Papers

Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.

Photon statistics have moved to the forefront of modern optics, as intensity fluctuations and correlations shape multiphoton interactions and reveal information beyond mean-intensity measurements. Developing high-quality photon sources with pronounced correlations is a fundamental necessity in these fields. Here we demonstrate fluctuation-driven nonlinear statistical amplification of quantum light in spontaneous four-wave mixing using filtered amplified spontaneous emission (ASE). Extending the coherent-pump framework to fluctuating fields, we show how nonlinear weighting of pump intensity combines with bosonic bunching to amplify quantum statistics and reshape temporal correlations. In a SiN microring, ASE pumping increases the zero-delay unconditional second-order correlation from 2.01 to 7.58 and extends the Hanbury Brown–Twiss correlation time by a factor of approximately 2.4. The super-bunched quantum source nevertheless retains heralded single-photon behaviour with $g_H^{(2)}(0)\simeq0.04$, while the same ASE pump supports time–energy entanglement in a silicon waveguide with a raw Franson visibility of 89.84%. These results establish driving-field statistics as a design dimension for quantum light, broadening the horizons for research into higher-order correlations and nonlinear physics.

Cytoplasmic streaming is believed crucial to large developing cells, such as oocytes, where flow provides a much faster route to transport and mixing of cellular components than molecular diffusion. Molecular motors carrying cargoes on cytoskeletal elements are known to drive streaming in a variety of biological systems and, in Drosophila oocytes, cell-spanning vortical flows are tied to kinesin motors moving on dense beds of microtubules. Recent theories and simulations suggest such streaming self-organizes through interacting microtubules, motors, and flow. Whether these theoretically derived flows are adequate to the tasks of transport and mixing has been unclear. Here we report on new observations of coherent waves traveling persistently through microtubule beds during streaming, and investigate their impact on fluid transport and mixing. Leveraging recent advances in simulating the complex fluid-structure problems of cellular environments, we probe a biophysical model set in an oocytal geometry. Our simulations identify previously unknown waving states that show concordance with our experimental observation and the capacity for functional transport and mixing.

We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional deterministic approaches often fail to provide the uncertainty quantification (UQ) necessary for ill-posed problems, while conventional Bayesian sampling methods such as Markov chain Monte Carlo (MCMC) suffer from the curse of dimensionality and slow convergence in high-dimensional model spaces. The proposed framework addresses these challenges through a three-tier computational strategy. First, we utilize the Fast Marching Method (FMM) to solve the eikonal equation, ensuring high numerical accuracy. Second, we reformulate the global tomographic objective into a decentralized consensus form, allowing the inversion to be decomposed into independent subproblems solved in parallel via the Alternating Direction Method of Multipliers (ADMM). This architecture eliminates the need for the explicit construction of large-scale sensitivity matrices, significantly reducing the memory footprint for 3D surveys. Finally, we integrate Stein Variational Gradient Descent (SVGD) within the ADMM workers to perform approximate posterior sampling. By evolving a set of model particles along a functional gradient direction that balances data-fitting forces with a repulsive kernel-based diversity force, we obtain an ensemble from which posterior summaries are computed. We derive a data-space Gauss-Newton update using the Woodbury matrix identity to further accelerate the particle evolution in large-scale 3D problems. Numerical experiments on complex 2D and 3D models demonstrate that the algorithm achieves stable convergence, produces high-fidelity velocity reconstructions, and provides posterior uncertainty maps.

Normal-conducting radiofrequency (rf) cavities for muon-collider ionization cooling must operate at high accelerating gradients in strong solenoidal magnetic fields, where rf breakdown can be enhanced by the magnetic focusing of field-emitted electrons. In this work, field-emitted electrons were tracked in the realistic field maps of rectilinear cooling-lattice cavities to test the validity of the previously developed localized-bombardment picture with simplified field maps. Under comparable reduced-field assumptions, the tracking results show good agreement with previous results. The full rf eigenmode fields and nonuniform solenoidal fields modify the idealized beamlet structure, producing rf phase dependent centroid shifts and broadened impact distributions from solenoid fringe fields. Nevertheless, the original model remains a useful framework for estimating limits on operating gradients. The thermal response is evaluated analytically using this model, with material properties varied to assess the coupled effects of heat transport and thermomechanical damage threshold. These results can inform cavity testing in strong solenoidal fields, muon ionization cooling channel designs, and other applications requiring high-gradient rf operation in magnetic fields.

