For a Bayes classifier whose input space is a graph, we study the structure of the boundary, which comprises those points for which at least one neighbor is classified differently. The scientific setting is assignment of DNA reads produced by next generations sequencers to candidate source genomes. We show that the boundary is both large and complicated in structure. A new measure of uncertainty, Neighbor Similarity, which compares the classifier result for an input point to the distribution of results for its neighbors, not only tracks two inherent uncertainty measures for the Bayes classifier, but also can be implemented for classifiers without inherent measures of uncertainty.

Aggregating subjective preferences in social choice traditionally assumes a trusted central authority. In contrast, this paper formalises Decentralised Preference Discovery (DPD): the reliable identification of a social choice parameter (e.g. the canonical outcome of an aggregation rule applied to the global preference profile) under conditions of partial information, asynchronous interaction, censorship resistance, and no central coordinator. To address DPD, we propose Snowveil, a gossip-based framework where agents repeatedly sample random peer rankings and update local beliefs to converge on the canonical outcome. Using a potential function, submartingale theory, and concentration bounds, we prove the system reaches this stable state with tunable high probability, in finite expected time. This single-winner process can then be iterated to construct a set of winning candidates for multi-winner scenarios. Snowveil is agnostic to specific aggregation rules, requiring only that the rule satisfies axioms such as Positive Responsiveness, thus offering a formal basis for a wider class of DPD protocols. Demonstrating Snowveil's modularity, we introduce the Constrained Hybrid Borda (CHB), an aggregation rule designed to balance broad consensus with plurality support. We provide an axiomatic analysis of CHB and present empirical results via extensive simulation, validating Snowveil's O(n) scalability. Overall, this work provides a foundation for how a stable consensus emerges from subjective, expressive, and diverse preference profiles in large-scale decentralised systems.

Several problems in machine learning are naturally expressed as the design and analysis of time-evolving probability distributions. This includes sampling via diffusion methods, optimizing the weights of neural networks, and analyzing the evolution of token distributions across layers of large language models. While the targeted applications differ (samples, weights, tokens), their mathematical descriptions share a common structure. A key idea is to switch from the Eulerian representation of densities to their Lagrangian counterpart through vector fields that advect particles. This dual view introduces challenges, notably the non-uniqueness of Lagrangian vector fields, but also opportunities to craft density evolutions and flows with favorable properties in terms of regularity, stability, and computational tractability. This survey presents an overview of these methods, with emphasis on two complementary approaches: diffusion methods, which rely on stochastic interpolation processes and underpin modern generative AI, and optimal transport, which defines interpolation by minimizing displacement cost. We illustrate how both approaches appear in applications ranging from sampling, neural network optimization, to modeling the dynamics of transformers for large language models.

Large language models (LLMs) have recently shown strong potential in vulnerability detection (VD). However, accurately detecting vulnerabilities in real-world repositories requires reasoning over complex contextual interactions. Existing LLM-based VD approaches remain limited because current datasets lack complete contextual information and high-quality reasoning supervision, while existing optimization methods primarily rely on coarse outcome-centric supervision signals that fail to model the vulnerability reasoning process. To address these limitations, we first construct ContextVul, a new dataset that augments high-quality function-level vulnerability benchmarks with repository-level contextual information and curated vulnerability reasoning traces. Building upon ContextVul, we introduce a two-stage optimization framework consisting of lightweight cold-start supervised fine-tuning followed by vulnerability-adaptive on-policy optimization (VULPO). VULPO incorporates multidimensional rewards that jointly evaluate vulnerability identification, vulnerability-relevant localization, and causal reasoning quality, along with difficulty-adaptive reward scaling to mitigate reward hacking and improve RL effectiveness. Extensive experiments demonstrate the superiority of VULPO for context-aware VD. Our VULPO-4B, the first specialized vulnerability reasoning LLM, substantially outperforms existing VD baselines, improving Pairwise Pass@1 by 203% relative to Qwen3-4B and achieving competitive performance against a 150% larger-scale LLM, DeepSeek-V3.1.

