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The appropriate selection of recurrence thresholds is a key problem in applications of recurrence quantification analysis and related methods across disciplines. Here, we discuss the distribution of pairwise distances between state vectors in the studied system's state space reconstructed by means of time-delay embedding as the key characteristic that should guide the corresponding choice for obtaining an adequate resolution of a recurrence plot. Specifically, we present an empirical description of the distance distribution, focusing on characteristic changes of its shape with increasing embedding dimension. Our results suggest that selecting the recurrence threshold according to a fixed percentile of this distribution reduces the dependence of recurrence characteristics on the embedding dimension in comparison with other commonly used threshold selection methods. Numerical investigations on some paradigmatic model systems with time-dependent parameters support these empirical findings., Comment: arXiv admin note: substantial text overlap with arXiv:1802.01605

Theoretically describing feature learning in neural networks is crucial for understanding their expressive power and inductive biases, motivating various approaches. Some approaches describe network behavior after training through a simple change in kernel scale from initialization, resulting in a generalization power comparable to a Gaussian process. Conversely, in other approaches training results in the adaptation of the kernel to the data, involving complex directional changes to the kernel. While these approaches capture different facets of network behavior, their relationship and respective strengths across scaling regimes remains an open question. This work presents a theoretical framework of multi-scale adaptive feature learning bridging these approaches. Using methods from statistical mechanics, we derive analytical expressions for network output statistics which are valid across scaling regimes and in the continuum between them. A systematic expansion of the network's probability distribution reveals that mean-field scaling requires only a saddle-point approximation, while standard scaling necessitates additional correction terms. Remarkably, we find across regimes that kernel adaptation can be reduced to an effective kernel rescaling when predicting the mean network output of a linear network. However, even in this case, the multi-scale adaptive approach captures directional feature learning effects, providing richer insights than what could be recovered from a rescaling of the kernel alone., Comment: 24 pages, 6 figures

The rapid growth of generative AI technologies has heightened the importance of effectively distinguishing between human and AI-generated content, as well as classifying outputs from diverse generative models. This paper presents a scalable framework that integrates perceptual hashing, similarity measurement, and pseudo-labeling to address these challenges. Our method enables the incorporation of new generative models without retraining, ensuring adaptability and robustness in dynamic scenarios. Comprehensive evaluations on the Defactify4 dataset demonstrate competitive performance in text and image classification tasks, achieving high accuracy across both distinguishing human and AI-generated content and classifying among generative methods. These results highlight the framework's potential for real-world applications as generative AI continues to evolve. Source codes are publicly available at https://github.com/ffyyytt/defactify4., Comment: Defactify4 @ AAAI 2025

The destruction of metallicity due to the mutual Coulomb interaction of quasiparticles gives rise to fascinating phenomena of solid state physics such as the Mott metal-insulator transition and the pseudogap. A key observable characterizing their occurrences is the single-particle spectral function, determined by the fermionic self-energy. In this paper we investigate in detail how real space fluctuations are responsible for a self-energy that drives the Mott-Hubbard metal-insulator transition. To this aim we first introduce a real space fluctuation diagnostics approach to the Hedin equation, which connects the fermion-boson coupling vertex $\lambda$ to the self-energy $\Sigma$. Second, by using cellular dynamical mean-field theory calculations for $\lambda$ we identify the leading physical processes responsible for the destruction of metallicity across the transition., Comment: 21 pages, 17 figures

This paper presents the first-rank solution for the Multi-Modal Action Recognition Challenge, part of the Multi-Modal Visual Pattern Recognition Workshop at the \acl{ICPR} 2024. The competition aimed to recognize human actions using a diverse dataset of 20 action classes, collected from multi-modal sources. The proposed approach is built upon the \acl{TSM}, a technique aimed at efficiently capturing temporal dynamics in video data, incorporating multiple data input types. Our strategy included transfer learning to leverage pre-trained models, followed by meticulous fine-tuning on the challenge's specific dataset to optimize performance for the 20 action classes. We carefully selected a backbone network to balance computational efficiency and recognition accuracy and further refined the model using an ensemble technique that integrates outputs from different modalities. This ensemble approach proved crucial in boosting the overall performance. Our solution achieved a perfect top-1 accuracy on the test set, demonstrating the effectiveness of the proposed approach in recognizing human actions across 20 classes. Our code is available online https://github.com/ffyyytt/TSM-MMVPR., Comment: ICPR2024, MMVPR, 12 pages

