Search

We investigate nonreciprocal supercurrent phenomena in superconducting quantum interference devices (SQUIDs) that integrate Josephson junctions with single and multiband order parameters, which may exhibit time-reversal symmetry breaking. Our results show that the magnetic field can independently control both the amplitude and direction of supercurrent rectification, depending on the multiband characteristics of the superconductors involved. We analyze the effects of zero and antiphase ({\pi}) pairing among different bands on the development of nonreciprocal effects and find that the rectification is not influenced by {\pi}-pairing. Furthermore, we demonstrate that incorporating multiband superconductors that break time-reversal symmetry produces significant signatures in rectification. The rectification exhibits an even parity dependence on the magnetic field and the average rectification amplitude across quantum flux multiples does not equal zero. These findings indicate that magnetic flux pumping can be accomplished with time-reversal symmetry broken multiband superconductors by adjusting the magnetic field. Overall, our findings provide valuable insights for identifying and utilizing phases with broken time-reversal symmetry in multiband superconductors., Comment: 11 pages, 4 figures. Comments, suggestions, missed citations are very welcome

Current guidelines for elective nodal irradiation in oropharyngeal squamous cell carcinoma (OPSCC) recommend including large portions of the contralateral lymph system in the clinical target volume (CTV-N), even for lateralized tumors with no clinical lymph node involvement in the contralateral neck. This study introduces a probabilistic model of bilateral lymphatic tumor progression in OPSCC to estimate personalized risks of occult disease in specific lymph node levels (LNLs) based on clinical involvement, T-stage, and tumor lateralization. Building on a previously developed hidden Markov model for ipsilateral spread, we extend the approach to the contralateral neck. The model represents LNLs I, II, III, IV, V, and VII on both sides of the neck as binary hidden variables (healthy/involved), connected via arcs representing spread probabilities. These probabilities are learned using Markov chain Monte Carlo (MCMC) sampling from a dataset of 833 OPSCC patients, enabling the model to reflect the underlying lymphatic progression dynamics. The model accurately and precisely describes observed patterns of involvement with a compact set of interpretable parameters. Midline extension of the primary tumor is identified as the primary risk factor for contralateral involvement, with advanced T-stage and extensive ipsilateral involvement further increasing risk. Occult disease in contralateral LNL III is highly unlikely if upstream LNL II is clinically negative, and in contralateral LNL IV, occult disease is exceedingly rare without LNL III involvement. For lateralized tumors not crossing the midline, the model suggests the contralateral neck may safely be excluded from the CTV-N. For tumors extending across the midline but with a clinically negative contralateral neck, the CTV-N could be limited to LNL II, reducing unnecessary exposure of normal tissue while maintaining regional tumor control., Comment: 32 pages, 11 figures, 4 tables

Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students., Comment: ACM CHI Conference on Human Factors in Computing Systems (CHI '25)

A subset of a topological space possesses the Baire property if it can be covered by an open set up to a meagre set. For the Cantor space of infinite words Finkel introduced the automatic Baire category where both sets, the open and the meagre, can be chosen to be definable by finite automata. Here we show that, given a Muller automaton defining the original set, resulting open and meagre sets can be constructed in polynomial time. Since the constructed sets are of simple topological structure, it is possible to construct not only Muller automata defining them but also the simpler B\"uchi automata. To this end we give, for a restricted class of Muller automata, a conversion to equivalent B\"uchi automata of at most quadratic size., Comment: 12 pages

Training machine learning models for fair decisions faces two key challenges: The \emph{fairness-accuracy trade-off} results from enforcing fairness which weakens its predictive performance in contrast to an unconstrained model. The incompatibility of different fairness metrics poses another trade-off -- also known as the \emph{impossibility theorem}. Recent work identifies the bias within the observed data as a possible root cause and shows that fairness and predictive performance are in fact in accord when predictive performance is measured on unbiased data. We offer a causal explanation for these findings using the framework of the FiND (fictitious and normatively desired) world, a "fair" world, where protected attributes have no causal effects on the target variable. We show theoretically that (i) classical fairness metrics deemed to be incompatible are naturally satisfied in the FiND world, while (ii) fairness aligns with high predictive performance. We extend our analysis by suggesting how one can benefit from these theoretical insights in practice, using causal pre-processing methods that approximate the FiND world. Additionally, we propose a method for evaluating the approximation of the FiND world via pre-processing in practical use cases where we do not have access to the FiND world. In simulations and empirical studies, we demonstrate that these pre-processing methods are successful in approximating the FiND world and resolve both trade-offs. Our results provide actionable solutions for practitioners to achieve fairness and high predictive performance simultaneously.

