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We report the determination of the magnetic phase diagram of the heavy fermion metal YbRh\textsubscript{2}Si\textsubscript{2} in magnetic fields up to 70\,mT applied perpendicular to the crystallographic c-axis. By a combination of heat capacity, magneto-caloric, and magneto-resistance measurements we map two antiferromagnetic phases: the electronic AFM1 below 70\,mK and electro-nuclear AFM2 below 1.5\,mK. The measurements extend into the microkelvin regime to explore the quantum phase transitions in this system. We demonstrate how the hyperfine interaction significantly modifies the phase diagram and the putative field-tuned quantum critical point. The determination of the rich magnetic properties of YbRh\textsubscript{2}Si\textsubscript{2} is essential to understanding the interplay of the two magnetic orders and superconductivity in this compound.

Pure as well as Ag- and Au-substituted YbInCu$_4$ single crystals were structurally and chemically characterized and investigated by means of heat capacity, magnetization, resistivity and ultrasonic measurements. We studied the influence of different compositions of the initial melt as well as of Au and Ag substitutions on the valence change and investigated whether this change occurs via a first-order phase transition or via crossover. We constructed a phase diagram of YbInCu$_4$ as a function of various substitutions and show that the position of the critical endpoint of the valence transition depends on the substituent and on the conditions under which the samples were grown. Multiple thermal cycles through the first-order transition lead to a significant modification of the physical properties which clearly demonstrated the influence of defects in substituted YbInCu$_4$.

The present study examines the effectiveness of applying Artificial Intelligence methods in an automotive production environment to predict unknown lead times in a non-cycle-controlled production area. Data structures are analyzed to identify contextual features and then preprocessed using one-hot encoding. Methods selection focuses on supervised machine learning techniques. In supervised learning methods, regression and classification methods are evaluated. Continuous regression based on target size distribution is not feasible. Classification methods analysis shows that Ensemble Learning and Support Vector Machines are the most suitable. Preliminary study results indicate that gradient boosting algorithms LightGBM, XGBoost, and CatBoost yield the best results. After further testing and extensive hyperparameter optimization, the final method choice is the LightGBM algorithm. Depending on feature availability and prediction interval granularity, relative prediction accuracies of up to 90% can be achieved. Further tests highlight the importance of periodic retraining of AI models to accurately represent complex production processes using the database. The research demonstrates that AI methods can be effectively applied to highly variable production data, adding business value by providing an additional metric for various control tasks while outperforming current non AI-based systems.

Training and fine-tuning deep learning models, especially large language models (LLMs), on limited and imbalanced datasets poses substantial challenges. These issues often result in poor generalization, where models overfit to dominant classes and underperform on minority classes, leading to biased predictions and reduced robustness in real-world applications. To overcome these challenges, we propose augmenting features in the embedding space by generating synthetic samples using a range of techniques. By upsampling underrepresented classes, this method improves model performance and alleviates data imbalance. We validate the effectiveness of this approach across multiple open-source text classification benchmarks, demonstrating its potential to enhance model robustness and generalization in imbalanced data scenarios., Comment: Accepted for presentation at IEEE SSCI 2025

The bispectrum of galaxy number counts is a key probe of large-scale structure (LSS), offering insights into the initial conditions of the Universe, the nature of gravity, and cosmological parameters. In this work, we derive the theoretical angular bispectrum of number counts for the first time without relying on the Limber approximation, while incorporating redshift binning. Notably, our analysis includes all Newtonian effects, leading relativistic projection effects, and general relativistic contributions, including radiation dynamics, up to second order in perturbation theory. For simplicity, however, we neglect any biasing effects. We have implemented these expressions in an open access code to evaluate the bispectrum for two redshift bins, $z=2 \pm 0.25$ and $z=0.6 \pm 0.05$, and compare our analytical results with simulations. For the contributions that already appear in a Newtonian treatment we find an interesting cancellation between the quadratic terms. At $z=2$, the projection effects and the dynamical effects have similar amplitude on large scales as we approach $k \sim \mathcal{H}$. In the squeezed limit, radiation effects are found to be the leading relativistic effects, by one order of magnitude. At $z=0.6$, we find the correct weak-field hierarchy between the terms, controlled by the ratio $\mathcal{H}/k$, but we still find that dynamical effects (from nonlinear evolution) are only a factor $2-3$ smaller than the projection effects. We compare our results with simulation measurements and find good agreement for the total bispectrum., Comment: 34 pages, 9 figures, Prepared for JCAP

Optical Coherence Tomography (OCT) has become an indispensable tool for investigating mesoscopic features in soft matter and fluid mechanics. Its ability to provide high-resolution, non-invasive measurements in both spatial and temporal domains bridges critical gaps in experimental instrumentation, enabling the study of complex, confined, and dynamic systems. This review serves as both an introduction to OCT and a practical guide for researchers seeking to adopt this technology. A set of tutorials, complemented by Python scripts, are provided for both intensity- and Doppler-based techniques. The versatility of OCT is illustrated through case studies, including time-resolved velocimetry, particle-based velocity measurements, slip velocity characterization, detection of shear-induced structures, and analysis of fluid-fluid and fluid-structure interactions. Drawing on our experiences, we also present a set of practical guidelines for avoiding common pitfalls.

