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$FeCr_2S_4$ is a ferrimagnet with two oppositely-ordered spin sublattices (Fe and Cr), connected via superexchange interaction, giving a non-zero net magnetic moment. We show how the magnetic dynamics of the sublattices can be selectively manipulated by resonantly perturbing the Fe sublattice using ultrashort laser pulses. The mid-infrared excitation through intra-atomic Fe $d-d$ transitions triggers a markedly slower magneto-optical Kerr effect dynamics compared to an off-resonant pumping affecting both the two sublattices simultaneously. The analysis and conclusions are supported by probing resonantly and off-resonantly to the Fe sublattice., Comment: 29 pages, 19 figures

Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records like ICD-10 or SNOMED-CT, is limited and has been particularly exposed in recent research. This paper examines the challenges LLMs face in processing medical codes due to the shortcomings of current tokenization methods. As a result, we introduce the UniStruct architecture to design a multimodal medical foundation model of unstructured text and structured data, which addresses these challenges by adapting subword tokenization techniques specifically for the structured medical codes. Our approach is validated through model pre-training on both an extensive internal medical database and a public repository of structured medical records. Trained on over 1 billion tokens on the internal medical database, the proposed model achieves up to a 23% improvement in evaluation metrics, with around 2% gain attributed to our proposed tokenization. Additionally, when evaluated on the EHRSHOT public benchmark with a 1/1000 fraction of the pre-training data, the UniStruct model improves performance on over 42% of the downstream tasks. Our approach not only enhances the representation and generalization capabilities of patient-centric models but also bridges a critical gap in representation learning models' ability to handle complex structured medical data, alongside unstructured text., Comment: NeurIPS 2024 Workshop on Unifying Representations in Neural Models (UniReps 2024)

A $k$-height on a graph $G=(V, E)$ is an assignment $V\to\{0, \ldots, k\}$ such that the value on ajacent vertices differs by at most $1$. We study the Markov chain on $k$-heights that in each step selects a vertex at random, and, if admissible, increases or decreases the value at this vertex by one. In the cases of $2$-heights and $3$-heights we show that this Markov chain is rapidly mixing on certain families of grid-like graphs and on planar cubic $3$-connected graphs. The result is based on a novel technique called block coupling, which is derived from the well-established monotone coupling approach. This technique may also be effective when analyzing other Markov chains that operate on configurations of spin systems that form a distributive lattice. It is therefore of independent interest., Comment: 31 pages, 8 figures. Supplemental code available at Zenodo, doi:10.5281/zenodo.13912818

This paper considers the problem of computing the operator norm of a linear map between finite dimensional Hilbert spaces when only evaluations of the linear map are available and under restrictive storage assumptions. We propose a stochastic method of random search type for the maximization of the Rayleigh quotient and employ an exact line search in the random search directions. Moreover, we show that the proposed algorithm converges to the global maximum (the operator norm) almost surely and illustrate the performance of the method with numerical experiments.

Extreme continuum variability in AGNs can indicate extreme changes in accretion flows onto supermassive black holes. We explore the multiwavelength nature of a continuum flare in the Seyfert LCRS B040659.9$-$385922. The all-sky X-ray surveys conducted by the eROSITA showed that its X-ray flux increased by a factor of roughly five over six months, and concurrent optical photometric monitoring with the ATLAS showed a simultaneous increase. We triggered a multiwavelength follow-up monitoring program (XMM, NICER; optical spectroscopy) to study the evolution of the accretion disk, broad-line region, and X-ray corona. During the campaign, X-ray and optical continuum flux subsided over roughly six months. We detected a soft X-ray excess near the flare peak and after it subsided, both exhibiting a power-law (nonthermal) behavior. We modeled the broadband optical/UV/X-ray spectral energy distribution at both the flare peak and post-flare times with the AGNSED model, incorporating thermal disk emission into the optical/UV and warm thermal Comptonization in the soft X-rays. Additionally, we find that the broad Heii $\lambda$4686 emission line fades significantly as the optical/UV/X-ray continuum fades, which could indicate a substantial flare of disk emission above 54 eV. We also observed a redshifted broad component in the H${\beta}$ emission line that is present during the high flux state of the source and disappears in subsequent observations. We witnessed a likely sudden strong increase in local accretion rate, which manifested itself via an increase in accretion disk emission and thermal Comptonization emission in the soft X-rays, followed by a decrease in accretion and Comptonized luminosity. The physical processes leading to such substantial variations are still an open question, and future continuous monitoring along with multi-wavelength studies will shed some light on it., Comment: 19 pages, 7 figures

