The widespread dissemination of multimodal content on social media has made misinformation detection increasingly challenging, as misleading narratives often arise not only from textual or visual content alone, but also from semantic inconsistencies between modalities and their evolution over time. Existing multimodal misinformation detection methods typically model cross-modal interactions statically and often show limited robustness across heterogeneous datasets, domains, and narrative settings. To address these challenges, we propose MOMENTA, a unified framework for multimodal misinformation detection that captures modality heterogeneity, cross-modal inconsistency, temporal dynamics, and cross-domain generalization within a single architecture. MOMENTA employs modality-specific mixture-of-experts modules to model diverse misinformation patterns, bidirectional co-attention to align textual and visual representations in a shared semantic space, and a discrepancy-aware branch to explicitly capture semantic disagreement between modalities. To model narrative evolution, we introduce an attention-based temporal aggregation mechanism with drift and momentum encoding over overlapping time windows, enabling the framework to capture both short-term fluctuations and longer-term trends in misinformation propagation. In addition, domain-adversarial learning and a prototype memory bank improve domain invariance and stabilize representation learning across datasets. The model is trained using a multi-objective optimization strategy that jointly enforces classification performance, cross-modal alignment, contrastive learning, temporal consistency, and domain robustness. Experiments on Fakeddit, MMCoVaR, Weibo, and XFacta show that MOMENTA achieves strong, consistent results across accuracy, F1-score, AUC, and MCC, highlighting its effectiveness for multimodal misinformation detection.

We examine the dependence of the ellipticity of globular clusters in the Milky Way on their X-ray luminosity using two modern catalogs and combine them with optical and X-ray data from the literature. Kolmogorov-Smirnov tests applied across multiple subsets reveal statistically significant differences in the ellipticity distributions when both $L_{\rm X}$ and optical luminosity are considered. Two X-ray luminosity thresholds, $L_{\rm X}^*(M_{\rm{V}}=-7)=10^{33.05}$ erg/s and $L_{\rm X}^*(M_{\rm{V}}=-7)=10^{32.01}$ erg/s, yield the most reliable distinction. In contrast to earlier findings based solely on optical data, our results demonstrate that globular clusters with the highest X-ray luminosity tend to have higher ellipticity on average.

We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the Planck satellite alongside HI data from HI4PI survey. Our training dataset is complemented by the targets represented by the two rotational transition lines of CO (J:1-0, J:2-1) provided by the Planck satellite. We ensure the robustness of our dataset by focusing on regions with a signal-to-noise ratio (SNR) exceeding 8. The outcomes, assessed utilizing angular power spectra and Minkowski functionals, confirm that our algorithm proficiently achieves the set goals, indicating that the amplitudes of the generated emission accurately reproduce the angular correlations and share the statistical properties of the employed CO targets. We thus aim at improving the current models of CO emission specifically in the high-Galactic latitude areas that have been hardly observed by the most recent surveys, and, in doing so, to address and overcome the limitations affecting current models regions. This research lays the groundwork for creating transformative synthetic simulations, leveraging convolutional neural networks tied to data procured from latest observations.

Magnetic switchbacks are large amplitude deflections of the magnetic field within the solar wind. They are Alfvénic in character and so are associated with a spike in velocity and a generally small variation in local plasma density. Early orbits of Parker Solar Probe revealed that the solar wind near the Sun is dominated by these structures, and therefore, they may be playing an important role in the energy budget and acceleration of the young solar wind. In this review, we present an overview of different mechanisms that have been proposed for how switchbacks could be formed. We group the mechanisms by whether they predominantly act in the low solar atmosphere or within the solar wind (in situ). We focus on mechanisms that can create reversals of the ambient magnetic field direction and, thus, account for the most extreme perturbations. The general consensus is that mechanisms in the lower solar atmosphere do not form such reversals on their own but provide the seed perturbations, flows, or particle beams necessary for in situ mechanisms to create switchbacks within the solar wind. Switchback observations thus likely contain an imprint of the coronal source of the seed perturbation or flow, which is evolved further locally by one of several plausible in situ mechanisms. We discuss the strengths and weaknesses of each mechanism and outline future observational and theoretical tests that could help differentiate between them.

