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One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-tuning stages. However, fine-tuning a pre-trained debiased language model on a downstream task can reintroduce biases into the model. Additionally, existing debiasing methods for downstream tasks either (i) require labels of protected attributes (e.g., age, race, or political views) that are often not available or (ii) rely on indicators of bias, which restricts their applicability to gender debiasing since they rely on gender-specific words. To address this, we introduce a novel debiasing regularization technique based on the class-wise variance of embeddings. Crucially, our method does not require attribute labels and targets any attribute, thus addressing the shortcomings of existing debiasing methods. Our experiments on encoder language models and three datasets demonstrate that our method outperforms existing strong debiasing baselines that rely on target attribute labels while maintaining performance on the target task., Comment: Accepted to EMNLP 2024

Trapped ions are among the most advanced platforms for quantum simulation and computation. Their capabilities can be further augmented by making use of electronically highly excited Rydberg states, which enable the realization of long-ranged electric dipolar interactions. Most experimental and theoretical studies so far focus on the excitation of ionic Rydberg states in linear Paul traps, which generate confinement by a combination of static and oscillating electric fields. These two fields need to be carefully aligned to minimize so-called micromotion, caused by the time-dependent electric field. The purpose of this work is to systematically understand the qualitative impact of micromotion on the Rydberg excitation spectrum, when the symmetry axes of the two electric fields do not coincide. Considering this scenario is not only important in the case of possible field misalignment, but becomes inevitable for Rydberg excitations in 2D and 3D ion crystals. We develop a minimal model describing a single trapped Rydberg ion, which we solve numerically via Floquet theory and analytically using a perturbative approach. We calculate the excitation spectra and analyze in which parameter regimes addressable and energetically isolated Rydberg lines persist, which are an important requirement for conducting coherent manipulations., Comment: 23 pages, 6 figures

We present the database of potential targets for the Large Interferometer For Exoplanets (LIFE), a space-based mid-infrared nulling interferometer mission proposed for the Voyage 2050 science program of the European Space Agency (ESA). The database features stars, their planets and disks, main astrophysical parameters, and ancillary observations. It allows users to create target lists based on various criteria to predict, for instance, exoplanet detection yields for the LIFE mission. As such, it enables mission design trade-offs, provides context for the analysis of data obtained by LIFE, and flags critical missing data. Work on the database is in progress, but given its relevance to LIFE and other space missions, including the Habitable Worlds Observatory (HWO), we present its main features here. A preliminary version of the LIFE database is publicly available on the German Astrophysical Virtual Observatory (GAVO)., Comment: RNAAS published, 3 pages, 1 figure

We present the first application of quantics tensor trains (QTTs) and tensor cross interpolation (TCI) to the solution of a full set of self-consistent equations for multivariate functions, the so-called parquet equations. We show that the steps needed to evaluate the equations (Bethe-Salpeter equations, parquet equation and Schwinger-Dyson equation) can be decomposed into basic operations on the QTT-TCI (QTCI) compressed objects. The repeated application of these operations does not lead to the loss of accuracy beyond a specified tolerance and the iterative scheme converges even for numerically demanding parameters. As examples we take the Hubbard model in the atomic limit and the single impurity Anderson model, where the basic objects in parquet equations, the two-particle vertices, depend on three frequencies, but not on momenta. The results show that this approach is able to overcome major computational bottlenecks of standard numerical methods. The applied methods allow for an exponential increase of the number of grid points included in the calculations leading to an exponentially improving computational error for a linear increase in computational cost., Comment: 20 pages, 16 figures

The semantics of notation systems can naturally be meta-modelled as a network of transformations, starting with the syntactic elements of the notation and ending with the parameters of an execution. In this context, a digital encoding format for music notation can be seen as selecting a subset of the data nodes of this network for storage, leaving others to evaluation. For such a selection, semantic properties are defined which have impact on the practical costs of maintenance, migration, extension, etc., Comment: Held on the MEC conference 2024, Trenton, TX

There is mounting evidence that blazars are the sources of part of the very-high-energy astrophysical neutrino flux detected by IceCube. In particular, there have been several spatial and temporal coincidences of individual IceCube neutrino events with flaring blazars, the most prominent of them being IceCube-170922A, coincident with a multi-wavelength flare of TXS~0506+056. Motivated by this, we used the time-dependent lepto-hadronic code OneHaLe to model the spectral energy distributions and light curves of a sample of bright $\gamma$-ray flares of blazars detected by Fermi-LAT, for which Kreter et al. (2020) provided calorimetric estimates of the expected neutrino detection rates. Flares were modelled with temporal changes of the proton injection spectra. Our analysis shows that the calorimetric approach overestimates the increase in neutrino production by a factor of typically $\sim 10$ if the $\gamma$-ray emission is dominated by proton-synchrotron radiation., Comment: 11 pages; Accepted for publication in ApJ

