Papers

Self-organized systems, from synthetic nanostructures to developing organisms, are composed of fluctuating units capable of forming robust functional structures despite noise. Here, we ask: are there fundamental bounds on the robustness of noisy self-organized systems? By viewing self-organization as noisy encoding, we prove that the positional information capacity of short-range classical systems with discrete states obeys a bound reminiscent of area laws for quantum information. We illustrate this principle with lattice models whose dynamics is captured by continuum models derived using exact coarse-graining techniques and validated through Dynamical Renormalization Group calculations. The universal bound is saturated by fine-tuning transport coefficients, which can be rationalized in the continuum limit upon considering the effects of boundaries on domain wall dynamics. We illustrate how this limit can be bypassed when long-range correlations are present by investigating a wave-pinning model motivated by biological mechanisms. In this class of models, global constraints reduce the need for fine-tuning by providing effective integral feedback. Our work identifies fundamental limits for the ability of natural and synthetic microsystems to self-assemble into patterns and rationalizes them on purely information-theoretic grounds.

Wave directionality plays a critical role in shaping coastal conditions and influencing local livelihoods, underscoring the importance of conducting detailed analyses. This study examines directional wave climate along the southwestern coast of Lake Michigan from 1979 to 2023 using the Directional Wave Entropy (DWE). Directionality was characterized in terms of inter-annual trends, monthly patterns, spatial variation, and extreme wave conditions. Overall, results exhibited a strong bi-directionality, with dominant northern and southern wave systems along the coast of our study site. A few annual trends for the inter-annual wave climate were observed, and there is a clear seasonal variation such that bi-directionality increases in the summer and winter seasons. As for spatial variation of wave directionality, all locations in the study sites presented a bi-directional wave climate. The two dominant directions of wave directionality: northern and southern mean significant wave heights were also characterized in all locations of study sites as 0.566 and 0.563 meters. Furthermore, the extreme wave heights in the northern direction are significantly greater than the extreme waves in the southern direction. In summary, these findings suggest the importance of wave directionality on coastal structural design and coastal morphology management along the coast of our study site.

Spin plays a fundamental role in understanding electronic structure, yet many real-space wavefunction methods fail to adequately consider it. We introduce the Spin-Adapted Antisymmetrization Method (SAAM), a general procedure that enforces exact total spin symmetry for antisymmetric many-electron wavefunctions in real space. In the context of neural network-based quantum Monte Carlo (NNQMC), SAAM leverages the expressiveness of deep neural networks to capture electron correlation while enforcing exact spin adaptation via group representation theory. This framework provides a principled route to embed physical priors into otherwise black-box neural network wavefunctions, yielding a compact representation of correlated system with neural network orbitals. Compared with existing treatments of spin in NNQMC, SAAM is more accurate and efficient, achieving exact spin purity without any additional tunable hyperparameters. To demonstrate its effectiveness, we apply SAAM to study the spin ladder of iron-sulfur clusters, a long-standing challenge for many-body methods due to their dense spectrum of nearly degenerate spin states. Our results reveal accurate resolution of low-lying spin states and spin gaps in [Fe$_2$S$_2$] and [Fe$_4$S$_4$] clusters, offering new insights into their electronic structures. In sum, these findings establish SAAM as a robust, hyperparameter-free standard for spin-adapted NNQMC, particularly for strongly correlated systems.

The impact of droplets on concave surfaces is poorly understood, although it is relevant to a mode of dispersal that has evolved independently in several species of plants and fungi. This mode relies on splash-cups, specialized organs that use raindrops to disperse reproductive units away from the parent organism. We investigated the impact of droplets on conical cavities that mimic splash-cups and we found that such impact may lead to the formation of two types of jets, which appear essential for dispersal in nature. We built a minimal kinematic model that explains jet formation, involving the motion of fluid particles along geodesics (shortest paths) on the cone surface and we predicted cone angles that optimize jet formation, consistent with the geometries of natural splash-cups

