Papers

We study first-return statistics for photons undergoing three-dimensional Henyey-Greenstein scattering in a semi-infinite medium. In previous work, we showed that one-dimensional first-passage probabilities can be expanded using Catalan and Motzkin generating functions. Extending this framework to three dimensions requires introducing a Boundary Truncation Factor (BTF), which accounts for the restricted angular phase space imposed by the boundary. Extensive Monte Carlo simulations are used to determine the BTF empirically as a function of scattering order and anisotropy. For moderate anisotropy, the BTF is accurately described by a Cauchy kernel, with parameters depending only on the Henyey-Greenstein asymmetry factor. This closed-form expression reproduces Monte Carlo results with 1-2% accuracy over a broad range of scattering orders. At higher anisotropy, systematic deviations from the Cauchy form are observed. These deviations can be reduced using a one-parameter generalized kernel. We further extend the framework to oblique incidence by replacing the normal-incidence return probability with a Legendre-series formulation. The BTF parameters and Motzkin counting machinery remain independent of the incidence angle, so only the anchor point of the algorithm changes. The resulting framework provides a computationally efficient mapping from three-dimensional anisotropic transport at arbitrary incidence to one-dimensional combinatorial first-passage theory.

This paper develops a process-based account of scientific explanation that reconceives grounding in terms of stabilisation. Grounding theories capture hierarchical dependence but lack criteria for when explanations remain adequate under model updates, perturbations, and theory change. Stabilisation is formally defined by a schema \(C \to P(I)\), where explanatory relations are sufficient when they preserve specified relational invariants under admissible transformations. This replaces the search for ultimate foundations with operational adequacy tests indexed to measurable invariance, resolving infinite regress worries while preserving a modest scientific realism. Applications show unifying power: theory change becomes an empirical question about structural continuity; quantum measurement becomes apparatus-dependent pattern selection; the effectiveness of mathematics reflects convergence on transformation-invariant descriptions; and emergence versus reduction reduces to stability of cross-level mappings. The black hole event horizon illustrates how ontologically identical states can diverge in admissible evolution, revealing process as explanatorily fundamental. Companion work develops apparatus-dependent adequacy protocols, including pointer-basis rotation and coupling-spectra methods, turning the framework into a falsifiable research programme across quantum, thermodynamic, and relativistic domains.

We present an NPT extension of Ewald summation with prolates (ESP), a spectrally accurate and scalable particle-mesh method for molecular dynamics simulations of periodic, charged systems. Building on the recently introduced ESP framework, this work focuses on rigorous and thermodynamically consistent pressure/stress evaluation in the isothermal–isobaric ensemble. ESP employs prolate spheroidal wave functions as both splitting and spreading kernels, reducing the Fourier grid size needed to reach a prescribed pressure accuracy compared with current widely used mesh-Ewald methods based on Gaussian splitting and B-spline spreading. We derive a unified pressure-tensor formulation applicable to isotropic, semi-isotropic, anisotropic, and fully flexible cells, and show that the long-range pressure can be evaluated with a single forward FFT followed by diagonal scaling, whereas force evaluation requires both forward and inverse transforms. We provide production implementations in LAMMPS and GROMACS and validate pressure and force accuracy on bulk water, LiTFSI ionic liquids, and a transmembrane system. Benchmarks on up to $3\times 10^3$ CPU cores demonstrate strong scaling and reduced communication cost at matched accuracy, particularly for NPT pressure evaluation.