Why do political parties polarise when most voters remain in the centre? Moving beyond conventional point-based models, we address this classic puzzle by developing a spatial voting framework that conceptualises political parties as continuous ideological distributions. Operating under endogenous turnout, these parties adapt their positioning to maximise a combination of total votes and parliamentary representation. We demonstrate that this theoretical formulation reveals a distinct phase transition in multiparty competition, moving from centripetal convergence to centrifugal separation. These dual objectives do not necessarily reward the same directional party movement as voters only cast ballots for ideologically acceptable parties. The numerical results map the precise boundaries of this transition: a higher party count destabilises the centrist equilibrium and expands the zone of divergence, whereas pronounced internal party dispersion and a stronger emphasis on representation contract it. Furthermore, electoral institutions critically govern this divergence: when voter preferences vary across geographic districts, the same national electorate can trigger entirely different phase transitions depending on how votes are converted into seats. Together, our results show how party differentiation emerges endogenously from the dynamics of electoral competition itself. Whether small initial differences grow or decay is not merely a reflection of voter preferences, but an outcome dictated by party goals, competitor count, internal party breadth, and vote-to-seat conversion rules.

Superfluorescence (SF), intense picosecond bursts from self-synchronizing dipoles, is promising for room-temperature quantum sources. Yet, whether this synchronization can convert weak structural anisotropies into robust macroscopic order remains an open question. Here, we demonstrate that polycrystalline ferroelectric 2D perovskite thin films exhibit strongly linearly polarized SF with a degree of polarization (DOP) up to 86%. Remarkably, macroscopic polarization emerges without external bias despite randomly oriented microscopic domains, suggesting a cooperative gain mechanism that amplifies weak local anisotropies by several orders of magnitude into coherent, linearly polarized light bursts. Our results establish cooperative superfluorescence as a general concept for translating microscopic anisotropy into robust macroscopic order, exemplified here for linear polarization, offering a sensitive probe of hidden material symmetries and a framework for polarized quantum light sources

This paper investigates dynamical properties of the Nonlinear Walecka Model (NLWM), also known as Quantum Hadrodynamics (QHD), which provides a relativistic framework for describing nuclear matter through nucleon-meson interactions. While the original linear model successfully captures qualitative features of nuclear matter, it overestimates nuclear compressibility; consequently, nonlinear extensions incorporating cubic and quartic self-interaction terms in the scalar field $(\sigma)$ are employed to achieve better agreement with experimental data. By treating the self consistent effective-mass equation as an iterative mapping, the study explores the emergence of complex behaviors such as periodic orbits and chaotic regimes. Using tools from nonlinear dynamics, including return maps and bifurcation diagrams, we characterize routes to chaos via period doubling cascades and identify the presence of "shrimps" stable isoperiodic islands within chaotic regions of the parameter space. Furthermore, we discusses the physical implications of these findings, suggesting that crisis phenomena and structural convergence may serve as dynamical signatures of macroscopic rearrangements in the scalar mean field, potentially influencing the stability and equation of state (EOS) of dense nuclear matter in extreme environments like neutron stars.

Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.

Myelin membranes are tightly connected to the axon, forming an electrical route for rapid saltatory conduction of action potentials from node to node. Beneath the myelin, adjacent to the node, lie voltage-gated potassium (K$^+$) channels, whose fundamental role in conduction remains poorly understood. Constrained by biological data, we developed a finite element model of a node of Ranvier encompassing an adaxonal membrane separating oligodendrocyte cytoplasm from the periaxonal space, and solved the full coupled set of physical equations governing electrodiffusion and voltage profiles at the nanoscale. Remarkably, we find that nodal action potentials invert at the inner adaxonal myelin membrane. The hyperpolarization-driven opening of myelinic Kir4.1 channels produces an instantaneous, powerful K$^+$ prebuffering, clamping the periaxonal K$^+$ concentration and enabling high-frequency action potential generation. Together, these findings indicate that the periaxon maintains an electrical feedback loop with the node, and suggest a key evolutionary advantage of juxtaparanodal K$^+$ channel positioning.

Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in resolution in coverage and computationally intensive. Recent generative diffusion model-based approaches capture statistical priors to support traditional inversion methods, but these approaches do not account for the top to bottom progression of information (layer stripping) involved in surface recorded data, where deep velocity information depends on the shallow. To address this issue, we propose a depth-progressive diffusion framework that constructs velocity models incrementally from shallow to deep by propagating prior information. Our method trains on paired shallow-deep velocity patches with variable overlap and explicit depth encoding, integrating multiple geophysical constraints including well logs and seismic images (representing structural information). During inference, we synthesize overlapping depth slices using a progressive algorithm and merge them with Gaussian-weighted blending to eliminate boundary artifacts. This approach leverages both learned geological distributions and observed shallow priors while providing uncertainty quantification. Extensive numerical experiments on in-distribution tests and an out-of-distribution test demonstrate excellent VMB accuracy with a strong correlation between predicted uncertainty and actual errors. As a proof of concept, this part I employs idealized structural constraints derived from vertical reflectivity to validate the methodological framework. The companion paper (Part II) extends the approach to realistic structural constraints relying on migrated images with field data applications.

When two photonic structures of different topology are joined, an optical mode appears at their junction. Its existence depends only on this topological difference and not on the precise dimensions of either structure. Here we realize the photonic analogue of a Jackiw-Rebbi (JR) state in hexagonal boron nitride (hBN) hosting negatively charged boron vacancies, optically addressable spin defects. Two partially etched gratings, in which the order of the radiating and dark band-edge modes is reversed, are joined side by side. Angle-resolved reflectivity and photoluminescence reveal this reversal and a non-dispersive JR state at the junction. The state proves remarkably tolerant: despite fabrication errors of up to 20 nm, its wavelength lies within a 1 nm of the design, as predicted by a systematic numerical tolerance analysis. JR gratings therefore offer a robust, monolithic platform for controlling the emission of luminescent defects.

The induction region of a model wind farm is investigated experimentally as an adverse pressure gradient (APG) turbulent boundary layer problem, using hot-wire anemometry. An array of porous discs, with diameter $D=50$\,mm representing the farm, in multiple configurations across two turbulent boundary layers of different depths, with farm-present and farm-absent configurations compared throughout. The farm imposes a spatially developing adverse pressure gradient in the approach flow. Spanwise-averaged measurements reveal systematic farm-scale global blockage extending to at least $10D$ upstream in all cases. The farm-induced turbulence intensity increase, which reaches up to $7\%$ relative to the farm-absent reference for the shallowest boundary layer, is shown by cumulative variance decomposition to be carried predominantly by large-scale motions of the boundary layer, whilst the small-scale turbulence intensity is only weakly affected. This selective large-scale energisation is consistent with the outer-layer amplification driven by an adverse pressure gradient. The concentration of farm-induced turbulence energy at scales comparable to or larger than the rotor diameter has direct implications for turbine fatigue loading, preferentially exciting coherent rotor-scale load fluctuations at the frequencies most damaging under fatigue. The results establish a quantitative link between wind farm global blockage and APG boundary layer physics.