The Korteweg-de Vries equation is a fundamental nonlinear equation that describes solitons with constant velocity. On the contrary, here we show that this equation also presents accelerated wavepacket solutions. This behavior is achieved by putting the Korteweg-de Vries equation in terms of the Painlev\'e I equation. The accelerated waveform solutions are explored numerically showing their accelerated behavior explicitly., Comment: 4 pages, 2 figures

This project explores methods to enhance sign language translation of German sign language, specifically focusing on disambiguation of homonyms. Sign language is ambiguous and understudied which is the basis for our experiments. We approach the improvement by training transformer-based models on various bodypart representations to shift the focus on said bodypart. To determine the impact of, e.g., the hand or mouth representations, we experiment with different combinations. The results show that focusing on the mouth increases the performance in small dataset settings while shifting the focus on the hands retrieves better results in larger dataset settings. Our results contribute to better accessibility for non-hearing persons by improving the systems powering digital assistants, enabling a more accurate interaction. The code for this project can be found on GitHub., Comment: LIMO2024 @ KONVENS 2024, 8 pages, 3 figures

Despite their impressive success, training foundation models remains computationally costly. This paper investigates how to efficiently train speech foundation models with self-supervised learning (SSL) under a limited compute budget. We examine critical factors in SSL that impact the budget, including model architecture, model size, and data size. Our goal is to make analytical steps toward understanding the training dynamics of speech foundation models. We benchmark SSL objectives in an entirely comparable setting and find that other factors contribute more significantly to the success of SSL. Our results show that slimmer model architectures outperform common small architectures under the same compute and parameter budget. We demonstrate that the size of the pre-training data remains crucial, even with data augmentation during SSL training, as performance suffers when iterating over limited data. Finally, we identify a trade-off between model size and data size, highlighting an optimal model size for a given compute budget., Comment: To appear in SLT 2024

Mode-locked lasers are of interest for applications such as biological imaging, non-linear frequency conversion, and single-photon generation. In the infrared, chip-integrated mode-locked lasers have been demonstrated through integration of laser diodes with low-loss photonic circuits. However additional challenges, such as a higher propagation loss and smaller alignment tolerances have prevented the realization of such lasers in the visible range. Here, we demonstrate the first chip-integrated mode-locked diode laser in the visible using an integrated photonic circuit for cavity extension. Based on a gallium arsenide gain chip and a low-loss silicon nitride feedback circuit, the laser is passively mode-locked using a saturable absorber implemented by focused ion beam milling. At a center wavelength of 642 nm, the laser shows an average output power of 3.4 mW, with a spectral bandwidth of 1.5 nm at a repetition rate of 7.84 GHz., Comment: 7 pages, 4 figures

State-of-the-art Large Language Models (LLMs) are accredited with an increasing number of different capabilities, ranging from reading comprehension, over advanced mathematical and reasoning skills to possessing scientific knowledge. In this paper we focus on their multi-hop reasoning capability: the ability to identify and integrate information from multiple textual sources. Given the concerns with the presence of simplifying cues in existing multi-hop reasoning benchmarks, which allow models to circumvent the reasoning requirement, we set out to investigate, whether LLMs are prone to exploiting such simplifying cues. We find evidence that they indeed circumvent the requirement to perform multi-hop reasoning, but they do so in more subtle ways than what was reported about their fine-tuned pre-trained language model (PLM) predecessors. Motivated by this finding, we propose a challenging multi-hop reasoning benchmark, by generating seemingly plausible multi-hop reasoning chains, which ultimately lead to incorrect answers. We evaluate multiple open and proprietary state-of-the-art LLMs, and find that their performance to perform multi-hop reasoning is affected, as indicated by up to 45% relative decrease in F1 score when presented with such seemingly plausible alternatives. We conduct a deeper analysis and find evidence that while LLMs tend to ignore misleading lexical cues, misleading reasoning paths indeed present a significant challenge., Comment: 15 pages, 3 figures, EMNLP 2024 Main Conference

