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Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topological guarantees, are limited to specific use cases, or impose high computational costs. In this work, we propose a novel, graph-based framework for topologically accurate image segmentation that is both computationally efficient and generally applicable. Our method constructs a component graph that fully encodes the topological information of both the prediction and ground truth, allowing us to efficiently identify topologically critical regions and aggregate a loss based on local neighborhood information. Furthermore, we introduce a strict topological metric capturing the homotopy equivalence between the union and intersection of prediction-label pairs. We formally prove the topological guarantees of our approach and empirically validate its effectiveness on binary and multi-class datasets. Our loss demonstrates state-of-the-art performance with up to fivefold faster loss computation compared to persistent homology methods.

Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality makes them particularly suited to run on current quantum processors, the task of verification at non-trivial scales is complicated for non-integrable systems. Here, we study a special class of maximally chaotic circuits known as dual unitary circuits -- exhibiting unitarity in both space and time -- that are known to have exact analytical solutions for certain correlation functions. With advances in noise learning and the implementation of novel error mitigation methods, we show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators. We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point, and compare our results to classical approximations with tensor networks. These results cement error-mitigated digital quantum simulation on pre-fault-tolerant quantum processors as a trustworthy platform for the exploration and discovery of novel emergent quantum many-body phases., Comment: 7 + 23 pages, 3 + 17 figures, 0 + 2 tables

The phase diagram of QCD may contain a moat regime in a large region of temperature $T$ and chemical potential $\mu\neq0$. A moat regime is characterized by quasiparticle moatons (pions) whose energy is minimal at nonzero spatial momentum. At $\mu\neq 0$, higher mass dimension operators play a critical role in a moat regime. At dimension six, there are nine possible gauge invariant couplings between scalars and photons. For back-to-back dilepton production, only one operator contributes, which significantly enhances production near a moat threshold. This enhancement is an experimental signature of moatons., Comment: 9 pages, 3 figures; 4 pages references & supplemental material

A device filled with pure xenon first demonstrated the ability to operate simultaneously as a bubble chamber and scintillation detector in 2017. Initial results from data taken at thermodynamic thresholds down to ~4 keV showed sensitivity to ~20 keV nuclear recoils with no observable bubble nucleation by $\gamma$-ray interactions. This paper presents results from further operation of the same device at thermodynamic thresholds as low as 0.50 keV, hardware limited. The bubble chamber has now been shown to have sensitivity to ~1 keV nuclear recoils while remaining insensitive to bubble nucleation by $\gamma$-rays. A robust calibration of the chamber's nuclear recoil nucleation response, as a function of nuclear recoil energy and thermodynamic state, is presented. Stringent upper limits are established for the probability of bubble nucleation by $\gamma$-ray-induced Auger cascades, with a limit of $<1.1\times10^{-6}$ set at 0.50 keV, the lowest thermodynamic threshold explored., Comment: 10 pages, 7 figures

Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations in handling complex use cases due to the restricted nature of individual functions. Serverless workflows address this limitation by orchestrating multiple functions into a cohesive application. However, existing serverless workflow platforms exhibit significant differences in their programming models and infrastructure, making fair and consistent performance evaluations difficult in practice. To address this gap, we propose the first serverless workflow benchmarking suite SeBS-Flow, providing a platform-agnostic workflow model that enables consistent benchmarking across various platforms. SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns. We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations. We make our benchmark suite open-source, enabling rigorous and comparable evaluations of serverless workflows over time.

Molecular beam epitaxy (MBE) is a state-of-the-art technique for depositing thin films with precise stoichiometric control. However, when depositing oxides of perovskite-type ABO3, this process becomes challenging as controlling the flux rate of A and B simultaneously in the presence of oxygen is difficult. In this work, by utilizing e-beam-assisted oxide MBE, we successfully deposited SrNbO3 epitaxial thin films. A buffer layer of SrTiO3 perovskite oxide, prepared by pulsed laser deposition (PLD), was used to improve the growth of this oxide significantly. This method overcomes the ultimatum of stabilizing perovskite oxide growth using MBE since the valence mismatch between the substrate and the films were effectively masked by the ultra-thin SrTiO3 and further supported interfacial charge transfer. Interestingly, we also discovered that the perovskite oxides SrNbO3 and SrTiO3 replace K in KTaO3 substrates by Sr, regardless of the deposition technique used, which is generally considered a reason for many interfacial effects. The growth of SrNbO3 was unaffected by the K deficiency when the stable buffer layer was introduced., Comment: 17 pages

Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize objective functions. This paper introduces PARCO (Parallel AutoRegressive Combinatorial Optimization), a novel approach that learns fast surrogate solvers for multi-agent combinatorial problems with reinforcement learning by employing parallel autoregressive decoding. We propose a model with a Multiple Pointer Mechanism to efficiently decode multiple decisions simultaneously by different agents, enhanced by a Priority-based Conflict Handling scheme. Moreover, we design specialized Communication Layers that enable effective agent collaboration, thus enriching decision-making. We evaluate PARCO in representative multi-agent combinatorial problems in routing and scheduling and demonstrate that our learned solvers offer competitive results against both classical and neural baselines in terms of both solution quality and speed. We make our code openly available at https://github.com/ai4co/parco.

We demonstrate that carefully adjusting the tokenizer of the Whisper speech recognition model significantly improves the precision of word-level timestamps when applying dynamic time warping to the decoder's cross-attention scores. We fine-tune the model to produce more verbatim speech transcriptions and employ several techniques to increase robustness against multiple speakers and background noise. These adjustments achieve state-of-the-art performance on benchmarks for verbatim speech transcription, word segmentation, and the timed detection of filler events, and can further mitigate transcription hallucinations. The code is available open https://github.com/nyrahealth/CrisperWhisper., Comment: Published at INTERSPEECH2024

A number of complexity measures for Boolean functions have previously been introduced. These include (1) sensitivity, (2) block sensitivity, (3) witness complexity, (4) subcube partition complexity and (5) algorithmic complexity. Each of these is concerned with "worst-case" inputs. It has been shown that there is "asymptotic separation" between these complexity measures and very recently, due to the work of Huang, it has been established that they are all "polynomially related". In this paper, we study the notion of distributional complexity where the input bits are independent and one considers all of the above notions in expectation. We obtain a number of results concerning distributional complexity measures, among others addressing the above concepts of "asymptotic separation" and being "polynomially related" in this context. We introduce a new distributional complexity measure, local witness complexity, which only makes sense in the distributional context and we also study a new version of algorithmic complexity which involves partial information. Many interesting examples are presented including some related to percolation. The latter connects a number of the recent developments in percolation theory over the last two decades with the study of complexity measures in theoretical computer science., Comment: 42 pages, 2 figures

Active atomic clocks are predicted to provide far better short-term stability and robustness against thermal fluctuations than typical feedback-based optical atomic clocks. However, continuous laser operation using an ensemble of clock atoms still remains an experimentally challenging task. We study spatial self-organization in a transversely driven ensemble of clock atoms inside an optical resonator and coherent light emission from the cavity. We focus on the spectral properties of the emitted light in the narrow atomic linewidth regime, where the phase coherence providing frequency stability is stored in the atomic dipoles rather than the cavity field. The atoms are off-resonantly driven by a standing-wave coherent laser transversely to the cavity axis allowing for atomic motion along the cavity axis as well as along the pump. In order to treat larger atom numbers we employ a second-order cumulant expansion which allows us to calculate the spectrum of the cavity light field. We identify the self-organization threshold where the atoms align themselves in a checkerboard pattern thus maximizing light scattering into the cavity which simultaneously induces cooling. For a larger driving intensity, more atoms are transferred to the excited state, reducing cooling but increasing light emission from the excited atoms. This can be enhanced via a second cavity mode at the atomic frequency spatially shifted by a quarter wavelength. For large enough atom numbers we observe laser-like emission close to the bare atomic transition frequency., Comment: 8 pages, 8 figures