We present an extension of the Frenkel exciton model to incorporate exchange interactions between monomers in molecular aggregates in conjunction with {a multi-reference electronic structure approach}. Our derivation, {which combines the Frenkel exciton Hamiltonian and the single-electron pair exchange approximation}, yields a non-perturbative, variational expression for the exchange coupling that naturally excludes any basis set superposition error. The method has been implemented in OpenMolcas and enables combination with multi-reference electronic structure techniques. The main objective of the present study is to assess the role of exchange in systems with strong multi-configurational character. Illustrative examples demonstrate how the inclusion of exchange at different levels of approximation can substantially alter the magnitude and sign of intermonomer couplings and thus, for instance, potentially converting the predicted classification of the aggregate from H-type to J-type. Comparison with TDDFT-based couplings highlights significant discrepancies arising from multi-reference effects, double excitations, and Rydberg transitions. Overall, this approach advances the predictive modeling of photophysical and photochemical processes in aggregates of polyacenes, carotenoids, and other systems where multi-configurational and Rydberg states are essential.

We demonstrate a laser-wakefield accelerator driven by a high-repetition-rate optical parametric chirped-pulse amplification (OPCPA) laser, producing stable, quasi-monoenergetic electron beams at 100 Hz. Using 70 mJ, 8.1 fs laser pulses, we obtain 47 MeV electron beams with 15 pC charge and an intrinsic energy spread below 10%. Measurements over 10,000 consecutive shots show rms fluctuations of only 1.4% in peak energy and 12% in charge, together with low pointing jitter. Principal-component analysis reveals that the beam fluctuations are described by five physically interpretable modes, dominated by slow variations below 1 Hz. Particle-in-cell simulations reproduce the measured spectrum and show that nonlinear self-focusing localizes ionization injection, resulting in the observed narrow energy distribution. The demonstrated combination of electron energy, charge, beam quality, and stability represents a significant step toward high-average-power laser-plasma accelerators.

This contribution presents the new phase-2 CMS baseline tracking strategy currently proposed for online event reconstruction, which can be similarly useful in offline reconstruction under the extreme pileup conditions of the High-Luminosity LHC (HL-LHC). By unifying diverse algorithmic paradigms and integrating machine learning (ML) techniques into a coherent sequence, CMS can maintain high-efficiency and high-resolution tracking while improving the fake track rate and the computational performance, a crucial aspect especially for the HL-LHC. The approach combines GPU-optimized algorithms (Patatrack, LST) with vectorized CPU methods (mkFit), exploiting modern hardware to maximize throughput. This heterogeneous strategy reduces the resource requirements and enhances the physics reach by incorporating a global displaced track reconstruction and extending sensitivity to rare, interesting signatures, such as long-lived particles.

Charged particle track reconstruction is one of the heaviest computational tasks in the event reconstruction chain at LHC experiments. Furthermore, projections for the High Luminosity LHC (HL-LHC) show that the required computing resources for single-threaded CPU algorithms will exceed those that are expected to be available. It follows that experiments at the HL-LHC will need to employ novel and evolved track reconstruction algorithms, within heterogeneous computing systems that include many-core CPUs as well as GPUs, in the attempt to maximize the computational performance while retaining the best possible reconstruction efficiency. In the context of the CMS High Level Trigger (HLT) at the HL-LHC, the mkFit algorithm, already in use for the CMS track reconstruction during the LHC Run 3, will exploit its parallelized and vectorized nature on CPUs to perform pattern recognition using seed tracks produced with algorithms that are designed to be fully parallelizable and hardware agnostic, thus suitable for heterogeneous systems: the Patatrack and the Line Segment Tracking (LST) algorithms. Patatrack is an established algorithm, already used for the CMS pixel track reconstruction at HLT during the Run 3 of the LHC, while LST is a novel algorithm, recently integrated in the CMS software, targeting the reconstruction of tracks in the outer tracker of the HL-LHC CMS detector. The state-of-the-art performance for the CMS HLT track reconstruction at the HL-LHC is presented, obtained using the combination of the mkFit, Patatrack, and LST algorithms, that in turn use machine-learning (ML) techniques such as deep neural networks and multi-objective particle swarm optimization to suppress duplicate and misreconstructed tracks. Prospects of further improvements are also presented, with a focus on the usage of ML techniques for track reconstruction at CMS.