The emergence of data-driven weather forecast models provides great promise for producing faster, computationally cheaper weather forecasts, compared to physics-based numerical models. However, while the performance of artificial intelligence (AI) models have been evaluated primarily for average conditions and single extreme weather events, less is known about their capability to capture sequences of extreme events, states that are usually accompanied by multiple hazards. The storm series in February 2020 provides a prime example to evaluate the performance of AI models for storm impacts. This event was associated with high surface impacts including intense surface wind speeds and heavy precipitation, amplified regionally due to the close succession of three extratropical storms. In this study, we compare the performance of data-driven models to physics-based models in forecasting the February 2020 storm series over the United Kingdom. We show that on weekly timescales, AI models tend to outperform the numerical model in predicting mean sea level pressure (MSLP), and, to a lesser extent, surface winds. Nevertheless, certain ensemble members within the physics-based forecast system can perform as well as, or occasionally outperform, the AI models. Moreover, weaker error correlations between atmospheric variables suggest that AI models may overlook physical constraints. This analysis helps to identify gaps and limitations in the ability of data-driven models to be used for impact warnings, and emphasizes the need to integrate such models with physics-based approaches for reliable impact forecasting.

Epsilon-near-zero (ENZ) photonics provides a powerful route to extreme dispersion engineering, strong field confinement, and unconventional wave phenomena. A closely related concept is photonic doping, where subwavelength nonmagnetic dielectric materials embedded in ENZ media enable exotic responses such as perfect-magnetic-conductor behavior and simultaneous epsilon- and mu-near-zero states. However, photonic doping has remained limited to microwave and far-infrared regimes due to the intrinsic losses of optical ENZ materials. Here, photonic doping is demonstrated at optical frequencies by embedding a periodic array of dielectric Mie resonators into an ultralow-loss, all-dielectric ENZ platform based on near-cutoff Bragg microcavities. The resulting structures support spectrally isolated, quasi-singular coupled Bragg–Mie resonances spanning electric and magnetic multipolar orders and their overtones. These modes exhibit effective near-zero-index dispersion with fields confined either within or between the nanoparticles. A representative \(14\,\mu\mathrm{m}\)-scale doped structure exhibits quality factors approaching \(10^{4}\) and magnetic-dipole Purcell enhancements exceeding \(5\times10^{3}\) in the near-infrared. The demonstrated platform elevates the photonic doping from a microwave-only concept to a fully optical, low-loss, and multipole-resolved platform, enabling ultra-narrowband Mie-like resonances, enhanced magnetic-light interactions, and new opportunities in multipolar-selective spectroscopy and lasing, low-threshold nonlinear optics and efficient single-photon emission.

Thrombus microstructure plays a critical role in determining the treatment success for thrombus-related diseases such as stroke and deep vein thrombosis. However, no in vivo diagnostic method can fully capture thrombus microstructure yet. Photoacoustic imaging is uniquely positioned to provide information on thrombi composition as it relays optical absorption information from diffuse photons at acoustic propagation depths. Computational modeling enables systematic exploration of microstructural effects on imaging signals, offering insights into developing improved in vivo diagnostic techniques. However, no photoacoustic simulation platform can model microstructural features within centimeter-scale phantoms at reasonable computational cost. Here, we present REFINE, a topology-driven framework for generating in silico thrombi replicating its key replicating their key microstructural traits. REFINE enables controlled, recursive optimization of thrombus topology, making it suitable for accurate photoacoustic modeling and potentially powerful for biomechanical analyses. These digital thrombi are embedded into a multiscale photoacoustic simulation platform that bridges microscale acoustic modeling with macroscale thrombus geometries, enabling efficient and realistic simulation of photoacoustic signal responses. We created unique thrombi microstructure for various compositions and porosities. Our simulation framework effectively links microstructural features to macroscale imaging outcomes, in agreement with previous empirical studies. Our results demonstrate that thrombus microstructure significantly affects photoacoustic spectral responses and can be modeled in silico. These findings highlight that multiscale photoacoustic simulation is a powerful tool for characterizing tissue microstructure and that our framework enables in silico thrombi analysis and diagnostic imaging strategy development