Time-reversal (TR) symmetry is crucial for understanding a wide range of physical phenomena, and plays a key role in constraining fundamental particle interactions and in classifying phases of quantum matter. In this work, we introduce an ensemble of random quantum circuits that are representative of the dynamics of generic TR-invariant many-body quantum systems. We derive a general statistical mechanics model describing entanglement, many-body quantum chaos and quantum information dynamics in such TR-invariant circuits. As an example of application of our formalism, we study the universal properties of measurement-induced phase transitions (MIPT) in monitored TR-invariant systems, with measurements performed in a TR-invariant basis. We find that TR-invariance of the unitary part of the dynamics does not affect the universality class, unless measurement outcomes are post-selected to satisfy the global TR-invariance of each quantum trajectory. We confirm these predictions numerically, and find, for both generic and Clifford-based evolutions, novel critical exponents in the case of ``strong'', i.e. global TR-invariance where each quantum trajectory is TR-invariant., Comment: 24 pages, 9 figures

One of the cornerstones of spintronics is the application of a spin-transfer torque to a nanomagnet, driving the magnetization of the nanomagnet into a steady-state precession and realizing a spin-torque oscillator. Such a steady state, sustained by a balance between driving and dissipation, could be a textbook example for a nonequilibrium situation. Nevertheless, most theoretical descriptions of spin-torque oscillators simply assume local equilibrium. Here, based on a simple model, we investigate the relevance of nonequilibrium effects in spin-torque oscillators. We use a nonequilibrium Keldysh description, which allows us to treat the effects of spin relaxation, and find that, in the absence of spin relaxation, persistent precessions of the magnetization are not allowed, if magnetic anisotropies are absent. However, introducing spin relaxation enables persistent precessions, where the strength of the spin relaxation has quantitative and qualitative effects on the magnetization dynamics. In the presence of magnetic anisotropy, we find that persistent precessions are allowed even if spin relaxation is absent, but spin relaxation causes a nonlinear relation between the oscillator power and the applied voltage bias. Finally, we consider an alternative spin relaxation mechanism and study the resulting magnetization precessions, highlighting the importance of understanding the exact nature of the relaxation in nanomagnets., Comment: 14 pages, 4 figures

We employ Bayesian optimization combined with three-dimensional particle-in-cell simulations to identify the optimal laser and plasma parameters that, for a given laser pulse energy, maximize the cut-off energy of an electron beam accelerated via laser wakefield acceleration. A Gaussian laser driver with a matched spot size and amplitude is assumed, interacting with both a uniform-density plasma and a preformed plasma channel of matched radius. To interpret the simulation results quantitatively, we derive novel analytical expressions for predicting the maximum electron energy and acceleration length, taking into account the diffraction and energy depletion of the laser pulse. Additionally, we discuss the potential scalability of the optimal parameters for high-energy lasers., Comment: 11 pages, 5 figures

To ensure resilience against the unavoidable noise in quantum computers, quantum information needs to be encoded using an error-correcting code, and circuits must have a particular structure to be fault-tolerant. Compilation of fault-tolerant quantum circuits is thus inherently different from the non-fault-tolerant case. However, automated fault-tolerant compilation methods are widely underexplored, and most known constructions are obtained manually for specific codes only. In this work, we focus on the problem of automatically synthesizing fault-tolerant circuits for the deterministic initialization of an encoded state for a broad class of quantum codes that are realizable on current and near-term hardware. To this end, we utilize methods based on techniques from classical circuit design, such as satisfiability solving, resulting in tools for the synthesis of (optimal) fault-tolerant state preparation circuits for near-term quantum codes. We demonstrate the correct fault-tolerant behavior of the synthesized circuits using circuit-level noise simulations. We provide all routines as open-source software as part of the Munich Quantum Toolkit (MQT) at https://github.com/cda-tum/mqt-qecc., Comment: 7 pages, 4 figures, accepted at DATE 2025

The restaurant industry is currently facing a challenging socio-economic situation caused by the rise of delivery services, inflation, and typically low margins. Often, technological opportunities for process optimization or customer retention are not fully utilized. In our design case study, we investigate which technologies are already being used to improve the customer experience in restaurants and explore a novel new approach to this issue. We designed, implemented, and evaluated a platform with customers and restaurateurs to increase visibility and emotional connection to nearby restaurants through their dishes. Some of our key findings include the enormous potential of combining location-based systems and conversational agents, but also the difficulties in creating content for such platforms. We contribute to the field of Human-Food Interaction by (1) identifying promising design spaces as well as customer and restaurateur requirements for technology in this domain, (2) presenting an innovative design case study to improve the user experience, and (3) exploring the broader implications of our design case study findings for approaching a real-world metaverse., Comment: 32 pages, 8 figures

The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce AutoConverter, an agentic framework that automatically converts these open-ended questions into multiple-choice format, enabling objective evaluation while reducing the costly question creation process. Our experiments demonstrate that AutoConverter can generate correct and challenging multiple-choice questions, with VLMs demonstrating consistently similar or lower accuracy on these questions compared to human-created ones. Using AutoConverter, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format, totaling 9,018 questions. We comprehensively evaluate 33 state-of-the-art VLMs on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation., Comment: Project page: https://yuhui-zh15.github.io/AutoConverter-Website/

Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weather dynamics for multiple variables across long time horizons is crucial. We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. After training, we condition on coarse climate model data to generate weather patterns consistent with the aggregate information. As this predictive task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. We evaluate our approach with high-resolution reanalysis information before applying it to the climate model downscaling task. We then demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output., Comment: 15 pages, 6 figures, additional supplementary text and figures

The analysis of dynamical systems is a fundamental tool in the natural sciences and engineering. It is used to understand the evolution of systems as large as entire galaxies and as small as individual molecules. With predefined conditions on the evolution of dy-namical systems, the underlying differential equations have to fulfill specific constraints in time and space. This class of problems is known as boundary value problems. This thesis presents novel approaches to learn time-reversible deterministic and stochastic dynamics constrained by initial and final conditions. The dynamics are inferred by machine learning algorithms from observed data, which is in contrast to the traditional approach of solving differential equations by numerical integration. The work in this thesis examines a set of problems of increasing difficulty each of which is concerned with learning a different aspect of the dynamics. Initially, we consider learning deterministic dynamics from ground truth solutions which are constrained by deterministic boundary conditions. Secondly, we study a boundary value problem in discrete state spaces, where the forward dynamics follow a stochastic jump process and the boundary conditions are discrete probability distributions. In particular, the stochastic dynamics of a specific jump process, the Ehrenfest process, is considered and the reverse time dynamics are inferred with machine learning. Finally, we investigate the problem of inferring the dynamics of a continuous-time stochastic process between two probability distributions without any reference information. Here, we propose a novel criterion to learn time-reversible dynamics of two stochastic processes to solve the Schr\"odinger Bridge Problem.

Machine learning (ML) will likely play a large role in many processes in the future, also for insurance companies. However, ML models are at risk of being attacked and manipulated. In this work, the robustness of Gradient Boosted Decision Tree (GBDT) models and Deep Neural Networks (DNN) within an insurance context will be evaluated. Therefore, two GBDT models and two DNNs are trained on two different tabular datasets from an insurance context. Past research in this domain mainly used homogenous data and there are comparably few insights regarding heterogenous tabular data. The ML tasks performed on the datasets are claim prediction (regression) and fraud detection (binary classification). For the backdoor attacks different samples containing a specific pattern were crafted and added to the training data. It is shown, that this type of attack can be highly successful, even with a few added samples. The backdoor attacks worked well on the models trained on one dataset but poorly on the models trained on the other. In real-world scenarios the attacker will have to face several obstacles but as attacks can work with very few added samples this risk should be evaluated., Comment: 40 pages, 14 figures

Large-scale Wizard-Of-Oz dialogue datasets have enabled the training of deep learning-based dialogue systems. While they are successful as benchmark datasets, they lack certain types of utterances, which would make them more realistic. In this work, we investigate the creation of synthetic communication errors in an automatic pipeline. Based on linguistic theory, we propose and follow a simple error taxonomy. We focus on three types of miscommunications that could happen in real-world dialogues but are underrepresented in the benchmark dataset: misunderstandings, non-understandings and vaguely related questions. Our two-step approach uses a state-of-the-art Large Language Model (LLM) to first create the error and secondly the repairing utterance. We perform Language Model-based evaluation to ensure the quality of the generated utterances. We apply the method to the MultiWOZ dataset and evaluate it both qualitatively and empirically as well as with human judges. Our results indicate that current LLMs can aid in adding post-hoc miscommunications to benchmark datasets as a form of data augmentation. We publish the resulting dataset, in which nearly 1900 dialogues have been modified, as CoPrUS-MultiWOZ to facilitate future work on dialogue systems., Comment: Accepted at COLING 2025 (main, long paper)

We discuss convergence in the Fourier algebra A(G) of a locally compact group G and provide a new characterisation of the local spectral sets of G., Comment: 18 pages. Comments are welcome!

Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect., Comment: 6 pages, 2 figures

How can we use the novel capacities of large language models (LLMs) in empirical research? And how can we do so while accounting for their limitations, which are themselves only poorly understood? We develop an econometric framework to answer this question that distinguishes between two types of empirical tasks. Using LLMs for prediction problems (including hypothesis generation) is valid under one condition: no ``leakage'' between the LLM's training dataset and the researcher's sample. No leakage can be ensured by using open-source LLMs with documented training data and published weights. Using LLM outputs for estimation problems to automate the measurement of some economic concept (expressed either by some text or from human subjects) requires the researcher to collect at least some validation data: without such data, the errors of the LLM's automation cannot be assessed and accounted for. As long as these steps are taken, LLM outputs can be used in empirical research with the familiar econometric guarantees we desire. Using two illustrative applications to finance and political economy, we find that these requirements are stringent; when they are violated, the limitations of LLMs now result in unreliable empirical estimates. Our results suggest the excitement around the empirical uses of LLMs is warranted -- they allow researchers to effectively use even small amounts of language data for both prediction and estimation -- but only with these safeguards in place.

