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We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm.

We present Gemini near-infrared integral field spectrograph (NIFS) K-band observations of the central 400 pc of NGC 1266, a nearby (D$\approx$30 Mpc) post-starburst galaxy with a powerful multi-phase outflow and a shocked ISM. We detect 7 H$_2$ ro-vibrational emission lines excited thermally to $T$$\sim$2000 K, and weak Br$\gamma$ emission, consistent with a fast C-shock. With these bright H$_2$ lines, we observe the spatial structure of the shock with an unambiguous tracer for the first time. The Br$\gamma$ emission is concentrated in the central $\lesssim$100 pc, indicating that any remaining star-formation in NGC 1266 is in the nucleus while the surrounding cold molecular gas has little on-going star-formation. Though it is unclear what fraction of this Br$\gamma$ emission is from star-formation or the AGN, assuming it is entirely due to star-formation we measure an instantaneous star-formation rate of 0.7 M$_\odot$ yr$^{-1}$, though the star-formation rate may be significantly higher in the presence of additional extinction. NGC 1266 provides a unique laboratory to study the complex interactions between AGN, outflows, shocks, and star-formation, all of which are necessary to unravel the evolution of the post-starburst phase., Comment: ApJ accepted

Accurate patient positioning is critical for precise radiotherapy dose delivery, as positioning errors can significantly affect treatment outcomes. This study introduces a novel method for tracking loco-regional tissue deformation through Cherenkov image analysis during fractionated breast cancer radiotherapy. The primary goal was to develop and test an algorithm for Cherenkov-based regional position accuracy quantification, specifically for loco-regional deformations, which lack ideal quantification methods in radiotherapy. Blood vessel detection and segmentation were developed in Cherenkov images using a tissue phantom with incremental movements, and later applied to images from fractionated whole breast radiotherapy in human patients (n=10). A combined rigid and non-rigid registration technique was used to detect inter- and intra-fractional positioning variations. This approach quantified positioning variations in two parts: a global shift from rigid registration and a two-dimensional variation map of loco-regional deformation from non-rigid registration. The methodology was validated using an anthropomorphic chest phantom experiment, where known treatment couch translations and respiratory motion were simulated to assess inter- and intra-fractional uncertainties, yielding an average accuracy of 0.83 mm for couch translations up to 20 mm. Analysis of clinical Cherenkov data from ten breast cancer patients showed an inter-fraction setup variation of 3.7 plus minus 2.4 mm relative to the first fraction and loco-regional deformations (95th percentile) of up to 3.3 plus minus 1.9 mm. This study presents a Cherenkov-based approach to quantify global and local positioning variations, demonstrating feasibility in addressing loco-regional deformations that conventional imaging techniques fail to capture., Comment: 25 pages, 4 figures, 1 table, journal under review

Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights answering the question of why the performance of a particular model really degraded. In this work, we propose a novel approach to explain the behavior of a black-box model under feature shifts by attributing an estimated performance change to interpretable input characteristics. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation (XPE). We analyze the underlying assumptions and demonstrate the superiority of our approach over several baselines on different data sets across various data modalities such as images, audio, and tabular data. We also indicate how the generated results can lead to valuable insights, enabling explanatory model monitoring by revealing potential root causes for model deterioration and guiding toward actionable countermeasures., Comment: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 24)

This paper describes the first Vertical Electron Cyclotron Emission (V-ECE) measurement of non-thermal electron distributions in the \textit{Tokamak \`a Configuration Variable}, TCV. These measurements were conducted in runaway electron scenarios and in the presence of Electron Cyclotron Current Drive. Measured intensities of linearly polarised X- and O-mode radiation from fast electrons allow the analysis of the energy distribution. The measurements were made possible through the creation of an operational regime for the diagnostic that is free of thermal background radiation, in relaxed electron density operations. This operational regime notably enables the cross-calibration of the diagnostic system, relying on thermal plasma measurements and modeling with the ray-tracing code SPECE., Comment: Submitted to: Plasma Phys. Control. Fusion