Space-based entanglement distribution has the potential to extend the range of quantum communication beyond that achievable through optical fibres that are constrained by exponential losses. Quantum repeaters have been proposed to mitigate the effects of channel losses for both fibre and satellite networks. Although quantum repeaters can improve entanglement distribution efficiency, the rate is constrained by classical communication latency in the entanglement swapping process. Direct dual downlink entangled pair distribution does not suffer such a latency restriction, hence can ``brute force'' the problem of high dual channel loss through increased source rate. Hence, the comparative requirements of direct pair distribution versus quantum repeater satellites are important for the design and deployment of space-based entanglement distribution systems. Here, we consider the simplest case of a single satellite establishing entanglement between two ground stations, comparing the performance of direct dual downlink to that of a space-based quantum repeater for general overpass geometries. We also study the long-term entanglement distribution performance for different ground station pairs and determine altitudinal dependence. Finally, we study the fidelity distribution of a satellite repeater system through Monte Carlo modelling of waiting times and rate statistics, exploring the effect of quantum memory capacity, decoherence rates, and operational policies. These results will inform mission design for future space-borne quantum repeater nodes, as well as requirements on space-based memory platforms.

Nonlinear spectroscopy is a cornerstone of quantum science, providing unique access to multi-point correlations, quantum coherence, and couplings that are invisible to linear methods. However, classical simulation of these phenomena is fundamentally limited by the exponential growth of the Hilbert space, and practical quantum algorithms for the nonlinear regime have remained largely unexplored. Here, we present a unified quantum algorithmic framework for computing $n$-th order nonlinear spectroscopies. By reformulating multi-time responses as a weighted sum of expectation values at finite pump amplitudes via a generalized parameter shift rule, our approach bypasses the costly evaluation of high-order commutators and time-dependent operator expansions. This reformulation enables efficient execution via real-time evolution on current quantum hardware, ensuring inherent noise resilience. We validate the framework on IBM's superconducting quantum processors, successfully obtain higher-order response functions of a 12-qubit XXZ spin-chain. Furthermore, the versatility of our method is demonstrated by resolving quasi-particle excitation spectra in spin-liquids and identifying interaction-induced cross-peaks in atomic systems. Our results establish a practical and scalable pathway for probing complex quantum dynamics on near-term quantum devices, extending the reach of quantum simulation into the nonlinear domain.

Collective rotations are common in active matter, enhancing cohesion, transport, and mixing. They are typically attributed to chiral non-reciprocal dynamics due to intrinsic particle chirality, torque-generating interactions among units, or geometric confinement. Here, we uncover a different mechanism for rotational order in active matter where a dynamic environment coordinates the self-organization of non-chiral active particles into living crystals exhibiting sustained collective solid-like rotations. At intermediate densities, feedback from a fluctuating landscape of passive Brownian particles stabilizes large living crystals of obstacle-avoiding run-and-tumble agents. Strikingly, this environmental feedback also produces living crystals with qualitatively distinct dynamics: collective solid-like spinning emerges for particles with long persistence times approaching ballistic motion, rather than for particles moving by conventional enhanced diffusion. Beyond revealing a new route to collective rotational order in active matter, these findings highlight the integral role of a dynamic environment in self-organization and suggest environment-mediated design principles for active materials with unconventional dynamical responses.