A constraint penalization method is introduced within the Lattice Boltzmann (LBM) framework to model fluid-structure interactions involving rigid bodies. The proposed approach extends the fictitious domain concept by enforcing the rigid-body motion through a penalization term directly applied to the fluid velocity field, eliminating the need for explicit Lagrange multipliers or interface force computation. This formulation preserves the locality and simplicity of the LBM algorithm while ensuring an implicit coupling between the fluid and solid regions. Numerical experiments demonstrate that the method accurately reproduces rigid-body motion and hydrodynamic interactions with minimal additional computational cost. The method is applied to particle sedimentation, starting with a simple example and progressing to increasingly complex cases.

Graphene is a promising platform for spin-based non-volatile memory, logic, and neuromorphic computing by combining long-distance spin transport with electrical tunability at room temperature. However, advancing beyond passive spin channels requires devices capable of generating large spin signals with efficient rectification capabilities, which are essential for active spintronic components. Here, we demonstrate a folded-bilayer graphene spin-valve device with giant non-local spin signals in the several mV range with pronounced spin-rectification effects. Efficient spin injection creates a significant spin accumulation of 20 meV and generates a spin diode effect with an asymmetry of over an order of magnitude between forward and reverse bias conditions. This spin-diode effect is expected to arise from nonlinear spin-charge interactions in the folded-bilayer graphene channel. These observed large spin signals and spin-diode effects in graphene systems offer a promising platform for developing active two-dimensional spintronic devices.

On Boxing Day, 1838, Jacobi found a solution to the problem of geodesics on a triaxial ellipsoid, with the course of the geodesic and the distance along it given in terms of one-dimensional integrals. Here, a numerical implementation of this solution is described. This entails accurately evaluating the integrals and solving the resulting coupled system of equations. The inverse problem, finding the shortest path between two points on the ellipsoid, can then be solved using a similar method as for biaxial ellipsoids.

The shift towards increased remote work and digital communication, driven by recent global developments, has led to the widespread adoption of i-voting systems, including in academic institutions. This paper critically evaluates the use of i-voting platforms for elections to academic senates at Czech public universities, focusing on the democratic and technical challenges they present. A total of 18 out of 26 Czech public universities have implemented remote electronic voting for these elections. Yet, the systems often lack the necessary transparency, raising significant concerns regarding their adherence to democratic norms, such as election security, voter privacy, and the integrity of the process. Through interviews with system developers and administrators, along with a survey of potential voters, the study underscores the critical need for transparency. Without it, a comprehensive assessment of the technical standards and the overall legitimacy of the i-voting systems remains unattainable, potentially undermining the credibility of the electoral outcomes.

Self-assembled monolayers of polyalanine $\alpha$-helices exhibit distinct structural phases with implications for chiral-induced spin selectivity. We combine scanning tunneling microscopy and theoretical modeling to reveal how chiral composition governs supramolecular organization. Enantiopure systems form hexagonal lattices, while racemic mixtures organize into rectangular phases with stripe-like features. Our interaction potentials derived from density-functional based tight binding calculations show that opposite-handed helix pairs exhibit stronger binding and closer packing, explaining the denser racemic structures. Crucially, we demonstrate that the observed STM contrast arises from anti-parallel alignment of opposite-handed helices rather than physical height variations. These findings establish fundamental structure-property relationships for designing peptide-based spintronic materials.

Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface. Data sparsity, signal noise, complex feature interdependencies, non-linear dynamics, massive design spaces, and the ill-posed nature of the inverse problem often constrain current methodologies for energy prediction. Machine learning enriched with prior knowledge is a promising direction for overcoming these constraints. Prior knowledge can be incorporated by acting on the input space, where the choice of data representation directly influences how effectively the model relates measured signals to impact energy. Despite its importance, the selection of effective features lacks a systematic procedure, with no consensus on how to choose among the many candidate descriptors available. The present study addresses that gap through a structured workflow that designs the input space using domain knowledge. Features are extracted from the time, frequency, and time-frequency domains, then filtered for statistical significance, correlation, dimensionality reduction, and robustness to noise. Exploratory data analysis further relates the retained descriptors to the dynamics. The resulting indicators form the input space for a fully connected neural network, which is trained and validated on experimental data from multiple impact scenarios, including pristine and damaged states. The model reduces the prediction error by a factor of three relative to conventional time-series techniques and purely data-driven baselines, while every retained or discarded descriptor remains traceable to the aspect it describes. Overall, the framework advances predictive performance, interpretability, and diagnostic confidence by embedding domain knowledge through targeted feature selection.

Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a new treatment modality focusing on the use of alpha particles against solid tumors. The introduction of Alpha DaRT in clinical settings calls for the development of detailed tumor dosimetry, which addresses biological responses such as cell survival and tumor control probabilities at the microscopic scale. In this study, we present a microdosimetric model that links the macroscopic alpha dose, cell survival, and tumor control probability while explicitly accounting for broad distributions of spherical nucleus radii. The model combines analytic expressions for nucleus-hit statistics by alpha particles with Monte Carlo-based specific-energy deposition to compute survival for cells whose nucleus radii are sampled from artificial and empirically derived distributions. The results indicate that introducing finite-width nucleus size distributions causes survival curves to depart from the exponential trend observed for uniform cell populations. We show that the width of the nucleus size distribution strongly influences the survival gap between radiosensitive and radioresistant populations, diminishing the influence of intrinsic radiosensitivity on cell survival. Tumor control probability is highly sensitive to the minimal nucleus size included in the size distribution, indicating that realistic lower thresholds are essential for credible predictions.Our findings highlight the importance of careful characterization of clonogenic nucleus sizes, with particular attention to the smallest nuclei represented in the data. Without addressing these small-nucleus contributions, tumor control probability may be substantially underestimated. Incorporating realistic nucleus size variability into microdosimetric calculations is a key step toward more accurate tumor control predictions for Alpha DaRT and other alpha-based treatment modalities.

A variational nodal partition of the correlation energy is introduced, $E_{\mathrm{cor}}=E_{\mathrm{sym}}+E_{\mathrm{stat}}$, relative to a chosen mean-field correlation baseline and its associated single-determinant node. Static correlation $E_{\mathrm{stat}}$ is the negative of the energy penalty incurred when the many-electron wavefunction is constrained to that reference node rather than the exact one. This nodal term isolates the antisymmetric, sign-structure component of correlation, while the complementary symmetric term $E_{\mathrm{sym}}$ necessarily contains dynamic correlation together with a distinct strong but nodeless contribution. The resulting state-based, method-independent framework clarifies the relation between dynamic, nondynamic, strong, and static correlation, places earlier node-based decompositions on rigorous footing, and explains why single-determinant fixed-node diffusion Monte Carlo can be highly accurate in some systems yet fail in others.

In the case of hyperbolic conservation laws, high-order methods, such as the classical DG method, experience the phenomenon of unwanted high-frequency oscillations in the vicinity of a shock. Shock-capturing methods such as artificial dissipation, solution, flux, or TVD limiting are generally used to eliminate non-physical oscillations and provide bounds on physical quantities. For entropy-stable schemes, the additional objective would be to retain provable entropy dissipation guarantees of the underlying scheme, i.e. subcell limiting or entropy filtering [1, 2, 3, 4]. The nonlinearly-stable flux reconstruction (NSFR) semi-discretization given in Eq. 7 with a suitable flux reconstruction scheme has been demonstrated to mitigate spurious oscillations in the presence of shock discontinuities and at CFL values substantially larger than the DG variant of the NSFR scheme whilst retaining the property of entropy stability [5]. NSFR schemes achieve this by introducing an alternative lifting operator for surface numerical flux penalization, albeit at the expense of accuracy. In this technical note, we present an adaptive approach to the choice of the lifting operator employed, which maintains higher accuracy and allows for larger CFL values while retaining the underlying provable attributes of the scheme. While it cannot eliminate oscillations such as the aforementioned shock-capturing methods, together with a positivity-preserving limiter, the scheme provides for solutions that are essentially oscillation-free.