In this paper, we present MCBench, a benchmark suite designed to assess the quality of Monte Carlo (MC) samples. The benchmark suite enables quantitative comparisons of samples by applying different metrics, including basic statistical metrics as well as more complex measures, in particular the sliced Wasserstein distance and the maximum mean discrepancy. We apply these metrics to point clouds of both independent and identically distributed (IID) samples and correlated samples generated by MC techniques, such as Markov Chain Monte Carlo or Nested Sampling. Through repeated comparisons, we evaluate test statistics of the metrics, allowing to evaluate the quality of the MC sampling algorithms. Our benchmark suite offers a variety of target functions with different complexities and dimensionalities, providing a versatile platform for testing the capabilities of sampling algorithms. Implemented as a Julia package, MCBench enables users to easily select test cases and metrics from the provided collections, which can be extended as needed. Users can run external sampling algorithms of their choice on these test functions and input the resulting samples to obtain detailed metrics that quantify the quality of their samples compared to the IID samples generated by our package. This approach yields clear, quantitative measures of sampling quality and allows for informed decisions about the effectiveness of different sampling methods. By offering such a standardized method for evaluating MC sampling quality, our benchmark suite provides researchers and practitioners from many scientific fields, such as the natural sciences, engineering, or the social sciences with a valuable tool for developing, validating and refining sampling algorithms.

3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challenges for practical applications. In this paper, we introduce an efficient compression technique that significantly reduces storage overhead by using compact representation. We propose a unified architecture that combines point cloud data and feature planes through a progressive tri-plane structure. Our method utilizes 2D feature planes, enabling continuous spatial representation. To further optimize these representations, we incorporate entropy modeling in the frequency domain, specifically designed for standard video codecs. We also propose channel-wise bit allocation to achieve a better trade-off between bitrate consumption and feature plane representation. Consequently, our model effectively leverages spatial correlations within the feature planes to enhance rate-distortion performance using standard, non-differentiable video codecs. Experimental results demonstrate that our method outperforms existing methods in data compactness while maintaining high rendering quality. Our project page is available at https://fraunhoferhhi.github.io/CodecGS

We report on the electron spin resonance (ESR), heat capacity, magnetization, nuclear magnetic resonance (NMR), magnetic circular and linear dichroism (XMCD, XMLD), as well as the electrical resistivity of EuMn$_{2}$P$_{2}$ single crystals. Antiferromagnetic order of Eu was observed in several quantities at $T^{\rm Eu}_{\rm N}\,=\,18\,\rm K$. The temperature dependencies of ESR linewidth and resonance shift show, when approaching the Eu-ordered state, a divergence towards $T^{\rm Eu}_{\rm N}$, indicating the growing importance of magnetic correlations and the build-up of internal magnetic fields. An additional temperature scale of $\approx 47\,\rm K$ has considerable impact on linewidth, resonance field and intensity. This points to the presence of weak Mn-based ordering. The observed ESR line is interpreted as an Eu$^{2+}$ resonance, which probes the weak magnetic background of the Mn subsystem. Such picture is suggested by the lineshape which keeps to be Lorentzian across the $47\,\rm K$ scale and by the ESR intensity which can be described by the same Curie-Weiss temperature above and below $47\,\rm K$. In the same temperature range anomalies were observed at $48.5\,\rm K$ and $51\,\rm K$ in the heat capacity data as well as a pronounced broadening of the NMR signal of the EuMn$_{2}$P$_{2}$ samples. In XMCD and XMLD measurements, this weak magnetic order could not be detected in the same temperature range which might be due to the small magnetic moment, with a potential $c$-component or frustration.

