Search

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image. Our method utilizes an image-conditioned 3D scene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene. Motivated by the ill-posed nature of the task and to obtain consistent scene reconstruction results, we learn a generative scene prior by conditioning on all scene objects simultaneously to capture the scene context and by allowing the model to learn inter-object relationships throughout the diffusion process. We further propose an efficient surface alignment loss to facilitate training even in the absence of full ground-truth annotation, which is common in publicly available datasets. This loss leverages an expressive shape representation, which enables direct point sampling from intermediate shape predictions. By framing the task of single RGB image 3D scene reconstruction as a conditional diffusion process, our approach surpasses current state-of-the-art methods, achieving a 12.04% improvement in AP3D on SUN RGB-D and a 13.43% increase in F-Score on Pix3D., Comment: Project Page: https://www.manuel-dahnert.com/research/scene-diffusion - Accepted at NeurIPS 2024

Nano-drones, with their small, lightweight design, are ideal for confined-space rescue missions and inherently safe for human interaction. However, their limited payload restricts the critical sensing needed for ego-velocity estimation and obstacle detection to single-bean laser-based time-of-flight (ToF) and low-resolution optical sensors. Although those sensors have demonstrated good performance, they fail in some complex real-world scenarios, especially when facing transparent or reflective surfaces (ToFs) or when lacking visual features (optical-flow sensors). Taking inspiration from bats, this paper proposes a novel two-way ranging-based method for ego-velocity estimation and obstacle avoidance based on down-and-forward facing ultra-low-power ultrasonic sensors, which improve the performance when the drone faces reflective materials or navigates in complete darkness. Our results demonstrate that our new sensing system achieves a mean square error of 0.019 m/s on ego-velocity estimation and allows exploration for a flight time of 8 minutes while covering 136 m on average in a challenging environment with transparent and reflective obstacles. We also compare ultrasonic and laser-based ToF sensing techniques for obstacle avoidance, as well as optical flow and ultrasonic-based techniques for ego-velocity estimation, denoting how these systems and methods can be complemented to enhance the robustness of nano-drone operations., Comment: This paper is extending "BatDeck: Advancing Nano-drone Navigation with Low-power Ultrasound-based Obstacle Avoidance" (SAS 2024), and is submitted to IEEE Transactions on Instrumentation and Measurements. arXiv admin note: text overlap with arXiv:2403.16696

SrNi$_2$P$_2$ is unique among the ThCr$_2$Si$_2$ class since it exhibits a temperature induced transition upon cooling from an uncollapsed tetragonal (ucT) state to a one-third-collapsed orthorhombic (tcO) state where one out of every three P-rows bond across the Sr layers. This compound is also known for exhibiting bulk superconductivity below 1.4 K at ambient pressure. In this work, we report on the effects of Rh substitution in Sr(Ni$_{1-x}$Rh$_x$)$_2$P$_2$ on the structural and superconducting properties. We studied the variation of the nearest P-P distances as a function of the Rh fraction at room temperature, as well as its temperature dependence for selected compositions. We find that increasing the Rh fraction leads to a decrease in the transition temperature between the ucT and tcO states, until a full suppression of the tcO state for $x\geq 0.166$. The superconducting transition first remains nearly insensitive to the Rh fraction, and then it increases to 2.3 K after the tcO state is fully suppressed. These results are summarized in a phase diagram, built upon the characterization by energy dispersive x-ray spectroscopy, x-ray diffraction, resistance, magnetization and specific heat measurements done on crystalline samples with varying Rh content. The relationship between band structure, crystal structure and superconductivity is discussed based on previously reported band structure calculations on SrRh$_2$P$_2$. Moreover, the effect of Rh fraction on the stress-induced structural transitions is also addressed by means of strain-stress studies done by uniaxial compression of single-crystalline micropillars of Sr(Ni$_{1-x}$Rh$_x$)$_2$P$_2$., Comment: 16 pages, 15 figures

A search for $D^0$ meson decays to the $\pi^+\pi^-e^+e^-$ and $K^+K^-e^+e^-$ final states is reported using a sample of proton-proton collisions collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb$^{-1}$. The decay $D^0 \rightarrow \pi^+\pi^-e^+e^-$ is observed for the first time when requiring that the two electrons are consistent with coming from the decay of a $\phi$ or $\rho^0/\omega$ meson. The corresponding branching fractions are measured relative to the $D^0 \rightarrow K^-\pi^-[e^+e^-]_{\rho^0/\omega}$ decay, where the two electrons are consistent with coming from the decay of a $\rho^0$ or $\omega$ meson. No evidence is found for the $D^0 \rightarrow K^+K^-e^+e^-$ decay and world-best limits are set on its branching fraction. The results are compared to, and found to be consistent with, the branching fractions of the $D^0 \rightarrow \pi^+\pi^-\mu^+\mu^-$ and $D^0 \rightarrow K^+K^-\mu^+\mu^-$ decays recently measured by LHCb and confirm lepton universality at the current precision., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1611/ (LHCb public pages)

Adversarial attacks on traffic sign classification models were among the first successfully tried in the real world. Since then, the research in this area has been mainly restricted to repeating baseline models, such as LISA-CNN or GTSRB-CNN, and similar experiment settings, including white and black patches on traffic signs. In this work, we decouple model architectures from the datasets and evaluate on further generic models to make a fair comparison. Furthermore, we compare two attack settings, inconspicuous and visible, which are usually regarded without direct comparison. Our results show that standard baselines like LISA-CNN or GTSRB-CNN are significantly more susceptible than the generic ones. We, therefore, suggest evaluating new attacks on a broader spectrum of baselines in the future. Our code is available at \url{https://github.com/KASTEL-MobilityLab/attacks-on-traffic-sign-recognition/}., Comment: Accepted for publication at ICMLA 2024

Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects, but its quadratic complexity presents a significant practical limitation. This problem is particularly pronounced in computational chemistry, where the stringent efficiency requirements of machine learning force fields (MLFFs) often preclude accurately modeling long-range interactions. To address this, we introduce Euclidean fast attention (EFA), a linear-scaling attention-like mechanism designed for Euclidean data, which can be easily incorporated into existing model architectures. A core component of EFA are novel Euclidean rotary positional encodings (ERoPE), which enable efficient encoding of spatial information while respecting essential physical symmetries. We empirically demonstrate that EFA effectively captures diverse long-range effects, enabling EFA-equipped MLFFs to describe challenging chemical interactions for which conventional MLFFs yield incorrect results.