We have used the Dark Energy Camera (DECam) on the CTIO Blanco 4-m telescope to perform a new emission-line survey of the Large Magellanic Cloud (LMC) using narrow-band H-alpha and [SII] filters in addition to a continuum band for use in creating pure emission-line images. We refer to this new survey as DeMCELS, to distinguish it from the earlier Magellanic Cloud Emission Line Survey (MCELS) that has been in service for nearly 25 years. DeMCELS covers $\sim 54$ degrees$^{2}$, encompassing most of the bright optical disk of the LMC. With DECam's pixel size of only 0.27", our DeMCELS survey provides a seeing-limited improvement of 3-5 times over MCELS and is comparable in depth, with surface brightness limits of 3.3E-17 erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$ in H-alpha and 2.9E-17 erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$ in H-alpha and [SII], respectively. DeMCELS provides detailed morphological information on nebulae of all scales, from the largest supershells to individual [HII] regions and supernova remnants, to bubbles of emission surrounding individual stars, and even to faint structures in the diffuse ionized gas of the LMC. Many complex regions of emission show significant variations in the ratio of [SII] to H-alpha, a sign of the mixture of shocks from stellar winds and/or supernovae with photoionization by embedded hot, young stars. We present the details of the observing strategy and data processing for this survey, and show selected results in comparison with previous data. A companion project for the Small Magellanic Cloud is in progress and will be reported separately. We are making these new data available to the community at large via the NOIRLab's Data Lab site., Comment: 30 pages, 16 figures Accepted for publication (ApJ)

Over the past three decades, multiple reverberation mapping (RM) campaigns conducted for the quasar PG 2130+099 have exhibited inconsistent findings with time delays ranging from $\sim$10 to $\sim$200 days. To achieve a comprehensive understanding of the geometry and dynamics of the broad-line region (BLR) in PG 2130+099, we continued an ongoing high-cadence RM monitoring campaign using the Calar Alto Observatory 2.2m optical telescope for an extra four years from 2019 to 2022. We measured the time lags of several broad emission lines (including He II, He I, H$\beta$, and Fe II) with respect to the 5100 {\AA} continuum, and their time lags continuously vary through the years. Especially, the H$\beta$ time lags exhibited approximately a factor of two increase in the last two years. Additionally, the velocity-resolved time delays of the broad H$\beta$ emission line reveal a back-and-forth change between signs of virial motion and inflow in the BLR. The combination of negligible ($\sim$10%) continuum change and substantial time-lag variation (over two times) results in significant scatter in the intrinsic $R_{\rm H\beta}-L_{\rm 5100}$ relationship for PG 2130+099. Taking into account the consistent changes in the continuum variability time scale and the size of the BLR, we tentatively propose that the changes in the measurement of the BLR size may be affected by 'geometric dilution'., Comment: 21 pages, 13 figures, 7 tables; accepted for publication in ApJ

Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical dialogue summarization, a low-resource domain where supervised data for fine-tuning is scarce, necessitating the use of ASR models as black-box solutions. Employing conventional data augmentation for enhancing the noise robustness of summarization models is not feasible either due to the unavailability of sufficient medical dialogue audio recordings and corresponding ASR transcripts. To address this challenge, we propose MEDSAGE, an approach for generating synthetic samples for data augmentation using Large Language Models (LLMs). Specifically, we leverage the in-context learning capabilities of LLMs and instruct them to generate ASR-like errors based on a few available medical dialogue examples with audio recordings. Experimental results show that LLMs can effectively model ASR noise, and incorporating this noisy data into the training process significantly improves the robustness and accuracy of medical dialogue summarization systems. This approach addresses the challenges of noisy ASR outputs in critical applications, offering a robust solution to enhance the reliability of clinical dialogue summarization.

We present an experimental implementation of a chirped mid-infrared (mid-IR) high-power laser source with variable center frequency between 4 THz to 30 THz and continuously tunable frequency sweep of up to 20 % within one pulse, with a pulse duration of 2 ps. The peak electric field obtained at 4 THz is 1.5 MV/cm. We generate the mid-IR light using a difference-frequency generation (DFG) process with two phase-locked, chirped IR pulses. The obtained mid-IR electric field waveform is characterized using electro-optic sampling. We compare our experimental results with the predictions of numerical simulations. The results indicate the potential for efficient driving of vibrational modes into a strongly anharmomic regime, in cases where using Fourier-transform-limited pulses to achieve similar vibrational amplitudes would lead to dielectric breakdown., Comment: 13 pages, 5 figures

Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation. Despite their success on these tasks, it is unclear how well LLMs perform on tasks that are traditionally pursued in the biomedical domain, such as structured information extration. To breach this gap, in this paper, we systematically benchmark LLM performance in Medical Classification and Named Entity Recognition (NER) tasks. We aim to disentangle the contribution of different factors to the performance, particularly the impact of LLMs' task knowledge and reasoning capabilities, their (parametric) domain knowledge, and addition of external knowledge. To this end we evaluate various open LLMs -- including BioMistral and Llama-2 models -- on a diverse set of biomedical datasets, using standard prompting, Chain-of-Thought (CoT) and Self-Consistency based reasoning as well as Retrieval-Augmented Generation (RAG) with PubMed and Wikipedia corpora. Counter-intuitively, our results reveal that standard prompting consistently outperforms more complex techniques across both tasks, laying bare the limitations in the current application of CoT, self-consistency and RAG in the biomedical domain. Our findings suggest that advanced prompting methods developed for knowledge- or reasoning-intensive tasks, such as CoT or RAG, are not easily portable to biomedical tasks where precise structured outputs are required. This highlights the need for more effective integration of external knowledge and reasoning mechanisms in LLMs to enhance their performance in real-world biomedical applications., Comment: 11 pages

Accurately describing the distribution of CO$_2$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench dataset, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric CO$_2$. More specifically, we center CO$_2$ input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill (90-day $R^2 > 0.99$), with physically plausible emulation even for forward runs of multiple years. This work paves the way forward towards high resolution forward and inverse modeling of inert trace gases with neural networks., Comment: Code: https://github.com/vitusbenson/carbonbench

Green hydrogen is a promising solution within carbon free energy systems with Sub-Saharan Africa being a possibly well-suited candidate for its production. However, green hydrogen in Sub-Saharan Africa is not yet investigated in detail. This work determines the green hydrogen cost-potential for green hydrogen within this region. Therefore, a potential analysis for PV, wind and hydropower, groundwater analysis, and energy systems optimization are conducted. The results are evaluated under local socio-economic factors. Results show that hydrogen costs start at 1.6 EUR/kg in Mauritania with a total potential of ~259 TWh/a under 2 EUR/kg in 2050. Two third of the regions experience groundwater limitations and need desalination at surplus costs of ~1% of hydrogen costs. Socio-economic analysis show, that green hydrogen deployment can be hindered along the Upper Guinea Coast and the African Great Lakes, driven by limited energy access, low labor costs in West Africa, and high labor potential in other regions.

We develop a feedback strategy based on optimal quantum feedback control for Gaussian systems to maximise the likelihood of steady-state entanglement detection between two directly interacting masses. We employ linear quadratic Gaussian (LQG) control to engineer the phase space dynamics of the two masses and propose Einstein-Podolsky-Rosen (EPR)-type variance minimisation constraints for the feedback to facilitate unconditional entanglement generation. This scheme allows for stationary entanglement in parameter regimes where strategies based on total energy minimisation ($cooling$) would fail. This feedback strategy, applied to the system of two masses driven out-of-thermal equilibrium [arXiv:2408.06251] enables unconditional entanglement generation under realistic experimental conditions.

We present a protocol that maximizes unconditional entanglement generation between two masses interacting directly through $1/r^{n}$ potential. The protocol combines optimal quantum control of continuously measured masses with their non-equilibrium dynamics, driven by a time-dependent interaction strength. Applied to a pair of optically trapped sub-micron particles coupled via electrostatic interaction, our protocol enables unconditional entanglement generation at the fundamental limit of the conditional state and with an order of magnitude smaller interaction between the masses compared to the existing steady-state approaches.

Acoustic waves (AW) have recently emerged as an energy-efficient ice removal procedure compatible with functional and industrial-relevant substrates. However, critical aspects at fundamental and experimental levels have yet to be disclosed to optimize their operational conditions. Identifying the processes and mechanisms by which different types of AWs induce de-icing are some of these issues. Herein, using model LiNbO3 systems and two types of interdigitated transducers, we analyze the de-icing and anti-icing efficiencies and mechanisms driven by Rayleigh surface acoustic waves (R-SAW) and Lamb waves with 120 and 510 um wavelengths, respectively. Through the experimental analysis of de-icing and active anti-icing processes and the finite element simulation of the AW generation, propagation, and interaction with small ice aggregates, we disclose that Lamb waves are more favorable than R-SAWs to induce de-icing and/or prevent the freezing of droplets. Prospects for applications of this study are supported by proof of concept experiments, including de-icing in an ice wind tunnel, demonstrating that Lamb waves can efficiently remove ice layers covering large LN substrates. Results indicate that the de-icing mechanism may differ for Lamb waves or R-SAWs and that the wavelength must be considered as an important parameter for controlling the efficiency.

