Papers

The GBAR experiment has measured the formation rate of antihydrogen from antiproton impact on a positronium cloud for antiproton kinetic energies of 4 and 6 keV. This is the first charge-exchange cross section measurement performed using antiproton beams, which are provided by the AD-ELENA facility at CERN. The measured cross section values are $(14.1 \pm 1.3 \mathrm{(stat)} ^{+2.2}_{-1.4} \mathrm{(sys)}) \times 10^{-16}$~cm$^2$ at 6.2 keV energy and $(8.7 \pm 2.4 \mathrm{(stat)} ^{+1.5}_{-0.09} \mathrm{(sys)}) \times 10^{-16}$~cm$^2$ at 4.15 keV energy, and agree with recent theoretical three-body calculations for antihydrogen formation. These measurements are an important input for experiments with antimatter, e.g the planned measurements of antihydrogen gravitational acceleration in the GBAR collaboration.

The African School of Fundamental Physics and Applications, also known as the African School of Physics (ASP), was initiated in 2010, as a three-week biennial event, to offer additional training in fundamental and applied physics to African students with a minimum of three-year university education. Since its inception, ASP has grown to be much more than a school. ASP has become a series of activities and events to support academic development of African students, teachers, high school pupils (learners) and faculties. We report on the ninth African School of Physics, ASP2026, organized in Kenya, on July 5–19, 2026. ASP2026 included programs for university students, high school teachers and learners.

We present a first-quantization, all-electron, full-potential Kohn-Sham density functional theory method for quantum computers, based on a classically optimized quantum-circuit eigensolver for the self-consistent field (SCF) eigenproblems in any orthonormal basis. The plane-wave-orthonormalized-orbital (PWOO) basis combines plane waves with atomic orbitals (AOs) orthonormalized against them and one another. The eigensolver has a dual-basis form: after full-basis bootstrap steps, it solves each SCF step in a contracted basis of $K$ functions ($\lceil\log_2 K\rceil$ qubits), updated in the full basis when the residual outside it exceeds a threshold. For an H$_8$ chain (SVWN, $K=8$), the state-vector-emulated SCF agrees with classical calculations within 0.02 mHa for 32768 plane waves and PWOO bases up to cc-pVTZ; contraction changes these PWOO energies by at most 5.7 $\mu$Ha. With emulated shot noise, the STO-3G PWOO SCF converges near the noise-free fixed point. On IonQ Forte-1, three-qubit-register readout reproduces the noise-free PWOO-AO energy difference within one standard deviation.

Richard Christopher Carrington carried out extensive sunspot observations from 1853 to 1861 and left numerous scientific legacies. However, despite his fame, consulting his original manuscripts, we have found Carrington's last sunspot drawing, which he never published. We date this drawing 23 Dec 1870, when Carrington was based at his Churt Observatory. The drawing allows us to count 3 sunspot groups and 10 individual sunspots and measure the group positions at N14{\deg} W37{\deg}, N11{\deg} W46{\deg}, and S08{\deg} W08{\deg}, where our longitude values represent longitudinal distance from the central meridian. In comparison with the existing datasets, his raw group counts look slightly lower than the contemporaneous observers. His position data look consistent. These additions contribute to the ongoing efforts for the sunspot number recalibration and butterfly diagram constructions, as long as we follow the standard assumption for the homogeneity of the "Schmidt backbone". Otherwise, this case instead questions the validity of the common assumption of "Schmidt backbone" keeping the data homogeneity. This sunspot drawing is unlikely to represent the tip of a huge, lost iceberg, but rather an isolated drawing that he left from his Churt era. Carrington may have temporarily revived his interest upon the solar eclipse of 22 Dec 1870. Otherwise, Carrington's references to his sunspot research in his Churt era are confined to priority claims in response to Chevalier's RAS obituary and a local newspaper report. The former confirms that Carrington was probably the first person, who counted and published sunspot data from Thomas Harriot's original sunspot drawings.