Understanding the physics of the integrable spin-1/2 XXZ chain has witnessed substantial progress, due to the development and application of sophisticated analytical and numerical techniques. In particular, infinite-temperature magnetization transport has turned out to range from ballistic, over superdiffusive, to diffusive behavior in different parameter regimes of the anisotropy. Since integrability is rather the exception than the rule, a crucial question is the change of transport under integrability-breaking perturbations. This question includes the stability of superdiffusion at the isotropic point and the change of diffusion constants in the easy-axis regime. In our work, we study this change of diffusion constants by a variety of methods and cover both, linear response theory in the closed system and the Lindblad equation in the open system, where we throughout focus on periodic boundary conditions. In the closed system, we compare results from the recursion method to calculations for finite systems and find evidence for a continuous change of diffusion constants over the full range of perturbation strengths. In the open system weakly coupled to baths, we find diffusion constants in quantitative agreement with the ones in the closed system in a range of nonweak perturbations, but disagreement in the limit of weak perturbations. Using a simple model in this limit, we point out the possibility of a diverging diffusion constant in such an open system., Comment: 6 pages, 4 figures (+ 5 pages, 7 figures)

This work investigates the political biases and personality traits of ChatGPT, specifically comparing GPT-3.5 to GPT-4. In addition, the ability of the models to emulate political viewpoints (e.g., liberal or conservative positions) is analyzed. The Political Compass Test and the Big Five Personality Test were employed 100 times for each scenario, providing statistically significant results and an insight into the results correlations. The responses were analyzed by computing averages, standard deviations, and performing significance tests to investigate differences between GPT-3.5 and GPT-4. Correlations were found for traits that have been shown to be interdependent in human studies. Both models showed a progressive and libertarian political bias, with GPT-4's biases being slightly, but negligibly, less pronounced. Specifically, on the Political Compass, GPT-3.5 scored -6.59 on the economic axis and -6.07 on the social axis, whereas GPT-4 scored -5.40 and -4.73. In contrast to GPT-3.5, GPT-4 showed a remarkable capacity to emulate assigned political viewpoints, accurately reflecting the assigned quadrant (libertarian-left, libertarian-right, authoritarian-left, authoritarian-right) in all four tested instances. On the Big Five Personality Test, GPT-3.5 showed highly pronounced Openness and Agreeableness traits (O: 85.9%, A: 84.6%). Such pronounced traits correlate with libertarian views in human studies. While GPT-4 overall exhibited less pronounced Big Five personality traits, it did show a notably higher Neuroticism score. Assigned political orientations influenced Openness, Agreeableness, and Conscientiousness, again reflecting interdependencies observed in human studies. Finally, we observed that test sequencing affected ChatGPT's responses and the observed correlations, indicating a form of contextual memory.

In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapping users' protected attributes (e.g., gender or ethnicity) from these user embeddings even without explicit access to them, resulting in models that may treat specific demographic user groups unfairly and raise privacy issues. While prior work has approached the removal of a single protected attribute of a user at a time, multiple attributes might come into play in real-world scenarios. In the work at hand, we present AdvXMultVAE which aims to unlearn multiple protected attributes (exemplified by gender and age) simultaneously to improve fairness across demographic user groups. For this purpose, we couple a variational autoencoder (VAE) architecture with adversarial training (AdvMultVAE) to support simultaneous removal of the users' protected attributes with continuous and/or categorical values. Our experiments on two datasets, LFM-2b-100k and Ml-1m, from the music and movie domains, respectively, show that our approach can yield better results than its singular removal counterparts (based on AdvMultVAE) in effectively mitigating demographic biases whilst improving the anonymity of latent embeddings.

As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common in other safety-critical industries such as aviation and nuclear power. In this paper, we explain why they may also be a useful tool in frontier AI governance, both in industry self-regulation and government regulation. We then discuss the practicalities of safety cases, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions., Comment: 25 pages, 6 figures, 5 tables

We present the implementation and performance analysis of a portable, rack-mounted standalone warm vapor quantum memory system, that also includes the laser package, control electronics and data processing hardware. The optical memory is based on long-lived hyperfine ground states of Cesium which are connected to an excited state via the $D_1$ line at 895 nm in a $\Lambda$-configuration. The memory is operated with weak coherent pulses containing on average $<1$ photons per pulse. The long-term stability of the memory efficiency and storage fidelity is demonstrated at the single-photon level together with operation in a non-laboratory environment., Comment: 8 pages, 5 figures. Comments are welcome

When comparing multiple groups in clinical trials, we are not only interested in whether there is a difference between any groups but rather the location. Such research questions lead to testing multiple individual hypotheses. To control the familywise error rate (FWER), we must apply some corrections or introduce tests that control the FWER by design. In the case of time-to-event data, a Bonferroni-corrected log-rank test is commonly used. This approach has two significant drawbacks: (i) it loses power when the proportional hazards assumption is violated [1] and (ii) the correction generally leads to a lower power, especially when the test statistics are not independent [2]. We propose two new tests based on combined weighted log-rank tests. One as a simple multiple contrast test of weighted log-rank tests and one as an extension of the so-called CASANOVA test [3]. The latter was introduced for factorial designs. We propose a new multiple contrast test based on the CASANOVA approach. Our test promises to be more powerful under crossing hazards and eliminates the need for additional p-value correction. We assess the performance of our tests through extensive Monte Carlo simulation studies covering both proportional and non-proportional hazard scenarios. Finally, we apply the new and reference methods to a real-world data example. The new approaches control the FWER and show reasonable power in all scenarios. They outperform the adjusted approaches in some non-proportional settings in terms of power.