The spatial distribution of the gaseous components of the Milky Way is of great importance for a number of different fields, e.g. Galactic structure, star formation and cosmic rays. However, obtaining distance information to gaseous clouds in the interstellar medium from Doppler-shifted line emission is notoriously difficult given our unique vantage point in the Galaxy. It requires precise knowledge of gas velocities and generally suffers from distance ambiguities. Previous works often assumed the optically thin limit (no absorption), a fixed velocity field, and lack resolution overall. We aim to overcome these issues and improve previous reconstructions of the gaseous constituents of the interstellar medium of the Galaxy. We use 3D Gaussian processes to model correlations in the interstellar medium, including correlations between different lines of sight, and enforce a spatially coherent structure in the prior. For modelling the transport of radiation from the emitting gas to us as observers, we take absorption effects into account. A special numerical grid ensures high resolution nearby. We infer the spatial distributions of HI, CO, their emission line-widths, and the Galactic velocity field in a joint Bayesian inference. We further constrain these fields with complementary data from Galactic masers and young stellar object clusters. Our main result consists of a set of samples that implicitly contain statistical uncertainties. The resulting maps are spatially coherent and reproduce the data with high fidelity. We confirm previous findings regarding the warping and flaring of the Galactic disc. A comparison with 3D dust maps reveals a good agreement on scales larger than approximately 400 pc. While our results are not free of artefacts, they present a big step forward in obtaining high quality 3D maps of the interstellar medium., Comment: 21 pages, 22 figures

Cosmic rays (CRs) play a major role in the dynamics of the interstellar medium (ISM). Their interactions and transport ionize, heat, and push the ISM thereby coupling different regions of it. The spatial distribution of CRs depends on the distribution of their sources as well as the ISM constituents they interact with, such as gas, starlight, and magnetic fields. Particularly, gas interacts closely with CRs, influencing CR fluxes and gamma -ray emission. We illustrate the influence of 3D gas structures on CR transport and gamma -ray emission. We use the PICARD code and multiple samples of recent 3D reconstructions of the HI and H$_2$ Galactic gas constituents to investigate the impact on the transport of CRs and emission of gamma -rays. We find the necessary transport parameters to reproduce local measurements of CR fluxes, and see that they depend on the local distribution of gas density and structure. The distribution of CR fluxes exhibits energy-dependent structures that vary for all CR species due to their corresponding loss processes. Regions of enhanced secondary (primary) species are spatially correlated (anti-correlated) with the gas density. We observe a high sensitivity of the gamma -ray emission on the contrast of gas structures, as those determine the 3D spatial distributions of hadronic interactions and bremsstrahlung. We find that corresponding gas-induced structures in the distribution of CR electrons are also visible in Inverse Compton (IC) emission. Due to the aforementioned sensitivity, the analysis of CR data for CR sources and transport parameters requires the usage of accurate 3D gas maps.

This work investigates the phase structure of the non-renormalizable (3+1)-dimensional Nambu-Jona-Lasinio (NJL) model with particular focus on inhomogeneous phases (IPs), where the chiral condensate is non-uniform in space, and the closely related moat regimes, where mesonic dispersion relations favor non-vanishing momenta. We use the mean-field approximation and consider five different regularization schemes including three lattice discretizations. The results within the different regularization schemes are systematically analyzed in order to study the dependence of the IP on the choice of regulatization scheme and regulator value. The IP exhibits a drastic dependence on the chosen regularization scheme rendering any physical interpretation of results on inhomogeneous phases in this model doubtful. In contrast, we find only a mild scheme dependence of the moat regime suggesting that its existence is a consequence of the action of the NJL model and its symmetries and, thus, that it might also exist in QCD., Comment: 16 pages, 11 figures, 12 pages appendix & references, v2: version as accepted for publication in Physical Review D, added a data publication containing all the plotted data