Traditional models of collective behavior rely on prescribed interaction rules, leaving unresolved the question of how behavior arises from neural representations of space. Here, we develop a first-principles theory in which movement, decision-making, and collective organization emerge by coarse-graining fast neural dynamics on a topological representation of directional space. For a ring manifold encoding heading, this reduction yields a macroscopic theory of behavior: a decision landscape over directions that admits a harmonic decomposition. In this framework, behavior is governed by a spectral organization of directional information rather than ad hoc rules. We demonstrate that target-seeking, avoidance, choice, spatial decision-making, and diverse forms of collective motion arise as distinct organizations of the underlying harmonic landscape. The theory unifies neural representations, behavioral decisions, and collective dynamics in a single mathematical description.

Cross-modality MRI synthesis using deep learning can streamline clinical workflows, compensate for missing image contrasts, and reduce examination time; however, existing methods often require substantial computational resources, limiting their practical accessibility. To address this limitation, we propose and investigate a lightweight yet effective framework, termed the Dual-path Adaptive Fusion Network (DAFNet), for T1-to-T2 images synthesis. DAFNet is based on cGAN (conditional generative adversarial networks) structure, and the generator employs a dual-path encoder that adaptively combines depth-wise separable convolutions and dilated convolutions to capture both fine-grained details and broader contextual features. A novel Dual-path Adaptive Fusion Module (DAFM) is introduced to dynamically combine these feature streams through channel-wise complementary weighting, enabling efficient and adaptive feature integration. This fusion mechanism is further applied to the final skip connection to enhance reconstruction fidelity, while the decoder adopts a standard transposed convolution architecture. Coupled with a conventional conditional GAN discriminator, the proposed model maintains low computational complexity while improving perceptual image fidelity. Experimental results demonstrate that DAFNet achieves superior performance compared with baseline U-Net and conventional cGAN models, reaching a peak signal-to-noise ratio (PSNR) of 26.43 dB while significantly reducing model size. This result indicates that DAFNet provides an effective balance between synthesis fidelity and computational efficiency, making it a promising solution for deployment on standard computing hardware and for broader clinical and research applications in MRI contrast synthesis.

Cloud computing and high-performance computing (HPC) typically follow different paradigms: cloud services are often orchestrated using Kubernetes, whereas HPC workloads are managed through batch schedulers such as Slurm. Growing demand for shared computational resources increases the need for interoperability between these environments. This study uses Podman as a user-accessible tool to benchmark quantum chemistry software distributed as precompiled executables. Containerized and on-system execution are compared across CPU-optimized builds and multiple compute nodes, without additional network optimizations. The results demonstrate that performance depends on software compilation, startup overhead, and execution configuration. Container preparation can dominate short-task workflows, while long-running calculations require monitoring of computational progress. These findings highlight the importance of workload-specific benchmarking and user-level monitoring when deploying scientific applications in containers on HPC systems.

We study circular-mode optics as a framework for transporting and accelerating intrinsically flat beams while maintaining a round transverse projection in physical space. Motivated by the strong degradation of flat beams under low-energy space-charge forces, we develop a coupled-optics description of circular modes, analyze their formation and preservation, and derive lattice design conditions for conserving canonical angular momentum and coupling phase. These conditions motivate rotationally invariant transport and ring cells based on solenoids, symmetric alternating-gradient structures, and indexed dipoles. We further derive space-charge tune-shift and tune-spread expressions for circular-mode beams in uncoupled and coupled lattices, showing that they preserve the leading-order space-charge behavior of round beams while retaining intrinsic eigenemittance flatness. Particle-in-cell simulations with TRACK and ImpactX confirm the generation, transport, and acceleration of circular-mode beams in representative lattices, including regimes with strong space charge. Circular-mode optics therefore provides a promising route toward high-intensity hadron beams and high-luminosity collider applications.

Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structure broadly through physics-motivated dynamical formulations or learning-motivated architectures. The former provide stronger priors but may constrain flexibility, whereas the latter are more flexible but leave the spatiotemporal coupling largely implicit. We therefore seek an approach that combines flexible learning with an explicit geometric bias for jointly modeling time and space. To this end, we propose Minkowski Positional Encoding (MinkowskiPE), which uses joint temporal and spatial coordinates to parameterize Lorentz transformations applied to query and key features. With MinkowskiPE, the query-key attention score depends on position only through the relative spacetime displacement between the two tokens and is therefore invariant to global translation of the coordinates. This paradigm retains the standard dot-product attention interface and remains compatible with efficient attention implementations. We evaluate MinkowskiPE on microscopic molecular dynamics and macroscopic video prediction tasks, achieving the best results on all nine multi-trajectory molecular evaluations and reducing KTH video-prediction MSE by 9.9% relative to the best baseline while using roughly one-tenth as many parameters.

