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Fixpoints are ubiquitous in computer science and when dealing with quantitative semantics and verification one is commonly led to consider least fixpoints of (higher-dimensional) functions over the nonnegative reals. We show how to approximate the least fixpoint of such functions, focusing on the case in which they are not known precisely, but represented by a sequence of approximating functions that converge to them. We concentrate on monotone and non-expansive functions, for which uniqueness of fixpoints is not guaranteed and standard fixpoint iteration schemes might get stuck at a fixpoint that is not the least. Our main contribution is the identification of an iteration scheme, a variation of Mann iteration with a dampening factor, which, under suitable conditions, is shown to guarantee convergence to the least fixpoint of the function of interest. We then argue that these results are relevant in the context of model-based reinforcement learning for Markov decision processes (MDPs), showing that the proposed iteration scheme instantiates to MDPs and allows us to derive convergence to the optimal expected return. More generally, we show that our results can be used to iterate to the least fixpoint almost surely for systems where the function of interest can be approximated with given probabilistic error bounds, as it happens for probabilistic systems, such as simple stochastic games, that can be explored via sampling.

Motivated by recent advances in digital quantum simulation and the overall prospective of solving correlated many-electron problems using quantum algorithms, we design a gate-based quantum circuit that emulates the dynamics of the Kondo impurity model. We numerically determine the impurity magnetization, entanglement between impurity and fermionic sites and energy as a function of time (i.e.~circuit depth) for various initial states and find universal long-time dynamics. We complement the numerical simulations for moderate system size with an asymptotically exact analytical solution that is effective in the limit of large system sizes and for starting states corresponding to a filled Fermi sea. This work opens up the perspective of studying the dynamics of electronic quantum many-body states on quantum chips of the NISQ era., Comment: 16+13 pages, 19 figures

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where agents are continuously reassigned new targets upon completing their current tasks, offers a more realistic approximation of real-world warehouse scenarios. While cache storage systems can enhance efficiency and reduce operational costs, existing approaches primarily rely on expectations and mathematical models, often without adequately addressing the challenges of multi-robot planning and execution. In this paper, we introduce a novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which integrates high-level cache storage with low-level path planning. We have involved a new type of map grid called cache for temporary item storage. Additionally, we involved a task assigner (TA) with a locking mechanism to bridge the gap between the new cache grid and L-MAPF algorithm. The TA dynamically allocates target locations to agents based on their status in various scenarios. We evaluated L-MAPF-CM using different cache replacement policies and task distributions. L-MAPF-CM has demonstrated performance improvements particularly with high cache hit rates and smooth traffic conditions., Comment: arXiv admin note: substantial text overlap with arXiv:2403.13421

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions. Coordinating multiple agents in a shared environment poses significant challenges, especially in continuous spaces where traditional optimization algorithms struggle with scalability. Moreover, these algorithms often depend on discretized representations of the environment, which can be impractical in image-based or high-dimensional settings. Recently, diffusion models have shown promise in single-agent path planning, capturing complex trajectory distributions and generating smooth paths that navigate continuous, high-dimensional spaces. However, directly extending diffusion models to MAPF introduces new challenges since these models struggle to ensure constraint feasibility, such as inter-agent collision avoidance. To overcome this limitation, this work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces. This unique combination directly produces feasible multi-agent trajectories that respect collision avoidance and kinematic constraints. The effectiveness of our approach is demonstrated across various challenging simulated scenarios of varying dimensionality.

We show rigidity results for the operator equations T(f.g) = Tf.Tg, T(f*g) = Tf.Tg and T(f.g) = Tf*Tg for bijective operators T acting on sufficently large spaces of smooth functions. Typically a condition like |T(f.g) - Tf.Tg| < a for all f, g with a fixed function a will imply T(f.g) = Tf.Tg. Theorems of Alesker, Artstein-Avidan, Faifman and Milman then yield characterizations (up to diffeomorphisms) of the Fourier transform by mapping products into convolutions and vice-versa on the Schwartz space.

A common starting point of traditional quantum algorithm design is the notion of a universal quantum computer with a scalable number of qubits. This convenient abstraction mirrors classical computations manipulating finite sets of symbols, and allows for a device-independent development of algorithmic primitives. Here we advocate an alternative approach centered on the physical setup and the associated set of natively available operations. We show that these can be leveraged to great benefit by sidestepping the standard approach of reasoning about computation in terms of individual qubits. As an example, we consider hybrid qubit-oscillator systems with linear optics operations augmented by certain qubit-controlled Gaussian unitaries. The continuous-variable (CV) Fourier transform has a native realization in such systems in the form of homodyne momentum measurements. We show that this fact can be put to algorithmic use. Specifically, we give a polynomial-time quantum algorithm in this setup which finds a factor of an $n$-bit integer $N$. Unlike Shor's algorithm, or CV implementations thereof based on qubit-to-oscillator encodings, our algorithm relies on the CV (rather than discrete) Fourier transform. The physical system used is independent of the number $N$ to be factored: It consists of a single qubit and three oscillators only., Comment: Main paper: pages 1-8, Supplementary material: pages 9-77; 16 figures, 2 tables