Excitons in Transition Metal Dichalcogenides (TMDs) acquire a spin-like quantum number, a pseudospin, originating from the crystal's discrete rotational symmetry. Here, we break this symmetry using a tunable uniaxial strain, effectively generating a pseudomagnetic field exceeding 40 Tesla. Under this large field, we demonstrate pseudospin analogs of spintronic phenomena such as the Zeeman effect and Larmor precession. Moreover, we determine previously inaccessible fundamental properties of TMDs, including the strength of the depolarizing field responsible for the loss of exciton coherence. Finally, we uncover the bosonic -- as opposed to fermionic -- nature of many-body excitonic species using the pseudomagnetic equivalent of the $g$-factor spectroscopy. Our work is the first step toward establishing this spectroscopy as a universal method for probing correlated many-body states and realizing pseudospin analogs of spintronic devices.

Phase transitions are fundamental in nature. A small parameter change near a critical point leads to a qualitative change in system properties. Across a regular phase transition, the system remains in thermal equilibrium and, therefore, experiences a change of static properties, like the emergence of a magnetisation upon cooling a ferromagnet below the Curie temperature. When driving a system far from equilibrium, novel, otherwise inaccessible quantum states of matter may arise. Such states are typically non-Hermitian, that is, their dynamics break time-reversal symmetry, a basic law of equilibrium physics. Phase transitions in non-Hermitian systems are of fundamentally new nature in that the dynamical behaviour rather than static properties may undergo a qualitative change at a critical, here called exceptional point. Here we experimentally realize a non-Hermitian phase transition in a bulk condensed-matter system. Optical excitation creates charge carriers in the ferromagnetic semiconductor EuO. In a temperature-dependent interplay with the Hermitian transition to ferromagnetic order, a non-Hermitian change of the relaxation dynamics occurs, manifesting in our time-resolved reflection data as a transition from bi-exponential real to single-exponential complex decay. Our theory models this behavior and predicts non-Hermitian phase transitions for a large class of condensed-matter systems, where they may be exploited to sensitively control bulk-dynamic properties., Comment: 15 pages, 10 figures

We present a study on using Markov Chain Monte Carlo (MCMC) techniques to explore the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders. To this end, we combine the BAT.jl package that provides implementations of a variety of MCMC algorithms with the Sherpa event generator framework. We discuss technical aspects of the implementation and the resulting algorithm and present first results for the process of $Z+3$ jets production at the LHC., Comment: 8 pages, 4 figures, Conference proceeding for CHEP 2024

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the $\texttt{sbi}$ toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.

Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing similar adversarial examples in the physical world has been difficult due to the non-differentiable image distortion functions in visual sensing systems. The existing algorithms for generating physically realizable adversarial examples often loosen their definition of adversarial examples by allowing unbounded perturbations, resulting in obvious or even strange visual patterns. In this work, we make adversarial examples imperceptible in the physical world using a straight-through estimator (STE, a.k.a. BPDA). We employ STE to overcome the non-differentiability -- applying exact, non-differentiable distortions in the forward pass of the backpropagation step, and using the identity function in the backward pass. Our differentiable rendering extension to STE also enables imperceptible adversarial patches in the physical world. Using printout photos, and experiments in the CARLA simulator, we show that STE enables fast generation of $\ell_\infty$ bounded adversarial examples despite the non-differentiable distortions. To the best of our knowledge, this is the first work demonstrating imperceptible adversarial examples bounded by small $\ell_\infty$ norms in the physical world that force zero classification accuracy in the global perturbation threat model and cause near-zero ($4.22\%$) AP50 in object detection in the patch perturbation threat model. We urge the community to re-evaluate the threat of adversarial examples in the physical world.

Laser diode injection-locking is a commonly used method to amplify laser light, while preserving its spectral properties. Fluctuations in the environmental conditions can cause injection-locking to fail, especially when operating with low seed powers or with a swept seed frequency. We present a method inspired by the H\"ansch-Couillaud scheme to monitor and actively stabilize the conditions required for injection-locking a laser diode. Using only a few optical components, our scheme can run continuously in the background and is modulation-free. We demonstrate its efficacy by showing its robustness to large fluctuations in diode temperature, seed frequency and power, effectively extending the reliable operating range and stability over time.

The quest for a global quantum internet is based on the realization of a scalable network which requires quantum hardware with exceptional performance. Among them are quantum light sources providing deterministic, high brightness, high-fidelity entangled photons and quantum memories with coherence times in the millisecond range and above. To operate the network on a global scale, the quantum light source should emit at telecommunication wavelengths with minimum propagation losses. A cornerstone for the operation of such a quantum network is the demonstration of quantum teleportation. Here we realize full-photonic quantum teleportation employing one of the most promising platforms, i.e. semiconductor quantum dots, which can fulfill all the aforementioned requirements. Two remote quantum dots are used, one as a source of entangled photon pairs and the other as a single-photon source. The frequency mismatch between the triggered sources is erased using two polarization-preserving quantum frequency converters, enabling a Bell state measurement at telecommunication wavelengths. A post-selected teleportation fidelity of up to 0.721(33) is achieved, significantly above the classical limit, demonstrating successful quantum teleportation between light generated by distinct sources. These results mark a major advance for the semiconductor platform as a source of quantum light fulfilling a key requirement for a scalable quantum network. This becomes particularly relevant after the seminal breakthrough of addressing a nuclear spin in semiconductor quantum dots demonstrating long coherence times, thus fulfilling another crucial step towards a scalable quantum network.

