral emitter position.

Generative adversarial networks (GANs) are machine learning models that are used to estimate the underlying statistical structure of a given dataset and as a result can be used for a variety of tasks such as image generation or anomaly detection. Despite their initial simplicity, designing an effective loss function for training GANs remains challenging, and various loss functions have been proposed aiming to improve the performance and stability of the generative models. In this study, loss function design for GANs is presented as an optimization problem solved using the genetic programming (GP) approach. Initial experiments were carried out using small Deep Convolutional GAN (DCGAN) model and the MNIST dataset, in order to search experimentally for an improved loss function. The functions found were evaluated on CIFAR10, with the best function, named GANetic loss, showing exceptionally better performance and stability compared to the losses commonly used for GAN training. To further evalute its general applicability on more challenging problems, GANetic loss was applied for two medical applications: image generation and anomaly detection. Experiments were performed with histopathological, gastrointestinal or glaucoma images to evaluate the GANetic loss in medical image generation, resulting in improved image quality compared to the baseline models. The GANetic Loss used for polyp and glaucoma images showed a strong improvement in the detection of anomalies. In summary, the GANetic loss function was evaluated on multiple datasets and applications where it consistently outperforms alternative loss functions. Moreover, GANetic loss leads to stable training and reproducible results, a known weak spot of GANs.

Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been proposed for a wide range of tasks affecting training and model performance. For classification tasks, the cross entropy is the de-facto standard and usually the first choice. Here, we try to experimentally challenge the well-known loss functions, including cross entropy (CE) loss, by utilizing the genetic programming (GP) approach, a population-based evolutionary algorithm. GP constructs loss functions from a set of operators and leaf nodes and these functions are repeatedly recombined and mutated to find an optimal structure. Experiments were carried out on different small-sized datasets CIFAR-10, CIFAR-100 and Fashion-MNIST using an Inception model. The 5 best functions found were evaluated for different model architectures on a set of standard datasets ranging from 2 to 102 classes and very different sizes. One function, denoted as Next Generation Loss (NGL), clearly stood out showing same or better performance for all tested datasets compared to CE. To evaluate the NGL function on a large-scale dataset, we tested its performance on the Imagenet-1k dataset where it showed improved top-1 accuracy compared to models trained with identical settings and other losses. Finally, the NGL was trained on a segmentation downstream task for Pascal VOC 2012 and COCO-Stuff164k datasets improving the underlying model performance.

Emitters in high refractive index materials like 4H-SiC suffer from reduced detection of photons because of losses caused by total internal reflection. Thus, integration into efficient nanophotonic structures which couple the emission of photons to a well defined waveguide mode can significantly enhance the photon detection efficiency. In addition, interfacing this waveguide to a classical fiber network is of similar importance to detect the photons and perform experiments. Here, we show a waveguide fiber interface in SiC. By careful measurements we determine efficiencies exceeding 93 % for the transfer of photons from SiC nanobeams to fibers. We use this interface to create a bright single photon source based on waveguide integrated V2 defects in 4H-SiC and achieve an overall photon count rate of 181 kilo-counts per second. We observe and quantify the strain induced shift of the ground state spin states and demonstrate coherent control of the electron spin with a coherence time of T2=42.5 $\rm\mu$s.

Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions., Comment: Paper: 22 pages, 12 figures, 1 table; Supplementary Material: 6 pages, 5 figures, 1 table; Submitted to Frontiers in Computer Science

An essential facet of achieving climate neutrality by 2045 is the decarbonization of municipal energy systems. To accomplish this, it is necessary to establish implementation concepts that detail the timing, location, and specific measures required to achieve decarbonization. This restructuring process involves identifying the measures that offer the most compelling techno-economic and ecological advantages. In particular, measures that contribute to the interconnection of energy vectors and domains, e.g. heating, cooling, and electricity supply, in the sense of decentralized multi-energy systems are a promising future development option. Due to the high complexity resulting from a multitude of decision options as well as a temporal coupling across the transformation path, the use of optimization methods is required, which enable a bottom-up identification of suitable transformation solutions in a high spatial resolution. For the design of reasonable concepts, we develop a multistage optimization problem for the derivation of transformation pathways in the context of a multi-location structure, expansion, and operation problem. The results show that the heat supply in the future will mainly be provided by heat pumps with a share of 60%. It can also be shown that an early dismantling of the gas network will lead to the need for transitional technologies such as pellet heating. Overall, the conversion of the municipal energy system can significantly reduce emissions (97%)., Comment: 13 pages, 11 figures