Control systems are ubiquitous in modern technology, comprising an engineered plant to be kept within specific, often fine-tuned, limits, and a separate controller that ensures this is the case. While modern controllers often employ digital computers, other examples are purely mechanical, or even biological. It is an open question whether computation is happening within all controllers by virtue of them being part of a control system. Abstraction/ Representation theory (ART) has been developed to tackle just this question of whether a physical system is computing. Here, we demonstrate how to use ART to model control systems, and analyse them for computational properties. We determine that the plant of a control system is (a proxy for) the representational entity necessary in ART for the existence of any computation: the plant is the user of the controller. We consider specific systems: a digital thermostat, an electro-mechanical thermostat, the purely mechanical centrifugal governor, and an open-loop human-controlled heating system. We show that all these systems, and control systems in general, are performing some degree of computation. As an initial use of these results, we apply them to computationalism within cognitive theory: we show the governor is computing, so it cannot play its role of counter-example in the question of whether the brain is too.

Purpose: To develop a robust deep learning framework for non-contrast-enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological non-stationarity. Methods: We introduce VQ-Wave (Ventilation/Q-perfusion Waveform-based Assessment of Variable Evolutions), a physics-driven spatio-temporal inception neural network trained on synthetic signal models to estimate ventilation and perfusion parameters. By processing local spatial context alongside temporal evolution, the network learns to decouple physiological signals from noise. The training generator simulated non-stationary dynamics, including amplitude modulations, frequency drifts, and noise. Performance was validated against matrix pencil (MP) decomposition using numerical phantoms and in-vivo lung MRI acquired in four healthy volunteers and two children with cystic fibrosis (CF) at 1.5T. Results: In numerical benchmarks, VQ-Wave demonstrated superior robustness to non-stationarity, maintaining low global and regional error rates where MP exhibited stochastic instability due to spectral leakage. In-vivo, VQ-Wave accurately captured functional defects in patients with CF yielding ventilation and perfusion maps with high quantitative stability (mean variation < 12%) even when scan time was reduced from 45s to 15s. Conversely, under irregular physiology and short scan lengths, MP decomposition severely degraded, exhibiting systematic amplitude instability, overestimation bias, and regional signal dropouts. Conclusion: VQ-Wave offers a robust, physics-driven neural network-based alternative to spectral decomposition. By effectively handling physiological irregularity and noise, it enables reliable functional lung imaging with substantially shortened acquisition protocols.

The purpose of the present work is based on two main observations: the tensions encountered by the standard $Λ$CDM model when confronted to precision small scale cosmological data and the finding that the matter distribution and the expansion of the Universe are axially symmetric roughly in the direction of the CMB dipole. Therefore, we propose, as a model for the inhomogeneous local universe, an axially symmetric Szekeres solution. After describing its main properties, we are left with three metric functions to be fitted to data between the observer and the transition to homogeneity which is an intrinsic feature of Szekeres spacetimes. So as to turn a difficult functional inference problem into a classical parameter estimation problem, we propose to use Chebyshev polynomial expansions, which, as a first step, we truncate after the second order terms. We are thus left with eight constant parameters: six for the metric functions, plus the observer's radial location and the cosmological constant. Here are the proper ingredients needed to implement the data fitting to the model in the future.

Let $G = V, E$ be a simple connected undirected graph. A set $X \subseteq V$ is \emph{geodesically convex} if for any pair of vertices $x, y \in X$, all vertices on all shortest paths in $G$ from $x$ to $y$ are contained in $X$. A set $H \subseteq V$ is said to be a {halfspace} if both $H$ and its complement (denoted by $H^c$) are convex. Given two sets $A, B \subseteq V$, the { halfspace separation} problem asks if there exist complementary halfspaces $H, H^c$ such that $A \subseteq H$ and $B \subseteq H^c$. The halfspace separation problem is known to be NP-complete for the geodesic convexity of general graphs. We show that geodesic halfspace separation is polynomial for weakly bridged graphs, pseudo-modular graphs, and the basis graphs of matroids.