An optimal guidance method is developed that reduces sensitivity to parameters in the dynamic model. The method combines a previously developed method for guidance and control using adaptive Legendre-Gauss-Radau (LGR) collocation and a previously developed approach for desensitized optimal control. Guidance updates are performed such that the desensitized optimal control problem is re-solved on the remaining horizon at the start of each guidance cycle. The effectiveness of the method is demonstrated on a simple example using Monte Carlo simulation. It is found that the method reduces variations in the terminal state as compared to either desensitized optimal control without guidance updates or a previously developed method for optimal guidance and control., Comment: 12 pages, 4 figures

This paper considers the problem of finding strategies that satisfy a mixture of sure and threshold objectives in Markov decision processes. We focus on a single $\omega$-regular objective expressed as parity that must be surely met while satisfying $n$ reachability objectives towards sink states with some probability thresholds too. We consider three variants of the problem: (a) strict and (b) non-strict thresholds on all reachability objectives, and (c) maximizing the thresholds with respect to a lexicographic order. We show that (a) and (c) can be reduced to solving parity games, and (b) can be solved in $\sf{EXPTIME}$. Strategy complexities as well as algorithms are provided for all cases., Comment: Paper accepted to RP 2024 - Full version

We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object trajectory. Due to the challenges in controlling a humanoid with dexterous hands, prior methods often use a disembodied hand and only consider vertical lifts or short trajectories. This limited scope hampers their applicability for object manipulation required for animation and simulation. To close this gap, we learn a controller that can pick up a large number (>1200) of objects and carry them to follow randomly generated trajectories. Our key insight is to leverage a humanoid motion representation that provides human-like motor skills and significantly speeds up training. Using only simplistic reward, state, and object representations, our method shows favorable scalability on diverse object and trajectories. For training, we do not need dataset of paired full-body motion and object trajectories. At test time, we only require the object mesh and desired trajectories for grasping and transporting. To demonstrate the capabilities of our method, we show state-of-the-art success rates in following object trajectories and generalizing to unseen objects. Code and models will be released., Comment: Project page: https://www.zhengyiluo.com/Omnigrasp/

For fast-tracking climate change response, green hydrogen is key for achieving greenhouse gas neutral energy systems. Especially Sub-Saharan Africa can benefit from it enabling an increased access to clean energy through utilizing its beneficial conditions for renewable energies. However, developing green hydrogen strategies for Sub-Saharan Africa requires highly detailed and consistent information ranging from technical, environmental, economic, and social dimensions, which is currently lacking in literature. Therefore, this paper provides a comprehensive novel approach embedding the required range of disciplines to analyze green hydrogen cost-potentials in Sub-Saharan Africa. This approach stretches from a dedicated land eligibility based on local preferences, a location specific renewable energy simulation, locally derived sustainable groundwater limitations under climate change, an optimization of local hydrogen energy systems, and a socio-economic indicator-based impact analysis. The capability of the approach is shown for case study regions in Sub-Saharan Africa highlighting the need for a unified, interdisciplinary approach.

We conduct a reverberation mapping (RM) campaign to spectroscopically monitor a sample of selected bright active galactic nuclei with large anticipated broad-line region (BLR) sizes adequate for spectroastrometric observations by the GRAVITY instrument on the Very Large Telescope Interferometer. We report the first results for five objects, IC 4329A, Mrk 335, Mrk 509, Mrk 1239, and PDS 456, among which Mrk 1239 and PDS 456 are for the first time spectroscopically monitored. We obtain multi-year monitoring data and perform multi-component spectral decomposition to extract the broad H$\beta$ profiles. We detect significant time lags between the H$\beta$ and continuum variations, generally obeying the previously established BLR size-luminosity relation. Velocity-resolved H$\beta$ time lags illustrate diverse, possibly evolving BLR kinematics. We further measure the H$\beta$ line widths from mean and rms spectra and the resulting virial products show good consistency among different seasons. Adopting a unity virial factor and the full width at half maximum of the broad H$\beta$ line from the mean spectrum as the measure of velocity, the obtained black hole mass averaged over seasons is $\log M_\bullet/M_\odot=8.02_{-0.14}^{+0.09}$, $6.92_{-0.12}^{+0.12}$, $8.01_{-0.25}^{+0.16}$, $7.44_{-0.14}^{+0.13}$, and $8.59_{-0.11}^{+0.07}$ for the five objects, respectively. The black hole mass estimations using other line width measures are also reported (up to the virial factors). For objects with previous RM campaigns, our mass estimates are in agreement with earlier results. In a companion paper, we will employ BLR dynamical modeling to directly infer the black hole mass and thereby determine the virial factors., Comment: 32 pages, 6 tables, 20 figures. To appear in ApJ