Frequency references based on solid state spins promise simplicity, compactness, robustness, multifunctionality, ease of integration, and high densities of emitters. Nitrogen-vacancy (NV) centers in diamond are a natural candidate, but the electronic zero-field splitting exhibits a large fractional temperature dependence, which has precluded its use as a stable clock transition. Here we show that this limitation can be overcome by forming a composite frequency reference that combines measurements of the electronic splitting D with the nuclear quadrupole splitting of the $^{14}$N nuclear spin intrinsic to the NV center. We further benchmark this composite approach against alternative strategies for mitigating temperature sensitivity. By implementing a specially designed pulse sequence with an eight-phase control scheme that suppresses pulse imperfections, we interleave measurements of D and Q in a high-density NV ensemble and demonstrate a temperature-compensated composite frequency reference. The stability of this composite diamond clock is characterized over a 10-day period at room temperature through a comparison to a Rb vapor-cell clock, yielding a fractional instability below $5 \times 10^{-9}$ for an averaging time of $\tau = 200$ s and below $1 \times 10^{-8}$ at $\tau = 2 \times 10^5$ s, corresponding to measured improvements by a factor of 4 and 200, respectively, over a clock based purely on the single frequency D for the same periods. By characterizing the residual sensitivity to magnetic fields, optical power, and radio-frequency drive amplitudes, we find that temperature is no longer the dominant source of instability. These results establish complementary electron- and nuclear-spin transitions in diamond as a viable route to thermally robust frequency metrology, providing a pathway toward compact, multifunctional solid-state clocks and quantum sensors.

Near-cloaks based on passive coatings can strongly suppress scattered-field energy in a narrow frequency band, yet an observer's ability to infer object parameters from noisy measurements need not decrease proportionally. We develop a fully theoretical two-dimensional (2D) framework for a coated acoustic cylinder in an air background. Using an exact cylindrical-harmonic solution of the Helmholtz equation, we compute the modal scattering coefficients a_m(omega) for a core of radius a surrounded by two concentric effective-fluid layers, and we design the coating to cancel the dominant low-order multipoles (monopole m=0 and dipole m=+/-1) at a target frequency, yielding a narrowband near-cloak. Beyond the conventional energetic metric (total scattering width), we quantify information-based detectability through the Fisher information matrix (FIM) and the associated Cramer-Rao lower bounds (CRLBs) for joint estimation of the size-material parameter vector x=[a, rho1, c1]^T from noisy far-field data. A representative air-background study exhibits an approximately 25 dB reduction in total scattering width near the design frequency, while tr(FIM) decreases by only a few dB, demonstrating that energy-based and inference-based notions of invisibility are distinct objectives. We further provide a low-order analytic argument clarifying the mechanism behind this energetic-informational decoupling and report design-space and local-robustness diagnostics that highlight persistent trade-offs between scattering suppression and parameter identifiability.

We report magnetic-field- and temperature-dependent transverse-voltage phenomena in hydrated genomic DNA. The sample is confined beneath a glass coverslip in a quasi-two-dimensional geometry and subjected to perpendicular magnetic fields, revealing sharp thresholds, staircase-like voltage plateaus, and oscillatory structures approximately periodic in inverse magnetic field. Fixed-field cooling reveals longitudinal reorganizations near 20.6 and 12.0 $^\circ$C, while a separate experiment shows large-amplitude transverse oscillations below approximately 12 $^\circ$C. Water controls and buffer-free DNA measurements support an association between the structured response and the hydrated DNA system. We propose a polarization-based interpretation of the transverse response, termed a "polarization Hall effect", and discuss the observed regimes within a phenomenological framework of nonlinear dynamics and metastable configurations. These findings identify hydrated DNA as a soft-matter system exhibiting structured electrical responses jointly regulated by magnetic field and temperature.

Despite well-meaning scenarios that propose global CO2 emissions will decline presented in every IPCC report since 1988, the trend of global CO2 increase continues without significant change. Even if any individual nation manages to flatten its emissions, what matters is the trajectory of the globe. Together the gulf between climate science and climate economics, plus the urgent need for alternative methods of estimation, provided the incentives for development of our Ocean-Heat-Content (OHC) Physics and Time Macro Economic Model (OPTiMEM) system. To link NOAA damages to climate required creating a carbon consumption model to drive a physics model of climate. How fast could carbon be burned and how much coal, oil and natural gas was reasonably available? A carbon model driving climate meant burning the carbon, and modelling how the earth heated up. We developed this using the most recent best greenhouse gas equations and production models for CO2, CH4, N2O, and halogenated gases. This developed an ocean heat content model for the globe. Each step is validated against Known carbon consumption, CO2, temperature, and ocean heat content. This allows a physics founded model of climate costs to be projected.