We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others. Intuitively, the hierarchical structure can be leveraged to reduce the size of prediction regions for the same coverage level. We implement this intuition by including a projection step (also called a reconciliation step) in the split conformal prediction [SCP] procedure, and prove that the resulting prediction regions are indeed globally smaller. We do so both under the classic objective of joint coverage and under a new and challenging task: component-wise coverage, for which efficiency results are more difficult to obtain. The associated strategies and their analyses are based both on the literature of SCP and of forecast reconciliation, which we connect. We also illustrate the theoretical findings, for different scales of hierarchies on simulated data.

Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy groups and clusters, yet no multiclass classifier simultaneously separates them from straight Fanaroff--Riley types (sFRI, sFRII) using visually inspected labels and unlabelled data. We release FIRST-2060, a four-class labelled dataset of 2060 RAGNs (sFRI, sFRII, WAT, NAT) constructed from three publicly available catalogues through multi-tier visual inspection, together with the semi-supervised RGC 1.0 model that leverages 20,000 unlabelled sources. We benchmark RGC against five supervised baselines. FIRST-2060 is provided in two preprocessing variants: $\mathbf{R}_{L1}$, which retains spurious sources, and $\mathbf{R}_{L2}$, from which they are removed. The RGC model integrates the self-supervised framework BYOL (Bootstrap Your Own Latent) with an $E(2)$-equivariant steerable CNN (E2CNN) encoder, pre-trained on the unlabelled data and fine-tuned on the labelled sets. All six models are evaluated with 5-fold cross-validation, Grad-CAM attention analysis, and controlled class-imbalance experiments. ConvNeXT ($M_1$) and RGC ($M_2$) form a top tier at macro-$F_1$ $0.80\pm0.02$ and $0.79\pm0.02$ respectively, a difference within one standard deviation. $M_2$ is the only model whose Grad-CAM contours consistently trace the morphological structure of RAGNs -- lobes, jets, and bends -- rather than defaulting to compact blobs or diffuse patterns. The four-class scheme introduced here enables WAT/NAT-resolved catalogues that can serve as environment probes and progenitor classifications for diffuse cluster radio emission. The complementary strengths of $M_1$ and $M_2$ -- in cross-type and within-type discrimination respectively -- suggest that an ensemble approach may offer a practical framework for survey-scale morphological catalogues.

Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solving linear systems involving the Gramian matrix. While matrix-free NGD methods based on the conjugate gradient (CG) method avoid explicit matrix inversion, the ill-conditioning of the Gramian significantly slows the convergence of CG. In this work, we extend matrix-free NGD to broader classes of problems than previously considered and propose the use of Randomized Numerical Linear Algebra (RandNLA) techniques for efficient preconditioning of the inner CG solver. The resulting algorithm demonstrates substantial performance improvements over existing NGD-based methods and other state-of-the-art optimizers on a range of PDE problems discretized using neural networks.

Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels, yet training reinforcement learning (RL) agents to adhere to them is challenging. The behavior of RL agents is shaped by the training scenarios they encounter, but creating scenarios that capture the complexity of maritime navigation is non-trivial, and real-world data alone is insufficient. To address this, we propose a falsification-driven RL approach that generates adversarial training scenarios in which the vessel under test violates maritime traffic rules, which are expressed as signal temporal logic specifications. Our experiments on open-sea navigation with two vessels demonstrate that the proposed approach provides more relevant training scenarios and achieves more consistent rule compliance.

Causal representation learning (CRL) has garnered increasing interest from the causal inference and artificial intelligence communities due to its potential to disentangle complex data-generating mechanism into causally interpretable latent features by leveraging the heterogeneity of modern datasets. In this paper, we further contribute to the CRL literature, by focusing on the stylized linear structural causal model over latent features and assuming a linear mixing function that maps latent features to the observed data or measurements. Existing linear CRL methods often rely on stringent assumptions, such as access to single-node interventional data or restrictive distributional constraints on latent features and/or exogenous measurement noise. However, these prerequisites can be easy to violate in practice. In this work, we propose a novel linear CRL algorithm that, unlike existing methods, operates under weaker assumptions on environment heterogeneity and data-generating distributions while still recovering latent causal features up to an equivalence class. We further validate our new algorithm via synthetic experiments and an interpretability analysis of large language models, demonstrating both its superiority over competing methods in finite samples and its potential in integrating causality into understanding artificial intelligence. The source code is available at https://github.com/utulie/code_for_linear_crl_paper_creator.