We present a novel image-based adaptive domain decomposition FEM framework to accelerate the solution of continuum damage mechanics problems. The key idea is to use image-processing techniques in order to identify the moving interface between the healthy subdomain and unhealthy subdomain as damage propagates, and then use an iterative Schur complement approach to efficiently solve the problem. The implementation of the algorithm consists of several modular components. Following the FEM solution of a load increment, the damage detection module is activated, a step that is based on several image-processing operations including colormap manipulation and morphological convolution-based operations. Then, the damage tracking module is invoked, to identify the crack growth direction using geometrical operations and ray casting algorithm. This information is then passed into the domain decomposition module, where the domain is divided into the healthy subdomain which contains only undamaged elements, and the unhealthy subdomain which comprises both damaged and undamaged elements. Continuity between the two regions is restored using penalty constraints. The computational savings of our method stem from the Schur complement, which allows for the iterative solution of the system of equations appertaining only to the unhealthy subdomain. Through an exhaustive comparison between our approach and single domain computations, we demonstrate the accuracy, efficiency, and robustness of the framework. We ensure its compatibility against local and non-local damage laws, structured and unstructured meshes, as well as in cases where different damage paths eventually merge. Since the key novelty lies in using image processing tools to inform the decomposition, our framework can be readily extended beyond damage mechanics and model several classes of non-linear problems such as plasticity and phase-field.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging., Comment: Part 3 of 3 in "Considerations and recommendations for preclinical diffusion MRI"

Free-space optical communication (FSOC) systems offer high-bandwidth and secure communication with minimal capital costs. Adaptive optics (AO) are typically added to these systems to decrease atmospheric channel losses; however, the performance of traditional AO wavefront sensors degrades in long-range, deep turbulence conditions. Alternative wavefront sensors using phase diversity can successfully reconstruct wavefronts in deep turbulence, but current implementations require bulky setups with high latency. In this work, we employ a nanostructured birefringent metasurface optic that enables low-latency phase diversity wavefront sensing in a compact form factor. We prove the effectiveness of this approach in mid-to-high turbulence (Rytov numbers from 0.2 to 0.6) through simulation and experimental demonstration. In both cases an average 16-fold increase in signal from the corrected beam is obtained. Our approach opens a pathway for compact, robust wavefront sensing that enhances range and accuracy of FSOC systems.

Increasing complexity in the power system and the transformation towards a smart grid lead to the necessity of new tools and methods for the development and testing of new technologies. One testing method is co-simulation, which allows coupling simulation components from different domains to test their interaction. Because the manual configuration of complex large-scale co-simulation scenarios can be error-prone, we propose an approach for assisting the user in the development of co-simulation scenarios. Our approach uses an information model, a component catalog implemented in a Semantic Media Wiki, and Semantic Web technologies to assist the high-level modeling of co-simulation scenarios, recommend suitable simulation components, and validate co-simulation scenarios. This assistance aims to improve the usability of co-simulation in the development of interdisciplinary, large-scale scenarios., Comment: 6 pages, 3 figures, conference proceedings Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) 2019

The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making their inference process more transparent. However, current explainability methods are seldom evaluated in the way they are intended to be used: by real-world end users. To address this, we conducted a large-scale user study with 85 healthcare practitioners in the context of human-AI collaborative chest X-ray analysis. We evaluated three types of explanations: visual explanations (saliency maps), natural language explanations, and a combination of both modalities. We specifically examined how different explanation types influence users depending on whether the AI advice and explanations are factually correct. We find that text-based explanations lead to significant over-reliance, which is alleviated by combining them with saliency maps. We also observe that the quality of explanations, that is, how much factually correct information they entail, and how much this aligns with AI correctness, significantly impacts the usefulness of the different explanation types., Comment: EMNLP 2024

Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the log-density, which may not always be available. Existing gradient-free versions of SVGD make use of simple Monte Carlo approximations or gradients from surrogate distributions, both with limitations. To improve gradient-free Stein variational inference, we combine SVGD steps with evolution strategy (ES) updates. Our results demonstrate that the resulting algorithm generates high-quality samples from unnormalized target densities without requiring gradient information. Compared to prior gradient-free SVGD methods, we find that the integration of the ES update in SVGD significantly improves the performance on multiple challenging benchmark problems.