The superconducting diode effect (SDE) is characterized by its nonreciprocal nature in critical supercurrents. However, realizing a longitudinal SDE typically requires simultaneous time-reversal ($\mathcal{T}$) and inversion ($\mathcal{P}$) symmetry breaking in the device, raising challenges in applications. In this Letter, we reveal that an off-axis direct-current bias applied to a planar anisotropic superconductor can convert intrinsic anisotropy into transverse nonreciprocity, generating ultra-tunable SDE without breaking either $\mathcal{P}$ or $\mathcal{T}$ symmetry. Using both Ginzburg-Landau theory and self-consistent mean-field calculations, we show that diode efficiency can be continuously tuned via bias current amplitude. Notably, when the injected bias current exceeds a critical threshold, the system is driven into a “unidirectional superconductivity" regime, where transverse dissipationless currents are permitted in only one direction. Based on this mechanism, we propose the “current-gated orthogonal superconducting transistor (CGOST)" and demonstrate its utility in tunable supercurrent range controllers and half-wave rectifiers. Our findings open new avenues for nonreciprocal superconducting electronics.

In this paper, we examine the significance of the lower geometric limit, defined as the trailing-edge height at which the reflected shock grazes the trailing edge, for both regular reflection (RR) and Mach reflection (MR). We show that this lower limit for MR is greater than that for RR, within the dual-solution domain away from its lower and left boundaries. We thus identify a dual-solution stability gap lying between the subcritical threshold (the lower limit for RR) and the supercritical threshold (the lower limit for MR). Within this gap RR is stable while MR is unstable, implying a new dynamic transition possiblity there: a steady RR configuration (start flow) can undergo a dynamic transition to an unstable MR state (unstart flow) under suitable disturbance of density or other flow parameters. Numerical simulations confirm the existence of this stability gap and illustrate the time history of dynamic transitions, including (1) direct transitions from RR to MR to unstart flow, with complex flow structures such as hybrid MR-type VI shock interference and double MR – MR reflections, and (2) inverted transitions, in which RR first shifts to MR and then returns back to RR.

Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating non-local charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parameterizations and ad hoc fitting.

Seismic data restoration is a fundamental task in seismic exploration, yet remains challenging under complex and unknown degradations. Traditional model-driven or task-specific learning methods often require retraining for each degradation type and fail to generalize effectively to unseen field data. In this work, we introduce an unsupervised Posterior Sampling Framework (PSF) built upon Score-based Generative Models (SGMs) for unified seismic data restoration. PSF leverages the pre-trained unconditional SGMs as a seismic-aware generative prior and derives a generalized conditional score function associated with the forward operator of each inverse problem. This enables posterior sampling across different seismic restoration tasks without retraining or supervision. Additionally, an adaptive noise-level estimation mechanism is incorporated to dynamically regulate the noise suppression strength during sampling, enhancing flexibility under varying signal-to-noise ratios and degradation conditions.Extensive experiments on seismic denoising, interpolation, compressed sensing, and deconvolution demonstrate that PSF delivers high-quality samples and exhibits robust generalization to out-of-distribution data. These results highlight the potential of SGMs as a universal prior for seismic inverse problems and establish PSF as a flexible framework for unsupervised posterior inference across diverse degradation scenarios.