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. While transportation resilience has been widely studied, understanding how random and targeted disruptions affect both structural connectivity and functional performance remains a key challenge. To address this, our study evaluates the robustness of the U.S. intermodal freight network, comprising rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. We examine disruption scenarios including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, we also consider flow-weighted degree centralities and partial node degradation. Two resilience indicators are used: the size of the giant connected component (GCC) to measure structural connectivity, and flow-weighted network efficiency (NE) to assess freight mobility under disruption. Results show that progressively degrading nodes ranked by Weighted Degree Centrality to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

Contrary to widespread belief, magnetostatic field lines do not ordinarily form closed loops. Why, then, are they in fact closed for so many familiar examples? What other topologies are possible, and what current configurations generate them?

Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.

Analysis of the urban population fraction data for sixteen populous countries over the last fifty years reveals a universal increase in urbanization, exhibiting four qualitatively distinct temporal patterns: (i) continuously accelerating growth, (ii) continuously decelerating growth, (iii) two-phase growth transitioning from acceleration to deceleration, and (iv) two-phase growth transitioning from deceleration to acceleration. To understand the origin of these diverse urbanization trajectories, we develop a simple coarse-grained model in which a country is segregated into two regions, a rural and an urban region. Population in each region evolves due to natural (sexual) growth and migration from rural to urban areas, with the migration rate governed by economic inequality, quantified through the difference in GDP per capita between the two regions. The GDP per capita of both regions is assumed to grow exponentially with distinct rates. We demonstrate that this minimal model, involving four dynamical variables and a small number of demographic and economic parameters, is capable of reproducing all four empirically observed urbanization patterns. Assuming demographic and economic parameters remain approximately constant over a 50-year timescale, we estimate coarse-grained parameters for the United States using empirical data and obtain optimized values that accurately reproduce its observed urbanization trajectory. Our results highlight how simple demographic-economic interactions can generate rich and diverse urbanization dynamics.

We introduce Heraclitean Dialectical Concept Spaces (HDCS), a topological framework for modelling stagewise structural change in systems of concepts. A stage consists of a nonempty concept set, a feasible family generating an external topology, and a neighbourhood assignment carrying additional relational structure. Neighbourhood overlap induces adjacency and channel ideals. Candidate emergence is represented by the CCER construction: an interaction profile, finite admissible cores, and anchored witnesses determine nonempty open candidate regions in uncovered remainder structure. Selected candidates may be reified as new atomic concepts at a later stage, while partial injective carry maps separately track persistence of existing identities. The resulting history space is obtained as a quotient of the disjoint union of stage spaces with the corresponding final topology. Product concept spaces extend the construction to cross-domain interaction. We derive support characterizations, mediated-interaction components, canonical CCER envelopes, monotonicity and impossibility criteria, product-space factorization, and invariance under concept-space isomorphism. Applications to monetary transformation, zero and representation, mammalian middle-ear evolution, and AI-related labour change illustrate the framework. HDCS is intended as a structural formalism rather than a causal or predictive theory of historical change.

We analyze the dynamics of Negribreen Glacier System, a polythermal glacier in Svalbard, during its ongoing surge and investigate the role of supraglacial (surface) water as both an indicator of ice-dynamic processes and a driver of surge evolution. We identify three distinct surge phases: the initial acceleration phase, mature phase, and return to quiescence. Comparing the quiescent supraglacial hydrological state to each of the surge phases, we observe a sudden increase in hydrological connectivity between the glacier surface and base during initial acceleration, followed by a gradual return to quiescent water extent. In the mature surge phase, emergent water-filled crevasses coincide with regions of compressive forcing and extensive deformation, follow local accelerations, and preceded smaller, secondary accelerations. Additionaly, rapid drainage of surface ponds is observed in the mature surge. A data-fusion approach, using Maxar WorldView(c) imagery, ICESat-2 altimetry, and Sentinel-1 Synthetic Aperture Radar, is taken to create a time series of supraglacial water maps, water volumes, surface velocity changes, and spatial ice surface roughness. These observations provide a qualitative (process understanding) and quantitative (water time series) basis for supraglacial water sources as a driver and indicator of surge activity for Arctic glaciers.