Electrochemiluminescence (ECL) produces light through electrochemical reactions and has shown promise for various analytic applications in biomedicine. However, the use of ECL devices (ECLDs) as light sources has been limited due to insufficient light output and low operational stability. In this study, we present a high-power pulsed operation strategy for ECLDs to address these limitations and demonstrate their effectiveness in optogenetic manipulation. By applying a biphasic voltage sequence with short opposing phases, we achieve intense and efficient ECL through an exciplex-formation reaction pathway. This approach results in an exceptionally high optical power density, exceeding 100 microW/mm2, for several thousand pulses. Balancing the ion concentration by optimizing the voltage waveform further improves device stability. By incorporating multiple optimized pulses into a burst signal separated by short rest periods, extended light pulses of high brightness and with minimal power loss over time were obtained. These strategies were leveraged to elicit a robust optogenetic response in fruit fly (Drosophila melanogaster) larvae expressing the optogenetic effector CsChrimson. The semi-transparent nature of ECLDs facilitates simultaneous imaging of larval behaviour from underneath, through the device. These findings highlight the potential of ECLDs as versatile optical tools in biomedical and neurophotonics research., Comment: 22 pages, 4 figures, 5 supplementary figures

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

Let $K$ be a number field with ring of integers $\mathcal{O}_K$, and let $f(x)\in\mathcal{O}_K[x]$ be a monic, irreducible polynomial. We establish necessary and sufficient conditions in terms of the critical points of $f(x)$ for the iterates of $f(x)$ to be monogenic polynomials. More generally, we give necessary and sufficient conditions for the backwards orbits of elements of $\mathcal{O}_K$ under $f(x)$ to be monogenerators. We apply our criteria to construct novel examples of dynamically monogenic polynomials, yielding infinite towers of monogenic number fields with the backward orbit of one monogenerator giving a monogenerator at the next level., Comment: There is an error in the main theorem that occurs when p is a non-vanishing prime that divides the exponent of a repeated irreducible factor of f(x) modulo p. We have been able to address the error by changing the criteria slightly. We anticipate posting a corrected version soon

Fermionic Hamiltonians play a critical role in quantum chemistry, one of the most promising use cases for near-term quantum computers. However, since encoding nonlocal fermionic statistics using conventional qubits results in significant computational overhead, fermionic quantum hardware, such as fermion atom arrays, were proposed as a more efficient platform. In this context, we here study the many-body entanglement structure of fermionic $N$-particle states by concentrating on $M$-body reduced density matrices (DMs) across various bipartitions in Fock space. The von Neumann entropy of the reduced DM is a basis independent entanglement measure which generalizes the traditional quantum chemistry concept of the one-particle DM entanglement, which characterizes how a single fermion is entangled with the rest. We carefully examine upper bounds on the $M$-body entanglement, which are analogous to the volume law of conventional entanglement measures. To this end we establish a connection between $M$-body reduced DM and the mathematical structure of hypergraphs. Specifically, we show that a special class of hypergraphs, known as $t$-designs, corresponds to maximally entangled fermionic states. Finally, we explore fermionic many-body entanglement in random states. We semianalytically demonstrate that the distribution of reduced DMs associated with random fermionic states corresponds to the trace-fixed Wishart-Laguerre random matrix ensemble. In the limit of large single-particle dimension $D$ and a non-zero filling fraction, random states asymptotically become absolutely maximally entangled., Comment: 21+2 pages, 5 figures

We present a spectral and timing analysis of NICER observations of the black hole X-ray binary 4U 1630-47 from 2018 to 2024. We find relativistic reflection features in the hard and soft intermediate states, and disk wind absorption features in the soft intermediate state and soft state. We fit the reflection features with RELXILLCP and find a stable and untruncated disk in the intermediate states; we fit the wind features with ZXIPCF and find a stable, highly ionized wind with high column density across different outbursts. Specifically, the heartbeat state is seen in two observations in 2021 and 2023 respectively. Through the phase-resolved spectral fitting, we find the flux to be correlated with the disk parameters while no strong correlation with the coronal parameters is observed, consistent with the scenario given by the inner disk radiation instability. A significant hard lag on the time scale of a second and high coherence is observed near the characteristic frequency of the heartbeat, which can be explained by the viscous propagation of mass accretion fluctuations in the disk. The positive relationship between the heartbeat fractional rms and energy can possibly be explained by a disk-originated oscillation which is then magnified by the corona scattering., Comment: 17 pages, 19 figures, comments welcome

While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widely adopted InceptionV3 model and the PlantVillage dataset. ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions. This approach reveals both effective disease-related patterns and incidental biases, such as those from background or lighting that can compromise model robustness. Through systematic experiments, ACE helped us to identify relevant features and pinpoint areas for targeted model improvement. Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning, which is essential for producing transparent tools for plant disease management in agriculture.

