Search

In a shattered pellet injection (SPI) system the penetration and assimilation of the injected material depends on the speed and size distribution of the SPI fragments. ASDEX Upgrade (AUG) was recently equipped with a flexible SPI to study the effect of these parameters on disruption mitigation efficiency. In this paper we study the impact of different parameters on SPI assimilation with the 1.5D INDEX code. Scans of fragment sizes, speeds and different pellet compositions are carried out for single SPI into AUG H-mode plasmas. We use a semi-empirical thermal quench (TQ) onset condition to study the material assimilation trends. For mixed deuterium-neon pellets, smaller/faster fragments start to assimilate quicker. However, at the expected onset of the global reconnection event (GRE),larger/faster fragments end up assimilating more material. Variations in the injected neon content lead to a large difference in the assimilated neon for neon content below $< 10^{21}$ atoms. For larger injected neon content, a self-regulating mechanism limits the variation in the amount of assimilated neon. We use a back-averaging model to simulate the plasmoid drift during pure deuterium injections with the back-averaging parameter determined by a interpretative simulation of an experimental pure deuterium injection discharge. Again, larger and faster fragments are found to lead to higher assimilation with the material assimilation limited to the plasma edge in general, due to the plasmoid drift. The trends of assimilation for varying fragment sizes, speeds and pellet composition qualitatively agree with the previously reported experimental observations.

The transition from individual to collective motion plays a significant role in many biological processes. While the implications of different types of particle-particle interactions for the emergence of particular modes of collective motion have been well studied, it is unclear how particular types of individual migration patterns influence collective motion. Here, motivated by swarming bacteria Myxococcus xanthus, we investigate the combined effects of the individual pattern of migration and of particle-particle interactions, on the emergence of collective migration. We analyze the effects of a feature of individual pattern migration, the persistence of motion, on the collective properties of the system that emerge from interactions among individuals; in particular, when nematic velocity alignment interaction mediates collective dynamics. We find, through computer simulations and mathematical analysis, that an initially disordered migratory state can become globally ordered by increasing either, the particle-particle alignment interaction strength or the persistence of individual migration. In contrast, we find that persistence prevents the emergence of global nematic order when both persistence and nematic alignment are comparatively high. We conclude that behavior at the population level does not only depend on interactions between individuals but also on the individuals' own intrinsic behavior.

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma (GBM) patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging characteristics of PsP and TP. This study therefore proposes a multimodal deep-learning approach utilizing complementary information from routine anatomical MR images, clinical parameters, and RT treatment planning information for improved predictive accuracy. The approach utilizes a self-supervised Vision Transformer (ViT) to encode multi-sequence MR brain volumes to effectively capture both global and local context from the high dimensional input. The encoder is trained in a self-supervised upstream task on unlabeled glioma MRI datasets from the open BraTS2021, UPenn-GBM, and UCSF-PDGM datasets to generate compact, clinically relevant representations from FLAIR and T1 post-contrast sequences. These encoded MR inputs are then integrated with clinical data and RT treatment planning information through guided cross-modal attention, improving progression classification accuracy. This work was developed using two datasets from different centers: the Burdenko Glioblastoma Progression Dataset (n = 59) for training and validation, and the GlioCMV progression dataset from the University Hospital Erlangen (UKER) (n = 20) for testing. The proposed method achieved an AUC of 75.3%, outperforming the current state-of-the-art data-driven approaches. Importantly, the proposed approach relies on readily available anatomical MRI sequences, clinical data, and RT treatment planning information, enhancing its clinical feasibility. The proposed approach addresses the challenge of limited data availability for PsP and TP differentiation and could allow for improved clinical decision-making and optimized treatment plans for GBM patients.

When reading a document, glancing at the spatial layout of a document is an initial step to understand it roughly. Traditional document layout analysis (DLA) methods, however, offer only a superficial parsing of documents, focusing on basic instance detection and often failing to capture the nuanced spatial and logical relations between instances. These limitations hinder DLA-based models from achieving a gradually deeper comprehension akin to human reading. In this work, we propose a novel graph-based Document Structure Analysis (gDSA) task. This task requires that model not only detects document elements but also generates spatial and logical relations in form of a graph structure, allowing to understand documents in a holistic and intuitive manner. For this new task, we construct a relation graph-based document structure analysis dataset (GraphDoc) with 80K document images and 4.13M relation annotations, enabling training models to complete multiple tasks like reading order, hierarchical structures analysis, and complex inter-element relation inference. Furthermore, a document relation graph generator (DRGG) is proposed to address the gDSA task, which achieves performance with 57.6% at mAP$_g$@0.5 for a strong benchmark baseline on this novel task and dataset. We hope this graphical representation of document structure can mark an innovative advancement in document structure analysis and understanding. The new dataset and code will be made publicly available at https://yufanchen96.github.io/projects/GraphDoc., Comment: Accepted by ICLR 2025. Project page: https://yufanchen96.github.io/projects/GraphDoc

Logic locking secures hardware designs in untrusted foundries by incorporating key-driven gates to obscure the original blueprint. While this method safeguards the integrated circuit from malicious alterations during fabrication, its influence on data confidentiality during runtime has been ignored. In this study, we employ path sensitization to formally examine the impact of logic locking on confidentiality. By applying three representative logic locking mechanisms on open-source cryptographic benchmarks, we utilize an automatic test pattern generation framework to evaluate the effect of locking on cryptographic encryption keys and sensitive data signals. Our analysis reveals that logic locking can inadvertently cause sensitive data leakage when incorrect logic locking keys are used. We show that a single malicious logic locking key can expose over 70% of an encryption key. If an adversary gains control over other inputs, the entire encryption key can be compromised. This research uncovers a significant security vulnerability in logic locking and emphasizes the need for comprehensive security assessments that extend beyond key-recovery attacks., Comment: 8 pages, accepted at 26th International Symposium on Quality Electronic Design (ISQED'25)