We experimentally characterized terahertz (THz) radiation emitted from laser-wakefield acceleration (LWFA) driven at 100-TW laser power. Simultaneous measurements of the laser energy, electron-bunch charge, and THz energy reveal a quadratic dependence of the THz energy on both charge and laser energy. This behavior indicates coherent collective emission in the generation process and provides a useful scaling law for THz output. Microbolometer-based beam profiling shows a relatively large THz beam divergence (~0.2 rad). Single-shot THz interferometry further shows that the emitted THz pulse is sub-picosecond in duration and broadband. Combining the beam-profile and interferometric measurements, the THz spectrum is expected to span approximately 1-20 THz. Together, these results support coherent acceleration radiation as the dominant mechanism for THz generation in 100-TW LWFA.

Practical applications of fragment embedding and closely related local correlation methods critically depend on a judicious choice of a low-level theory to define the local embedding subspace and to capture long-range electrostatic and correlation effects outside the embedding region. Second-order Møller-Plesset perturbation theory (MP2) is by far the most widely used correlated low-level theory; however, its applicability becomes questionable in systems where MP2 is known to fail either quantitatively or qualitatively. In this work, we present the random phase approximation (RPA) as a promising alternative low-level theory to MP2 within the local natural orbital-based coupled-cluster (LNO-CC) framework. We demonstrate that RPA-based LNO-CC closely matches the performance of its MP2-based counterpart for systems with sizable energy gaps, while delivering significantly faster convergence toward the canonical coupled-cluster limit for metallic systems, particularly as the thermodynamic limit is approached. These results highlight the critical role of the low-level theory in fragment embedding and local correlation methods and identify RPA as a compelling alternative to the commonly used MP2.

Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.

In this work, we identify three considerations that are essential for realizing practical photonic AI systems at scale: (1) dynamic tensor operation support for modern models rather than only weight-static kernels, especially for attention/Transformer-style workloads; (2) systematic management of conversion, control, and data-movement overheads, where multiplexing and dataflow must amortize electronic costs instead of letting ADC/DAC and I/O dominate; and (3) robustness under hardware non-idealities that become more severe as integration density grows. To study these coupled tradeoffs quantitatively, and to ensure they remain meaningful under real implementation constraints, we build a cross-layer toolchain that supports photonic AI design from early exploration to physical realization. SimPhony provides implementation-aware modeling and rapid cross-layer evaluation, translating physical costs into system-level metrics so architectural decisions are grounded in realistic assumptions. ADEPT and ADEPT-Z enable end-to-end circuit and topology exploration, connecting system objectives to feasible photonic fabrics under practical device and circuit constraints. Finally, Apollo and LiDAR provide scalable photonic physical design automation, turning candidate circuits into manufacturable layouts while accounting for routing, thermal, and crosstalk constraints.

Skipper Charge-Coupled Devices (skipper-CCDs) are pixelated silicon detectors with deep sub-electron resolution. Their radiation hardness and capability to reconstruct energy deposits with unprecedented precision make them a promising technology for space-based X-ray astronomy. In this scenario, optical and near-infrared photons may saturate the sensor, distorting the reconstructed signal. We present a light-tight shield for skipper-CCDs to suppress optical backgrounds while preserving X-ray detection efficiency. We deposited thin aluminum layers on the CCD surface using an e-beam evaporator and evaluated their blinding performance across wavelengths from 650 to 1000 nm using a monochromator, as well as the X-ray transmission using an $^{55}$Fe source. We find that 50 and 100 nm layers provide >99.6% light suppression, with no efficiency loss for 5.9 and 6.4 keV X-rays. In addition, we used Geant4 simulations to extend these results to a broader energy range and quantify the efficiency loss for different aluminum thicknesses. Results show that thin aluminum coatings are an effective, low-cost solution for optical suppression in skipper-CCDs intended for X-ray detection and space instrumentation.