A sub-grid closure of the scalar-dissipation rate for the Large Eddy Simulation (LES) of turbulent premixed flames is assessed experimentally. Hotwire anemometry is performed to characterize the background turbulent flow, and the Rayleigh scattering measurements are conducted to acquire instantaneous temperature fields, which are used to obtain the scalar dissipation rate. Either methane or hydrogen-enriched (40% by volume) methane is used as the fuel. The Karlovitz number varies between 1.7 and 34.5. The LES filter size is changed from 0.1 to 3.0 times the thermal thickness of the corresponding laminar flames. Both Gaussian and top-hat filtering methods are used. Statistical analyses are performed to investigate the characteristics of the measured, resolved, and unresolved scalar dissipation rate as well as how these are influenced by the normalized filter size, filter type, and the Karlovitz number. It is shown that the algebraic model allows for acceptable prediction of the Favre-filtered, temporally and spatially averaged scalar dissipation rate across the examined test conditions. The experimental measurements are used to gain insight into a key parameter used in the algebraic model. While differences between the previously proposed and our measured value of the model parameter for small Karlovitz number flames are reported, good agreement between our measured values and previously suggested values is obtained for relatively larger Karlovitz number flames. This finding is shown to be consistent across the examined filter sizes and filtering methods, despite the differences in the examined flame configuration of the present experimental study and those of past numerical investigations. This motivates future investigations to assess use of the algebraic model for simulating turbulent premixed flames with an extended range of operation.

Conventional reflectance photoplethysmography (PPG) typically reduces tissue optical responses to one or a few detector channels. Matrix Pinhole Image Sensing (MAPIS) instead simultaneously samples multiple aperture-dependent optical signals. We analyzed 44 recordings of 30 seconds each from a single MAPIS device to characterize the spatial relationships among DC intensity, pulsatile AC, perfusion index (PI), and temporal coherence across Red and infrared channels. AC increased sublinearly with DC at both wavelengths, resulting in higher PI toward lower-DC apertures, consistent with a first-order relative path-sensitivity interpretation. A second counterintuitive spatial trend was observed in temporal coherence: lower-DC and lower-AC apertures exhibited higher autocorrelation and greater cardiac-periodic spectral concentration. IR signals also showed consistently higher temporal coherence than corresponding Red signals. These observations demonstrate that multi-aperture PPG resolves reproducible spatial relationships among optical intensity, fractional pulsatile sensitivity, and waveform coherence that are largely averaged together in conventional single-channel PPG. The present study establishes within-device relationships and motivates controlled studies of the underlying photon-path mechanisms.

Structural observations in polymer chemistry are often rationalized through underlying mechanistic insights. The study of families of mechanisms - and the phenomenology and lawlike behavior that can accompany them - is less common. Here we construct a theory that links a mechanistic feature - the number of nearest neighbors $k$ that influence monomer incorporation - with the accessible copolymer sequence space. We find that the reaction mechanisms can be mapped 1-to-1 to a regular de Bruijn graph of degree $k$. We calculate the periodic copolymer sequences as function of neighbor index $k$, and number of monomers $s$. We show that the number of periodic sequences grows explosively with $s,k$, bounded from below by $s!^{s^{k-1}}/s^k$. We find that periodic k-polymerization is governed by several simple laws describing allowed sequences, number of specific reactions, repeating unit length and the allowed and disallowed sequence coexistence in block copolymers. We formulate Simple Assembly Rules (SAR) that capture commonly observed behavior and design of literature systems, and characterize the possible k-polymerizations they are allowed to access ($k\leq2$) as function of symmetries and illustrate our findings with reported examples from the coordination chemistry and supramolecular chemistry literature. We discuss chemical-structural contributions by which nontrivial ($k>1$) sequences are formed, highlighting the importance steric hindrance, long-range interactions and degrees of freedom.