Scanning Electron Microscopy (SEM) experiments provide detailed insights into material microstructures, enabling high-resolution imaging as well as crystallographic analysis through advanced techniques like Electron Backscatter Diffraction (EBSD). However, the interaction of the high-energy electron beam with the material can lead to localized heating, which may significantly impact specimen integrity, especially in applications requiring prolonged beam exposure, for instance when mapping the crystal structure using EBSD. This study examines electron-beam-induced heating effects on a model metal sample (iron), directly measuring the locally deposited electron beam energy with a MEMS-based heating device and validating these measurements through simulations, including Monte Carlo and Finite Element methods. The analysis focuses on the effects of various experimental parameters such as acceleration voltage (from 5 to 30 kV), beam current (from 0.17 nA to 22 nA), dwell time (from 1$\mu$s to 1ms) and sample tilt (0{\deg} to 70{\deg}). The findings reveal that local sample temperatures can increase by up to 70 {\deg}C during EBSD experiments, primarily affected by the choice in beam current and acceleration voltage, with beam current having the most significant impact.

We apply NER to a particular sub-genre of legal texts in German: the genre of legal norms regulating administrative processes in public service administration. The analysis of such texts involves identifying stretches of text that instantiate one of ten classes identified by public service administration professionals. We investigate and compare three methods for performing Named Entity Recognition (NER) to detect these classes: a Rule-based system, deep discriminative models, and a deep generative model. Our results show that Deep Discriminative models outperform both the Rule-based system as well as the Deep Generative model, the latter two roughly performing equally well, outperforming each other in different classes. The main cause for this somewhat surprising result is arguably the fact that the classes used in the analysis are semantically and syntactically heterogeneous, in contrast to the classes used in more standard NER tasks. Deep Discriminative models appear to be better equipped for dealing with this heterogenerity than both generic LLMs and human linguists designing rule-based NER systems.

In this article, we reconsider the formulation of Yangian symmetry for planar N=4 supersymmetric Yang-Mills theory, and we investigate to what extent this symmetry lifts to the beta/gamma-deformation of the model. We first apply cohomology of variational forms towards a thorough derivation of the invariance statement for the undeformed action from covariance of the equations of motion under the Yangian algebra. We then apply a twist deformation to these statements paying particular attention to cyclicity aspects. We find that the equations of motion remain covariant while invariance of the action only holds for the Yangian subalgebra that is uncharged under the twist., Comment: 27 pages

Finite-dimensional Reedy algebras form a ring-theoretic analogue of Reedy categories and were recently proved to be quasi-hereditary. We identify Reedy algebras as quasi-hereditary algebras admitting a triangular (or Poincar\'e-Birkhoff-Witt type) decomposition into the tensor product of two oppositely directed subalgebras over a common semisimple subalgebra. This exhibits homological and representation-theoretic structure of the ingredients of the Reedy decomposition and it allows to give a characterisation of Reedy algebras in terms of idempotent ideals occurring in heredity chains, providing an analogue for Reedy algebras of a result of Dlab and Ringel on quasi-hereditary algebras., Comment: 12 pages

Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of these DL models are often hidden in the unstructured text. As a result, critical information about how these models are designed, trained, and evaluated is challenging to access and comprehend. To address this issue, in this work, we use five different open-source Large Language Models (LLMs): Llama-3 70B, Llama-3.1 70B, Mixtral-8x22B-Instruct-v0.1, Mixtral 8x7B, and Gemma 2 9B in combination with Retrieval-Augmented Generation (RAG) approach to extract and process DL methodological details from scientific publications automatically. We built a voting classifier from the outputs of five LLMs to accurately report DL methodological information. We tested our approach using biodiversity publications, building upon our previous research. To validate our pipeline, we employed two datasets of DL-related biodiversity publications: a curated set of 100 publications from our prior work and a set of 364 publications from the Ecological Informatics journal. Our results demonstrate that the multi-LLM, RAG-assisted pipeline enhances the retrieval of DL methodological information, achieving an accuracy of 69.5% (417 out of 600 comparisons) based solely on textual content from publications. This performance was assessed against human annotators who had access to code, figures, tables, and other supplementary information. Although demonstrated in biodiversity, our methodology is not limited to this field; it can be applied across other scientific domains where detailed methodological reporting is essential for advancing knowledge and ensuring reproducibility. This study presents a scalable and reliable approach for automating information extraction, facilitating better reproducibility and knowledge transfer across studies.

The ATLAS Muon Drift Tube (MDT) ReadOut Drivers (MROD), 204 VME modules that are an essential part of the readout chain of the 1,150 MDT chambers, have been in operation for more than 15 years and are expected to remain in operation until about 2026. In the event of extensive failures the number of spare MROD modules may be insufficient. However, deployment of an adapted version of the Front-End LInk eXchange (FELIX) system, a new component of the ATLAS data acquisition (DAQ) infrastructure, may overcome potential MROD failures. This paper describes the design, functionality and performance of this adapted version, referred to as FELIX-MROD, and the test results of its integration into the ATLAS DAQ system., Comment: 24 pages, 17 figures

