Web measurements are a well-established methodology for assessing the security and privacy landscape of the Internet. However, existing top lists of popular websites are unlabeled and lack semantic information about the nature of the included websites, making targeted web measurements challenging, as researchers often rely on ad-hoc techniques to bias datasets toward specific website classes of interest. In this paper, we investigate the use of Large Language Models (LLMs) to enable targeted web measurement studies. Building on prior literature, we identify key website classification tasks relevant to web measurements and highlight limitations in state-of-the-art classification approaches. We construct carefully curated datasets to evaluate different LLMs on these tasks. Our results show that LLMs can achieve strong performance across multiple classification scenarios, but the choice of model and configuration plays a significant role. Motivated by the observed trade-off between classification accuracy and computational efficiency, we propose a practical two-step methodology for scalable targeted web measurements starting from the Tranco list. Finally, we conduct LLM-assisted web measurement studies inspired by prior work using our methodology and assess the validity of the resulting research inferences, showing that LLMs can effectively enable targeted measurements of security and privacy trends on the Web.

Near threshold, cross sections for the production of heavy particles are sensitive to large logarithmic terms, which must be resummed to all orders in perturbation theory. Current state-of-the art calculations for inclusive slepton pair production at hadron colliders has focused on higher-order logarithms in the leading power of the threshold variable. Here, we evaluate the next-to-leading power contribution in the threshold variable to leading logarithmic accuracy. We find that the next-to-leading power contributions can be significant compared to the next-to-leading logarithmic terms at leading power, and that existing calculations underestimate the scale error for large slepton masses. We include results for a potential future FCC-hh machine at $\sqrt{s}=85$ TeV.

Space-based gravitational-wave observatories will detect the early inspiral of stellar-mass binary black holes and can track their eccentricity evolution. However, untargeted searches in the space band are computationally demanding and require relatively high detection thresholds (signal-to-noise ratio $\sim 15$). Information from ground-based detections can significantly shrink the parameter space for space-band analyses and thereby substantially reduce the detection threshold. We present a Bayesian inference pipeline for ground-triggered archival space-band analyses that includes eccentricity. Using ground-informed priors, we demonstrate that with one year of LISA or TianQin data a GW190521-like source with signal-to-noise ratio $\sim 7$ can be distinguished and tightly constrained. In this setup, space observations sharpened the redshifted chirp mass from $\mathcal{O}(10^{-3})M_\odot$ to $\mathcal{O}(10^{-5})M_\odot$, and constrain the eccentricity to $\mathcal{O}(10^{-5})$ around the injected value $e_{0.01\mathrm{Hz}}=0.1$. These results demonstrate that inference of eccentric stellar-mass binary black holes in noisy space-band data is practically feasible, supports an expanded yield of multiband detections, and strengthens prospects for future astrophysical and gravitational tests.

Higher-order interactions fundamentally shape collective dynamics in oscillator networks. The topological Kuramoto model captures these effects by extending synchronization models to include interactions between cells of arbitrary dimension within simplicial and cell complexes. We introduce the topological nonlinear Kirchhoff conditions to characterize all phase-locked states of the topological Kuramoto model. These states are organized by winding numbers associated with generalized independent cycles, which quantify how phases wind around these cycles. Using rings, Platonic solids, and regular simplices as illustrative examples, we uncover a universal rule: boundaries must have at least five elements for multistability to arise. We further find that independent winding numbers associated with lower- and higher-dimensional boundaries generate cascades of multistability across dimensions. These results show how the topology and boundary structure of cell complexes influence phase locking and multistability, and provide a general framework for collective dynamics on cell complexes.

Following up a previous article of the authors which studies the interpolation of certain anticyclotomic $p$-adic $L$-functions associated to quaternionic modular forms in a Hida family, we extend the work of F. Castella on the interpolation and specialization of big Heegner points to the quaternionic setting. We prove an explicit reciprocity law relating the big $p$-adic $L$-function to the big Heegner points in this quaternionic setting.

We use Serre--Tate expansions of modular forms to construct power series attached to quaternionic ordinary families of modular forms. We associate to these power series a big $p$-adic $L$-function interpolating the $p$-adic $L$-functions constructed by Burungale and Magrone at classical specializations. A crucial ingredient is the generalization of some results of Ohta to the quaternionic setting.

