Papers

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls—environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

This paper is an English translation of a contribution to the latest German edition (2017) of Hermann Weyl's classic text "Symmetry", edited and annotated by Erhard Scholz, Klaus Volkert, and myself. In this contribution I discuss various physical aspects of the notion of symmetry and place them in context from the perspective of theoretical physics. In particular, a distinction is drawn between symmetries of individual configurations (solutions of equilibrium equations or equations of motion) and symmetries of the laws themselves. Likewise, a distinction is made between those operations that alter physical states and so-called gauge symmetries, which leave physical states unchanged and merely represent a redundancy in the mathematical description. Besides gauge symmetries, I comment on the connection to conservation laws, symmetry breaking, and the philosophy of space.

We propose that instructional effectiveness may depend on an interaction between two design dimensions: dosage—the total duration of student-centered active learning allocated to a topic—and precision—the degree to which an intervention targets a specific, documented cognitive bottleneck with a mechanism-matched representational tool. This paper formalizes the framework, anchors it in cognitive load theory, derives its falsifiable predictions, and clarifies its incremental contribution relative to existing instructional-design literature. The central theoretical move is to decompose precision into three components—targeting ($P_t$), representational match ($P_r$), and scope restriction ($P_s$)—and to argue that only representational match acts directly on extraneous load. This decomposition yields a compact model in which the effective dosage is $D_{\mathrm{eff}}=D P_s$, the germane processing is $G=D P_s\eta(P_r)$ with $\eta$ the representational efficiency, and learning is $E=g(G)$ with $g$ increasing and concave \rev{(here $E$ is the cumulative learning outcome on the target construct, not a rate)}. The framework's core proposition (P3)—that precision can compensate for dosage—then becomes a quantifiable substitutability claim, $\mathrm{d}D/\mathrm{d}P_r=-\eta/\eta'$, rather than a mere existence statement; its scope is bounded by a precision ceiling and a minimum dosage threshold. We distinguish the framework from Carroll's classic time-needed model by the directional character of precision and by the estimability of the substitution rate, and from the ICAP framework by distinguishing “matching of participation content to the bottleneck” from “mode of participation.” As an illustrative case (not a test of the framework),

In this article we revisit Aristarchus' ancient experiment to determine the ratios between the Earth-Moon and Earth-Sun distances. We present here the construction of a simple instrument, basically a tube that, by projecting its shadow, that allows one to estimate the Sun-Earth-Moon angle at the moment of first (or last) quarter. The simplicity of its construction and the accuracy of the measurements obtained may come as a surprise. We discuss the complexities involved in determining the exact moment of first quarter, based on the shape of the elliptical shadow projected on the Moon. Finally, we analyze and implement a strategy that allows us to approximate the measurement of this angle and estimate its value using observations that can be performed twice a month, and more accurately a couple of times during the year. We place Aristarchus' problem in its historical context and detail the physical and mathematical topics that can be developed and motivated through this experiment, in addition to carrying out a very thorough analysis of the errors associated with this measurement.

We review the conceptual and theoretical status of twin-universe models, from Sakharov's early ideas on baryogenesis, CPT symmetry, and reversed arrows of time to recent quantum-cosmological scenarios involving pair-created entangled universes. Particular attention is given to the distinction between Sakharov-type cosmological symmetry, modern bimetric theories of gravity, and speculative models involving negative mass sectors. We summarize the main consistency conditions faced by bimetric gravity, including the Boulware-Deser ghost, Higuchi-type bounds, covariant conservation laws, and no-go results for positive- and negative-mass sectors. We then examine the Janus cosmological model and related black-hole replacement proposals, emphasizing the difference between heuristic twin-universe narratives and fully specified relativistic field theories. We argue that while twin-universe ideas remain a fertile framework for questions about the arrow of time, matter-antimatter asymmetry, and the quantum origin of the universe, models combining bimetric gravity, negative mass, dark-sector phenomenology, and alternatives to Kerr black holes face a substantial burden of mathematical and observational proof.