We propose a novel and efficient scheme to engineer arbitrary (local) many-body Hamiltonians by interleaving the free evolution of a fixed system Hamiltonian with layers of single-qubit Pauli or Clifford gates. These sequences are constructed by solving a linear program (LP) which minimizes the total evolution time. The target Hamiltonians that can be engineered by our method are only limited by the locality of the Pauli terms in the system Hamiltonian. We then construct an efficient relaxation of this LP with a classical runtime that depends on the number of Pauli terms in the Pauli decomposition of the system Hamiltonian and is thus a low-degree polynomial in the number of qubits in practice. With our method, it is also possible to engineer Hamiltonians if only partial knowledge of the system Hamiltonian is available, which can be used to cancel unknown unwanted Pauli terms. We show the classical efficiency of our method by engineering an arbitrary two-body Hamiltonian on a 2D square lattice with $225$ qubits in only $60$ seconds. We also provide numerical simulations of our method modelling an ion trap device with an Ising Hamiltonian and engineering a general Heisenberg Hamiltonian. Moreover, we address dominant error sources in practical applications., Comment: 20 pages, 7 figures

Recent years have witnessed a steady progress towards blending 2D quantum materials into technology, with future applications often rooted in the electronic structure. Since crossings and inversions of electronic bands with different orbital characters determine intrinsic quantum transport properties, knowledge of the orbital character is essential. Here, we benchmark angle-resolved photoelectron emission spectroscopy (ARPES) as a tool to experimentally derive orbital characters. For this purpose we study the valence electronic structure of two technologically relevant quantum materials, graphene and WSe$_2$, and focus on circular dichroism that is believed to provide sensitivity to the orbital angular momentum. We analyze the contributions related to angular atomic photoionization profiles, interatomic interference, and multiple scattering. Regimes in which initial-state properties could be disentangled from the ARPES maps are critically discussed and the potential of using circular-dichroic ARPES as a tool to investigate the spin polarization of initial bands is explored. For the purpose of generalization, results from two additional materials, GdMn$_6$Sn$_6$ and PtTe$_2$ are presented in addition. This research demonstrates rich complexity of the underlying physics of circular-dichroic ARPES, providing new insights that will shape the interpretation of both past and future circular-dichroic ARPES studies., Comment: 12 pages, 7 figures

Magnetic fields are considered to be key components of massive stars, with a far-reaching impact on their evolution and ultimate fate. A magnetic mechanism was suggested for the collimated explosion of massive stars, relevant for long-duration gamma-ray bursts, X-ray flashes, and asymmetric core collapse supernovae. However, the origin of the observed stable, globally organized magnetic fields in massive stars is still a matter of debate: it has been argued that they can be fossil, dynamo generated, or generated by strong binary interactions or merging events. Taking into account that multiplicity is a fundamental characteristic of massive stars, observational evidence is accumulating that the magnetism originates through interaction between the system components, both during the initial mass transfer or when the stellar cores merge., Comment: 6 pages, 4 figures, to be published in the International Astronomical Union Proceedings Series for the IAU GA 2024 (Astronomy in Focus, Focus Meeting 8: Advances and Challenges in Understanding the Solar and Stellar Dynamos)

Given a graph $G$, we study the $2$-edge-connected subgraph polytope $\mathrm{TECSP}(G)$, which is given by the convex hull of the incidence vectors of all $2$-edge-connected subgraphs of $G$. We describe the lattice points of this polytope by linear inequalities which provides an ILP-algorithm for finding a $2$-edge-connected subgraph of maximum weight. Furthermore, we characterize when these inequalities define facets of $\mathrm{TECSP}(G)$. We also consider further types of supporting hyperplanes of $\mathrm{TECSP}(G)$ and study when they are facet-defining. Finally, we investigate the efficiency of our considered inequalities practically on some classes of graphs.

Leveraging Large Language Models (LLM) like GPT4 in the auto generation of code represents a significant advancement, yet it is not without its challenges. The ambiguity inherent in natural language descriptions of software poses substantial obstacles to generating deployable, structured artifacts. This research champions Model Driven Development (MDD) as a viable strategy to overcome these challenges, proposing an Agile Model Driven Development (AMDD) approach that employs GPT4 as a code generator. This approach enhances the flexibility and scalability of the code auto generation process and offers agility that allows seamless adaptation to changes in models or deployment environments. We illustrate this by modeling a multi agent Unmanned Vehicle Fleet (UVF) system using the Unified Modeling Language (UML), significantly reducing model ambiguity by integrating the Object Constraint Language (OCL) for code structure meta modeling, and the FIPA ontology language for communication semantics meta modeling. Applying GPT4 auto generation capabilities yields Java and Python code that is compatible with the JADE and PADE frameworks, respectively. Our thorough evaluation of the auto generated code verifies its alignment with expected behaviors and identifies enhancements in agent interactions. Structurally, we assessed the complexity of code derived from a model constrained solely by OCL meta models, against that influenced by both OCL and FIPA ontology meta models. The results indicate that the ontology constrained meta model produces inherently more complex code, yet its cyclomatic complexity remains within manageable levels, suggesting that additional meta model constraints can be incorporated without exceeding the high risk threshold for complexity.