This study demonstrates that two-dimensional talc, a naturally abundant mineral, supports hyperbolic phonon-polaritons (HPhPs) at mid-infrared wavelengths, thus offering a low-cost alternative to synthetic polaritonic materials. Using scattering scanning near-field optical microscopy (s-SNOM) and synchrotron infrared nano spectroscopy (SINS), we reveal tunable HPhP modes in talc flakes of a long lifetime. These results highlight the potential of natural 2D talc crystals to constituting an effective platform for establishing scalable optoelectronic and photonic devices., Comment: 8 pages, 4 figures

Color centers in hexagonal boron nitride (hBN) are promising candidates as quantum light sources for future technologies. In this work, we utilize a scattering-type near-field optical microscope (s-SNOM) to study the photoluminescence (PL) emission characteristics of such quantum emitters in metalorganic vapor phase epitaxy grown hBN. On the one hand, we demonstrate direct near-field optical excitation and emission through interaction with the nanofocus of the tip resulting in a sub-diffraction limited tip-enhanced PL hotspot. On the other hand, we show that indirect excitation and emission via scattering from the tip significantly increases the recorded PL intensity. This demonstrates that the tip-assisted PL (TAPL) process efficiently guides the generated light to the detector. We apply the TAPL method to map the in-plane dipole orientations of the hBN color centers on the nanoscale. This work promotes the widely available s-SNOM approach to applications in the quantum domain including characterization and optical control.

Feedback from black hole-powered jets has been invoked in many cosmological simulations to regulate star formation and quench galaxies. Despite this, observational evidence of how jets might be able to affect their hosts remains scarce, especially for low power jets in halos smaller than clusters. Recent observations of outflows around FR0 galaxies, that host compact radio-loud sources, imply that lower-power jetted active galactic nuclei (AGN) may have a significant impact on their hosts through jet interactions with the interstellar medium (ISM). Using the Arepo code, we launch jets of low and intermediate power (10$^{38}$ - 10$^{43}$ erg s$^{-1}$) within a ~kpc-scale periodic box with driven turbulence to study how the jets propagate through a turbulent ISM. Our simulation results broadly fit into three different scenarios $\unicode{x2013}$ jets penetrating easily through the ISM, becoming completely stalled, or the interesting intermediate stage, when jets are highly disturbed and redirected. We suggest that intermediate power jets do not have enough ram pressure to affect the turbulent structure of the ISM, and so only fill pre-existing cavities. Low-power jets are able to drive outflows in a hot phase ($>10^{4.4}$ K). However, warm (~$10^4$ K) ionized gas outflows appear under certain conditions. This work is part of the ''Learning the Universe'' collaboration, aiming to build next-generation cosmological simulations that incorporate a new prescription for AGN feedback., Comment: 20 pages, 14 figures, accepted in ApJ

Vortices are ubiquitous in nature; they appear in a variety of phenomena ranging from galaxy formation in astrophysics to topological defects in quantum fluids. In particular, wave vortices have attracted enormous attention and found applications in optics, acoustics, electron microscopy, etc. Such vortices carry quantized phase singularities accompanied by zero intensity in the center, and quantum-like orbital angular momentum, with the minimum localization scale of the wavelength. Here we describe a conceptually novel type of wave vortices, which can appear around arbitrarily small `holes' (i.e., excluded areas or defects) in a homogeneous 2D plane. Such vortices are characterized by high intensity and confinement at the edges of the hole and hence subwavelength localization of the angular momentum. We demonstrate the appearance of such vortices in: (i) optical near fields around metallic nanodiscs on a dielectric substrate, (ii) phonon-polariton fields around nanoholes in a polaritonic slab, and (iii) ocean tidal waves around islands of New Zealand and Madagascar. We also propose a simple toy model of the generation of such subwavelength vortices via the interference of a point-dipole source and a plane wave, where the vortex sign is controlled by the mutual phase between these waves. Our findings open avenues for subwavelength vortex/angular-momentum-based applications in various wave fields., Comment: 11 pages, 4 figures

The KOTO II experiment is proposed to measure the branching ratio of the decay $K_L\to\pi^0\nu\bar{\nu}$ at J-PARC. With a beamline to extract long-lived neutral kaons at 5 degrees from a production target, the single event sensitivity of the decay is $8.5\times 10^{-13}$, which is much smaller than the Standard Model prediction $3\times 10^{-11}$. This allows searches for new physics beyond the Standard Model and the first discovery of the decay with a significance exceeding $5\sigma$. As the only experiment proposed in the world dedicated to rare kaon decays, KOTO II will be indispensable in the quest for a complete understanding of flavor dynamics in the quark sector. Moreover, by combining efforts from the kaon community worldwide, we plan to develop the KOTO II detector further and expand the physics reach of the experiment to include measurements of the branching ratio of the $K_L\to\pi^0\ell^+\ell^-$ decays, studies of other $K_L$ decays, and searches for dark photons, axions, and axion-like particles. KOTO II will therefore obtain a comprehensive understanding of $K_L$ decays, providing further constraints on new physics scenarios with existing $K^+$ results., Comment: Submitted to the J-PARC PAC. arXiv admin note: substantial text overlap with arXiv:2110.04462

Inspired by previous studies and pioneers of the field, we present new results on an extensive EPR-Bell experiment using photons generated by parametric down conversion, where one of the photons is deliberately phase-shifted. Our experiments show some surprising results for particular angles of this phase shift., Comment: 20 pages, 12 figures

Phonon polaritons (PhPs) -- light coupled to lattice vibrations -- in polar van der Waals (vdW) crystals are promising candidates for controlling the flow of energy at the nanoscale due to their strong field confinement, anisotropic propagation, and ultra-long lifetime in the picosecond range \cite{ref1,ref2,ref3,ref4,ref5}. However, the lack of tunability in their narrow and material-specific spectral range -- the Reststrahlen Band (RB) -- severely limits their technological implementation. Here, we demonstrate that the intercalation of Na atoms in the vdW semiconductor $\alpha$-V$_2$O$_5$ enables a broad spectral shift of RBs, and that the PhPs excited exhibit ultra-low losses (lifetime of $4 \pm 1$~ps), similar to PhPs in the non-intercalated crystal (lifetime of $6 \pm 1$ ps). We expect our intercalation method to be applicable to other vdW crystals, opening the door for the use of PhPs in broad spectral bands in the mid-infrared domain., Comment: 15 pages, 5 figures

We propose an experimental setup for manipulating the spontaneous emission of trapped ions, based on a spatial light modulator. Anticipated novelties include the potential to entangle more than two ions through a single photon detection event and control the visibility for spatially distinguishable emitters. The setup can be adapted to most of the existing ion traps commonly used in quantum technology.