Let $s \in (0, 1]$ and $N > 2s$. It is known that positive solutions to the (fractional) fast diffusion equation $\partial_t u + (-\Delta)^s (u^\frac{N-2s}{N+2s}) = 0$ on $(0, \infty) \times \mathbb R^N$ with regular enough initial datum extinguish after some finite time $T_* > 0$. More precisely, one has $\frac{u(t,\cdot)}{U_{T_*, z, \lambda}(t,\cdot)} - 1 =o(1)$ as $t \to T_*^-$ for a certain extinction profile $U_{T_*, z, \lambda}$, uniformly on $\mathbb R^N$. In this paper, we prove the quantitative bound $ \frac{u(t,\cdot)}{U_{T_*, z, \lambda}(t,\cdot)} - 1 = \mathcal O( (T_*-t)^\frac{N+2s}{N-2s+2})$, in a natural weighted energy norm. The main point here is that the exponent $\frac{N+2s}{N-2s+2}$ is sharp. This is the analogue of a recent result by Bonforte and Figalli (CPAM, 2021) valid for $s = 1$ and bounded domains $\Omega \subset \mathbb R^N$. Our result is new also in the local case $s = 1$. The main obstacle we overcome is the degeneracy of an associated linearized operator, which generically does not occur in the bounded domain setting. For a smooth bounded domain $\Omega \subset \mathbb R^N$, we prove similar results for positive solutions to $\partial_t u + (-\Delta)^s (u^m) = 0$ on $(0, \infty) \times \Omega$ with Dirichlet boundary conditions when $s \in (0,1)$ and $m \in (\frac{N-2s}{N+2s}, 1)$, under a non-degeneracy assumption on the stationary solution. An important step here is to prove the convergence of the relative error, which is new for this case., Comment: 33 pages, comments welcome!

This study investigates the influence of Herbig-Haro jets on initiating star formation in dense environments. When molecular clouds are nearing gravitational instability, the impact of a protostellar jet could provide the impetus needed to catalyse star formation. A high-energy-density experiment was carried out at the LULI2000 laser facility, where a supersonic jet generated by a nanosecond laser was used to compress a foam or plastic ball, mimicking the interaction of a Herbig-Haro jet with a molecular cloud. Simulations using the 3D radiation hydrodynamics code TROLL provided comprehensive data for analysing ball compression and calculating jet characteristics. After applying scaling laws, similarities between stellar and experimental jets were explored. Diagnostic simulations, including density gradient, emission and X-ray radiographies, showed strong agreement with experimental data. The results of the experiment, supported by simulations, demonstrated that the impact of a protostellar jet on a molecular cloud could reduce the Bonnor-Ebert mass by approximately 9%, thereby initiating collapse., Comment: Submitted to the Astrophysical Journal

In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one importance score for each feature. But often, predicting the target variable requires interactions between several features (such as in the XOR function), and features might have complex statistical dependencies that allow to partially replace one feature with another one. In commonly used feature importance scores these cooperative effects are conflated with the features' individual contributions, making them prone to misinterpretations. In this work, we derive DIP, a new mathematical decomposition of individual feature importance scores that disentangles three components: the standalone contribution and the contributions stemming from interactions and dependencies. We prove that the DIP decomposition is unique and show how it can be estimated in practice. Based on these results, we propose a new visualization of feature importance scores that clearly illustrates the different contributions., Comment: GK and EG contributed equally to this article

We initiate the study of state complexity for continuous-variable quantum systems. Concretely, we consider a setup with bosonic modes and auxiliary qubits, where available operations include Gaussian one- and two-mode operations, single- and two-qubit operations, as well as qubit-controlled phase-space displacements. We define the (approximate) complexity of a bosonic state by the minimum size of a circuit that prepares an $L^1$-norm approximation to the state. We propose a new circuit which prepares an approximate Gottesman-Kitaev-Preskill (GKP) state $|\mathsf{GKP}_{\kappa,\Delta}\rangle$. Here $\kappa^{-2}$ is the variance of the envelope and $\Delta^2$ is the variance of the individual peaks. We show that the circuit accepts with constant probability and -- conditioned on acceptance -- the output state is polynomially close in $(\kappa,\Delta)$ to the state $|\mathsf{GKP}_{\kappa,\Delta}\rangle$. The size of our circuit is linear in $(\log 1/\kappa,\log 1/\Delta)$. To our knowledge, this is the first protocol for GKP-state preparation with fidelity guarantees for the prepared state. We also show converse bounds, establishing that the linear circuit-size dependence of our construction is optimal. This fully characterizes the complexity of GKP states., Comment: 85 pages, 12 figures

The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level. Recent studies have reported improved protein-ligand binding free energy results based on ML/MM with mechanical embedding, arguing that intramolecular interactions like torsion potentials of the ligand are often the limiting factor for accuracy. This claim is evaluated based on 108 relative binding free energy calculations for four different benchmark systems. As an alternative strategy, we also tested a tool that fits the MM dihedral potentials to the ML level of theory. Overall, the relative binding free energy results from MM with Open Force Field 2.2.0, MM with ML-fitted torsion potentials, and the corresponding ML/MM end-state corrected simulations show no statistically significant differences in the mean absolute errors (between 0.8 and 0.9 kcal/mol). Therefore, a well-parameterized force field is on a par with simple mechanical embedding ML/MM simulations for protein-ligand binding. In terms of computational costs, the reparametrization of poor torsional potentials is preferable over employing computationally intensive ML/MM simulations of protein-ligand complexes with mechanical embedding. Also, the refitting strategy leads to lower variances of the protein-ligand binding free energy results than the ML/MM end-state corrections., Comment: 47 pages, 13 figures, 10 tables