Barocalorics (BCs) are emerging as promising alternatives to vapour-phase refrigerants, which are problematic as they exacerbate climate change when they inevitably leak into the atmosphere. However, the commercialisation of BC refrigerants is significantly hindered by hysteresis in the solid-solid phase transition that would be exploited in a refrigeration cycle. Here, we provide new insight into the hysteresis that is a critical step towards the rational design of viable BCs. By studying the archetypal BC plastic crystal, neopentyl glycol (NPG), we observe for the first time the liberation of the hydroxyl rotational modes that unlock the hydrogen bonding network. We use high-resolution inelastic fixed-window neutron scattering studies in combination with quasielastic neutron scattering (QENS) measurements to build a comprehensive microscopic understanding of the NPG phase transition. Hysteresis previously observed in calorimetric studies of NPG is now observed directly as hysteresis in molecular rotational modes, and hence in the formation and disruption of hydrogen bonding. Furthermore, by tracking the thermal activation of three main reorientation modes, we suggest that their fractional excitations may resolve an outstanding discrepancy between measured and calculated entropy change. These results allow for direct study of the molecular dynamics that govern the thermal hysteresis of small molecule energy materials. They will be broadly applicable, as many promising BC material families possess first-order transitions involving molecular reorientations.

In studying the time evolution of isolated many-body quantum systems, a key focus is determining whether the system undergoes relaxation and reaches a steady state at a given point in time. Traditional approaches often rely on specific local operators or a detailed understanding of the stationary state. In this letter, we introduce an alternative method that assesses relaxation directly from the time-dependent state by focusing on the evolution speed of the subsystem. The proposed indicator evaluates the rate of change in the reduced density matrix of the subsystem over time. We demonstrate that in systems reaching relaxation, as the overall system size increases, the evolution speed of sufficiently small yet still finite-sized subsystems notably diminishes. This leads to small fluctuations in the expectation values of operators, which is also consistent with the predictions made by the eigenstate thermalization hypothesis. We apply this approach across various models, including the chaotic Ising chain, XXZ chains with and without many-body localization, and the transverse field Ising chain. Our results confirm the robustness and accuracy of subsystem evolution speed as a reliable indicator for relaxation., Comment: 12 pages, 3+4 figures

The gradients used to train neural networks are typically computed using backpropagation. While an efficient way to obtain exact gradients, backpropagation is computationally expensive, hinders parallelization, and is biologically implausible. Forward gradients are an approach to approximate the gradients from directional derivatives along random tangents computed by forward-mode automatic differentiation. So far, research has focused on using a single tangent per step. This paper provides an in-depth analysis of multi-tangent forward gradients and introduces an improved approach to combining the forward gradients from multiple tangents based on orthogonal projections. We demonstrate that increasing the number of tangents improves both approximation quality and optimization performance across various tasks.

Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology, yet our understanding of how bias manifests in the parameters of LMs remains incomplete. We introduce local contrastive editing that enables the localization and editing of a subset of weights in a target model in relation to a reference model. We deploy this approach to identify and modify subsets of weights that are associated with gender stereotypes in LMs. Through a series of experiments, we demonstrate that local contrastive editing can precisely localize and control a small subset (< 0.5%) of weights that encode gender bias. Our work (i) advances our understanding of how stereotypical biases can manifest in the parameter space of LMs and (ii) opens up new avenues for developing parameter-efficient strategies for controlling model properties in a contrastive manner., Comment: Accepted at EMNLP 2024

In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrographs (namely Gemini-S/GPI and VLT/SPHERE-IFS) that we calibrated homogeneously, and in which we injected a handful of synthetic planetary signals (ground truth) to be characterized by the data challenge participants. The tasks of the participants consist of (1) extracting the precise astrometry of each injected planetary signals, and (2) extracting the precise spectro-photometry of each injected planetary signal. Additionally, the participants may provide the 1-sigma uncertainties on their estimation for further analyses. When available, the participants can also provide the posterior distribution used to estimate the position/spectrum and uncertainties. The data are permanently available on a Zenodo repository and the participants can submit their results through the EvalAI platform. The EvalAI submission platform opened on April 2022 and closed on the 31st of May 2024. In total, we received 4 valid submissions for the astrometry estimation and 4 valid submissions for the spectrophotometry (each submission, corresponding to one pipeline, has been submitted by a unique participant). In this communication, we present an analysis and interpretation of the results., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, Adaptive Optics Systems IX, Paper 13097-40

The study investigated the effects of atmospheric plasma on various dielectric materials (FR-4, PTFE, Al$_2$O$_3$) and electrode materials (copper, silver, gold-plated copper) used in surface micro-discharge plasma sources. XPS and laser microscopy were used to analyze changes in surface properties and chemical composition after 10 hours of plasma exposure. Al$_2$O$_3$ showed the highest resistance, with no significant changes in Al/O ratio or oxidation state. PTFE underwent oxidation, with fluorine substituted by oxygen, forming carbonyl, hydroxyl and aldehyde groups. FR-4 showed the most substantial changes, with etching of the epoxy matrix exposing glass fibers. This was in line with the XPS results, which show a higher O/C-ratio, less epoxy groups and higher nitrogen signals. Silver electrodes were most resilient, with sintered particles redistributed across the Al$_2$O$_3$ surface. Copper and gold-plated copper electrodes were more susceptible to oxidation and degradation, especially in areas exposed to plasma filaments. The results highlight the importance of carefully selecting materials based on resistance to plasma exposure and compatibility with the application.