A unified linear theory is developed for the unsteady loading of finite-chord aerofoils, rigid or porous, in compressible subsonic flow. The formulation combines Possio's integral operator with a convective permeable boundary condition, allowing chordwise-varying admittance while retaining the wake and unsteady Kutta condition. The loading exponents at aerofoil edges and admittance discontinuities keep their incompressible form in terms of the local permeability parameter, so weighted-Jacobi collocation carries over. The solution is verified against published incompressible results and an independent compressible formulation. Gust and heave responses and their indicial counterparts reveal that permeability controls the sensitivity of unsteady loading to compressibility. As permeability increases, the material impedance rather than the surrounding flow sets the pressure jump, but only gradually: at Mach number 0.7 the gust load on a weakly permeable surface changes by a third to a half, and closed-form steady and high-frequency limits for resistive surfaces show that it vanishes only when the permeability parameter greatly exceeds the Mach number. The porous-to-impermeable load ratio, by which a treatment is judged, cannot be obtained by combining incompressible porous and compressible rigid theories, which misjudge it by up to a factor of two. Incompressible theory overestimates this ratio for an acoustically compact chord and underestimates it for a non-compact one, by factors of 1.4 to 4.3 at Mach numbers 0.5 to 0.7 and reduced frequencies 5 to 20, overstating the load reduction. Since compactness depends on Mach number times reduced frequency, the error persists at low speed, exceeding 10 per cent at Mach number 0.05 for reduced frequencies above 10.

Groups differing in social and economic circumstances often differ markedly in depressive symptom levels, yet their estimated symptom networks show few clear differences. Such null results are often read as evidence that the groups' symptom systems are the same, but they only show that no difference was detected in how symptoms interact. How readily symptoms become present, and how this depends on the contextual conditions under which they are observed, is rarely compared. Building on a slow-fast perspective, we treat depressive symptoms as a relatively fast-changing system embedded in more persistent social, economic, psychosocial, health, and lifestyle conditions that shape symptom activation. Using a cohort of 23,689 adults from Amsterdam, we illustrate this with PHQ-9 depressive symptoms and a composite index of persistent socioeconomic, psychosocial, and health-related conditions, which we call Slow Risk Load (SRL). Participants with high SRL had substantially higher symptom levels than those with low SRL. A standard Network Comparison Test detected no overall difference in network structure, although global strength was modestly higher in the high-SRL group. Ising models, which separate how symptoms co-occur from how readily each becomes present, indicate higher activation under high SRL for eight of nine symptoms, and model comparison consistently favored group-specific activation over group-specific interactions. The slow-fast perspective reframes group network comparisons as a multiple-timescale problem in which network structure, symptom activation, and context should be considered jointly. It places symptom-focused and context-focused interventions at different layers of the same coupled system rather than in competition.

Semantic mismatch between independently trained AI-native agents in heterogeneous networks can impair semantic communications. Hybrid analog-digital semantic equalization can align the incompatible latent representations without retraining the semantic transceivers. We study a practical realization of this approach based on a fabricated dynamic metasurface antenna (DMA), an emerging low-cost, low-power, ultracompact technology for hybrid analog-digital beamforming. We model the DMA-assisted channel using multiport-network theory (MNT), accounting for mutual coupling (MC), structural scattering, and binary lossy tuning states. We use the experimentally estimated MNT parameters of our fabricated 19-GHz DMA prototype with strong MC. We jointly optimize digital pre- and post-equalizers and the DMA at a reference receiver geometry for latent-space alignment, then freeze the digital stages and adapt only the DMA after receiver motion. In our CIFAR-10 image-classification task at the receiver, the jointly optimized system achieves 94.5% accuracy, while DMA-only adaptation restores a median accuracy of 90.3% after receiver motion. Our semantic-aware wave-domain adaptation substantially outperforms semantic-unaware benchmarks. We further observe that varying the DMA configuration across channel uses provides little additional benefit for either semantic-aware or semantic-unaware optimization.