Atmospheric neutrinos provide a unique avenue to probe theories beyond the Standard Model (BSM) over a wide range of energies and path lengths. The theory of non-standard interactions (NSI) of neutrinos is one of the important BSM scenarios, which can modify flavor oscillations of atmospheric neutrinos traveling through the Earth. In this work, we use a high-purity $ν_μ$ charged-current (CC) sample of atmospheric neutrinos from IceCube DeepCore with a livetime of 7.5 years to search for the NSI parameters $\varepsilon_{eμ}$, $\varepsilon_{eτ}$, and $\varepsilon_{ee}-\varepsilon_{μμ}$. The $ν_μ$ CC events mainly come from the $ν_μ$ survival channel having no significant dependence on $δ_{\rm CP}$. Therefore, the constraints on $\varepsilon_{eμ}$ and $\varepsilon_{eτ}$ obtained using this $ν_μ$ CC sample are expected to be free from the $δ_{\rm CP}$-degeneracy. The data sample is found to be in agreement with the standard neutrino interactions. Therefore, we place bounds on these NSI parameters that are consistent with and comparable to existing experimental constraints. These $δ_{\rm CP}$-free constraints from IceCube DeepCore are complementary to those from the long-baseline neutrino oscillation experiments, where the appearance channel depends on $δ_{\rm CP}$.

Solar coronal mass ejections (CME) are routinely observed, but as of yet there exist few convincing detections of stellar CMEs. A reason for this could be the stronger magnetic fields of these stars, compared to that of our Sun, would prevent CME to form and escape. Here we combined astrophysical simulations, measurements of scaled high-energy laser-driven plasma flows, and 3D magneto-hydrodynamic modeling to test this hypothesis. Simulations show that in a 100 G stellar dipole field, low-plasma beta CMEs become magnetically confined. In the laboratory, a laser-produced plasma stream scaled to stellar CME conditions propagates freely at low applied magnetic fields (approximately 30 G stellar equivalent) but becomes unstable and halts entirely when the field is increased to 3e5 G (i.e., a 100 G equivalent). Numerical simulations suggest that the sudden disruption of the flow is induced by a kink instability. These results provide the first laboratory-scale evidence that strong stellar magnetic fields can fully suppress CME propagation, offering a physical explanation for their lack in stellar observations and highlighting the role of magnetic confinement in stellar evolution and exoplanet space weather.

Future cellular networks will sustainably integrate computing, intelligence and services within a network of networks ecosystem that includes IoT devices and subnetworks for local communications and distributed processing. This integration creates an IoT-edge-cloud continuum that enables opportunistic task offloading across the continuum, enhancing network performance, reducing response times and allowing a flexible resource allocation that can facilitate the system to scale according to demand. Future networks should also natively support deterministic service levels for critical and time-sensitive vertical applications. In this paper, we propose a deterministic task offloading and resource allocation scheme for the joint management of communication and computing resources in the IoT-edge-cloud continuum. The proposed scheme prioritizes task completion before deadlines over minimizing the latency in the execution of individual tasks. The scheme leverages flexible latencies across tasks to support a higher number of tasks through a more efficient management of computing and communication resources that better adapts to scenarios with constrained resources.

The QBF Gallery 2023, the last QBF evaluation event, continues the tradition to survey and document the state of the art in solving quantified Boolean formulas (QBFs). It provides a detailed overview by collecting newly developed solvers and formulas as benchmarks. This report documents the solvers and formulas submitted by the community and introduces a new, consolidated benchmark set that combines well-evaluated formulas with the submitted instances. The resulting formula set is made publicly available. With this benchmark set, we conduct a comparative analysis of the submitted solvers and publicly available solvers, assessing their performance and current capabilities. In addition, we report on the present status of the QBF Gallery and discuss ideas and directions for future editions to further support research and benchmarking within the QBF community.