An integral procedure in every coherent multidimensional spectroscopy experiment is to suppress undesired background signals. For that purpose, one can employ a particular phase-matching geometry or phase cycling, a procedure that was adapted from nuclear magnetic resonance (NMR) spectroscopy. In optical multidimensional spectroscopy, phase cycling has been usually carried out in a "nested" fashion, where pulse phases are incremented sequentially with linearly spaced increments. Another phase-cycling approach which was developed for NMR spectroscopy is "cogwheel phase cycling," where all pulse phases are varied simultaneously in increments defined by so-called "winding numbers". Here we explore the concept of cogwheel phase cycling in the context of population-based coherent multidimensional spectroscopy. We derive selection rules for resolving and extracting fourth-order and higher-order nonlinear signals by cogwheel phase cycling and describe how to perform a numerical search for the winding numbers for various population-detected 2D spectroscopy experiments. We also provide an expression for a numerical search for nested phase-cycling schemes and predict the most economical schemes of both approaches for a wide range of nonlinear signals. The signal selectivity of the technique is demonstrated experimentally by acquiring rephasing and nonrephasing fourth-order signals of a laser dye by both phase-cycling approaches. We find that individual nonlinear signal contributions are, in most cases, captured with fewer steps by cogwheel phase cycling compared to nested phase cycling., Comment: Main manuscript: 21 pages, 5 figures, 2 tables; Supplementary material: 3 pages, 2 figures

Reinforcement learning is employed to optimize the periodic forcing signal of a pulsed blowing system that controls flow separation in a fully-turbulent $Re_\theta = 1000$ diffuser flow. Based on the state of the wind tunnel experiment that is determined with wall shear-stress measurements, Proximal Policy Optimization is used to iteratively adjust the forcing signal. Out of the reward functions investigated in this study, the incremental reduction of flow reversal per action is shown to be the most sample efficient. Less than 100 episodes are required to find the parameter combination that ensures the highest control authority for a fixed mass flow consumption. Fully consistent with recent studies, the algorithm suggests that the mass flow is used most efficiently when the actuation signal is characterized by a low duty cycle where the pulse duration is small compared to the pulsation period. The results presented in this paper promote the application of reinforcement learning for optimization tasks based on turbulent, experimental data.

G\"odel proved in the 1930s in his famous Incompleteness Theorems that not all statements in mathematics can be proven or disproven from the accepted ZFC axioms. A few years later he showed the celebrated result that Cantor's Continuum Hypothesis is consistent. Afterwards, G\"odel raised the question whether, despite the fact that there is no reasonable axiomatic framework for all mathematical statements, natural statements, such as Cantor's Continuum Hypothesis, can be decided via extending ZFC by large cardinal axioms. While this question has been answered negatively, the problem of finding good axioms that decide natural mathematical statements remains open. There is a compelling candidate for an axiom that could solve G\"odel's problem: V = Ultimate-L. In addition, due to recent results the Sealing scenario has gained a lot of attention. We describe these candidates as well as their impact and relationship.

The search for highly coherent degrees of freedom in noisy solid-state environments is a major challenge in condensed matter. In disordered dipolar systems, such as magnetically doped insulators, compact clusters of two-level systems (TLS) have recently been shown to have significantly longer coherence times than typical single TLS. Coupling weakly to their environment, they sense and probe its many-body dynamics through the induced dephasing. However, it has remained an open question whether further mechanisms exist that protect the coherence of such solid-state qubits. Here we show that symmetric clusters of few TLS couple even more weakly to their surroundings, making them highly sensitive quantum sensors of slow many-body dynamics. Furthermore, we explore their use as qubits for quantum information storage, detailing the techniques required for their preparation and manipulation. Our findings elucidate the role of symmetry in enhancing quantum coherence in disordered and noisy systems, opening a route toward a sensitive experimental probe of many-body quasi-localization dynamics as well as the development of quantum technologies in solid-state systems.

As main result, we show that a pseudodifferential operator in the Weyl calculus, whose symbol has compact Fourier support, lies in the Schatten class $\mathcal S^p$ if and only if its symbol lies in the Lebesgue space $L^p$ on phase space. As an immediate consequence, this gives an alternative and very lucid proof of a recent result by Luef and Samuelsen, who had discovered that for compactly supported measures $\mu,$ classical Fourier restriction estimates with respect to the measure $\mu$ are equivalent to quantum restriction estimates for the Fourier-Wigner transform for Schatten classes.