Understanding how light interstitials migrate in chemically complex alloys is essential for predicting defect dynamics and long-term stability. Here, we introduce a spectral sampling framework to quantify boron diffusion activation energies in Ni and demonstrate how substitutional solutes (Cr, Mo) reshape interstitial point defect transport in both the bulk and along crystallographic defects. In the bulk, boron migration energy distributions exhibit distinct modality tied to solute identity and spatial arrangement: both Cr and Mo raise barriers in symmetric cages but induce directional asymmetry in partially decorated environments. Extending this framework to a $\Sigma5\langle100\rangle{210}$ symmetric tilt grain boundary reveals solute-specific confinement effects. Cr preserves low-barrier in-plane mobility while suppressing out-of-plane transport, guiding boron into favorable midplane voids. Mo, by contrast, imposes an across-the-board reduction in boron mobility, suppressing average diffusivity by two additional orders of magnitude at 800 $^\circ$C and reducing out-of-plane transport by five orders of magnitude relative to Cr. Both elements promote segregation by producing negative segregation energies, but their roles diverge: Cr facilitates rapid redistribution and stabilization at interfacial sites, consistent with Cr-rich boride formation, while Mo creates deeper and more uniform segregation wells that strongly anchor boron. Together, these complementary behaviors explain the experimental prevalence of Cr- and Mo-rich borides at grain boundaries and carbide interfaces in Ni-based superalloys. More broadly, we establish spectral sampling as a transferable framework for interpreting diffusion in disordered alloys and for designing dopant strategies that control transport across complex interfaces.

Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analysis or pulse arrival time, making them vulnerable to physiological and experimental confounders that undermine their accuracy and clinical utility. Here, we developed a smartwatch device with real-time electrical bioimpedance (BioZ) sensing for cuffless hemodynamic monitoring. We elucidate the biophysical relationship between BioZ and BP via a multiscale analytical and computational modeling framework, and identify physiological, anatomical, and experimental parameters that influence the pulsatile BioZ signal at the wrist. A signal-tagged physics-informed neural network incorporating fluid dynamics principles enables calibration-free estimation of BP and radial and axial blood velocity. We successfully tested our approach with healthy individuals at rest and after physical activity including physical and autonomic challenges, and with patients with hypertension and cardiovascular disease in outpatient and intensive care settings. Our findings demonstrate the feasibility of BioZ technology for cuffless BP and blood velocity monitoring, addressing critical limitations of existing cuffless technologies.

Acoustic levitation is widely used for contactless droplet manipulation, yet the standard Gor'kov description obtained by time averaging the acoustic field lacks a quantitative validity criterion. In this work, we derive Gor'kov theory as the leading-order slow time limit of the instantaneous radiation force, compute the first finite-inertia correction, and obtain a simple breakdown parameter. The correction reduces the effective trapping drift and predicts fast time oscillations of amplitude $x_1^{\mathrm{max}}\sim\lambda/8$, corresponding to hundreds of micron for typical ultrasonic levitation experiments. This sets a measurable criterion for experiments using phased transducer arrays. Our results provide a universal rule of thumb for acoustic trap design and clarify where time-averaged radiation force models fail.