The topological classes of coherent structures in magnetohydrodynamic turbulence are studied using the irreducible representations of the SO(3) and SO(2) rotation groups. We probe the coherent structures around axisymmetric magnetic fields in the universe. Such axisymmetric fields can be found around filamentary structures as well as cosmic strings. We have considered a Gaussian and a non Gaussian magnetic field. We find that the field distribution leads to different coherent structures for the two cases. Our results show that the scattering of particles from the Gaussian structure depends predominantly on two lengthscales. In the case of the non Gaussian distribution, we can define a third intermediate lengthscale, similar to the Taylor lengthscale. In both the cases, we find that there is a dominance of the polarization anisotropy which indicates that the field gradients are forced into rigid spatial configurations. In the early universe, this will change the mean free path of the baryons and leptons in the coherent structures. Trapping of charged particle as well as their acceleration will be more frequent in the case of a magnetic field with a heavy tailed distribution. The lengthscale of density inhomogeneities close to these structures will thus be determined by the magnetic field distribution of the fields.

Topological edge states offer a robust route to photonic devices and systems that remain functional despite structural imperfections. Topological waveguide arrays have been shown to deliver robust coupling, but only for full power transfer between the two edge waveguides—a robust route to an arbitrary intermediate ratio has remained unknown. Here we show that the topological edge states of a finite Su-Schrieffer-Heeger chain, realized with only four identical waveguides, form an arbitrary-ratio power splitter whose ratio is robust to both the device length and fabrication errors. Two near-degenerate edge supermodes beat to produce a step-like power transfer with a flat, length-insensitive plateau, in contrast to the sinusoidal transfer of a conventional directional coupler. The splitting ratio at this plateau is governed by the difference between the two waveguide gaps, making it immune to correlated fabrication offsets. We derive this behavior analytically and confirm it numerically and experimentally on a conventional silicon-on-insulator platform. Beyond this device, our analysis can be applied to general topological waveguide arrays, while the robust splitter offers a practical building block for fabrication-tolerant photonic circuits.

We show how genuine three-body phase information can enter the invariant mass of a relativistic three-particle Dirac system in $(1+1)$ dimensions. As a solvable reference system, we consider the Sakamoto–Munakata–Ino model with pairwise contact interactions $g_{ij}(1-\alpha_i\alpha_j)\delta(x_i-x_j)$. These singular interactions can be transferred into sector-dependent phases and matching conditions by a discontinuous unitary transformation. Although the explicit contact terms are thereby removed, the nonzero constituent-mass operator is rotated and retains nontrivial spectral information. We introduce a genuine three-body holonomy generated by $Q_3=\alpha_1\alpha_2\alpha_3$. The kinetic and pair-interaction parts commute with $Q_3$, while the constituent-mass operator anticommutes with it. Consequently, the massless system separates into the $Q_3=\pm1$ sectors, which acquire opposite holonomy phases $e^{\pm i\theta_3}$, whereas nonzero constituent masses mix the two sectors. This phase-sector-mixing mechanism makes the relative three-body phase dynamically accessible to the bound-state spectrum and establishes an operator-level mechanism through which the three-body holonomy generates a $\theta_3$ dependence of the physical three-body invariant mass. We further emphasize that the topological three-body holonomy is not automatically equivalent to a bare triple-contact potential; such an equivalence requires a regulated self-adjoint realization and a compatible interaction-dependent boost satisfying the Poincaré algebra. The resulting framework therefore connects genuine three-body phase information to the mass spectrum of a relativistic composite system while clearly separating the controlled holonomy construction from the unresolved short-distance triple-contact realization.

A recent article explores some respective contributions of the initial velocity and effective mass of a striking implement to the ultimately induced, post-collision velocity of a stationary strike target, in a combat sports context. Therein, it was found that multiplicative increases in the effective mass associated with a punch to the human body can have a greater quantitative effect on target velocity than that of its initial, pre-collision speed. Given that fold-increases in effective mass may be limited to low-integer multiples of a modest, literature-supported value that spans martial arts styles – and the fact that increases in the strike velocity are likely bounded by a factor of two, even for trained fighters – translating the aforementioned findings into incremental changes in either variable may be of more benefit to coaches who intend to apply the theoretical physics of fighting to develop practical training programs. Here, we build on previous work, studying the effects of marginal increases in effective mass on target velocity, and relating them to equivalent, single-unit changes in strike speed.