We present a trajectory-resolved framework for charge transport in graphene and related two-dimensional carbon systems beyond the ideal ballistic and fully coherent limits. Transport is described by kinetic Monte Carlo hopping on a predefined atomic lattice, allowing the combined treatment of disorder, thermal activation, and external fields. Current and effective transmittance are extracted directly from stochastic carrier trajectories, without phenomenological transport coefficients. We apply the method to graphene under bias voltage (0-0.10 V), temperature (300-900 K), magnetic field (0-10 T), in-plane strain (2-10%, uniaxial and biaxial), and vacancy concentration (0-10%). Pristine graphene shows an almost ohmic response, with currents of about 7-8 uA, effective transmittance near 0.98-1.00, and conductance of about (5.8-7.8) x 10^-5 S at 0.10 V, depending on direction. Vacancies strongly suppress transport, reducing transmittance to about 0.45-0.75 at 10% vacancy. Higher temperature accelerates hopping and partly restores transport, but cannot overcome severe connectivity loss. Magnetic fields further reduce transport, especially in disordered networks. The framework provides a unified computational scheme for realistic two-dimensional carbon materials and also yields diffusion coefficients and effective mobilities from carrier displacements and transit times.

The binding number $b(G)$ of a graph, introduced by Woodall [J. Combin. Theory, Ser. B, 1973], is a central topic of both structural and extremal graph theory. It is closely related to fundamental combinatorial and structural properties of graphs. The graphs with $b(G)\geq1$ exhibit strong expansion properties and a highly connected global structure. In contrast, the structure for graphs with $b(G)<1$ remains far less well understood. Kane et al. [J. Graph Theory, 1981] proved that if $b(G)<1$, then every binding set of $G$ is independent. Goddard and Swart [Quaest. Math., 1990] showed that if $b(G)\leq1$, then the toughness $τ(G)\leq b(G).$ This makes it particularly interesting to investigate extremal problems for graphs with \(b(G)<1\). For any integer $r\geq1,$ we completely characterize the unique extremal graph that maximizes the size (spectral radius) among all graphs of order $n$ satisfying $b(G)<\frac{1}{r}.$ For any bipartite graph $G=(X,Y)$ on $n$ vertices, it is readily seen that $b(G)\leq\min\{|X|/|Y|,|Y|/|X|\}\leq1.$ Notably, the complete balanced bipartite graph $K_{\frac{n}{2}, \frac{n}{2}}$ achieves the maximum size (spectral radius) among all bipartite graphs with $b(G)=1$. In this paper, we completely determine the extremal graphs maximizing the size or the spectral radius among all bipartite graphs with $b(G)<\frac{1}{r}$, where $r\geq1$ is an integer.

We present a high-yield method for fabricating concave micromirror templates in silica using feedback-controlled CO2 laser ablation with precise in situ positioning. Real-time monitoring of the white-light emission generated during ablation is used to terminate laser exposure, thereby reducing shot-to-shot variability in mirror depth and radius of curvature. To ensure reproducible single-shot processing across different substrates, the sample position relative to the laser focus is calibrated using an in situ phase-scanning interferometric microscope integrated into the fabrication workflow. The method enables reliable fabrication of shallow mirror templates with tunable radii of curvature spanning from approximately 20$\mathrm{μm}$ to several hundred micrometers, with relative geometric variances as low as 3%. The suitability of the fabricated mirrors for optical resonators is verified by realizing a compact plano-concave Fabry--Perot microcavity with a finesse of 37000 at telecom wavelengths. The setup provides a simple and automated route to reproducible micromirror fabrication for applications in cavity quantum electrodynamics and cavity optomechanics.