This paper presents a novel approach for hazard analysis in dashcam footage, addressing the detection of driver reactions to hazards, the identification of hazardous objects, and the generation of descriptive captions. We first introduce a method for detecting driver reactions through speed and sound anomaly detection, leveraging unsupervised learning techniques. For hazard detection, we employ a set of heuristic rules as weak classifiers, which are combined using an ensemble method. This ensemble approach is further refined with differential privacy to mitigate overconfidence, ensuring robustness despite the lack of labeled data. Lastly, we use state-of-the-art vision-language models for hazard captioning, generating descriptive labels for the detected hazards. Our method achieved the highest scores in the Challenge on Out-of-Label in Autonomous Driving, demonstrating its effectiveness across all three tasks. Source codes are publicly available at https://github.com/ffyyytt/COOOL_2025., Comment: WACV 2025, 5 pages

Learning from tabular data is of paramount importance, as it complements the conventional analysis of image and video data by providing a rich source of structured information that is often critical for comprehensive understanding and decision-making processes. We present Multi-task Contrastive Masked Tabular Modeling (MT-CMTM), a novel method aiming to enhance tabular models by leveraging the correlation between tabular data and corresponding images. MT-CMTM employs a dual strategy combining contrastive learning with masked tabular modeling, optimizing the synergy between these data modalities. Central to our approach is a 1D Convolutional Neural Network with residual connections and an attention mechanism (1D-ResNet-CBAM), designed to efficiently process tabular data without relying on images. This enables MT-CMTM to handle purely tabular data for downstream tasks, eliminating the need for potentially costly image acquisition and processing. We evaluated MT-CMTM on the DVM car dataset, which is uniquely suited for this particular scenario, and the newly developed HIPMP dataset, which connects membrane fabrication parameters with image data. Our MT-CMTM model outperforms the proposed tabular 1D-ResNet-CBAM, which is trained from scratch, achieving a relative 1.48% improvement in relative MSE on HIPMP and a 2.38% increase in absolute accuracy on DVM. These results demonstrate MT-CMTM's robustness and its potential to advance the field of multi-modal learning.

The spectrum of cosmic-ray electrons depends sensitively on the history and spatial distribution of nearby sources. Given our limited observational handle on cosmic-ray sources, any model remains necessarily probabilistic. Previously, predictions were performed in a Monte Carlo fashion, summing the contributions from individual, simulated sources to generate samples from the statistical ensemble of possible electron spectra. Such simulations need to be re-run if the cosmic-ray transport parameters (e.g. diffusion coefficient, maximum energy) are changed, rendering any parameter study computationally expensive. In addition, a proper statistical analysis of observations and comparison with such probabilistic models requires the joint probability distribution of the full spectrum instead of only samples. Note that parametrising this joint distribution is rendered difficult by the non-Gaussian statistics of the cosmic-ray fluxes. Here, we employ machine learning to compute the joint probability distribution of cosmic-ray electron fluxes. Specifically, we employ masked autoregressive density estimation (MADE) for a representation of the high-dimensional joint probability distribution. In a first step, we train the network on a Monte Carlo simulation for a fixed set of transport parameters, thus significantly accelerating the generation of samples. In a second step, we extend this setup to SECRET (Stochasticity Emulator for Cosmic Ray Electrons), allowing to reliably interpolate over the space of transport parameters. We make the MADE and SECRET codes available at https://git.rwth-aachen.de/pmertsch/secret ., Comment: 25 pages, 13 figures

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

Although the search for quantum many-body phenomena in magnetic materials has a strong focus on highly frustrated systems, even unfrustrated quantum magnets show a multitude of unconventional phenomena in their spin excitation spectra. YbBr$_3$ is an excellent realization of the $S = 1/2$ antiferromagnetic Heisenberg model on the honeycomb lattice, and we have performed detailed spectroscopic experiments with both unpolarized and polarized neutrons at all applied magnetic fields up to saturation. We observe extensive excitation continua, which cause strong renormalization and the decay of single magnons at higher fields, while coherent features include field-induced ``shadows'' of the single magnons and the spectacular emergence of a roton-like excitation. To guide and interpret our experiments, we performed systematic calculations by the method of cylinder matrix-product states that provide quantitative agreement with the neutron scattering data and a qualitative benchmark for the spectral signatures of strong quantum fluctuations even in the absence of magnetic frustration., Comment: 7+8 pages, 4+7 figures