In the evolving NLP landscape, benchmarks serve as yardsticks for gauging progress. However, existing Legal NLP benchmarks only focus on predictive tasks, overlooking generative tasks. This work curates LexSumm, a benchmark designed for evaluating legal summarization tasks in English. It comprises eight English legal summarization datasets, from diverse jurisdictions, such as the US, UK, EU and India. Additionally, we release LexT5, legal oriented sequence-to-sequence model, addressing the limitation of the existing BERT-style encoder-only models in the legal domain. We assess its capabilities through zero-shot probing on LegalLAMA and fine-tuning on LexSumm. Our analysis reveals abstraction and faithfulness errors even in summaries generated by zero-shot LLMs, indicating opportunities for further improvements. LexSumm benchmark and LexT5 model are available at https://github.com/TUMLegalTech/LexSumm-LexT5., Comment: Accepted to NLLP Workshop, EMNLP 2024

To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected units and captures complex dependencies among connections and attributes, while retaining the virtues of linear regression, logistic regression, and other regression models by being interpretable and widely applicable. On the computational side, we show that the regression framework is amenable to scalable statistical computing based on convex optimization of pseudo-likelihoods using minorization-maximization methods. On the theoretical side, we establish convergence rates for pseudo-likelihood estimators based on a single observation of dependent connections and attributes. We demonstrate the regression framework using simulations and an application to hate speech on the social media platform X in the six months preceding the insurrection at the U.S. Capitol on January 6, 2021.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.

Modelling the cosmic large-scale structure can be done through numerical N-body simulations or by using perturbation theory. Here, we present an N-body approach that effectively implements a multi-step forward model based on Lagrangian Perturbation Theory (LPT) in a $\Lambda$CDM Universe. This is achieved by introducing the second-order accurate BullFrog integrator, which automatically performs 2LPT time steps to second order without requiring the explicit computation of 2LPT displacements. Importantly, we show that BullFrog trajectories rapidly converge to the exact solution as the number of time steps increases, at any moment in time, even though 2LPT becomes invalid after shell-crossing. As a validation test, we compare BullFrog against other N-body integrators and high-order LPT, both for a realistic $\Lambda$CDM cosmology and for simulations with a sharp UV cutoff in the initial conditions. The latter scenario enables controlled experiments against LPT and, in practice, is particularly relevant for modelling coarse-grained fluids arising in the context of effective field theory. We demonstrate that BullFrog significantly improves upon other LPT-inspired integrators, such as FastPM and COLA, without incurring any computational overhead compared to standard N-body integrators. Implementing BullFrog in any existing N-body code is straightforward, particularly if FastPM is already integrated., Comment: 31 pages plus appendices and references, 14 figures, v2: added clarifications and results for cross-correlation, JCAP accepted

This paper investigates the performance of a likelihood ratio test in combination with a polynomial subspace projection approach to detect weak transient signals in broadband array data. Based on previous empirical evidence that a likelihood ratio test is advantageously applied in a lower-dimensional subspace, we present analysis that highlights how the polynomial subspace projection whitens a crucial part of the signals, enabling a detector to operate with a shortened temporal window. This reduction in temporal correlation, together with a spatial compaction of the data, also leads to both computational and numerical advantages over a likelihood ratio test that is directly applied to the array data. The results of our analysis are illustrated by examples and simulations.

Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-consuming. The use of generative models in designing antibodies therefore holds great promise, as it can reduce the time and resources required. Recently, the class of diffusion models has gained considerable traction for their ability to synthesize diverse and high-quality samples. In their basic form, however, they lack mechanisms to optimize for specific properties, such as binding affinity to an antigen. In contrast, the class of offline Reinforcement Learning (RL) methods has demonstrated strong performance in navigating large search spaces, including scenarios where frequent real-world interaction, such as interaction with a wet lab, is impractical. Our novel method, BetterBodies, which combines Variational Autoencoders (VAEs) with RL guided latent diffusion, is able to generate novel sets of antibody CDRH3 sequences from different data distributions. Using the Absolut! simulator, we demonstrate the improved affinity of our novel sequences to the SARS-CoV spike receptor-binding domain. Furthermore, we reflect biophysical properties in the VAE latent space using a contrastive loss and add a novel Q-function based filtering to enhance the affinity of generated sequences. In conclusion, methods such as ours have the potential to have great implications for real-world biological sequence design, where the generation of novel high-affinity binders is a cost-intensive endeavor.