In-depth characterization of heterogeneous materials has long been a challenge in non-destructive testing. Here, a method is proposed to determine the elastic constants of metallic polycrystalline materials using back-scattered ultrasound. The waves scattered by the microstructure are analyzed to image the effective bulk velocities. To this end, a reflection matrix is acquired with an array of transducers. The projection of this matrix onto a focused basis is used to estimate an average point spread function. Optimizing this function with respect to the propagation model leads to an estimation of the longitudinal velocity. Additional treatments are developed to adapt the method to map the shear wave velocity. The local Poisson's ratio is then deduced from the ratio between those two velocities. Young's modulus and shear modulus can also be obtained assuming known densities. This matrix approach is experimentally validated on different polycrystalline materials. A sample displaying heterogeneous mechanical properties is then simulated to assess the accuracy and the resolution of the method. Its strengths and limitations are discussed, demonstrating its potential for quantitative non-destructive material characterization.

Surfactant adsorption at solid-fluid interfaces strongly influences contact line dynamics, yet most surfactant-laden phase-field models neglect solid adsorption and rely on Cahn-Hilliard-type equations. We develop a thermodynamically consistent and conservative Allen-Cahn-Navier-Stokes model for surfactant-laden contact line dynamics with solid adsorption. A unified free-energy formulation accounts for surfactant transport, adsorption, and wetting dynamics, yielding consistent wetting and adsorption boundary conditions. Second-order Allen-Cahn-type equations govern both the phase field and surfactant concentration, avoiding the numerical complexity of fourth-order formulations. A classical linear constraint ensures exact conservation of total surfactant mass, whereas a generalized nonlinear conserved quantity is introduced for the phase field to improve geometric volume conservation. The resulting weighted Lagrange-multiplier correction vanishes in the bulk phases and is confined to the diffuse interface, suppressing the leading-order curvature-induced bulk shift associated with the linear constraint while preserving the energy-dissipation structure. Matched asymptotic analysis shows that the nonlinear formulation can achieve formal second-order geometric-volume accuracy. A lattice Boltzmann method is developed to solve the coupled system. Numerical results confirm the predicted second-order convergence, demonstrate enhanced volume preservation for high-curvature droplets, and reproduce wetting responses induced by preferential solid adsorption, including hydrophilization and autophobing. Under shear flow, the results also show that solid adsorption can significantly influence contact line recession and droplet detachment.

With advances in high-speed imaging there is a growing need for robust high-speed deconvolution algorithms. Wiener deconvolution remains one of the fastest and simplest algorithms available, however it suffers from ringing artifacts that can limit its applicability. In this work, we develop two complementary, computationally efficient heuristic methods to suppress these ringing artifacts by using physical non-negativity and sparsity priors.

Conflict events in Middle Eastern countries exhibit complex temporal fluctuations. As a simple baseline, we examine whether their conflict-frequency time series contain measurable linear temporal structure using the autoregressive integrated moving average (ARIMA) framework, which assumes linear dependence on past observations and errors. Despite this simple linear assumption, the temporal dependence is largely captured for most countries by ARIMA. Predictions based on the selected ARIMA models reproduce the overall temporal behavior to some extent, although abrupt changes remain difficult to predict. We further examine the short-term persistence of changes in conflict activity using the first zero-crossing point $k^*$, which characterizes the duration of the initial positive temporal correlation, and the integrated correlation time $\tau_{\mathrm{int}}$, which additionally reflects its strength and decay. The resulting $k^*$ values are approximately 2–6 days, while $\tau_{\mathrm{int}}$ ranges from approximately 0.6 to 1.7 days. The significant negative association of $\tau_{\mathrm{int}}$ with mean GDP per capita suggests a possible relation between short-term conflict persistence and broader socioeconomic conditions. These results reveal measurable linear temporal structure and country-dependent short-term persistence in the temporal patterns of conflict activity across the Middle East.