Point of interest (POI) recommendation can play a pivotal role in enriching tourists' experiences by suggesting context-dependent and preference-matching locations and activities, such as restaurants, landmarks, itineraries, and cultural attractions. Unlike some more common recommendation domains (e.g., music and video), POI recommendation is inherently high-stakes: users invest significant time, money, and effort to search, choose, and consume these suggested POIs. Despite the numerous research works in the area, several fundamental issues remain unresolved, hindering the real-world applicability of the proposed approaches. In this paper, we discuss the current status of the POI recommendation problem and the main challenges we have identified. The first contribution of this paper is a critical assessment of the current state of POI recommendation research and the identification of key shortcomings across three main dimensions: datasets, algorithms, and evaluation methodologies. We highlight persistent issues such as the lack of standardized benchmark datasets, flawed assumptions in the problem definition and model design, and inadequate treatment of biases in the user behavior and system performance. The second contribution is a structured research agenda that, starting from the identified issues, introduces important directions for future work related to multistakeholder design, context awareness, data collection, trustworthiness, novel interactions, and real-world evaluation.

Automatic Speech Recognition (ASR) systems' growing use warrants robust auditing approaches to ensure equitable transcription quality, especially for people with speech disorders like aphasia who disproportionately depend on ASR. While academic and industry audits have revealed performance disparities across user populations, standard auditing practices often overlook nuances that risk masking harm to marginalized groups. We identify three common pitfalls in standard ASR audits: (1) adhering to one method of text standardization, which can mask variance in ASR performance and ignore the standardization preferences of marginalized communities; (2) displaying high-level demographic findings without considering performance disparities by nuanced intersectional subgroups, or conditioning on relevant acoustic properties; and (3) reporting only one gold-standard metric (Word Error Rate), which inadequately quantifies common generative AI errors like hallucinations. We propose a holistic auditing framework addressing these pitfalls, and in a case study of six popular ASR systems, find consistently worse ASR performance for speakers with aphasia relative to a control group. We call on practitioners to implement these robust, community-driven ASR auditing practices better suited for the rapidly changing ASR landscape.

In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework using a dynamic weighting mechanism to enhance exploration. We analyze the algorithm under two operating modes. First, we show that the Exact Mode matches the best-known theoretical guarantees for shuffling variance-reduced methods in both strongly convex and non-convex settings. Second, to address large-scale regimes, we introduce an Inexact Mode that utilizes mini-batch estimators. A key contribution of our work is proving that this Inexact Mode achieves a total complexity independent of the dataset size, making it significantly more scalable than existing shuffling methods when the sample size is large.

To address the lack of access to ultrasound in remote communities, previous work introduced human teleoperation, a mixed reality and haptics-based tele-ultrasound system. In this approach, a novice takes the role of a cognitive robot controlled remotely by an expert through mixed reality. In this manuscript we summarize new developments to this system and describe a feasibility study assessing its use for long-distance remote abdominal ultrasound examinations. To provide simple but effective haptic feedback, we used an ellipsoid model of the patient with its parameters calibrated using our system's position and force sensors. We tested the system in Skidegate, Haida Gwaii, Canada, with the experts positioned 754 km away in Vancouver, Canada. We performed 11 total scans with 10 novices and 2 sonographers. The sonographers were tasked with acquiring 5 target images in the epigastric region. The image acquisition quality was assessed by 2 radiologists. We collected alignment data and the novices completed task load and usability questionnaires. Both the novices and sonographers provided written and verbal feedback to inform future design iterations. 92% of the acquired images had sufficient quality for interpretation by both radiologists. The mean task load reported by the novices was below reference values reported in literature and the usability was unanimously positive. No correlation was found between image quality and the follower's alignment error with the virtual transducer. Overall, we show that human teleoperation enables sonographers to perform remote abdominal ultrasound imaging with high performance, even across large distances and with novice followers. Future work will compare human teleoperation to conventional, robotic and tele-mentored ultrasound.

Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning, planning, and tool use. Despite impressive leaderboard gains on benchmarks such as SWE-bench, little is understood about how these agents take actions, where they fail, and how their behavior compares to that of human developers. This paper presents the first systematic analysis of five state-of-the-art APR agents across 500 real-world repair tasks, tracing their full decision-making pipelines -- from issue description to patch validation. Our study reveals that while agents excel at simple fixes, they struggle with logic-intensive bugs, often producing verbose or overfitted patches that merely satisfy existing tests. We find that test generation and regression test selection remain major bottlenecks, with agents frequently failing to reproduce issues or run relevant regression tests. Moreover, most agents operate with primitive tooling (e.g., bash scripts) and lack access to debuggers or program analyzers, which constrains their reasoning and patch quality. These findings highlight key limitations in current APR systems and motivate a shift-left approach -- emphasizing early, high-quality test generation and validation -- to reduce spurious fixes and improve semantic correctness. We further outline concrete directions for next-generation APR design: (1) richer and more integrated tool ecosystems, (2) diversified agentic architectures that combine complementary strengths, and (3) benchmarks that prioritize semantic repair quality and test generation fidelity over surface-level success metrics.

Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class.

Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recently, there has been a notable increase in interest in utilizing machine learning and deep learning techniques for unsupervised learning of protein representations. However, these approaches often focus solely on the amino acid sequence of proteins and lack factual knowledge about proteins and their interactions, thus limiting their performance. In this study, we present GOProteinGNN, a novel architecture that enhances protein language models by integrating protein knowledge graph information during the creation of amino acid level representations. Our approach allows for the integration of information at both the individual amino acid level and the entire protein level, enabling a comprehensive and effective learning process through graph-based learning. By doing so, we can capture complex relationships and dependencies between proteins and their functional annotations, resulting in more robust and contextually enriched protein representations. Unlike previous methods, GOProteinGNN uniquely learns the entire protein knowledge graph during training, which allows it to capture broader relational nuances and dependencies beyond mere triplets as done in previous work. We perform a comprehensive evaluation on several downstream tasks demonstrating that GOProteinGNN consistently outperforms previous methods, showcasing its effectiveness and establishing it as a state-of-the-art solution for protein representation learning.

Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization method followed by multitask basis pursuit. Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries. We provide theoretical and experimental results justifying this method. For problems involving coordinate selection and diversification, it offers a synergistic alternative to greedy and brute force search.

Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Auto-Encoder (IWAE), Variational Rényi (VR) and VR-IWAE bounds. Yet, it remains unclear how the joint choice of bound and gradient estimator impacts the behavior of the resulting variational inference (VI) algorithms. This paper provides a unified theoretical comparison of reparameterized (REP) and doubly-reparameterized (DREP) gradient estimators tied to the IWAE, VR and VR-IWAE bounds. Through asymptotic analyses of the Signal-to-Noise Ratio as the number of Monter Carlo samples $N$ goes to infinity, we identify a bias-variance tradeoff in these gradient estimators and we formally justify the superiority of DREP over REP in importance-weighted VI. An additional asymptotic analysis for challenging regimes, where both $N$ and the Kullback-Leibler divergence between the variational and posterior densities go to infinity, indicates that importance-weighted VI gradient estimators point in a well-founded direction even when the variational approximation deteriorates. Together, these complementary results characterize the optimization trajectory in importance-weighted VI from poor initialization to final convergence. Importantly, our proof techniques establish general theoretical tools for the study of sample means ratios whose scope extend beyond VI and constitute an independent contribution to the field of Monte Carlo methods.

A radiology report comprises presentation-style vocabulary, which ensures clarity and organization, and factual vocabulary, which provides accurate and objective descriptions based on observable findings. While manually writing these reports is time-consuming and labor-intensive, automatic report generation offers a promising alternative. A critical step in this process is to align radiographs with their corresponding reports. However, existing methods often rely on complete reports for alignment, overlooking the impact of presentation-style vocabulary. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method. In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions. In Stage 2, we present evidence-driven report generation that enhances diagnostic accuracy by integrating insights from similar historical cases structured as factual serialization. Experiments on MIMIC-CXR and IU X-ray datasets across specific and general scenarios demonstrate that FSE outperforms state-of-the-art approaches in both natural language generation and clinical efficacy metrics. Ablation studies further emphasize the positive effects of factual serialization in Stage 1 and Stage 2. The code is available at https://github.com/mk-runner/FSE.

Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space. Most existing works rely on a single Gaussian process (GP) based surrogate model, where the kernel function form is typically preselected using domain knowledge. To bypass such a design process, this paper leverages an ensemble (E) of GPs to adaptively select the surrogate model fit on-the-fly, yielding a GP mixture posterior with enhanced expressiveness for the sought function. Acquisition of the next evaluation input using this EGP-based function posterior is then enabled by Thompson sampling (TS) that requires no additional design parameters. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. The novel EGP-TS readily accommodates parallel operation. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret for both sequential and parallel settings. Tests on synthetic functions and real-world applications showcase the merits of the proposed method.

The antiferromagnet on the ruby lattice is expected to host a range of exotic emergent phenomena, yet its material realization has remained elusive. Here we show that the layered transition metal fluorides CsBaFe$_3$F$_{12}$ and CsBaCr$_3$F$_{12}$ with Fe$^{3+}$ and Cr$^{3+}$ ions realize only slightly distorted ruby lattice geometries with spin moments $S=5/2$ and $S=3/2$, respectively. Their microscopic Hamiltonians, calculated with DFT energy mapping, are dominated by short-ranged antiferromagnetic interactions within the ruby layers. Classical Monte Carlo simulations reveal strong frustration in both compounds, with local Néel correlations on the hexagonal plaquettes and distinct long-range ordering tendencies governed by weaker triangular links. For CsBaFe$_3$F$_{12}$, the calculated thermodynamic behaviour is consistent with the experimentally reported magnetic ordering scale. For CsBaCr$_3$F$_{12}$, classical Monte Carlo and Luttinger-Tisza analysis reveal competing low-energy ordering wave vectors, strong finite-size sensitivity, and a tendency toward incommensurate order. Overall, our results establish these fluorides as experimentally accessible ruby-lattice antiferromagnets and provide quantitative predictions for future neutron-scattering studies.

We present a control-variate method for reducing the variance of power spectrum covariance matrix estimates from simulations of large-scale structure. The key idea is to pair each mock simulation with a cheap Zeldovich-approximation realization sharing the same initial conditions, and to use the known statistical properties of the Zeldovich field to remove correlated sample variance from the covariance estimator. Under a Gaussian disconnected approximation, we derive fully analytic expressions for both the optimal control-variate coefficient, $β(k,\ell;k',\ell')$, and the corresponding correlation, $ρ(k,\ell;k',\ell')$, in terms of the auto- and cross-power spectra of the target and control fields. In the monopole case, the correlation takes the particularly simple form $ρ(k,k') = r^2(k),r^2(k')$, where $r(k)$ is the standard cross-correlation coefficient between the target and Zeldovich fields, implying that covariance estimation remains highly efficient whenever the two fields are strongly correlated. For masked redshift-space lognormal mocks, resembling Luminous Red Galaxies from the Dark Energy Spectroscopic Instrument (DESI), we find that the control-variate estimator reduces the variance of the covariance matrix by approximately an order of magnitude on large scales, $k \lesssim 0.05\,h\,{\rm Mpc}^{-1}$, precisely where accurate covariance estimation is most challenging. The gains are smaller for higher $k$ but typically accelerate convergence by a factor of 2-3, substantially lowering the computational cost of covariance estimation for current and upcoming large-scale structure surveys. Due to its simplicity, this method is readily implementable in current imaging and spectroscopic surveys (e.g., DESI, Euclid, LSST, PFS, SPHEREx).

Two-dimensional van der Waals materials exhibit a variety of correlated electron phases, and optical driving offers a promising route toward manipulating them. For example, cavity-enhanced, continuous-wave (CW) Raman excitation has been suggested as a way to coherently and superradiantly populate phonons or charge density waves via material excitons. A steady-state phonon population may be sustained with sufficiently strong electron-phonon coupling to drive novel collective response. We describe an apparatus built to meet the requirements of such an experimental program: Namely, an ultrahigh-vacuum system housing a length-tunable confocal Fabry-Pérot cavity with an intracavity sample, both cryogenically cooled and stabilized against vibrations. A four-axis nanopositioner aligns the sample and supports electrical leads for sample carrier density modulation and transport measurements. Transmission through the multimode cavity enables in situ sample imaging for alignment; the sample is a transition metal dichalcogenide in this work. Operating near the confocal geometry concentrates the optical field into a localized supermode that substantially enhances light-matter coupling. This enhancement is preserved despite the millimeter-scale cavity length, which provides room for sample alignment and exchange.

