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While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide due to their opaque internal mechanisms. Feature importance (FI) methods provide useful insights into the DGP under certain conditions. Since the results of different FI methods have different interpretations, selecting the correct FI method for a concrete use case is crucial and still requires expert knowledge. This paper serves as a comprehensive guide to help understand the different interpretations of global FI methods. Through an extensive review of FI methods and providing new proofs regarding their interpretation, we facilitate a thorough understanding of these methods and formulate concrete recommendations for scientific inference. We conclude by discussing options for FI uncertainty estimation and point to directions for future research aiming at full statistical inference from black-box ML models.

Debugging is an essential part of software maintenance and evolution since it allows software developers to analyze program execution step by step. Understanding a program is required to fix potential flaws, alleviate bottlenecks, and implement new desired features. Thus, software developers spend a large percentage of their time validating and debugging software, resulting in high software maintenance and evolution cost. We aim to reduce this cost by providing a novel visual debugging tool to software developers to foster program comprehension during debugging. Our debugging tool visualizes program execution information graphically as an object diagram and is fully integrated into the popular Java development environment IntelliJ IDEA. Moreover, the object diagram allows interactions to explore program execution information in more detail. A demonstration of our tool is available at https://www.youtube.com/watch?v=lU_OgotweRk.

When materials are patterned in three dimensions, there exist opportunities to tailor and create functionalities associated with an increase in complexity, the breaking of symmetries, and the introduction of curvature and non-trivial topologies. For superconducting nanostructures, the extension to the third dimension may trigger the emergence of new physical phenomena, as well as advances in technologies. Here, we harness three-dimensional (3D) nanopatterning to fabricate and control the emergent properties of a 3D superconducting nanostructure. Not only are we able to demonstrate the existence and motion of superconducting vortices in 3D but, with simulations, we show that the confinement leads to a well-defined bending of the vortices within the volume of the structure. Moreover, we experimentally observe a strong geometrical anisotropy of the critical field, through which we achieve the reconfigurable coexistence of superconducting and normal states in our 3D superconducting architecture, and the local definition of weak links. In this way, we uncover an intermediate regime of nanosuperconductivity, where the vortex state is truly three-dimensional and can be designed and manipulated by geometrical confinement. This insight into the influence of 3D geometries on superconducting properties offers a route to local reconfigurable control for future computing devices, sensors, and quantum technologies., Comment: 22 pages, 12 figures

Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weights. With these rules being in the focus of research, ANNs have become a branch of machine learning developing independently from neuroscience. Although likely required for the development of truly intelligent machines, the integration of neuroscience into ANNs has remained a neglected proposition. Here, we demonstrate that designing an ANN along biological principles results in drastically improved task performance. As a challenging real-world problem, we choose real-time ocean-wave prediction which is essential for various maritime operations. Motivated by the similarity of ocean waves measured at a single location to sound waves arriving at the eardrum, we redesign an echo state network to resemble the brain's auditory system. This yields a powerful predictive tool which is computationally lean, robust with respect to network parameters, and works efficiently across a wide range of sea states. Our results demonstrate the advantages of integrating neuroscience with machine learning and offer a tool for use in the production of green energy from ocean waves., Comment: 23 pages, 6 figures