Stars stripped of their hydrogen-rich envelopes through binary interaction are thought to be responsible for both hydrogen-poor supernovae and the hard ionizing radiation observed in low-$Z$ galaxies. A population of these stars was recently observed for the first time, but their prevalence remains unknown. In preparation for such measurements, we estimate the mass distribution of hot, stripped stars using a population synthesis code that interpolates over detailed single and binary stellar evolution tracks. We predict that for a constant star-formation rate of $1 \,M_\odot$/yr and regardless of metallicity, a population contains $\sim$30,000 stripped stars with mass $M_{\rm strip}>1M_\odot$ and $\sim$4,000 stripped stars that are sufficiently massive to explode ($M_{\rm strip}>2.6M_\odot$). Below $M_{\rm strip}=5M_\odot$, the distribution is metallicity-independent and can be described by a power law with the exponent $\alpha \sim -2$. At higher masses and lower metallicity ($Z \lesssim 0.002$), the mass distribution exhibits a drop. This originates from the prediction, frequently seen in evolutionary models, that massive low-metallicity stars do not expand substantially until central helium burning or later and therefore cannot form long-lived stripped stars. With weaker line-driven winds at low metallicity, this suggests that neither binary interaction nor wind mass loss can efficiently strip massive stars at low metallicity. As a result, a "helium-star desert" emerges around $M_{\rm strip} =15\, M_\odot$ at $Z=0.002$, covering an increasingly large mass range with decreasing metallicity. We note that these high-mass stars are those that potentially boost a galaxy's He$^+$-ionizing radiation and that participate in the formation of merging black holes. This "helium-star desert" therefore merits further study., Comment: 16 pages, 9 figures, submitted to A&A

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach., Comment: 13 pages, 7 figures

The ITk is a new silicon tracker for the ATLAS experiment designed to increase detector resolution, readout capacity, and radiation hardness, in preparation for the larger number of simultaneous proton-proton interactions at the High Luminosity LHC. This paper presents the first direct measurement of the material budget of an ATLAS ITk pixel module, performed at a testbeam at the CERN Proton Synchrotron via the multiple scattering of low energy positrons within the module volume. Using a four plane telescope of thin monolithic pixel detectors from the MALTA collaboration, scattering datasets were recorded at a beam energy of $1.2\,\text{GeV}$. Kink angle distributions were extracted from tracks derived with and without information from the ITk pixel module, and were fit to extract the RMS scattering angle, which was converted to a fractional radiation length $x/X_0$. The average $x/X_0$ across the module was measured as $[0.89 \pm 0.01 \text{ (resolution)} \pm 0.01 \text{ (subtraction)} \pm 0.08 \text{ (beam momentum band)}]\%$, which agrees within uncertainties with an estimate of $0.88\%$ derived from material component expectations., Comment: 12 pages, 12 figures

Magnetron sputtering is an essential technique in combinatorial materials science, enabling the efficient synthesis of thin-film materials libraries with continuous compositional gradients. For exploring multidimensional search spaces, minimizing preliminary experiments is essen-tial, as numerous materials libraries are required to adequately cover the space, making it crucial to fabricate only those libraries that are absolutely necessary. This can be achieved by Monte Carlo particle simulations to model the deposition profile, e.g. by SIMTRA, which is an established package mainly designed for single cathode simulations. A strong enhance-ment of its capabilities is the development of a Python-based wrapper, designed to simulate multi-cathode sputter processes through parallel Monte Carlo simulations. By modeling a sputter chamber and determining the relationship between deposition power and rate for an exemplary quaternary system Ni-Pd-Pt-Ru, we achieve a match between simulated and measured compositions, with a mean Euclidean distance of 3.5%. The object-oriented design of the package allows easy customization and enables the definition of complex sputter sys-tems. Due to parallelization, simulating multiple cathodes results in no additional simulation time. These additions extend the capabilities of SIMTRA making it applicable in combinatorial materials research.