In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quality segmentation masks by informing the network about the objects position with the help of user guidance. In our case the guidance consists of click prompts. For this task, we first present a baseline architecture which is specifically geared towards quickly responding after each click. Afterwards, we motivate and describe a number of architectural modifications which improve the performance when tasked with segmenting winter sports equipment on the WSESeg dataset. With regards to the average NoC@85 metric on the WSESeg classes, we outperform SAM and HQ-SAM by 2.336 and 7.946 clicks, respectively. When applied to the HQSeg-44k dataset, our system delivers state-of-the-art results with a NoC@90 of 6.00 and NoC@95 of 9.89. In addition to that, we test our model on a novel dataset containing masks for humans during skiing., Comment: 4 figures, 6 tables, 12 pages

The amplituhedron is a semialgebraic set given as the image of the non-negative Grassmannian under a linear map subject to a choice of additional parameters. We define the limit amplituhedron as the limit of amplituhedra by sending one of the parameters, namely the number of particles $n$, to infinity. We study this limit amplituhedron for $m = 2$ and any $k$, relating to the number of negative helcity particles. We determine its algebraic boundary in terms of Chow hypersurfaces. This hypersurface in the Grassmannian is stratified by singularities in terms of higher order secants of the rational normal curve. In conclusion, we show that the limit amplituhedron is a positive geometry with a residual arrangement that is empty., Comment: 22 pages

Modern particle physics experiments, e.g. at the Large Hadron Collider (LHC) at CERN, crucially depend on the precise description of the scattering processes in terms of the known fundamental forces. This is limited by our current understanding of the strong nuclear force, as quantified by the strong coupling, $\alpha_s$, between quarks and gluons. Relating $\alpha_s$ to experiments poses a major challenge as the strong interactions lead to the confinement of quarks and gluons inside hadronic bound states. At high energies, however, the strong interactions become weaker ("asymptotic freedom") and thus amenable to an expansion in powers of the coupling. Attempts to relate both regimes usually rely on modeling of the bound state problem in one way or another. Using large scale numerical simulations of a first principles formulation of Quantum Chromodynamics on a space-time lattice, we have carried out a model-independent determination of $\alpha_s$ with unprecedented precision. The uncertainty, about half that of all other results combined, originates predominantly from the statistical Monte Carlo evaluation and has a clear probabilistic interpretation. The result for $\alpha_s$ describes a variety of physical phenomena over a wide range of energy scales. If used as input information, it will enable significantly improved analyses of many high energy experiments, by removing an important source of theoretical uncertainty. This will increase the likelihood to uncover small effects of yet unknown physics, and enable stringent precision tests of the Standard Model. In summary, this result boosts the discovery potential of the LHC and future colliders, and the methods developed in this work pave the way for even higher precision in the future., Comment: 49 pages, LaTeX

Quantum sensors offer exceptional sensitivity to nanoscale magnetic field fluctuations, where non-stationary effects such as spin diffusion and non-Markovian dynamics arising from coupling to few environmental degrees of freedom play critical roles. Here, we demonstrate how quantum sensors can characterize the statistical properties of noise sources, distinguishing between stationary and non-stationary behaviors, as well as Markovian and non-Markovian dynamics. Using nitrogen-vacancy (NV) centers in diamond as a platform, we develop a physical noise model that analytically predicts Ramsey decay curves under different noise regimes. These predictions are experimentally validated by measuring Ramsey decay for NV centers subject to injected noise of each type. Our results showcase the capability of quantum sensors to unravel complex noise behaviors induced by nanoscale environments, shedding light on their physical origins and guiding the development of strategies to mitigate decoherence. This work deepens our understanding of noise dynamics at the nanoscale and lays the foundation for enhancing the performance and robustness of quantum technologies.

We study the semisimplification of the full karoubian subcategory generated by the irreducible finite dimensional representations of the algebraic supergroup $GL(m|n)$ over an algebraically closed field of characteristic zero. This semisimplification is equivalent to the representations of a pro-reductive group $H_{m|n}$. We show that there is a canonical decomposition $H_{m|n} \cong GL(m\!-\! n) \times H_{n|n}$, thereby reducing the determination of $H_{m|n}$ to the equal rank case $m\! =\! n$ which was treated in a previous paper., Comment: v4: Minor changes in Section 6 (reordering); v3: Added an appendix ("A variant of the $\eta$ functor"); v2: very minor changes (typos etc)

Low-field magnetars have dipolar magnetic fields that are 10-100 times weaker than the threshold, $B \gtrsim 10^{14}$ G, used to define classical magnetars, yet they produce similar X-ray bursts and outbursts. Using the first direct numerical simulations of magneto-thermal evolution starting from a dynamo-generated magnetic field, we show that the low-field magnetars can be produced as a result of a Tayler--Spruit dynamo inside the proto-neutron star. We find that these simulations naturally explain key characteristics of low-field magnetars: (1) weak ($\lesssim 10^{13}$ G) dipolar magnetic fields, (2) strong small-scale fields, and (3) magnetically induced crustal failures producing X-ray bursts. These findings suggest two distinct formation channels for classical and low-field magnetars, potentially linked to different dynamo mechanisms., Comment: Submitted to a journal

