Search

Cosmic Voids are a promising probe of cosmology for spectroscopic galaxy surveys due to their unique response to cosmological parameters. Their combination with other probes promises to break parameter degeneracies. Due to simplifying assumptions, analytical models for void statistics are only representative of a subset of the full void population. We present a set of neural-based emulators for void summary statistics of watershed voids, which retain more information about the full void population than simplified analytical models. We build emulators for the void size function and void density profiles traced by the halo number density using the Quijote suite of simulations for a broad range of the $\Lambda\mathrm{CDM}$ parameter space. The emulators replace the computation of these statistics from computationally expensive cosmological simulations. We demonstrate the cosmological constraining power of voids using our emulators, which offer orders-of-magnitude acceleration in parameter estimation, capture more cosmological information compared to analytic models, and produce more realistic posteriors compared to Fisher forecasts. We find that the parameters $\Omega_m$ and $\sigma_8$ in this Quijote setup can be recovered to $14.4\%$ and $8.4\%$ accuracy respectively using void density profiles; including the additional information in the void size function improves the accuracy on $\sigma_8$ to $6.8\%$. We demonstrate the robustness of our approach to two important variables in the underlying simulations, the resolution, and the inclusion of baryons. We find that our pipeline is robust to variations in resolution, and we show that the posteriors derived from the emulated void statistics are unaffected by the inclusion of baryons with the Magneticum hydrodynamic simulations. This opens up the possibility of a baryon-independent probe of the large-scale structure., Comment: 13 pages, 10 figures

Differentiable simulators have recently shown great promise for training autonomous vehicle controllers. Being able to backpropagate through them, they can be placed into an end-to-end training loop where their known dynamics turn into useful priors for the policy to learn, removing the typical black box assumption of the environment. So far, these systems have only been used to train policies. However, this is not the end of the story in terms of what they can offer. Here, for the first time, we use them to train world models. Specifically, we present three new task setups that allow us to learn next state predictors, optimal planners, and optimal inverse states. Unlike analytic policy gradients (APG), which requires the gradient of the next simulator state with respect to the current actions, our proposed setups rely on the gradient of the next state with respect to the current state. We call this approach Analytic World Models (AWMs) and showcase its applications, including how to use it for planning in the Waymax simulator. Apart from pushing the limits of what is possible with such simulators, we offer an improved training recipe that increases performance on the large-scale Waymo Open Motion dataset by up to 12% compared to baselines at essentially no additional cost.

Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal space is essential for enhancing human-computer interaction (HCI) and smart environments. Personal space preferences vary due to individual traits, cultural background, and contextual factors. Advanced multimodal sensing technologies, including eye-tracking and wristband sensors, offer opportunities to develop adaptive systems that dynamically adjust to user comfort levels. Integrating physiological and behavioral data enables a deeper understanding of spatial interactions. This study develops a sensor-based model to estimate comfortable personal space and identifies key features influencing spatial preferences. Our findings show that multimodal sensors, particularly eye-tracking and physiological wristband data, can effectively predict personal space preferences, with eye-tracking data playing a more significant role. An experimental study involving controlled human interactions demonstrates that a Transformer-based model achieves the highest predictive accuracy (F1 score: 0.87) for estimating personal space. Eye-tracking features, such as gaze point and pupil diameter, emerge as the most significant predictors, while physiological signals from wristband sensors contribute marginally. These results highlight the potential for AI-driven personalization of social space in adaptive environments, suggesting that multimodal sensing can be leveraged to develop intelligent systems that optimize spatial arrangements in workplaces, educational institutions, and public settings. Future work should explore larger datasets, real-world applications, and additional physiological markers to enhance model robustness.

Hypervelocity stars (HVSs) are produced by the Hills mechanism when a stellar binary is disrupted by a supermassive black hole (SMBH). The HVS Survey detected 21 unbound B-type main-sequence stars in the Milky Way's outer halo that are consistent with ejection via the Hills mechanism. We revisit the trajectories of these stars in light of proper motions from {\it Gaia} DR3 and modern constraints on the Milky Way -- Large Magellanic Cloud (LMC) orbit. We find that half of the unbound HVSs discovered by the HVS Survey trace back not the Galactic Center, but to the LMC. Motivated by this finding, we construct a forward-model for HVSs ejected from an SMBH in the LMC and observed through the selection function of the HVS Survey. The predicted spatial and kinematic distributions of simulated HVSs are remarkably similar to the observed distributions. In particular, we reproduce the conspicuous angular clustering of HVSs around the constellation Leo. This clustering occurs because HVSs from the LMC are boosted by $\sim300\,{\rm km\,s^{-1}}$ by the orbital motion of the LMC, and stars launched parallel to this motion are preferentially selected as HVS candidates. We find that the birth rate and clustering of LMC HVSs cannot be explained by supernova runaways or dynamical ejection scenarios not involving a SMBH. From the ejection velocities and relative number of Magellanic vs. Galactic HVSs, we constrain the mass of the LMC SMBH to be $10^{5.8^{+0.2}_{-0.4}} M_{\odot}$ ($\simeq 6\times10^5 M_{\odot}$)., Comment: submitted to ApJ; commments welcome