Charge-self-consistent (CSC) DFT+DMFT delivers quantitative correlated-electron physics one configuration at a time, making ensemble sampling dependent on reliable warm starts for its expensive fixed-point iteration. Here we compare retrieval from the most similar converged structure with an E(3)-equivariant model that predicts a physics-structured self-energy and Fermi level. Because the full CSC loop refines both initializations, every production result remains a converged DFT+DMFT solution. Across metallic Fe, correlated FeO, and Mott-insulating NiO, learning reduces the typical iterations to sustained convergence from 8 to 3, 8 to 3, and 6 to 1. Retrieval matches this median speed when a dense same-state archive is available, but several poor transplants reveal that structural similarity does not predict transplant quality. In a pre-registered volume window excluded from training and donor pools, learning retains its speed, whereas retrieval starts $3.4\times$ farther from the fixed point and approaches cold-start cost. Either initialization can select a distinct near-degenerate branch on a rugged CSC landscape. Applied end to end, the workflow generates over one thousand correlated energy and force labels for iron at Earth's-core conditions and trains an equivariant interatomic potential. Solid–liquid coexistence with 9216 atoms gives $T_m = 6225 \pm 42\,\mathrm{K}_{\mathrm{stat}}$ at 330~GPa, consistent with recent experiments. A 50-configuration DFT+DMFT audit resolves the potential's energy calibration but does not justify a corrected melting temperature, because four-atom cells cannot realize a liquid. The resulting regime map favors retrieval within dense coverage, amortized learning at and beyond its boundary, and solver refinement throughout.

We formalise the self-referential definition of physical laws using monotone operators on a lattice of theories, resolving the pathologies of naive set-theoretic formulations. By invoking Tarski fixed point theorem, we identify physical theories as the least fixed points of admissibility constraints derived from Galois connections. We demonstrate that QED and GR can be represented in such a logical structure with respect to their symmetry and locality principles.

The main result of the paper is a construction of an infinite-dimensional symplectic manifold, associated with spacetime, its coisotropic submanifold defined as the zero locus of a moment map, and a family of Lagrangian submanifolds determined by the equation of state and the spacetime metric, such that their intersection is precisely the covariant Lichnerowicz–Carter system for a relativistic perfect fluid. On this basis, we clarify the notion of canonical velocity and hydrodynamic invariants, such as the asymptotic Hopf invariant and the Ertel invariant, and obtain a generalization of Kelvin's circulation theorem. We also clarify the relation between the canonical velocity, the four-velocity, and other choices of frames. We point out a five-dimensional origin of the formalism underlying the covariant formulation of hydrodynamics. Our formalism extends naturally to fluids with additional degrees of freedom, including multi-component fluids, charged fluids, and superfluids, and incorporates the chiral anomaly and Onsager quantization. It also suggests a direction of research connecting hydrodynamics to Poisson sigma models and topological field theories. Furthermore, we argue that a similar approach applies to the theory of self-dual fields, such as chiral bosons in 1+1 dimensions, tensor fields of (2,0) theory in 1+5 dimensions, and the 4-form field of type-IIB supergravity in 1+9 dimensions.

Spatiotemporal optical vortices carry transverse orbital angular momentum (t-OAM), which give rise to spatiotemporal topological charge (ST-TC). To unleash the full potential of t-OAM in expanding the capacity of communication and computing, we demonstrate the first optical information-processing pipeline capable of performing addition and subtraction on ST-TC values, regardless of whether they are integer or fractional. Additionally, we established a readout method for those mathematical operations through imaging spectral analysis, providing a robust optical basis toward arithmetic operations and verification. These new capabilities mark crucial advancements toward full arithmetic operations on the ST-TC of light for bosonic state computation and information processing.

Microstructured surfaces are central to photonics, biointerfaces, and coating technologies, but they are typically fabricated through multistep workflows involving masks, masters,development, and etching. Azopolymers offer a direct light-driven route to surface structuring, although holographic photopatterning of flat films has largely remained limited to smooth, shallow, and engraving-like reliefs with limited vertical modulation. Here we show that computer-generated holograms with co-designed bright and dark regions can spatially confine inward mass transport and directly generate isolated protruding microstructures with surface modulations of several micrometers from pristine flat azopolymer films. Single exposures produce individual protrusions and arrays, whereas sequential tailored exposures generate flattopped and hierarchical microreliefs. The same workflow produces connected freeform structures and enables optical erasure and rewriting within the same surface region. We further transfer the optically programmed azopolymer morphologies into another transparent polymer and demonstrate replicated microlenses operating at multiple visible wavelengths, with focal response controlled through the master-writing parameters. These results establish an alloptical framework for generating and reconfiguring complex, high-modulation azopolymer microreliefs from flat films through combined control of holographic illumination geometry and exposure history, with replication extending their use to materials selected for their final functional properties.