We theoretically study the conformational and dynamical properties of semiflexible active polar ring polymers under linear shear flow. A ring is described as a continuous Gaussian polymer with a tangential active force of a constant density along its contour. The linear but non-Hermitian equation of motion is solved using an eigenfunction expansion, which yields activity-independent, but shear-rate-dependent, relaxation times and activity-dependent frequencies. As a consequence, the ring's stationary-state properties are independent of activity, and its conformations as well as rheological properties are equal to those of a passive ring under shear. The presence of characteristic time scales by the relaxation and the frequency gives rise to a particular dynamical behavior. A tank-treading-like motion emerges for large relaxation times and high frequencies, specifically for stiffer rings, governed by the activity-dependent frequencies. In the case of very flexible polymers, the relaxation behavior dominates over tank-treading. Shear strongly affects the crossover from a tank-treading to a relaxation-time dominated dynamics and suppresses tank-treading. This is reflected in the tumbling frequency, which exhibits two shear-rate dependent regimes, with an activity-dependent plateau at low shear rates followed by a power-law regime with increasing tumbling frequency for large shear rates., Comment: 7 figures

Magnetic skyrmions are promising candidates for information and storage technologies. In the last years, magnetic multilayer systems have been tuned to enable room-temperature skyrmions, stable even in the absence of external magnetic field. There are several models describing the properties of an isolated skyrmion in a homogeneous background for single repetition multilayer stack, however, the description on how the equilibrium skyrmion size in lattices scales with increasing the number of repetitions of the stack remains unaddressed. This question is essential for fundamental and practical perspectives, as the behaviour of an ensemble of skyrmions differs from the isolated case. Based on a multilayer stack hosting a skyrmion lattice, we have carried out a series of imaging experiments scaling up the dipolar interaction by repeating $n$ times the multilayer unit, from $n =1$ up to $n=30$. We have developed an analytical description for the skyrmion radius in the whole multilayer regime, $i.e.$, from thin to thick film limits. Furthermore, we provide insight on how nucleation by an externally applied field can give rise to a lattice with more skyrmions (thus, overfilled) than the predicted by the calculations., Comment: 9 pages, 3 figures

Systems of motile microorganisms exhibit a multitude of collective phenomena, including motility-induced phase separation and turbulence. Sensing of the environment and adaptation of movement plays an essential role in the emergent behavior. We study the collective motion of wet self-steering polar microswimmers, which align their propulsion direction hydrodynamically with that of their neighbors, by mesoscale hydrodynamics simulations. The simulations of the employed squirmer model reveal a distinct dependence on the swimmer flow field, i.e., pullers versus pushers. The collective motion of pushers is characterized by active turbulence, with nearly homogeneous density and a Gaussian velocity distribution. Pullers exhibit a strong tendency for clustering and display velocity and vorticity distributions with fat exponential tails; their dynamics is chaotic, with a temporal appearance of vortex rings and fluid jets. Our results show that the collective behavior of intelligent microswimmers is very diverse and still offers many surprises to be discovered., Comment: 6 figures

We present HTMPC, a Heavily Templated C++ library for large-scale simulations implementing multi-particle collision dynamics (MPC), a particle-based mesoscale hydrodynamic simulation method. The implementation is plugin-based, and designed for distributed computing over an arbitrary number of MPI ranks. By abstracting the hardware-dependent parts of the implementation, we provide an identical application-code base for various architectures, currently supporting CPUs and CUDA-capable GPUs. We have examined the code for a system of more than a trillion MPC particles distributed over a few thousand MPI ranks (GPUs), demonstrating the scalability of the implementation and its applicability to large-scale hydrodynamic simulations. As showcases, we examine passive and active suspension of colloids, which confirms the extensibility and versatility of our plugin-based implementation., Comment: 7 figures