The birth mass function of neutron stars encodes rich information about supernova explosions, double star evolution, and properties of matter under extreme conditions. To date, it has remained poorly constrained by observations, however. Applying probabilistic corrections to account for mass accreted by recycled pulsars in binary systems to mass measurements of 90 neutron stars, we find that the birth masses of neutron stars can be described by a unimodal distribution that smoothly turns on at $\mathbf{\unit[1.1]{\mathrm{M}_{\odot}}}$, peaks at $\mathbf{\approx \unit[1.27]{\mathrm{M}_{\odot}}}$, before declining as a steep power law. Such a ``turn-on" power-law distribution is strongly favoured against the widely-adopted empirical double-Gaussian model at the $\mathbf{3\sigma}$ level. The power-law shape may be inherited from the initial mass function of massive stars, but the relative dearth of massive neutron stars implies that single stars with initial masses greater than $\mathbf{\approx \unit[18]{\mathrm{M}_{\odot}}}$ do not form neutron stars, in agreement with the absence of massive red supergiant progenitors to supernovae., Comment: 57 pages, 14 figures, 4 tables

Autoregressive transformer language models (LMs) possess strong syntactic abilities, often successfully handling phenomena from agreement to NPI licensing. However, the features they use to incrementally process language inputs are not well understood. In this paper, we fill this gap by studying the mechanisms underlying garden path sentence processing in LMs. We ask: (1) Do LMs use syntactic features or shallow heuristics to perform incremental sentence processing? (2) Do LMs represent only one potential interpretation, or multiple? and (3) Do LMs reanalyze or repair their initial incorrect representations? To address these questions, we use sparse autoencoders to identify interpretable features that determine which continuation - and thus which reading - of a garden path sentence the LM prefers. We find that while many important features relate to syntactic structure, some reflect syntactically irrelevant heuristics. Moreover, while most active features correspond to one reading of the sentence, some features correspond to the other, suggesting that LMs assign weight to both possibilities simultaneously. Finally, LMs do not re-use features from garden path sentence processing to answer follow-up questions., Comment: Code and data available at https://github.com/hannamw/GP-mechanisms

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) tags neutrons via their capture on gadolinium or hydrogen, which release $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the key features and the first results of the XENONnT NV when operated with demineralized water in the initial phase of the experiment. Its efficiency for detecting neutrons is $(82\pm 1)\,\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of $(53\pm 3)\,\%$ for the tagging of WIMP-like neutron signals, inside a tagging time window of $250\,\mathrm{\mu s}$ between TPC and NV, leading to a livetime loss of $1.6\,\%$ during the first science run of XENONnT.

The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model training on a fixed language data budget of 100 million words or less. This year, we released improved text corpora, as well as a vision-and-language corpus to facilitate research into cognitively plausible vision language models. Submissions were compared on evaluation tasks targeting grammatical ability, (visual) question answering, pragmatic abilities, and grounding, among other abilities. Participants could submit to a 10M-word text-only track, a 100M-word text-only track, and/or a 100M-word and image multimodal track. From 31 submissions employing diverse methods, a hybrid causal-masked language model architecture outperformed other approaches. No submissions outperformed the baselines in the multimodal track. In follow-up analyses, we found a strong relationship between training FLOPs and average performance across tasks, and that the best-performing submissions proposed changes to the training data, training objective, and model architecture. This year's BabyLM Challenge shows that there is still significant room for innovation in this setting, in particular for image-text modeling, but community-driven research can yield actionable insights about effective strategies for small-scale language modeling.

Background: Over the past few decades, the process and methodology of automated question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course, and address the cognitive dimension understand. Methods: Unlike previous attempts that might use basic methods like ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects. Results and Conclusions: Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content., Comment: Accepted at Journal of Computer Assisted Learning (2024)

We present a detailed spectroscopic study of the $^4\mathrm{I}_{13/2}$ and $^4\mathrm{I}_{15/2}$ Er$^{3+}$ multiplets of Er:CaWO$_4$ grown without a co-dopant. Using photoluminescence and photoluminescence excitation measurements, we find multiple environments into which the Erbium ions are incorporated in the crystal. For the four most prevalent environments, we provide a complete assignment of the $^4\mathrm{I}_{13/2}$ and $^4\mathrm{I}_{15/2}$ sublevel energies. Polarization-dependent absorption and emission spectroscopy allow us to identify the irreducible representations of the states and determine the dipole nature of transitions. In addition, we compare the different relaxation paths within $^4\mathrm{I}_{13/2}$ and $^4\mathrm{I}_{15/2}$ multiplets via line broadening and lifetime measurements. Finally, spectral, lifetime, and polarization measurements indicate similarities between the studied environments.

Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines. However, generating holistic multi-view consistent images from prompts still remains an important and challenging task. To address this challenge, we propose a diffusion process that attends to time-dependent spatial frequencies of features with a novel attention mechanism as well as novel noise initialization technique and cross-attention loss. This Fourier-based attention block focuses on features from non-overlapping regions of the generated scene in order to better align the global appearance. Our noise initialization technique incorporates shared noise and low spatial frequency information derived from pixel coordinates and depth maps to induce noise correlations across views. The cross-attention loss further aligns features sharing the same prompt across the scene. Our technique improves SOTA on several quantitative metrics with qualitatively better results when compared to other state-of-the-art approaches for multi-view consistency., Comment: WACV 2025

Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-Rings and Gaussian Shading, represent a novel class of watermarking techniques that are easy to implement and highly robust against various perturbations. However, our work demonstrates a fundamental security vulnerability of semantic watermarks. We show that attackers can leverage unrelated models, even with different latent spaces and architectures (UNet vs DiT), to perform powerful and realistic forgery attacks. Specifically, we design two watermark forgery attacks. The first imprints a targeted watermark into real images by manipulating the latent representation of an arbitrary image in an unrelated LDM to get closer to the latent representation of a watermarked image. We also show that this technique can be used for watermark removal. The second attack generates new images with the target watermark by inverting a watermarked image and re-generating it with an arbitrary prompt. Both attacks just need a single reference image with the target watermark. Overall, our findings question the applicability of semantic watermarks by revealing that attackers can easily forge or remove these watermarks under realistic conditions., Comment: 23 pages, 21 figures, 6 tables

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.