Colour centres in silicon carbide emerge as a promising semiconductor quantum technology platform with excellent spin-optical coherences.However, recent efforts towards maximising the photonic efficiency via integration into nanophotonic structures proved to be challenging due to reduced spectral stabilities. Here, we provide a large-scale systematic investigation on silicon vacancy centres in thin silicon carbide membranes with thicknesses down to $0.25\,\rm\mu m$. Our membrane fabrication process involves a combination of chemical mechanical polishing, reactive ion etching, and subsequent annealing. This leads to highly reproducible membranes with roughness values of $3-4\,\rm\r{A}$, as well as negligible surface fluorescence. We find that silicon vacancy centres show close-to lifetime limited optical linewidths with almost no signs of spectral wandering down to membrane thicknesses of $0.7 \,\rm\mu m$. For silicon vacancy centres in thinner membranes down to $0.25\,\rm\mu m$, we observe spectral wandering, however, optical linewidths remain below $200\,\rm MHz$, which is compatible with spin-selective excitation schemes. Our work clearly shows that silicon vacancy centres can be integrated into sub-micron silicon carbide membranes, which opens the avenue towards obtaining the necessary improvements in photon extraction efficiency based on nanophotonic structuring., Comment: 10 pages, 4 figures, plus supplementary information

We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. EGIC is based on two novel building blocks: i) OASIS-C, a conditional pre-trained semantic segmentation-guided discriminator, which provides both spatially and semantically-aware gradient feedback to the generator, conditioned on the latent image distribution, and ii) Output Residual Prediction (ORP), a retrofit solution for multi-realism image compression that allows control over the synthesis process by adjusting the impact of the residual between an MSE-optimized and GAN-optimized decoder output on the GAN-based reconstruction. Together, EGIC forms a powerful codec, outperforming state-of-the-art diffusion and GAN-based methods (e.g., HiFiC, MS-ILLM, and DIRAC-100), while performing almost on par with VTM-20.0 on the distortion end. EGIC is simple to implement, very lightweight, and provides excellent interpolation characteristics, which makes it a promising candidate for practical applications targeting the low bit range., Comment: ECCV 2024 Camera Ready

Soft magnetic dots in the form of thin rings have unique topological properties. They can be in a vortex state with no vortex core. Here, we study the magnon modes of such systems both analytically and numerically. In an external magnetic field, magnetic rings are characterized by easy-cone magnetization and shows a giant splitting of doublets for modes with the opposite value of the azimuthal mode quantum number. The effect of the splitting can be refereed as a magnon analog of the topology-induced Aharonov-Bohm effect. For this we develop an analytical theory to describe the non-monotonic dependence of the mode frequencies on the azimuthal mode number, influenced by the balance between the local exchange and non-local dipole interactions., Comment: 8 pages, 3 figures

We construct a sequence $\{\Sigma_\ell\}_{\ell=1}^\infty$ of closed, axially symmetric surfaces $\Sigma_\ell\subset \mathbb{R}^3$ that converges to the unit sphere in $W^{2,p}\cap C^1$ for every $p\in[1,\infty)$ and such that, for every $\ell$, $$ \int_{\Sigma_{\ell}}H_{\Sigma_\ell}-\sqrt{16\,\pi\,|\Sigma_{\ell}|}<0 $$ where $H_{\Sigma_\ell}$ is the mean curvature of $\Sigma_\ell$. This shows that the Minkowski inequality with optimal constant fails even for perturbations of a round sphere that are small in $W^{2,p}\cap C^1$ unless additional convexity assumptions are imposed., Comment: All comments welcome

We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and supply of liquidity. Limit and market orders mutually excite each other so that liquidity is mean reverting. We use the theory of Meyer-$\sigma$-fields to introduce a short-term signal process from which a trader learns about imminent changes in order flow. In this setting, we examine an optimal execution problem and derive the Hamilton--Jacobi--Bellman (HJB) equation for the value function. The HJB equation is solved numerically and we illustrate how the trader uses the signal to enhance the performance of execution problems and to execute speculative strategies.