The Lagoon Nebula (M8) is host to multiple regions with recent and ongoing massive star formation. With M8-Main and M8 East, two prominent regions of massive star formation have been studied in detail over the past years, while large parts of the nebula have received little attention. These largely unexplored regions comprise a large sample of molecular clumps that are affected by the presence of massive O- and B-type stars. We establish an inventory of species observed towards 37 known molecular clumps in M8 by conducting an unbiased line survey for each clump. For this, we used APEX and the IRAM 30m telescope for pointed on-off observations on the clumps. These observations cover bandwidths of 53GHz and 40GHz in frequency ranges from 210GHz to 280GHz and from 70GHz to 117GHz, respectively. Temperatures are derived from rotational transitions of CH3CN, CH3C2H and para-H2CO. Additional archival data from the Spitzer, Herschel, MSX, APEX, WISE, JCMT and AKARI telescopes are used to derive physical parameters of the dust emission by fitting spectral energy distributions to the observed flux densities. Across the observed M8 region, we identify 346 transitions from 70 different molecular species, including isotopologues. We detect tracers of photo-dissociation regions across all the clumps and 38% of these clumps show signs of star formation. We find that PDR tracers are most abundant in clumps with relatively lower H2 column densities. When comparing M8 clumps to ATLASGAL sources at similar distances, we find them to be slightly less massive and have compatible luminosities and radii. This possibly indicates a fragmentation of the gas caused by the O- and B-type stars. In contrast, dust temperatures of the clumps in M8 are found to be increased by approximately 5K (25%) indicating substantial external heating of the clumps by radiation of the present massive stars., Comment: Accepted for publication in A&A Corrected headings of Tab. A.1. and included language editing

Topological defects, or singularities, play a key role in the statics and dynamics of complex systems. In magnetism, Bloch point singularities represent point defects that mediate the nucleation of textures such as skyrmions and hopfions. However, while the textures are typically stabilised in chiral magnets, the influence of chirality on the Bloch point singularities remains relatively unexplored. Here we harness advanced three-dimensional nanofabrication to explore the influence of chirality on Bloch point singularities by introducing curvature-induced symmetry breaking in a ferromagnetic nanowire. Combining X-ray magnetic microscopy with the application of in situ magnetic fields, we demonstrate that Bloch point singularity-containing domain walls are stabilised in straight regions of the sample, and determine that curvature can be used to tune the energy landscape of the Bloch points. Not only are we able to pattern pinning points but, by controlling the gradient of curvature, we define asymmetric potential wells to realise a robust Bloch point shift-register with non-reciprocal behaviour. These insights into the influence of symmetry and chirality on singularities offers a route to the controlled nucleation and propagation of topological textures, providing opportunities for logic and computing devices.

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent also presents a challenge. The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents. Several algorithms, including CBM, CBS-TA, and ITA-CBS, optimally solve the TAPF problem, with ITA-CBS being the leading algorithm for minimizing flowtime. However, the only existing bounded-suboptimal algorithm ECBS-TA is derived from CBS-TA rather than ITA-CBS. So, it faces the same issues as CBS-TA, such as searching through multiple constraint trees and spending too much time on finding the next-best target assignment. We introduce ITA-ECBS, the first bounded-suboptimal variant of ITA-CBS. Transforming ITA-CBS to its bounded-suboptimal variant is challenging because different constraint tree nodes can have different assignments of targets to agents. ITA-ECBS uses focal search to achieve efficiency and determines target assignments based on a new lower bound matrix. We show that it runs faster than ECBS-TA in 87.42% of 54,033 test cases.

Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills some specified requirements is an important task. Previous research in algorithm selection for MAPF built a standard workflow and showed that machine learning can help. In this paper, we study general solvers for MAPF, which further include suboptimal algorithms. We propose different groups of optimization objectives and learning tasks to handle the new tradeoff between runtime and solution quality. We conduct extensive experiments to show that the same loss can not be used for different groups of optimization objectives, and that standard computer vision models are no worse than customized architecture. We also provide insightful discussions on how feature-sensitive pre-processing is needed for learning for MAPF, and how different learning metrics are correlated to different learning tasks.

Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavior, they hint users towards possible actions and give grounds for contesting decisions. As a crucial factor in achieving these goals, counterfactuals must be plausible, i.e., describing realistic alternative scenarios within the data manifold. This paper leverages a recently developed generative modeling technique -- adversarial random forests (ARFs) -- to efficiently generate plausible counterfactuals in a model-agnostic way. ARFs can serve as a plausibility measure or directly generate counterfactual explanations. Our ARF-based approach surpasses the limitations of existing methods that aim to generate plausible counterfactual explanations: It is easy to train and computationally highly efficient, handles continuous and categorical data naturally, and allows integrating additional desiderata such as sparsity in a straightforward manner., Comment: SD, KB, TB, and GK contributed equally as first authors

Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge efficiently. In this paper, we propose a general training framework, MARL-LNS, to algorithmically address these issues by training on alternating subsets of agents using existing deep MARL algorithms as low-level trainers, while not involving any additional parameters to be trained. Based on this framework, we provide three algorithm variants based on the framework: random large neighborhood search (RLNS), batch large neighborhood search (BLNS), and adaptive large neighborhood search (ALNS), which alternate the subsets of agents differently. We test our algorithms on both the StarCraft Multi-Agent Challenge and Google Research Football, showing that our algorithms can automatically reduce at least 10% of training time while reaching the same final skill level as the original algorithm.

We propose efficient classical algorithms which (strongly) simulate the action of bosonic linear optics circuits applied to superpositions of Gaussian states. Our approach relies on an augmented covariance matrix formalism to keep track of relative phases between individual terms in a linear combination. This yields an exact simulation algorithm whose runtime is polynomial in the number of modes and the size of the circuit, and quadratic in the number of terms in the superposition. We also present a faster approximate randomized algorithm whose runtime is linear in this number. Our main building blocks are a formula for the triple overlap of three Gaussian states and a fast algorithm for estimating the norm of a superposition of Gaussian states up to a multiplicative error. Our construction borrows from earlier work on simulating quantum circuits in finite-dimensional settings, including, in particular, fermionic linear optics with non-Gaussian initial states and Clifford computations with non-stabilizer initial states. It provides algorithmic access to a practically relevant family of non-Gaussian bosonic circuits., Comment: 43 pages and 11 figures

How galaxies regulate nuclear growth through gas accretion by supermassive black holes (SMBHs) is one of the most fundamental questions in galaxy evolution. One potential way to regulate nuclear growth is through a galactic wind that removes gas from the nucleus. It is unclear whether galactic winds are powered by jets, mechanical winds, radiation, or via magnetohydrodynamic (MHD) processes. Compact obscured nuclei (CONs) represent a significant phase of galactic nuclear growth. These galaxies hide growing SMBHs or unusual starbursts in their very opaque, extremely compact (r $<$ 100 pc) centres. They are found in approximately 30 % of the luminous and ultra-luminous infrared galaxy (LIRG and ULIRG) population. Here, we present high-resolution ALMA observations ($\sim$30 mas, $\sim$5 pc) of ground-state and vibrationally excited HCN towards ESO 320-G030 (IRAS 11506-3851). ESO 320-G030 is an isolated luminous infrared galaxy known to host a compact obscured nucleus and a kiloparsec-scale molecular wind. Our analysis of these high-resolution observations excludes the possibility of a starburst-driven wind, a mechanically or energy driven active galactic nucleus (AGN) wind, and exposes a molecular MDH wind. These results imply that the nuclear evolution of galaxies and the growth of SMBHs are similar to the growth of hot cores or protostars where gravitational collapse of the nuclear torus drives a MHD wind. These results mean galaxies are capable, in part, of regulating the evolution of their nuclei without feedback., Comment: 9 pages, 10 figures

For $2 < p < p_0 \simeq 26.265$, the hyperplane section of the $l_p^n$-unit ball $B_p^n$ perpendicular to a^(n) = 1/sqrt(n) (1, ... ,1) for large $n$ has larger volume than the one orthogonal to a^(2) = 1/sqrt(2) (1,1,0, ...,0), as shown by Oleszkiewicz. This is different from the case of $l_\infty^n$ considered by Ball. We give a quantitative estimate for which dimensions $n$ this happens, namely for $n > c (\frac 1 {p_0-p} + \frac 1 {p-2})$ for some absolute constant $c>0$. Correspondingly for projections of $B_q^n$ onto hyperplanes, Barthe and Naor showed that projections onto hyperplanes perpendicular to $a^{(n)}$ have smaller volume for large $n$ than onto the one orthogonal to $a^{(2)}$, if $\frac 4 3 < q < 2$, different from the case $q=1$. We show that this happens for all $n > 5 (\frac 1 {q-\frac 4 3} + \frac 1 {2-q})$., Comment: 19 pages

Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial targets, offers a more accurate approximation of real-world warehouse planning. In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF. We have developed a new type of map grid called cache for temporary item storage and replacement, and created a locking mechanism to improve the planning solution's stability. A task assigner (TA) is designed for CAL-MAPF to allocate target locations to agents and control agent status in different situations. CAL-MAPF has been evaluated using various cache replacement policies and input task distributions. We have identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. In general, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, map and agent configurations.

Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability, eliminating the need for predefined agents or agent-specific experience buffers in multi-agent reinforcement learning. Extensive evaluations across various traffic scenarios demonstrate our method's significant potential in improving traffic flow at critical bottleneck points. Moreover, we address the challenges posed by conservative autonomous vehicle driving behaviors that adhere strictly to traffic rules, showing that our cooperative policy effectively alleviates potential slowdowns without compromising safety., Comment: accepted by IROS 2024

We consider a quantum system coupled to environment at a fixed temperature and describe the reduced evolution of the system by means of a Redfield equation with effective time-dependent contact temperature obeying a universal law. At early times, after the system and environment start in a product state, the effective contact temperature appears to be much higher, yet eventually it settles down towards the true environment value. In this way, we obtain a method which includes non-Markovian effects and can be further applied to various types of GKSL equations, beyond the secular approximation and time-averaging methods. We derive the theory from first principles and discuss its application using a simple example of a single quantum dot., Comment: 13 pages, 10 figures

Electronic transport through nanostructures can be suppressed by coherent population trapping, in which quantum coherence leads to a dark state that decouples from the drain electrode. Finite transport, then, relies on decoherence of the dark state. An alternative scenario for reduced transport is weak coupling of a state, referred to as a blocking state, to the drain. This raises the question of whether and how these two scenarios can be distinguished in the transport features. For the example of electron transport through a carbon nanotube, we identify regimes, in which this distinction is possible by analyzing the full counting statistics in terms of Lee-Yang zeros and factorial cumulants., Comment: 11 pages, 9 figures

The conventional process of building Ontologies and Knowledge Graphs (KGs) heavily relies on human domain experts to define entities and relationship types, establish hierarchies, maintain relevance to the domain, fill the ABox (or populate with instances), and ensure data quality (including amongst others accuracy and completeness). On the other hand, Large Language Models (LLMs) have recently gained popularity for their ability to understand and generate human-like natural language, offering promising ways to automate aspects of this process. This work explores the (semi-)automatic construction of KGs facilitated by open-source LLMs. Our pipeline involves formulating competency questions (CQs), developing an ontology (TBox) based on these CQs, constructing KGs using the developed ontology, and evaluating the resultant KG with minimal to no involvement of human experts. We showcase the feasibility of our semi-automated pipeline by creating a KG on deep learning methodologies by exploiting scholarly publications. To evaluate the answers generated via Retrieval-Augmented-Generation (RAG) as well as the KG concepts automatically extracted using LLMs, we design a judge LLM, which rates the generated content based on ground truth. Our findings suggest that employing LLMs could potentially reduce the human effort involved in the construction of KGs, although a human-in-the-loop approach is recommended to evaluate automatically generated KGs.