NGC 4151 is the brightest Seyfert-1 active galaxy in the pass band of the Resolve calorimeter spectrometer aboard XRISM. It has been observed on 14 occasions, resulting in a total exposure of 893 ks. Herein, we report on an analysis of the time-averaged spectrum. The narrow Fe K$_α$ emission line complex requires contributions from the torus and the optical broad line region (BLR). Models assuming an emissivity index of $q=2$ for these components are statistically preferred over models assuming $q=3$ for a flat disk (where $J\propto r^{-q}$). A smooth shoulder on the red wing of these line components is likely best interpreted as Compton scattering in a medium with bound electrons, potentially signaling the presence of dust at the base of the BLR and in the torus. The data statistically prefer the addition of relativistic reflection from the innermost accretion disk, extending down to a radius of $r = 3.2^{+3.5}_{-2.0}~GM/c^{2}$ and with an inclination of $θ= 29.7^{+0.5}_{-0.4}$ degrees. The Fe K edge at 7.1 keV is best modeled with contributions from multiple charge states, consistent with obscuration due to cool, $kT \simeq 5$ eV collisional gas or photoionized gas. Dust is not evident in the Fe K absorption edge. A spectrum of outflows is clearly revealed, with slow ``warm absorber'' winds spanning Fe XX-XXVI, fast winds primarily seen via Fe XXV and Fe XXVI lines, and ultra-fast outflows (or, UFOs) seen as broad Fe~XXVI lines. The warm absorbers are almost certainly ``failed'' winds that return to the central engine; the data constrain their radius, density, filling factor, and distribution. For the most conservative volume filling factors, the UFOs may not deliver the kinetic feedback needed to halt star formation, on average. However, they may generate galaxy-altering feedback for larger filling factors and/or during certain intervals. (abridged)

We propose an effective non-relativistic framework in which wave-function collapse emerges as a deterministic dynamical instability induced by gravitational self-interaction and regulated by short-distance repulsion. The dynamics is described by a nonlinear Schrödinger equation supplemented by a phenomenological repulsive sector ensuring regularity at high densities. Using a variational Gaussian ansatz, we derive an explicit effective energy functional and show that extended quantum states lose stability beyond a critical mass scale. This loss of stability is associated with a bifurcation in the reduced dynamical system governing the wave-function width, leading to the emergence of stable localized configurations. Within this picture, collapse corresponds to the dynamical selection of one of these localized attractors, driven by infinitesimal asymmetries in the initial state and occurring without stochastic noise or environmental coupling. The mechanism provides a controlled and quantitative realization of gravity-induced localization, extending Schrödinger--Newton-type models while avoiding their pathological short-distance behavior. Possible implications for mesoscopic systems probing the quantum-to-classical transition are briefly discussed.

Modern vehicles are embedding increasing levels of automation, connectivity, and intelligence, which require advanced in-vehicle networks and computational platforms to support the dependability and deterministic requirements of critical in-vehicle functions. To this end, the automotive industry is shifting towards software-defined vehicles (SDVs) and zonal E/E architectures with centralized computing nodes. Realizing the full potential of these new architectures requires an efficient management of the in-vehicles computational workload. In this context, this paper introduces a deterministic task scheduling approach for in-vehicle networks (IVN), and demonstrates that it can better guarantee deterministic service levels than alternative approaches based on the shortest path or the objective to minimize task execution time. Our evaluation also demonstrates that a deterministic task scheduling can satisfactorily support increasing in-vehicle computational workloads and tasks, and achieve a more balanced workload and resource utilization across the IVN. These gains are validated across a variety of IVN topologies, and in hybrid wireless-wired IVN implementations, where a gradual introduction of wireless offers increased in-vehicle connectivity diversity.

Stable dissemination of terahertz (THz) signals over long distances is important for next-generation synchronization networks, radio astronomy, and high-capacity wireless systems. Optical fiber provides a low-loss platform for coherent frequency transfer; however, when a THz carrier is encoded as the difference between two optical wavelengths, chromatic dispersion introduces differential phase noise that degrades spectral purity. Here, we demonstrate phase-coherent distribution of opto-THz carriers over 38 km of standard single-mode fiber using a dual-wavelength Brillouin laser (DWBL) combined with a dual-channel round-trip noise-cancellation architecture. By extracting the differential phase noise between the two optical lines via a dual-channel round-trip measurement, dispersion-mediated phase fluctuations are compensated, and the intrinsic stability of the source is effectively preserved at the remote end within the measurement sensitivity. Opto-THz carriers at 150, 300, and 600 GHz exhibit sub-femtosecond timing stability and fractional frequency instabilities below 1e-17 at 10,000 seconds of averaging.

Let I be a finite partially ordered set and let (Sym(Δi),Δi)i be a sequence of symmetric groups indexed by I. Construct the generalised wreath product (F, Δ) on this sequence of permutation groups. We determine the minimum number d(F) of generators required for this generalised wreath product.