The azimuthal hadronic flow observed in ultra-relativistic ion-ion collisions provides a sensitive probe of many-body ground-state correlations in the colliding nuclei. In particular, collective correlations associated with nuclear "intrinsic deformation" are expected to leave pronounced fingerprints on specific final-state observables. However, such effects are commonly interpreted within a classical rigid-rotor picture, despite the intrinsically quantum nature of nuclei. In this Letter, the validity of this interpretation is assessed systematically across the nuclear chart by comparing the quantum quadrupole rotor with its classical rigid-rotor limit. Quantum contributions associated with the fermionic nature of the nucleons are shown to be largely independent of shell effects, and hence of the intrinsic deformation. While they account for nearly all of the quantum rotor effective quadrupole deformation in light and/or spherical nuclei, they drop below 10% in intrinsically well deformed heavy nuclei. The present letter demonstrates that a quantitative interpretation of nuclear-structure effects in final-state observables requires going beyond the classical rigid-rotor paradigm. Beyond the quantum contributions quantified presently, correlations associated with collective vibrations and with the non-collective nucleonic motion must be further included and characterized.

Microwave devices capable of near-quantum-limited signal processing are essential components in the toolbox of solid-state quantum technologies. The manipulation and readout of single-photon microwave signals through amplifiers, mixers, isolators, etc. must fulfill strict requirements in terms of signal integrity to ensure reliable operation. These active microwave quantum devices operate in complex cryo-electronic setups. This poses challenges to their characterization, since all relevant figures of merit must be expressed at the reference planes of their ports. Even though cryogenic S-parameter calibration is non-trivial, metrological approaches are converging toward rigorous methods. Furthermore, preserving signal integrity must be quantified via absolute noise levels at the ports of the Device Under Test (DUT), requiring an absolute power reference. In this work, we present an in situ noise metrology protocol based on substituting a controllable noise source for the DUT. We motivate this choice by showing that placing the noise source at the DUT input impacts the separability of the calibration from the DUT characteristics. Our proposed architecture combines Planck spectroscopy using a Variable Temperature Stage with Short-Open-Load-Reciprocal scattering-parameter calibration, so that noise and scattering quantities are referred to the same cryogenic reference planes. In this configuration, the readout-chain calibration is separated from the internal dynamics of the DUT. As a demanding use case, we apply the protocol to a Josephson Traveling Wave Parametric Amplifier and extract its gain and input-referred added noise under pump conditions activating multimode nonlinear behavior. This illustrates how our device-agnostic protocol supports portable noise characterization of nonlinear cryogenic microwave devices.

Gravitino masses above the electroweak scale provide the simplest solution to the gravitino problem, but such large mass scales lie far beyond the reach of collider experiments. We show that the stochastic gravitational wave background offers a direct probe of this otherwise inaccessible regime. Despite decaying before Big-Bang Nucleosynthesis (BBN), these gravitinos naturally generate a period of early matter domination in the early universe. This non-standard epoch leaves a characteristic imprint on any primordial gravitational wave background, characterised by two frequencies corresponding to the onset and end of this phase. We demonstrate that these features can be used to directly infer both the gravitino mass and its initial abundance in a direct mapping. Future gravitational wave observatories span a vast frequency range, enabling sensitivity to gravitino masses from the BBN bound of $\mathcal{O}(100)\,\text{TeV}$ all the way up to $\mathcal{O}(10^{10})\,\text{TeV}$, with recent signal by NANOGrav already probing masses in the range $500$-$10^4$ TeV. Gravitational wave observables therefore probe an enormous region of parameter space, far beyond the reach of collider experiments. We are entering an era in which supergravity can be probed through gravitational wave backgrounds alongside collider experiments.

In this work, we present the logistic branching Brownian motion with selection (Log-BBM), a modification of the N-BBM defined by Groisman et. al (2020), in which birth and competition events are decoupled to allow for a variable population size that follows the branching process with logistic growth defined by Lambert (2005). We study the representation of the Log-BBM as a microscopic model for a FKPP-type equation. In the large population limit, the renormalised empirical measure of the Log-BBM converges weakly to a probability measure whose density solves a nonlocal version of the FKPP equation, while its cumulative distribution function solves the classical FKPP equation. We also show that this model exhibits behaviour that is characteristic of the Brunet-Derrida family of systems, in particular the so-called weak selection principle. Indeed, we show that, in the Log-BBM, the particles select the minimal propagation speed, in contrast to the FKPP equation, where the selected front speed depends on the initial condition.

Ferroelectric materials can be used for the development of multiple device concepts combining non-volatility, small dimensions, low-power actuation, and electrical tunability. Such development demands efficient and precise design of simulation tools describing the polarization texture. However, most existing ferroelectric solvers are CPU-based and rely on simplified electrostatic treatments and reduced-dimensional representations of the polarization field. These approximations limit their ability to capture finite-size and boundary effects and restrict the range of domain structures and domain walls that can be realistically simulated. Here, we present a fully GPU (graphics processing units)-accelerated and scalable numerical solver, named PETASPIN_microelectrics, for computing the full polarization vector field of ferroelectric systems using the Ginzburg-Landau formalism. Our solver incorporates an optimized and validated calculation of the full electrostatic field and enables the parallel execution of multiple simulations. We systematically validated the solver with several benchmark problems, including phase transitions in BaTiO3 and ferroelectric domain wall profiles. Our simulations reproduce temperature-driven hysteretic phase transitions in BaTiO3. We also reproduce hysteresis loops and demonstrate stabilization of a three-dimensional hybrid skyrmion in a PbTiO3/SrTiO3 bilayer system. Our results show quantitative agreement with predictions from an analytical theory and prior experimental studies. The proposed solver provides an efficient, accurate platform for large-scale simulations of ferroelectric materials including stabilization of topological textures supporting predictive modeling for next-generation of ferroelectric device design.

Experimental synthesis of theoretically predicted materials with controlled elemental coordination environments can lead to realization of useful properties, such as facile ion transport or ferroelectric switching. Among such materials are new ternary nitrides in the Mg-W-N composition space, where several new stable and metastable compounds have been predicted and synthesized recently in bulk and film forms. Here, we report for the first time on the bulk synthesis of Mg$_3$WN$_4$ in a wurtzite-derived crystal structure via a solid state metathesis reaction. $In$ $situ$ synchrotron powder X-ray diffraction shows how the ion exchange proceeds from Li$_6$WN$_4$ + 3 MgCl$_2$ precursors to Mg$_3$WN$_4$ + 6 LiCl products, with the reaction starting slowly near 380 $^\circ$C and completing by 600 $^\circ$C, including the presence of a competing disordered rocksalt-derived phase (Mg,W)N above 440 $^\circ$C. The follow up $ex$ $situ$ powder synthesis at 400 $^\circ$C for 0.5 hour with 10% excess MgCl$_2$ reveals the cation-ordered nature of the wurtzite-derived Mg$_3$WN$_4$ structure with polar symmetry confirmed by second harmonic generation measurements. Optical absorption spectra, chemical composition analysis, and electron microscopy imaging suggests that bulk wurtzite Mg$_3$WN$_4$ is prone to defect formation. Overall, this study shows that selective $ex$ $situ$ synthesis of the phase pure ternary nitrides, informed by \textit{in situ} measurements, is possible by carefully controlling the thermal budget of the reaction, and paves a way towards property characterization of wurtzite Mg$_3$WN$_4$.

Liquid water is fundamentally important, and its accurate computer simulation has been the driving force for myriad methodological developments. Ab initio molecular dynamics with forces obtained from density functional theory (DFT) is now a standard tool widely used by researchers. However, we reveal that previous studies of liquid water using the same widely-used density functional (revPBE-D3) exhibit significant discrepancies with one another, varying by over 20% in the diffusion coefficient and 10% in the density, raising fundamental questions about reproducibility. By combining modern long-range machine-learning interatomic potentials that enable robust statistical sampling with carefully converged DFT training data, we resolve these discrepancies, achieving consensus across six diverse community codes. Our predictions differ markedly from previous literature: we show that most previous results overestimate the density and underestimate the diffusion coefficient of revPBE-D3 water due to basis set incompleteness and pseudopotential inconsistencies, coupled with limitations in statistical sampling (in some cases). These benchmark values provide a reliable reference for validating current and future implementations of DFT-based ab initio molecular dynamics. Reaching agreement establishes confidence and credibility and serves as a prerequisite for the systematic assessment of new density functionals and numerical approximations.