{O2 stars define the hottest end of the normal O-star sequence, but their identification requires blue-optical nitrogen diagnostics absent from many surveys.} {We reassess three luminous Large Magellanic Cloud (LMC) sources with multi-epoch Sloan Digital Sky Survey V (SDSS-V) spectroscopy obtained with the Baryon Oscillation Spectroscopic Survey (BOSS) spectrographs: \wsi, \lh, and \sk.} {We measure visit-resolved radial velocities and H, He, and N equivalent widths (EWs), compare robust combinations with LMC O2 templates at a common effective resolution, and use OSTAR2002 spectral energy distribution (SED) fits and a Gaia colour–magnitude diagram (CMD) as broad-band checks.} {The weighted $\log|{\rm EW}(\niv~\lambda4058)/{\rm EW}(\niii~\lambda4640)|$ values are $0.64\pm0.29$, $0.48\pm0.23$, and $0.34\pm0.44$ for \wsi, \lh, and \sk. The detection of \nv~$\lambda\lambda4604,4620$ supports the O2~If$^\ast$ and O2~V–III classifications adopted for \wsi\ and \lh, respectively. For \sk, the uncertain \niv/\niii\ ratio and non-detection of \nv\ motivate O2–3~V–III. Weak \hei~$\lambda4471$ may indicate unresolved later-type contamination in \wsi\ and \sk. The radial velocities of \sk\ show suggestive but non-significant variability. OSTAR2002 fits over 45–55~kK and the Gaia CMD place all three sources in the luminous very-early-O regime; \wsi\ shows a strong mid-infrared (MIR) excess, \lh\ is nearly photospheric, and \sk\ shows only a modest $3$–$8\,\mu$m excess.} {The BOSS spectra add \wsi\ and \lh\ to the LMC very-early-O population and identify \sk\ as an additional O2–3 source. These cases show how incomplete blue coverage and ambiguous catalogue associations can conceal the hottest massive stars.}

We present the Quantum Atom Control Kit (QuACK), a low-cost FPGA-based control system that provides memory-efficient precise signal timing and low-delay measurement-based decision making for operating neutral atom optical tweezer array experiments. The system consists of an FPGA module connected to a host computer via USB 3.0 and multiple cable-connected modular daughterboards enabling a myriad of configurable I/O formats. In this work, digital input and output as well as analog outputs are demonstrated. Multiple experimental sequence segments can be encoded with 5 ns timing resolution. On-chip condition kernels enable fast branching between segments based on digital inputs, eliminating host intervention for time-critical decisions, while high-speed USB communication allows rapid sequence updates and host-level orchestration. We demonstrate initial system loading and rearranging neutral ytterbium-171 atoms in optical tweezers. The combination of high-resolution timing, on-chip logic, and distributed orchestration architecture makes this system well suited for atomic, molecular, and optical (AMO) experiments pursuing applications in quantum information science.

Near flutter, modal frequency can be well resolved from accumulated phase while the sign of a small modal growth rate remains uncertain because the response envelope changes only weakly over a finite record. This work tests whether a validated physical relation between frequency and damping can exploit the better-resolved phase information to improve near-flutter stability identification. A coupling-audited SU2 Euler campaign for a modified Isogai-parameterized NACA 64A010 section at Mach 0.85 is subjected to systematic grid and temporal verification, multichannel critical-pole tracking, bootstrap trajectory closure, finite-record information analysis, and nonlinear Monte Carlo validation. The temporal study shows that 50 steps per structural period predict the wrong damping sign although frequency is already close to the finer solutions. The verified critical trajectory gives a flutter-speed index of 0.537665 and a local frequency-to-damping slope of 3.754. Restricting a free pole estimate to this trajectory gives an approximately 3.89-fold damping-precision gain, reproduced by Monte Carlo with a median RMSE gain of 3.915. At a representative operating offset and 30 dB amplitude SNR, the minimum tested record exceeding 95% correct-sign probability decreases from 15 to 5 cycles. Physically validated pole trajectories can therefore substantially reduce the observation time required for reliable near-boundary aeroelastic stability identification.

Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that remain meaningful across varying geometries, spatial prediction fields, and engineering quantities of interest. We investigate how established uncertainty quantification (UQ) approaches behave when adapted to geometry-conditioned neural surrogates. We compare one closed-form and two sampling-based approaches-a Gaussian process (GP)-based method, concrete Monte Carlo (MC) dropout, and deep ensembles-and evaluate them on three large, industry-relevant CAE datasets for external aerodynamics and crash dynamics. We examine whether predicted uncertainties have credible magnitudes, identify locations with larger prediction errors, respond to unfamiliar inputs, and remain informative for derived engineering quantities. On the DrivAerStar dataset, where all three methods are compared, each generally assigns higher uncertainty to locations with larger prediction errors, and validation-based rescaling brings interval coverage close to nominal on a disjoint in-distribution test set. Results on AirFRANS and automotive crash also show useful error ranking and interval estimates, but the relative performance of the methods changes with the dataset and evaluation criterion. UQ methods and evaluation metrics should therefore be selected based on the intended downstream CAE decision.