Monopoles and braids are exotic but elusive aspects of fundamental theories of light and matter. In lattice systems, monopoles of band-structure degeneracies are subject to well-established no-go (doubling) theorems that appear to universally apply in closed Hermitian systems and open non-Hermitian systems alike. However, the non-Abelian braid topology of non-Hermitian multi-band systems provides a remarkable loophole to these constraints. Here we make use of this loophole, and experimentally implement, for the first time, a monopole degeneracy in a non-Hermitian three-band system in the form of a single third-order exceptional point. We explicitly demonstrate the intricate braiding topology and the non-Abelian fusion rules underlying the monopole degeneracy. The experiment is carried out using a new design of single-photon interferometry, enabling eigenstate and spectral resolutions for non-Hermitian multi-band systems with widely tunable parameters. Thus, the union of state-of-the-art experiments, fundamental theory, and everyday concepts such as braids paves the way toward the highly exotic non-Abelian topology unique to non-Hermitian settings., Comment: 11 pages, 7 figures

The increasing size of deep learning models has created the need for more efficient alternatives to the standard error backpropagation algorithm, that make better use of asynchronous, parallel and distributed computing. One major shortcoming of backpropagation is the interlocking between the forward phase of the algorithm, which computes a global loss, and the backward phase where the loss is backpropagated through all layers to compute the gradients, which are used to update the network parameters. To address this problem, we propose a method that parallelises SGD updates across the layers of a model by asynchronously updating them from multiple threads. Furthermore, since we observe that the forward pass is often much faster than the backward pass, we use separate threads for the forward and backward pass calculations, which allows us to use a higher ratio of forward to backward threads than the usual 1:1 ratio, reducing the overall staleness of the parameters. Thus, our approach performs asynchronous stochastic gradient descent using separate threads for the loss (forward) and gradient (backward) computations and performs layer-wise partial updates to parameters in a distributed way. We show that this approach yields close to state-of-the-art results while running up to 2.97x faster than Hogwild! scaled on multiple devices (Locally-Partitioned-Asynchronous-Parallel SGD). We theoretically prove the convergence of the algorithm using a novel theoretical framework based on stochastic differential equations and the drift diffusion process, by modeling the asynchronous parameter updates as a stochastic process., Comment: 16 pages, 4 figures

Symmetry protected topological (SPT) phases are fundamental quantum many-body states of matter beyond Landau's paradigm. Here, we introduce the concept of quantum restored SPTs (QRSPTs), where the protecting symmetry is spontaneously broken at each instance in time, but restored after time average over quantum fluctuations, so that topological features re-emerge. To illustrate the concept, we study a one-dimensional fermionic Su-Schrieffer-Heeger model with fluctuating superconducting order. We solve this problem in several limiting cases using a variety of analytical methods and compare them to numerical (density matrix renormalization group) simulations which are valid throughout the parameter regime. We thereby map out the phase diagram and identify a QRSPT phase with topological features which are reminiscent from (but not identical to) the topology of the underlying free fermion system. The QRSPT paradigm thereby stimulates a new perspective for the constructive design of novel topological quantum many-body phases., Comment: 7 pages, 6 pages supplement, 10 figures

Recently, there has been a growing interest in Multimodal Large Language Models (MLLMs) due to their remarkable potential in various tasks integrating different modalities, such as image and text, as well as applications such as image captioning and visual question answering. However, such models still face challenges in accurately captioning and interpreting specific visual concepts and classes, particularly in domain-specific applications. We argue that integrating domain knowledge in the form of an ontology can significantly address these issues. In this work, as a proof of concept, we propose a new framework that combines ontology with MLLMs to classify images of plant diseases. Our method uses concepts about plant diseases from an existing disease ontology to query MLLMs and extract relevant visual concepts from images. Then, we use the reasoning capabilities of the ontology to classify the disease according to the identified concepts. Ensuring that the model accurately uses the concepts describing the disease is crucial in domain-specific applications. By employing an ontology, we can assist in verifying this alignment. Additionally, using the ontology's inference capabilities increases transparency, explainability, and trust in the decision-making process while serving as a judge by checking if the annotations of the concepts by MLLMs are aligned with those in the ontology and displaying the rationales behind their errors. Our framework offers a new direction for synergizing ontologies and MLLMs, supported by an empirical study using different well-known MLLMs.

We use the rms and lag spectra of the type-C quasi-periodic oscillation (QPO) to study the properties of the Comptonisation region (aka corona) during the low/hard and hard-intermediate states of the main outburst and reflare of MAXI J1348$-$630. We simultaneously fit the time-averaged energy spectrum of the source and the fractional rms and phase-lag spectra of the QPO with the time-dependent Comptonization model vKompth. The data can be explained by two physically connected coronae interacting with the accretion disc via a feedback loop of X-ray photons. The best-fitting model consists of a corona of $\sim$10$^3$ km located at the inner edge of the disc and a second corona of $\sim$10$^4$ km horizontally extended and covering the inner parts of the accretion disc. The properties of both coronae during the reflare are similar to those during the low/hard state of the main outburst, reinforcing the idea that both the outburst and the reflare are driven by the same physical mechanisms. We combine our results for the type-C QPO with those from previous work focused on the study of type-A and type-B QPOs with the same model to study the evolution of the geometry of the corona through the whole outburst, including the reflare of MAXI J1348$-$630. Finally, we show that the sudden increase in the phase-lag frequency spectrum and the sharp drop in the coherence function previously observed in MAXI J1348$-$630 are due to the type-C QPO during the decay of the outburst and can be explained in terms of the geometry of the coronae., Comment: 18 pages, 8 figures, 1 table. Submitted to ApJ

In the past few years, Large Language Models (LLMs) have exploded in usefulness and popularity for code generation tasks. However, LLMs still struggle with accuracy and are unsuitable for high-risk applications without additional oversight and verification. In particular, they perform poorly at generating code for highly complex systems, especially with unusual or out-of-sample logic. For such systems, verifying the code generated by the LLM may take longer than writing it by hand. We introduce a solution that divides the code generation into two parts; one to be handled by an LLM and one to be handled by formal methods-based program synthesis. We develop a benchmark to test our solution and show that our method allows the pipeline to solve problems previously intractable for LLM code generation.

Despite their ubiquity, systematic characterization of multifold exceptional points, $n$-fold exceptional points (EP$n$s), remains a significant unsolved problem. In this article, we characterize Abelian topology of eigenvalues for generic EP$n$s and symmetry-protected EP$n$s for arbitrary $n$. The former and the latter emerge in a $(2n-2)$- and $(n-1)$-dimensional parameter space, respectively. By introducing resultant winding numbers, we elucidate that these EP$n$s are stable due to topology of a map from a base space (momentum or parameter space) to a sphere defined by these resultants. Our framework implies fundamental doubling theorems of both generic EP$n$s and symmetry-protected EP$n$s in $n$-band models., Comment: 10pages, 2 figures

Background: Patient-reported survey data are used to train prognostic models aimed at improving healthcare. However, such data are typically available multi-centric and, for privacy reasons, cannot easily be centralized in one data repository. Models trained locally are less accurate, robust, and generalizable. We present and apply privacy-preserving federated machine learning techniques for prognostic model building, where local survey data never leaves the legally safe harbors of the medical centers. Methods: We used centralized, local, and federated learning techniques on two healthcare datasets (GLA:D data from the five health regions of Denmark and international SHARE data of 27 countries) to predict two different health outcomes. We compared linear regression, random forest regression, and random forest classification models trained on local data with those trained on the entire data in a centralized and in a federated fashion. Results: In GLA:D data, federated linear regression (R2 0.34, RMSE 18.2) and federated random forest regression (R2 0.34, RMSE 18.3) models outperform their local counterparts (i.e., R2 0.32, RMSE 18.6, R2 0.30, RMSE 18.8) with statistical significance. We also found that centralized models (R2 0.34, RMSE 18.2, R2 0.32, RMSE 18.5, respectively) did not perform significantly better than the federated models. In SHARE, the federated model (AC 0.78, AUROC: 0.71) and centralized model (AC 0.84, AUROC: 0.66) perform significantly better than the local models (AC: 0.74, AUROC: 0.69). Conclusion: Federated learning enables the training of prognostic models from multi-center surveys without compromising privacy and with only minimal or no compromise regarding model performance.

The maximal hyperplane section of the $l_\infty^n$-ball, i.e. of the $n$-cube, is the one perpendicular to 1/sqrt 2 (1,1,0, ... ,0), as shown by Ball. Eskenazis, Nayar and Tkocz extended this result to the $l_p^n$-balls for very large $p \ge 10^{15}$. We show that the analogue of Ball's result essentially holds for all $26.265 \simeq p_0 \le p < \infty$. By Oleszkiewicz, Ball's result does not transfer to $l_p^n$ for $2 < p < p_0$. Then the hyperplane section perpendicular to the main diagonal yields a counterexample for large dimensions $n$. In this case, we give an asymptotic upper bound for $20 < p < p_0$.

In their comment (1), Hawreliak et al. claims that our observation of stacking fault formation and transonic dislocation propagation in diamond (2) is not valid as they interpret the observed features as cracks. In this response letter, we describe our rationale for interpreting the observed features as stacking faults. We also address other points raised in their comments, including the clarifications of how the results of Makarov et al. (3) are not in conflict with our study., Comment: Comment on arXiv:2401.04213. 10 pages, 2 figures

In open quantum many-body systems, the theoretical description of resonant states of many particles strongly coupled to the continuum can be challenging. Such states are commonplace in, for example, exotic nuclei and hadrons, and can reveal important information about the underlying forces at play in these systems. In this work, we demonstrate that the complex-augmented eigenvector continuation (CA-EC) method, originally formulated for the two-body problem with uniform complex scaling, can reliably perform bound-to-resonance extrapolations for genuine three-body resonances having no bound subsystems. We first establish that three-body bound-to-resonance extrapolations are possible by benchmarking different few-body approaches, and we provide arguments to explain how the extrapolation works in the many-body case. We furthermore pave the way towards scalable resonance extrapolations in many-body systems by showing that the CA-EC method also works in the Berggren basis, studying a realistic application using the Gamow shell model., Comment: 11 pages, 6 figures