The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challenge by supporting "hybrid" plasticity, which combines the concepts of programmability and inherently parallel emulation. In particular, observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits. The evaluation of the observables, the decision to perform an update, and the magnitude of an update, are all conducted in a conventional program that runs simultaneously with the analog neural network. Consequently, these systems can offer a scalable and flexible solution in such cases. While previous work on the platform has already reported on the use of different kinds of plasticity, the descriptions for the spiking neural network experiment topology and protocol, and the plasticity algorithm have not been connected. In this work, we introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language for the BrainScaleS-2 platform and demonstrate its use.

Assistive technology can be leveraged by blind people when searching for objects in their daily lives. We created ObjectFinder, an open-vocabulary interactive object-search prototype, which combines object detection with scene description and navigation. It enables blind persons to detect and navigate to objects of their choice. Our approach used co-design for the development of the prototype. We further conducted need-finding interviews to better understand challenges in object search, followed by a study with the ObjectFinder prototype in a laboratory setting simulating a living room and an office, with eight blind users. Additionally, we compared the prototype with BeMyEyes and Lookout for object search. We found that most participants felt more independent with ObjectFinder and preferred it over the baselines when deployed on more efficient hardware, as it enhances mental mapping and allows for active target definition. Moreover, we identified factors for future directions for the development of object-search systems.

I argue for an approach to the Foundations of Physics that puts the question in the title center stage, rather than asking "what is the case in the world?". This approach, Algorithmic Idealism, attempts to give a mathematically rigorous in-principle-answer to this question both in the usual empirical regime of physics and more exotic regimes of cosmology, philosophy, and science fiction (but soon perhaps real) technology. I begin by arguing that quantum theory, in its actual practice and in some interpretations, should be understood as telling an agent what they should expect to observe next (rather than what is the case), and that the difficulty of answering this former question from the usual "external" perspective is at the heart of persistent enigmas such as the Boltzmann brain problem, extended Wigner's friend scenarios, Parfit's teletransportation paradox, or our understanding of the simulation hypothesis. Algorithmic Idealism is a conceptual framework, based on two postulates that admit several possible mathematical formalizations, cast in the language of algorithmic information theory. Here I give a non-technical description of this view and show how it dissolves the aforementioned enigmas: for example, it claims that you should never bet on being a Boltzmann brain regardless of how many there are, that shutting down computer simulations does not generally terminate its inhabitants, and it predicts the apparent embedding into an objective external world as an approximate description., Comment: 25 pages, 2 figures

As numerical simulations grow in size and complexity, they become increasingly resource-intensive in terms of time and energy. While specialized hardware accelerators often provide order-of-magnitude gains and are state of the art in other scientific fields, their availability and applicability in computational neuroscience is still limited. In this field, neuromorphic accelerators, particularly mixed-signal architectures like the BrainScaleS systems, offer the most significant performance benefits. These systems maintain a constant, accelerated emulation speed independent of network model and size. This is especially beneficial when traditional simulators reach their limits, such as when modeling complex neuron dynamics, incorporating plasticity mechanisms, or running long or repetitive experiments. However, the analog nature of these systems introduces new challenges. In this paper we demonstrate the capabilities and advantages of the BrainScaleS-1 system and how it can be used in combination with conventional software simulations. We report the emulation time and energy consumption for two biologically inspired networks adapted to the neuromorphic hardware substrate: a balanced random network based on Brunel and the cortical microcircuit from Potjans and Diesmann.

As numerical simulations grow in complexity, their demands on computing time and energy increase. Hardware accelerators offer significant efficiency gains in many computationally intensive scientific fields, but their use in computational neuroscience remains limited. Neuromorphic substrates, such as the BrainScaleS architectures, offer significant advantages, especially for studying complex plasticity rules that require extended simulation runtimes. This work presents the implementation of a calcium-based plasticity rule that integrates calcium dynamics based on the synaptic tagging-and-capture hypothesis on the BrainScaleS-2 system. The implementation of the plasticity rule for a single synapse involves incorporating the calcium dynamics and the plasticity rule equations. The calcium dynamics are mapped to the analog circuits of BrainScaleS-2, while the plasticity rule equations are numerically solved on its embedded digital processors. The main hardware constraints include the speed of the processors and the use of integer arithmetic. By adjusting the timestep of the numerical solver and introducing stochastic rounding, we demonstrate that BrainScaleS-2 accurately emulates a single synapse following a calcium-based plasticity rule across four stimulation protocols and validate our implementation against a software reference model.

The development of mechanistic models of physical systems is essential for understanding their behavior and formulating predictions that can be validated experimentally. Calibration of these models, especially for complex systems, requires automated optimization methods due to the impracticality of manual parameter tuning. In this study, we use an autoencoder to automatically extract relevant features from the membrane trace of a complex neuron model emulated on the BrainScaleS-2 neuromorphic system, and subsequently leverage sequential neural posterior estimation (SNPE), a simulation-based inference algorithm, to approximate the posterior distribution of neuron parameters. Our results demonstrate that the autoencoder is able to extract essential features from the observed membrane traces, with which the SNPE algorithm is able to find an approximation of the posterior distribution. This suggests that the combination of an autoencoder with the SNPE algorithm is a promising optimization method for complex systems.