Utilizing nonlinear interferometers for sensing with undetected light enables new sensing and imaging techniques in spectral ranges that are difficult to detect. To enhance this method for future applications, it is advantageous to extract both amplitude and phase information of an object. This study introduces two approaches for synthetic quantum holography with undetected light, which allows for obtaining an object's amplitude and phase information in a nonlinear interferometer by capturing only a single image. One method is based on quasi-phase-shifting holography using superpixel structures displayed on a spatial light modulator. The other method relies on synthetic off-axis holography implemented through a linear phase gradient on a spatial light modulator. Both approaches are experimentally analyzed for applicability and compared against available multi-acquisition methods., Comment: 13 pages, 9 figures

The surprisingly long-lasting oscillations observed in the dynamics of highly excited states of chains of Rydberg atoms defy the expectation that interacting systems should thermalize fast. The phenomenon is reminiscent of wavepackets in quantum billiards that trace classical periodic orbits. While analogs of the associated scarred eigenfunctions have been found for Rydberg chains, an underlying classical limit hosting periodic orbits has remained elusive. Here we generalize the Rydberg (pseudospin $S=1/2$) system to a chain of arbitrary spin $S$. Its classical limit features unexpectedly stable periodic orbits that are essential to understand the emergence of robust, parametrically suppressed quantum chaoticity, with semi-classical coherence times diverging as $\sqrt{S}$. The classical limit successfully explains several empirical features of the quantum limit.

Despite growing interest in incorporating feedback to improve language models, most efforts focus only on sequence-level annotations. In this work, we explore the potential of utilizing fine-grained span-level annotations from offline datasets to improve model quality. We develop a simple finetuning algorithm, called Training with Annotations (TWA), to directly train machine translation models on such annotated data. TWA utilizes targeted span-level error information while also flexibly learning what to penalize within a span. Moreover, TWA considers the overall trajectory of a sequence when deciding which non-error spans to utilize as positive signals. Experiments on English-German and Chinese-English machine translation show that TWA outperforms baselines such as Supervised FineTuning on sequences filtered for quality and Direct Preference Optimization on pairs constructed from the same data.

Structure determination is a key application of XFELs and 4th generation synchrotron sources, particularly using the coherent and pulsed X-ray radiation from X-ray free-electron lasers (XFEL). Scientific interest focuses on understanding the physical, biological, and chemical properties of samples at the nanometer scale. The X-rays from XFELs enable Coherent X-ray Diffraction Imaging (CXDI), where coherent X-rays irradiate a sample, and a far-field diffraction pattern is captured by an imaging detector. By the nature of the underlying physics, the resolution, at which the sample can be probed with the CXDI technique, is limited by the wavelength of the X-ray radiation and the exposure time if a detector can record the diffraction pattern to very large scattering angles. The resolution that can be achieved under real experimental conditions, depends strongly on additional parameters. The Shannon pixel size, linked to the detector resolution, the coherent dose that can be deposited in the sample without changing its structure, the image contrast, and the signal-to-noise ratio of the detected scattered radiation at high $q$, i.e. at high scattering angles $2\Theta$, have a strong influence on the resolution. The signal-to-noise ratio at high $q$ defines the "effective" maximum solid angle in a specific experiment setup up to which a detector can efficiently detect a signal and in consequence determines the achievable resolution. The image contrast defines how well bright image features can be distinguished from dark ones. We present the preliminary results of our study on the influence of the PSF on SNR, image contrast, position resolution, and achievable sample resolution for different pixel sizes., Comment: 7 pages, 4 figures, submitted to the Journal of Physics: Conference Series (JPCS)

We present a computational framework for the design of magnonic transducers, where waveguide antennas generate and pick up spin-wave signals. Our method relies on the combination of circuit-level models with micromagnetic simulations and allows simulation of complex geometries in the magnonic domain. We validated our model with experimental measurements, which showed good agreement witch the predicted scattering parameters of the system. Using our model we identified scaling rules of the antenna radiation resistance and we show strategies to maximize transduction efficiency between the electric and magnetic domains. We designed a transducer pair on YIG with 5dB insertion loss in a 100 MHz band, an unusually low value for micron-scale spin-wave devices. This demonstrates that magnonic devices can be very efficient and competitive in RF applications.