We prove analogues of the Green-Tao-Ziegler theorem on linear constellations in primes, in which the primes under consideration are restricted by certain arithmetic conditions. Our first main result is conditional upon Hooley's Riemann hypothesis and imposes the extra condition that the primes have prescribed primitive roots. Our second main result is unconditional and imposes the extra condition that the primes have prescribed Artin symbols in given Galois number fields. In the appendix we present an application of the second result in inverse Galois theory., Comment: 59 pages; minor corrections; added appendix with improved Galois-theoretic application; main article by by Christopher Frei and Magdal\'ena Tinkov\'a; appendix by Christopher Frei, Joachim K\"onig and Magdal\'ena Tinkov\'a

Many interesting questions in arithmetic dynamics revolve, in one way or another, around the (local and/or global) reducibility behavior of iterates of a polynomial. We show that for very general families of integer polynomials $f$ (and, more generally, rational functions over number fields), the set of stable primes, i.e., primes modulo which all iterates of $f$ are irreducible, is a density zero set. Compared to previous results, our families cover a much wider ground, and in particular apply to $100\%$ of polynomials of any given odd degree, thus adding evidence to the conjecture that polynomials with a "large" set of stable primes are necessarily of a very specific shape, and in particular are necessarily postcritically finite., Comment: Includes strengthened version of Corollary 1.2

We present a recently established setup for laser spectroscopy at CRYRING@ESR at the GSI Helmholtz Centre for Heavy Ion Research. Here, laser spectroscopy can be performed on stored and cooled ion bunches and coasting beams. First spectra of $^{24,25}$Mg$^+$ ions are presented that were recorded by classical Doppler-limited fluorescence spectroscopy as well as $\Lambda$-spectroscopy using counter- and copropagating laser beams that are Doppler-shifted by several nm., Comment: 10 pages, 8 figures, submitted to conference proceedings of "1st Workshop on Polarized Beam and Target-Physics and Applications"

A simple device for balancing for a continuous covariate in clinical trials is to stratify by whether the covariate is above or below some target value, typically the predicted median. This raises an issue as to which model should be used for modelling the effect of treatment on the outcome variable, $Y$. Should one fit, the stratum indicator, $S$, the continuous covariate, $X$, both or neither? This question has been investigated in the literature using simulations targetting the overall effect on inferences about treatment . However, when a covariate is added to a model there are three consequences for inference: 1) The mean square error effect, 2) The variance inflation factor and 3) second order precision. We consider that it is valuable to consider these three factors separately even if, ultimately, it is their joint effect that matters. We present some simple theory, concentrating in particular on the variance inflation factor, that may be used to guide trialists in their choice of model. We also consider the case where the precise form of the relationship between the outcome and the covariate is not known. We conclude by recommending that the continuous coovariate should always be in the model but that, depending on circumstances, there may be some justification in fitting the stratum indicator also., Comment: 17 pages, 4 Figures

Many business process models contain control flow errors, such as deadlocks or livelocks, which hinder proper execution. In this paper, we introduce a new tool that can instantaneously identify control flow errors in BPMN models, make them understandable for modelers, and suggest corrections to resolve them. We demonstrate that detection is instantaneous by benchmarking our tool against synthetic BPMN models with increasing size and state space complexity, as well as realistic models. Moreover, the tool directly displays detected errors in the model, including an interactive visualization, and suggests fixes to resolve them. The tool is open source, extensible, and integrated into a popular BPMN modeling tool., Comment: arXiv admin note: substantial text overlap with arXiv:2407.03965

The black hole X-ray binary H1743-322 lies in a region of the Galaxy with high extinction, and therefore it has not been possible to make a dynamical mass measurement. In this paper we make use of a recent model which uses the X-ray reflection spectrum to constrain the ratio of the black hole mass to the source distance. By folding in a reported distance measurement, we are able to estimate the mass of the black hole to be $12\pm2~\text{M}_\odot$ ($1\sigma$ credible interval). We are then able to revise a previous disc continuum fitting estimate of black hole spin $a_*$ (previously relying on a population mass distribution) using our new mass constraint, finding $a_*=0.47\pm0.10$. This work is a proof of principle demonstration of the method, showing it can be used to find the mass of black holes in X-ray binaries., Comment: 13 pages, 7 figures. Accepted for publication in MNRAS

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a fast initial solution is iteratively optimized by destroying and repairing selected paths of the solution. Current MAPF-LNS variants commonly use an adaptive selection mechanism to choose among multiple destroy heuristics. However, to determine promising destroy heuristics, MAPF-LNS requires a considerable amount of exploration time. As common destroy heuristics are non-adaptive, any performance bottleneck caused by these heuristics cannot be overcome via adaptive heuristic selection alone, thus limiting the overall effectiveness of MAPF-LNS in terms of solution cost. In this paper, we propose Adaptive Delay-based Destroy-and-Repair Enhanced with Success-based Self-Learning (ADDRESS) as a single-destroy-heuristic variant of MAPF-LNS. ADDRESS applies restricted Thompson Sampling to the top-K set of the most delayed agents to select a seed agent for adaptive LNS neighborhood generation. We evaluate ADDRESS in multiple maps from the MAPF benchmark set and demonstrate cost improvements by at least 50% in large-scale scenarios with up to a thousand agents, compared with the original MAPF-LNS and other state-of-the-art methods., Comment: Accepted to AAAI 2025