Controlling the spatial distribution of absorbed optical energy is central to nanoscale photothermal chemistry, plasmonics, and ultrafast materials control. Here, we show that a metallic AuPt superlattice concentrates electronic energy in Pt within a few hundred femtoseconds, regardless of the initial energy distribution between the two constituents. Ultrafast X-ray diffraction follows this energy redistribution through the amplitude of a coherent 570 GHz superlattice phonon driven by the stress imbalance at the AuPt interfaces. Despite the nearly homogeneous absorption at 400 nm, the observed lattice motion is identical to that produced by 800 nm excitation, which is absorbed predominantly in Pt. This dissipation driven localization of energy arises from the large electronic heat capacity of Pt and rapid electronic transport through the superlattice, providing a route to femtosecond control of nanoscale energy distributions.

Sheet-like structures are ubiquitous in magnetohydrodynamic systems and are known to play a crucial role in astrophysical plasma transport. Here, by applying a theoretical framework to data from numerical simulations, we demonstrate that current-sheet formation is a direct consequence of energy conversion between kinetic and magnetic fields. The strain-magnetic field organisation associated with this conversion favours the emergence of sheet-like structures, and analytical calculations show that sheet-like geometry constitutes the dominant flow configuration. We further show that the same conversion-driven reorganisation of the strain motion is responsible for the depletion of the mean Inertial energy transfer, a previously observed phenomenon whose physical origin has remained unclear. Finally, we identify experimentally accessible diagnostics through which these predictions can be tested in multi-spacecraft observations of turbulent plasmas.

Autonomous vehicles increasingly rely on high-end multi-frequency GNSS receivers that track modernized civil signals, such as L2C, L5, and L1C, alongside legacy GPS L1~C/A. Although multi-frequency operation improves robustness against natural error sources, its resilience against synchronous multi-band spoofing attacks remains insufficiently characterized. This paper presents a method for quantifying the inter-band alignment accuracy required for a coherent spoofer to evade cross-frequency consistency monitoring that a resilient receiver may employ. As a foundational study, the presented method is implemented and evaluated using the GPS L1 C/A and L2C signal pair within a controlled software-defined environment. This environment encompasses deterministic signal synthesis, programmable inter-band code offsets realized through fractional-delay filtering, and processing by a dual-frequency GNSS-SDR receiver. An inter-frequency pseudorange-difference consistency detector is defined and calibrated using a constant-false-alarm-rate (CFAR) approach, yielding a threshold of approximately 19 m at a false-alarm probability of $10^{-3}$. Signal fidelity and receiver capture are verified through correlation-function analysis using a multi-tap correlator bank. The inter-band code offset is then swept from one code chip to 0.005 chips to characterize detector performance. Results show that a perfectly aligned coherent attack yields a detection probability of zero, while detection probability increases rapidly with inter-band misalignment, reaching approximately 50% at 0.05 chips (about 15 m) and approaching unity for offsets of 0.2 chips or greater. These results quantify the alignment precision required for successful coherent dual-frequency spoofing and establish a practical framework for future evaluation of L5, L1C, and hardware-based multi-frequency spoofing scenarios.

The application of machine learning to molecular property prediction has become increasingly prevalent in drug discovery, yet most models operate as black boxes, returning a prediction without revealing which structural features drive it. MolExplain addresses this gap by combining property prediction with sub-structure level visual explainability in an interactive web interface. The system featurizes molecules as Morgan fingerprints, classifies them using a trained XGBoost model, and applies SHAP attribution to produce a smooth heatmap overlay indicating which regions of the molecule contribute for or against the predicted property. Applied to cyclic peptide membrane permeability, the tool's attribution independently recovers the known role of backbone N-methylation in improving passive membrane diffusion, consistent with established chemistry. While demonstrated on cyclic peptides, the framework is designed to generalize to other molecular properties, positioning MolExplain as a platform for interactive, explainability-driven molecular design.