We propose and study a novel efficient algorithm for clustering and classification tasks based on the famous MBO scheme. On the one hand, inspired by Jacobs et al. [J. Comp. Phys. 2018], we introduce constraints on the size of clusters leading to a linear integer problem. We prove that the solution to this problem is induced by a novel order statistic. This viewpoint allows us to develop exact and highly efficient algorithms to solve such constrained integer problems. On the other hand, we prove an estimate of the computational complexity of our scheme, which is better than any available provable bounds for the state of the art. This rigorous analysis is based on a variational viewpoint that connects this scheme to volume-preserving mean curvature flow in the big data and small time-step limit., Comment: 61 pages, 9 figures

Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets dataset, consisting of approximately 180M high $p_T$ jets derived from CMS 2016 Open Data. We show how pre-training the OmniJet-$\alpha$ foundation model on AspenOpenJets improves performance on generative tasks with significant domain shift: generating boosted top and QCD jets from the simulated JetClass dataset. In addition to demonstrating the power of pre-training of a jet-based foundation model on actual proton-proton collision data, we provide the ML-ready derived AspenOpenJets dataset for further public use., Comment: 11 pages, 4 figures, the AspenOpenJets dataset can be found at http://doi.org/10.25592/uhhfdm.16505

This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the unique challenges of dental CBCT imaging. Our method achieved a mean Dice coefficient of 0.9253 and HD95 of 18.472 on the test set, securing a mean rank of 4.6 and with it the first place in the ToothFairy2 challenge. The source code is publicly available, encouraging further research and development in the field., Comment: Fabian Isensee and Yannick Kirchhoff contributed equally

The Medium-Resolution Spectrometer on the Mid-Infrared Instrument on JWST obtained spectra of three carbon stars in the Large Magellanic Cloud. Two of the spectra differ significantly from spectra obtained ~16-19 years earlier with the Infrared Spectrograph on the Spitzer Space Telescope. The one semi-regular variable among the three has changed little. The long-period Mira variable in the sample shows changes consistent with its pulsation cycle. The short-period Mira shows dramatic changes in the strength of its molecular absorption bands, with some bands growing weaker and some stronger. Whether these variations result from its pulsation cycle or its evolution is not clear., Comment: 7 pages, 3 figures

Shock-generated transients, such as hot flow anomalies (HFAs), upstream of planetary bow shocks, play a critical role in electron acceleration. Using multi-mission data from NASA's Magnetospheric Multiscale (MMS) and ESA's Cluster missions, we demonstrate the transmission of HFAs through Earth's quasi-parallel bow shock, associated with acceleration of electrons up to relativistic energies. Energetic electrons, initially accelerated upstream, are shown to remain broadly confined within the transmitted transient structures downstream, where betatron acceleration further boosts their energy due to elevated compression levels. Additionally, high-speed jets form at the compressive edges of HFAs, exhibiting a significant increase in dynamic pressure and potentially contributing to driving further localized compression. Our findings emphasize the efficiency of quasi-parallel shocks in driving particle acceleration far beyond the immediate shock transition region, expanding the acceleration region to a larger spatial domain. Finally, this study underscores the importance of multi-scale observational approach in understanding the convoluted processes behind collisionless shock physics and their broader implications.

Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence -- are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics., Comment: 4 pages, 3 figures, To be presented at Machine Learning for Jets Conference (ML4JETS), 2024; v2: Numerical results updated and improved