We consider the non-interacting Bose gas of $N$ bosons in dimension $d\geq 3$ in a trap in a mean-field setting with a vanishing factor $a_N$ in front of the kinetic energy. The choice $a_N=N^{-2/d}$ is the semi-classical setting and was analysed in great detail in a special, interacting case in Deuchert and Seiringer (2021). Using a version of the well-known Feynman--Kac representation and a further representation in terms of a Poisson point process, we derive precise asymptotics for the reduced one-particle density matrix, implying off-diagonal long-range order (ODLRO, a well-known criterion for Bose--Einstein condensation) for $a_N$ above a certain threshold and non-occurrence of ODLRO for $a_N$ below that threshold. In particular, we relate the condensate and its total mass to the amount of particles in long loops in the Feynman--Kac formula, the order parameter that Feynman suggested in Feynman (1953). For $a_N\ll N^{-2/d}$, we prove that all loops have the minimal length one, and for $a_N\gg N^{-2/d}$ we prove 100 percent condensation and identify the distribution of the long-loop lengths as the Poisson--Dirichlet distribution., Comment: 24 pages, comments welcome

Identification of crystallographic slip in metals and alloys is crucial to understand and improve their mechanical behavior. Recently, a novel slip system identification framework, termed SSLIP (for Slip System-based Local Identification of Plasticity), was introduced to leap from conventional trace-based identification to automated, point-by-point identification that exploits the full deformation kinematics. Using microstructure-correlated deformation data, SSLIP matches the measured in-plane displacement gradient tensor to the kinematics of the optimal combination of multiple slip system activities, at each DIC datapoint. SSLIP was applied and demonstrated to be successful on virtual and experimental case studies of FCC and BCC metals. However, for more advanced and anisotropic HCP crystal structures the complete identification of all slip systems was found to be more challenging, posing limitations on automation and flexibility. Here, we propose a significant extension to the SSLIP framework with the aim of automated slip system identification of HCP. The main extensions of the SSLIP method, hereinafter referred to as the +SSLIP method, include (i) a pre-selection of slip systems using a Radon transform, (ii) robustness to measured rigid body rotation by simultaneous identification of the local elastic rotation field, (iii) identification of the two best matching slip systems for each data point, and (iv) a procedure to deal with slip systems with in-plane displacement gradient tensors that cannot be discriminated, yielding the full slip system activity maps with all slip systems for each grain. The resulting objective identification method does not rely on, e.g., the Schmid factor to select which slip system is active at each point. We show how slip systems from multiple slip families are successfully identified on virtual and real experiments on a Zn polycrystalline coating.

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation., Comment: 8 pages, 1 figure, 9 references

Shared controls in platform trials comprise concurrent and non-concurrent controls. For a given experimental arm, non-concurrent controls refer to data from patients allocated to the control arm before the arm enters the trial. The use of non-concurrent controls in the analysis is attractive because it may increase the trial's power of testing treatment differences while decreasing the sample size. However, since arms are added sequentially in the trial, randomization occurs at different times, which can introduce bias in the estimates due to time trends. In this article, we present methods to incorporate non-concurrent control data in treatment-control comparisons, allowing for time trends. We focus mainly on frequentist approaches that model the time trend and Bayesian strategies that limit the borrowing level depending on the heterogeneity between concurrent and non-concurrent controls. We examine the impact of time trends, overlap between experimental treatment arms and entry times of arms in the trial on the operating characteristics of treatment effect estimators for each method under different patterns for the time trends. We argue under which conditions the methods lead to type 1 error control and discuss the gain in power compared to trials only using concurrent controls by means of a simulation study in which methods are compared.

This article presents a new relation between the basic representation of split real simply-laced affine Kac-Moody algebras and finite dimensional representations of its maximal compact subalgebra $\mathfrak{k}$. We provide infinitely many $\mathfrak{k}$-subrepresentations of the basic representation and we prove that these are all the finite dimensional $\mathfrak{k}$-subrepresentations of the basic representation such that the quotient of the basic representation by the subrepresentation is a finite dimensional representation of a certain parabolic algebra and of the maximal compact subalgebra. By this result we provide an infinite composition series with a cosocle filtration of the basic representation. Finally, we present examples of the results and applications to supergravity., Comment: 45 + 11 pages

A Kondo model with symplectic symmetry was recently put forward as the effective low-energy theory of a superconducting-island device coupled to multiple leads. This model, which possesses non-Fermi liquid physics and effective anyons, was argued to belong to the class of topological Kondo effects. Here, we clarify the extent of stability of its exotic fixed point using perturbative and numerical renormalization group in conjunction with bosonization and conformal field theory. In contrast to previous claims, we show that asymmetry in the coupling to the leads destabilizes the non-Fermi liquid. Other destabilizing perturbations include asymmetry in the superconducting pairing or internal energy of the individual quantum dots in the island. Nevertheless, these perturbations all generate the same relevant operators. Thus, only a small number of couplings need to be tuned individually, and these can be selected according to experimental convenience. Our results highlight a common misconception that anisotropy in single-channel Kondo couplings is always irrelevant. As demonstrated, relevant terms will emerge whenever the group generators do not span the full space of impurity operators. This calls for a more detailed inspection of models that exhibit this property, such as large-spin impurities and SO(M) Kondo models., Comment: 28 pages, 7 figures, accepted version