This paper reviews the field of extreme nonlinear optics in optical fibers, highlighting key phenomena and advancements. It discusses multiple ionization effects caused by femtosecond laser pulses that generate plasma and induce permanent material modifications, as well as plasma luminescence and its dependence on material imperfections. The formation and dynamics of plasma filaments, including helical structures, are explored, along with the rainbow spiral emission pattern useful in communications and particle manipulation. The review covers the generation of spatial-temporal waves, supercontinuum broadening, and advanced modeling techniques, such as multimode unidirectional pulse propagation equations for describing optical pulse evolution. Experimental demonstrations involving discretized conical waves and supercontinuum generation optimization are detailed. The paper emphasizes the unique capabilities of photonic crystal fibers, especially hollow-core variants, in achieving broad supercontinua and Raman frequency combs, ultrashort pulse compression, high-harmonic generation, plasma formation, and nonclassical light production. Our outlook highlights ongoing research into spatiotemporal helicon waves, ultrashort pulse propagation, vacuum ultraviolet and mid-infrared supercontinuum generation, and innovative fiber technologies. Future directions focus on enhancing fiber performance, understanding multimodal wave dynamics, and expanding applications in telecommunications, sensing, and quantum science.

Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease that leads to increased pulmonary pressures, vascular remodeling, and eventual right ventricular (RV) failure. Pediatric PAH remains understudied due to limited data and the lack of targeted diagnostic and therapeutic strategies. In this study, we developed and calibrated multi-scale, patient-specific cardiovascular models for four pediatric PAH patients using longitudinal MRI and catheterization data collected approximately two years apart. Using the CRIMSON simulation framework, we coupled three-dimensional fluid-structure interaction (FSI) models of the pulmonary arteries with zero-dimensional (0D) lumped-parameter heart and Windkessel models to simulate patient hemodynamics. An automated Python-based optimizer was developed to calibrate boundary conditions by minimizing discrepancies between simulated and clinical metrics, reducing calibration time from weeks to days. Model-derived metrics such as arterial stiffness, pulse wave velocity, resistance, and compliance were found to align with clinical indicators of disease severity and progression. Our findings demonstrate that computational modeling can non-invasively capture patient-specific hemodynamic adaptation over time, offering a promising tool for monitoring pediatric PAH and informing future treatment strategies.

It is often assumed that the most general transformation between two inertial reference frames is affine linear in their Cartesian coordinates, an assumption which is however not true. We provide a complete derivation of the most general inertial frame transformation, which is indeed nonlinear; along the way, we shall find that the conditions of preserving the Law of Inertia take the form of Schwarzian differential equations, providing perhaps the simplest possible physics setting in which the Schwarzian derivative appears. We then demonstrate that the most general such inertial transformation which further preserves the speed of light in all directions is, however, still affine linear. Physically, this paper may be viewed as a reduction of the number of postulates needed to uniquely specify special relativity by one, as well as a proof that inertial transformations automatically imbue spacetime with a vector space structure, albeit in one higher dimension than might be expected. Mathematically, this paper may be viewed as a derivation of the higher-dimensional analog of the Schwarzian differential equation and its most general solution.

A Detector Response Matrix (DRM) is a discrete representation of an instrument's Detector Response Function (DRF), which quantifies how many discrete energy depositions occur in a detector volume for a given distribution of particles incident on the detector. For simple radiation detectors that can count such energy depositions (such as scintillators, Proportional Counter Tubes (PCTs), etc), we consider the ideal counting DRF, $\mathbf{G}_\varphi (E_\mathrm{in}, E_\mathrm{dep})$, which relates the detector's counting histogram (number of energy depositions within a given channel) to an incident particles characterization, $\varphi$ (e.g. incident flux, fluence, intensity). From the counting DRF we can derive the counting DRM, the effective area, and the flash effective area (which measures the total energy deposited in the detector from a large, instantaneous fluence).