In this work, a globally stiffly accurate Implicit-Explicit (IMEX) Runge-Kutta scheme is developed and implemented in the GBS code [Ricci et al., Plasma Phys. Control. Fusion, 2012], for two-fluid plasma turbulence simulations. The stiffest phenomena, governed by shear Alfvén waves and parallel diffusion, are treated implicitly, while the remaining non-stiff terms are advanced explicitly. This splitting enables time steps well beyond the Courant-Friedrichs-Lewy limit, without incurring the full computational cost of a globally implicit formulation. To efficiently solve the implicit subsystem at each time step, a three-dimensional physics-based preconditioner, inspired by techniques developed in the magnetohydrodynamic (MHD) context, is used. The resulting framework is verified through the method of manufactured solutions and exhibits both algorithmic and parallel scalability. Significant advantages in numerical stability and computational efficiency are demonstrated with respect to an adaptive explicit Runge-Kutta scheme.

An analytical criterion for the disappearance of turbulent flow in a channel is developed within the Alexeev hydrodynamic equations (AHE). The analysis builds on an analytical representation of the streamwise velocity as a superposition of a parabolic component and a nonlinear turbulent component coupled to the transverse velocity field. The critical Reynolds number is obtained from the balance of viscous dissipation of kinetic energy associated with the turbulent component. The resulting critical Reynolds number is expressed analytically as \(Re_c=f(\delta,\gamma,n)\), where \(\delta\) is the Reynolds-independent AHE similarity parameter, \(\gamma\) is the weighting coefficient of the turbulent component, and \(n\) is the transverse-velocity mode number. The criterion describes the disappearance of the turbulent solution as the Reynolds number is decreased and therefore addresses the turbulent-to-laminar transition rather than the linear instability of the laminar solution. Unlike a criterion based on a single universal critical Reynolds number, the AHE criterion predicts a transition threshold that depends on the parameters characterizing the flow. For representative channel-flow parameters, the predicted critical Reynolds number is approximately \(Re_c=1000\text{--}1200\). The predicted threshold is compared with experimental observations of transition and of the persistence of laminar flow in channel flows. The analysis suggests that viscous dissipation provides a mechanism limiting the persistence of the turbulent solution below a critical Reynolds number.%

The wave-particle decomposition is presented as a framework for multiscale kinetic transport, comprising a theory and an algorithm. Along the characteristics of the kinetic equation, the equilibrium integral of the solution over a local kinetic horizon defines a wave, which depends only on the conservative variables, and its exact complement defines a particle. Both obey kinetic equations whose moments are extended Navier-Stokes equations, and the conservation laws together with the particle equation form a closed system that is equivalent to the kinetic equation for every horizon. Parametrized by the ratio of the horizon to the relaxation time, these systems form a continuous spectrum from kinetic theory to Navier-Stokes hydrodynamics that adapts to the flow from cell to cell. The algorithm is a coupled wave-particle iteration for steady flows. It alternates a macroscopic iteration for the conservative variables, with the particle frozen, and a kinetic iteration for the particle, with the wave fixed. Its fixed point is the discrete kinetic solution, it is asymptotic preserving, and its near-continuum convergence factor is bounded by the share of the transport carried by the particle, a share that decays exponentially with the horizon-to-relaxation ratio. The collision model enters only through a conservative remainder, and the equations and the iteration are written out for the Bhatnagar-Gross-Krook, Shakhov and ellipsoidal statistical models, the Boltzmann and Landau equations, neutron transport and radiative transfer. The decomposition is related to the unified gas-kinetic wave-particle method, the micro-macro decomposition, penalization and synthetic acceleration. Computations of hypersonic flows past a cylinder and around the three-dimensional X38 vehicle illustrate how the division of the distribution adapts to the flow and how strongly the iteration is accelerated.