A multi-agent AI model is used to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge including insights from physics via atomistic simulations. Our multi-agent system features three key components: (a) a suite of LLMs responsible for tasks such as reasoning and planning, (b) a group of AI agents with distinct roles and expertise that dynamically collaborate, and (c) a newly developed graph neural network (GNN) model for rapid retrieval of key physical properties. A set of LLM-driven AI agents collaborate to automate the exploration of the vast design space of MPEAs, guided by predictions from the GNN. We focus on the NbMoTa family of body-centered cubic (bcc) alloys, modeled using an ML-based interatomic potential, and target two key properties: the Peierls barrier and solute/screw dislocation interaction energy. Our GNN model accurately predicts these atomic-scale properties, providing a faster alternative to costly brute-force calculations and reducing the computational burden on multi-agent systems for physics retrieval. This AI system revolutionizes materials discovery by reducing reliance on human expertise and overcoming the limitations of direct all-atom simulations. By synergizing the predictive power of GNNs with the dynamic collaboration of LLM-based agents, the system autonomously navigates vast alloy design spaces, identifying trends in atomic-scale material properties and predicting macro-scale mechanical strength, as demonstrated by several computational experiments. This approach accelerates the discovery of advanced alloys and holds promise for broader applications in other complex systems, marking a significant step forward in automated materials design.

Superfluid ${}^4$He is an ideal candidate for the direct detection of light dark matter via nuclear recoils thanks to its low nuclear mass and the possibility to reach a low detection energy threshold by exploiting the generated quasiparticles. The design of future detectors based on this target, such as the DELight experiment, requires a proper understanding of the formation and partitioning of the signal for different energy depositions from various sources. This work presents an overview of the physical processes involved in the energy deposition of recoiling electrons and ions, and describes a Monte Carlo approach to the partitioning of the signal into different channels. Despite an overall good agreement with existing literature, differences in the region of interest for light dark matter searches below 200\,eV are observed., Comment: 14 pages, 16 figures, 1 table

Reliable execution of large-scale quantum algorithms requires robust underlying operations and this challenge is addressed by quantum error correction (QEC). Most modern QEC protocols rely on measurements and feed-forward operations, which are experimentally demanding, and often slow and prone to high error rates. Additionally, no single error-correcting code intrinsically supports the full set of logical operations required for universal quantum computing, resulting in an increased operational overhead. In this work, we present a complete toolbox for fault-tolerant universal quantum computing without the need for measurements during algorithm execution by combining the strategies of code switching and concatenation. To this end, we develop new fault-tolerant, measurement-free protocols to transfer encoded information between 2D and 3D color codes, which offer complementary and in combination universal sets of robust logical gates. We identify experimentally realistic regimes where these protocols surpass state-of-the-art measurement-based approaches. Moreover, we extend the scheme to higher-distance codes by concatenating the 2D color code with itself and by integrating code switching for operations that lack a natively fault-tolerant implementation. Our measurement-free approach thereby provides a practical and scalable pathway for universal quantum computing on state-of-the-art quantum processors., Comment: 16 pages, 9 figures

Self-supervised learning on tabular data seeks to apply advances from natural language and image domains to the diverse domain of tables. However, current techniques often struggle with integrating multi-domain data and require data cleaning or specific structural requirements, limiting the scalability of pre-training datasets. We introduce PORTAL (Pretraining One-Row-at-a-Time for All tabLes), a framework that handles various data modalities without the need for cleaning or preprocessing. This simple yet powerful approach can be effectively pre-trained on online-collected datasets and fine-tuned to match state-of-the-art methods on complex classification and regression tasks. This work offers a practical advancement in self-supervised learning for large-scale tabular data., Comment: Accepted at Table Representation Learning Workshop at NeurIPS 2024

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Large language models (LLMs) have shown impressive alignment with human cognitive processes, raising questions about the extent of their similarity to human cognition. This study investigates whether LLMs, specifically ChatGPT, possess metacognitive monitoring abilities akin to humans-particularly in predicting memory performance on an item-by-item basis. We employed a cross-agent prediction model to compare the metacognitive performance of humans and ChatGPT in a language-based memory task involving garden-path sentences preceded by either fitting or unfitting context sentences. Both humans and ChatGPT rated the memorability of these sentences; humans then completed a surprise recognition memory test. Our findings reveal a significant positive relationship between humans' memorability ratings and their actual recognition performance, indicating reliable metacognitive monitoring. In contrast, ChatGPT did not exhibit a similar predictive capability. Bootstrapping analyses demonstrated that none of the GPT models tested (GPT-3.5-turbo, GPT-4-turbo, GPT-4o) could accurately predict human memory performance on a per-item basis. This suggests that, despite their advanced language processing abilities and alignment with human cognition at the object level, current LLMs lack the metacognitive mechanisms that enable humans to anticipate their memory performance. These results highlight a fundamental difference between human and AI cognition at the metacognitive level. Addressing this gap is crucial for developing AI systems capable of effective self-monitoring and adaptation to human needs, thereby enhancing human-AI interactions across domains such as education and personalized learning., Comment: 28 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:2403.05152

The shift towards decentralized and renewable energy sources has introduced significant challenges to traditional power systems, necessitating innovative market designs. Local energy markets present a viable solution for integrating distributed energy resources such as photovoltaic systems, electric vehicles, and heat pumps within various grid topologies. This study investigates the techno-economic benefits of local energy markets compared to conventional market designs, focusing on their impact on average energy prices and operational peak power, using a self-developed agent-based energy system simulation tool. Through comprehensive simulations across the countryside, rural, suburban, and urban grid topologies with varying penetration levels of the distributed energy resources, totaling 400 simulation setups, we demonstrate that local energy markets can enhance economic efficiency and grid stability with 99 % of the scenarios boasting lower average energy prices and 80 % lower operational peak power levels. Our findings suggest that local energy markets can play a role in the future energy system, especially in areas with high shares of PV and HP, provided that additional infrastructure, management costs, and bureaucratic complexity are kept to a minimum., Comment: 39 pages, 9 figures, 4 tables