The fractional Caffarelli-Kohn-Nirenberg inequality states that $$ \int_{\mathbb{R}^n}\int_{\mathbb{R}^n} \frac{(u(x)-u(y))^2}{|x|^\alpha |x-y|^{n+2s} |y|^\alpha} \mathrm{d} x \, \mathrm{d} y \geq \Lambda_{n, s, p, \alpha,\beta} \|u |x|^{-\beta}\|_{L^p}^2, $$ for $0

A common approach to deal with gate errors in modern quantum-computing hardware is zero-noise extrapolation. By artificially amplifying errors and extrapolating the expectation values obtained with different error strengths towards the zero-error (zero-noise) limit, the technique aims at rectifying errors in noisy quantum computing systems. For an accurate extrapolation, it is essential to know the exact factors of the noise amplification. In this article, we propose a simple method for estimating the strength of errors occurring in a quantum circuit and demonstrate improved extrapolation results. The method determines the error strength for a circuit by appending to it the inverted circuit and measuring the probability of the initial state. The estimation of error strengths is easy to implement for arbitrary circuits and does not require a previous characterisation of noise properties. We compare this method with the conventional zero-noise extrapolation method and show that the novel method leads to a more accurate calculation of expectation values. Our method proves to be particularly effective on current hardware, showcasing its suitability for near-term quantum computing applications., Comment: 16 pages, 15 figures

We show that a dynamical sequence $(f_n)_{n\in \mathbb{N}}$ of polynomials over a number field whose set of stable primes is of positive density must necessarily have a very restricted, and in particular ``near-solvable" dynamical Galois group. Together with existing heuristics, our results suggest moreover that a polynomial $f$ all of whose iterates are irreducible modulo a positive density subset of the primes must necessarily be a composition of linear functions, monomials and Dickson polynomials., Comment: To appear in Bull. London Math. Soc

Compact obscured nuclei (CONs) are an extremely obscured (N$_{H2}$ >10$^{25}$ cm$^{-2}$) class of galaxy nuclei thought to exist in 20-40 per cent of nearby (ultra-)luminous infrared galaxies. While they have been proposed to represent a key phase of the active galactic nucleus (AGN) feedback cycle, the nature of these CONs - what powers them, their dynamics, and their impact on the host galaxy - remains unknown. This work analyses the large-scale optical properties of the local CON, NGC4418 (z=0.00727). We present new, targeted integral field unit observations of the galaxy with the Multi-Unit Spectroscopic Explorer (MUSE). For the first time, we map the ionised and neutral gas components of the galaxy, along with their dynamical structure, to reveal several previously unknown features of the galaxy. We confirm the presence of a previously postulated blueshifted outflow along the minor axis of NGC4418. We find this outflow to be decelerating and, for the first time, show it to extend bilaterally from the nucleus. We report the discovery of two further outflow structures: a redshifted southern outflow connected to a tail of ionised gas surrounding the galaxy and a blueshifted bubble to the north. In addition to these features, we find the [OIII] emission reveals the presence of knots across the galaxy, which are consistent with regions of the galaxy that have been photoionised by an AGN. Based on the properties of these features, we conclude that the CON in NGC4418 is most likely powered by AGN activity., Comment: 17 pages, 15 figures, accepted to A&A

Nuclear regions of ultraluminous and luminous infrared galaxies (U/LIRGs) are powered by starbursts and/or active galactic nuclei (AGNs). These regions are often obscured by extremely high columns of gas and dust. Molecular lines in the submillimeter windows have the potential to determine the physical conditions of these compact obscured nuclei (CONs). We aim to reveal the distributions of HCN and HCO$^+$ emission in local U/LIRGs and investigate whether and how they are related to galaxy properties. Using ALMA, we have conducted sensitive observations of the HCN J=3--2 and HCO$^+$ J=3--2 lines toward 23 U/LIRGs in the local Universe (z < 0.07) with a spatial resolution of ~0.3" (~50--400 pc). We detected both HCN and HCO$^+$ in 21 galaxies, only HCN in one galaxy, and neither in one galaxy. The global HCN/HCO$^+$ line ratios, averaged over scales of ~0.5--4 kpc, range from 0.4 to 2.3, with an unweighted mean of 1.1. These line ratios appear to have no systematic trend with bolometric AGN luminosity or star formation rate. The line ratio varies with position and velocity within each galaxy, with an average interquartile range of 0.38 on a spaxel-by-spaxel basis. In eight out of ten galaxies known to have outflows and/or inflows, we found spatially and kinematically symmetric structures of high line ratios. These structures appear as a collimated bicone in two galaxies and as a thin spherical shell in six galaxies. Non-LTE analysis suggests that the high HCN/HCO$^+$ line ratio in outflows is predominantly influenced by the abundance ratio. Chemical model calculations indicate that the enhancement of HCN abundance in outflows is likely due to high-temperature chemistry triggered by shock heating. These results imply that the HCN/HCO$^+$ line ratio can aid in identifying the outflow geometry when the shock velocity of the outflows is sufficiently high to heat the gas., Comment: 52 pages, 35 figures, accepted for publication in Astronomy & Astrophysics