We propose to design atomtronic circuits with Bose-Einstein condensates (BECs) in circuit-like traps that are controlled via mobile barriers. Using classical-field simulations, we demonstrate a universal set of logical gates and show how to assemble them into circuits. We first demonstrate an AND gate based on a T-shaped BEC, utilizing a combination of mobile and static barriers. The mobile barriers provide the logical input of the gate, while the static barrier functions as a Josephson junction that generates the AND output of the gate via a density imbalance across the barrier. Next we show how to combine three AND gates into a circuit, with a design composed of two T-shapes and an H-shape. Furthermore, we demonstrate how to use Josephson oscillations to create a NOT gate and combine it with an AND gate, thereby showcasing a universal set of gates and their assembly into circuits., Comment: 9 pages, 8 figures

Context: The small- to intermediate-mass ($M <0.8 M_\odot$), most metal-poor stars that formed in the infancy of the Universe are still shining today in the sky. They are very rare, but their discovery and investigation brings new knowledge on the formation of the first stellar generations. Aims: SDSS J102915.14+172927.9 is one of the most metal-poor star known to date. Since no carbon can be detected in its spectrum, a careful upper limit is important, both to classify this star and to distinguish it from the carbon-enhanced stars that represent the majority at these metallicities. Methods: We undertook a new observational campaign to acquire high-resolution UVES spectra. The new spectra were combined with archival spectra in order to increase the signal-to-noise ratio. From the combined spectrum, we derived abundances for seven elements (Mg, Si, Ca, Ti, Fe, Ni, and a tentative Li) and five significant upper limits (C, Na, Al, Sr, and Ba). Results: The star has a carbon abundance A(C) <4.68 and therefore is not enhanced in carbon, at variance with the majority of the stars at this Fe regime, which typically show A(C)> 6.0. A feature compatible with the Li doublet at 670.7 nm is tentatively detected. Conclusions: The upper limit on carbon implies $Z<1.915 \times 10^{-6}$, more than 20 times lower than the most iron-poor star known. Therefore, the gas cloud out of which the star was formed did not cool via atomic lines but probably through dust. Fragmentation of the primordial cloud is another possibility for the formation of a star with a metallicity this low.

In this contribution, we compute the $^1$H nuclear magnetic resonance (NMR) relaxation rate of liquid water at ambient conditions. We are using structural and dynamical information from Coupled Cluster Molecular Dynamics (CCMD) trajectories generated at CCSD(T) electronic structure accuracy while considering also nuclear quantum effects in addition to consulting information from X-ray and neutron scattering experiments. Our analysis is based on a recently presented computational framework for determining the frequency-dependent NMR dipole-dipole relaxation rate of spin $1/2$ nuclei from Molecular Dynamics (MD) simulations, which allows for an effective disentanglement of its structural and dynamical contributions, and is including a correction for finite-size effects inherent to MD simulations with periodic boundary conditions. A close to perfect agreement with experimental relaxation data is achieved if structural and dynamical informations from CCMD trajectories are considered including a re-balancing of the rotational and translational dynamics, according to the product of the self-diffusion coefficient and the reorientational correlation time of the H-H vector $D_0\times\tau_\mathrm{HH}$. The simulations show that this balance is significantly altered when nuclear quantum effects are taken into account. Our analysis suggests that the intermolecular and intramolecular contribution to the $^1$H NMR relaxation rate of liquid water are almost similar in magnitude, unlike to what was predicted earlier from classical MD simulations., Comment: 13 pages, 8 figures, added references, corrected authors list in metadata

Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global Climate Models (GCMs) and Regional Climate Models (RCMs) provide forecasts over multi-decadal periods. However, their outputs vary substantially, and higher-resolution models come with increased computational demands. In this study, we analyze how the spatial resolution of different GCMs and RCMs affects the reliability of simulated wind speeds and wind power, using ERA5 data as a reference. We present a systematic procedure for model evaluation for wind resource assessment as a downstream task. Our results show that higher-resolution GCMs and RCMs do not necessarily preserve wind speeds more accurately. Instead, the choice of model, both for GCMs and RCMs, is more important than the resolution or GCM boundary conditions. The IPSL model preserves the wind speed distribution particularly well in Europe, producing the most accurate wind power forecasts relative to ERA5 data., Comment: Pre-print

Grain boundaries (GBs) trigger structure-specific chemical segregation of solute atoms. According to the three-dimensional (3D) topology of grains, GBs - although defined as planar defects - cannot be free of curvature. This implies formation of topologically-necessary arrays of secondary GB dislocations. We report here that these pattern-forming secondary GB dislocations can have an additional and, in some cases, even a much stronger effect on GB segregation than defect-free GBs. Using nanoscale correlative tomography combining crystallography and chemical analysis, we quantified the relationship between secondary GB dislocations and their segregation energy spectra for a model Fe-W alloy. This discovery unlocks new design opportunities for advanced materials, leveraging the additional degrees of freedom provided by topologically-necessary secondary GB dislocations to modulate segregation.

Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes and locations, incorporating scattered radiation simulation, and including Poisson noise, the pipeline enables the expansion of limited datasets for training deep neural networks. We validate the proposed pipeline by a qualitative and quantitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.