Symmetry-based classification of quantum phases of matter is one of the most foundational organizing principles in physics; however, an analogous framework for mixed, decohered quantum states has only begun to emerge. A central new concept is strong-to-weak spontaneous symmetry breaking (SW-SSB), a sharp transition in mixed quantum states that is invisible to any observable linear in the density matrix and that has since been predicted across a broad class of open and monitored quantum systems. It also provides a unifying language for phenomena as disparate as the decodability of topological quantum memories and the emergence of classical hydrodynamics from decohered quantum dynamics. Here we report the first experimental observation of SW-SSB, in dephased single-component fermionic matter imaged by a quantum gas microscope. A quantum-classical estimator built on a machine-learned Gaussian reference state gives direct access to the nonlinear Rényi-1 and Rényi-2 correlators that diagnose SW-SSB, and reveals long-range Rényi order in the dephased Fermi liquid. Adding a commensurate superlattice drives the underlying fermions through a metal-to-insulator transition that, after full dephasing, manifests as a sharp SW-SSB phase transition. Our results uncover the symmetry principle behind information-theoretic transitions in open quantum systems, and extend Landau's symmetry paradigm into the regime of real, decohering quantum devices.

We theoretically study how quantum measurement noise can be engineered in a hybrid cavitymagnomechanical platform for precision force sensing. The proposed configuration consists of a driven optomechanical cavity, with a movable mirror on one side plus a fixed semi-transparent mirror on the other side, coupled to a magnon mode, with an OPA placed inside the cavity. We show that the magnon mediated dynamics reshapes the added-noise spectrum leading to improved sensitivity compared to a conventional optomechanical sensor. In particular, by satisfying the coherent quantum noise cancellation (CQNC) criterion, radiation-pressure back-action can be fully suppressed. In addition, a larger OPA pump gain permits operation beyond the standard quantum limit at substantially reduced laser power, thereby mitigating power-related constraints without sacrificing performance. These combined advantages provide a practical pathway to below-SQL weak force detection and can outperform existing approaches based on squeezing in magnomechanics.

We extend our recent work on the cavity-modified spin Zeeman effect of an effective spin-1/2-system[J. Chem. Phys. 163, 174307 (2025)] to a relativistic Jahn-Teller scenario under strong light-matter coupling. Here, the effective spin-1/2-system is realized via a single electron or a single hole in a doubly-degenerate molecular orbital system of trigonal symmetric transition metal complexes. Both single-particle and single-hole systems are subject to both vibronic and spin-orbit coupling (SOC) augmented by interactions with a quantized cavity field via the cavity Zeeman interaction. Methodologically, we combine the relativistic $E\times e$-Jahn-Teller model with a recently introduced effective Hamiltonian formalism based on quasi-degenerate perturbation theory, which treats the cavity-spin interaction in leading order beyond the dipole approximation. We derive analytic expressions for Kramers pair energies in weak and strong SOC regimes as well as related cavity-modified effective electronic g-factors. We find cavity-induced modifications of the electronic g-factor to become relevant in the weak SOC regime for both single-particle and single-hole systems while being effectively quenched under strong SOC. Alternating signs of the cavity-Zeeman correction render single-particle and single-hole scenarios distinct in their response to the cavity field from a g-factor perspective.

Geminal wavefunctions, introduced in the late 1950s, have long been recognized for their ability to compactly capture strong electron correlation. Despite their promise, they were historically overshadowed by more computationally efficient methods. Advances in both computational resources and theoretical frameworks have renewed interest in geminal-based approaches, particularly as researchers seek accurate yet tractable wavefunctions for complex electronic systems. Recent developments highlight their versatility: from serving as efficient starting points for correlated wavefunctions, to hybrid formulations that blend geminal concepts with coupled-cluster theory, to emerging applications in quantum algorithms where orbital-pairing provides a natural structure. In this mini-review, we summarize key advances in geminal wavefunction theory, with a focus on their modern resurgence, new methodological innovations, and potential directions for electronic structure theory and quantum computation.

