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Reliable evaluation of AI models is critical for scientific progress and practical application. While existing VLM benchmarks provide general insights into model capabilities, their heterogeneous designs and limited focus on a few imaging domains pose significant challenges for both cross-domain performance comparison and targeted domain-specific evaluation. To address this, we propose three key contributions: (1) a framework for the resource-efficient creation of domain-specific VLM benchmarks enabled by task augmentation for creating multiple diverse tasks from a single existing task, (2) the release of new VLM benchmarks for seven domains, created according to the same homogeneous protocol and including 162,946 thoroughly human-validated answers, and (3) an extensive benchmarking of 22 state-of-the-art VLMs on a total of 37,171 tasks, revealing performance variances across domains and tasks, thereby supporting the need for tailored VLM benchmarks. Adoption of our methodology will pave the way for the resource-efficient domain-specific selection of models and guide future research efforts toward addressing core open questions.

We present two precision tools for the simulation of Higgs-pair production via vector-boson fusion in the kappa framework for the parameterization of non-standard Higgs couplings. A new implementation of the process is developed in the framework of the POWHEG BOX program that can be used to provide predictions at the next-to-leading order (NLO) of QCD matched to parton showers (PS). In addition, the existing proVBFHH program for the computation of next-to-next-to-leading order (NNLO) QCD and next-to-next-to-next-to-leading order QCD corrections is extended to account for values of the Higgs couplings different from the expectation of the Standard Model. We systematically compare and analyse predictions obtained with the two programs and find that the NLO+PS predictions provide a good approximation of the NNLO results for observables of the tagging jets and Higgs bosons. The results turn out to be very sensitive to the values of the modified Higgs couplings., Comment: 18 pages, code can be obtained from https://powhegbox.mib.infn.it/

Ethanolamine (EA), a key component of phospholipids, has recently been detected in the interstellar medium within molecular clouds. To understand this observation, laboratory studies of its formation and destruction are essential and should be complemented by astrochemical models. This study investigates the photostability of EA ice under Lyman (Ly)-$\alpha$ (10.2 eV) irradiation at 10 K, and explores its potential role in the formation of simple and complex organic molecules in molecular clouds. The UV destruction cross section of EA was estimated to be ($4.7\pm0.3)\times10^{-18}$ cm$^2$, providing insight into its half-life of $6.5\times10^{7}$ yr in dense interstellar clouds. Fourier transform infrared spectroscopy and quadrupole mass spectrometry were used to identify various photoproducts, with their formation pathways discussed. Ethylene glycol and serine were tentatively detected during the warming up process following irradiation, suggesting that EA could contribute to the formation of prebiotic molecules such as sugars, peptides and their derivatives. High mass signals detected in the mass spectrometer suggest the presence of several complex organic molecules, and further analysis of residues at room temperature is planned for future work. The results suggest that EA could contribute to the formation of prebiotic molecules in space, with implications for the origin of life.

Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles. This work leverages quaternions and the two-dimensional orthogonal planes split framework (splitting of a quaternion - representing a pixel - into pairs of orthogonal 2D planes) for natural/biomedical image analysis through the following computational workflows and outcomes: natural/biomedical image re-colorization, natural image de-colorization, natural/biomedical image contrast enhancement, computational re-staining and stain separation in histological images, and performance gains in machine/deep learning pipelines for histological images. The workflows are analyzed separately for natural and biomedical images to showcase the effectiveness of the proposed approaches. The proposed workflows can regulate color appearance (e.g. with alternative renditions and grayscale conversion) and image contrast, be part of automated image processing pipelines (e.g. isolating stain components, boosting learning models), and assist in digital pathology applications (e.g. enhancing biomarker visibility, enabling colorblind-friendly renditions). Employing only basic arithmetic and matrix operations, this work offers a computationally accessible methodology - in the hypercomplex domain - that showcases versatility and consistency across image processing tasks and a range of computer vision and biomedical applications. The proposed non-data-driven methods achieve comparable or better results (particularly in cases involving well-known methods) to those reported in the literature, showcasing the potential of robust theoretical frameworks with practical effectiveness. Results, methods, and limitations are detailed alongside discussion of promising extensions, emphasizing the potential of feature-rich mathematical/computational frameworks for natural and biomedical images., Comment: 24 pages, 18 figures, 14 tables

Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artefacts, morphological heterogeneity, age-related comorbidities, time-dependent signal dynamics, instrumental variability, and limited labelled data. Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics that focus on mean performance, neglecting anatomical, subpopulation, and acquisition-dependent variability. Here, we present a high-performance DWI lesion segmentation tool addressing these challenges through optimized vision transformer-based architectures, integration of 3563 annotated lesions from multi-site data, and algorithmic enhancements, achieving state-of-the-art results. We further propose a novel evaluative framework assessing model fidelity, equity (across demographics and lesion subtypes), anatomical precision, and robustness to instrumental variability, promoting clinical and research utility. This work advances stroke imaging by reconciling model expressivity with domain-specific challenges and redefining performance benchmarks to prioritize equity and generalizability, critical for personalized medicine and mechanistic research., Comment: 29 pages, 7 figures, 2 tables, 1 supplementary table, 2 supplementary figures

A well-known challenge for the lattice community is calculating the spectral function from the Euclidean correlator. We have approximated the spectral function and derived the mass and thermal width of particles through the time derivatives of the lattice correlator moments. We have focused on extracting the properties of bottomonium states, specifically $\Upsilon$ and $\chi_{b1}$. We will give an overview of the time-derivative moments approach and present results for the temperature dependence of the mass and width of both bottomonium states. The zero temperature results are consistent with experimental values, while results at higher temperatures are similar to those obtained using other methods., Comment: 9 pages, 4 figures, talk presented at the 41st International Symposium on Lattice Field Theory (Lattice2024), July 28th - August 3rd, 2024, Liverpool, UK