Padé resummation of the correlation energy from Møller-Plesset (MP) perturbation theory is not size-consistent and is therefore seldom used. We present an orbital-based Padé resummation of the MP2 and MP3 self-energy operators, whose trace yields a size-consistent correlation energy. We find that damping the third-order same-spin contribution leads to substantially increased accuracy, with the resulting same-spin damped orbital Padé resummed (SSD-OPMP3) approach attaining near-chemical accuracy (mean absolute error $\sim$ 1 kcal/mol) for total atomization energies of non-multireference molecules in the W4-17 dataset against the gold standard CCSD(T) method. Excellent performance surpassing CCSD is also obtained for other molecular datasets such as those containing barrier heights, noncovalent interactions, dipole moments, static polarizabilities, and transition-metal chemistry. SSD-OPMP3 is thus a highly accurate electronic structure method with the same noniterative $O(N^6)$ cost as MP3, filling a gap in the cost/accuracy hierarchy of wavefunction methods.

Temporal rainfall distributions are widely used in design storm construction; however, many countries with limited sub-daily observations, such as Brazil, still rely on frameworks developed under different hydroclimatic conditions. This mismatch may introduce bias in hydrological design and water resources estimation. Here, we develop the first national-scale empirical Huff curves for Brazil using sub-daily rainfall observations. We compiled data from 3,164 stations and, after quality control, retained 290,164 storm events from 1,045 stations spanning 2010 to 2025. Empirical cumulative rainfall mass curves were constructed and fitted using seventh-degree polynomials at station, biome, state, and municipality scales. Our findings show a dominance of first-quartile (Q1; front-loaded) storm patterns, occurring at 94.4% of stations nationally, increasing to 99.2% in the Amazon and Cerrado biomes and decreasing to 88.7% in the Atlantic Forest. The national Q1 median curve closely matches the Huff (1967) reference (MAE = 0.045; Dmax = 0.097), with narrow bootstrap uncertainty. Q1 dominance is robust to inter-event time definition, with 84.7% of stations showing consistent classification across 2 to 12 h thresholds. A Soil Conservation Service Curve Number experiment across 579 headwater catchments shows that Brazilian curves increase design peak discharge by a median of 8% and up to 11% in the Cerrado relative to the Illinois reference, indicating potential underestimation when using non-local distributions. Biome-, state-, and municipality-scale parameters are provided as open data and through an interactive platform, offering locally calibrated design-storm alternatives for Brazil.

Transforming textile fibres into sensors can facilitate continuous physiological monitoring for improving human healthcare. Coating fibres with electronic conductors can help detect physiological stimuli but their sensitivity scales with thickness and often lowers the fibre mechanical flexibility. Proton conductors are potential alternatives; however, they suffer from fragile physical interfaces, and sluggish kinetics. Here, we report a bio-templated, scalable strategy to create superprotonic interfaces using natural silk fibres. We exploit ordered nanofibrils on silk fibres to selectively grow continuous metal-organic framework (MOF) nanostrips. This patterned, ca. $10\,\mu\mathrm{m}$-diameter fibre-structure provides low-defect interfacial pathways for ultrafast proton transport, yielding conductivities up to 40 $S\,cm^{-1}$ - two orders of magnitude above the state of the art - with millisecond response and high mechanical robustness. The individual micro-thread sensors facilitate respiratory monitoring with a mean bias of 0.01 breaths $min^{-1}$ in volunteer testing, unaffected by human movement and readily capture breathing signature in high-flow oxygen therapy devices. We integrate arrays of micro-thread sensors in textiles and exploit deep learning to demonstrate high-resolution spatiotemporal humidity mapping in dynamic flow-fields and resolve asymmetric respiratory patterns that elude conventional single-point detectors. Our results should have broad-ranging implications, e.g. in wearable health monitoring, bioelectronics, and human-machine interfaces.