Quantum computing offers a promising route for electronic-structure calculations, but practical condensed-matter applications often require solving large families of related Hamiltonians arising from $\mathbf{k}$-point sampling, compositional variation, defects, and changes in system size. Here, we develop strategies for transferring variational information between related Hamiltonians to accelerate quantum simulations. For Hamiltonians of the same embedded size, optimized variational parameters are transferred between related calculations. For different sizes, we introduce Augmented Ansatz Reuse for Target Initialization (AARTI), which transfers compatible Pauli-generator structure and optimized source parameters, then augments the ansatz with target-specific generators. Our workflow combines density functional theory, Wannier downfolding, embedded tight-binding Hamiltonians, variational quantum algorithms, and neural-network quantum states, with quantum-circuit simulations implemented using NVIDIA's CUDA-Q platform. Using Li$_x$CoO$_2$ as a representative battery cathode material, we study Hamiltonian families spanning multiple delithiation levels, dense $\mathbf{k}$-point meshes, and increasing sizes. Parameter and ansatz transfer substantially reduce the optimization steps required to solve these related Hamiltonians while maintaining the common target accuracy, with lower terminal errors in several cases. The calculated electronic-structure evolution with delithiation exhibits pronounced non-rigid-band behavior consistent with experiment. These results establish a framework for exploiting similarity among related Hamiltonians to improve the efficiency and scalability of quantum workflows for realistic condensed-matter systems.

Success-driven imitation is a fundamental aspect of social learning, yet group interactions beyond pairwise require more subtle imitating rules since success can be observed at either the group or individual level. How such heuristic imitating rules based on these distinct levels of social information compete and coevolve with collective behavior, particularly cooperation, remains poorly understood. Here, we address this issue by studying the coevolutionary dynamics of cooperation in public goods games and individual- and group-based imitating rules on hypergraphs. We distinguish three rules according to whether payoff-biased imitation operates at the individual level, the group level, or both. Our results identify a mutual reinforcement between cooperation and group-biased imitation: preferentially learning from successful groups promotes cooperation, while cooperators in turn favor group-biased imitation. We confirm this synergy between cooperation and group-biased imitation across diverse synthetic and empirical higher-order network populations. We further show that the subtle scale and organization of group interactions critically shape this coevolution, with intermediate group sizes providing the greatest advantage for cooperation. Our results reveal how learning whom to imitate can jointly evolve to shape collective cooperation.

Critical slowing down (CSD), the slowing of recovery from perturbations as a system approaches a critical transition, is widely used for early warning, but whether such signals appear in environmental transitions subject to rapidly changing external forcing has rarely been tested with observations. Here we show, using 2,427 icing-onset events in 1-min observations from 121 South Korean road weather stations, that such signals do not consistently precede road icing. Icing began from different prior surface states, but conditions at onset varied continuously with weak separation among pathways. Cold dose, deposition dose and freeze-thaw accumulation at onset exceeded those at non-icing control times, with cold-dose discrimination highest at decay timescales of 30 min or less. However, the increases in variance and lag-1 autocorrelation expected under CSD did not distinguish icing from control times; the same procedure recovered these increases in synthetic series with a declining recovery rate, and the null result was replicated for 3,091 events at 127 independent stations. Predictive information was nevertheless present: adding recent changes and cumulative antecedent conditions to current conditions improved discrimination, and surrounding meteorological fields increased the area under the ROC curve by 0.030-0.032 for forecast windows of 1 to 12 h and reduced false alarms by 62-67% at 3 and 6 h. These results suggest that road icing is more consistent with recent meteorological forcing moving the road surface into icing-favourable conditions than with a progressive decline in internal stability, and they indicate where early warning should look when a transition is externally forced.

During plasma heating and current ramps the thermal and magnetic states of a tokamak evolve on different timescales. We present a rapid integrated tokamak modelling approach, which couples a variational fixed-boundary Grad–Shafranov equilibrium to sparse particle and energy flux matching and to weak-form current diffusion. An ITER-like pure-deuterium scenario is examined: a current ramp from 10 to 15~MA over 20~s, a 20~s flat-top and a 20~s ramp-down to 14~MA, under 80~MW of electron-cyclotron heating applied at the start. The toroidal current density at the pedestal top more than doubles during the ramp-up and then decreases during the flat-top as current redistributes inward. Thermal stored energy rises from 185.52 to 252.70~MJ and approaches its final value within the first few seconds. An independent calculation with the Fusion Synthesis Engine (FUSE) gives a stored-energy difference of 0.02% at 60~s, whereas the largest profile differences occur in the core current density. Halving the coupling interval changes stored energy by at most 0.006% at the selected radial resolution. The 60~s pulse, advanced through 600 coupling intervals of 0.1~s, takes 8.62~s on an Apple M4 Pro, which is at least two orders of magnitude faster than conventional integrated tokamak modelling solvers.