The Scintillating Bubble Chamber (SBC) collaboration purchased 32 Hamamatsu VUV4 silicon photomultipliers (SiPMs) for use in SBC-LAr10, a bubble chamber containing 10~kg of liquid argon. A dark-count characterization technique, which avoids the use of a single-photon source, was used at two temperatures to measure the VUV4 SiPMs breakdown voltage ($V_{\text{BD}}$), the SiPM gain ($g_{\text{SiPM}}$), the rate of change of $g_{\text{SiPM}}$ with respect to voltage ($m$), the dark count rate (DCR), and the probability of a correlated avalanche (P$_{\text{CA}}$) as well as the temperature coefficients of these parameters. A Peltier-based chilled vacuum chamber was developed at Queen's University to cool down the Quads to $233.15\pm0.2$~K and $255.15\pm0.2$~K with average stability of $\pm20$~mK. An analysis framework was developed to estimate $V_{\text{BD}}$ to tens of mV precision and DCR close to Poissonian error. The temperature dependence of $V_{\text{BD}}$ was found to be $56\pm2$~mV~K$^{-1}$, and $m$ on average across all Quads was found to be $(459\pm3(\rm{stat.})\pm23(\rm{sys.}))\times 10^{3}~e^-$~PE$^{-1}$~V$^{-1}$. The average DCR temperature coefficient was estimated to be $0.099\pm0.008$~K$^{-1}$ corresponding to a reduction factor of 7 for every 20~K drop in temperature. The average temperature dependence of P$_{\text{CA}}$ was estimated to be $4000\pm1000$~ppm~K$^{-1}$. P$_{\text{CA}}$ estimated from the average across all SiPMs is a better estimator than the P$_{\text{CA}}$ calculated from individual SiPMs, for all of the other parameters, the opposite is true. All the estimated parameters were measured to the precision required for SBC-LAr10, and the Quads will be used in conditions to optimize the signal-to-noise ratio., Comment: 25 pages, 19 figures

In contrast to free space, in waveguides the dispersive and dissipative dipole-dipole interactions among quantum emitters exhibit a periodic behavior over remarkably long distances. We propose a novel setup exploiting this long-range periodicity in order to create highly excited subradiant states and facilitate fast controlled collective energy transport amongst far-apart ensembles coupled to a waveguide. For sufficiently large ensembles collective superradiant emission into the fiber modes dominates over its free space counterpart. We show that for a large number of emitters a fast transverse coherent pulse can create almost perfect subradiant states with up to $50\%$ excitation. On the other hand, for a coherent excitation of one sub-ensemble above an overall excitation fraction of $50\%$ we find a nearly lossless and fast energy transfer to the ground state sub-ensemble. This transport can be enhanced or suppressed by controlling the positions of the ensembles relative to each other, while it can also be realized with a random position distribution. In the optimally enhanced case this fast transfer appears as superradiant emission with subsequent superabsorption, yet, without a superradiant decay after the absorption. The highly excited subradiant states as well as the superradiant excitation transfer appear as suitable building blocks in applications like active atomic clocks, quantum batteries, quantum information protocols and quantum metrology procedures such as fiber-based Ramsey schemes., Comment: 9 pages, 7 figures

On a locally finite, infinite tree $T$, let $p_c(T)$ denote the critical probability for Bernoulli percolation. We prove that every positively associated, finite-range dependent percolation model on $T$ with marginals $p > p_c(T)$ must percolate. Among finite-range dependent models on trees, positive association is thus a favourable property for percolation to occur. On general graphs of bounded degree, Liggett, Schonmann and Stacey (1997) proved that finite-range dependent percolation models with sufficiently large marginals stochastically dominate product measures. Under the additional assumption of positive association, we prove that stochastic domination actually holds for arbitrary marginals. Our result thereby generalises Proposition 3.4 in Liggett, Schonmann and Stacey (1997) which was restricted to the special case $G = \mathbb{Z}$. Studying the class of 1-independent percolation models has proven useful in bounding critical probabilities of various percolation models via renormalization. In many cases, the renormalized model is not only 1-independent but also positively associated. This motivates us to introduce the smallest parameter $p_a^+(G)$ such that every positively associated, 1-independent bond percolation model on a graph $G$ with marginals $p > p_a^+(G)$ percolates. We obtain quantitative upper and lower bounds on $p_a^+(\mathbb{Z}^2)$ and on $p_a^+(\mathbb{Z}^n)$ as $n\to \infty$, and also study the case of oriented bond percolation. In proving these results, we revisit several techniques originally developed for Bernoulli percolation, which become applicable thanks to a simple but seemingly new way of combining positive association with finite-range dependence., Comment: 41 pages, 5 figures

The topic of fairness in AI, as debated in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) communities, has sparked meaningful discussions in the past years. However, from a legal perspective, particularly from the perspective of European Union law, many open questions remain. Whereas algorithmic fairness aims to mitigate structural inequalities at design-level, European non-discrimination law is tailored to individual cases of discrimination after an AI model has been deployed. The AI Act might present a tremendous step towards bridging these two approaches by shifting non-discrimination responsibilities into the design stage of AI models. Based on an integrative reading of the AI Act, we comment on legal as well as technical enforcement problems and propose practical implications on bias detection and bias correction in order to specify and comply with specific technical requirements.

Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexplored in multi-class segmentation scenarios, where topological errors are common. We propose a general loss function for topologically faithful multi-class segmentation extending the recent Betti matching concept, which is based on induced matchings of persistence barcodes. We project the N-class segmentation problem to N single-class segmentation tasks, which allows us to use 1-parameter persistent homology, making training of neural networks computationally feasible. We validate our method on a comprehensive set of four medical datasets with highly variant topological characteristics. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.

Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. Due to the complexity of this task, large training datasets are rare in many domains, which makes the training of large networks challenging. This data sparsity necessitates the establishment of pre-training strategies akin to the state-of-the-art in computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension transfer learning for image-to-graph transformers. We propose (1) a regularized edge sampling loss for sampling the optimal number of object relationships (edges) across domains, (2) a domain adaptation framework for image-to-graph transformers that aligns features from different domains, and (3) a simple projection function that allows us to pretrain 3D transformers on 2D input data. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we pretrain our models on 2D satellite images before applying them to vastly different target domains in 2D and 3D. Our method consistently outperforms a series of baselines on challenging benchmarks, such as retinal or whole-brain vessel graph extraction.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.

Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and process tomography to machine learning and error mitigation. Recently, the notion of measurement dual frames, in which classical shadows are generalized to dual operators of POVM effects, resurfaced in the literature. This brought attention to additional degrees of freedom in the post-processing stage of randomized measurements that are often neglected by established techniques. In this work, we leverage dual frames to construct improved observable estimators from informationally complete measurement samples. We introduce novel classes of parametrized frame superoperators and optimization-free dual frames based on empirical frequencies, which offer advantages over their canonical counterparts while retaining computational efficiency. Remarkably, this comes at almost no quantum or classical cost, thus rendering dual frame optimization a valuable addition to the randomized measurement toolbox., Comment: 9+3 pages, 4 figures

In this proceedings article we present a selected set of our lattice results regarding the effect that background electromagnetic fields have on the topology of QCD. In particular, we report on the lattice spacing-dependence of the axion-photon coupling as well as on the response of the topological susceptibility to strong magnetic fields at nonzero temperatures. We demonstrate that the ratio of topological susceptibilities at finite to zero magnetic field has a well behaved continuum limit at low temperatures using a reweighting technique. Moreover, we study the scaling of the axion-photon coupling towards the continuum limit and show that it is less severely affected by discretisation effects., Comment: 9 pages, 5 figures, Proceedings of the 40th International Symposium on Lattice Field Theory, July 31st - August 4th 2023, Chicago, United States of America

We continue previous investigations of the (inhomogeneous) phase structure of the Gross-Neveu model in a noninteger number of spatial dimensions ($1 \leq d < 3$) in the limit of an infinite number of fermion species ($N \to \infty$) at (non)zero chemical potential $\mu$. In this work, we extend the analysis from zero to nonzero temperature $T$. The phase diagram of the Gross-Neveu model in $1 \leq d < 3$ spatial dimensions is well known under the assumption of spatially homogeneous condensation with both a symmetry broken and a symmetric phase present for all spatial dimensions. In $d = 1$ one additionally finds an inhomogeneous phase, where the order parameter, the condensate, is varying in space. Similarly, phases of spatially varying condensates are also found in the Gross-Neveu model in $d = 2$ and $d = 3$, as long as the theory is not fully renormalized, i.e., in the presence of a regulator. For $d = 2$, one observes that the inhomogeneous phase vanishes, when the regulator is properly removed (which is not possible for $d = 3$ without introducing additional parameters). In the present work, we use the stability analysis of the symmetric phase to study the presence (for $1 \leq d < 2$) and absence (for $2 \leq d < 3$) of these inhomogeneous phases and the related moat regimes in the fully renormalized Gross-Neveu model in the $\mu, T$-plane. We also discuss the relation between "the number of spatial dimensions" and "studying the model with a finite regulator" as well as the possible consequences for the limit $d \to 3$., Comment: 12 pages, 9 figures, 21 pages appendix & references, data & code will be published together with the journal publication, continuation of arXiv:2306.16290