Complex Langevin (CL) is a computational method to circumvent the numerical sign problem with applications in finite-density quantum chromodynamics and the real-time dynamics of quantum field theories. It has long been known that, depending on the simulated system, CL does not always converge correctly. In this work, we provide numerical evidence that the success or failure of the complex Langevin method is deeply tied to the Lefschetz thimble structure of the simulated system. This is demonstrated by constructing weight function regularizations that deform the thimbles of systems with compact domains. Our results indicate that CL converges correctly when the regularized system exhibits a single relevant compact thimble. We introduce a bias correction to retrieve the values of the original theory for parameter sets where a direct complex Langevin approach fails. The effectiveness of this method is illustrated using several toy models, including the cosine model and the SU(2) and SU(3) Polyakov chains. Finally, we discuss the opportunities and limitations of this regularization approach for lattice field theories., Comment: 28 pages + appendixes, 18 figures, 19 tables

Determining the internal structure of Uranus is a key objective for planetary science. Knowledge of Uranus's bulk composition and the distribution of elements is crucial to understanding its origin and evolutionary path. In addition, Uranus represents a poorly understood class of intermediate-mass planets (intermediate in size between the relatively well studied terrestrial and gas giant planets), which appear to be very common in the Galaxy. As a result, a better characterization of Uranus will also help us to better understand exoplanets in this mass and size regime. Recognizing the importance of Uranus, a Keck Institute for Space Studies (KISS) workshop was held in September 2023 to investigate how we can improve our knowledge of Uranus's internal structure in the context of a future Uranus mission that includes an orbiter and a probe. The scientific goals and objectives of the recently released Planetary Science and Astrobiology Decadal Survey were taken as our starting point. We reviewed our current knowledge of Uranus's interior and identified measurement and other mission requirements for a future Uranus spacecraft, providing more detail than was possible in the Decadal Survey's mission study and including new insights into the measurements to be made. We also identified important knowledge gaps to be closed with Earth-based efforts in the near term that will help guide the design of the mission and interpret the data returned., Comment: Study Report prepared for the W. M. Keck Institute for Space Studies (KISS). Study title: Determining the Interior Structure of Uranus: A Case Study for Leveraging Cross-Discipline Science to Answer Tough Questions. Study dates: September 11-15, 2023. Team Leads: Mark Hofstadter, Ravit Helled, and David Stevenson

We elaborate on the method introduced in arXiv:2403.02165 for holographic duals of Boundary Conformal Field Theories (BCFTs) with multiple boundaries. Using these advances we calculate the entanglement entropy as a function of time for 1+1-dimensional CFTs that are split into $N$ subsystems. We give explicit results for $N = 4$ and $N = 17$. We find that all qualitative differences that arise for larger $N$ are present for $N = 4$., Comment: 21 pages, 12 figures

Cosmology and GR remain largely inaccessible to high-school teaching due to the advanced prerequisites to master these topics. Integrating them into upper secondary teaching is a significant challenge that remains unresolved. This contribution reports on an implementation study of a GR and cosmology course for upper secondary school students as part of an educational project launched during the centenary of GR and tested ever since for several years. The course aimed to expand students' knowledge to include current physics topics while highlighting their foundations in areas of classical physics such as Newtonian mechanics, electromagnetism, and waves. Targeted teaching and learning materials are focused on conceptual and qualitative understanding, while systematically combined with a mathematical treatment accessible at the upper secondary level, avoiding oversimplification. A key element is an active learning approach, incorporating activities and tasks such as engaging applications related to current research, reflective exercises, thought experiments, and hands-on tasks. The main research objective was to explore whether a conceptually deep and educationally effective GR and cosmology course could be successfully implemented for non-specialist upper secondary students. A pre-post study assessed both conceptual learning and affective outcomes, including interest, curiosity, self-concept, and perceived relevance of science. Results showed encouraging gains in both learning and motivation, with large to very large effect sizes for conceptual learning of core principles. Additionally, no or small effects of predictors such as gender were observed. We conclude that the integration of GR and cosmology into upper secondary physics teaching, in the form of courses and materials that are engaging, comprehensible, and impactful, is feasible.

We perform five 3D magnetohydrodynamic (MHD) core-collapse supernova simulations for non-rotating progenitors between 9.5 $M_\odot$ and 24 $M_\odot$. Four of the five models produce explosions while one fails. The exploding models are extended to between 0.9 s and 1.6 s post-bounce to study a possible impact of magnetic fields on explosion and remnant properties. Diagnostic explosion energies grow at a similar pace as in previous non-magnetic models. They reach between 0.11 foe and 0.61 foe, but are still growing by the end of the simulations. Neutron star kicks reach no more than 300 km s$^{-1}$, and although these are also still growing, they are unlikely to be in conflict with observed pulsar velocities. Extrapolated neutron star spin periods are between 45 ms and 1.8 s, consistent with observed birth spin rates. Magnetic torques only contribute about 10% to the spin-up of the neutron star. The inclusion of magnetic fields does not provide a mechanism for spin-kick alignment in our simulations. Surface dipole fields are in the range of $10^{12}-10^{13}$ G, much smaller than the root-mean-square field strength. Different from previous simulations, magnetic fields in the gain region only reach at most O(1%) of kinetic equipartition, likely because relatively early shock revival cuts off accretion as a power source for field amplification, which appears to be driven primarily by shear flows at the bottom of the gain region., Comment: 20 pages, 21 figures, submitted to PRD

We report the spectroscopic analysis of ten satellite galaxy candidates in the sphere of influence of the Sombrero galaxy (M104, NGC4594), based on data obtained with IFUM (Integral Field Units for Magellan). Based on their newly-observed recessional velocities, we confirm that nine of these candidates are satellite galaxies of M104, with one being a background dwarf galaxy. All ten dwarfs have stellar masses $2\times10^{7}\,M_{\odot}$ to $1\times10^{9}\,M_{\odot}$ and mean weighted metallicities $-1.7<\langle{[\mathrm{M/H}]}\rangle<-0.3$. Although these dwarfs are predominantly old, with stellar populations $\sim5-11\,$Gyr. However, this sample contains a local example of a low-mass "Green Pea" candidate, it exhibits extreme optical emission features and broad emission line features ($\sigma\sim250\,\mathrm{km\,s^{-1}}$) reminiscent of high-redshift Ly$\alpha$/LyC photon leaking galaxies. Using the newly-acquired recessional velocities of the nine satellites of M104, we find no evidence of coherent satellite motions unlike other nearby $L_*$ galaxy environments. Given the small sample, this results does not statistically rule out such coherent motions. There remain 60 satellite candidates of M104 for which future spectroscopy can more reliably test for such motion. Using the observed dwarf galaxies as tracers of the gravitational potential of M104, we estimate the dynamical mass of M104, $M_{dyn}=(12.4\pm6.5)\times10^{12}M_{\odot}$, and find that, making a reasonable estimate of M104's gas mass, $>90\%$ of its baryons are missing. These results agree with previous measurements of M104's dynamical mass., Comment: 15 pages, 11 figures. Accepted to the MNRAS