PRefLexOR (Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning) combines preference optimization with concepts from Reinforcement Learning to enable models to self-teach through iterative reasoning improvements. We propose a recursive learning approach that engages the model in multi-step reasoning, revisiting, and refining intermediate steps before producing a final output in training and inference phases. Through multiple training stages, the model first learns to align its reasoning with accurate decision paths by optimizing the log odds between preferred and non-preferred responses. During this process, PRefLexOR builds a dynamic knowledge graph by generating questions from random text chunks and retrieval-augmentation to contextualize relevant details from the entire training corpus. In the second stage, preference optimization enhances model performance by using rejection sampling to fine-tune reasoning quality by continually producing in-situ training data while masking the reasoning steps. Recursive optimization within a thinking token framework introduces iterative feedback loops, where the model refines reasoning, achieving deeper coherence, consistency, and adaptability. Implemented in small language models with only 3 billion parameters, we should that even tiny models can iteratively teach themselves to reason with greater depth and reflectivity. Our implementation is straightforward and can be incorporated into any existing pretrained LLM. We focus our examples on applications in biological materials science and demonstrate the method in a variety of case studies that range from in-domain to cross-domain applications. Using reasoning strategies that include thinking and reflection modalities we build a multi-agent recursive self-improving inference approach to successively improve responses via repeated sampling in inference time.

Competing magnetic interactions may stabilize smooth magnetization textures that can be characterized by a topological winding number. Such textures, which are spatially localized within a two-dimensional plane, are commonly known as skyrmions. On the classical level, their significance for fundamental science and their potential for applications, ranging from spintronic devices to unconventional computation platforms, have been intensively investigated in recent years. This Colloquium considers quantum effects associated with skyrmion textures: their theoretical origins, the experimental and material challenges associated with their detection, and the promise of exploiting them for quantum operations. Starting with classical skyrmions, we discuss their magnon and electron excitations and show how hybrid architectures offer new platforms for engineering quantum orders, including topological superconductivity. We then focus on the quantization of the skyrmion texture itself and formulate the long-time skyrmion dynamics in terms of collective coordinates. Next, we discuss the quantization of helicity and phenomena of macroscopic quantum tunneling: key concepts that fundamentally distinguish quantum skyrmions from their classical counterparts. Looking ahead, we propose material classes suitable for the realization of skyrmions in quantum spin systems and identify device architectures with the promise of achieving quantum operations. We close by addressing the advances in experimental methods which will be a prerequisite for resolving the quantum aspects of topological spin patterns, sensing their local dynamical response, and achieving their predicted functionalities in magnetic systems., Comment: 26 pages, 12 figures

In this paper we study the bounds that can be inferred on New Physics couplings to heavy sterile neutrinos $N$ from the recent measurements performed by the Belle collaboration of the angular analysis of $B\to D^*\ell\bar\nu_\ell$ decays, with $\ell=e,\mu$. Indeed, a sterile neutrino $N$ may lead to competing $B\to D^*\ell\bar N$ decays and Belle might have measured an incoherent sum of these two independent channels. After reviewing the theoretical formalism required to describe this phenomenon in full generality, we first perform a bump hunt in the $M_{\rm miss}^2$ Belle distribution to search for evidences of an additional massive neutrino. We found in such a way a small hint at $M_{\rm miss}^2 \sim (350\ {\rm MeV})^2$. However, the Belle angular analysis is sensitive to $N$ masses up to $\mathcal{O}$(50 MeV), preventing us to further inspect this hint. Nevertheless, we study the potential impact of this additional channel in the allowed mass range on the measured angular distributions and extract model-independent bounds on the new-physics couplings which could mediate such an interaction. In particular, in the mass window here inspected, we obtain the most stringent bounds for vector and left-handed scalar operators to date., Comment: 22 pages, 6 figures, 2 tables. Comments are welcome!

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.

Seamless forecasts are based on a combination of different sources to produce the best possible forecasts. Statistical multimodel postprocessing helps to combine various sources to achieve these seamless forecasts. However, when one of the combined sources of the forecast is not available due to reaching the end of its forecasting horizon, forecasts can be temporally inconsistent and sudden drops in skill can be observed. To obtain a seamless forecast, the output of multimodel postprocessing is often blended across these transitions, although this unnecessarily worsens the forecasts immediately before the transition. Additionally, large differences between the latest observation and the first forecasts can be present. This paper presents an idea to preserve a smooth temporal prediction until the end of the forecast range and increase its predictability. This optimal seamless forecast is simply accomplished by not excluding any model from the multimodel by using the latest possible lead time as model persistence into the future. Furthermore, the gap between the latest available observation and the first model step is seamlessly closed with the persistence of the observation by using the latest observation as additional predictor. With this idea, no visible jump in forecasts is observed and the verification presents a seamless quality in terms of scores. The benefit of accounting for observation and forecast persistence in multimodel postprocessing is illustrated using a simple temperature example with linear regression but can also be extended to other predictors and postprocessing methods.