Given a bipartite graph that has a perfect matching, a prefect proportional allocation is an assignment of positive weights to the nodes of the right partition so that every left node is fractionally assigned to its neighbors in proportion to their weights, and these assignments define a fractional perfect matching. We prove that a bipartite graph has a perfect proportional allocation if and only if it is matching covered, by using a classical result on matrix scaling. We also present an extension of this result to provide a simple allocation strategy in non-matching covered bipartite graphs.

We show that the fermionic exclusion principle in scattering problems manifests itself through constraints implied by unitarity and the optical theorem. Configurations that formally allow identical fermions to appear in the same quantum state at the level of intermediate amplitudes are not pathological. Instead, they turn out to be essential for implementing the Pauli principle in scattering processes. Making this connection explicit resolves an apparent tension between the exclusion principle and unitarity and provides a clarified view of how fermionic statistics manifests itself within the $S$-matrix framework.

We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of aggregation units, such as geographical areas. Existing methods for this problem, also known as ecological inference, implicitly make strong assumptions about the aggregation process. We first formalize weaker conditions for identification which hold conditionally on covariates. To efficiently control for many covariates, we propose a debiased machine learning estimator that is based on nuisance functions restricted to a partially linear form. Our estimator admits a semiparametric sensitivity analysis which allows researchers to evaluate the impact of violations of the key identifying assumption. We also propose a nonparametric test for the identifying assumption itself. Finally, we derive asymptotically valid confidence intervals for local, unit-level estimates under additional assumptions. Simulations and validation on real-world data where ground truth is available demonstrate the advantages of our approach over existing methods. Open-source software is available which implements the proposed methods.

Axions and axion-like particles are ubiquitous in extensions of the Standard Model and offer a unifying framework for addressing open problems in cosmology. Depending on their mass and interactions, axions can act as dark matter, drive cosmic acceleration as dark energy, or contribute to the relativistic background as dark radiation. Motivated by the plenary talk at TAUP 2025, this proceeding reviews the phenomenology of light bosons in the early and late Universe, with a focus on the theoretical foundations, observational signatures, and experimental prospects. This contribution is intended as a compact mini-review, emphasizing representative mechanisms and observational targets rather than an exhaustive survey.

We study the complexity of Decoded Quantum Interferometry (DQI), a quantum algorithm for approximate optimization. First, we show that the algorithm resists classical simulation strategies based on locating outputs with large probabilities. We then prove that DQI can be simulated at a low level of the polynomial hierarchy, posing challenges to standard quantum supremacy arguments. We further show that DQI is a constructive solution to a classical coding-theoretic bound based on the MacWilliams identity. Lastly, we interpret DQI as preparing low-energy states of a quantum simple harmonic oscillator, a viewpoint we believe suggests a physics-motivated route to generalizing DQI.

The Vehicle Routing Problem (VRP) is a popular generalization of the Traveling Salesperson Problem. Instead of one salesperson traversing the entire weighted, undirected graph $G$, there are $k$ vehicles available to jointly cover the set of clients $C \subseteq V(G)$. Every vehicle must start at one of the depot vertices $D \subseteq V(G)$ and return to its start. Capacitated Vehicle Routing (CVRP) additionally restricts the route of each vehicle by limiting the number of clients it can cover, the distance it can travel, or both. In this work, we study the complexity of VRP and the three variants of CVRP for several parameterizations, in particular focusing on the treewidth of $G$. We present an FPT algorithm for VRP parameterized by treewidth. For CVRP, we prove paraNP- and $W[\cdot]$-hardness for various parameterizations, including treewidth, thereby rendering the existence of FPT algorithms unlikely. In turn, we provide an XP algorithm for CVRP when parameterized by both treewidth and the vehicle capacity.