We entangle each individual matter-qubit in a register of ten to a separate travelling photon. The qubits are encoded in a string of cotrapped atomic ions. By switching the trap confinement, ions are brought one at a time into the waist of an optical cavity and emit a photon via a laser-driven cavity-mediated Raman transition. The result is a train of photonic-qubits, each near-maximally entangled by their polarisation with a different ion-qubit in the string. An average ion-photon Bell state fidelity of 92(1)% is achieved, for an average probability for detecting each single photon of 9.1(8)%. The technique is directly scalable to larger ion-qubit registers and opens up the near-term possibility of entangling distributed networks of trapped-ion quantum processors, sensing arrays and clocks.

In these notes we propose and analyze an inertial type method for obtaining stable approximate solutions to nonlinear ill-posed operator equations. The method is based on the Levenberg-Marquardt (LM) iteration. The main obtained results are: monotonicity and convergence for exact data, stability and semi-convergence for noisy data. Regarding numerical experiments we consider: i) a parameter identification problem in elliptic PDEs, ii) a parameter identification problem in machine learning; the computational efficiency of the proposed method is compared with canonical implementations of the LM method.

There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack of understanding of the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain: a fundamental pre-requisite for success on down-stream tasks. Addressing this gap, we use Multiple Choice and Abstractive Question Answering to conduct a large-scale empirical study on 22 datasets in three generalist and three specialist biomedical sub-domains. Our multifaceted analysis of the performance of 15 LLMs, further broken down by sub-domain, source of knowledge and model architecture, uncovers success factors such as instruction tuning that lead to improved recall and comprehension. We further show that while recently proposed domain-adapted models may lack adequate knowledge, directly fine-tuning on our collected medical knowledge datasets shows encouraging results, even generalising to unseen specialist sub-domains. We complement the quantitative results with a skill-oriented manual error analysis, which reveals a significant gap between the models' capabilities to simply recall necessary knowledge and to integrate it with the presented context. To foster research and collaboration in this field we share M-QALM, our resources, standardised methodology, and evaluation results, with the research community to facilitate further advancements in clinical knowledge representation learning within language models., Comment: Accepted at ACL 2024 (Findings)

Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields; new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods.

We present the online service cosmICweb (COSMological Initial Conditions on the WEB) - the first database and web interface to store, analyze, and disseminate initial conditions for zoom simulations of objects forming in cosmological simulations: from galaxy clusters to galaxies and more. Specifically, we store compressed information about the Lagrangian proto-halo patches for all objects in a typical simulation merger tree along with properties of the halo/galaxy across cosmic time. This enables a convenient web-based selection of the desired zoom region for an object fitting user-specified selection criteria. The information about the region can then be used with the MUSIC code to generate the zoom ICs for the simulation. In addition to some other simulations, we currently support all objects in the EAGLE simulation database, so that for example the Auriga simulations are easily reproduced, which we demonstrate explicitly. The framework is extensible to include other simulations through an API that can be added to an existing database structure and with which cosmICweb can then be interfaced. We make the web portal and database publicly available to the community., Comment: To be submitted to The Open Journal of Astrophysics. The cosmICweb portal is accessible under https://cosmicweb.eu. v2: fixed rendering issues (Safari) in Fig. 3

We present a general, systematic theory of electromagnetism and chirality in crystalline solids. Symmetry is its fundamental guiding principle. We use the formal similarity between space inversion $i$ and time inversion $\theta$ to identify two complementary, comprehensive classification of crystals, based on five categories of electric and magnetic multipole order -- called polarizations -- and five categories of chirality. The five categories of polarizations (parapolar, electropolar, magnetopolar, antimagnetopolar, and multipolar) expand the familiar notion of electric dipolarization in ferroelectrics and magnetization in ferromagnets to higher-order multipole densities. The five categories of chirality (parachiral, electrochiral, magnetochiral, antimagnetochiral, and multichiral) expand the familiar notion of enantiomorphism due to non-superposable mirror images to the inversion symmetries $i$, $\theta$, and $i\theta$. In multichiral systems, all these inversion symmetries are absent so that these systems have four distinct enantiomorphs. Each category of chirality arises from distinct superpositions of electric and magnetic multipole densities. We provide a complete theory of minimal effective models characterizing the different categories of chirality in different systems. Jointly these two schemes yield a classification of all 122 magnetic crystallographic point groups into 15 types that treat the inversion symmetries $i$, $\theta$, and $i\theta$ on the same footing. The group types are characterized via distinct physical properties and characteristic features in the electronic band structure. At the same time, the formal similarities between the inversion symmetries $i$, $\theta$, and $i\theta$ imply striking correspondences between apparently dissimilar systems and their physical properties., Comment: 41 pages, 7 figures