This paper investigates the problem of controlling nonlinear dynamical systems subject to state and input constraints while minimizing time-varying and a priori unknown cost functions. We propose a modular approach that combines the online convex optimization framework and reference governors to solve this problem. Our method is general in the sense that we do not limit our analysis to a specific choice of online convex optimization algorithm or reference governor. We show that the dynamic regret of the proposed framework is bounded linearly in both the dynamic regret and the path length of the chosen online convex optimization algorithm, even though the online convex optimization algorithm does not account for the underlying dynamics. We prove that a linear bound with respect to the online convex optimization algorithm's dynamic regret is optimal, i.e., cannot be improved upon. Furthermore, for a standard class of online convex optimization algorithms, our proposed framework attains a bound on its dynamic regret that is linear only in the variation of the cost functions, which is known to be an optimal bound. Finally, we demonstrate implementation and flexibility of the proposed framework by comparing different combinations of online convex optimization algorithms and reference governors to control a nonlinear chemical reactor in a numerical experiment., Comment: 17 pages

An amplitude analysis of $B^-\rightarrow D^- D^0 K_\mathrm{S}^0$ decays is performed using proton-proton collision data, corresponding to an integrated luminosity of $9\,\text{fb}^{-1}$, collected with the LHCb detector at center-of-mass energies of 7, 8, and 13$\mathrm{\,Te\kern -0.1em V}$. A resonant structure of spin-parity $0^+$ is observed in the $D^0 K_\mathrm{S}^0$ invariant-mass spectrum with a significance of $5.3\,\sigma$. The mass and width of the state, modeled with a Breit$-$Wigner lineshape, are determined to be $2883\pm11\pm6\mathrm{\,Me\kern -0.1em V\!/}c^2$ and $87_{-47}^{+22}\pm6\mathrm{\,Me\kern -0.1em V}$ respectively, where the first uncertainties are statistical and the second systematic. These properties and the quark content are consistent with those of the open-charm tetraquark state $T_{cs 0}^{*}(2870)^0$ observed previously in the $D^+ K^-$ final state of the $B^-\rightarrow D^- D^+ K^-$ decay. This result confirms the existence of the $T_{cs 0}^{*}(2870)^0$ state in a new decay mode. The $T_{cs1}^{*}(2900)^0$ state, reported in the $B^-\rightarrow D^- D^+ K^-$ decay, is also searched for in the $D^0 K_\mathrm{S}^0$ invariant-mass spectrum of the $B^- \rightarrow D^- D^0 K_\mathrm{S}^0$ decay, without finding evidence for it., Comment: All figures and tables, along with machine-readable versions and any supplementary material and information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3162/ (LHCb public pages)

S-embeddings were introduced by Chelkak as a tool to study the conformal invariance of the thermodynamic limit of the Ising model. Moreover, Chelkak, Laslier and Russkikh introduced a lift of s-embeddings to Lorentz space, and showed that in the limit the lift converges to a maximal surface. They posed the question whether there are s-embeddings that lift to maximal surfaces already at the discrete level, before taking the limit. We answer this question in the positive. In a previous paper we identified a subclass of s-embeddings--isothermic s-embeddings--that lift to (discrete) S-isothermic surfaces, which were introduced by Bobenko and Pinkall as a discretization of isothermic surfaces. In this paper we identify a special class of isothermic s-embeddings that correspond to discrete S-maximal surfaces, translating an approach of Bobenko, Hoffmann and Springborn introduced for discrete S-minimal surfaces in Euclidean space. Additionally, each S-maximal surface comes with a 1-parameter family of associated surfaces that are isometric. This enables us to obtain an associated family of s-embeddings for each maximal s-embedding. We show that the Ising weights are constant in the associated family., Comment: 24 pages, 8 figures

The ability of the Quantum Approximate Optimization Algorithm (QAOA) to deliver a quantum advantage on combinatorial optimization problems is still unclear. Recently, a scaling advantage over a classical solver was postulated to exist for random 8-SAT at the satisfiability threshold. At the same time, the viability of quantum error mitigation for deep circuits on near-term devices has been put in doubt. Here, we analyze the QAOA's performance on random Max-$k$XOR as a function of $k$ and the clause-to-variable ratio. As a classical benchmark, we use the Mean-Field Approximate Optimization Algorithm (MF-AOA) and find that it performs better than or equal to the QAOA on average. Still, for large $k$ and numbers of layers $p$, there may remain a window of opportunity for the QAOA. However, by extrapolating our numerical results, we find that reaching high levels of satisfaction would require extremely large $p$, which must be considered rather difficult both in the variational context and on near-term devices.

Quantum computing holds immense promise for simulating quantum systems, a critical task for advancing our understanding of complex quantum phenomena. One of the primary goals in this domain is to accurately approximate the ground state of quantum systems. The Fermi-Hubbard model, particularly, is of profound interest due to its implications for high-temperature superconductivity and strongly correlated electron systems. The quantum eigenvalue transformation of unitary matrices (QETU) algorithm offers a novel approach for ground state estimation by utilizing a controlled Hamiltonian time evolution operator, circumventing the resource-intensive block-encoding required by previous methods. In this work, we apply the QETU algorithm to the $2 \times 2$ Fermi-Hubbard model, presenting circuit simplifications tailored to the model and introducing a mapping to a 9-qubit grid-like hardware architecture inspired by fermionic swap networks. We investigate how the selection of a favorable hardware architecture can benefit the circuit construction. Additionally, we explore the feasibility of this method under the influence of noise, focusing on its robustness and practical applicability.