Small-scale dynamos (SSDs) amplify magnetic fields in turbulent plasmas. Theory predicts nonlinear magnetic energy growth $E_\mathrm{mag} \propto t^{p_\mathrm{nl}}$, but this scaling has not been tested across flow regimes. Using a large ensemble of SSD simulations spanning subsonic to supersonic turbulence, we measure linear growth ($p_\mathrm{nl} = 1$) in subsonic flows and quadratic growth ($p_\mathrm{nl} = 2$) in supersonic flows. In all cases, the nonlinear dynamo converts a nearly constant fraction $\sim 1/100$ of the turbulent kinetic energy flux into magnetic energy, and the nonlinear phase has a characteristic duration $Δt \approx 20\,t_0$, where $t_0$ is the outer-scale turnover time. By isolating the onset of magnetic backreaction in SSDs, our statistical ensemble approach identifies a robust efficiency and duration for the nonlinear SSD that can be used to interpret more complex astrophysical and laboratory plasmas.

In the coming decades, as the low frequency sensitivity of detectors improves, the time that gravitational-wave signals remain in the sensitive band will increase, leading to new challenges in analyzing data, namely non-stationary noise and data gaps. Time-frequency (wavelet) methods can efficiently handle non-stationary noise, but data gaps still lead to spectral leakage due to the finite length of the wavelet filters. It was previously shown that Bayesian data augmentation - "gap filling" - could mitigate spectral leakage in frequency domain analyses, but the computational cost associated with the matrix operations needed in that approach is prohibitive. Here we present a new, computationally efficient approach to Bayesian data augmentation in the time-frequency domain that avoids repeated, costly matrix operations. We show that our approach efficiently solves the problem of data gaps in simulated LISA data, and can be smoothly integrated into the LISA Global Fit. The same approach can also be used for future 3G ground-based interferometers.

We establish a Bombieri-Vinogradov theorem for sectors in real quadratic number fields.

An end-to-end strategy for hybrid quantum-classical computations of Green's functions in many-body systems is presented and applied to the pairing model. The scheme makes explicit use of the spectral representation of the Green's function, which entails the calculation of the $N$-body ground state as well as eigenstates and associated energies of the $(N\pm1)$-body neighbors. While the former is accessed via variational techniques, the latter are constructed by means of the quantum subspace expansion method. Different ansatzes for the ground-state wave function, originating from either classical or quantum approaches, are tested and compared to exact calculations. The resulting one-body Green's functions prove to be accurate approximations of the exact one for a large range of parameters, including across the normal-to-superfluid transition. As a byproduct, this approach yields a good description of odd systems provided that the starting even system is well reproduced by the variational ansatz.

We recently showed that macroscopic nuclear spin ensembles prepared in coherent spin states can dramatically enhance the interaction rates of weakly interacting cosmic relics-such as dark matter and the cosmic neutrino background-through collective quantum effects analogous to Dicke superradiance, where the de-excitation and excitation rates scale as the square of the number of spins, $N^2$. We thus coined these processes superradiant interactions. In this paper, we propose a protocol to realize this enhancement and boost the discovery potential for such relics. We show how concepts from quantum optics can be adapted to nuclear spins coupled to superconducting circuits, enabling high-sensitivity systems. The spins are first initialized into a coherent spin state via a $π/2$ Rabi pulse from the ground state. When the circuit is sufficiently detuned from resonance, the spin-circuit interaction implements a squeezing Hamiltonian. Because squeezing must outpace spin relaxation and dephasing, the protocol favors macroscopic ensembles and high-quality superconducting circuits. During this squeezing phase, the standard quantum variance is reduced by up to 4.8 orders of magnitude-equivalent to 48 dB of squeezing-for circuits with quality factors $Q \sim 10^8$-$10^9$. The signal imprinted on the spins during the squeezing protocol can be magnified by further utilizing the squeezing interactions, easing the requirement for shot-noise-limited readout. This protocol has the potential to significantly accelerate axion and dark photon dark matter searches and extend the reach of existing axion experiments to probe QCD axion-nuclear spin couplings. More broadly, it paves the way for detecting coherent inelastic interactions from other cosmic relics-most notably the cosmic neutrino background-and establishes nuclear-spin-based systems as a new class of quantum, ultra-low-threshold detectors.