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

The digital age has significantly changed how music is consumed, promoted, and monetized. Social media platforms like TikTok are playing a pivotal role in this transformation. This shift has sparked a debate within the music industry: While some stakeholders see social media platforms like TikTok as opportunities to boost songs to viral status, others raise concerns about potential cannibalization effects, fearing that such exposure might reduce revenue from streaming services like Spotify. In this paper, we evaluate the effect of a song's presence - or absence - on social media on its demand on music streaming services using a quasi-natural experiment: Universal Music Group's (UMG) - one of "The Big 3" record labels - decision to remove its entire content library from TikTok in February 2024. We use representative samples covering close to 50% of the US and 94% of the German streaming markets, employing a difference-in-differences approach to compare the streaming consumption of songs that were removed from TikTok with those that were not. We find that UMG's removal of music from TikTok led to a 2-3% increase in streams on audio platforms for affected songs, indicating substitution effects. However, this average treatment effect masks significant heterogeneity: older songs and songs with less promotional support elsewhere saw a decrease in streaming consumption, suggesting that TikTok helps consumers discover or rediscover content that is not top of mind for consumers.

Oblivious transfer (OT) is a fundamental primitive for secure two-party computation. It is well known that OT cannot be implemented with information-theoretic security if the two players only have access to noiseless communication channels, even in the quantum case. As a result, weaker variants of OT have been studied. In this work, we rigorously establish the impossibility of cheat-sensitive OT, where a dishonest party can cheat, but risks being detected. We construct a general attack on any quantum protocol that allows the receiver to compute all inputs of the sender and provide an explicit upper bound on the success probability of this attack. This implies that cheat-sensitive quantum Symmetric Private Information Retrieval cannot be implemented with statistical information-theoretic security. Leveraging the techniques devised for our proofs, we provide entropic bounds on primitives needed for secure function evaluation. They imply impossibility results for protocols where the players have access to OT as a resource. This result significantly improves upon existing bounds and yields tight bounds for reductions of 1-out-of-n OT to a resource primitive. Our results hold in particular for transformations between a finite number of primitives and for any error., Comment: The main results are unchanged. We have added some explanations and corrected typos and a mistake in the calculation of the error terms of Theorems 3 and 4

A promising solution for scalable integrated optics of trapped-ion quantum processors are curved waveguides guiding visible light within sapphire bulk material. To the best of our knowledge, no curved waveguides were investigated in sapphire so far, and no measurements of waveguides with visible light in undoped planar sapphire substrates were reported. Here, we demonstrate femtosecond laser writing of depressed cladding waveguides in sapphire. Laser parameters, such as pulse energy, pulse duration, and repetition rate, as well as waveguide geometry parameters, were optimized to guide 728 nm light. This resulted in single-mode waveguides with a propagation loss of 1.9(3) dB/cm. The investigation of curved waveguides showed a sharp increase in total loss for curvature radii below 15 mm. Our results demonstrate the potential of femtosecond laser writing as a powerful technique for creating integrated optical waveguides in the volume of sapphire substrates. Such waveguides could be a building block for integrated optics in trapped-ion quantum processors.

Generative modeling via stochastic processes has led to remarkable empirical results as well as to recent advances in their theoretical understanding. In principle, both space and time of the processes can be discrete or continuous. In this work, we study time-continuous Markov jump processes on discrete state spaces and investigate their correspondence to state-continuous diffusion processes given by SDEs. In particular, we revisit the $\textit{Ehrenfest process}$, which converges to an Ornstein-Uhlenbeck process in the infinite state space limit. Likewise, we can show that the time-reversal of the Ehrenfest process converges to the time-reversed Ornstein-Uhlenbeck process. This observation bridges discrete and continuous state spaces and allows to carry over methods from one to the respective other setting. Additionally, we suggest an algorithm for training the time-reversal of Markov jump processes which relies on conditional expectations and can thus be directly related to denoising score matching. We demonstrate our methods in multiple convincing numerical experiments.