Although spin and charge stripes in high-Tc cuprates have been extensively studied, the exact range of carrier concentration over which they form a static order remains uncertain, complicating efforts to understand their significance. In La2-xSrxCuO4 (LSCO) and in zero external magnetic field, static spin stripes are confined to a doping range well below p*, the pseudogap boundary at zero temperature. However, when high fields suppress the competing effect of superconductivity, spin stripe order is found to extend up to p*. Here, we investigated La1.8-xEu0.2SrxCuO4 (Eu-LSCO) using 139La nuclear magnetic resonance and observe field-dependent spin fluctuations suggesting a similar competition between superconductivity and spin order as in LSCO. Nevertheless, we find that static spin stripes are present practically up to p* irrespective of field strength: the stronger stripe order in Eu-LSCO prevents superconductivity from enforcing a non-magnetic ground state, except very close to p*. Thus, spin-stripe order is consistently bounded by p* in both LSCO and Eu-LSCO, despite their differing balances between stripe order and superconductivity. This indicates that the canonical stripe order, where spins and charges are intertwined in a static pattern, is fundamentally tied to the pseudogap phase, though the exact nature of this connection has yet to be elucidated. Any stripe order beyond the pseudogap endpoint must then be of a different nature: either spin and charge orders remain intertwined, but both fluctuating, or only spin order fluctuates while charge order remains static. The presence of spin-stripe order up to p*, the pervasive, slow, and field-dependent spin-stripe fluctuations, as well as the electronic inhomogeneity documented in this work, must all be carefully considered in discussions of Fermi surface transformations, quantum criticality, and strange metal behavior.

Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the background expectation in a simple cut and count setup. In contrast to a traditional bump hunt, no fit to the invariant mass distribution is needed, thereby avoiding the potential problem of background sculpting. Furthermore, direct background estimation allows working with large background rejection rates, where resonant anomaly detection methods typically show their greatest improvement in significance., Comment: 26 pages, 9 figures, 2 tables

We use the Hubble Space Telescope to analyse the extended [O III] 5007A emission in seven bright radio-quiet type 1 quasars (QSO1s), focusing on the morphology and physical conditions of their extended Narrow-Line Regions (NLRs). We find NLRs extending 3-9 kpc, with four quasars showing roughly symmetrical structures (b/a=1.2-1.5) and three displaying asymmetric NLRs (b/a=2.4-5.6). When included with type 1 and type 2 AGNs from previous studies, the sizes of the extended [O III] regions scale with luminosity as $R[O III] \sim L[O III]^{0.5}$, consistent with photoionisation. However, when analysed separately, type 1s exhibit a steeper slope ($\gamma=0.57\pm0.05$) compared to type 2 AGNs ($\gamma=0.48\pm0.02$). We use photoionisation modeling to estimate the maximum NLRs sizes, assuming a minimum ionisation parameter of $\log(U) = -3$, an ionising luminosity based on the $L[O III]$-derived bolometric luminosity, and a minimum gas number density $n_H \sim 100\,\text{cm}^{-3}$, assuming that molecular clouds provide a reservoir for the ionised gas. The derived sizes agree well with direct measurements for a sample of type 2 quasars, but are underestimated for the current sample of QSO1s. A better agreement is obtained for the QSO1s using bolometric luminosities derived from the 5100A continuum luminosity. Radial mass profiles for the QSO1s show significant extended mass in all cases, but with less [O III]-emitting gas near the central AGN compared to QSO2s. This may suggest that the QSO1s are in a later evolutionary stage than QSO2s, further past the blow-out stage., Comment: Accepted for publication on MNRAS

Despite substantial progress in recent years, probabilistic solvers with adaptive step sizes can still not solve memory-demanding differential equations -- unless we care only about a single point in time (which is far too restrictive; we want the whole time series). Counterintuitively, the culprit is the adaptivity itself: Its unpredictable memory demands easily exceed our machine's capabilities, making our simulations fail unexpectedly and without warning. Still, dropping adaptivity would abandon years of progress, which can't be the answer. In this work, we solve this conundrum. We develop an adaptive probabilistic solver with fixed memory demands building on recent developments in robust state estimation. Switching to our method (i) eliminates memory issues for long time series, (ii) accelerates simulations by orders of magnitude through unlocking just-in-time compilation, and (iii) makes adaptive probabilistic solvers compatible with scientific computing in JAX.

Hydrogen, while a promising sustainable energy carrier, presents challenges such as the embrittlement of materials due to its ability to penetrate and weaken their crystal structures. Here we investigate Fe4N nitride layers, formed on iron through a cost-effective gas nitriding process, as an effective hydrogen permeation barrier. A combination of screening using advanced characterization, density functional theory calculations, and hydrogen permeation analysis reveals that a nitride layer reduces hydrogen diffusion by a factor of 20 at room temperature. This reduction is achieved by creating energetically unfavorable states due to stronger H-binding at the surface and high energy barriers for diffusion. The findings demonstrate the potential of Fe4N as a cost-efficient and easy-to-process solution to protecting metallic materials exposed to hydrogen, with great advantages for large-scale applications.