This Letter establishes the analytical structure of the dipole radiation transition operator, taking into account non-dipole effects for a multielectron atom. The results summarize those of Komninos Y. et al., Phys. Rev. A 86, 023420 (2012), obtained in an approximation of a "split" electron of a continuous spectrum. The result for the complete operator of a multipole radiation transition [see formula (10)] can be specified within the framework of the methods of the theory of irreducible tensor operators and for higher multipoles.

Laser-driven proton-boron fusion offers a path for generation of $\alpha$ particles on a compact scale. Conventional solid-target experiments, however, are fundamentally limited by target degradation and low shot-repetition capacity. Here, we demonstrate a continuous laser-driven $\alpha$ particle source utilizing target-normal sheath acceleration from a continuously replenishable ultrathin water-leaf jet target. The proton beam interacts with a downstream boron nitride converter, driving the $^{11}\text{B}(p,\alpha)^{8}\text{Be}$ reaction which yields three $\alpha$ particles. The measurement was repeated across $18{,}000$ consecutive shots at a rate of $10\,\text{Hz}$. The outgoing $\alpha$-particle yield and spectral fluence were measured and benchmarked against comprehensive particle-transport simulations. The numerical and experimental results reveal a pronounced flattening in the energy-differential $\alpha$-particle spectrum escaping from the converter, driven by depth-dependent reaction yield and stopping power effects. The sustained stable $\alpha$-particle production at high cumulative yields paves the way for applications in targeted particle therapy and laser-driven plasma-fusion research.

Molecular qubits held in conservative optical traps may lose coherence when they scatter photons from the trapping lasers. The rate for this process has two parts, one equal to the mean Raman scattering rate of the two qubit states, and the other proportional to the square of the difference in their Rayleigh scattering amplitudes. This paper calculates the decoherence rate, expressing results in terms of the vector and tensor parts of the molecular polarizability, $\alpha_1$ and $\alpha_2$. While the decoherence rate is generally much larger for molecules than for atoms, it is suppressed at wavelengths where $\alpha_1$ and $\alpha_2$ are both small. The wavelengths where this occurs are identified for CaF molecules.

Semiconductor electronic devices are increasingly constrained by fundamental quantum tunneling effects and charge-based mechanisms, which severely limit further miniaturization, write-speed scaling, and environmental robustness of silicon-based technologies. These limitations are particularly prohibitive for deep-space exploration, where extreme temperatures, ultra-strong magnetic fields, and intense radiation rapidly incapacitate conventional electronics without massive shielding. Here, we present an intrinsically resilient, strain-mediated antiferromagnetic MnIr/PMN-PT edge processor that operates reliably as a bare die under temperatures up to 500 K, magnetic fields of 55 T, and radiation doses of 1.5 Mrad. By exploiting an input-modulated in situ self-refreshing encoding mechanism, the device performs nonlinear feature extraction and classification directly from raw analog signals, enabling an analog computing architecture that requires no time-frequency transformation. This architecture achieves 99.8% accuracy in speech recognition without digital preprocessing and 100% accuracy in astronaut visual object recognition. Furthermore, an all-hardware integrated drone vision system demonstrates real-time in situ command execution and autonomous navigation, delivering a terahertz-level response frequency and an ultra-low energy consumption of approximately 0.2 fJ per operation. This work expands the functional scope of antiferromagnetic devices beyond memory and logic, establishing them as a promising materials platform for energy-efficient physical computing and autonomous intelligence in extreme environments.