Bottomonia play a crucial role in our understanding of the quark gluon plasma. We present lattice non-relativistic QCD calculations of bottomonia at temperatures in the range $T \in [47, 380]$ MeV using the Fastsum Generation 2L anisotropic $N_f = 2 + 1$ ensembles. The use of a basis of smeared operators allows the extraction of excited-state masses at zero temperature and an investigation of their thermal properties at non-zero temperature. We find that the ground state signal is substantially improved by this variational approach at finite temperature. We also apply the time-derivative moments approach to the projected or optimal correlation functions at finite temperature., Comment: 13 pages, 3 figures, talk presented at the 41st International Symposium on Lattice Field Theory (Lattice2024), July 28th - August 3rd, 2024, Liverpool, UK

Understanding how the properties of heavy mesons change as temperature increases is crucial for gaining valuable insights into the quark-gluon plasma. Information about meson masses and decay widths is encoded in the meson spectral function, which, in principle, can be extracted from Euclidean correlation functions via generalised Laplace transformations. However, this inverse problem is ill-posed for lattice correlation functions and requires regularisation. In this work, we present the latest results for bottomonium spectral functions obtained within the lattice NRQCD framework using the Backus-Gilbert regularisation, along with two other variants, one of which is commonly referred to as the HLT method. Our analysis employs Generation 2L anisotropic lattice configurations produced by the \textsc{Fastsum} collaboration., Comment: 7 pages, 2 figures, Contribution to The 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK

A fully disentangled variational auto-encoder (VAE) aims to identify disentangled latent components from observations. However, enforcing full independence between all latent components may be too strict for certain datasets. In some cases, multiple factors may be entangled together in a non-separable manner, or a single independent semantic meaning could be represented by multiple latent components within a higher-dimensional manifold. To address such scenarios with greater flexibility, we develop the Partially Disentangled VAE (PDisVAE), which generalizes the total correlation (TC) term in fully disentangled VAEs to a partial correlation (PC) term. This framework can handle group-wise independence and can naturally reduce to either the standard VAE or the fully disentangled VAE. Validation through three synthetic experiments demonstrates the correctness and practicality of PDisVAE. When applied to real-world datasets, PDisVAE discovers valuable information that is difficult to find using fully disentangled VAEs, implying its versatility and effectiveness.

The first few hours of a supernova contain significant information about the progenitor system. The most modern wide-field surveys that scan the sky repeatedly every few days can discover all kinds of transients in those early epochs. At such times, some progenitor footprints may be visible, elucidating critical explosion parameters and helping to distinguish between leading explosion models. A dedicated spectroscopic classification programme using the optical spectrograph OSIRIS mounted to the Gran Telescopio de Canarias was set up to try to obtain observations of supernova at those early epochs. With the time awarded, we obtained spectra for 10 SN candidates, which we present here. Half of them were thermonuclear SNe, while the other half were core-collapse SNe. Most (70\%) were observed within the first six days of the estimated explosion, with two being captured within the first 48 hours. We present a characterization of the spectra, together with other public ancillary photometry from ZTF and ATLAS. This programme shows the need for an accompanying rapid-response spectroscopic programme to existing and future deep photometric wide-field surveys located at the right longitude to be able to trigger observations in a few hours after the discovery of the supernova candidate. Both the future La Silla Southern Supernova Survey (LS4) and the Legacy Survey of Space and Time (LSST) both located in Chile will be providing discovery and follow up of most of the transients in the southern hemisphere. This paper demonstrates that with a rapid spectroscopic programme and stringent triggering criteria, obtaining a sample of SN with spectra within a day of the explosion is possible., Comment: 20 pages, 16 figures. Submitted to A&A

The electric dipole moments~(EDM) of fundamental particles inherently violate parity~(P) and time-reversal~(T) symmetries. By virtue of the CPT theorem in quantum field theory, the latter also implies the violation of the combined charge-conjugation and parity~(CP) symmetry. We aim to measure the EDM of the muon using the frozen-spin technique within a compact storage trap. This method exploits the high effective electric field, \$E \approx 165\$ MV/m, experienced in the rest frame of the muon with a momentum of about 23 MeV/c when it passes through a solenoidal magnetic field of \$|\vec{B}|=2.5\$ T. In this paper, we outline the fundamental considerations for a muon EDM search and present a conceptual design for a demonstration experiment to be conducted at secondary muon beamlines of the Paul Scherrer Institute in Switzerland. In Phase~I, with an anticipated data acquisition period of 200 days, the expected sensitivity to a muon EDM is 4E-21 ecm. In a subsequent phase, Phase~II, we propose to improve the sensitivity to 6E-23 ecm using a dedicated instrument installed on a different beamline that produces muons of momentum 125 MeV/c}., Comment: 34 pages, submitted to EPJC

3D Gaussian Splatting (3DGS) has emerged as a powerful approach for 3D scene reconstruction using 3D Gaussians. However, neither the centers nor surfaces of the Gaussians are accurately aligned to the object surface, complicating their direct use in point cloud and mesh reconstruction. Additionally, 3DGS typically produces floater artifacts, increasing the number of Gaussians and storage requirements. To address these issues, we present FeatureGS, which incorporates an additional geometric loss term based on an eigenvalue-derived 3D shape feature into the optimization process of 3DGS. The goal is to improve geometric accuracy and enhance properties of planar surfaces with reduced structural entropy in local 3D neighborhoods.We present four alternative formulations for the geometric loss term based on 'planarity' of Gaussians, as well as 'planarity', 'omnivariance', and 'eigenentropy' of Gaussian neighborhoods. We provide quantitative and qualitative evaluations on 15 scenes of the DTU benchmark dataset focusing on following key aspects: Geometric accuracy and artifact-reduction, measured by the Chamfer distance, and memory efficiency, evaluated by the total number of Gaussians. Additionally, rendering quality is monitored by Peak Signal-to-Noise Ratio. FeatureGS achieves a 30 % improvement in geometric accuracy, reduces the number of Gaussians by 90 %, and suppresses floater artifacts, while maintaining comparable photometric rendering quality. The geometric loss with 'planarity' from Gaussians provides the highest geometric accuracy, while 'omnivariance' in Gaussian neighborhoods reduces floater artifacts and number of Gaussians the most. This makes FeatureGS a strong method for geometrically accurate, artifact-reduced and memory-efficient 3D scene reconstruction, enabling the direct use of Gaussian centers for geometric representation., Comment: 16 pages, 9 figures, 7 tables

Forests are an essential part of our biosphere, regulating climate, acting as a sink for greenhouse gases, and providing numerous other ecosystem services. However, they are negatively impacted by climatic stressors such as drought or heat waves. In this paper, we introduce FORTE, an open-source system for environmental monitoring with the aim of understanding how forests react to such stressors. It consists of two key components: (1) a wireless sensor network (WSN) deployed in the forest for data collection, and (2) a Data Infrastructure for data processing, storage, and visualization. The WSN contains a Central Unit capable of transmitting data to the Data Infrastructure via LTE-M and several spatially independent Satellites that collect data over large areas and transmit them wirelessly to the Central Unit. Our prototype deployments show that our solution is cost-effective compared to commercial solutions, energy-efficient with sensor nodes lasting for several months on a single charge, and reliable in terms of data quality. FORTE's flexible architecture makes it suitable for a wide range of environmental monitoring applications beyond forest monitoring. The contributions of this paper are three-fold. First, we describe the high-level requirements necessary for developing an environmental monitoring system. Second, we present an architecture and prototype implementation of the requirements by introducing our FORTE platform and demonstrating its effectiveness through multiple field tests. Lastly, we provide source code, documentation, and hardware design artifacts as part of our open-source repository.

We study SU$(N_C)$ gauge theories with a single fermion in the two-index antisymmetric representation to predict the mesonic spectrum of supersymmetric $\mathcal{N}=1$ SYM theories. Using gradient flow methods, we investigate fractional topological charges in $N_C = 4$ ensembles with varying lattice spacings. We show that the use of overimproved gauge actions (specifically the DBW2 action) in the smearing kernel stabilises the values of the topological charge already at moderate values of the flow time, while this is not the case for the standard Wilson flow., Comment: 9 pages, 2 tables, 4 figures, Contribution to The 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK

We report the discovery of the new isotope $^{252}$Rf. With its extremely short half-life of $60^{+90}_{-30}$~ns, it expands the range of half-lives of the known superheavy nuclei by about two orders of magnitude. This nucleus was synthesized in its high-$K$ isomeric state, for which we measured a half-life of $13^{+4}_{-3}$~$\mu$s. Our results confirm a smooth onset of decreasing ground-state spontaneous fission half-lives in the neutron-deficient Rf isotopes towards the isotopic border of $10^{-14}$~s, which is the time needed to form an atomic shell. Our findings set a new benchmark for further exploration of phenomena associated with high-$K$ states and inverted fission-stability in the heaviest nuclei.

We formalize a rigorous connection between barren plateaus (BP) in variational quantum algorithms and exponential concentration of quantum kernels for machine learning. Our results imply that recently proposed strategies to build BP-free quantum circuits can be utilized to construct useful quantum kernels for machine learning. This is illustrated by a numerical example employing a provably BP-free quantum neural network to construct kernel matrices for classification datasets of increasing dimensionality without exponential concentration.

We present optical photometric and spectroscopic observations of the peculiar Type Ia supernova ASASSN-20jq/SN 2020qxp. It is a low-luminosity object with a peak absolute magnitude of $M_B=-17.1\pm0.5$ mag. Despite its low luminosity, its post-peak light-curve decline rate ($\Delta m_{15}(B)=1.35\pm0.09$ mag) and color-stretch parameter (sBV>0.82) are similar to normal SNe Ia, making it an outlier in the luminosity-width and luminosity-color-stretch relations. Early light curves suggest a "bump" during the first 1.4 days of explosion. ASASSN-20jq synthesized a low radioactive $^{56}$Ni mass of $0.09\pm0.01M_\odot$. Near-maximum light spectra reveal strong Si II absorption lines, indicating a cooler photosphere than normal SNe Ia, but lack Ti II absorption lines. Unusually strong O I $\lambda$7773 and Ca II near-infrared triplet absorption features are present. Nebular spectra show a strong, narrow forbidden [Ca II] $\lambda\lambda$7291,7324 doublet emission, rarely seen in SNe Ia except in some Type Iax events. Marginal detection of [O I] $\lambda\lambda$6300,6364 doublet emission, which is extremely rare, is observed. Both [Ca II] and [O I] lines are redshifted by $\sim2000$ km/s. A strong [Fe II] $\lambda$7155 emission line with a tilted-top profile, identical to the [Fe II] $\lambda$16433 profile, is also observed. These asymmetric [Fe II] profiles and redshifted [Ca II] and [O I] emissions suggest a high central density white dwarf progenitor undergoing an off-center delayed-detonation explosion mechanism, producing roughly equal amounts of $^{56}$Ni in deflagration and detonation phases. This distinguishes ASASSN-20jq from normal and subluminous SNe Ia. ASASSN-20jq's light curve and spectra do not align with any single SNe Ia subclass but show similarities to 2002es-like objects. Thus, we add it as an extreme candidate within the heterogeneous parameter space of 2002es-like SNe Ia., Comment: 27 pages, 20 figures, 5 tables, submitted to A&A

This study investigates the rheological behavior of shear-thickening suspensions made of different types of nanoparticles upon the addition of large intruders referred to as granules. The size ratio ranges from 20 to 120. We examine the effects of granule size, volume fraction, and surface properties on shear-thickening characteristics. Starting with a fumed silica suspension exhibiting discontinuous shear thickening (DST) without granules, the addition of granules at different volume fractions, shifts the onset of thickening to lower shear rates. Concomitantly, the strength of the thickening, quantified by the thickening index, decreases, transitioning from DST to continuous shear thickening (CST). Comparison with suspensions of nanosilica spheres reveals a similar trend, suggesting generality across different systems. However, these results contrast with cornstarch-based suspensions, where granule addition enhances thickening. This difference is attributed to the large size ratio studied here: When the granules are much larger than the particles in the interstitial suspension, the granules introduce a spread in the local shear rate and disrupt the particles' ability to form an extended fabric of force chains. The findings highlight the critical role of particle size ratio in determining the rheology of complex suspensions, paving the way for tailoring material properties in industrial and scientific applications.

In the QCD phase diagram, the dependence of the pseudo-critical temperature, $T_{\rm pc}$, on the baryon chemical potential, $\mu_B$, is of fundamental interest. The variation of $T_{\rm pc}$ with $\mu_B$ is normally captured by $\kappa$, the coefficient of the leading (quadratic) term of the polynomial expansion of $T_{\rm pc}$ with $\mu_B$. In this work, we present the first calculation of $\kappa$ using hadronic quantities. Simulating $N_f=2+1$ flavours of Wilson fermions on {\sc Fastsum} ensambles, we calculate the $\mathcal{O}(\mu_B^2)$ correction to mesonic correlation functions. By demanding degeneracy in the vector and axial vector channels we obtain $T_{\rm pc}(\mu_B)$ and hence $\kappa$. While lacking a continuum extrapolation and being away from the physical point, our results are consistent with previous works using thermodynamic observables (renormalised chiral condensate, strange quark number susceptibility) from lattice QCD simulations with staggered fermions., Comment: 7 pages, 5 figures and 3 tables

Device driver isolation is a promising approach for protecting the kernel from faulty or malicious drivers, but the actual security provided by such frameworks is often not well understood. Recent research has identified Compartment Interface Vulnerabilities (CIVs) in userspace compartmentalized applications, yet their impact on driver isolation frameworks remains poorly understood. This paper provides a comprehensive survey of the design and security guarantees of existing driver isolation frameworks and systemizes existing CIV classifications, evaluating them under driver isolation. The analysis shows that different classes of CIVs are prevalent across the studied drivers under a baseline threat model, with large drivers having more than 100 instances of different CIVs and an average of 33 instances across the studied drivers. Enforcing extra security properties, such as CFI, can reduce the number of CIVs to around 28 instances on average. This study provides insights for understanding existing driver isolation security and the prevalence of CIVs in the driver isolation context, and extracts useful insights that can provide security guidance for future driver isolation systems.

We present observations of ASASSN-22ci (AT2022dbl), a nearby tidal disruption event (TDE) discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) at a distance of d$_L \simeq 125$ Mpc. Roughly two years after the initial ASAS-SN discovery, a second flare was detected coincident with ASASSN-22ci. UV/optical photometry and optical spectroscopy indicate that both flares are likely powered by TDEs. The striking similarity in flare properties suggests that these flares result from subsequent disruptions of the same star. Each flare rises on a timescale of $\sim$30 days, has a temperature of $\approx$30,000 K, a peak bolometric luminosity of $L_{UV/Opt} = 10^{43.6 - 43.9} \textrm{ erg} \textrm{ s}^{-1}$, and exhibits a blue optical spectrum with broad H, He, and N lines. No X-ray emission is detected during either flare, but X-ray emission with an unabsorbed luminosity of $L_{X} = 3\times10^{41} \textrm{ erg} \textrm{ s}^{-1}$ and $kT = 0.042$ eV is observed between the flares. Pre-discovery survey observations rule out the existence of earlier flares within the past $\approx$6000 days, indicating that the discovery of ASASSN-22ci likely coincides with the first flare. If the observed flare separation of $720 \pm 4.7$ days is the orbital period, the next flare of ASASSN-22ci should occur near MJD 61075 (2026 February 04). Finally, we find that the existing sample of repeating TDE candidates is consistent with Hills capture of a star initially in a binary with a total mass between $\sim$$1 - 4$ M$_{\odot}$ and a separation of $\sim$$0.01 - 0.1$ AU., Comment: 30 pages, 15 figures, 4 tables. Comments welcome

Cohesive granular materials are found in many natural and industrial environments, but experimental platforms for exploring the innate mechanical properties of these materials are often limited by the difficulty of adjusting cohesion strength. Granular particles levitated in an acoustic cavity form a model system to address this. Such particles self-assemble into free-floating, quasi-two-dimensional raft structures which are held together by acoustic scattering forces; the strength of this attraction can be changed simply by modifying the sound field. We investigate the mechanical properties of acoustically bound granular rafts using substrate-free micro-scale shear tests. We first demonstrate deformation of rafts of spheres and the dependence of this deformation on acoustic pressure. We then apply these methods to rafts composed of anisotropic sand grains and smaller spheres, in which the smaller spheres have a thin layer of air separating them from other grain surfaces. These spheres act as soft, effectively frictionless particles that populate the interstices between the larger grains, which enables us to investigate the effect of lubricating the mixture in the presence of large-grain cohesion., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. A version of this article has been accepted for publication in Granular Matter. This will be updated with a link to the Version of Record once the article has been published

Accurately estimating the performance of quantum hardware is crucial for comparing different platforms and predicting the performance and feasibility of quantum algorithms and applications. In this paper, we tackle the problem of benchmarking a quantum register based on the NV center in diamond operating at room temperature. We define the connectivity map as well as single qubit performance. Thanks to an all-to-all connectivity the 2 and 3 qubit gates performance is promising and competitive among other platforms. We experimentally calibrate the error model for the register and use it to estimate the quantum volume, a metric used for quantifying the quantum computational capabilities of the register, of 8. Our results pave the way towards the unification of different architectures of quantum hardware and evaluation of the joint metrics., Comment: 10 pages, 5 figures

End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literature on end-to-end architectures and training strategies, the impact of the training dataset is often overlooked. In this work, we make a first attempt at end-to-end driving for Leaderboard 2.0. Instead of investigating architectures, we systematically analyze the training dataset, leading to new insights: (1) Expert style significantly affects downstream policy performance. (2) In complex data sets, the frames should not be weighted on the basis of simplistic criteria such as class frequencies. (3) Instead, estimating whether a frame changes the target labels compared to previous frames can reduce the size of the dataset without removing important information. By incorporating these findings, our model ranks first and second respectively on the map and sensors tracks of the 2024 CARLA Challenge, and sets a new state-of-the-art on the Bench2Drive test routes. Finally, we uncover a design flaw in the current evaluation metrics and propose a modification for future challenges. Our dataset, code, and pre-trained models are publicly available at https://github.com/autonomousvision/carla_garage., Comment: Technical report for the CVPR 2024 Workshop on Foundation Models for Autonomous Systems. Runner-up of the track 'CARLA Autonomous Driving Challenge' in the 2024 Autonomous Grand Challenge (https://opendrivelab.com/challenge2024/)

We propose schemes to design and control a time-dependent decoherence-free subspace (DFS) in a dissipative atom-cavity system. These schemes use atoms with three internal energy levels, which allows for the DFS to be multi-dimensional--a condition important for quantum sensing, simulation, and computation. We consider the use of tunable external driving lasers to transfer the system from a coherent spin state to a highly degenerate DFS. We find that the typical state in the DFS is highly entangled. Throughout evolution the state is kept in an instantaneous DFS, thereby allowing for pure states to be prepared. We develop adiabatic shortcuts to carry out this evolution with higher purity and fidelity than standard adiabatic and dissipative methods., Comment: 17 pages, 5 figures

Data grows in value when joined and combined; likewise the power of voice grows in ensemble. With 15 UK choirs, we explore opportunities for bottom-up data governance of a jointly created Choral AI Dataset. Guided by a survey of chorister attitudes towards generative AI models trained using their data, we explore opportunities to create empowering governance structures that go beyond opt in and opt out. We test the development of novel mechanisms such as a Trusted Data Intermediary (TDI) to enable governance of the dataset amongst the choirs and AI developers. We hope our findings can contribute to growing efforts to advance collective data governance practices and shape a more creative, empowering future for arts communities in the generative AI ecosystem., Comment: Presented at the NeurIPS 2024 Workshop on Creativity & Generative AI

Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existing GNN methods struggle with the complexity of the heterogeneous graphs induced by databases with numerous tables and relations. Traditional approaches either consider all possible relational meta-paths, thus failing to scale with the number of relations, or rely on domain experts to identify relevant meta-paths. A recent solution does manage to learn informative meta-paths without expert supervision, but assumes that a node's class depends solely on the existence of a meta-path occurrence. In this work, we present a self-explainable heterogeneous GNN for relational data, that supports models in which class membership depends on aggregate information obtained from multiple occurrences of a meta-path. Experimental results show that in the context of relational databases, our approach effectively identifies informative meta-paths that faithfully capture the model's reasoning mechanisms. It significantly outperforms existing methods in both synthetic and real-world scenario.

The Kaon bag parameter $ {\hat{B}}_K $ plays a critical role in constraining the parameters of the CKM matrix and in probing physics beyond the Standard Model. In this work, we improve the precision of $ \hat{B}_K $ to next-to-next-to-leading order (NNLO) and provide world averages for both $3$- and $4$-flavour theories. In the course of this, as our main technical development, we carry out the two-loop matching between the RI-(S)MOM and $\overline{\mathrm{MS}}$ schemes. Our world averages combine all available lattice data, including conversion between the 3- and 4-flavour theories as appropriate. We obtain the result $\hat B_{K}^{(f=3)} = 0.7627(60)$, which comprises the complete set of $3$- and $4$-flavour lattice results and can be used directly in phenomenological applications. The error is dominated by lattice uncertainties and missing higher-order corrections (residual scale dependence). Our averages include a PDG rescaling factor of 1.28 reflecting a mild tension among the lattice inputs after inclusion of NNLO corrections in the scheme conversion and matching across flavour thresholds. Our averages imply an updated value $|\epsilon_K|=2.171(65)_\text{pert.}(71)_\text{non-pert.}(153)_\text{param.} \times 10^{-3}$. We briefly discuss applications of our results to $D$-meson mixing., Comment: 33 pages, 11 figures, 9 tables

Vision-Language Models (VLMs) have great potential in medical tasks, like Visual Question Answering (VQA), where they could act as interactive assistants for both patients and clinicians. Yet their robustness to distribution shifts on unseen data remains a critical concern for safe deployment. Evaluating such robustness requires a controlled experimental setup that allows for systematic insights into the model's behavior. However, we demonstrate that current setups fail to offer sufficiently thorough evaluations, limiting their ability to accurately assess model robustness. To address this gap, our work introduces a novel framework, called SURE-VQA, centered around three key requirements to overcome the current pitfalls and systematically analyze the robustness of VLMs: 1) Since robustness on synthetic shifts does not necessarily translate to real-world shifts, robustness should be measured on real-world shifts that are inherent to the VQA data; 2) Traditional token-matching metrics often fail to capture underlying semantics, necessitating the use of large language models (LLMs) for more accurate semantic evaluation; 3) Model performance often lacks interpretability due to missing sanity baselines, thus meaningful baselines should be reported that allow assessing the multimodal impact on the VLM. To demonstrate the relevance of this framework, we conduct a study on the robustness of various fine-tuning methods across three medical datasets with four different types of distribution shifts. Our study reveals several important findings: 1) Sanity baselines that do not utilize image data can perform surprisingly well; 2) We confirm LoRA as the best-performing PEFT method; 3) No PEFT method consistently outperforms others in terms of robustness to shifts. Code is provided at https://github.com/IML-DKFZ/sure-vqa.

The inclusive cross sections of prompt $J/\psi$ and $\psi(3686)$ production are measured at center-of-mass energies from 3.808 to 4.951 GeV. The dataset used is 22 fb$^{-1}$ of $e^{+}e^{-}$ annihilation data collected with the BESIII detector operating at the BEPCII storage ring. The results obtained are in agreement with the previous BESIII measurements of exclusive $J/\psi$ and $\psi(3686)$ production. The average values obtained for the cross sections measured in the center-of-mass energy ranges from 4.527 to 4.951 GeV for $J/\psi$ and from 4.843 to 4.951 GeV for $\psi(3686)$, where the impact of known resonances is negligible, are $14.0\pm1.7\pm3.1$ pb and $15.3\pm3.0$ pb, respectively. For $J/\psi$, the first and the second uncertainties are statistical and systematic, respectively. For $\psi(3686)$, the uncertainty is total. These values are useful for testing charmonium production models., Comment: 20 pages, 6 figures Accepted for publication as a Regular Article in Physical Review D

The FASTSUM Collaboration has developed a comprehensive research programme in thermal lattice QCD using 2+1 flavour ensembles. We review our recent hadron spectrum analyses of open charm mesons and charm baryons at non-zero temperature. We also detail our determination of the interquark potential in the bottomonium system using NRQCD quarks. All of our work uses anisotropic lattices where the temporal lattice spacing is considerably finer than the spatial one allowing better resolution of temporal correlation functions., Comment: 6 pages. Contribution to 42nd International Conference on High Energy Physics (ICHEP2024), 18-24 July 2024, Prague, Czech Republic

Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment Anything Model (SAM) enables prompt-based segmentation and offers zero-shot generalization to unfamiliar objects. In our work, we leverage SAM's abstract object understanding for medical image segmentation to provide pseudo labels for semi-supervised learning, thereby mitigating the need for extensive annotated training data. Our approach refines initial segmentations that are derived from a limited amount of annotated data (comprising up to 43 cases) by extracting bounding boxes and seed points as prompts forwarded to SAM. Thus, it enables the generation of dense segmentation masks as pseudo labels for unlabelled data. The results show that training with our pseudo labels yields an improvement in Dice score from $74.29\,\%$ to $84.17\,\%$ and from $66.63\,\%$ to $74.87\,\%$ for the segmentation of bones of the paediatric wrist and teeth in dental radiographs, respectively. As a result, our method outperforms intensity-based post-processing methods, state-of-the-art supervised learning for segmentation (nnU-Net), and the semi-supervised mean teacher approach. Our Code is available on GitHub., Comment: Presented at MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI 2024; Code and data: https://github.com/multimodallearning/SamCarriesTheBurden

Current interactive segmentation approaches, inspired by the success of META's Segment Anything model, have achieved notable advancements, however, they come with substantial limitations that hinder their practical application in 3D radiological scenarios. These include unrealistic human interaction requirements, such as slice-by-slice operations for 2D models on 3D data, a lack of iterative interactive refinement, and insufficient evaluation experiments. These shortcomings prevent accurate assessment of model performance and lead to inconsistent outcomes across studies. The RadioActive benchmark overcomes these challenges by offering a comprehensive and reproducible evaluation of interactive segmentation methods in realistic, clinically relevant scenarios. It includes diverse datasets, target structures, and interactive segmentation methods, and provides a flexible, extendable codebase that allows seamless integration of new models and prompting strategies. We also introduce advanced prompting techniques to enable 2D models on 3D data by reducing the needed number of interaction steps, enabling a fair comparison. We show that surprisingly the performance of slice-wise prompted approaches can match native 3D methods, despite the domain gap. Our findings challenge the current literature and highlight that models not specifically trained on medical data can outperform the current specialized medical methods. By open-sourcing RadioActive, we invite the research community to integrate their models and prompting techniques, ensuring continuous and transparent evaluation of interactive segmentation models in 3D medical imaging., Comment: Undergoing Peer-Review

Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by reporting fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants' engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that reporting fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for reporting fatigue were associated with substantially improved estimation accuracy relative to models with no reporting fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach adressing reporting fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across various out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms on three test sets. In addition, we also evaluated pre-existing AI frameworks--which, differing from algorithms, are more flexible and can support different algorithms--including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain., Comment: Accepted to NeurIPS-2024

Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, its adoption in 3D medical image computing has been limited by three key pitfalls: Small pre-training dataset sizes, architectures inadequate for 3D medical image analysis, and insufficient evaluation practices. In this paper, we address these issues by i) leveraging a large-scale dataset of 39k 3D brain MRI volumes and ii) using a Residual Encoder U-Net architecture within the state-of-the-art nnU-Net framework. iii) A robust development framework, incorporating 5 development and 8 testing brain MRI segmentation datasets, allowed performance-driven design decisions to optimize the simple concept of Masked Auto Encoders (MAEs) for 3D CNNs. The resulting model not only surpasses previous SSL methods but also outperforms the strong nnU-Net baseline by an average of approximately 3 Dice points setting a new state-of-the-art. Our code and models are made available here., Comment: Arxiv Preprint. Revised and under review

Ethanolamine (NH$_2$CH$_2$CH$_2$OH, EA) has been identified in the gas phase of the ISM within molecular clouds. Although EA has not been directly observed in the molecular ice phase, a solid state formation mechanism has been proposed. However, the current literature lacks an estimation of the infrared band strengths of EA ices. We conducted an experimental investigation of solid EA ice at low temperatures to ascertain its infrared band strengths, phase transition temperature, and multilayer binding energy. The commonly used laser interferometry method was not applied. Infrared band strengths were determined using three distinct methods. The obtained lab spectrum of EA was compared with the publicly available MIRI MRS James Webb Space Telescope observations toward a low mass protostar. The phase transition temperature for EA ice falls within the range of 175 to 185 K. Among the discussed methods, the simple pressure gauge method provides a reasonable estimate of band strength. We derive a band strength value of about $1\times10^{-17}$ cm molecule$^{-1}$ for the NH$_2$ bending mode in the EA molecules. Additionally, temperature-programmed desorption analysis yielded a multilayer desorption energy of 0.61$\pm$0.01 eV. By comparing the laboratory data documented in this study with the JWST spectrum of the low mass protostar IRAS 2A, an upper-limit for the EA ice abundances was derived., Comment: 8 pages, 5 figures

Contemporary machine learning methods will try to approach the Bayes error, as it is the lowest possible error any model can achieve. This paper postulates that any decision is composed of not one but two Bayesian decisions and that decision-making is, therefore, a double-Bayesian process. The paper shows how this duality implies intrinsic uncertainty in decisions and how it incorporates explainability. The proposed approach understands that Bayesian learning is tantamount to finding a base for a logarithmic function measuring uncertainty, with solutions being fixed points. Furthermore, following this approach, the golden ratio describes possible solutions satisfying Bayes' theorem. The double-Bayesian framework suggests using a learning rate and momentum weight with values similar to those used in the literature to train neural networks with stochastic gradient descent., Comment: 14 pages, 5 figures, draft

Type II supernovae (SNeII) mark the endpoint in the lives of hydrogen-rich massive stars. Their large explosion energies and luminosities allow us to measure distances, metallicities, and star formation rates into the distant Universe. To fully exploit their use in answering different astrophysical problems, high-quality low-redshift data sets are required. Such samples are vital to understand the physics of SNeII, but also to serve as calibrators for distinct - and often lower-quality - samples. We present uBgVri optical and YJH near-infrared (NIR) photometry for 94 low-redshift SNeII observed by the Carnegie Supernova Project (CSP). A total of 9817 optical and 1872 NIR photometric data points are released, leading to a sample of high-quality SNII light curves during the first ~150 days post explosion on a well-calibrated photometric system. The sample is presented and its properties are analysed and discussed through comparison to literature events. We also focus on individual SNeII as examples of classically defined subtypes and outlier objects. Making a cut in the plateau decline rate of our sample (s2), a new subsample of fast-declining SNeII is presented. The sample has a median redshift of 0.015, with the nearest event at 0.001 and the most distant at 0.07. At optical wavelengths (V), the sample has a median cadence of 4.7 days over the course of a median coverage of 80 days. In the NIR (J), the median cadence is 7.2 days over the course of 59 days. The fast-declining subsample is more luminous than the full sample and shows shorter plateau phases. Of the non-standard SNeII highlighted, SN2009A particularly stands out with a steeply declining then rising light curve, together with what appears to be two superimposed P-Cygni profiles of H-alpha in its spectra. We outline the significant utility of these data, and finally provide an outlook of future SNII science., Comment: Accepted for publication in A&A. Photometric data will be uploaded to the CDS and the CSP website, and can also be requested from the first author

Inverse design (ID) is a computational method that systematically explores a design space to find optimal device geometries based on specific performance criteria. In silicon photonics, ID often leads to devices with design features that degrade significantly due to the fabrication process, limiting the applicability of these devices in scalable silicon photonic fabrication. We demonstrate a solution to this performance degradation through fabrication-aware inverse design (FAID), integrating lithography models for deep-ultraviolet (DUV) lithography and electron beam lithography (EBL) into the shape optimization approach of ID. A Y-branch and an SWG-to-strip converter were generated and fabricated with this new approach. Simulated and measured results verify that the FAID yields devices with up to 0.6 dB lower insertion loss per device. The modified workflow enables designers to use ID to generate devices that adjust for process bias predicted by lithography models., Comment: 4 pages

We measure the response of open-cell polyurethane foams filled with a dense suspension of fumed silica particles in polyethylene glycol at compression speeds spanning several orders of magnitude. The gradual compressive stress increase of the composite material indicates the existence of shear rate gradients in the interstitial suspension caused by wide distributions in pore sizes in the disordered foam network. The energy dissipated during compression scales with an effective internal shear rate, allowing for the collapse of three data sets for different pore-size foams. When scaled by this effective shear rate, the most pronounced energy increase coincides with the effective shear rate corresponding to the onset of shear thickening in our bulk suspension. Optical measurements of the radial deformation of the foam network and of the suspension flow under compression provide additional insight into the interaction between shear thickening fluid and foam. This optical data, combined with a simple model of a spring submerged in viscous flow, illustrates the dynamic interaction of viscous drag with foam elasticity as a function of compression rate, and identifies the foam pore size distribution as a critically important model parameter. Taken together, the stress measurements, dissipated energy, and relative motion of the fluid and the foam can be rationalized by knowing the pore size distribution and the average pore size of the foam.

The various limitations of Generative AI, such as hallucinations and model failures, have made it crucial to understand the role of different modalities in Visual Language Model (VLM) predictions. Our work investigates how the integration of information from image and text modalities influences the performance and behavior of VLMs in visual question answering (VQA) and reasoning tasks. We measure this effect through answer accuracy, reasoning quality, model uncertainty, and modality relevance. We study the interplay between text and image modalities in different configurations where visual content is essential for solving the VQA task. Our contributions include (1) the Semantic Interventions (SI)-VQA dataset, (2) a benchmark study of various VLM architectures under different modality configurations, and (3) the Interactive Semantic Interventions (ISI) tool. The SI-VQA dataset serves as the foundation for the benchmark, while the ISI tool provides an interface to test and apply semantic interventions in image and text inputs, enabling more fine-grained analysis. Our results show that complementary information between modalities improves answer and reasoning quality, while contradictory information harms model performance and confidence. Image text annotations have minimal impact on accuracy and uncertainty, slightly increasing image relevance. Attention analysis confirms the dominant role of image inputs over text in VQA tasks. In this study, we evaluate state-of-the-art VLMs that allow us to extract attention coefficients for each modality. A key finding is PaliGemma's harmful overconfidence, which poses a higher risk of silent failures compared to the LLaVA models. This work sets the foundation for rigorous analysis of modality integration, supported by datasets specifically designed for this purpose.

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice., Comment: Paper accepted at MICCAI 2024 conference

Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limited scope, examining only a handful of XAI methods and ignoring underlying design parameters for performance, such as the model architecture or the nature of input data. Moreover, they often rely on one or a few metrics and neglect thorough validation, increasing the risk of selection bias and ignoring discrepancies among metrics. These shortcomings leave practitioners confused about which method to choose for their problem. In response, we introduce LATEC, a large-scale benchmark that critically evaluates 17 prominent XAI methods using 20 distinct metrics. We systematically incorporate vital design parameters like varied architectures and diverse input modalities, resulting in 7,560 examined combinations. Through LATEC, we showcase the high risk of conflicting metrics leading to unreliable rankings and consequently propose a more robust evaluation scheme. Further, we comprehensively evaluate various XAI methods to assist practitioners in selecting appropriate methods aligning with their needs. Curiously, the emerging top-performing method, Expected Gradients, is not examined in any relevant related study. LATEC reinforces its role in future XAI research by publicly releasing all 326k saliency maps and 378k metric scores as a (meta-)evaluation dataset. The benchmark is hosted at: https://github.com/IML-DKFZ/latec., Comment: Accepted at NeurIPS 2024

Luminescent coupling is a characteristic of multi-junction solar cells which has often been neglected in models of their performance. The effect describes the absorption of light emitted from a higher band gap semiconductor by a lower band gap semiconductor. In this way, light which might have been lost can be utilized for current generation. We present a framework for modeling this effect in both planar layer stacks and devices with periodic nanostructuring. As a case study, we evaluate how luminescent coupling is affected by the inclusion of nanostructuring in a perovskite silicon tandem solar cell. We find that nanostructuring, while reducing the reflection loss for tandem solar cells also reduces the luminescent coupling, allowing more light to be emitted to the surroundings, when compared to planar devices. This highlights the need to include modeling of this effect into optimization schemes in order to find the trade-off between these two effects. The published version of this work is available at https://doi.org/10.1117/12.3023941., Comment: 7 pages, 3 figures

In this note, we generalise the concept of topo-isomorphic extensions and define finite topomorphic extensions as topological dynamical systems whose factor map to the maximal equicontinuous factor is measure-theoretically at most $m$-to-one for some $m\in\mathbb{N}$. We further define multivariate versions of mean equicontinuity, complementing the notion of multivariate mean sensitivity introduced by Li, Ye and Yu, and then show that any $m$-to-one topomorphic extension is mean $(m+1)$-equicontinuous. This falls in line with the well-known result, due to Downarowicz and Glasner, that strictly ergodic systems are isomorphic extensions if and only if they are mean equicontinuous. While in the multivariate case we can only conjecture that the converse direction also holds, the result provides an indication that multivariate equicontinuity properties are strongly related to finite extension structures. For minimal systems, an Auslander-Yorke type dichotomy between multivariate mean equicontinuity and sensitivity is shown as well.

We introduce ExcitationSolve, a fast globally-informed gradient-free optimizer for physically-motivated ans\"atze constructed of excitation operators, a common choice in variational quantum eigensolvers. ExcitationSolve is to be classified as an extension of quantum-aware and hyperparameter-free optimizers such as Rotosolve, from parameterized unitaries with generators $G$ of the form $G^2=I$, e.g., rotations, to the more general class of $G^3=G$ exhibited by the physically-inspired excitation operators such as in the unitary coupled cluster approach. ExcitationSolve is capable of finding the global optimum along each variational parameter using the same quantum resources that gradient-based optimizers require for a single update step. We provide optimization strategies for both fixed- and adaptive variational ans\"atze, as well as a multi-parameter generalization for the simultaneous selection and optimization of multiple excitation operators. Finally, we demonstrate the utility of ExcitationSolve by conducting electronic ground state energy calculations of molecular systems and thereby outperforming state-of-the-art optimizers commonly employed in variational quantum algorithms. Across all tested molecules in their equilibrium geometry, ExcitationSolve remarkably reaches chemical accuracy in a single sweep over the parameters of a fixed ansatz. This sweep requires only the quantum circuit executions of one gradient descent step. In addition, ExcitationSolve achieves adaptive ans\"atze consisting of fewer operators than in the gradient-based adaptive approach, hence decreasing the circuit execution time., Comment: 45 pages, 10 figures

Quantum optimization has emerged as a promising frontier of quantum computing, providing novel numerical approaches to mathematical optimization problems. The main goal of this paper is to facilitate interdisciplinary research between the Operations Research (OR) and Quantum Computing communities by providing an OR scientist's perspective on selected quantum-powered methods for discrete optimization. To this end, we consider three quantum-powered optimization approaches that make use of different types of quantum hardware available on the market. To illustrate these approaches, we solve three classical optimization problems: the Traveling Salesperson Problem, Weighted Maximum Cut, and Maximum Independent Set. With a general OR audience in mind, we attempt to provide an intuition behind each approach along with key references, describe the corresponding high-level workflow, and highlight crucial practical considerations. In particular, we emphasize the importance of problem formulations and device-specific configurations, and their impact on the amount of resources required for computation (where we focus on the number of qubits). These points are illustrated with a series of experiments on three types of quantum computers: a neutral atom machine from QuEra, a quantum annealer from D-Wave, and a gate-based device from IBM., Comment: 43 pages, 18 figures, 7 tables. Technical supplement: https://alex-bochkarev.github.io/qopt-overview . Source code, problem instances, and other raw data: https://github.com/alex-bochkarev/qopt-overview

In this work we determine the metric dimension of $ K_a \times K_b \times K_c$ for all $a,b,c\in \mathbb N$ with $ a \le b \le c $ as follows. For $3a c$, it is $\left \lfloor \frac{2}{3}(b+c-1) \right \rfloor$, and for $3a=b+c$, it is $\left \lfloor \frac{a+b+c}{2} \right \rfloor -1 $. The only open case is $3a>b+c$, where two values are possible, namely $\left \lfloor \frac{a+b+c}{2} \right \rfloor -1 $ and $\left \lfloor \frac{a+b+c}{2} \right \rfloor $. This result extends previous results of C\'acere et al., who computed the metric dimension of $ K_a \times K_b$, and of Drewes and J\"ager, who computed the metric dimension of $ K_a \times K_a \times K_a$. We prove our result by introducing and analyzing a new variant of Static Black-Peg Mastermind, in which each peg has its own permitted set of colors. For all cases, we present strategies which we prove to be both feasible and optimal. Our main result follows, as the number of questions of these strategies is equal to the metric dimension of $K_a \times K_b \times K_c$., Comment: 41 pages

Software-defined networking (SDN) is a centralized, dynamic, and programmable network management technology that enables flexible traffic control and scalability. SDN facilitates network administration through a centralized view of the underlying physical topology; tampering with this topology view can result in catastrophic damage to network management and security. To underscore this issue, we introduce Marionette, a new topology poisoning technique that manipulates OpenFlow link discovery packet forwarding to alter topology information. Our approach exposes an overlooked yet widespread attack vector, distinguishing itself from traditional link fabrication attacks that tamper, spoof, or relay discovery packets at the data plane. Unlike localized attacks observed in existing methods, our technique introduces a globalized topology poisoning attack that leverages control privileges. Marionette implements a reinforcement learning algorithm to compute a poisoned topology target, and injects flow entries to achieve a long-lived stealthy attack. Our evaluation shows that Marionette successfully attacks five open-source controllers and nine OpenFlow-based discovery protocols. Marionette overcomes the state-of-the-art topology poisoning defenses, showcasing a new class of topology poisoning that initiates on the control plane. This security vulnerability was ethically disclosed to OpenDaylight, and CVE-2024-37018 has been assigned., Comment: 17 pages, 12 figures, CCS 2024