In this paper we propose a novel $Q$-learning algorithm allowing to solve distributionally robust Markov decision problems for which the ambiguity set of probability measures can be chosen arbitrarily as long as it comprises only a finite amount of measures. Therefore, our approach goes beyond the well-studied cases involving ambiguity sets of balls around some reference measure with the distance to reference measure being measured with respect to the Wasserstein distance or the Kullback--Leibler divergence. Hence, our approach allows the applicant to create ambiguity sets better tailored to her needs and to solve the associated robust Markov decision problem via a $Q$-learning algorithm whose convergence is guaranteed by our main result. Moreover, we showcase in several numerical experiments the tractability of our approach.

The widespread adoption of large language models (LLMs) has created a pressing need for an efficient, secure and private serving infrastructure, which allows researchers to run open source or custom fine-tuned LLMs and ensures users that their data remains private and is not stored without their consent. While high-performance computing (HPC) systems equipped with state-of-the-art GPUs are well-suited for training LLMs, their batch scheduling paradigm is not designed to support real-time serving of AI applications. Cloud systems, on the other hand, are well suited for web services but commonly lack access to the computational power of HPC clusters, especially expensive and scarce high-end GPUs, which are required for optimal inference speed. We propose an architecture with an implementation consisting of a web service that runs on a cloud VM with secure access to a scalable backend running a multitude of LLM models on HPC systems. By offering a web service using our HPC infrastructure to host LLMs, we leverage the trusted environment of local universities and research centers to offer a private and secure alternative to commercial LLM services. Our solution natively integrates with the HPC batch scheduler Slurm, enabling seamless deployment on HPC clusters, and is able to run side by side with regular Slurm workloads, while utilizing gaps in the schedule created by Slurm. In order to ensure the security of the HPC system, we use the SSH ForceCommand directive to construct a robust circuit breaker, which prevents successful attacks on the web-facing server from affecting the cluster. We have successfully deployed our system as a production service, and made the source code available at \url{https://github.com/gwdg/chat-ai}, Comment: Various improvements to explanations and form and updated graphs to include data points up to 30.07.2024

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95\% signal-encircling region during the new run, while one is expected. The new data improve the axion-photon coupling limit to 5.7$\times 10^{-11}\,$GeV$^{-1}$ at 95\% C.L., the most restrictive experimental limit to date., Comment: 8 pages, 4 figures (plus 6 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

Using feature attributions for post-hoc explanations is a common practice to understand and verify the predictions of opaque machine learning models. Despite the numerous techniques available, individual methods often produce inconsistent and unstable results, putting their overall reliability into question. In this work, we aim to systematically improve the quality of feature attributions by combining multiple explanations across distinct methods or their variations. For this purpose, we propose a novel approach to derive optimal convex combinations of feature attributions that yield provable improvements of desired quality criteria such as robustness or faithfulness to the model behavior. Through extensive experiments involving various model architectures and popular feature attribution techniques, we demonstrate that our combination strategy consistently outperforms individual methods and existing baselines., Comment: International Conference on Machine Learning (ICML) 2024

This paper describes how to propagate wavefields for arbitrary numbers of traditional time steps in a single step, called a superstep. We show how to construct operators that accomplish this task for finite-difference time domain schemes, including temporal first-order schemes in isotropic, anisotropic and elastic media, as well as temporal second-order schemes for acoustic media. This task is achieved by implementing a computational tradeoff differing from traditional single step wavefield propagators by precomputing propagator matrices for each model location for k timesteps (a superstep) and using these propagator matrices to advance the wavefield k time steps at once. This tradeoff separates the physics of the propagator matrix computation from the computer science of wavefield propagation and allows each discipline to provide their optimal modular solutions.