Let $(M,g)$ be a complete, connected, non-compact Riemannian three-manifold with non-negative Ricci curvature satisfying $Ric\geq\varepsilon\,\operatorname{tr}(Ric)\,g$ for some $\varepsilon>0$. In this note, we give a new proof based on inverse mean curvature flow that $(M,g)$ is either flat or has non-Euclidean volume growth. In conjunction with results of J. Lott and of M.-C. Lee and P. Topping, this gives an alternative proof of a conjecture of R. Hamilton recently proven by A. Deruelle, F. Schulze, and M. Simon using Ricci flow., Comment: Final version to appear in J. Reine Angew. Math

We use a pure spin current originating from the spin Hall effect to generate a spin-orbit torque (SOT) strongly reducing the effective damping in an adjacent ferromagnet. Due to additional microwave excitation, large spin-wave amplitudes are achieved exceeding the threshold for 4-magnon scattering, thus resulting in additional spin-wave signals at discrete frequencies. Two or more modes are generated below and above the directly pumped mode with equal frequency spacing. It is shown how this nonlinear process can be controlled in magnonic waveguides by the applied dc current and the microwave pumping power. The sudden onset of the nonlinear effect after exceeding the thresholds can be interpreted as spiking phenomenom which makes the effect potentially interesting for neuromorphic computing applications. Moreover, we investigated this effect under microwave frequency and external field variation. The appearance of the additional modes was investigated in the time-domain revealing a time delay between the directly excited and the simultaneously generated nonlinear modes. Furthermore, spatially resolved measurements show different spatial decay lengths of the directly pumped mode and nonlinear modes.

Let $(M,g)$ be an asymptotically flat Riemannian manifold of dimension $3\leq n\leq 7$ with non-negative scalar curvature. R. Schoen has conjectured that $(M,g)$ is isometric to Euclidean space if it admits a non-compact area-minimizing hypersurface $\Sigma \subset M$. This has been proved by O. Chodosh and the first-named author in the case where $n = 3$. In this paper, we confirm this conjecture in the case where $3

The development of SQUID current sensors with a thermally actuated input current limiter (TCL) integrated into the input circuit of the sensor is presented. The TCL is based on an unshunted Josephson Junction (JJ) series array, around which meanders a galvanically isolated, but tightly, thermally coupled, resistive heater element. By applying a current to the heater, the JJ critical currents can be reduced or completely suppressed, while the other on-chip SQUID circuit elements remain unaffected. The functional parameters of the TCL are determined by direct transport measurements and by static and dynamic flux coupling tests via the input circuit. In liquid Helium, a heater power, which reliably suppresses the critical current of the JJ array to zero, of $\sim$1.5 mW was needed, with the TCL normal state resistance being at 500 $\Omega$. In this configuration, typical TCL switching times below 10 $\mu$s were observed in the direct transport measurements. The TCL can be used to temporarily or even permanently disable malfunctioning channels in multichannel SQUID magnetometer systems, when feedback into their otherwise superconducting input circuits is not possible. In doing so, significant signal distortions in neighbouring channels from screening currents in these input circuits are avoided. Furthermore, by completely suppressing and restoring the TCL critical current, dc offset currents in the superconducting input circuits can be prevented. This is relevant, for instance, in SQUID-based spin precession experiments on hyperpolarized noble gases, in which even dc currents of a few $\mu$A in the input circuit can lead to significant magnetic field distortions in the adjacent sample region contributing to the transverse spin-spin relaxation rate $1/T_{2}$, Comment: 7 pages, 6 figures, ASC 2022, submitted to Superconducting Science and Technology

Nuclear spin hyperpolarization enables real-time observation of metabolism and intermolecular interactions in vivo. 1-13C-Pyruvate is the leading hyperpolarized tracer currently under evaluation in several clinical trials as a promising molecular imaging agent. Still, the quest for a simple, fast, and efficient hyperpolarization technique is ongoing. Here, we describe that continuous, weak irradiation in the audio-frequency range of the 13C spin at 121 {\mu}T magnetic field (\sim twiceEarth\apos s field) enables spin order transfer from parahydrogen to 13C magnetization of 1-13C-pyruvate. These so-called LIGHT-SABRE pulses couple nuclear spin states of parahydrogen and pyruvate via the J-coupling network of reversibly exchanging Ir-complexes. Using \sim 100% parahydrogen at ambient pressure, we polarized 51 mM of 1-13C-pyruvate in the presence of 5.1 mM Ir-complex continuously and repeatedly to a polarization of 1.1% averaged over free and catalyst-bound pyruvate. The experiments were conducted at -8{\deg}C), where almost exclusively bound pyruvate was observed, corresponding to an estimated 11% polarization on bound pyruvate. The obtained hyperpolarization levels closely match those obtained via SABRE-SHEATH under otherwise identical conditions. The creation of three different types of spin orders was observed: transverse 13C magnetization along the applied magnetic field, 13C z-magnetization along the main field B_0, and 13C-1H zz-spin-order. With a superconducting quantum interference device (SQUID) for detection, we found that the generated spin orders result from tiny 1H-13C J-coupling interactions, which are not visible even with our narrow linewidth below 0.3 Hz.