We present spectra of 12 of the reddest, and hence dustiest, S stars in the Milky Way, observed with the FORCAST grisms on SOFIA. S stars are asymptotic giant branch (AGB) stars with C/O$\sim$1, so their molecular and dust chemistries are dominated by neither O nor C, often leading to atypical spectral features from their molecules and dust grains. All of the stars in our sample have strong dust emission features at 10--11 $\mu$m, but the shape of the feature in most of the stars differs from the shapes commonly observed in either oxygen-rich or carbon-rich AGB stars. Two stars also show the 13 $\mu$m feature associated with crystalline alumina. Two have a water absorption band at $\sim$6.5--7.5 $\mu$m, and a third has a tentative detection, but only one of these three has the more common SiO absorption band at 7.5 $\mu$m. Three others show a red 6.3 $\mu$m emission feature from complex hydrocarbons consistent with ``Class C'' objects, and in a fourth it appears at 6.37 $\mu$m, redder than even the standard Class C hydrocarbon feature. Class C spectra typically indicate complex hydrocarbons which have been less processed by UV radiation, resulting in more aliphatic bonds relative to aromatic bonds. None of the S stars shows a strong 11.3 $\mu$m hydrocarbon feature, which is also consistent with the presence of aliphatic hydrocarbons.

Practical implementations of Gaussian smoothing algorithms have received a great deal of attention in the last 60 years. However, almost all work focuses on estimating complete time series (''fixed-interval smoothing'', $\mathcal{O}(K)$ memory) through variations of the Rauch--Tung--Striebel smoother, rarely on estimating the initial states (''fixed-point smoothing'', $\mathcal{O}(1)$ memory). Since fixed-point smoothing is a crucial component of algorithms for dynamical systems with unknown initial conditions, we close this gap by introducing a new formulation of a Gaussian fixed-point smoother. In contrast to prior approaches, our perspective admits a numerically robust Cholesky-based form (without downdates) and avoids state augmentation, which would needlessly inflate the state-space model and reduce the numerical practicality of any fixed-point smoother code. The experiments demonstrate how a JAX implementation of our algorithm matches the runtime of the fastest methods and the robustness of the most robust techniques while existing implementations must always sacrifice one for the other.

During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle, Comment: 14 pages, 3 figures

The generation of single photons using solid-state quantum emitters is pivotal for advancing photonic quantum technologies, particularly in quantum communication. As the field continuously advances towards practical use cases and beyond shielded laboratory environments, specific demands are placed on the robustness of quantum light sources during operation. In this context, the robustness of the quantum light generation process against intrinsic and extrinsic effects is a major challenge. Here, we present a robust scheme for the coherent generation of indistinguishable single-photon states with very low photon number coherence (PNC) using a three-level system in a semiconductor quantum dot. Our novel approach combines the advantages of adiabatic rapid passage (ARP) and stimulated two-photon excitation (sTPE). We demonstrate robust quantum light generation while maintaining the prime quantum-optical quality of the emitted light state. Moreover, we highlight the immediate advantages for the implementation of various quantum cryptographic protocols., Comment: 12 pages, 6 figures

We investigate the star formation-AGN connection in the Seyfert 1 NGC 7469 using James Webb Space Telescope (JWST) mid-infrared spectroscopic integral field unit (IFU) data. We use the IFU data to generate maps of different emission lines present in the spectrum, such as the star formation (SF) tracer [Ne II] 12.81{\mu}m, and the AGN tracer [Ne V] 14.32{\mu}m. We can separate the AGN- and SF-dominated regions using spatially resolved mid-infrared diagnostic diagrams, and further investigate the ionization sources powering each region by constructing photoionization models. We find that the previously detected eastern wind populates an intermediary region of the diagrams, between our star-forming and AGN points. Although it is possible that the star-forming ring may inherently not be uniform, this wind also coincides with a reduction in the [Ne II] emission in the ring, which suggests that the ionization cone intersects the ring in this direction., Comment: 14 pages, 5 figures, accepted for publication to ApJ