Fourier Neural Operators (FNOs) have demonstrated exceptional accuracy in mapping functional spaces by leveraging Fourier transforms to establish a connection with underlying physical principles. However, their opaque inner workings often constitute an obstacle to physical interpretability. This work introduces Hierarchical Fourier Neural Operators (HFNOs), a novel FNO-based architecture tailored for reduced-order modeling of turbulent fluid flows, designed to enhance interpretability by explicitly separating fluid behavior across scales. The proposed architecture processes wavenumber bins in parallel, enabling the approximation of dispersion relations and non-linear interactions. Inputs are lifted to a higher-dimensional space, Fourier-transformed, and partitioned into wavenumber bins. Each bin is processed by a Fully Connected Neural Network (FCNN), with outputs subsequently padded, summed, and inverse-transformed back into physical space. A final transformation refines the output in physical space as a correction model, by means of one of the following architectures: Convolutional Neural Network (CNN) and Echo State Network (ESN). We evaluate the proposed model on a series of increasingly complex dynamical systems: first on the one-dimensional Kuramoto-Sivashinsky equation, then on the two-dimensional Kolmogorov flow, and finally on the prediction of wall shear stress in turbulent channel flow, given the near-wall velocity field. In all test cases, the model demonstrates its ability to decompose turbulent flows across various scales, opening up the possibility of increased interpretability and multiscale modeling of such flows.

Volcanic regions have shaped human settlements for millennia, placing over 500 million people worldwide within close proximity to active volcanoes. Predicting eruptions that threaten both lives and property remains a critical challenge, given the complexity and diversity of seismic signals and recording formats. Effective early warning systems require tools that are not only powerful but also user-friendly, computationally efficient, and capable of handling large seismic datasets and complex mathematical and probabilistic models used in Machine Learning and Artificial Intelligence. We present a Shannon Entropy Estimator for the Characterization of Seismic-Volcanic Signals using Python designed to meet these demands. Featuring intuitive graphical user interfaces and a low learning curve, the system enables real-time analysis of seismic data using four key metrics: Shannon Entropy, Kurtosis, Frequency Index, and Energy. These metrics provide robust insights into a volcanoes state across different stages, independently of the type of event (VT, VLP, Tremor, Hybrid, Explosions) or the data format (SAC, MSEED, SEISAN, etc.). In this work, the efficacy of this tool is demonstrated through the analysis and graphical presentation of these metrics using real seismic records from active volcanoes, providing concrete examples of its practical application. This approach allows reliable monitoring, accurate categorization, and early detection of critical changes in volcanic activity, supporting the development of predictive models and enhancing the effectiveness of early warning systems. This practical, low-cost solution meets the analytical needs of research institutes and observatories while contributing to the mitigation of volcanic hazards worldwide.

Wave-sea ice interactions shape the transition zone between open ocean and pack ice in the polar regions. Most theoretical paradigms, implemented in coupled wave-sea ice models, predict exponential decay of the wave energy but some recent observations deviate from this behaviour. Expanding on a framework based on wave energy dissipation due to ice-water drag, we account for drifting sea ice to derive an improved model for wave energy attenuation. Analytical solutions replicate the observed non-exponential wave energy decay and the spatial evolution of the effective attenuation rate in Antarctic sea ice.

This study presents a dynamic modeling approach for precipitation in electrolyte systems, focusing on the crystallization of an aromatic amine through continuous processes. A novel model, integrating equilibrium and crystallization kinetics, is formulated and applied to a continuous oscillatory baffled reactor. The approach assumes rapid equilibrium establishment and is formulated as a set of differential algebraic equations. Key features include a population balance equation model to describe the particle size distribution and the modeling of dynamically changing equilibria. The predictions of the dynamic model show good agreement with the available experimental measurements. The model is aimed at aiding the transition from a batch process to continuous process by forming the basis for numerical optimization and advanced control.

Transport and mixing of tracers in the ocean is thought to be preferentially along neutral planes defined by the potential temperature and salinity fields. This gives rise to a conceptual model of ocean transport in which water parcel trajectories are everywhere neutral, that is, tangent to the neutral planes. Because the distribution of neutral planes is not integrable, neutral transport, while locally two dimensional, is globally three dimensional. We describe this form of transport, building on its connection with contact and sub-Riemannian geometry. We discuss a Lie-bracket interpretation of local dianeutral transport, the quantitative meaning of helicity and the implications of the accessibility theorem. We compute sub-Riemnanian geodesics for climatological neutral planes and put forward the use of the associated Carnot–Carathéodory distance as a diagnostic of the strong anisotropy of neutral transport. We propose a stochastic toy model of neutral transport which represents motion along neutral planes by a Brownian motion. The corresponding diffusion process is degenerate and not (strongly) elliptic. The non-integrability of the neutral planes however ensures that the diffusion is hypoelliptic. As a result, trajectories are not confined to surfaces but visit the entire three-dimensional ocean. The short-time behaviour is qualitatively different from that obtained with a non-degenerate highly anisotropic diffusion. We examine both short- and long-time behaviours using Monte Carlo simulations. The simulations provide an estimate for the time scale of ocean vertical transport implied by the constraint of neutrality.