So-called engineering or analytical wind farm flow solvers typically build upon two submodels: one for the velocity deficit and one for the wake-added turbulence intensity. While velocity deficit modelling has received considerable attention, wake-added turbulence models are less prevalent in comparison. Yet, accurate estimates of local turbulence intensity are essential for predicting flow interactions and energy yield, as turbine wakes are both sensitive to, and sources of turbulence. Existing wake-added turbulence models are typically empirical or assume axial symmetry despite the inherently three-dimensional nature of turbulent wake fields. In this work, we present a new model for wake-added turbulence intensity. Our approach is based on the analysis of the TKE and the streamwise Reynolds stress budget, incorporating classical RANS modelling assumptions and far-wake approximations. The resulting model maintains a simple and practical form, demonstrating strong agreement with LES and wind tunnel measurements. Our model provides a more physically consistent and predictive tool for wind farm flow modelling and performance estimation.

We study Isobaric Analog Resonances in even-even nuclei along the $ N=50 $ isotonic chain. First, Hartree-Fock-Bogoliubov calculations, using the Gogny D1M effective interaction, have been performed to provide energies and occupation probabilities of nucleon orbitals. The Isobaric Analog Resonances are calculated with the charge exchange QRPA approach on top of then HFB calculations. Fermi transition mechanism is interpreted with the existence of collective modes in the final nucleus. The fragmentation of the Fermi Strength results from the fractional occupation of nucleon shells, a direct consequence of nuclear pairing. The theoretical Fermi transition probabilities along the isotopic chain $ Z=50 $ are also analyzed and confirm our conclusions.

We present the first real-time predictions of coronal mass ejection (CME) magnetic structure and resulting geomagnetic impact at Earth for two events using far-upstream observations from Solar Orbiter during March 2024. While our approach assumes idealized conditions for CME propagation and scaling, in situ magnetic field data from upstream monitors still produced realistic predictions despite the large heliocentric distance between Solar Orbiter and L1 (0.53 and 0.60 au). Geomagnetic index predictions were made 15.3 and 4.3 hours before the CME shock arrival at L1, and 33.9 and 10.3 hours ahead of peak storm time; a large improvement over current L1-based nowcasting capabilities. We find that observationally constraining the simple drag-based models using the upstream in situ observations improved arrival time estimates for the two events in this study, although arrival time errors of several hours still remain. Our results show that good predictions of CME magnetic structure and geomagnetic indices with actionable lead-times can be made with far upstream spacecraft, even with longitudinal separations up to 10° from the Sun-Earth line, over heliocentric distance ranges where radial evolution effects dominate over longitudinal effects. Limitations include different expansion behaviors for individual CMEs and regions within. Future missions providing continuous data, including solar wind plasma parameters alongside magnetic field measurements, could account for preexisting disturbed conditions and improve geomagnetic prediction accuracy. Our findings demonstrate the substantial value of real-time upstream solar wind measurements for enhancing geomagnetic forecasting accuracy at Earth and provide critical validation for future dedicated upstream space weather missions.

Angle-resolved photoemission spectroscopy is a powerful experimental technique for studying anisotropic many-body interactions through the electron spectral function. Existing attempts to decompose the spectral function into non-interacting dispersions and electron-phonon, electron-electron, and electron-impurity self-energies rely on linearization of the bands and manual assignment of self-energy magnitudes. Here, we show how self-energies can be extracted consistently for curved dispersions. We extend the maximum-entropy method to Eliashberg-function extraction with Bayesian inference, optimizing the parameters describing the dispersions and the magnitudes of electron-electron and electron-impurity interactions. We compare these novel methodologies with state-of-the-art approaches on model data, then demonstrate their applicability with two high-quality experimental data sets. With the first set, we identify the phonon modes of a two-dimensional electron liquid on TiO$_2$-terminated SrTiO$_3$. With the second set, we obtain unprecedented agreement between two Eliashberg functions of Li-doped graphene extracted from separate dispersions. We release these functionalities in the novel Python code xARPES.