We report on single crystal growth and physical properties of the quantum critical Kondo-lattice system YbNi$_4$(P$_{1-x}$As$_{x}$)$_2$ with $0\leq x\leq 1$ which hosts a ferromagnetic quantum critical point at $x \approx 0.1$. We performed measurements of the magnetization, electrical resistivity, thermopower, heat capacity, and resonant X-ray emission spectroscopy. Arsenic substitution leads to a homogeneous increase of the unit-cell volume, with well-defined As-concentrations in large parts of the single crystals. All data consistently show that with increasing $x$ the Kondo temperature increases, while the magnetic anisotropy observed at low $x$ fully vanishes towards $x=1$. Consequently, at low temperatures, the system shows a crossover from pronounced non-Fermi liquid behaviour for $x \leq 0.2$ to a Fermi liquid behavior for $x > 0.2$ with weak correlations. There is a continuous change in Yb valence from nearly trivalent at low $x$ to a slightly lower value for $x = 0.6$, which correlates with the Kondo temperature. Interestingly, specific heat measurements at very low temperatures show that $C/T$ strongly increases towards lower $T$ for $x = 0.13$ and $x = 0.2$ with a very similar power law. This suggests that in YbNi$_4$(P$_{1-x}$As$_{x}$)$_2$ a quantum critical region rather than a quantum critical point might exist.

The automotive industry is currently expanding digital display options with every new model that comes onto the market. This entails not just an expansion in dimensions, resolution, and customization choices, but also the capability to employ novel display effects like overlays while assembling the content of the display cluster. Unfortunately, this raises the need for appropriate monitoring systems that can detect rendering errors and apply appropriate countermeasures when required. Classical solutions such as Cyclic Redundancy Checks (CRC) will soon be no longer viable as any sort of alpha blending, warping of scaling of content can cause unwanted CRC violations. Therefore, we propose a novel monitoring approach to verify correctness of displayed content using telltales (e.g. warning signs) as example. It uses a learning-based approach to separate "good" telltales, i.e. those that a human driver will understand correctly, and "corrupted" telltales, i.e. those that will not be visible or perceived correctly. As a result, it possesses inherent resilience against individual pixel errors and implicitly supports changing backgrounds, overlay or scaling effects. This is underlined by our experimental study where all "corrupted" test patterns were correctly classified, while no false alarms were triggered., Comment: 9 pages

In this paper, we study how generative AI and specifically large language models (LLMs) impact learning in coding classes. We show across three studies that LLM usage can have positive and negative effects on learning outcomes. Using observational data from university-level programming courses, we establish such effects in the field. We replicate these findings in subsequent experimental studies, which closely resemble typical learning scenarios, to show causality. We find evidence for two contrasting mechanisms that determine the overall effect of LLM usage on learning. Students who use LLMs as personal tutors by conversing about the topic and asking for explanations benefit from usage. However, learning is impaired for students who excessively rely on LLMs to solve practice exercises for them and thus do not invest sufficient own mental effort. Those who never used LLMs before are particularly prone to such adverse behavior. Students without prior domain knowledge gain more from having access to LLMs. Finally, we show that the self-perceived benefits of using LLMs for learning exceed the actual benefits, potentially resulting in an overestimation of one's own abilities. Overall, our findings show promising potential of LLMs as learning support, however also that students have to be very cautious of possible pitfalls.

Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination., Comment: 10 pages, 4 figures, 4 tables, preprint

The UN Office of Outer Space Affairs identifies synergy of space development activities and international cooperation through data and infrastructure sharing in their Sustainable Development Goal 17 (SDG17). Current multilateral space exploration paradigms, however, are divided between the Artemis and the Roscosmos-CNSA programs to return to the moon and establish permanent human settlements. As space agencies work to expand human presence in space, economic resource consolidation in pursuit of technologically ambitious space expeditions is the most sensible path to accomplish SDG17. This paper compiles a budget dataset for the top five federally-funded space agencies: CNSA, ESA, JAXA, NASA, and Roscosmos. Using time-series econometric anslysis methods in STATA, this work analyzes each agency's economic contributions toward space exploration. The dataset results are used to propose a multinational space mission, Vela, for the development of an orbiting space station around Mars in the late 2030s. Distribution of economic resources and technological capabilities by the respective space programs are proposed to ensure programmatic redundancy and increase the odds of success on the given timeline., Comment: 13 pages, 4 figures, Manuscript presented at the 73rd International Astronautical Congress, IAC 2022, Paris, France, 18 - 22 September 2022. Slideshow presentation: [https://hollydinkel.github.io/assets/pdf/IAC2022_presentation.pdf]