We present a massively parallel solver that accelerates DC loadflow computations for power grid topology optimization tasks. Our approach leverages low-rank updates of the Power Transfer Distribution Factors (PTDFs) to represent substation splits, line outages, and reconfigurations without ever refactorizing the system. Furthermore, we implement the core routines on Graphics Processing Units (GPUs), thereby exploiting their high-throughput architecture for linear algebra. A two-level decomposition separates changes in branch topology from changes in nodal injections, enabling additional speed-ups by an in-the-loop brute force search over injection variations at minimal additional cost. We demonstrate billion-loadflow-per-second performance on power grids of varying sizes in workload settings which are typical for gradient-free topology optimization such as Reinforcement Learning or Quality Diversity methods. While adopting the DC approximation sacrifices some accuracy and prohibits the computation of voltage magnitudes, we show that this sacrifice unlocks new scales of computational feasibility, offering a powerful tool for large-scale grid planning and operational topology optimization.

Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin-films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions., Comment: 9 pages, 7 figures

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both labor-intensive and time-consuming, creating a bottleneck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution by automating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an in-house dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving., Comment: Accepted at WACV2025

Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL., Comment: 29 pages, 19 figures, 3 tables

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0

Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient execution, individual resource allocations, such as memory and CPU cores, must meet the specific resource requirements of the job. An alternative to selecting a static resource allocation for a job execution is autoscaling as implemented for example by Spark. In this paper, we evaluate the resource efficiency of autoscaling batch data processing jobs based on resource demand both conceptually and experimentally by analyzing a new dataset of Spark job executions on Google Dataproc Serverless. In our experimental evaluation, we show that there is no significant resource efficiency gain over static resource allocations. We found that the inherent conceptual limitations of such autoscaling approaches are the inelasticity of node size as well as the inelasticity of the ratio of memory to CPU cores., Comment: 6 pages, 3 figures, 6 tables. IEEE Big Data 2024

Declines in resilience have been observed in several climate tipping elements over the past decades, including the Atlantic Meridional Overturning Circulation (AMOC) and the Amazon rainforest (AR). Large-scale nonlinear and possibly irreversible changes in system state, such as AMOC weakening or rainforest-savanna transitions in the Amazon basin, would have severe impacts on ecosystems and human societies worldwide. In order to improve future tipping risk assessments, understanding interactions between tipping elements is crucial. The AMOC is known to influence the Intertropical Convergence Zone, potentially altering precipitation patterns over the AR and affecting its stability. However, AMOC-AR interactions are currently not well understood. Here, we identify a previously unknown stabilising interaction pathway from the AMOC onto the Southern AR, applying an established causal discovery and inference approach to tipping element interactions for the first time. Analysing observational and reanalysis data from 1982-2022, we show that AMOC weakening leads to increased precipitation in the Southern AR during the critical dry season, in line with findings from recent Earth system model experiments. Specifically, we report a 4.8% increase of mean dry season precipitation in the Southern AR for every 1 Sv of AMOC weakening. This finding is consistent across multiple data sources and AMOC strength indices. We show that this stabilising interaction has offset 17% of dry season precipitation decrease in the Southern AR since 1982. Our results demonstrate the potential of causal discovery methods for analysing tipping element interactions based on reanalysis and observational data. By improving the understanding of AMOC-AR interactions, we contribute toward better constraining the risk of potential climate tipping cascades under global warming.