We show how to realize a general quantum circuit involving gates between arbitrary pairs of qubits by means of geometrically local quantum operations and efficient classical computation. We prove that circuit-level local stochastic noise modeling an imperfect implementation of our derived schemes is equivalent to local stochastic noise in the original circuit. Our constructions incur a constant-factor increase in the quantum circuit depth and a polynomial overhead in the number of qubits: To execute an arbitrary quantum circuit on $n$ qubits, we give a 3D quantum fault-tolerance architecture involving $O(n^{3/2} \log^3 n)$ qubits, and a quasi-2D architecture using $O(n^2 \log^3 n)$ qubits. Applied to recent fault-tolerance constructions, this gives a fault-tolerance threshold theorem for universal quantum computations with local operations, a polynomial qubit overhead and a quasi-polylogarithmic depth overhead. More generally, our transformation dispenses with the need for considering the locality of operations when designing schemes for fault-tolerant quantum information processing., Comment: 38 pages, 4 figures

Eskenazis, Nayar and Tkocz have shown recently some resilience of Ball's celebrated cube slicing theorem, namely its analogue in $l^n_p$ for large $p$. We show that the complex analogue, i.e. resilience of the polydisc slicing theorem proven by Oleszkiewicz and Pelczy\'nski, holds for large $p$ and small $n$, but does not hold for any $p > 2$ and large $n$.

Photoreduction of carbon dioxide (CO$_2$) on plasmonic structures is of great interest in photocatalysis to aid selectivity. While species commonly found in reaction environments and associated intermediates can steer the reaction down different pathways by altering the potential energy landscape of the system, they are often not addressed when designing efficient plasmonic catalysts. Here, we perform an atomistic study of the effect of the hydroxyl group (OH) on CO$_2$ activation and hot electron generation and transfer using first-principle calculations. We show that the presence of OH is essential in breaking the linear symmetry of CO$_2$, which leads to a charge redistribution and a decrease in the O=C=O angle to 134{\deg}, thereby activating CO$_2$. Analysis of the partial density of states (pDOS) demonstrates that the OH group mediates the orbital hybridization between Au and CO$_2$ resulting in more accessible states, thus facilitating charge transfer. By employing time-dependent density functional theory (TDDFT), we quantify the fraction of hot electrons directly generated into hybridized molecular states at resonance, demonstrating a broader energy distribution and a 11% increase in charge-transfer in the presence of OH groups. We further show that the spectral overlap between excitation energy and plasmon resonance plays a critical role in efficiently modulating electron transfer processes. These findings contribute to the mechanistic understanding of plasmon-mediated reactions and demonstrate the importance of co-adsorbed species in tailoring the electron transfer processes, opening new avenues for enhancing selectivity.