Integer programs for resource-constrained project scheduling problems are notoriously hard to solve due to their weak linear relaxations. Several papers have proposed reformulating project scheduling problems via Dantzig-Wolfe decomposition to strengthen their linear relaxation and decompose large problem instances. The reformulation gives rise to a master problem that has a large number of variables. Therefore, the master problem is solved by a column generation procedure embedded in a branching framework, also known as branch-and-price. While branch-and-price has been successfully applied to many problem classes, it turns out to be ineffective for most project scheduling problems. This paper identifies drivers of the ineffectiveness by analyzing the structure of the reformulated problem and the strength of different branching schemes. Our analysis shows that the reformulated problem has an unfavorable structure for column generation: It is highly degenerate, slowing down the convergence of column generation, and for many project scheduling problems, it yields the same or only slightly stronger linear relaxations as classical formulations at the expense of large increases in runtime. Our computational experiments complement our theoretical findings.

Let $N$ be a finite set and $\mathcal{F}$, an intersection-closed family of subsets. Frankl conjectured that there always exists an element in $N$ which is contained in at most half the number of sets in $\mathcal{F}$ unless $\mathcal{F} =\{E\}$. We prove the validity of a class of inequalities which imply Frankl's conjecture.

Thanks to the rapidly evolving integration of LLMs into decision-support tools, a significant transformation is happening across large-scale systems. Like other medical fields, the use of LLMs such as GPT-4 is gaining increasing interest in radiation oncology as well. An attempt to assess GPT-4's performance in radiation oncology was made via a dedicated 100-question examination on the highly specialized topic of radiation oncology physics, revealing GPT-4's superiority over other LLMs. GPT-4's performance on a broader field of clinical radiation oncology is further benchmarked by the ACR Radiation Oncology In-Training (TXIT) exam where GPT-4 achieved a high accuracy of 74.57%. Its performance on re-labelling structure names in accordance with the AAPM TG-263 report has also been benchmarked, achieving above 96% accuracies. Such studies shed light on the potential of LLMs in radiation oncology. As interest in the potential and constraints of LLMs in general healthcare applications continues to rise5, the capabilities and limitations of LLMs in radiation oncology decision support have not yet been fully explored., Comment: Officially published in the Red Journal

Sum-frequency generation (SFG) is a second-order nonlinear process widely used for characterizing surfaces and interfaces with monolayer sensitivity. Recently, optical field enhancement in plasmonic nanocavities has enabled SFG with continuous wave (CW) lasers from nanoscale areas of molecules, promising applications like nanoscale SFG spectroscopy and coherent upconversion for mid-infrared detection at visible frequencies. Here, we demonstrate CW SFG from individual nanoparticle-on-mirror (NPoM) cavities, which are resonant at visible frequencies and filled with a monolayer of molecules, when placed beneath a metal scanning probe tip. The tip acts as an efficient broadband antenna, focusing incident CW infrared illumination onto the nanocavity. The cascaded near-field enhancement within the NPoM nanocavity yields nonlinear optical responses across a broad range of infrared frequencies, achieving SFG enhancements of up to 14 orders of magnitude. Further, nanomechanical positioning of the tip allows for in-operando control of SFG by tuning the local field enhancement rather than the illumination intensities. The versatility of tip-enhanced nanocavities allows for SFG studies of a wide range of molecular species in the few-molecule regime without the need for complex nanofabrication of doubly-resonant nanocavities. Our results also promise SFG nanoimaging with tips providing strong visible and IR field enhancement at their apex, offering a robust platform for future applications in nonlinear nanooptics., Comment: 34 pages, 17 figures (Supplementary Material included)

Nerve impulses, the currency of information flow in the brain, are generated by an instability of the neuronal membrane potential dynamics. Neuronal circuits exhibit collective chaos that appears essential for learning, memory, sensory processing, and motor control. However, the factors controlling the nature and intensity of collective chaos in neuronal circuits are not well understood. Here we use computational ergodic theory to demonstrate that basic features of nerve impulse generation profoundly affect collective chaos in neuronal circuits. Numerically exact calculations of Lyapunov spectra, Kolmogorov-Sinai-entropy, and upper and lower bounds on attractor dimension show that changes in nerve impulse generation in individual neurons moderately impact information encoding rates but qualitatively transform phase space structure. Specifically, we find a drastic reduction in the number of unstable manifolds, Kolmogorov-Sinai entropy, and attractor dimension. Beyond a critical point, marked by the simultaneous breakdown of the diffusion approximation, a peak in the largest Lyapunov exponent, and a localization transition of the leading covariant Lyapunov vector, networks exhibit sparse chaos: prolonged periods of near stable dynamics interrupted by short bursts of intense chaos. Analysis of large, more realistically structured networks supports the generality of these findings. In cortical circuits, biophysical properties appear tuned to this regime of sparse chaos. Our results reveal a close link between fundamental aspects of single-neuron biophysics and the collective dynamics of cortical circuits, suggesting that nerve impulse generation mechanisms are adapted to enhance circuit controllability and information flow.