Photosynthesis is vital for the survival of entire ecosystems on Earth. While light is fundamental to this process, excessive exposure can be detrimental to plant cells. Chloroplasts, the photosynthetic organelles, actively move in response to light and self-organize within the cell to tune light absorption. These disk-shaped motile organelles must balance dense packing for enhanced light absorption under dim conditions with spatial rearrangements to avoid damage from excessive light exposure. Here, we reveal that the packing characteristics of chloroplasts within plant cells show signatures of optimality. Combining measurements of chloroplast densities and three-dimensional cell shape in the water plant Elodea densa, we construct an argument for optimal cell shape versus chloroplast size to achieve two targets: dense packing into a two-dimensional monolayer for optimal absorption under dim light conditions and packing at the sidewalls for optimal light avoidance. We formalize these constraints using a model for random close packing matched with packing simulations of polydisperse hard disks confined within rectangular boxes. The optimal cell shape resulting from these models corresponds closely to that measured in the box-like plant cells, highlighting the importance of particle packing in the light adaptation of plants. Understanding the interplay between structure and function sheds light on how plants achieve efficient photo adaptation. It also highlights a broader principle: how cell shape relates to the optimization of packing finite and relatively small numbers of organelles under confinement. This universal challenge in biological systems shares fundamental features with the mechanics of confined granular media and the jamming transitions in dense active and passive systems across various scales and contexts.

In semiconductor hole spin qubits, low magnetic field ($B$) operation extends the coherence time ($T_\mathrm{2}^*$) but proportionally reduces the gate speed. In contrast, singlet-triplet (ST) qubits are primarily controlled by the exchange interaction ($J$) and can thus maintain high gate speeds even at low $B$. However, a large $J$ introduces a significant charge component to the qubit, rendering ST qubits more vulnerable to charge noise when driven. Here, we demonstrate a highly coherent ST hole spin qubit in germanium, operating at both low $B$ and low $J$. By modulating $J$, we achieve resonant driving of the ST qubit, obtaining an average gate fidelity of $99.68\%$ and a coherence time of $T_\mathrm{2}^*=1.9\,\mu$s. Moreover, by applying the resonant drive continuously, we realize a dressed ST qubit with a tenfold increase in coherence time ($T_\mathrm{2\rho}^*=20.3\,\mu$s). Frequency modulation of the driving signal enables universal control, with an average gate fidelity of $99.64\%$. Our results demonstrate the potential for extending coherence times while preserving high-fidelity control of germanium-based ST qubits, paving the way for more efficient operations in semiconductor-based quantum processors.

Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations. This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications like coordinating autonomous vehicles. Quantum computing (QC) is a promising candidate in overcoming such limits. However, current quantum hardware is still in its infancy and thus limited in terms of computing power and error robustness. In this work, we present the first optimal hybrid quantum-classical MAPF algorithm which is based on branch-and-cut-and-prize. QC is integrated by iteratively solving QUBO problems, based on conflict graphs. Experiments on actual quantum hardware and results on benchmark data suggest that our approach dominates previous QUBO formulations and baseline MAPF solvers.

This paper exploits the linkage of German administrative social security data (GER: Integrierte Erwerbsbiografien) and survey data from the socio-economic panel (GER: Sozio-\"okonomisches Panel, SOEP) for the characterization of measurement error in metrics quantifying individual-specific labor earnings in Germany. We find that survey participants' decision whether to consent to linkage is non-random based on observables. In that sense, the studied sample does not constitute a random sample of SOEP. Measurement error is not classical: we observe underreporting of income on average, autocorrelation, and non-zero correlation with the true signal and other observable characteristics. In levels, calculated reliability ratios above 0.94 hint at a relaitvely small attenuation bias in simple linear univariate regressions with earnings as the explanatory variable. For changes in income, i.e. first differences, the bias from measurement error is exacerbated., Comment: Updated the title page, main body still based on Master's thesis from Nov. 18, 2024

This work aims to implement Long Short-Term Memory mixture density networks (LSTM-MDNs) for Value-at-Risk forecasting and compare their performance with established models (historical simulation, CMM, and GARCH) using a defined backtesting procedure. The focus was on the neural network's ability to capture volatility clustering and its real-world applicability. Three architectures were tested: a 2-component mixture density network, a regularized 2-component model (Arimond et al., 2020), and a 3-component mixture model, the latter being tested for the first time in Value-at-Risk forecasting. Backtesting was performed on three stock indices (FTSE 100, S&P 500, EURO STOXX 50) over two distinct two-year periods (2017-2018 as a calm period, 2021-2022 as turbulent). Model performance was assessed through unconditional coverage and independence assumption tests. The neural network's ability to handle volatility clustering was validated via correlation analysis and graphical evaluation. Results show limited success for the neural network approach. LSTM-MDNs performed poorly for 2017/2018 but outperformed benchmark models in 2021/2022. The LSTM mechanism allowed the neural network to capture volatility clustering similarly to GARCH models. However, several issues were identified: the need for proper model initialization and reliance on large datasets for effective learning. The findings suggest that while LSTM-MDNs provide adequate risk forecasts, further research and adjustments are necessary for stable performance., Comment: A thesis presented for the degree of Master of Science (MSc.) in Applied Statistics and Datamining, supervised by Prof. Dr. Valentin Popov

Representational analysis explores how input data of a neural system are encoded in high dimensional spaces of its distributed neural activations, and how we can compare different systems, for instance, artificial neural networks and brains, on those grounds. While existing methods offer important insights, they typically do not account for local intrinsic geometrical properties within the high-dimensional representation spaces. To go beyond these limitations, we explore Ollivier-Ricci curvature and Ricci flow as tools to study the alignment of representations between humans and artificial neural systems on a geometric level. As a proof-of-principle study, we compared the representations of face stimuli between VGG-Face, a human-aligned version of VGG-Face, and corresponding human similarity judgments from a large online study. Using this discrete geometric framework, we were able to identify local structural similarities and differences by examining the distributions of node and edge curvature and higher-level properties by detecting and comparing community structure in the representational graphs., Comment: Presented at NeuReps workshop, NeurIPS 2024

Decentralised learning enables the training of deep learning algorithms without centralising data sets, resulting in benefits such as improved data privacy, operational efficiency and the fostering of data ownership policies. However, significant data imbalances pose a challenge in this framework. Participants with smaller datasets in distributed learning environments often achieve poorer results than participants with larger datasets. Data imbalances are particularly pronounced in medical fields and are caused by different patient populations, technological inequalities and divergent data collection practices. In this paper, we consider distributed learning as an Stackelberg evolutionary game. We present two algorithms for setting the weights of each node's contribution to the global model in each training round: the Deterministic Stackelberg Weighting Model (DSWM) and the Adaptive Stackelberg Weighting Model (ASWM). We use three medical datasets to highlight the impact of dynamic weighting on underrepresented nodes in distributed learning. Our results show that the ASWM significantly favours underrepresented nodes by improving their performance by 2.713% in AUC. Meanwhile, nodes with larger datasets experience only a modest average performance decrease of 0.441%.

Fault zones exhibit geometrical complexity and are often surrounded by multiscale fracture networks within their damage zones, influencing rupture dynamics and near-field ground motions. We investigate the ground-motion characteristics of cascading ruptures across damage zone fracture networks of moderate-sized earthquakes using high-resolution 3D dynamic rupture simulations. Our models feature a listric fault surrounded by over 800 fractures, emulating a major fault and its associated damage zone. We analyze three cases: a cascading rupture propagating within the fracture network, a non-cascading main-fault rupture with off-fault fracture slip, and a main-fault rupture without a fracture network. Cascading ruptures within the fracture network produce distinct ground-motion signatures with high-frequency content, arising from simultaneous slip of multiple fractures and parts of the main fault, resembling source coda-wave-like signatures. This case shows elevated near-field characteristic frequency (fc) and stress drop, approximately an order of magnitude higher than the estimation directly on the fault of the dynamic rupture simulation. The inferred fc of the modeled vertical components reflects the complexity of the radiation pattern and rupture directivity of cascading earthquakes. We show that this is consistent with observations of strong azimuthal dependence of corner frequency in the 2009-2016 Central Apennines, Italy, earthquake sequence. Simulated ground motions from cascading ruptures also show pronounced azimuthal variations in peak ground acceleration (PGA), peak ground velocity, and pseudo-spectral acceleration, with average PGA nearly double that of the non-cascading cases. Such outcomes emphasize the critical role of fault-zone complexity in affecting rupture dynamics and seismic radiation and have important implications for physics-based seismic hazard assessment.

Pellets of frozen material travelling into a magnetically confined fusion plasma are accelerated by the so-called pellet rocket effect. The non-uniform plasma heats the pellet ablation cloud asymmetrically, producing pressure-driven, rocket-like propulsion of the pellet. We present a semi-analytical model of this process by perturbing a spherically symmetric ablation model. Predicted pellet accelerations match experimental estimates in current tokamaks ($\sim 10^5 \;\rm m/s^2$). Projections for ITER high-confinement scenarios ($\sim 10^6 \;\rm m/s^2$) indicate significantly shorter pellet penetration than expected without this effect, which could limit the effectiveness of disruption mitigation., Comment: 6 pages, 4 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Time series forecasting is a valuable tool for many applications, such as stock price predictions, demand forecasting or logistical optimization. There are many well-established statistical and machine learning models that are used for this purpose. Recently in the field of quantum machine learning many candidate models for forecasting have been proposed, however in the absence of theoretical grounds for advantage thorough benchmarking is essential for scientific evaluation. To this end, we performed a benchmarking study using real data of various quantum models, both gate-based and annealing-based, comparing them to the state-of-the-art classical approaches, including extensive hyperparameter optimization. Overall we found that the best classical models outperformed the best quantum models. Most of the quantum models were able to achieve comparable results and for one data set two quantum models outperformed the classical ARIMA model. These results serve as a useful point of comparison for the field of forecasting with quantum machine learning., Comment: 5 pages, 5 figures

Random Forests have become a widely used tool in machine learning since their introduction in 2001, known for their strong performance in classification and regression tasks. One key feature of Random Forests is the Random Forest Permutation Importance Measure (RFPIM), an internal, non-parametric measure of variable importance. While widely used, theoretical work on RFPIM is sparse, and most research has focused on empirical findings. However, recent progress has been made, such as establishing consistency of the RFPIM, although a mathematical analysis of its asymptotic distribution is still missing. In this paper, we provide a formal proof of a Central Limit Theorem for RFPIM using U-Statistics theory. Our approach deviates from the conventional Random Forest model by assuming a random number of trees and imposing conditions on the regression functions and error terms, which must be bounded and additive, respectively. Our result aims at improving the theoretical understanding of RFPIM rather than conducting comprehensive hypothesis testing. However, our contributions provide a solid foundation and demonstrate the potential for future work to extend to practical applications which we also highlight with a small simulation study.

Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher is trained with privileged task information, while a student tries to predict the actions of the teacher with more limited observations, e.g., in a robot navigation task, the teacher might have access to distances to nearby obstacles, while the student only receives visual observations of the scene. However, privileged imitation learning faces a key challenge: the student might be unable to imitate the teacher's behavior due to partial observability. This problem arises because the teacher is trained without considering if the student is capable of imitating the learned behavior. To address this teacher-student asymmetry, we propose a framework for joint training of the teacher and student policies, encouraging the teacher to learn behaviors that can be imitated by the student despite the latters' limited access to information and its partial observability. Based on the performance bound in imitation learning, we add (i) the approximated action difference between teacher and student as a penalty term to the reward function of the teacher, and (ii) a supervised teacher-student alignment step. We motivate our method with a maze navigation task and demonstrate its effectiveness on complex vision-based quadrotor flight and manipulation tasks.

We report on an instance in quantum gravity where a topological expansion resums into an effective description on a single geometry. The original theory whose gravitational path integral we study is JT quantum gravity with one asymptotic boundary at nonperturbatively low temperatures. The effective theory we derive is a deformation of JT gravity by a highly quantum and nonlocal interaction for the dilaton, evaluated only on a disk topology. This emergent description addresses a strongly quantum gravitational regime where all genera contribute at the same order, successfully capturing the doubly nonperturbative physics of the original theory., Comment: 5 pages, 2 figures, 1 geometry

When finetuning multiple tasks altogether, it is important to carefully weigh them to get a good performance, but searching for good weights can be difficult and costly. Here, we propose to aid the search with fast previews to quickly get a rough idea of different reweighting options. We use model merging to create previews by simply reusing and averaging parameters of models trained on each task separately (no retraining required). To improve the quality of previews, we propose a Bayesian approach to design new merging strategies by using more flexible posteriors. We validate our findings on vision and natural-language transformers. Our work shows the benefits of model merging via Bayes to improve multitask finetuning.

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

We measure the dynamical behavior of colloidal singlets and dumbbells on an inclined magnetic moir\'e pattern, subject to a precessing external homogeneous magnetic field. At low external field strength single colloidal particles and dumbbells move everywhere on the pattern: At stronger external field strengths colloidal singlets and dumbbells are localized in generic locations. There are however nongeneric locations of flat channels that cross the moir\'e Wigner Seitz cell. In the flat channels we find gravitational driven translational and non-translational dynamic phase behavior of the colloidal singlets and dumbbells depending on the external field strength and the precession angle of the external homogeneous magnetic field.

Time-resolved X-ray absorption spectroscopy (tr-XAS) has been shown to be a versatile measurement technique for investigating non-equilibrium dynamics. Novel X-ray free electron laser (XFEL) facilities like the European XFEL offer increased repetition rates for stroboscopic XAS experiments through a burst operation mode, which enables measurements with up to 4.5 MHz. These higher repetition rates lead to higher data acquisition rates but can also introduce long-lived excitations that persist and thus build up during each burst. Here, we report on such long-lived effects in Ni and NiO thin film samples that were measured at the European XFEL. We disentangle the long-lived excitations from the initial pump-induced change and perform a detailed modelling-based analysis of how they modify transient X-ray spectra. As a result, we link the long-lived effects in Ni to a local temperature increase, as well as the effects in NiO to excited charge carrier trapping through polaron formation. In addition, we present possible correction methods, as well as discuss ways in which the effects of these long-lived excitations could be minimized for future time-resolved X-ray absorption spectroscopy measurements.

We derive a generalisation of the Boyd-Grinstein-Lebed (BGL) parametrization. Most form factors (FFs) in $b$-hadron decays exhibit additional branch cuts -- namely subthreshold and anomalous branch cuts -- beyond the ``standard'' unitarity cut. These additional cuts cannot be adequately accounted for by the BGL parametrization. For instance, these cuts arise in the FFs for $B\to D^{(*)}$, $B\to K^{(*)}$, and $\Lambda_b\to \Lambda$ processes, which are particularly relevant from a phenomenological standpoint. We demonstrate how to parametrize such FFs and derive unitarity bounds in the presence of subthreshold and/or anomalous branch cuts. Our work paves the way for a wide range of new FF analyses based solely on first principles, thereby minimising systematic uncertainties., Comment: 7 pages, 2 figures, minor changes

In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling processes, a key component in high-precision manufacturing. To train our model, we use a custom data set acquired by an extensive series of milling experiments using different spindle speeds, enhanced with a custom feature map. We show that the resulting model predicts the stability limits observed in our physical setup accurately, demonstrating that quantum computing is capable of deploying ML models for real-world applications.

Distribution factors are indispensable tools in the design and analysis of power transmission grids. Recently, they received a renewed interest in the field of topology optimization, leading to the definition of bus merge and bus split distribution factors. In this article, we introduce a unified derivation of the most relevant distribution factors based on matrix algebraic manipulations. This approach facilitates the generalization to more complex grid modification, in particular simultaneous switching events or bus splits., Comment: 11 pages, 1 figure

TL;DR: Gaussian Splatting is a widely adopted approach for 3D scene representation that offers efficient, high-quality 3D reconstruction and rendering. A major reason for the success of 3DGS is its simplicity of representing a scene with a set of Gaussians, which makes it easy to interpret and adapt. To enhance scene understanding beyond the visual representation, approaches have been developed that extend 3D Gaussian Splatting with semantic vision-language features, especially allowing for open-set tasks. In this setting, the language features of 3D Gaussian Splatting are often aggregated from multiple 2D views. Existing works address this aggregation problem using cumbersome techniques that lead to high computational cost and training time. In this work, we show that the sophisticated techniques for language-grounded 3D Gaussian Splatting are simply unnecessary. Instead, we apply Occam's razor to the task at hand and perform weighted multi-view feature aggregation using the weights derived from the standard rendering process, followed by a simple heuristic-based noisy Gaussian filtration. Doing so offers us state-of-the-art results with a speed-up of two orders of magnitude. We showcase our results in two commonly used benchmark datasets: LERF and 3D-OVS. Our simple approach allows us to perform reasoning directly in the language features, without any compression whatsoever. Such modeling in turn offers easy scene manipulation, unlike the existing methods -- which we illustrate using an application of object insertion in the scene. Furthermore, we provide a thorough discussion regarding the significance of our contributions within the context of the current literature. Project Page: https://insait-institute.github.io/OccamLGS/, Comment: Project Page: https://insait-institute.github.io/OccamLGS/

3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. While there exist numerous datasets approaching the former two problems, the task of understanding interactable and articulated objects is underrepresented and only partly covered by current works. In this work, we address this shortcoming and introduce (1) an expertly curated dataset in the Universal Scene Description (USD) format, featuring high-quality manual annotations, for instance, segmentation and articulation on 280 indoor scenes; (2) a learning-based model together with a novel baseline capable of predicting part segmentation along with a full specification of motion attributes, including motion type, articulated and interactable parts, and motion parameters; (3) a benchmark serving to compare upcoming methods for the task at hand. Overall, our dataset provides 8 types of annotations - object and part segmentations, motion types, movable and interactable parts, motion parameters, connectivity, and object mass annotations. With its broad and high-quality annotations, the data provides the basis for holistic 3D scene understanding models. All data is provided in the USD format, allowing interoperability and easy integration with downstream tasks. We provide open access to our dataset, benchmark, and method's source code.

Machine learning models often struggle with generalization in small, heterogeneous datasets due to domain shifts caused by variations in data collection and population differences. This challenge is particularly pronounced in biological data, where data is high-dimensional, small-scale, and decentralized across institutions. While federated domain adaptation methods (FDA) aim to address these challenges, most existing approaches rely on deep learning and focus on classification tasks, making them unsuitable for small-scale, high-dimensional applications. In this work, we propose freda, a privacy-preserving federated method for unsupervised domain adaptation in regression tasks. Unlike deep learning-based FDA approaches, freda is the first method to enable the federated training of Gaussian Processes to model complex feature relationships while ensuring complete data privacy through randomized encoding and secure aggregation. This allows for effective domain adaptation without direct access to raw data, making it well-suited for applications involving high-dimensional, heterogeneous datasets. We evaluate freda on the challenging task of age prediction from DNA methylation data, demonstrating that it achieves performance comparable to the centralized state-of-the-art method while preserving complete data privacy.

Marangoni surfers are simple, cost-effective tabletop experiments that, despite their simplicity, exhibit rich dynamics and collective behaviors driven by physicochemical mechanisms, hydrodynamic interactions, and inertial motion. This work introduces self-propelled particles designed and manufactured through 3D printing to move on the air-water interface. We develop particles with tunable motility and controlled particle-particle interactions by leveraging surface tension-mediated forces, such as the Marangoni effect for propulsion and the Cheerios effect for interactions. Rapid prototyping through 3D printing facilitates the exploration of a wide design space, enabling precise control over particle shape and function. We exemplify this by creating translational and chiral particles. Additionally, we investigate self-assembly in this system and highlight its potential for modular designs where mechanically linked particles with varying characteristics follow outlined trajectories. This research offers a flexible, low-cost approach to designing active interfacial systems and opens new possibilities for further advancements of adaptive, multifunctional devices., Comment: 12 pages, 7 figures

For rank 1 flat symmetric spaces, continuous orbital measures admit absolutely continuous convolution squares, except for Cartan type AI. Hence $L^1$-$L^2$ dichotomy for these spaces holds true in parallel to the compact and non-compact rank 1 symmetric spaces. We also study $L^1$-$L^2$ dichotomy for flat symmetric spaces of ranks $p=2,3$ of type AIII, i.e.\ associated with $SU(p,q)/S(U(p)\times U(q))$ where $q\geq p$. For continuous orbital measures given by regular points $L^1$-$L^2$ dichotomy holds. We study such measures given by certain singular points when $p=2$, and show that $L^1$-$L^2$ dichotomy fails. This is the first time such results are observed for any type of symmetric spaces of rank 2., Comment: 21 pages

In this work we investigate discrete-time transport in a generic U(1)-symmetric disordered model tuned across an array of different dynamical regimes. We develop an aggregate quantity, a circular statistical moment, which is a simple function of the magnetization profile and which elegantly captures transport properties of the system. From this quantity we extract transport exponents, revealing behaviors across the phase diagram consistent with localized, diffusive, and - most interestingly for a disordered system - superdiffusive regimes. Investigation of this superdiffusive regime reveals the existence of a prethermal "swappy" regime unique to discrete-time systems in which excitations propagate coherently; even in the presence of strong disorder., Comment: Comments are welcome!

We propose a theory for neutrino oscillations, in which the flavour neutrinos are treated as waves of massless particles propagating in a "refractive quantum vacuum" and obeying a relativistically covariant equation of motion. The difference in strength between weak interactions and mass-generating interactions is argued to allow for the production and detection of flavour neutrinos in weak interactions as massless particles. They experience the mass-generating interactions as coherent forward scattering in the Brout-Englert-Higgs vacuum, which induces macroscopically multi-refringent effects. The flavour neutrino wave is then found to have a universal effective refractive mass in vacuum and a unique group velocity for a given energy. The coherence of the wave is manifest throughout and, at every moment of the propagation, the energy of the waves is the same. The standard oscillation probability in vacuum is obtained and the effects of matter are incorporated in a natural way., Comment: 24 pages, 4 figures

Guarded Kleene Algebra with Tests (GKAT) provides a sound and complete framework to reason about trace equivalence between simple imperative programs. However, there are still several notable limitations. First, GKAT is completely agnostic with respect to the meaning of primitives, to keep equivalence decidable. Second, GKAT excludes non-local control flow such as goto, break, and return. To overcome these limitations, we introduce Control-Flow GKAT (CF-GKAT), a system that allows reasoning about programs that include non-local control flow as well as hardcoded values. CF-GKAT is able to soundly and completely verify trace equivalence of a larger class of programs, while preserving the nearly-linear efficiency of GKAT. This makes CF-GKAT suitable for the verification of control-flow manipulating procedures, such as decompilation and goto-elimination. To demonstrate CF-GKAT's abilities, we validated the output of several highly non-trivial program transformations, such as Erosa and Hendren's goto-elimination procedure and the output of Ghidra decompiler. CF-GKAT opens up the application of Kleene Algebra to a wider set of challenges, and provides an important verification tool that can be applied to the field of decompilation and control-flow transformation.

Disorder in the heterogeneous material stack of semiconductor spin qubit systems introduces noise that compromises quantum information processing, posing a challenge to coherently control large-scale quantum devices. Here, we exploit low-disorder epitaxial strained quantum wells in Ge/SiGe heterostructures grown on Ge wafers to comprehensively probe the noise properties of complex micron-scale devices comprising of up to ten quantum dots and four rf-charge sensors arranged in a two-dimensional array. We demonstrate an average charge noise of $\sqrt{S_{0}}=0.3(1)$ $\mu\mathrm{eV}/\sqrt{\mathrm{Hz}}$ at 1 Hz across different locations on the wafer, providing a benchmark for quantum confined holes. We then establish hole-spin qubit control in these heterostructures and extend our investigation from electrical to magnetic noise through spin echo measurements. Exploiting dynamical decoupling sequences, we quantify the power spectral density components arising from the hyperfine interaction with $^{73}$Ge spinful isotopes and identify coherence modulations associated with the interaction with the $^{29}$Si nuclear spin bath near the Ge quantum well. We estimate an integrated hyperfine noise amplitude $\sigma_f$ of 180(8) kHz from $^{73}$Ge and of 47(5) kHz from $^{29}$Si, underscoring the need for full isotopic purification of the qubit host environment.

Pressure evolution of RuO2 is studied using single-crystal x-ray diffraction in a diamond anvil cell, combined with \textit{ab initio} band-structure calculations. The tetragonal rutile structure transforms into the orthorhombic CaCl$_2$-type structure above 13 GPa under quasi-hydrostatic pressure conditions. This second-order transition is ferroelastic in nature and accompanied by tilts of the RuO$_6$ octahedra. Orthorhombic RuO$_2$ is expected to be paramagnetic metal, similar to ambient-pressure RuO$_2$. It shows the increased $t_{2g}-e_g$ crystal-field splitting that is responsible for the pressure-induced color change. It further features the Dirac nodal line that shifts across the Fermi level upon compression., Comment: 8 pages with appendix, 8 figures

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures; Replaced to match accepted ApJ version. New version contains small editorial changes throughout in response to referee comments, no changes to results

In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of premisses as absolute truths as is done in predicate logic. Viewing the set of premisses as a set of assumptions, however, it is possible to deduce useful conclusions from an inconsistent set of premisses. In this paper a logic for reasoning with inconsistent knowledge is described. This logic is a generalization of the work of N. Rescher [15]. In the logic a reliability relation is used to choose between incompatible assumptions. These choices are only made when a contradiction is derived. As long as no contradiction is derived, the knowledge is assumed to be consistent. This makes it possible to define an argumentation-based deduction process for the logic. For the logic a semantics based on the ideas of Y. Shoham [22, 23], is defined. It turns out that the semantics for the logic is a preferential semantics according to the definition S. Kraus, D. Lehmann and M. Magidor [12]. Therefore the logic is a logic of system P and possesses all the properties of an ideal non-monotonic logic., Comment: The original version was published in the Artificial Intelligence journal. This original version uses 'justifications' in the proof system, which we would call nowadays 'arguments'. The current version presents the same results but now using the terminology of an assumption-based argumentation system

We aim at insights about how localization of the background field impacts nonlinear quantum vacuum signatures probed by photons in purely magnetic, electric and crossed fields. The starting point of our study are the one-loop results for the Heisenberg-Euler effective Lagrangian and the photon polarization tensor in quantum electrodynamics (QED) evaluated in a uniform constant electromagnetic field. As is well known and often employed, especially in the weak-field limit, within certain restrictions these results also allow for the reliable analysis of vacuum polarization effects in slowly varying background fields. Here, our main interest is in manifestly non-perturbative effects. To this end, we make use of the fact that for the particular case of background field inhomogeneities of Lorentzian shape with $0\leq d\leq3$ inhomogeneous directions analytical insights are possible. We study the scaling of conventional nonlinear QED signatures, such as probe-photon polarization flip and probe-photon induced electron-positron pair production, with relevant parameters. Special attention is put on the $d$ dependence of the considered effects., Comment: 18 pages