Building on previous works of H. L. Bray, of P. Miao, and of S. Almaraz, E. Barbosa, and L. L. de Lima, we develop a doubling procedure for asymptotically flat half-spaces $(M,g)$ with horizon boundary $\Sigma\subset M$ and mass $m\in\mathbb{R}$. If $3\leq \dim(M)\leq 7$, $(M,g)$ has non-negative scalar curvature, and the boundary $\partial M$ is mean-convex, we obtain the Riemannian Penrose-type inequality $$ m\geq\left(\frac{1}{2}\right)^{\frac{n}{n-1}}\,\left(\frac{|\Sigma|}{\omega_{n-1}}\right)^{\frac{n-2}{n-1}} $$ as a corollary. Moreover, in the case where $\partial M$ is not totally geodesic, we show how to construct local perturbations of $(M,g)$ that increase the scalar curvature. As a consequence, we show that equality holds in the above inequality if and only if the exterior region of $(M,g)$ is isometric to a Schwarzschild half-space. Previously, these results were only known in the case where $\dim(M)=3$ and $\Sigma$ is a connected free boundary hypersurface., Comment: The final version has appeared in Comm. Math. Phys

Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more., Comment: 10 pages, 9 figures. Submitted to "IEEE Quantum Week 2023". The data of the work are available on https://jugit.fz-juelich.de/qnet-public/home/

We present an experimental and numerical study of three-magnon splitting in a micrometer-sized magnetic disk with the vortex state strongly deformed by static in-plane magnetic fields. Excited with a large enough power at frequency $f_\mathrm{RF}$, the primary radial magnon modes of a cylindrical magnetic vortex can decay into secondary azimuthal modes via spontaneous three-magnon splitting. This nonlinear process exhibits selection rules leading to well-defined and distinct frequencies $f_\mathrm{RF}/2\pm \Delta f$ of the secondary modes. Here, we demonstrate that three-magnon splitting in vortices can be significantly modified by deforming the magnetic vortex with in-plane magnetic fields, leading to a much richer three-magnon response. We find that, with increasing field, an additional class of secondary modes is excited which are localized to the highly-flexed regions adjacent to the displaced vortex core. While these modes satisfy the same selection rules of three-magnon splitting, they exhibit a much lower three-magnon threshold power compared to regular secondary modes of a centered vortex. The applied static magnetic fields are small ($\simeq$ 10 mT), providing an effective parameter to control the nonlinear spectral response of confined vortices. Our work expands the understanding of nonlinear magnon dynamics in vortices and advertises these for potential neuromorphic applications based on magnons.

Magnons are elementary excitations in magnetic materials and undergo nonlinear multimode scattering processes at large input powers. In experiments and simulations, we show that the interaction between magnon modes of a confined magnetic vortex can be harnessed for pattern recognition. We study the magnetic response to signals comprising sine wave pulses with frequencies corresponding to radial mode excitations. Three-magnon scattering results in the excitation of different azimuthal modes, whose amplitudes depend strongly on the input sequences. We show that recognition rates above 95\% can be attained for four-symbol sequences using the scattered modes, with strong performance maintained with the presence of amplitude noise in the inputs.