We show that the performance of critical quantum metrology protocols, counter-intuitively, can be enhanced by finite temperature. We consider a toy-model squeezing Hamiltonian, the Lipkin-Meshkov-Glick model and the paradigmatic Ising model. We show that the temperature enhancement of the quantum Fisher information can be achieved by adiabatic preparation of the critical state and by preparing it directly in the proximity of the critical point. We also find a relatively simple, however, non-optimal measurement capable of harnessing finite temperature to increase the parameter estimation sensitivity. Therefore, we argue that temperature can be considered as a resource in critical quantum metrology., Comment: 6 pages, 2 figures, improved presentation and discussions

We present methods to constrain fermionic condensates on the level of the path integral, which grant access to the quantum effective potential in the infinite volume limit. In the case of a spontaneously broken symmetry, this potential possesses a manifestly flat region, which is inaccessible to the standard approach on the lattice. However, by constraining the appropriate order parameters such as the chiral condensate, one is then able to probe the flat region. We demonstrate our method of constraining fermionic condensates in the 2-dimensional Gross-Neveu model, which exhibits a spontaneously broken chiral symmetry. We show how the potential flattens for increasing volume and that the flat region is dominated by inhomogeneous field configurations., Comment: 8 pages, 4 figures, contains ancillary files with plot data; talk given at the 40th International Symposium on Lattice Field theory (LATTICE 2023) at Fermilab; July 31 - August 4 2023

Major Depressive Disorder (MDD) is one of the most common causes of disability worldwide. Unfortunately, about one-third of patients do not benefit sufficiently from available treatments and not many new drugs have been developed in this area in recent years. We thus need better and faster ways to evaluate many different treatment options quickly. Platform trials are a possible remedy - they facilitate the evaluation of more investigational treatments in a shorter period of time by sharing controls, as well as reducing clinical trial activation and recruitment times. We discuss design considerations for a platform trial in MDD, taking into account the unique disease characteristics, and present the results of extensive simulations to investigate the operating characteristics under various realistic scenarios. To allow the testing of more treatments, interim futility analyses should be performed to eliminate treatments that have either no or negligible treatment effect. Furthermore, we investigate different randomisation and allocation strategies as well as the impact of the per-treatment arm sample size. We compare the operating characteristics of such platform trials to those of traditional randomised controlled trials and highlight the potential advantages of platform trials.

Due to our vantage point in the disk of the Galaxy, its 3D structure is not directly accessible. However, knowing the spatial distribution, e.g. of atomic and molecular hydrogen gas is of great importance for interpreting and modelling cosmic ray data and diffuse emission. Using novel Bayesian inference techniques, we reconstruct the 3D densities of atomic and molecular hydrogen in the Galaxy together with (part of) the galactic velocity field. In order to regularise the infinite number of degrees of freedom and obtain information in regions with missing or insufficient data, we incorporate the correlation structure of the gas fields into our prior. Basis for these reconstructions are the data-sets from the HI4PI-survey on the 21-cm emission line and the CO-survey compilation by Dame et al. (2001) on the ($1\rightarrow0$) rotational transition together with a variable gas flow model. We present the preliminary estimated mean surface mass densities and corrections to the prior assumption of the galactic velocity field. In the future, we plan to relax assumptions on the optical thickness and include additional data to further constrain either the galactic velocity field or the gas densities., Comment: 8 pages, 3 figures, prepared for the proceedings of the 38th International Cosmic Ray Conference (ICRC2023)

This paper presents a fully automated approach for identifying speech anomalies from voice recordings to aid in the assessment of speech impairments. By combining Connectionist Temporal Classification (CTC) and encoder-decoder-based automatic speech recognition models, we generate rich acoustic and clean transcripts. We then apply several natural language processing methods to extract features from these transcripts to produce prototypes of healthy speech. Basic distance measures from these prototypes serve as input features for standard machine learning classifiers, yielding human-level accuracy for the distinction between recordings of people with aphasia and a healthy control group. Furthermore, the most frequently occurring aphasia types can be distinguished with 90% accuracy. The pipeline is directly applicable to other diseases and languages, showing promise for robustly extracting diagnostic speech biomarkers.