A deformation technique, known as the warped convolution, takes quantum fields in Minkowski spacetime to quantum fields in noncommutative Minkowski space-time. Since a quantum field is an operator valued regular distribution and the warped convolution is (weakly) an oscillatory integral of Rieffel type, we prove that the symbol classes introduced by Hormander admit extensions which are suited to the warped convolutions of scalar quantum field operators. We further show that, if a particular vector state on the undeformed algebra of field operators fulfills the microlocal spectrum condition, then every vector state on the deformed algebra generated by these warped convolutions fulfills the microlocal spectrum condition., Comment: 37 pages

Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise--a common issue in real-world datasets--has been largely overlooked. Label noise can mislead model optimization, thereby exacerbating the challenges of open-set recognition in novel domains. In this study, we take the first step towards addressing Open-Set Domain Generalization under Noisy Labels (OSDG-NL) by constructing dedicated benchmarks derived from widely used OSDG datasets, including PACS and DigitsDG. We evaluate baseline approaches by integrating techniques from both label denoising and OSDG methodologies, highlighting the limitations of existing strategies in handling label noise effectively. To address these limitations, we propose HyProMeta, a novel framework that integrates hyperbolic category prototypes for label noise-aware meta-learning alongside a learnable new-category agnostic prompt designed to enhance generalization to unseen classes. Our extensive experiments demonstrate the superior performance of HyProMeta compared to state-of-the-art methods across the newly established benchmarks. The source code of this work is released at https://github.com/KPeng9510/HyProMeta., Comment: The source code of this work is released at https://github.com/KPeng9510/HyProMeta

Plasma-terminating disruptions represent a critical outstanding issue for reactor-relevant tokamaks. ITER will use shattered pellet injection (SPI) as its disruption mitigation system to reduce heat loads, vessel forces, and to suppress the formation of runaway electrons. In this paper we demonstrate that reduced kinetic modelling of SPI is capable of capturing the major experimental trends in ASDEX Upgrade SPI experiments, such as dependence of the radiated energy fraction on neon content, or the current quench dynamics. Simulations are also consistent with the experimental observation of no runaway electron generation with neon and mixed deuterium-neon pellet composition. We also show that statistical variations in the fragmentation process only have a notable impact on disruption dynamics at intermediate neon doping, as was observed in experiments.

Dynamic resistance occurs in a superconducting tape carrying a dc transport current while being exposed to an alternating magnetic field. This effect is caused by flux movements interacting with the transport current. The dynamic resistance is already applied in many superconducting applications, for example superconducting flux pumps or persistent current switches. The resistance is highly dependent on the magnetic field and the frequency the superconductor is subjected to and its properties. When the dynamic resistance exceeds a certain value and thus enters the magnitude of the resistances of the normal conducting layers of the HTS tape, these normal conducting layers play a significant role in the total resistance of the tape. In this paper, modifications were made to the silver stabilizer and the total resistance of the HTS tape has been investigated. The experimental results with frequencies up to 1000 Hz and magnetic field up to 277 mT show significant increases in resistance. Additionally, a multilayer model based on H-formulation is presented to calculate the losses of the superconductor. The results also show significant heating due to the losses and therefore a temperature rise, which effects the measured total resistance. These results can be further used for applications where high switchable resistances are required with zero dc resistance when the magnet is turned off.

Nanoconfined water plays an indispensable role in various phenomena in biology, chemistry, and engineering. It exhibits many abnormal properties compared to bulk water, especially under strong confinement. However, the origin of those anomalies is still elusive due to the lack of structural information on hydrogen-bonding networks. Considering the inhomogeneity of the nanocavity and the tiny amount of water molecules, conventional optical spectroscopies and nuclear magnetic resonance (NMR) fail to realize the structure analysis of nanoconfined water. Here, we addressed this issue by combining scanning probe microscopy (SPM) with advanced quantum sensing(QS) based on an atomic-size quantum sensor like nitrogen-vacancy (NV) center in diamond, which can apply the nanoscale-NMR for characterizing both the dynamics and structure of confined water at ambient conditions. We built a two-dimensional (2D) nanoconfined water system with a hexagonal-boron nitride (hBN) flake and a hydrophilic diamond surface. By using the SPM tip to measure the confinement size precisely, we observed a critical confinement size of ~2 nm, below which the water diffusion was significantly suppressed and the hydrogen-bonding network of water showed an ordered structure. Meanwhile, molecular dynamics (MD) simulation revealed a solid-like water contact layer on the diamond surface under strong confinement, which also reproduced the measured nanoscale-NMR spectra and confirmed the liquid-solid phase transition observed in the experiments. Notably, with this new SPM-QS platform, our results showed a promising way to elucidate the abnormal properties of nanoconfined water in future applications.

From life sciences and ecology to quantum physics and finance, anomalous diffusion appears in countless complex systems as a signature of emergent transport properties beyond Brownian motion. Despite substantial theoretical progress, the experimental study of real-world systems exhibiting anomalous diffusion remains challenging due to an intrinsically elusive ground truth and the limited information contained in typical single trajectories. Here, unlike previous experimental systems, we demonstrate the controlled generation of two-dimensional trajectories with fully tailored statistics spanning the entire spectrum of anomalous diffusion, from subdiffusion to superdiffusion, and over statistically significant temporal and spatial scales (covering at least two decades). We achieve this feat by simultaneously tuning the step-length distribution and, critically, the velocity autocorrelation function of microscopic superparamagnetic colloidal walkers with magnetic fields during extended acquisitions. Supported by theoretical reasoning, fine control of these two quantities combined allows us to generate trajectories compatible with L\'evy walks and fractional Brownian motion with tailored anomalous diffusion exponents. We envisage our approach will offer a robust, controllable experimental framework for validating and advancing theoretical models, analysis techniques, and predictive tools to study anomalous diffusion in real-life phenomena. These include transport in physical and biological systems, animal movement, human ecology, and financial markets.

This study pioneers the two-field correlation spectroscopy (COSY) in zero to ultralow field (ZULF) liquid state NMR, employing hyperpolarized [1-$^{13}$C]pyruvate as a model system. We demonstrate the successful integration of signal amplification by reversible exchange (SABRE) for hyperpolarization, enabling the detection of ZULF COSY spectra with increased sensitivity. The use of field cycling allows the acquisition of two-field COSY spectra at varying magnetic field strengths, including zero-field conditions. This enables insight into both J-coupling and Zeeman-dominated regimes benefiting from ULF field observation sensitivity and mitigation of low-frequency noise by conducting readout at higher fields (>5 $\mu$T). Our study explores the effects of polarization transfer, apodization techniques, and the potential for further application of ZULF NMR in chemical analysis exemplified for three X-nuclei and three corresponding molecules: [1-$^{13}$C]pyruvate, [$^{15}$N]acetonitrile and [3-$^{19}$F]pyridine. These findings pave the way for more sensitive and cost-effective NMR spectroscopy in low-field regimes.