Quantum computing promises to solve problems previously deemed infeasible. However, high error rates necessitate quantum error correction for practical applications. Seminal experiments with zoned neutral atom architectures have shown remarkable potential for fault-tolerant quantum computing. To fully harness their potential, efficient software solutions are vital. A key aspect of quantum error correction is the initialization of physical qubits representing a logical qubit in a highly entangled state. This process, known as state preparation, is the foundation of most quantum error correction codes and, hence, a crucial step towards fault-tolerant quantum computing. Generating a schedule of target-specific instructions to perform the state preparation is highly complex. First software tools exist but are not suitable for the zoned neutral atom architectures. This work addresses this gap by leveraging the computational power of SMT solvers and generating minimal schedules for the state preparation of logical arrays. Experimental evaluations demonstrate that actively utilizing zones to shield idling qubits consistently results in higher fidelities than solutions disregarding these zones. The complete code is publicly available in open-source as part of the Munich Quantum Toolkit (MQT) at https://github.com/cda-tum/mqt-qmap/tree/main/src/na/nasp., Comment: 7 pages, 4 figures

Precise control of topologically protected excitations, such as quantum vortices in atomtronic circuits, opens new possibilities for future quantum technologies. We theoretically investigate the dynamics of Josephson vortices (rotational fluxons) induced by coupled persistent currents in a system of coplanar double-ring atomic Bose-Einstein condensates. We study the Josephson effect in an atomic Josephson junction formed by coaxial ring-shaped condensates. Tunneling superflows, initiated by an imbalance in atomic populations between the rings, are significantly influenced by the persistent currents in the inner and outer rings. This results in pronounced Josephson oscillations in the population imbalance for both co-rotating and non-rotating states. If a linear acceleration is applied to the system, our analysis reveals peculiar azimuthal tunneling patterns and dynamics of Josephson vortices which leads to non-zero net tunneling current and shows sensitivity to the acceleration magnitude. When multiple Josephson vortices are present, asymmetric vortex displacements that correlate with both the magnitude and direction of acceleration can be measured, offering potential for quantum sensing applications., Comment: 10 pages, 9 figures

We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthetic dense image captions with web-scale alt-text to generate factually grounded image captions. Our two-stage approach leverages large vision-language models and language models to create knowledge-augmented captions, which are then used to train a specialized VLM for scaling up the dataset. We train vision-language models on KALE and demonstrate improvements on vision-language tasks. Our experiments show the utility of KALE for training more capable and knowledgeable multimodal models. We release the KALE dataset at https://huggingface.co/datasets/Salesforce/blip3-kale

Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shape of the LLF's optimized remap function, the characteristics of the style transfer can be inferred and reliability of the algorithm can be ensured. Moreover, we enable the LLF to capture complex X-ray style features by replacing the remap function with a Multi-Layer Perceptron (MLP) and adding a trainable normalization layer. We demonstrate the effectiveness of the proposed method by transforming unprocessed mammographic X-ray images into images that match the style of target mammograms and achieve a Structural Similarity Index (SSIM) of 0.94 compared to 0.82 of the baseline LLF style transfer method from Aubry et al.

Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents remains unclear. In this study, forty-six participants interacted with a virtual agent whose persona was manipulated as extravert or introvert in three different conversational tasks (small talk, knowledge test, convincing). Social-evaluation, emotional experience, and realism were assessed using ratings. Interactive engagement was measured by quantifying participants' words and conversational turns. Finally, we measured participants' willingness to ask the agent for help during the knowledge test. Our findings show that the extraverted agent was more positively evaluated, elicited a more pleasant experience and greater engagement, and was assessed as more realistic compared to the introverted agent. Whereas persona did not affect the tendency to ask for help, participants were generally more confident in the answer when they had help of the LLM. Variation of personality traits of LLM-controlled embodied virtual agents, therefore, affects social-emotional processing and behavior in virtual interactions. Embodied virtual agents allow the presentation of naturalistic social encounters in a virtual environment., Comment: 11 pages, 5 figures

Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for the speedup in materials discovery. We show and discuss three models, using property data from two ternary systems (Ag-Pd-Ru; Ag-Pd-Pt), to predict material performance in the shared quaternary system (Ag-Pd-Pt-Ru). First, we apply Gaussian Process Regression (GPR) based on composition, which includes both Ag and Pd, achieving an initial correlation coefficient for the prediction ($r$) of 0.63 and a determination coefficient ($r^2$) of 0.08. Second, we present a version of the GPR model using word embedding-derived materials vectors as representations. Using materials-specific embedding vectors significantly improves the predictive capability, evident from an improved $r^2$ of 0.65. The third model is based on a `standard vector method' which synthesizes weighted vector representations of material properties, then creating a reference vector that results in a very good correlation with the quaternary system's material performance (resulting $r$ of 0.89). Our approach demonstrates that existing experimental data combined with latent knowledge of word embedding-based representations of materials can be used effectively for materials discovery where data is typically sparse., Comment: 17 pages, 12 figures, pre-print

Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. Moreover, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins., Comment: Accepted to WACV 2025 (IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