We report the discovery of an extended emission line region (EELR) in MUSE observations of Markarian 950, a nearby ($z=0.01628$) post-starburst (PSB) galaxy that hosted the tidal disruption event (TDE) iPTF-16fnl. The EELR requires a non-stellar ionizing continuum with a luminosity L$_{\rm ion, min} \gtrsim 10^{43}$ erg s$^{-1}$, inconsistent with the current weak state (L$_{\rm IR,AGN} < 2.5 \times 10^{42}$ erg s$^{-1}$) of the galactic nucleus. The ionized gas has low velocity ($\sim$-50 km s$^{-1}$) and low turbulence ($\sigma_{\rm gas} \lesssim$ 50 km s$^{-1}$), and is kinematically decoupled from the stellar motions, indicating that the gas kinematics are not AGN driven. Markarian 950 is the third post-starburst galaxy to host a weak nuclear ionizing source as well as an EELR and a TDE. The overall properties of these three galaxies, including the kinematics and accretion history, are unusual but strikingly similar. We estimate that the incidence of EELRs in PSB-TDE hosts is a factor of $\sim 10 \times$ higher than in other PSB galaxies. This suggests that a gas-rich post-merger environment is a key ingredient in driving elevated TDE rates. Based on the current observations, we cannot rule out that the EELRs may be powered through an elevated TDE rate in these galaxies. If the EELRs are not TDE-powered, the presence of intermittent AGN activity, and in particular the fading of the AGN, may be associated with an increased TDE rate and/or an increased rate of detecting TDEs., Comment: Accepted for publication in ApJ Letters. In a companion paper we present an IFU analysis of QPE host galaxies (Wevers et al.)

Advancements in neutral atom quantum computers have positioned them as a valuable framework for quantum computing, largely due to their prolonged coherence times and capacity for high-fidelity gate operations. Recently, neutral atom computers have enabled coherent atom shuttling to facilitate long-range connectivity as a high-fidelity alternative to traditional gate-based methods. However, these inherent advantages are accompanied by novel constraints, making it challenging to create optimal movement schedules. In this study I present, to the best of my knowledge, the first compiler pass designed to optimize reconfigurable coupling in zoned neutral atom architectures, while adhering to the reconfigurability constraints of these systems. I approach qubit mapping and movement scheduling as a max-cut and layered cross-minimization problem while enhancing support for spatially complex algorithms through a novel "stacking" feature that balances the qubit array's spatial dimensions with algorithmic parallelism. I compare the method across various algorithms sourced from Supermarq and Qasmbench where the compiler pass represents the first exclusively movement-based technique to achieve compilation times consistently within seconds. Results also demonstrate that the approach reduces pulse counts by up to 5x and increases fidelity by up to 7x compared to existing methods on currently available technology.

A system that possesses translational symmetry but breaks orientational symmetry is known as a nematic phase. While there are many examples of nematic phases in a wide range of contexts, such as in liquid crystals, complex oxides, and superconductors, of particular interest is the magnetic analogue, where the spin, charge, and orbital degrees of freedom of the electron are intertwined. The difficulty of spin nematics is the unambiguous realization and characterization of the phase. Here we present an entirely new type of magnetic nematic phase, which replaces the basis of individual spins with magnetic helices. The helical basis allows for the direct measurement of the order parameters with soft X-ray scattering and a thorough characterization of the nematic phase and its thermodynamic transitions. We discover two distinct nematic phases with unique spatio-temporal correlation signatures. Using coherent X-ray methods, we find that near the phase boundary between the two nematic phases, fluctuations coexist on the timescale of both seconds and sub-nanoseconds. Additionally, we have determined that the fluctuations occur simultaneously with a reorientation of the magnetic helices, indicating that there is spontaneous symmetry breaking and new degrees of freedom become available. Our results provide a novel framework for characterizing exotic phases and the phenomena presented can be mapped onto a broad class of physical systems.