This paper is concerned with the automation and simulation of pick and place processes in the domain of CFK aircraft production. We introduce a workflow which starts from a CAD construction, extracts relevant data out of it, assigns grippers to the CFK pieces and schedules the single steps using a PDDL solver. Finally, the result is visualized in Blender where also prior mistakes can be identified., Comment: 10 pages, 2 figures

Visual perception is driven by the focus on relevant aspects in the surrounding world. To transfer this observation to the digital information processing of computers, attention mechanisms have been introduced to highlight salient image regions. Here, we introduce a parameter-free attention mechanism called PfAAM, that is a simple yet effective module. It can be plugged into various convolutional neural network architectures with a little computational overhead and without affecting model size. PfAAM was tested on multiple architectures for classification and segmentic segmentation leading to improved model performance for all tested cases. This demonstrates its wide applicability as a general easy-to-use module for computer vision tasks. The implementation of PfAAM can be found on https://github.com/nkoerb/pfaam.

Spin-bearing color centers in the solid state are promising candidates for the realization of quantum networks and distributed quantum computing. A remaining key challenge is their efficient and reliable interfacing to photons. Incorporating minimally processed membranes into open-access microcavities represents a promising route for Purcellenhanced spin-photon interfaces: it enables significant emission enhancement and efficient photon collection, minimizes deteriorating influence on the quantum emitter, and allows for full spatial and spectral tunability, key for controllably addressing suitable emitters with desired optical and spin properties. Here, we study the properties of a high-finesse fiber Fabry-P\'erot microcavity with integrated single-crystal diamond membranes by scanning cavity microscopy. We observe spatially resolved the effects of the diamond-air interface on the cavity mode structure: a strong correlation of the cavity finesse and mode structure with the diamond thickness and surface topography, significant transverse-mode mixing under diamond-like conditions, and mode-character-dependent polarization-mode splitting. Our results reveal the influence of the diamond surface on the achievable Purcell enhancement, which helps to clarify the route towards optimized spin-photon interfaces.

Many characterization techniques for magnetic nanoparticles depend on the usage of external fields. This is not the case in Thermal Noise Magnetometry (TNM), where thermal fluctuations in the magnetic signal of magnetic nanoparticle ensembles are measured without any external excitation. This can provide valuable information about the fundamental dynamical properties of the particles, due to the purely observative experiments of this relatively new technique. Until now, TNM signals have been detected only by a superconducting quantum interference device (SQUID) sensor. We present a tabletop setup using Optically Pumped Magnetometers (OPMs) in a small magnetic shield, offering a flexible and accessible alternative and show the agreement between both measurement systems for two different commercially available nanoparticle samples. We argue that the OPM setup with high accessibility complements the SQUID setup with high sensitivity and bandwidth. Furthermore, because of its excellent sensitivity in the lower frequencies, the OPM tabletop setup is well suited to monitor aggregation processes where the magnetization dynamics of the particles tend to slow down, e.g. in biological processes. As a proof of concept, we show for three different immobilization and clustering processes the changes in the noise spectrum measured in the tabletop setup: 1) the aggregation of particles due to the addition of ethanol, 2) the formation of polymer structures in the sample due to UV exposure, and 3) the cellular uptake of the particles by THP-1 cells. From our results we conclude that the tabletop setup offers a flexible and widely adoptable sensor measurement unit to monitor the immobilization and clustering of magnetic nanoparticles over time for different applications.

1D spin-wave conduits are envisioned as nanoscale components of magnonics-based logic and computing schemes for future generation electronics. `A-la-carte methods of versatile control of the local magnetization dynamics in such nanochannels are highly desired for efficient steering of the spin waves in magnonic devices. Here, we present a study of localized dynamical modes in 1-$\mu$m-wide Permalloy conduits probed by microresonator ferromagnetic resonance technique. We clearly observe the lowest-energy edge mode in the microstrip after its edges were finely trimmed by means of focused Ne$^+$ ion irradiation. Furthermore, after milling the microstrip along its long axis by focused ion beams, creating consecutively $\sim$50 and $\sim$100 nm gaps, additional resonances emerge and are attributed to modes localized at the inner edges of the separated strips. To visualize the mode distribution, spatially resolved Brillouin light scattering microscopy was used showing an excellent agreement with the ferromagnetic resonance data and confirming the mode localization at the outer/inner edges of the strips depending on the magnitude of the applied magnetic field. Micromagnetic simulations confirm that the lowest-energy modes are localized within $\sim$15-nm-wide regions at the edges of the strips and their frequencies can be tuned in a wide range (up to 5 GHz) by changing the magnetostatic coupling (i.e. spatial separation) between the microstrips., Comment: 10 pages, 4 figures