The kagome lattice, known for its strong frustration in two dimensions, hosts a variety of exotic magnetic and electronic states. A variation of this geometry, where the triangular motifs are twisted to further reduce symmetry, has recently revealed even more complex physics. HoAgGe exemplifies such a structure, with magnetic and electronic properties believed to be driven by strong in-plane anisotropy of the Ho spins, effectively acting as a two-dimensional spin ice. In this study, using a combination of magnetization, Hall conductivity measurements, and density functional theory calculations, we demonstrate how various spin-ice states, stabilized by external magnetic fields, influence the Fermi surface topology. More interestingly, we observe sharp transitions in Hall conductivity without concurrent changes in magnetization when an external magnetic field is applied along a particular crystallographic direction, underscoring the role of strong magnetic frustration and providing a new platform for exploring the interplay between magnetic frustration, electronic topology, and crystalline symmetry. These results also highlight the limitations of a simple spin-ice model, suggesting that a more sophisticated framework is necessary to capture the subtle experimental nuances observed., Comment: 17 pages, 11 figures

In this paper we introduce and study a family of self-adjoint realizations of the Laplacian in $L^2(\mathbb{R}^2)$ with a new type of transmission conditions along a closed bi-Lipschitz curve $\Sigma$. These conditions incorporate jumps in the Dirichlet traces both of the functions in the operator domains and of their Wirtinger derivatives and are non-local. Constructing a convenient generalized boundary triple, they may be parametrized by all compact hermitian operators in $L^2(\Sigma;\mathbb{C}^2)$. Whereas for all choices of parameters the essential spectrum is stable and equal to $[0, +\infty)$, the discrete spectrum exhibits diverse behaviour. While in many cases it is finite, we will describe also a class of parameters for which the discrete spectrum is infinite and accumulates at $-\infty$. The latter class contains a non-local version of the oblique transmission conditions. Finally, we will connect the current model to its relativistic counterpart studied recently in [L. Heriban, M. Tu\v{s}ek: Non-local relativistic $\delta$-shell interactions]., Comment: 36 pages; comments welcome!

The covariance for clean data given a noisy observation is an important quantity in many conditional generation methods for diffusion models. Current methods require heavy test-time computation, altering the standard diffusion training process or denoiser architecture, or making heavy approximations. We propose a new framework that sidesteps these issues by using covariance information that is available for free from training data and the curvature of the generative trajectory, which is linked to the covariance through the second-order Tweedie's formula. We integrate these sources of information using {\em (i)} a novel method to transfer covariance estimates across noise levels and (ii) low-rank updates in a given noise level. We validate the method on linear inverse problems, where it outperforms recent baselines, especially with fewer diffusion steps., Comment: 24 pages, 11 figures

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

Several gamma-ray observatories have discovered photons of cosmic origin with energies in the PeV ($10^{15}\,\text{eV}$) range. Photons at these energies might be produced as by-products from particle acceleration in so-called PeVatrons, which are widely assumed to be the sources of a large part of galactic cosmic rays. Based on recent measurements of these PeV $\gamma$-sources by LHAASO and HAWC, we extrapolate the energy spectra of selected sources up to the ultra-high-energy (UHE, ${\geq}10\,\text{PeV}$) regime. The goal of this study is to evaluate if (and under what conditions) giant air-shower observatories, for example the Pierre Auger Observatory, could contribute to testing the UHE luminosity of PeV $\gamma$-sources. Possible propagation effects are investigated as well as the required discrimination power to distinguish photon- and hadron-initiated air showers. For present detector setups, it turns out to be challenging to achieve the required sensitivity due to the energy threshold being too high or the detection area too small. Dedicated detector concepts appear to be needed to explore the UHE frontier. Ultimately, this could provide complementary information on the sources of cosmic rays beyond the PeV regime -- a key objective of current efforts in multimessenger astronomy., Comment: 8 pages, 3 figures, 2 tables

Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machineonly, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.

TScore is both an abstract formalism and its computer implementation to construct models of arbitrary kinds of time-related data. It is a research project about the semantics of musical notation, applying the method of computer-aided re-modelling to diverse formalisms and semantics of time-related data. Here we present the application to German tablature notation. While the current implemention is merely a proof of concept, the lean architecture of TScore allows easy adaptation and extension.

We present a proposal for a future light dark matter search experiment at the Electron Stretcher Accelerator ELSA in Bonn: Lohengrin. It employs the fixed-target missing momentum based technique for searching for dark-sector particles. The Lohengrin experiment uses a high intensity electron beam that is shot onto a thin target to produce mainly SM bremsstrahlung and - in rare occasions - possibly new particles coupling feebly to the electron. A well motivated candidate for such a new particle is the dark photon, a new massive gauge boson arising from a new gauge interaction in a dark sector and mixing kinetically with the standard model photon. The Lohengrin experiment is estimated to reach sensitivity to couplings small enough to explain the relic abundance of dark matter in various models for dark photon masses between approximately 1 MeV and approximately 100 MeV.

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.