The log-rank test and the Cox proportional hazards model are commonly used to compare time-to-event data in clinical trials, as they are most powerful under proportional hazards. But there is a loss of power if this assumption is violated, which is the case for some new oncology drugs like immunotherapies. We consider a two-stage test procedure, in which the weighting of the log-rank test statistic depends on a pre-test of the proportional hazards assumption. I.e., depending on the pre-test either the log-rank or an alternative test is used to compare the survival probabilities. We show that if naively implemented this can lead to a substantial inflation of the type-I error rate. To address this, we embed the two-stage test in a permutation test framework to keep the nominal level alpha. We compare the operating characteristics of the two-stage test with the log-rank test and other tests by clinical trial simulations.

Motivated by the importance of studying topological superconductors beyond the mean-field approximation, we here investigate mesoscopic islands of time reversal invariant topological superconductors (TRITOPS). We characterize the spectrum in the presence of strong order parameter fluctuations in the presence of an arbitrary number of Kramers pairs of Majorana edge states and study the effect of coupling the Coulomb blockaded island to external leads. In the case of an odd fermionic parity on the island, we derive an unconventional Kondo Hamiltonian in which metallic leads couple to both topological Majorana degrees of freedom (which keep track of the parity in different leads) and the overall spin-1/2 in the island. For the simplest case of a single wire (two pairs of Majorana edge states), we demonstrate that anisotropies are irrelevant in the weak coupling renormalization group flow. This permits us to solve the Kondo problem in the vicinity of a Toulouse-like point using Abelian Bosonization. We demonstrate a residual ground state entropy of $\ln(2)$, which is protected by spin-rotation symmetry, but reduced to $\ln(\sqrt{2})$ (as in the spinless topological Kondo effect) by symmetry breaking perturbations. In the symmetric case, we further demonstrate the simultaneous presence of both Fermi liquid and non-Fermi liquid like thermodynamics (depending on the observable) and derive charge and spin transport signatures of the Coulomb blockaded island., Comment: 10+13 pages, 4+1 figures

Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while minimizing the sum of travel time. Since solving the MAPF problem optimally is NP-hard, anytime algorithms based on Large Neighborhood Search (LNS) are promising to find good-quality solutions in a scalable way by iteratively destroying and repairing the paths. We propose Destroy-Repair Operation Parallelism for LNS (DROP-LNS), a parallel framework that performs multiple destroy and repair operations concurrently to explore more regions of the search space within a limited time budget. Unlike classic MAPF approaches, DROP-LNS can exploit parallelized hardware to improve the solution quality. We also formulate two variants of parallelism and conduct experimental evaluations. The results show that DROP-LNS significantly outperforms the state-of-the-art and the variants., Comment: Accepted as an extended abstract in AAMAS 2024

Inclusive e$^+$e$^-$ production has been studied with HADES in $\pi^-$ + p, $\pi^-$ + C and $\pi^- + \mathrm{CH}_2$ reactions, using the GSI pion beam at $\sqrt{s_{\pi p}}$ = 1.49 GeV. Invariant mass and transverse momentum distributions have been measured and reveal contributions from Dalitz decays of $\pi^0$, $\eta$ mesons and baryon resonances. The transverse momentum distributions are very sensitive to the underlying kinematics of the various processes. The baryon contribution exhibits a deviation up to a factor seven from the QED reference expected for the dielectron decay of a hypothetical point-like baryon with the production cross section constrained from the inverse $\gamma$ n$\rightarrow \pi^-$ p reaction. The enhancement is attributed to a strong four-momentum squared dependence of the time-like electromagnetic transition form factors as suggested by Vector Meson Dominance (VMD). Two versions of the VMD, that differ in the photon-baryon coupling, have been applied in simulations and compared to data. VMD1 (or two-component VMD) assumes a coupling via the $\rho$ meson and a direct coupling of the photon, while in VMD2 (or strict VMD) the coupling is only mediated via the $\rho$ meson. The VMD2 model, frequently used in transport calculations for dilepton decays, is found to overestimate the measured dielectron yields, while a good description of the data can be obtained with the VMD1 model assuming no phase difference between the two amplitudes. Similar descriptions have also been obtained using a time-like baryon transition form factor model where the pion cloud plays the major role., Comment: (HADES collaboration)