Quantum computing in atom tweezers requires high-fidelity implementations of quantum operations. Here, we demonstrate the optimal implementation of the transition $|0\rangle \rightarrow |1\rangle$ of two levels, serving as a qubit, of an atom in a tweezer potential, driven by a single-photon Rabi pulse. The Rabi pulse generates a photon recoil of the atom, due to the Lamb-Dicke coupling between the internal and motional degree of freedom, driving the system out of the logical subspace. This detrimental effect is strongly suppressed in the protocols that we propose. Using pulse engineering, we generate optimal protocols composed of a Rabi protocol and a force protocol, corresponding to dynamically displacing the tweezer. We generate these for a large parameter space, from small to large values of the Rabi frequency, and a range of pulse lengths. We identify three main regimes for the optimal protocols, and discuss their properties. In all of these regimes, we demonstrate infidelity well below the current technological standard, thus mitigating a universal challenge in atom tweezers and other quantum technology platforms.

Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations.

Using a quantum gas setup consisting of a Bose-Einstein condensate strongly coupled to a high-finesse optical cavity by a transverse pump laser, we experimentally observe an instability of a dissipative continuous time crystal (CTC) towards a time crystalline state exhibiting two prominent oscillation frequencies. Applying a mean-field approximation model and a Floquet analysis, we theoretically confirm that this transition is a manifestation in a many-body system of a torus bifurcation between a limit cycle (LC) and a limit torus (LT). We theoretically illustrate the LC and LT attractors using the minimal model and experimentally reconstruct them using Takens' embedding theorem applied to the non-destructively measured intracavity photon dynamics.

Conformal inference is a method that provides prediction sets for machine learning models, operating independently of the underlying distributional assumptions and relying solely on the exchangeability of training and test data. Despite its wide applicability and popularity, its application in graph-structured problems remains underexplored. This paper addresses this gap by developing an approach that leverages the rich information encoded in the graph structure of predicted classes to enhance the interpretability of conformal sets. Using a motivating example from genomics, specifically imaging-based spatial transcriptomics data and single-cell RNA sequencing data, we demonstrate how incorporating graph-structured constraints can improve the interpretation of cell type predictions. This approach aims to generate more coherent conformal sets that align with the inherent relationships among classes, facilitating clearer and more intuitive interpretations of model predictions. Additionally, we provide a technique to address non-exchangeability, particularly when the distribution of the response variable changes between training and test datasets. We implemented our method in the open-source R package scConform, available at https://github.com/ccb-hms/scConform.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

Controlling the coupling between different degrees of freedom in many-body systems is a powerful technique for engineering novel phases of matter. We create a bilayer system of two-dimensional (2D) ultracold Bose gases and demonstrate the controlled generation of bulk coherence through tunable interlayer Josephson coupling. We probe the resulting correlated phases using matter-wave interferometry, measuring both the symmetric and antisymmetric phase modes of the bilayer system. These modes exhibit a crossover from short-range to quasi-long-range order above a coupling-dependent critical point, providing direct evidence of bilayer superfluidity mediated by interlayer coupling. We map out the phase diagram and interpret it with renormalization-group theory and Monte Carlo simulations. Additionally, we elucidate the underlying mechanism through the observation of suppressed vortex excitations in the antisymmetric mode.

Blazars are beamed active galactic nuclei known for their strong multi-wavelength variability on timescales from years down to minutes. We aim to investigate the suitability of the twisting jet model presented in previous works to explain the multi-wavelength behaviour of BL Lacertae, the prototype of one of the blazar classes. According to this model, the jet is inhomogeneous, curved, and twisting, and the long-term variability is due to changes in the Doppler factor due to variations in the orientation of the jet-emitting regions. We analysed optical data of the source obtained during monitoring campaigns organised by the Whole Earth Blazar Telescope (WEBT) in 2019-2022, together with radio data from the WEBT and other teams, and gamma-ray data from the Fermi satellite. In this period, BL Lacertae underwent an extraordinary activity phase, reaching its historical optical and gamma-ray brightness maxima. The application of the twisting jet model to the source light curves allows us to infer the wiggling motion of the optical, radio, and gamma-ray jet-emitting regions. The optical-radio correlation shows that the changes in the radio viewing angle follow those in the optical viewing angle by about 120 days, and it suggests that the jet is composed of plasma filaments, which is in agreement with some radio high-resolution observations of other sources. The gamma-ray emitting region is found to be co-spatial with the optical one, and the analysis of the gamma-optical correlation is consistent with both the geometric interpretation and a synchrotron self-Compton (SSC) origin of the high-energy photons. We propose a geometric scenario where the jet is made up of a pair of emitting plasma filaments in a sort of double-helix curved rotating structure, whose wiggling motion produces changes in the Doppler beaming and can thus explain the observed multi-wavelength long-term variability., Comment: In press for A&A