Understanding why researchers cite each other has been a longstanding conjecture in studying scientific networks. Prior research suggests relevance, group cohesion, or honest source crediting as possible factors. However, the dual nature of cognitive and social dimensions underlying citation is often overlooked by not considering the intermediary steps leading up to a citation. For one work to be cited by another, it must first be published by a set of authors. Therefore, we investigate the reasons behind researchers' citations, explicitly examining the interplay of socio-cognitive ties through the interdependence of coauthorship and citation networks. We assess our claims in an empirical analysis by employing the Author-Oriented Relational HyperEvent Model (AuthRHEM) to study Chilean astronomers' citation and collaboration behavior between 2013 and 2015 in a joint framework. We find evidence that when deciding which work to cite, authors prefer other work with novelty and cognitive ties, such as work-to-work relations. At the same time, coherent groups are relevant because coauthors are cocited more frequently in subsequent publications.

Inspired by the success of the Transformer architecture in natural language processing and computer vision, we investigate the use of Transformers in Reinforcement Learning (RL), specifically in modeling the environment's dynamics using Transformer Dynamics Models (TDMs). We evaluate the capabilities of TDMs for continuous control in real-time planning scenarios with Model Predictive Control (MPC). While Transformers excel in long-horizon prediction, their tokenization mechanism and autoregressive nature lead to costly planning over long horizons, especially as the environment's dimensionality increases. To alleviate this issue, we use a TDM for short-term planning, and learn an autoregressive discrete Q-function using a separate Q-Transformer (QT) model to estimate a long-term return beyond the short-horizon planning. Our proposed method, QT-TDM, integrates the robust predictive capabilities of Transformers as dynamics models with the efficacy of a model-free Q-Transformer to mitigate the computational burden associated with real-time planning. Experiments in diverse state-based continuous control tasks show that QT-TDM is superior in performance and sample efficiency compared to existing Transformer-based RL models while achieving fast and computationally efficient inference., Comment: Accepted by IEEE Robotics and Automation Letters (RA-L)

It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC) -- a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned ``integrator'' and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems., Comment: 16 pages, 12 figures. Figures added, References added; Comments welcome

We present a review of popular simulation engines and frameworks used in reinforcement learning (RL) research, aiming to guide researchers in selecting tools for creating simulated physical environments for RL and training setups. It evaluates nine frameworks (Brax, Chrono, Gazebo, MuJoCo, ODE, PhysX, PyBullet, Webots, and Unity) based on their popularity, feature range, quality, usability, and RL capabilities. We highlight the challenges in selecting and utilizing physics engines for RL research, including the need for detailed comparisons and an understanding of each framework's capabilities. Key findings indicate MuJoCo as the leading framework due to its performance and flexibility, despite usability challenges. Unity is noted for its ease of use but lacks scalability and simulation fidelity. The study calls for further development to improve simulation engines' usability and performance and stresses the importance of transparency and reproducibility in RL research. This review contributes to the RL community by offering insights into the selection process for simulation engines, facilitating informed decision-making., Comment: 11 pages, 3 figures

Vision-language models have shown impressive progress in recent years. However, existing models are largely limited to turn-based interactions, where each turn must be stepped (i.e., prompted) by the user. Open-ended, asynchronous interactions, where an AI model may proactively deliver timely responses or feedback based on the unfolding situation in real-time, are an open challenge. In this work, we present the QEVD benchmark and dataset, which explores human-AI interaction in the challenging, yet controlled, real-world domain of fitness coaching -- a task which intrinsically requires monitoring live user activity and providing immediate feedback. The benchmark requires vision-language models to recognize complex human actions, identify possible mistakes, and provide appropriate feedback in real-time. Our experiments reveal the limitations of existing state-of-the-art vision-language models for such asynchronous situated interactions. Motivated by this, we propose a simple end-to-end streaming baseline that can respond asynchronously to human actions with appropriate feedback at the appropriate time., Comment: Accepted to the 2024 NeurIPS Datasets and Benchmarks track; data and code are available at: https://www.qualcomm.com/developer/software/qevd-dataset and https://github.com/Qualcomm-AI-research/FitCoach

The signal induced by a temperate, terrestrial planet orbiting a Sun-like star is an order of magnitude smaller than the host stars' intrinsic variability. Understanding stellar activity is, therefore, a fundamental obstacle in confirming the smallest exoplanets. We present the Lowell Observatory Solar Telescope (LOST), a solar feed for the EXtreme PREcision Spectrometer (EXPRES) at the 4.3-m Lowell Discovery Telescope (LDT). EXPRES is one of the newest high-resolution spectrographs that accurately measure extreme radial velocity. With LOST/EXPRES, we observe disk-integrated sunlight autonomously throughout the day. In clear conditions, we achieve a ~137,500 optical spectrum of the Sun with a signal-to-noise of 500 in ~150s. Data is reduced using the standard EXPRES pipeline with minimal modification to ensure the data are comparable to the observations of other stars with the LDT. During the first three years of operation, we find a daily RMS of 71 cm/s. Additionally, having two EPRV spectrometers located in Arizona gives us an unprecedented opportunity to benchmark the performance of these planet-finders. We find a RMS of just 55 cm/s when comparing data taken simultaneously with EXPRES and NEID., Comment: SPIE Astronomical Telescopes & Instrumentation proceedings paper