We give illustrative examples of how the computer algebra system OSCAR can support research in commutative algebra and algebraic geometry. We start with a thorough introduction to Groebner basis techniques, with particular emphasis on the computation of syzygies, then apply these techniques to deal with ideal and ring theoretic concepts such as primary decomposition and normalization, and finally use them for geometric case studies which concern curves and surfaces, both from a local and global point of view., Comment: 50 pages, 10 figures

Runaway electrons (REs) are a concern for tokamak fusion reactors from discharge startup to termination. A sudden localized loss of a multi-megaampere RE beam can inflict severe damage to the first wall. Should a disruption occur, the existence of a RE seed may play a significant role in the formation of a RE beam and the magnitude of its current. The application of central electron cyclotron resonance heating (ECRH) in the Tokamak \`a Configuration Variable (TCV) reduces an existing RE seed population by up to three orders of magnitude within only a few hundred milliseconds. Applying ECRH before a disruption can also prevent the formation of a post-disruption RE beam in TCV where it would otherwise be expected. The RE expulsion rate and consequent RE current reduction are found to increase with applied ECRH power. Whereas central ECRH is effective in expelling REs, off-axis ECRH has a comparatively limited effect. A simple 0-D model for the evolution of the RE population is presented that explains the effective ECRH-induced RE expulsion results from the combined effects of increased electron temperature and enhanced RE transport.

Star-forming galaxies can reach quiescence via rapid transition through merger-triggered starbursts that consequently affect both their kinematics and star formation rates. In this work, we analyze the spatially-resolved kinematics of 92 post-starburst galaxies (PSBs) with data from the MaNGA survey and place them in context with early-type galaxies (ETGs) to study the impact of merger history on galaxy kinematics. We measure the specific angular momentum to characterize them as slow or fast rotators. We find that the MaNGA PSB sample has $5\%$ slow rotators, which is less than the $14\%$ slow rotators in the ATLAS$^{3D}$ ETG sample and $22\%$ slow rotators in the MaNGA ETGs. The PSBs have lower specific angular momentum than the star-forming galaxies (SFs) and a greater fraction of slow rotators. This implies that for the PSBs to evolve into the ETGs, they must still lose some angular momentum. While ETGs with higher stellar mass tend to be slow rotators, PSBs do not follow this trend. We find significant correlations between specific angular momentum and mass-weighted age for the SF and ETG samples, but do not see any significant trends within the short PSB phase. These results indicate that significant evolution in angular momentum must continue to take place as the galaxy ages after the PSB phase. For PSBs to evolve directly into ETGs, they must undergo dry mergers to shed excess angular momentum without causing further epochs of star formation., Comment: submitted to AAS journals

Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the question of how these models can support the adoption of FAIR dataspaces. In this work, we demonstrate the potential of LLMs in dataspaces with a concrete example. We also derive a research agenda for exploring this emerging field., Comment: 8 pages. Preprint. Under review

We document the ground state phase diagram of the one-dimensional Kitaev chain with quasi-periodic disorder in the presence of two-body interactions. Our data was obtained for systems of $L=1000$ sites using large-scale density matrix renormalization group numerics and is benchmarked against known results for the clean system. We demonstrate that moderate quasi-periodic disorder stabilizes the topological phase both for repulsive and attractive interactions. For larger disorder strengths, the system features re-entrance behavior and multiple phase transitions.

Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the structure of dialogue beyond the sequence of utterances. However, studying this high-level structure is a complex task that we try to approach by first segmenting dialogues into smaller topically coherent sets of utterances. Understanding the interactions between these segments would then enable us to propose a model of topic organisation at a dialogue level. In this paper we work with open-domain conversations and try to reach a comparable level of accuracy as recent machine learning based topic segmentation models but with a formal approach. The features we identify as meaningful for this task help us understand better the topical structure of a conversation.