Inspired by the recent progress in the simulation of tight-binding Hamiltonians on large lattices using tensor networks, we introduce a systematic matrix product operator (MPO) construction for single-particle Hamiltonians on recursively structured lattices. Taking advantage of this recursive structure, we encode the lattice geometry in a finite-state automaton and, adapting ideas from Abelian-symmetric tensor networks, obtain an exact and analytical MPO representation of the Hamiltonian, where the number of tensors grows logarithmically with the system size and the bond dimension is set by the number of automaton states. Several examples demonstrate the generality of the framework: regular lattices, fractals, Cayley trees, hyperbolic lattices, and one- and two-dimensional Fibonacci quasicrystals. Combined with the kernel polynomial method, these MPO representations enable large-scale calculations of spectral properties without explicitly constructing the full Hamiltonian. This provides a unified route to the exploration of electronic properties across a broad class of lattices at exponentially large system sizes.

In a tetrad formulation of general relativity, an ideal Ohm-type condition for the gravitational field implies the conservation of gravitational fluxes and the preservation of the connectivity of gravitational field sheets. We investigate the consequences of departures from ideal gravitational evolution by developing a theory of gravitational reconnection, defined as a change in the connectivity of gravitational field sheets arising from the breakdown of the ideal frozen-in evolution of the gravitational field. We derive quantitative measures of the gravitational reconnection rate, show that gravitational reconnection alters the energy-momentum exchange between the gravitational and matter sectors, and determine how it modifies the evolution of gravitational helicity. Our results provide a quantitative framework for describing changes in gravitational connectivity and their consequences in dynamical spacetimes.

Spacetime can undergo complex nonlinear evolution, as governed by the Einstein field equations, and a central challenge is to understand the geometric structures that arise, persist, and interact throughout its evolution. Using a formulation of Einstein equations that parallels nonlinear electrodynamics of continuous media, we show that general relativity admits gravitational field connections—two-surfaces and associated field lines whose connectivity is maintained by the spacetime dynamics. This gravitational frozen-in behavior is enabled by an ideal Ohm-type condition for the gravitational field. We further show that the same framework naturally leads to a conserved “gravitational magnetic” flux. A conserved gravitational helicity also emerges, with a clear topological interpretation in terms of gravitational field-line structures. These results identify well-defined topological constraints on admissible spacetime evolution and provide an organizing principle underlying the nonlinear dynamics of spacetime.

After four decades of null-results, the experimental quest to discover a dark matter particle ardently continues. While confidence in the leading candidate, the Weakly Interacting Massive Particle (WIMP), is waning, its explanatory success still shapes current experimental approaches and theoretical expectations - from underground detecting efforts in Europe, China, and the U.S. to modern theories of cosmic structure. This paper traces the origins of this particle-based paradigm to a critical predecessor: the neutrino-dominated universe. Proposed in the early 1970s as a solution to extra-galactic anomalies, neutrinos became the leading explanation of 'missing matter' following experimental hints of neutrino mass in 1980. Although short-lived, the model's coherent picture of cosmic evolution and structure formation set strong conceptual and methodological standards - providing critical impetus to the field of particle cosmology. Examining this history invites renewed reflection on how such past aims and expectations continue to guide dark matter inquiry today.

Nanophotonic cavities provide strong atom-photon coupling in a compact, directly fiber-coupled geometry, while a reconfigurable array of $^{171}$Yb atoms combines high-fidelity gates with a metastable qubit state coupled directly to a telecom-band transition, making the two a natural interface between neutral-atom quantum processors and telecom quantum networks. Here, we realize strong atom-cavity coupling on the ${}^{3}\mathrm{P}_0 \leftrightarrow {}^{3}\mathrm{D}_1$ transition of ${}^{171}\mathrm{Yb}$, establishing, to our knowledge, the first neutral-atom cavity-QED system to reach the single-atom strong-coupling regime on a telecom-band atomic transition. Reflection spectroscopy of a single-sided cavity yields a single-atom internal cooperativity of $C_{\mathrm{in}}=5.6(1.0)$ and collective coupling following $\sqrt{N}$ scaling up to $N=5$ atoms, with homogeneous coupling at antinodes over $\sim$200 $\mathrm{\mu}$m along the nanofiber. Thermal tuning of a fiber Bragg grating provides in situ control of the cavity outcoupling rate over two orders of magnitude from the undercoupled to the overcoupled regime, relevant for various atom-cavity protocols. We confirm compatibility with reconfigurable atom arrays by demonstrating 99.95(6)% imaging fidelity and trap lifetimes comparable to free-space array, at 1 $\mathrm{\mu}$m from the nanofiber axis. Moreover, the diameter-engineered antireflection design of the nanofiber enables tweezer-based atom transport across the nanofiber with no measurable loss or heating at 4.5 $\mathrm{\mu}$m above the nanofiber, supporting a two-layer architecture for the nanofiber-integrated atom array. From the demonstrated atom-cavity coupling, atom capacity, and cavity mode profile, we project time-multiplexed remote atom-atom entanglement generation at 40 kHz, with prospects for further scaling through technical enhancements and channel multiplexing.