This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023., Comment: Forecasting competition, conflict forecasting, forecasting with uncertainty

Our understanding of neutrino flavor conversion in the innermost regions of core-collapse supernovae and neutron star mergers is mostly limited to spherically symmetric configurations that facilitate the numerical solution of the quantum kinetic equations. In this paper, we simulate neutrino quantum kinetics within a (2+1+1) dimensional setup: we model the flavor evolution during neutrino decoupling from matter in two spatial dimensions, one neutrino momentum variable, and time; taking into account non-forward neutral current and charged current collisions of neutrinos with the matter background, as well as neutrino advection. In order to mimic fluctuations in the neutrino emission and matter background, and explore their effect on the flavor evolution, we introduce perturbations in the collision term as well as in the vacuum term of the Hamiltonian. Because of such perturbations, the initial symmetry of the neutrino field across the simulation annulus is broken and flavor conversion is qualitatively affected, with regions of larger flavor conversion alternating across the simulation annulus. In addition, neutrino advection is responsible for spreading flavor waves across neighboring spatial regions. Although based on a simplified setup, our findings highlight the importance of modeling neutrino quantum kinetics in multi-dimensions to assess the impact of neutrinos on the physics of compact astrophysical sources and nucleosynthesis., Comment: 24 pages, including 13 figures and three appendices. References and appendix added, minor changes in text. Matches version accepted for publication in JCAP. Supplemental material is available at this link: https://sid.erda.dk/share_redirect/A8yryUX901/index.html

Generating diverse samples under hard constraints is a core challenge in many areas. With this work we aim to provide an integrative view and framework to combine methods from the fields of MCMC, constrained optimization, as well as robotics, and gain insights in their strengths from empirical evaluations. We propose NLP Sampling as a general problem formulation, propose a family of restarting two-phase methods as a framework to integrated methods from across the fields, and evaluate them on analytical and robotic manipulation planning problems. Complementary to this, we provide several conceptual discussions, e.g. on the role of Lagrange parameters, global sampling, and the idea of a Diffused NLP and a corresponding model-based denoising sampler.

In this work, we tackle the problems of efficiency and scalability for predictive coding networks in machine learning. To do so, we first propose a library called PCX, whose focus lies on performance and simplicity, and provides a user-friendly, deep-learning oriented interface. Second, we use PCX to implement a large set of benchmarks for the community to use for their experiments. As most works propose their own tasks and architectures, do not compare one against each other, and focus on small-scale tasks, a simple and fast open-source library adopted by the whole community would address all of these concerns. Third, we perform extensive benchmarks using multiple algorithms, setting new state-of-the-art results in multiple tasks and datasets, as well as highlighting limitations inherent to PC that should be addressed. Thanks to the efficiency of PCX, we are able to analyze larger architectures than commonly used, providing baselines to galvanize community efforts towards one of the main open problems in the field: scalability. The code for PCX is available at \textit{https://github.com/liukidar/pcax}., Comment: 33 pages, 25 figures

Thin organic films and two-dimensional (2D) molecular assemblies on solid surfaces yield the potential for applications in molecular electronics, optoelectronics, catalysis, and sensing. These applications rely on the intrinsic electronic properties of the hybrid organic/inorganic interface. Here, we investigate the energy dispersion of 2D electronic states at the interface between an atomically thin self-assembled molecular film, comprised of flat, noncovalently bonded 9,10-dicyanoanthracene (DCA) molecules, and a Ag(111) surface. Using Fourier-transformed scanning tunnelling spectroscopy (FT-STS), we determined that the 2D electronic wave functions with wavevectors within ~80% of the first Brillouin zone (BZ) area close to the Gamma-point are free-electron-like, suggesting a weak electronic interaction between the 2D molecular film and the metal surface. Via a perturbative second-order correction to the free electron energy dispersion, we further established an upper bound for the amplitude of the scattering potential resulting from the self-assembled molecular film that the interface electrons are subject to, on the order of 1.5 eV. Our approach allows for quantifying electronic interactions at hybrid 2D interfaces and heterostructures.