The quadratic embedding constant (QEC) of a finite, simple, connected graph originated from the classical work of Schoenberg [Ann. of Math., 1935] and [Trans. Amer. Math. Soc., 1938] on Euclidean distance geometry. In this article, we study the QEC of graphs in terms of two graph operations: the Cartesian product and the join of graphs. We derive a general formula for the QEC of the join of an arbitrary graph with a regular graph and with a complete multipartite graph. As an application of these results, we explicitly compute the QEC for several classes of graphs and provide new examples of graphs of QE class. We also establish a lower bound for the quadratic embedding constant of the Cartesian product of two arbitrary connected graphs. Furthermore, as an extremal case, we derive concise formulas for the quadratic embedding constants of the Cartesian product of an arbitrary graph G with a complete graph and with a complete bipartite graph, expressed in terms of $\qec(G)$.

The simulation of electronic systems is an anticipated application for quantum-centric computers, i.e. heterogeneous architectures where classical and quantum processing units operate in concert. An important application is the computation of radical chain reactions, including those responsible for the photodegradation of composite materials used in aerospace engineering. Here, we compute the activation energy and reaction energy for hydrogen abstraction from 2,2-diphenyldipropane, used as a minimal model for a step in a radical chain reaction. Calculations are performed using a superconducting quantum processor of the IBM Heron family and classical computing resources. To this end, we combine a qubit-reduction technique called entanglement forging (EF) with sample-based quantum diagonalization (SQD), a method that projects the Schrödinger equation into a subspace of configurations sampled from a quantum device. In conventional quantum simulations, a qubit represents a spin-orbital. In contrast, EF maps a qubit to a spatial orbital, reducing the required number of qubits by half. We provide a complete derivation and a detailed description of the combined EF and SQD approach, and we assess its accuracy across active spaces of varying sizes upto (39e,39o).

Electric-field-driven electrolyte transport through nanoporous membranes is important for applications including osmotic power generation, sensing and iontronics. We derive an analytical equation in the Debye--Hückel regime and a semi-analytical equation for arbitrary surface potentials for the electric-field-driven electric current through a pore in an ultrathin membrane, which predict scaling with fractional powers of the pore size and Debye length. We show that our theory for arbitrary electric potentials accurately quantifies the ionic conductance through an ultrathin membrane in finite-element method numerical simulations for a wide range of parameters, and generalizes a widely used theory for the access electrical conductance of a membrane nanopore to a broader range of conditions. Our theory predicts that fractional scaling of the ionic conductance with electrolyte concentration at low concentrations is an intrinsic property of charged ultrathin membranes and also occurs for thicker membranes for which the access contribution to the conductance dominates, which could help to explain experimental observations of this widely debated phenomenon.

In this paper, we propose a novel class of Piecewise Deterministic Markov Processes (PDMPs) that are designed to sample from probability distributions $π$ supported on a convex set $\mathcal{M}$. This class of PDMPs adapts the concept of a mirror map from convex optimisation to address sampling problems. The corresponding algorithms provide unbiased samples that respect the constraints and, moreover, allow for exact subsampling. We demonstrate the advantages of these algorithms against a range of constrained sampling problems where the proposed algorithms outperform state of the art stochastic differential equation-based methods.

Given that any subsystem of a closed out-of-equilibrium quantum system is an open quantum system, its dynamics (reduced from the full system's unitary evolution) can be either Markovian (memory-less) or non-Markovian, with the latter necessarily impeding the process of relaxation and thermalization. Seemingly independently, such non-ergodic dynamics occurs when an initial state has spectral weight on the so-called quantum many-body scar states, which are non-thermalizing eigenstates embedded deep in the spectrum of otherwise thermal eigenstates. In this article, we present numerical evidence that, in the class of systems which exhibit scars-induced entanglement oscillations, the presence of quantum scars is a microscopic ingredient that enables and enhances non-Markovianity of the dynamics of subsystems. We exemplify this with the PXP model and its deformations which either enhance or erase the signatures of scarred dynamics when quenched from simple product states with significant overlaps with the scarred states. The effect of thermalizing or scarring initial states is also similarly investigated. By probing information backflows with the dynamical behaviour of the distances between temporally-separated transient states of small subsystems, systematic signatures of subsystem non-Markovianity in these models are presented. It is seen that scarring-enhancing (erasing) deformations also exhibit enhanced (diminished) subsystem non-Markovianity. Likewise, results relating scarring (thermalizing) initial states to stronger (weaker) subsystem non-Markovianity are also presented. The retention of memory and revivals between transient subsystem states is a finer form of memory effect than captured by the revivals of full system's fidelity with the initial states. This sheds new light on the dynamical memories associated with quantum scarring (abstract shortened due to arxiv limitations).