We present a deep-learning surrogate for the Earth matter effect on solar neutrinos. The model uses a residual convolutional network conditioned on the solar neutrino oscillation parameters and is trained on numerical solutions computed over a grid of parameter values. It predicts the Earth-induced transition probabilities across neutrino energy and zenith angle, providing a fast approximation to the direct numerical calculation. For the reference electron-neutrino survival probability, the surrogate achieves a pointwise relative accuracy of approximately $2\%$ and a speed-up of about a factor of 60 in a laptop-CPU test. Since the network learns only the Earth-crossing propagation, it can be applied without retraining to new-physics scenarios that leave this propagation unchanged. The surrogate can also be retrained using alternative numerical implementations or extended parameter sets, providing a flexible and computationally efficient approach for solar-neutrino analyses. The code and simulation data are publicly available at \href{https://github.com/AI-Driven-HEP/NuMatterSurrogate}{\faGithub}.

Decoherence is conventionally regarded as a detrimental process that suppresses quantum coherence and limits the performance of quantum technologies. Contrary to this view, we show that spatially localized decoherence can instead be exploited as an interferometric tool. Beyond its conceptual implications, localized decoherence provides a novel approach to prepare arbitrary macroscopic spatial superpositions for testing quantum physics with massive particles. We demonstrate the proof of principle by making use of the correspondence between optics and matter-waves through the equivalence of the respective propagation functions. By employing stochastic phase fields to the optical counterpart to model the decoherence, our experiments confirm the expected interference. The results establish localized decoherence as a resource for quantum state engineering.

As generative AI agents are deployed at scale, safety will depend not only on technical safeguards and individual model design, but also on collective equilibria that determine how agent populations process information, prioritize actions, and respond to uncertainty. Yet the same equilibria that enable agents to coordinate also create a social attack surface. The standard framework to assess this vulnerability is critical mass dynamics: the minimum fraction of adversarial agents required to overturn an equilibrium through direct competition. Here, we show that this approach risks underestimating system vulnerability by reducing the problem to the identification of singular tipping points, and ignoring indirect but potentially more efficient routes through which collective behavior can be redirected. Through experiments with populations of LLM agents and an analytic framework that captures their collective dynamics at scale, we map critical-mass thresholds that define a directed, weighted topology over the space of coordination equilibria, and treat this topology as a navigable landscape. We show that indirect tipping through intermediate stepping-stone equilibria can reduce the committed minority required to reach an alternative state, bypass majority requirements, and make possible transitions inaccessible through direct challenges. The diversity of available alternatives and timing of the attack further reshape this landscape, creating opportunities for control as well as risks of unintended destabilization. These results show that an equilibrium's resistance to committed intervention is not an intrinsic property but a structural feature of its competitive relations with alternative states. Securing populations of interacting AI agents therefore requires mapping this social landscape alongside individual agent capabilities and the technical channels through which they interact.

Quantum simulators are beginning to prepare long-sought phases of matter that remain difficult to realize cleanly in materials. Yet identifying such phases remains a major challenge when their defining properties are inherently non-local and cannot be captured by conventional local measurements. Here we establish multiple-quantum coherences (MQCs) as phase-sensitive diagnostics for gapped $\mathbb{Z}_2$ quantum spin liquids. Focusing on the extended toric code and using large-scale quantum Monte Carlo simulations, we build a direct correspondence between the elementary anyonic excitations and the weights of different MQC sectors. MQCs thereby reveal anyon condensation across the phase transitions through characteristic signatures that remain robust against fluctuations that obscure conventional diagnostics. Subsystem-resolved MQCs further provide a bipartite entanglement witness that distinguishes a classical loop gas from a quantum-coherent closed-loops gas. We develop and benchmark a practical protocol to extract MQCs based on the return fidelity of adiabatic round trips, and show that local coherence measurements retain experimentally accessible signatures of the phase transitions. Our results establish MQCs as a practical diagnostic for the excitations, phase structure and global constraints of quantum spin liquids.