The bulk-metallicity determination of giant exoplanets is essential to constrain their formation and evolution pathways and to compare them to the solar system. Previous studies inferred an inverse relation between the mass and bulk metallicity. However, the data almost exclusively contained planets that orbit FGK stars. The recent discoveries of giant exoplanets around M-dwarf stars present an opportunity to probe whether they follow a mass-metallicity trend different from that of their FGK counterparts. Using evolution models we characterised the interiors of giant exoplanets with reliable mass-radius measurements that orbit FGK and M-dwarf stars. We then inferred the mass-metallicity trends for both populations. We found that the bulk metallicity of giant planets around M stars is overall lower compared to those around FGK stars. This yielded mass-metallicity relations for the two populations with similar slopes but significantly different offsets. The lack of metal-rich giant planets around M dwarfs could explain the difference in the inferred offset and be a result of different formation conditions. However, there were only 20 successful bulk-metallicity retrievals for the giant planets around M dwarfs, which resulted in rather large uncertainties. Therefore, it is of great importance to continue detecting these planets with both transit and radial velocities. Additionally, the characterisation of the atmospheres of giant planets around M-stars can further help to constrain their interiors and to investigate the atmosphere-interior connection. This will significantly contribute towards understanding the possible formation pathways of giant planets., Comment: 12 pages, 10 figures, 3 tables, accepted for publication at Astronomy & Astrophysics; corrected arXiv abstract

We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey programs, which both used MegaCam on the Canada-France-Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional S\'ersic function, which provides effective radii (half number radii) and S\'ersic indices, and estimate background contamination by adding a constant term to the S'ersic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two 2D S\'ersic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian Mixture Modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with S\'ersic functions. Finally, we measure the total number of GCs based on our fitted S\'ersic function, and calculate the GC specific frequency., Comment: 31 pages, 10 Figures (115 additional figures are available in the ApJS online Journal), 2 Tables, accepted for publication in ApJS

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent., Comment: 22 pages, 11 figures, Euclid consortium paper, A&A submitted

Cataract surgery is the most common surgical procedure globally, with a disproportionately higher burden in developing countries. While automated surgical video analysis has been explored in general surgery, its application to ophthalmic procedures remains limited. Existing works primarily focus on Phaco cataract surgery, an expensive technique not accessible in regions where cataract treatment is most needed. In contrast, Manual Small-Incision Cataract Surgery (MSICS) is the preferred low-cost, faster alternative in high-volume settings and for challenging cases. However, no dataset exists for MSICS. To address this gap, we introduce Sankara-MSICS, the first comprehensive dataset containing 53 surgical videos annotated for 18 surgical phases and 3,527 frames with 13 surgical tools at the pixel level. We benchmark this dataset on state-of-the-art models and present ToolSeg, a novel framework that enhances tool segmentation by introducing a phase-conditional decoder and a simple yet effective semi-supervised setup leveraging pseudo-labels from foundation models. Our approach significantly improves segmentation performance, achieving a $23.77\%$ to $38.10\%$ increase in mean Dice scores, with a notable boost for tools that are less prevalent and small. Furthermore, we demonstrate that ToolSeg generalizes to other surgical settings, showcasing its effectiveness on the CaDIS dataset.

Peer review in grant evaluation informs funding decisions, but the contents of peer review reports are rarely analyzed. In this work, we develop a thoroughly tested pipeline to analyze the texts of grant peer review reports using methods from applied Natural Language Processing (NLP) and machine learning. We start by developing twelve categories reflecting content of grant peer review reports that are of interest to research funders. This is followed by multiple human annotators' iterative annotation of these categories in a novel text corpus of grant peer review reports submitted to the Swiss National Science Foundation. After validating the human annotation, we use the annotated texts to fine-tune pre-trained transformer models to classify these categories at scale, while conducting several robustness and validation checks. Our results show that many categories can be reliably identified by human annotators and machine learning approaches. However, the choice of text classification approach considerably influences the classification performance. We also find a high correspondence between out-of-sample classification performance and human annotators' perceived difficulty in identifying categories. Our results and publicly available fine-tuned transformer models will allow researchers and research funders and anybody interested in peer review to examine and report on the contents of these reports in a structured manner. Ultimately, we hope our approach can contribute to ensuring the quality and trustworthiness of grant peer review.

MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging for 2D image analysis, these methods have not been applied to 3D medical images. Furthermore, deep learning models often lack explainability due to their "black-box" nature. This study aims to extend 2D self-supervised models, specifically DINOv2, to 3D medical imaging while evaluating their potential for explainable outcomes. We introduce the Medical Slice Transformer (MST) framework to adapt 2D self-supervised models for 3D medical image analysis. MST combines a Transformer architecture with a 2D feature extractor, i.e., DINOv2. We evaluate its diagnostic performance against a 3D convolutional neural network (3D ResNet) across three clinical datasets: breast MRI (651 patients), chest CT (722 patients), and knee MRI (1199 patients). Both methods were tested for diagnosing breast cancer, predicting lung nodule dignity, and detecting meniscus tears. Diagnostic performance was assessed by calculating the Area Under the Receiver Operating Characteristic Curve (AUC). Explainability was evaluated through a radiologist's qualitative comparison of saliency maps based on slice and lesion correctness. P-values were calculated using Delong's test. MST achieved higher AUC values compared to ResNet across all three datasets: breast (0.94$\pm$0.01 vs. 0.91$\pm$0.02, P=0.02), chest (0.95$\pm$0.01 vs. 0.92$\pm$0.02, P=0.13), and knee (0.85$\pm$0.04 vs. 0.69$\pm$0.05, P=0.001). Saliency maps were consistently more precise and anatomically correct for MST than for ResNet. Self-supervised 2D models like DINOv2 can be effectively adapted for 3D medical imaging using MST, offering enhanced diagnostic accuracy and explainability compared to convolutional neural networks.