Quantum state preparation is an important subroutine in many quantum algorithms. The goal is to encode classical information directly to the quantum state so that it is possible to leverage quantum algorithms for data processing. However, quantum state preparation of arbitrary states scales exponentially in the number of two-qubit gates, and this makes quantum state preparation a very difficult task on quantum computers, especially on near-term noisy devices. This represents a major challenge in achieving quantum advantage. We present and analyze a novel two-step state preparation method where we first minimize the entanglement entropy of the target quantum state, thus transforming the state to one that is easier to prepare. The state with reduced entanglement entropy is then represented as a matrix product state, resulting in a high accuracy preparation of the target state. Our method is suitable for NISQ devices and we give rigorous lower bounds on the accuracy of the prepared state in terms of the entanglement entropy. We benchmark our method with 2D normal distribution and Ricker wavelet states with 6--20 qubits.

We develop a feedback control framework for stabilizing the McKean-Vlasov PDE on the torus. Our goal is to steer the dynamics toward a prescribed stationary distribution or accelerate convergence to it using a time-dependent control potential. We reformulate the controlled PDE in a weighted, zero-mean space and apply the ground-state transform to obtain a Schrodinger-type operator. The resulting operator framework enables spectral analysis, verification of the infinite-dimensional Hautus test, and construction of a Riccati-based feedback law derived from the linearized dynamics, yielding local exponential stabilization with a chosen convergence rate. We rigorously prove local exponential stabilization via maximal regularity arguments and nonlinear estimates. Numerical experiments on well-studied models in one and two dimensions (the noisy Kuramoto model for synchronization, the O(2) spin model in a magnetic field, and the von Mises attractive interaction potential) showcase the effectiveness of our control strategy, demonstrating convergence acceleration and stabilization of unstable equilibria.

We present a theoretical approach to investigate the scattering of polarized electrons from light nuclei using the multipole expansion for the scattering cross section within the framework of the unified electroweak theory. Scattering processes corresponding to different electron polarizations are analyzed and compared with the unpolarized electron scattering investigated earlier. Besides, the contribution of both polarized and unpolarized terms to the scattering cross section is examined. Numerical calculations for stable $^{6,7}$Li and unstable $^7$Be nuclei using the Weinberg-Salam model show that the longitudinal polarization and weak interaction are not explicitly correlated when electrons scatter at $ θ\simeq 0^{\circ}$ across all energy scales. A strong correlation emerges at the other scattering angles when the electron energy exceeds 10 GeV. This study provides additional information about nuclear structure and uncovers the role of electron polarization and its correlation with the weak interaction in each process, thus offering a more complete picture of electron-nucleus scattering.

The chiral magnetoelectric insulator Cu$_2$OSeO$_3$ hosts a rich and anisotropic magnetic phase diagram that includes helical, conical, field-polarised, tilted conical, and skyrmion lattice phases. Using resonant elastic x-ray scattering (REXS), we uncover a new spiral state confined to the surface of Cu$_2$OSeO$_3$. This surface-confined spiral state (SSS) displays a real-space pitch of $\sim$120 nm, which remarkably is twice the length of the incommensurate structures observed to-date in Cu$_2$OSeO$_3$. The SSS phase emerges at temperatures below 30~K when the magnetic field is applied between $3^\circ$ to $18^\circ$ away from the $\langle\text{110}\rangle$ crystallographic axes. Its surface localisation is demonstrated through a combination of REXS in reflection and transmission geometries, with complementary small-angle neutron scattering measurements suggesting its absence from the bulk. We attribute the stabilisation of the SSS to competing anisotropic interactions at the crystal surface. The discovery of a robust, surface-confined spiral paves the way for engineering energy-efficient, nanoscale spin-texture platforms for next-generation devices.