Objective. This work aims to accelerate our four-dimensional (4D) proton pencil beam delivery strategy, which incorporates respiratory motion into a dynamic treatment plan, to improve dose conformity and treatment efficiency for gantry-less and magnet-free scanner designs. Approach. To accelerate the generation and adaptation of 4D treatment plans, we propose a conditional hybrid ResNet18-Transformer model for predicting pencil beam weights. The model predicts either the full set of pencil beam weights or a subset thereof. Main Results. The conditional model can successfully predict the remaining beam weights of a treatment, and the results suggest that extending this approach to full treatment prediction is feasible but requires further investigation. Significance. The acceleration of 4D treatment generation via pencil beam weight prediction takes us one step closer to the feasibility of treating mobile targets with simplified, gantry-less, and magnet-free scanner designs. Reducing system complexity while preserving dosimetric conformity may offer a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours. Our code is available here: https://github.com/NairVonMuehlenen/Pencil-Beam-Weight-Prediction

Single-beam ptychography (SBP) requires overlapping scan positions for reliable reconstruction, and imaging larger areas demands more measurements. Multi-beam ptychography (MBP) illuminates several regions simultaneously, but its measurement gain at a fixed resolution remains unclear when diffraction intensities from different beams overlap on the detector. Using numerical simulations, we determined the minimum number of patterns needed to reach a target resolution by varying the scan step size for SBP and fully overlapped MBP with up to 15 beams. We define gain as the pattern count for repeated SBP scans divided by that for MBP at the same total imaged area and achieved resolution. Although the required MBP pattern count increased approximately linearly with beam number, it remained below that of repeated SBP scans. At 67% of the spatial Nyquist frequency using a one-bit threshold, fully overlapped MBP achieved a gain of approximately two. Detector cropping and lower-resolution targets reduced this gain, showing its dependence on usable diffraction information and the reconstruction criterion. Because overlapping intensities provide no spatial identification of individual beam contributions, this gain is a conservative benchmark; separation on the detector offers a route to larger gains.

When does a collection of autonomous cells become a multicellular individual? We propose that coherence provides a physical description of this transition. Coherence is treated as a global property arising when distinguishable constituents admit a physically meaningful collective state-space description. Using the center of mass and an interaction-based construction, we show that such collective states can be defined for classical bodies before dynamics is introduced, with normal modes emerging as a particular dynamical realization. We apply this framework to multicellular organization, where cells retain their identities while their independent individuality is replaced by participation in the organized whole. In Dictyostelium discoideum, cAMP-mediated coupling produces population-level collective modes, while starvation provides an experimentally controlled energetic constraint on the transition to multicellularity. The framework yields direct tests through interaction-derived collective eigenstates and the energetic cost of maintaining autonomous versus collective organization. Coherence may thus provide a general physical description of multicellular individuality without requiring microscopic quantum coherence or intrinsic wave character.

The debate over pairwise and higher-order models is often framed as a contest of expressive power. This framing is misleading. A graph with arbitrary multivariate node functions can reproduce the node-level dynamics of many hypergraph models, but the hypergraph's grouping information does not thereby disappear: it may simply move from the structure to the rule. We separate four notions frequently conflated here: structural representation, functional representability, statistical identifiability, and mechanistic adequacy. We then prove that interaction order, the number of variables that must act jointly in some term of any additive decomposition of a node's update, is the same in every exact representation: no change of structural language can lower it. As a description-length problem, at the unrestricted algorithmic level a fixed compiler redistributes information between structure and rule at constant overhead, so expressiveness alone cannot privilege either language. Preferences arise only relative to explicit model classes, code families, and data. This yields an operational minimum-description-length criterion combining structural cost, conditional rule cost, and imperfect fit. Within it, for $M$ disjoint groups of size $k$, the clique projection's edge list is asymptotically $k-1$ times longer than the hyperedge list it replaces: the projection encodes the same grouping at higher cost. Examples from diffusion, Boolean dynamics, ecology, and ambiguous projections give graph-preferred, hypergraph-preferred, and unresolved cases; for bipartite and multilayer lifts, cost, fit, and identifiability tie, and the choice turns on which entities are posited as primitive. The position is symmetric: higher-order structure should not be inferred from phenomenology alone, nor does a graph's ability to emulate a system make it the most parsimonious or adequate description.