Prompt Gamma Timing (PGT) is a promising technique for in vivo range verification in particle therapy, exploiting the time-of-flight between primary particles and prompt gamma rays emitted by nuclear interactions. PGT distribution is highly sensitive to beam energy and target density, which, under controlled detector positioning, enables real-time monitoring of particle range, detection of morphological changes, and support for adaptive treatment strategies. This study investigates for the first time the application of PGT in carbon ion therapy. Measurements were performed using a dedicated detection system composed of a silicon strip sensor for primary ion timing and a LaBr3(Ce) read out by a SiPM for secondary radiation. Carbon ion beams with energies of 166.41, 268.86, and 398.84 MeV/u irradiated a homogeneous 30.0 cm PMMA target at CNAO. The secondary radiation detector was positioned at four off-beam positions to assess the robustness of the PGT technique. Simulations based on Geant4 were conducted for all configurations to evaluate agreement and predictive capability. A bin-by-bin comparison of experimental and simulated PGT intensities demonstrated strong agreement within the 95% confidence interval, with no incompatible bins at 166.41 MeV/u, at most 1% at 268.86 MeV/u, and up to 8% at 398.84 MeV/u, depending on detector position. Photons were identified as the dominant contribution to the detected signals, particularly for detector positions upstream with respect to the primary particle beam, minimizing signal contamination from neutrons and charged fragments. The validated experimental-simulation framework confirms the capability of the proposed PGT system to resolve energy-dependent differences and highlights its potential for detecting clinically relevant changes in the particle beam range, supporting further development toward real-time monitoring in carbon ion therapy.

We propose a non-holomorphic modular $A_4$ model under the assumption of universal couplings. In this framework, a charged lepton mass hierarchy is not created through parameter fine tuning or hierarchical Yukawa couplings, but instead is determined by the modulus $τ$, with certain modular weight assignments of right handed charged leptons. The experimental charged lepton masses are reproduced with high precision for values of $τ$ located near modular fixed points. In neutrino sector the couplings are imposed to be equal in magnitude with different relative phases. By fixing the modulus $τ$ from charged lepton sector, we perform a comprehensive scan over the phase parameters and modular weight assignments of the right handed neutrinos. We find that viable solutions arise only for normal neutrino mass ordering and a unique right handed neutrino modular weight, $k_N=-1$. The model yields strong correlations among mixing angles, the effective neutrinoless double beta decay parameter $m_{ee}$, and the total neutrino mass $\sum m_i$. These results underscore the predictive quality of non-holomorphic modular symmetry with minimal parameter inputs and offer implications for neutrino experiments and cosmological observations that can be tested.

Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In 2022, Google mandated the Data Safety Section (DSS) on Google Play, requiring developers to summarize their data practices. However, compiling accurate DSS disclosures is challenging, as they must remain consistent with the corresponding privacy policy (PP), and no automated tool currently verifies this alignment. Prior studies indicate that nearly 80% of popular apps contain incomplete or misleading DSS declarations. We present PolicyGapper, an LLM-based methodology for automatically detecting discrepancies between DSS disclosures and privacy policies. PolicyGapper operates in four stages: scraping, pre-processing, analysis, and post-processing, without requiring access to application binaries. We evaluate PolicyGapper on a dataset of 330 top-ranked apps spanning all 33 Google Play categories, collected in Q3 2025. The approach identifies 2,689 omitted disclosures, including 2,040 related to data collection and 649 to data sharing. Manual validation on a stratified 10% subset, repeated across three independent runs, yields an average Precision of 0.75, Recall of 0.77, Accuracy of 0.69, and F1-score of 0.76. To support reproducibility, we release a complete replication package, including the dataset, prompts, source code, and results available at https://github.com/Mobile-IoT-Security-Lab/PolicyGapper and https://doi.org/10.5281/zenodo.19628493.