A study of $\it{\Lambda}_{\it{b}}^\rm{0}$ and $\it{\Xi}_{\it{b}}^\rm{0}$ decays to $\it{\Lambda} h^{+} h^{\prime -}$ $(h^{(\prime)}=\pi, K)$ is performed using $pp$ collision data collected by the LHCb experiment during LHC Runs 1$-$2, corresponding to an integrated luminosity of $9~\rm{fb}^{-1}$. The branching fractions for these decays are measured using the $\it{\Lambda}_{\it{b}}^\rm{0}\to\it{\Lambda}_{\it{c}}^+(\to\it{\Lambda}\pi^+)\pi^-$ decay as control channel. The decays $\it{\Lambda}_{\it{b}}^\rm{0}\to\it{\Lambda}\pi^+\pi^-$ and $\it{\Xi}_{\it{b}}^\rm{0}\to\it{\Lambda}K^-\pi^+$ are observed for the first time. For decay modes with sufficient signal yields, $CP$ asymmetries are measured in the full and localized regions of the final-state phase space. Evidence is found for $CP$ violation in the $\it{\Lambda}_{\it{b}}^\rm{0}\to\it{\Lambda}K^+K^-$ decay, interpreted as originating primarily from an asymmetric $\it{\Lambda}_{\it{b}}^\rm{0} \to \it{N}^{*+} \it{K}^-$ decay amplitude. The measured $CP$ asymmetries for the other decays are compatible with zero., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-043.html (LHCb public pages)

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within 10-20\,MeV/$c^2$ and electron absorption of axion-like particles and dark photons for $m_\chi$ below 0.186\,keV/$c^2$., Comment: 9 pages, 5 figures

Metal-halide perovskites (MHPs) have exciting optoelectronic properties and are under investigation for various applications, such as photovoltaics, light-emitting diodes, and lasers. An essential step toward exploiting the full potential of this class of materials is their large-scale, on-chip integration with high-resolution, top-down patterning. The development of such patterning methods for perovskite films is challenging because of their ionic behavior and adverse reactions with the solvents used in standard lithography processes. Here, we introduce a versatile and precise method comprising photolithography and reactive ion etching (RIE) processes that can be tuned to accommodate different perovskite compositions and morphologies, including 3D, quasi-2D, and quasi-0D structures. Our method utilizes conventional photoresists at reduced temperatures to create micron-sized features down to 1 $\mu$m, providing high reproducibility from chip to chip. The patterning technique is validated through atomic force microscopy (AFM), X-ray diffraction (XRD), optical spectroscopy, and scanning electron microscopy (SEM). It enables the scalable and high-throughput on-chip monolithic integration of MHPs., Comment: 30 pages

Existing methods to summarize posterior inference for mixture models focus on identifying a point estimate of the implied random partition for clustering, with density estimation as a secondary goal (Wade and Ghahramani, 2018; Dahl et al., 2022). We propose a novel approach for summarizing posterior inference in nonparametric Bayesian mixture models, prioritizing density estimation of the mixing measure (or mixture) as an inference target. One of the key features is the model-agnostic nature of the approach, which remains valid under arbitrarily complex dependence structures in the underlying sampling model. Using a decision-theoretic framework, our method identifies a point estimate by minimizing posterior expected loss. A loss function is defined as a discrepancy between mixing measures. Estimating the mixing measure implies inference on the mixture density and the random partition. Exploiting the discrete nature of the mixing measure, we use a version of sliced Wasserstein distance. We introduce two specific variants for Gaussian mixtures. The first, mixed sliced Wasserstein, applies generalized geodesic projections on the product of the Euclidean space and the manifold of symmetric positive definite matrices. The second, sliced mixture Wasserstein, leverages the linearity of Gaussian mixture measures for efficient projection., Comment: 37 pages, 2 figures, 6 tables

We consider systems of interacting particles which are described by a second order Langevin equation, i.e., particles experiencing inertia. We introduce an associated equation of fluctuating hydrodynamics, which can be interpreted as stochastic version of a Vlasov-Fokker-Planck equation. We show that this stochastic partial differential equation exhibits the same dichotomy as the corresponding first order (inertial-free) equation, the so-called Dean-Kawasaki equation: Solutions exist only for suitable atomic initial data, but not for smooth initial data. The class of systems covered includes several models of active matter.

Let $F$ be a graph on $r$ vertices and let $G$ be a graph on $n$ vertices. Then an $F$-factor in $G$ is a subgraph of $G$ composed of $n/r$ vertex-disjoint copies of $F$, if $r$ divides $n$. In other words, an $F$-factor yields a partition of the $n$ vertices of $G$. The study of such $F$-factors in the Erd\H{o}s-R\'enyi random graph dates back to Erd\H{o}s himself. Decades later, in 2008, Johansson, Kahn and Vu established the thresholds for the existence of an $F$-factor for strictly 1-balanced $F$ -- up to the leading constant. The sharp thresholds, meaning the leading constants, were obtained only recently by Riordan and Heckel, but only for complete graphs $F=K_r$ and for so-called nice graphs. Their results rely on sophisticated couplings that utilize the recent, celebrated solution of Shamir's problem by Kahn. We extend the couplings by Riordan and Heckel to any strictly 1-balanced $F$ and thereby obtain the sharp threshold for the existence of an $F$-factor. In particular, we confirm the thirty year old conjecture by Ruc\'inski that this sharp threshold indeed coincides with the sharp threshold for the disappearance of the last vertices which are not contained in a copy of $F$., Comment: 17 pages