We present ALMA observations of two dense gas tracers, HCN(1-0) and HCO$^{+}$(1-0), for three galaxies in the green valley and two galaxies on the star-forming main sequence with comparable molecular gas fractions as traced by the CO(1-0) emissions, selected from the ALMaQUEST survey. We investigate whether the deficit of molecular gas star formation efficiency (SFE$_{\rm mol}$) that leads to the low specific star formation rate in these green valley galaxies is due to a lack of dense gas (characterized by the dense gas fraction $f_{\rm dense}$) or the low star formation efficiency of dense gas (SFE$_{\rm dense}$). We find that SFE$_{\rm mol}$ as traced by the CO emissions, when considering both star-forming and retired spaxels together, is tightly correlated with SFE$_{\rm dense}$ and depends only weakly on $f_{\rm dense}$. The specific star formation rate (sSFR) on kpc scales is primarily driven by SFE$_{\rm mol}$ and SFE$_{\rm dense}$, followed by the dependence on $f_{\rm mol}$, and is least correlated with $f_{\rm dense}$ or the dense-to-stellar mass ratio ($R_{\rm dense}$). When compared with other works in the literature, we find that our green valley sample shows lower global SFE$_{\rm mol}$ as well as lower SFE$_{\rm dense}$ while exhibiting similar dense gas fractions when compared to star-forming and starburst galaxies. We conclude that the star formation of the 3 green valley galaxies with a normal abundance of molecular gas is suppressed mainly due to the reduced SFE$_{\rm dense}$ rather than the lack of dense gas., Comment: 20 pages, 13 figures, ApJ accepted

AT 2019azh is a H+He tidal disruption event (TDE) with one of the most extensive ultraviolet and optical data sets available to date. We present our photometric and spectroscopic observations of this event starting several weeks before and out to approximately two years after the g-band peak brightness and combine them with public photometric data. This extensive data set robustly reveals a change in the light-curve slope and a possible bump in the rising light curve of a TDE for the first time, which may indicate more than one dominant emission mechanism contributing to the pre-peak light curve. Indeed, we find that the MOSFiT-derived parameters of AT 2019azh, which assume reprocessed accretion as the sole source of emission, are not entirely self-consistent. We further confirm the relation seen in previous TDEs whereby the redder emission peaks later than the bluer emission. The post-peak bolometric light curve of AT 2019azh is better described by an exponential decline than by the canonical t^{-5/3} (and in fact any) power-law decline. We find a possible mid-infrared excess around the peak optical luminosity, but cannot determine its origin. In addition, we provide the earliest measurements of the Halpha emission-line evolution and find no significant time delay between the peak of the V-band light curve and that of the Halpha luminosity. These results can be used to constrain future models of TDE line formation and emission mechanisms in general. More pre-peak 1-2 days cadence observations of TDEs are required to determine whether the characteristics observed here are common among TDEs. More importantly, detailed emission models are needed to fully exploit such observations for understanding the emission physics of TDEs., Comment: Submitted to ApJ

In this paper we argue that topic plays a fundamental role in conversations, and that the concept is needed in addition to that of genre to define interactions. In particular, the concepts of genre and topic need to be separated and orthogonally defined. This would enable modular, reliable and controllable flexible-domain dialogue systems.

Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data is typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and interactivity of the analysis process. These stem from the difficulty to discern the analysis steps, parameters, and data flow from the results, making knowledge transfer and findability challenging in collaborative settings. Provenance information tracks data lineage and processes applied to it, and provenance capture during the execution of an analysis script can address those challenges. We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts. Alpaca records inputs, outputs, and function parameters and structures information according to the W3C PROV standard. We demonstrate the tool using a realistic use case involving multichannel local field potential recordings of a neurophysiological experiment, highlighting how the tool makes result details known in a standardized manner in order to address the challenges of the analysis process. Ultimately, using Alpaca will help to represent results according to the FAIR principles, which will improve research reproducibility and facilitate sharing the results of data analyses.