De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue that discovery is better served by separating the two. A crystal structure prediction (CSP) model is a physical prior that should be improved by likelihood training, while rewards, including novelty measured against the search's own history, should act on a search over compositions. We introduce ANCHOR, a GRPO composition policy trained with multi-objective rewards around a frozen CSP model, and continuous adaptive novelty (CAN), a graded novelty score against known structures and a growing discovery history. Using the frozen CSP model as a fixed ruler under one evaluator, we test where adaptation should act. Replacing DNG compositions with ANCHOR's policy on the same CSP backbone raises MSUN from 11.4% to 47.6% and SUN from 1.1% to 22.1% at 99.9% formula uniqueness. Fine-tuning DNG models directly on the same rewards instead moves their composition marginal without raising their on-hull fraction. We show that KL-regularized fine-tuning of a DNG model can only reweight chemistry the pretrained model already supports by a bounded factor, while unregularized DNG fine-tunes move toward known or less stable chemistry. Even a stability-only reward routed into ANCHOR's CSP backbone roughly halves SUN relative to the frozen backbone, whereas likelihood training on structures found during search can improve a CSP backbone. Under MatterGen's evaluation pipeline, ANCHOR raises state-of-the-art MSUN from 29.2% to 41.3%, transfers without retraining to two further CSP backbones, and reaches 47.1% after distillation into Crystalite-CSP. As with any model optimised against a potential, its on-hull rate depends on that potential.

Marcuse theory of pulse distortion in singlemode fibers provides closed-form results for Gaussian pulses launched from sources of arbitrary spectral width. We extend the theory to supergaussian pulses of arbitrary order with linear or edge-concentrated chirp, single or multiline sources and an arbitrary number of dispersion coefficients. The rms output width is obtained exactly from the moments of the Wigner distribution, and all the pulse moments involved reduce to finite sums of Gamma functions. Supergaussian pulses are considerably more sensitive than Gaussian pulses to higher order dispersion and are compressed less efficiently by chirp. Near the zero-dispersion wavelength their rms width is governed by weak spectral side lobes and overestimates the broadening of the pulse energy. For pulse sequences, interference between neighboring pulses leaves all time moments unchanged when the input pulses do not overlap, but it redistributes energy locally with a visibility set by the source spectrum filtered by the pulse spectrum.

Quadratically nonlinear systems driven by additive noise arise throughout science and engineering, both as stochastic dynamical systems in their own right and as reduced-order models (ROMs) of turbulent flows. We propose stochastic physics-constrained operator inference (SPOpInf), a non-intrusive method that learns a stochastic differential equation, i.e., a quadratic–linear drift and a diffusion amplitude, from data. Unlike operator inference (OpInf), which fits a deterministic drift by least squares, SPOpInf maximizes an Euler–Maruyama increment likelihood in which the residual covariance is itself an unknown, so the deterministic and stochastic components are calibrated jointly; it enforces the energy-preserving quadratic structure exactly, through linear equality constraints imposed inside the optimization; and it works in an upper-triangular parametrization that removes the structural non-identifiability of the full quadratic tensor. The resulting optimization is solved by an expectation–maximization (EM)-based iterative learning algorithm that updates the drift coefficients and the noise levels in turn: each iteration requires only an inexpensive weighted least-squares solve for the drift and a residual-variance update for the noise. With five stochastic benchmark systems that show intermittency and chaos, SPOpInf recovers the deterministic operators more accurately than OpInf, satisfies the imposed constraints to machine precision, accurately estimates the noise amplitudes, and reproduces long-time statistics. Applied to a stochastic Burgers equation and a quasi-geostrophic double-gyre flow, the same estimator yields non-intrusive stochastic ROMs that remain stable over long times where unconstrained alternatives blow up.

Criticality in the Ising model is conventionally generated by Hamiltonian dynamics and controlled by temperature. Here we show that critical like behavior can emerge in an Ising system whose dynamics is completely independent of the Ising Hamiltonian and contains no temperature like parameter. We introduce an adaptive cluster dynamics in which spin connectivity is controlled by a local quiet time variable, the time elapsed since a spin was last updated. The Ising Hamiltonian enters only as an observable characterizing the resulting configurations. As the parameter $\alpha$, controlling the connectivity strength, is varied, the system undergoes spontaneous symmetry breaking accompanied by strong collective fluctuations. The cluster size distribution develops an extended power law regime, terminated by a small finite size hump at the largest scales, while the Ising energy exhibits a singular response to the variation of $\alpha$. These results show that memory dependent connectivity alone can generate collective critical behavior, revealing a route to nonequilibrium criticality without energy based dynamics.