Efficiently estimating energy expectation values of lattice Hamiltonians on quantum computers is a serious challenge, where established techniques can require excessive sample numbers. Here we introduce geometric partitioning as a scalable alternative. It splits the Hamiltonian into subsystems that extend over multiple lattice sites, for which transformations between their local eigenbasis and the computational basis can be efficiently found. This allows us to reduce the number of measurements as we sample from a more concentrated distribution without diagonalizing the problem. For systems in an energy eigenstate, we prove a lower bound on the sampling number improvement over the "naive" mutually commuting local operator grouping, which grows with the considered subsystem size, consistently showing an advantage for our geometric partitioning strategy. Notably, our lower bounds do not decrease but increase for more correlated states (Theorem 1). For states that are weakly isotropically perturbed around an eigenstate, we show how the sampling number improvement translates to imperfect eigenstate improvements, namely measuring close to the true eigenbasis already for smaller perturbations (Theorem 2). We illustrate our findings on multiple two-dimensional lattice models incl. the transverse field XY- and Ising model as well as the Fermi Hubbard model., Comment: 17 + 12 pages, 5 + 0 figures

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

Standard approaches to quantum computing require significant overhead to correct for errors. The hardware size for conventional quantum processors in solids often increases linearly with the number of physical qubits, such as for transmon qubits in superconducting circuits or electron spin qubits in quantum dot arrays. While photonic circuits based on flying qubits do not suffer from decoherence or lack of potential scalability, they have encountered significant challenges to overcome photon loss in long delay circuits. Here, we propose an alternative approach that utilizes flying electronic wave packets propagating in solid-state quantum semiconductor circuits. Using a novel time-bin architecture for the electronic wave packets, hardware requirements are drastically reduced because qubits can be created on-demand and manipulated with a common hardware element, unlike the localized approach of wiring each qubit individually. The electronic Coulomb interaction enables reliable coupling and readout of qubits. Improving upon previous devices, we realize electronic interference at the level of a single quantized mode that can be used for manipulation of electronic wavepackets. This important landmark lays the foundation for fault-tolerant quantum computing with a compact and scalable architecture based on electron interferometry in semiconductors.

Climate model large ensembles are an essential research tool for analysing and quantifying natural climate variability and providing robust information for rare extreme events. The models simulated representations of reality are susceptible to bias due to incomplete understanding of physical processes. This paper aims to correct the bias of five climate variables from the CRCM5 Large Ensemble over Central Europe at a 3-hourly temporal resolution. At this high temporal resolution, two variables, precipitation and radiation, exhibit a high share of zero inflation. We propose a novel bias-correction method, VBC (Vine copula bias correction), that models and transfers multivariate dependence structures for zero-inflated margins in the data from its error-prone model domain to a reference domain. VBC estimates the model and reference distribution using vine copulas and corrects the model distribution via (inverse) Rosenblatt transformation. To deal with the variables' zero-inflated nature, we develop a new vine density decomposition that accommodates such variables and employs an adequately randomized version of the Rosenblatt transform. This novel approach allows for more accurate modelling of multivariate zero-inflated climate data. Compared with state-of-the-art correction methods, VBC is generally the best-performing correction and the most accurate method for correcting zero-inflated events.

We study the photoconductivity response of graphene nanoribbons with armchair edges in the presence of dissipation using a Lindblad-von Neumann master equation formalism. We propose to control the transport properties by illuminating the system with light that is linearly polarized along the finite direction of the nanoribbon while probing along the extended direction. We demonstrate that the largest steady-state photocurrent occurs for a driving frequency that is slightly blue-detuned to the electronic band gap proportional to the width of the nanoribbon. We compare the photoconductivity in the presence of coherent and incoherent light and conclude that the enhancement of the photoconductivity for blue-detuned driving relies on the coherence of the driving term. Based on this result we propose a switching protocol for fast control of the photocurrent on a time scale of a few picoseconds. Furthermore, we suggest a design for a heterostructure of a graphene nanoribbon and a high-Tc superconductor, that is operated as a transistor as a step towards next-generation coherent electronics., Comment: 8 pages including references, 5 figures

The integer quantum Hall effect (QHE) is one of the most fundamental and important phenomena in condensed matter physics. Its most outstanding application is the resistance standard of the SI units, while new applications are likely to emerge in the advent of quantum technologies. Nevertheless, our microscopic understanding of the QHE is not yet complete. For decades, there has been a controversal discussion about the current density distribution for a quantized Hall resistance. This question is essential for applications as it effects the stability of the quantized Hall states as well as the quantum mechanical phase of the charge carriers. It is also important for the understanding of the fractional, spin or anomalous quantum Hall effects. Here, we discuss the current density distribution based on the screening theory of the QHE. The current flow is chiral and consists of the externally applied imposed and a closed loop persistent current, where the latter descreases as the imposed current is increased.