Automatic differentiation is everywhere, but there exists only minimal documentation of how it works in complex arithmetic beyond stating "derivatives in $\mathbb{C}^d$" $\cong$ "derivatives in $\mathbb{R}^{2d}$" and, at best, shallow references to Wirtinger calculus. Unfortunately, the equivalence $\mathbb{C}^d \cong \mathbb{R}^{2d}$ becomes insufficient as soon as we need to derive custom gradient rules, e.g., to avoid differentiating "through" expensive linear algebra functions or differential equation simulators. To combat such a lack of documentation, this article surveys forward- and reverse-mode automatic differentiation with complex numbers, covering topics such as Wirtinger derivatives, a modified chain rule, and different gradient conventions while explicitly avoiding holomorphicity and the Cauchy--Riemann equations (which would be far too restrictive). To be precise, we will derive, explain, and implement a complex version of Jacobian-vector and vector-Jacobian products almost entirely with linear algebra without relying on complex analysis or differential geometry. This tutorial is a call to action, for users and developers alike, to take complex values seriously when implementing custom gradient propagation rules -- the manuscript explains how.

The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g., for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research., Comment: 34 pages, 14 figures, 1 table

In the past, quasi coherent modes were reported for nearly all tokamaks. The general definition describes modes as quasi coherent when the magnitude squared coherence is in the range of \SIrange{0.3}{0.6}{}. Quasi coherent modes are observed in the plasma core as well as in the plasma edge and can have quite different physical origins. The one in the core are observed in plasmas with low collisionality, where the electron temperature exceeds the ion temperature in the plasma core. This is the case for electron cyclotron heating in general. The origin of these modes are electrons trapped within a magnetic mirror, as reported in the past from various fusion devices. The so-called trapped-electron modes (TEMs) belong to drift wave instabilities and can be destabilized by electron-temperature gradients in the plasma core. From the diagnostic point of view, quasi coherent modes appear as fluctuations in electron density and temperature. Therefore, the microwave reflectometer is very well suited to monitor these modes. This paper describes experiments, conducted at the Wendelstein 7-X stellarator (W7-X), which aim at detecting quasi coherent modes at low wave numbers. A Poloidal Correlation Reflectometer (PCR) installed at W7-X, is able to measure low wave numbers ($k_\perp\le 3.5$ cm$^{-1}$). For different magnetic configurations and plasma parameters, broad quasi-coherent structures are observed in the coherence spectra. From the analysis of the rotation and the poloidal structure, these quasi coherent (QC) modes show the properties of electron-temperature-gradient driven TEMs. A linear relation between the mode velocity and the rotation frequency is found. The relation is uniform and confirms the nature of QC-mode observation as TEM in tokamaks, too., Comment: Some revisions asked by referees included

A comparative vacuum ultraviolet spectroscopy study conducted at ISOLDE-CERN of the radiative decay of the $^{229m}$Th nuclear clock isomer embedded in different host materials is reported. The ratio of the number of radiative decay photons and the number of $^{229m}$Th embedded are determined for single crystalline CaF$_2$, MgF$_2$, LiSrAlF$_6$, AlN, and amorphous SiO$_2$. For the latter two materials, no radiative decay signal was observed and an upper limit of the ratio is reported. The radiative decay wavelength was determined in LiSrAlF$_6$ and CaF$_2$, reducing its uncertainty by a factor of 2.5 relative to our previous measurement. This value is in agreement with the recently reported improved values from laser excitation.

Supersymmetry is an algebraic property of a class of quantum Hamiltonians, a guiding principle used in the development of nuclear theory and a concept that may underpin physics beyond the Standard Model. In condensed matter, any symmetry between bosonic and fermionic entities is usually strongly broken, as for the spin and charge sectors of the $t$-$J$ model describing the dynamics of fractionalized electrons in one-dimensional materials. While the triplet excitations of a quantum spin ladder in an applied magnetic field provide a close analogue of the $t$-$J$ chain, the supersymmetric nature of this system has not been explored. Here we perform neutron spectroscopy on the spin-ladder compounds (C$_5$D$_{12}$N)$_2$CuBr$_4$ and (C$_5$D$_{12}$N)$_2$CuCl$_4$ over a range of applied fields and temperatures, and apply matrix-product-state (MPS) methods to the ladder and equivalent chain models, to analyse the full, momentum-resolved dynamics of a single charge excitation in a bath of fractionalized spins. We find a dramatic difference in the effects of thermal fluctuations, where strong and coherent shifts of spectral weight at the zone edge contrast with a strict pole at the zone centre, whose persistence at all temperatures constitutes an observable consequence of supersymmetry in a quantum spin ladder.