Range uncertainties remain a major limitation to the full clinical exploitation of proton therapy, motivating reliable in-vivo range-verification techniques. This work presents an experimental investigation of a hybrid Prompt-Gamma Imaging (PGI)–Positron-Emission Tomography (PET) concept at the isochronous cyclotron of the West German Proton Therapy Centre under clinically representative pencil-beam-scanning conditions. Because this accelerator delivers a quasi-continuous beam, a pulsed shoot-and-step structure was imposed to mimic the interruptions associated with clinical energy-layer switching. This enabled PGI during irradiation and PET and delayed Compton imaging during beam-off intervals within the same sequence. Four two-plane Compton cameras were arranged in a co-planar cross-shaped geometry around the beam isocenter. Polyethylene phantoms were irradiated with 100~MeV protons at known positions along the beam axis. Their positions were estimated from reconstructed one-dimensional profiles using supervised machine-learning models trained exclusively on Monte Carlo simulations. PET achieved the best performance, with a root-mean-square deviation of 0.8~mm, followed by PGI with 1.6~mm. Delayed Compton imaging based on e$^{+}$ annihilation photons and $^{10}$C$^{*}$ decays yielded 2.4~mm and 3.8~mm, respectively. PET retained good performance down to 1% of the original event statistics, whereas PGI became increasingly statistics-limited. These results demonstrate the complementary strengths of hybrid PGI–PET and support its development towards millimetre-scale proton-range verification under clinically relevant irradiation conditions.

The trajectories of point vortices after a division using reverse collapse route are studied. In this setting, an unexpected peculiar phenomenon appears, corresponding to the shadowing of the vortex breakup. This occurs at least for a finite time, when the system evolves from three to five vortices after a breakup. An analytical study reveals that this observed numerical phenomenon is related to the condition of scale invariance, a condition necessary for the finite time singularity. Further simulations agree with our analytical findings and indicate that shadowing can occur with more than five vortices, and as well with higher order singularities. This phenomenon leads to question what a vortex is actually measuring, triggering speculations if this shadowing property could be generalized to other Hamiltonian systems with finite-time singularities.

The idea that emotions in biological systems are analogous to intrinsic colored noise is advanced and justified. A model describing the dynamics of operation of biological networks under the influence of colored noise is suggested. The agents of a biological network can be represented either by biological species, such as humans and animals, or by neurons of the brain, or by the nodes of a neural network. Operational actions, or decisions, in a biological noisy network are based not only on the evaluation of utility of alternatives, but also on the agents emotions. The model is probabilistic, with the choice of alternatives characterized by the related probabilities. At the initial step, the agents make decisions individually and then start exchanging information with each other and imitating the actions of other agents, thus forming a biological network of interacting agents. Numerical simulations are accomplished for a heterogeneous society consisting of three groups of agents, one group possessing long-range memory, the other group, short-range memory, and the third group of super-rational agents acting strictly on the basis of utility, being deprived of emotions. Dynamics of opinions in different groups can be smooth, oscillatory, or chaotic. Altogether, eight types of operation are found, depending on the dynamics of group decisions. Under strong imitation effect, there appears chaotic motion in the evolution of decision choice. It is shown how the Ellsberg paradox can be resolved and how the exchange of information influences the dynamics of this paradox.

To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~$^\circ$C during the day and 2.6~$^\circ$C at night, while UTCI varies by up to 7.9~$^\circ$C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.

Finite-memory optical tracers can encode transport properties through their previously experienced excitation. Using such memory features, harboured in the population of excitable long-lived electronic states within the emitters, instantaneous localization assessments are not needed to determine their transport properties. Here we formulate this principle for monitoring of stochastic transport extracted from transport-history integral over conditional transport histories. Under structured illumination, this history yields a measurable transfer-function response from which scalar and tensor diffusion are reconstructed without time-resolved acquisition. Fisher-information analysis identifies the optimal operating regime and yields first-principles, parameter-free predictions of scalar and tensor reconstruction precision, in quantitative agreement with independent Monte Carlo simulations. These results establish finite optical memory as a general principle for stochastic transport imaging.