In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict $\textit{when}$ and $\textit{what}$ error pattern is most likely to occur in the future for a given car. Our approach leverages the temporal dynamics and contextual relationships of our event data from a fleet of cars. Event data is composed of discrete values of error codes as well as continuous values such as time and mileage. Modelled by two causal Transformers, we can anticipate vehicle failures and malfunctions before they happen. Thus, we introduce $\textit{CarFormer}$, a Transformer model trained via a new self-supervised learning strategy, and $\textit{EPredictor}$, an autoregressive Transformer decoder model capable of predicting $\textit{when}$ and $\textit{what}$ error pattern will most likely occur after some error code apparition. Despite the challenges of high cardinality of event types, their unbalanced frequency of appearance and limited labelled data, our experimental results demonstrate the excellent predictive ability of our novel model. Specifically, with sequences of $160$ error codes on average, our model is able with only half of the error codes to achieve $80\%$ F1 score for predicting $\textit{what}$ error pattern will occur and achieves an average absolute error of $58.4 \pm 13.2$h $\textit{when}$ forecasting the time of occurrence, thus enabling confident predictive maintenance and enhancing vehicle safety., Comment: 10 pages, 8 figures, accepted to AAAI 2025

Models for the transport of high energy charged particles through strong magnetic turbulence play a key role in space and astrophysical studies, such as describing the propagation of solar energetic particles and high energy cosmic rays. Inspired by the recent advances in high-performance machine learning techniques, we investigate the application of generative diffusion models to synthesizing test particle trajectories obtained from a turbulent magnetohydrodynamics simulation. We consider velocity increment, spatial transport and curvature statistics, and find excellent agreement with the baseline trajectories for fixed particle energies. Additionally, we consider two synthetic turbulence models for comparison. Finally, challenges towards an application-ready transport model based on our approach are discussed., Comment: 19 pages, 12 figures, accepted for publication in The Astrophysical Journal Supplement Series

Tracer diffusion of Na in natural alkali feldspars including sanidine, adularia and orthoclase with different Na:K ratios is measured using the radiotracer technique and applying the 22Na radioisotope. The tracer diffusion measurements along the crystallographic directions perpendicular to (001) and (010) in alularia feldspar reveled a slight (within a factor of two to three) anisotropy of Na diffusion with the faster diffusion rates along the perpendicular to (001) directions, while this difference was less prominent in orthoclase. The anisotropy of Na diffusion is specifically addressed for orthoclase and the full diffusion tensor is determined. The impact of temperature and composition on diffusion in natural alkali feldspars is discussed with respect to impurities and intrinsic defects. The state of Al/Si ordering is proposed to have a significant influence on the Na diffusion rates in alkali feldspars., Comment: conflict of interests with co-authors. All written records can be received from the corresponding author

We present JWST-NIRCam narrow-band, 4.05 $\mu$m Brackett-$\alpha$ images of the Sgr C HII region, located in the Central Molecular Zone (CMZ) of the Galaxy. Unlike any HII region in the Solar vicinity, the Sgr C plasma is dominated by filamentary structure in both Brackett-$\alpha$ and the radio continuum. Some bright filaments, which form a fractured arc with a radius of about 1.85 pc centered on the Sgr C star-forming molecular clump, likely trace ionization fronts. The brightest filaments form a `$\pi$-shaped' structure in the center of the HII region. Fainter filaments radiate away from the surface of the Sgr C molecular cloud. The filaments are emitting optically thin free-free emission, as revealed by spectral index measurements from 1.28 GHz (MeerKAT) to 97 GHz (ALMA). But, the negative in-band 1 to 2 GHz spectral index in the MeerKAT data alone reveals the presence of a non-thermal component across the entire Sgr C HII region. We argue that the plasma flow in Sgr C is controlled by magnetic fields, which confine the plasma to rope-like filaments or sheets. This results in the measured non-thermal component of low-frequency radio emission plasma, as well as a plasma $\beta$ (thermal pressure divided by magnetic pressure) below 1, even in the densest regions. We speculate that all mature HII regions in the CMZ, and galactic nuclei in general, evolve in a magnetically dominated, low plasma $\beta$ regime., Comment: 31 pages, 19 figures. Accepted for publication in The Astrophysical Journal

Spontaneous neural activity observed in resting-state fMRI is characterized by complex spatio-temporal dynamics. Different measures related to local and global brain connectivity and fluctuations in low-frequency amplitudes can quantify individual aspects of these neural dynamics. Even though such measures are derived from the same functional signals, they are often evaluated separately, neglecting their interrelations and potentially reducing the analysis sensitivity. In our study, we present a fusion searchlight (FuSL) framework to combine the complementary information contained in different resting-state fMRI metrics and demonstrate how this can improve the decoding of brain states. Moreover, we show how explainable AI allows us to reconstruct the differential impact of each metric on the decoding, which additionally increases spatial specificity of searchlight analysis. In general, this framework can be adapted to combine information derived from different imaging modalities or experimental conditions, offering a versatile and interpretable tool for data fusion in neuroimaging.

Context. Low-mass bodies, such as comets, asteroids, planetesimals, and free-floating planets, are continuously injected into the intra-cluster environment after expulsion from their host planetary systems. These can be modeled as massless particles (MLPs, hereafter). The dynamics of large populations of MLPs, however, has yet received little attention in literature. Aims. We investigate the dynamical evolution of MLP populations in star clusters, and characterize their kinematics and ejection rates. Methods. We present NBODY6++GPU-MASSLESS, a modified version of the N-body simulation code NBODY6++GPU, that allows fast integration of star clusters that contain large numbers of massless particles (MLPs). NBODY6++GPU-MASSLESS contains routines specifically directed at the dynamical evolution of low-mass bodies, such as planets. Results. Unlike stars, MLPs do not participate in the mass segregation process. Instead, MLPs mostly follow the gravitational potential of the star cluster, which gradually decreases over time due to stellar ejections and stellar evolution. The dynamical evolution of MLPs is primarily affected by the evolution of the core of the star cluster. This is most apparent in the outer regions for clusters with higher initial densities. High escape rates of MLPs are observed before the core-collapse, after which escape rates remain stable. Denser star clusters undergo a more intense core collapse, but this does not impact the dynamical evolution of MLPs. The speeds of escaping stars are similar to those of escaping MLPs, when disregarding the high-velocity ejections of neutron stars during the first 50 Myr., Comment: 11 Pages, 9 Figures, Accepted for publication in A&A

The ALICE detector at the LHC has undergone a major upgrade in the long shutdown 2019-2021 to be able to take data at much-increased rates in Runs 3 and 4. The upgrades of the detector systems used for analysing double gap events are described, and the improvement in data taking capability for such double gap events is presented., Comment: 6 pages, 3 figures, Proceedings Workshop Diffraction and Low-x 2024, Trabia (Palermo, Italy), September 8-14, 2024

This study examines the AI-powered grading tool "AI Grading Assistant" by the German company Fobizz, designed to support teachers in evaluating and providing feedback on student assignments. Against the societal backdrop of an overburdened education system and rising expectations for artificial intelligence as a solution to these challenges, the investigation evaluates the tool's functional suitability through two test series. The results reveal significant shortcomings: The tool's numerical grades and qualitative feedback are often random and do not improve even when its suggestions are incorporated. The highest ratings are achievable only with texts generated by ChatGPT. False claims and nonsensical submissions frequently go undetected, while the implementation of some grading criteria is unreliable and opaque. Since these deficiencies stem from the inherent limitations of large language models (LLMs), fundamental improvements to this or similar tools are not immediately foreseeable. The study critiques the broader trend of adopting AI as a quick fix for systemic problems in education, concluding that Fobizz's marketing of the tool as an objective and time-saving solution is misleading and irresponsible. Finally, the study calls for systematic evaluation and subject-specific pedagogical scrutiny of the use of AI tools in educational contexts., Comment: 38 pages, in German language, with an update from 2025-01-21 as appendix

Active Flux is a modified Finite Volume method that evolves additional Degrees of Freedom for each cell that are located on the interface by a non-conservative method to compute high-order approximations to the numerical fluxes through the respective interface to evolve the cell-average in a conservative way. In this paper, we apply the method to the Vlasov-Poisson system describing the time evolution of the time-dependent distribution function of a collisionless plasma. In particular, we consider the evaluation of the flux integrals in higher dimensions. We propose a dimensional splitting and three types of formulations of the flux integral: a one-dimensional reconstruction of second order, a third-order reconstruction based on information along each dimension, and a third-order reconstruction based on a discrepancy formulation of the Active Flux method. Numerical results in 1D1V phase-space compare the properties of the various methods.

This study explores the prevalence of dark patterns in mobile games that exploit players through temporal, monetary, social, and psychological means. Recognizing the ethical concerns and potential harm surrounding these manipulative strategies, we analyze user-generated data of 1496 games to identify relationships between the deployment of dark patterns within "dark" and "healthy" games. Our findings reveal that dark patterns are not only widespread in games typically seen as problematic but are also present in games that may be perceived as benign. This research contributes needed quantitative support to the broader understanding of dark patterns in games. With an emphasis on ethical design, our study highlights current problems of revenue models that can be particularly harmful to vulnerable populations. To this end, we discuss the relevance of community-based approaches to surface harmful design and the necessity for collaboration among players/users and practitioners to promote healthier gaming experiences.

Multiphase gas, with hot ($\sim10^6$K) and cold ($\sim10^4$K) gas, is ubiquitous in astrophysical media across a wide range of scales. However, simulating multiphase gas has been a long-standing challenge, due to the large separation between the size of cold gas structures and the scales at which such gas impacts the evolution of associated systems. In this study, we introduce a new subgrid framework for such multiphase gas, MOGLI: Model for Multiphase Gas using Multifluid hydrodynamics, in multifluid AREPO. We develop this approach based on first principles and theoretical results from previous studies with resolved small-scale simulations, leading to a minimal number of free parameters in the formulation. We divide the interactions in the model into three sources: drag, turbulent mixing and cold gas growth. As part of the model, we also include two methods for estimating the local turbulent velocities, one using the Kolmogorov scaling, and the other using the local velocity gradients. We verify the different components of the framework through extensive comparison with benchmark single-fluid simulations across different simulation parameters, such as how resolved the cold gas is initially, the turbulent Mach number, spatial resolution, and random initialisation of turbulence. We test the complete scheme and a reduced version, with and without cold gas growth. We find a very good qualitative and quantitative agreement across the different simulation parameters and diagnostics for both local turbulent velocity estimation methods. We also reproduce behaviour like the cold gas survival criteria as an emergent property. We discuss the applications and possible extensions of MOGLI and demonstrate its capability by running a simulation which would be computationally prohibitive to run as a resolved single-fluid simulation., Comment: 20 pages, 19 figures, 1 appendix. Comments are welcome

Assistive technology can be leveraged by blind people when searching for objects in their daily lives. We created ObjectFinder, an open-vocabulary interactive object-search prototype, which combines object detection with scene description and navigation. It enables blind persons to detect and navigate to objects of their choice. Our approach used co-design for the development of the prototype. We further conducted need-finding interviews to better understand challenges in object search, followed by a study with the ObjectFinder prototype in a laboratory setting simulating a living room and an office, with eight blind users. Additionally, we compared the prototype with BeMyEyes and Lookout for object search. We found that most participants felt more independent with ObjectFinder and preferred it over the baselines when deployed on more efficient hardware, as it enhances mental mapping and allows for active target definition. Moreover, we identified factors for future directions for the development of object-search systems.

This paper investigates the origin and orbital evolution of S-stars in the Galactic Center using models of binary disruption and relaxation processes. We focus on explaining the recently discovered "zone of avoidance" in S-star orbital parameters, defined as a region where no S-stars are observed with pericenters $\log(r_p / {\rm AU}) \leq 1.57 + 2.6(1 - e)$ pc. We demonstrate that the observed S-star orbital distributions, including this zone of avoidance and their thermal eccentricity distribution, can be largely explained by continuous disruption of binaries near the central supermassive black hole, followed by orbital relaxation. Our models consider binaries originating from large scales (5--100 pc) and incorporate empirical distributions of binary properties. We simulate close encounters between binaries and the black hole, tracking the remnant stars' orbits. The initially highly eccentric orbits of disrupted binary remnants evolve due to non-resonant and resonant relaxation in the Galactic Center potential. While our results provide insights into the formation mechanism of S-stars, there are limitations, such as uncertainties in the initial binary population and mass-function and simplifications in our relaxation models. Despite these caveats, our study demonstrates the power of using S-star distributions to probe the dynamical history and environment of the central parsec of our Galaxy., Comment: 12 pages, 9 figures + appendices. Submitted to A&A

Comment to article published in Proc. Natl. Acad. Sci. U. S. A.: Garaizar, A. et al. 'Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments.' Proc. Natl. Acad. Sci. U. S. A. 121, e2404555121 (2024)

Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diagnosis and patient treatment planning. In this work we retrain a multimodal transformer-based model for disease classification. To this end we use the text-image pair dataset from OpenI consisting of 2D chest X-rays associated with clinical reports. Our focus is on fusion methods for merging text and vision information extracted from medical datasets. Different architecture structures with a LLaMA II backbone model are tested. Early fusion of modality specific features creates better results with the best model reaching 97.10% mean AUC than late fusion from a deeper level of the architecture (best model: 96.67% mean AUC). Both outperform former classification models tested on the same multimodal dataset. The newly introduced multimodal architecture can be applied to other multimodal datasets with little effort and can be easily adapted for further research, especially, but not limited to, the field of medical AI., Comment: 9 pages, 6 figures, conference: AIRoV -- The First Austrian Symposium on AI, Robotics, and Vision 25.-27.3.2024, Innsbruck

The stability of toroidal magnetic fields within the interior of stars remains a significant unresolved issue in contemporary astrophysics. In this study, we combine a nonlocal linear analysis with 3D direct numerical simulations to examine the instability of toroidal fields within nonrotating, stably stratified stellar interiors in spherical geometry. Both analyses start from an equilibrium solution derived from balancing the Lorentz force with an anisotropic component of the fluid pressure, which is unstable to the (nonaxisymmetric) Tayler instability, and account for the combined effects of gravity and thermal diffusion. The numerical simulations incorporate finite magnetic resistivity and fluid viscosity while reaching a regime of highly stable stratification that has never been explored before. The linear analysis, which is global in the radial direction, shows that gravity significantly reduces the growth rate of the instability and uncovers the importance of unstable modes with low radial wavenumbers operating at low latitudes. The simulations trace the entire evolution of the instability from the linear to the nonlinear phase and strongly corroborate the findings of the linear analysis. Our results reveal that in highly stratified stellar interiors, the newly configured magnetic fields remain unstable only on the thermal diffusion timescale. Combining the linear analysis results with stellar evolution models of low-mass stars, we find that the limiting toroidal field strength for Tayler instability in red giant cores decreases with the stellar evolution. The predicted field strengths align with the ones expected from recent asteroseismic observations, suggesting that the observed fields may be remnants of a Tayler instability during the transition from the main sequence to the giant phase., Comment: 13 pages, 13 figures, to be submitted to A&A

The first "seeds" of supermassive black holes (BHs) continue to be an outstanding puzzle, and it is currently unclear whether the imprints of early seed formation survive today. Here we examine the signatures of seeding in the local Universe using five $[18~\mathrm{Mpc}]^3$ BRAHMA simulation boxes run to $z=0$. They initialize $1.5\times10^5~M_{\odot}$ BHs using different seeding models. The first four boxes initialize BHs as heavy seeds using criteria that depend on dense & metal-poor gas, Lyman-Werner radiation, gas spin, and environmental richness. The fifth box initializes BHs as descendants of lower mass seeds ($\sim10^3~M_{\odot}$) using a new stochastic seed model built in our previous work. We find that strong signatures of seeding survive in $\sim10^5-10^6~M_{\odot}$ local BHs hosted in $M_*\lesssim10^{9}~M_{\odot}$ dwarf galaxies. The signatures survive due to two reasons: 1) there is a substantial population of local $\sim10^5~M_{\odot}$ BHs that are ungrown relics of early seeds from $z\sim5-10$; 2) BH growth up to $\sim10^6~M_{\odot}$ is dominated by mergers all the way down to $z\sim0$. As the contribution from gas accretion increases, the signatures of seeding start to weaken in more massive $\gtrsim10^6~M_{\odot}$ BHs, and they eventually disappear for $\gtrsim10^7~M_{\odot}$ BHs. This is in contrast to high-z ($z\gtrsim5$) BH populations wherein the BH growth is fully merger dominated, which causes the seeding signatures to persist at least up to $\sim10^8~M_{\odot}$. The different seed models predict abundances of local $\sim10^6~M_{\odot}$ BHs ranging from $\sim0.01-0.05~\mathrm{Mpc}^{-3}$ with occupation fractions of $\sim20-100\%$ in $M_*\sim10^{9}~M_{\odot}$ galaxies. Our results highlight the potential for local $\sim10^5-10^6~M_{\odot}$ BH populations in dwarf galaxies to serve as a promising probe for BH seeding models., Comment: 20 pages, 9 Figures