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

The RD50-MPW4 is the latest HV-CMOS pixel sensor from the CERN-RD50-CMOS working group, designed to evaluate the HV-CMOS technology in terms of spatial resolution, radiation hardness and timing performance. Fabricated by LFoundry using a 150nm process, it features an improved architecture to mitigate crosstalk, which has been an issue with the predecessor RD50-MPW3, allowing more sensitive threshold settings and full matrix operation. Enhancements include separated power domains for peripheral and in-pixel digital readout, a new backside-biasing step, and an improved guard ring structure supporting biasing up to 500V, significantly boosting radiation hardness. Laboratory measurements and test beam results presented in this paper show significant improvements over its predecessor regarding noise behavior, spatial resolution, and efficiency., Comment: Preprint version of Proceedings of Pisa meeting 2024

Outflowing gas from supermassive black holes in the centers of active galaxies has been postulated as a major contributor to galactic evolution. To explore the interaction between narrow-line region (NLR) outflows and their host galaxies, we use Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) spectra and Wide Field Camera 3 (WFC3) images of 15 nearby (z < 0.02) active galactic nuclei (AGN) to determine the extents and geometries of their NLRs. We combine new HST WFC3 continuum and [O III] $\lambda$5007A images of 11 AGN with 4 archival AGN to match existing spectra from HST STIS. For the 6 AGN with suitable long-slit coverage of their NLRs, we use isophotal fitting of ground-based images, continuum-subtracted [O III] images, and the STIS spectra, to resolve, measure, and de-project the gas kinematics to the plane of the host galaxy disk and distinguish NLR outflows from galaxy rotation and/or kinematically disturbed gas. We find an average [O III] extent of $\sim$680pc with a correlation between gas extent and [O III] luminosity of R$_\mathrm{[O III]}$ $\propto$ L$_{\text{[O III]}}^{0.39}$. The measured extents depend strongly on the depth of the [O III] images, highlighting the importance of adopting uniform thresholds when analyzing scaling relationships. The outflows reach from 39-88% of the full NLR extents, and we find that all 6 of the AGN with STIS coverage of their entire NLRs show strong kinematic evidence for outflows, despite previous uncertainty for these AGN. This suggests that NLR outflows are ubiquitous in moderate luminosity AGN and that standard criteria for kinematic modeling are essential for identifying outflows., Comment: Accepted for publication in ApJ on July 9, 2024. The paper has 21 pages, 10 figures, and 6 tables. Version two includes minor corrections to match the journal publication. The calibrated data are available through MAST at: https://archive.stsci.edu/hlsp/nlr-agn

Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech ($F_1 = 0.89$), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1 = 0.69$). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises. The resources related to the study are available at https://claws-lab.github.io/violence-provoking-speech/., Comment: Accepted to ACL 2024 Main

We investigate the theoretical impedance equations for several near-field antenna positions. In the standard model one computes the currents at the antennas for given voltages using the impedance matrix of the antennas, which is only possible if the determinant of the impedance matrix is non-zero. We consider Hertzian group antennas, its relative corresponding impedance and two approximations (mid and far) of it. For the approximations we show that for many situations the determinant is zero. We find three antenna configurations for three antennas, i.e., on a line, on a right triangle, and an isosceles triangle, which result in a zero determinant of the impedance for the far-field approximation. This means that with existing methods, one cannot determine the behavior of this antenna system. For the better mid approximation, we find a configuration of 15 triangular-positioned antennas resulting in a singular impedance matrix. Furthermore, we investigate $n\times n$ grid placed antennas in the more accurate Hertzian impedance model and find that for $d \approx 0.65$ wavelengths of grid distance for n = 2, ..., 8 the absolute value of the determinant of the corresponding impedance matrix decreases by an order of magnitude with each increased grid size., Comment: 13 pages, 11 figures

We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 5th International Conference, DeLTA 2024 Proceedings, and will be available after the conference