A briefly formulated update of the recently published review [1] on the topic of multidimensional solitons (chiefly, in nonlinear optics and atomic Bose-Einstein condensates (BECs)) is presented. The update briefly summarizes some notable results on this topic that have been reported very recently, and offers a compact outline of the directions of the current experiment and theoretical work in this and related fields. In particular, as concerns newest experimental findings, included are the creation of multiple quantum droplets in prolate BEC, and the observation of expanding toroidal light structures in linear optics. The update of theoretical results includes the analysis of 2D solitons in the optical system with the quadratic nonlinearity and fractional diffraction, and the prediction of stable 3D vortex solitons in various BEC schemes with long-range interactions.

Optical periodic structures exhibiting a degenerate band edge (DBE) are of significant interest for various applications such as switching, sensing, high-power amplification, and lasing. At the edge of the bandgap in such structures, a fourth-order exceptional point degeneracy arises, leading to an extremely flat dispersion band. We propose and study a Silicon-on-Insulator-compatible structure composed of two coupled waveguides with asymmetric gratings. The dispersion relations and the field profiles are obtained using three-dimensional finite-difference time-domain simulations, and we provide a set of practical guidelines for the design and optimization of such structures, in order to obtain a DBE. We analyze the transmission and reflection spectra of finite-size devices, and investigate their spectral properties near the stationary points. The scaling of the resonance quality factor with the number of unit cells is studied, revealing a performance that surpasses that of conventional periodic structures. Finally, we examine the robustness of the DBE with respect to fabrication tolerances and structural imperfections.

The genomic DNA of most bacteria is significantly underwound, which constrains the DNA molecule to adopt a branched plectoneme geometry. Moreover, biological functions like replication and transcription require that the two DNA strands be transiently opened, which generates waves of positive (respectively, negative) supercoiling downstream (respectively, upstream) of the molecular motor, a feature known as Twin Supercoiled Domain (TSD). In this work, we used coarse-grained modeling and Brownian Dynamics simulations to investigate the interactions between a TSD and the plectonemes of bacterial DNA. Simulations indicate that the slithering dynamics of short plasmids is not significantly affected by a TSD. In contrast, the TSD potently stimulates the spontaneous displacement modes (diffusion and growth/shrinkage) of the plectonemes of longer DNA molecules. This results in the motor trailing a growing plectoneme behind itself if it translocates more slowly than the maximum slithering speed of plectonemes. In contrast, if the motor translocates more rapidly than this limit, then quasi immobile plectonemes nucleate almost periodically upstream of the motor, grow up to several kbp, detach from the motor, shrink and disappear. The effect of an eventual static bend imposed by the motor and of topological barriers was also investigated.

By reducing resolution, coarse-grained models greatly accelerate molecular simulations, unlocking access to long-timescale phenomena, though at the expense of microscopic information. Recovering this fine-grained detail is essential for tasks that depend on atomistic accuracy, making backmapping a central challenge in molecular modeling. We introduce split-flows, a novel flow-based approach that reinterprets backmapping as a continuous-time measure transport across resolutions. Unlike existing generative strategies, split-flows establish a direct probabilistic link between resolutions, enabling expressive conditional sampling of atomistic structures and – for the first time – a tractable route to computing mapping entropies, an information-theoretic measure of the irreducible detail lost in coarse-graining. We demonstrate these capabilities on diverse molecular systems, including chignolin, a lipid bilayer, and alanine dipeptide, highlighting split-flows as a principled framework for accurate backmapping and systematic evaluation of coarse-grained models.

We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference.