Papers

Topological lasers and random lasers embody two contrasting strategies for disorder management in photonics: the former suppresses disorder via protected edge transport, while the latter exploits multiple scattering for feedback. Here, we theoretically demonstrate that these seemingly incompatible paradigms can be unified through a topological Anderson random laser (TARL), where disorder itself induces a topological phase that enables robust lasing. Starting from a trivial photonic lattice, we show that engineered disorder drives the system into a topological Anderson insulator regime, generating emergent chiral edge states that serve as boundary-selective lasing channels. Remarkably, the TARL exhibits rapid mode selection toward a single edge state, producing an ultranarrow emission spectrum and enhanced slope efficiency optimized near disorder strength with maximal topological mobility gap. Furthermore, they exhibit single-mode-like coherence properties, deviating from Kardar-Parisi-Zhang behavior in conventional chiral topological lasers, while remaining significantly more robust against local perturbations than conventional random lasers. Our findings establish a disorder-enabled flexible route to topologically protected single-mode lasing and introduce a fundamentally new design principle for robust, high-coherence photonic light sources.

Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two dynamical regimes of a single recurrent network (RNN) modeling hippocampal CA3 as a predictive autoencoder. The network receives simulated, partially occluded “experience vectors" containing spatial patterns (location-specific activity sampled during environmental traversal) and/or temporal patterns (correlated activity pairs separated by “void" intervals), and is trained to reconstruct missing input. During spatial navigation, the network generates stable attractor-like place fields. But trained on temporally structured inputs, the network produces sequentially broadened fields, recapitulating time cells. By varying spatio-temporal input patterning, we observe hidden units transition smoothly between time cell-like and place cell-like representations. These results suggest a shared origin, but task-driven difference, between place and time cells.

Weatherall and Manchak (2014) show that, under reasonable assumptions, Reichenbachean universal effects, constrained to a rank-2 tensor field representation in the geodesic equation, always exist in non-relativistic gravity but not so for relativistic spacetimes. Thus general relativity is less susceptible to underdetermination than its Newtonian predecessor. Dürr and Ben-Menahem (2022) argue these assumptions are exploitable as loopholes, effectively establishing a (rich) no-go theorem. I disambiguate between two targets of the proof, which have previously been conflated: the existence claim of at least one alternative geometry to a given one and Reichenbach's (in)famous “theorem theta", which amounts to a universality claim that any geometry can function as an alternative to any other. I show there is no (rich) no-go theorem to save theorem theta. I illustrate this by explicitly breaking one of the assumptions and generalising the proof to torsionful spacetimes. Finally, I suggest a programmatic attitude: rather than undermining the proof one can use it to systematically and rigorously articulate stronger propositions to be proved, thereby systematically exploring the space of alternative spacetime theories.

Negative and complex numbers are so familiar in modern mathematics, physics, and engineering that it is easy to forget how uncertain their status once was. They did not become established through a single route. This article follows four linked processes in their stabilization: operational use, formal legitimation, pedagogical normalization, and physical naturalization. Negative quantities appear early in Chinese rod arithmetic and Indian debt–fortune rules, were reshaped in medieval Islamic algebra, and remained conceptually unstable in early modern Europe even when they worked in practice. Complex quantities followed a different path: they first appeared as troubling by-products of algebraic formulas, then gained stability through Bombelli's rules, geometric representation, nineteenth-century analysis, and later applications in circuits, wave theory, optics, and quantum mechanics. Franklin's electrical plus and minus helped make sign physically intelligible, while electrical engineering turned impedance and complex amplitudes into routine tools. The broader lesson is that these quantities became natural through repeated interaction among calculation, representation, teaching, and experiment.

This paper presents a physics-based channel modeling and optimization framework for reconfigurable intelligent surface (RIS)-assisted downlink multi-user multiple-input single-output (MU-MISO) communication systems in site-specific environments. A hybrid ray-tracing (RT) and full-wave electromagnetic analysis approach is developed to construct a deterministic channel model that explicitly captures multipath propagation, RIS scattering behavior, and mutual coupling effects through a non-diagonal load impedance representation. Based on this model, an alternating optimization scheme jointly updates the base-station (BS) beamformer and RIS load impedances to maximize the minimum achievable rate under a total transmit power constraint and practical capacitance limits. The objective of the proposed framework is to provide a reliable initial assessment of the system-level impact of RIS deployment in realistic propagation scenarios. To evaluate this capability, the RIS is operated in a column-paired 1-bit control mode that enables exhaustive evaluation of all realizable configurations in both simulation and measurement. Performance is compared at the distribution level through achievable-rate histograms across all configurations and further examined under small user-location variations. The observed agreement between simulation and measurement demonstrates that the proposed framework reliably captures practical performance trends and provides useful guidance for the design and deployment of RIS-assisted MU-MISO systems in site-specific environments.

Thin films are preferred for high photocurrent conversion efficiency, but strong photon absorption at photon energies below the bandgap (near and shortwave infrared) typically requires thicker semiconductor layers. To address this tradeoff, various optical approaches have been proposed, including light scattering within the active layer, reducing surface reflection, and using resonant structures to improve light confinement, trapping, and coupling. However, resonant structures often operate over a narrow spectral range, limiting their use of the full solar spectrum, and can involve complex fabrication and careful structural design. In this work, I propose a new method to enhance absorption in semiconductor solar cells across wide angular and spectral ranges for both polarization states (transverse electric (TE) and transverse magnetic (TM)). The method is based on a coupled plasmonic waveguide resonance (CPWR) configuration excited in a planar layered structure that can be fabricated using simple deposition techniques. Using the proposed approach, as an example, the thickness of the required Silicon (Si) layer can be reduced from approximately 130 to 180 {\mu}m (the typical Si thickness in commercial solar cells) to only a few microns. The method enables efficient harvesting of the infrared portion of the solar spectrum. By exciting CPWRs, the method overcomes the sharp drop in the absorption spectrum of conventional Si solar cells at wavelengths longer than 1100 nm. Field calculations demonstrate that light is efficiently absorbed in the Si layer at the resonant wavelengths. The proposed approach is general and can be applied to different types of semiconducting and prism materials. To maintain high absorption at wavelengths below 1100nm, an additional semiconductor metal semiconductor configuration is proposed, in which a thinner Si layer is added beneath the metal layer.

We investigate the rotational response of a confined, two-dimensional quantum droplet, which emerges in an attractive binary Bose mixture that is stabilized against collapse by beyond-mean-field effects. We consider both a harmonic and an anharmonic form for the external confining potential. We go beyond the widely employed “phase-locked" single-order-parameter model, maintaining two separate order parameters for the two components, and calculating the lowest-energy state for various values of the angular momentum. For a population-balanced quantum droplet and sufficiently tight confinement, we find that near certain half-integer values of the angular momentum the droplet is excited in a “heterosymmetric" manner, with the two components carrying different vorticities. This mode is naturally missed by the single-order-parameter model. We additionally investigate the effects of a small population imbalance in the droplet. Apart from an energy increase associated with the population difference, the imbalance also lifts the double degeneracy of the heterosymmetric states, which characterizes the $\mathbb{Z}_2$-symmetric balanced droplet. The heterosymmetric mode is found to be favored by the energy term which captures the beyond-mean-field effects in the mixture.

All-optical logic gates have significantly advanced over a diverse range of photonic systems, boosted by intricate nonlinearities that facilitate the engineering of complex logic operations. Here, we demonstrate that in semiconductor microcavities, polariton condensates trapped in Ouroboros-shaped rings form specifically charged vortices, determined by the strength of nonlinearity and the excitation method. Quantized vortex phases encode binary digits that can be nonresonantly controlled by optical pulses incident directly upon the ring, enabling logic operations. By interconnecting three polariton Ouroboros rings, we realize a universal set of logic gates (AND, OR, NIMPLY) fundamental to functional polaritonic devices. The Ouroboros structures are highly customizable, providing a robust and promising platform for exploring more complex logic operations.

Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping algorithms to fail. Another limitation of existing learning-based unwrapping methods is their reliance on fixed and relatively small input sizes, while real InSAR interferograms are typically large-scale and spatially heterogeneous. This mismatch restricts the applicability of many neural network approaches to real-world data. In this work, we present a phase unwrapping framework based on a diffusion model, developed to process large-scale interferograms and to address phase discontinuities caused by deformation. By leveraging a diffusion model architecture, the proposed method can recover physically consistent unwrapped phase fields even in the presence of fault-related phase jumps. Experimental results on both synthetic and real datasets demonstrate that the method effectively addresses discontinuities associated with near-surface deformation and scales well to large InSAR images, offering a practical alternative to manual unwrapping in challenging scenarios.

Ground states of materials with orientational order ranging from solid ferromagnets and ferroelectrics to liquid crystals often contain spatially varying vector-like order parameter caused by inner factors such as the shape of building units or by the geometry of confinement. This review presents examples of how the shapes, chirality, and polarity of molecules and spatial confinement induce deformed equilibrium and polydomain states with parity breaking, splay, bend, and twist-bend deformations of the order parameter in paraelectric and ferroelectric nematic liquid crystals. Parity breaking results either from chirality of the constituent molecules, as a replacement of energetically costly splay and bend in paraelectric nematics, or in response to depolarization field in the ferroelectric nematic. Both paraelectric and ferroelectric nematics exhibit a splay cancellation effect, in which the elastic and electrostatic energies of splay along one direction are reduced by an additional splay along orthogonal directions.

This study introduces a vortex gust generation method for isolated vortices impacting a downstream airfoil that is applicable to both numerical simulations and experiments. The vortex gust is generated by a symmetric airfoil undergoing a rapid pitching maneuver during a prescribed heaving motion. The resulting vortices propagate along trajectories nearly parallel to the incoming flow, while the associated wake extends obliquely from the vortex core. Despite differences in Reynolds number, rapid pitching duration and detailed vortex structure between simulations and experiments, consistent trends are observed in how the vortex rotation orientation, strength, and position vary with the prescribed motion parameters. Analysis of the lift response of the downstream airfoil shows that the aerodynamic influence associated with the wake does not persist over extended time scales. These results demonstrate that the proposed method enables the controlled generation of vortex gusts with prescribed characteristics, providing a flexible approach for systematic studies of vortex-airfoil interaction.

Advances in the study of relativistic quantum phase space have established the set of Linear Canonical Transformations (LCTs) as a candidate for the fundamental symmetry group associated with relativistic quantum physics. In this framework, for a spacetime of signature $(N_+,N_-)$, the symmetry of the relativistic quantum phase space is described by the LCT group, isomorphic to the symplectic Lie group $Sp(2N_+,2N_-)$, which preserves the canonical commutation relations (CCRs) and treats spacetime coordinates and momenta operators on an equal footing. In this work, we investigate the contraction structure of the Lie algebra associated with the LCT group for signature $(1,4)$, clarifying how familiar spacetime symmetry groups emerge from this more fundamental quantum phase space symmetry. Using the Inönü-Wigner group contraction formalism, we examine each limit case corresponding to the possible combinations of asymptotic values of two fundamental length scale parameters associated with the theory, namely a minimum length $\ell$ and a maximum length $L$, which may be identified respectively with the Planck length and the de Sitter radius. We explicitly analyze how contractions of the LCT Lie algebra lead to the physically relevant de Sitter algebra $\mathfrak{so}(1,4)$ and, in the flat-curvature limit, to the Poincaré algebra $\mathfrak{iso}(1,3)$ of four-dimensional spacetime. This provides an explicit mechanism through which relativistic spacetime symmetry can emerge from a deeper symplectic structure of quantum phase space.

The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohibits their routine use in TS optimization. Here, we present a machine-learning-driven TS optimizer that directly predicts the leftmost Hessian eigenvector (LMHE), the critical mode that locally approximates the reaction coordinate encompassing the TS. We demonstrate that our LMHE optimizer recovers TS solutions at the same rate as full Hessian optimizers, and more robustly from degraded initial guess geometries, thereby eliminating the excessively long wall times characteristic of full-Hessian approaches and reducing total gradient evaluations compared to standard quasi-Newton methods. We further improve accuracy and robustness using uncertainty quantification for identifying occasional LMHE prediction failures, that then falls back to a full Hessian update from the machine learned potential at that optimization step, avoiding expensive active learning. Overall our methodology and semi-automated workflow delivers second-order stability at first-order computational expense to provide a highly efficient engine for high-throughput reaction discovery.

The rapid growth of AI and accelerator-driven workloads is forcing a fundamental rethinking of optical interconnect architectures in datacenters. Co-packaged optics and three-dimensional photonic integration have emerged as promising solutions to overcome the energy and bandwidth limitations of electrical I/O. Yet, as optics move closer to compute, packaging, thermal management, and system-level robustness increasingly dominate performance and scalability. Here, we argue that co-packaged optics should not be viewed as a component-level optimization, but as an architectural commitment that reshapes the boundaries between photonics, electronics, and system design. We examine how heterogeneous integration strategies, chiplet-based optics, and emerging packaging platforms redefine scaling laws for AI systems, often introducing trade-offs that are underappreciated in device-centric analyses. Looking forward, we discuss why standardization, serviceability, and thermal-aware co-design will be decisive in determining whether co-packaged optics can transition from early deployment to widespread adoption in AI-scale datacenters.

Chiral surface waves are surface-localized modes that propagate unidirectionally along a boundary, enabling directed transport and minimal back-scattering. While first identified in quantum systems, they were recently shown to emerge in classical metamaterials in the presence of `odd elasticity'. Owing to the non-reciprocality of odd elasticity, these waves exhibit growing amplitudes during propagation, reminiscent of the non-Hermitian skin effect. To date, studies of odd elastic systems have mainly focused on ordered structures. Whether structurally-disordered materials can host non-Hermitian chiral surface waves (NHCSW) remains unexplored. We address this question using a minimal model of torque-driven disordered odd solids. Such solids are abundant, from biological gels such as the cytoskeleton driven by motor-proteins to synthesized systems such as magnetic colloidal gels. We find that torque-driven disordered odd solids have unique NHCSW with stronger surface localization and stable boundary velocity, in contrast to previous lattice models of odd solids. These distinct features stem from an intrinsic interplay between boundary torques and odd elasticity in torque-driven odd solids. Our results offer a new strategy to control NHCSW using active torques.

We demonstrate precision laser spectroscopy of a trapped $^{173}$Yb$^+$ ion that is not directly laser cooled by coupling it to ultracold atoms. The atomic bath continuously cools the internal degrees of freedom of the ion to its hyperfine ground state via spin-exchange collisions. Successful laser excitation is detected via state-selective charge transfer and subsequent ion loss. We probe the $6^2S_{1/2}\rightarrow 6^2P_{3/2}$ transition at 329 nm and measure the magnetic and electric hyperfine interaction constants for the $6^2P_{3/2}$ state to be $A=-241(1)$ MHz and $B=1460(8)$ MHz, respectively. Our results are in agreement with a previous measurement obtained in a hollow-cathode discharge experiment but are a factor of 6-9 more precise. The techniques demonstrated in this work may be extended to perform precision spectroscopy on other ions with complex level structures.

We propose closed-form conditional diffusion models for data assimilation. Diffusion models use data to learn the score function (defined as the gradient of the log-probability density of a data distribution), allowing them to generate new samples from the data distribution by reversing a noise injection process. While it is common to train neural networks to approximate the score function, we leverage the analytical tractability of the score function to assimilate the states of a system with measurements. To enable the efficient evaluation of the score function, we use kernel density estimation to model the joint distribution of the states and their corresponding measurements. The proposed approach also inherits the capability of conditional diffusion models of operating in black-box settings, i.e., the proposed data assimilation approach can accommodate systems and measurement processes without their explicit knowledge. The ability to accommodate black-box systems combined with the superior capabilities of diffusion models in approximating complex, non-Gaussian probability distributions means that the proposed approach offers advantages over many widely used filtering methods. We evaluate the proposed method on nonlinear data assimilation problems based on the Lorenz-63 and Lorenz-96 systems of moderate dimensionality and nonlinear measurement models. Results show the proposed approach outperforms the widely used ensemble Kalman and particle filters when small to moderate ensemble sizes are used.

Microorganisms ofter move in confined, disordered environments, where hydrodynamic couplings can modify their transport behavior. Using extensive finite-element simulations, we investigate the dynamics of microswimmers – modeled as squirmers – in two-dimensional disordered porous media by resolving the full hydrodynamic interactions. We reveal that the deterministic coupling between activity, hydrodynamics, and disorder is sufficient to generate effective diffusive transport. Strong pushers and pullers become localised in the porous medium either by trapping at corners or dynamic trapping, depending on swimmer type and obstacle packing fraction. Squirmers can escape from dynamic traps, leading to a prominent “hopping-and–trapping” dynamics. Strikingly, we find a pusher-puller asymmetry in the trapping probability that can be reversed by short-range swimmer-obstacle interactions, highlighting the sensitivity of transport to near-field effects.

Weather forecasting is a fundamental problem for protecting lives and infrastructure from high-impact atmospheric events. Recently, data-driven weather forecasting methods based on deep learning have demonstrated strong performance, often reaching accuracy levels competitive with operational numerical systems. However, many existing models rely on large-scale training regimes and compute-intensive architectures, which raises the practical barrier for academic groups with limited compute resources. Here we introduce Sonny, an efficient hierarchical transformer that achieves competitive medium-range forecasting performance while remaining feasible within reasonable compute budgets. At the core of Sonny is a two-stage StepsNet design: a narrow slow path first models large-scale atmospheric dynamics, and a subsequent full-width fast path integrates thermodynamic interactions. To stabilize medium-range rollout without an additional fine-tuning stage, we apply exponential moving average (EMA) during training. On WeatherBench2, Sonny yields robust medium-range forecast skill, remains competitive with operational baselines, and demonstrates clear advantages over FastNet, particularly at extended tropical lead times. In practice, Sonny can be trained to convergence on a single NVIDIA A40 GPU in approximately 5.5 days.

Graphene supports surface plasmon polaritons with extreme field confinement and electrical tunability, but these waves are typically short-lived due to ohmic loss in the sheet. We show that embedding graphene in an active dielectric can counteract this loss and we derive closed-form design rules for lossless propagation within the local linear model. Specifically, from the full Maxwell model of a conductive sheet we obtain the gain values required to make the propagation constant real, $q''=0$, and we separately discuss the $r=0$ boundary obtained from the real part of the complex-index radicand. The formulas are expressed directly in terms of the complex conductivity of graphene and the surrounding media, making them easy to evaluate and implement. We verify the theory with full-wave simulations based on the finite element method in COMSOL, showing dispersion and attenuation/amplification trends with and without gain for single- and double-layer graphene plasmonic structures.

Coupled partial differential equations underpin a wide range of multiphysics systems, yet existing neural PDE solvers still struggle to resolve localized high-risk regions and often fail to preserve structural admissibility across coupled fields. To address these limitations, we propose the Partition-of-unity Information-geometric Certified Solver (PICS), a closed-loop framework that strictly enforces structural admissibility at the level of representation rather than relying on an additional soft penalty. By constructing a gate-structured admissible manifold coupled with a restricted jet prolongation, PICS ensures that geometry-sensitive approximations and closure-essential differential coordinates enter the solver as a strongly enforced, structure-preserving ansatz. Furthermore, the framework integrates entropic tail-risk control and a posteriori certificate-driven empirical measure transport, dynamically reallocating training efforts toward uncertified, error-prone transition zones. Evaluated against standard baseline methods across three two-dimensional coupled benchmarks, PICS achieves more consistently accurate and balanced cross-field recovery while retaining practical computational efficiency, thereby providing a rigorous route toward highly reliable multiphysics simulation.

We propose a landmark-constrained algorithm, LA-VDM (Landmark Accelerated Vector Diffusion Maps), to accelerate the Vector Diffusion Maps (VDM) framework built upon the Graph Connection Laplacian (GCL), which captures pairwise connection relationships within complex datasets. LA-VDM introduces a novel two-stage normalization that effectively address nonuniform sampling densities in both the data and the landmark sets. Under a manifold model with the frame bundle structure, we show that we can accurately recover the parallel transport with landmark-constrained diffusion from a point cloud, and hence asymptotically LA-VDM converges to the connection Laplacian. The performance and accuracy of LA-VDM are demonstrated through experiments on simulated datasets and an application to nonlocal image denoising.

We investigate the evolution of Anomalous Hall Conductivity (AHC) in a coplanar and collinear antiferromagnetic system with varying spin canting angles. A tight-binding model based on three t2g-orbitals in a body-centered tetragonal lattice is constructed, where the inclusion of third-nearest neighbor hopping is demonstrated to be essential for capturing the characteristic energy band splitting of altermagnetic materials. By employing a symmetry analysis based on spin space groups and treating spin-orbit coupling (SOC) as a perturbation, we theoretically distinguish and numerically verify two origins of the transverse transport: the conventional anomalous Hall effect (AHE) induced by net magnetization and the Crystal Hall Effect (CHE) arising from specific crystal symmetries. Our results show that the conductivity components driven by these two mechanisms follow distinct trigonometric dependencies on the canting angle. Crucially, we identify a hidden C110 rotational symmetry that has been previously overlooked in static magnetic group analyses. By expanding the AHC in terms of spin orientation vectors, we demonstrate that this symmetry acts as a bridge connecting distinct magnetic configurations with different canting angles, thereby strictly protecting the equivalence of orthogonal conductivity components in the collinear system.

Accurately resolving steady electrohydrodynamic (EHD) flows presents a formidable computational challenge due to the strong nonlinear coupling between charged-particle density, velocity fields, and electric potential. These interactions frequently induce sharp transition layers, crossing fronts, and multiscale spatial structures, which notoriously degrade the predictive accuracy of standard mesh-free solvers like Physics-Informed Neural Networks (PINNs). To systematically address this bottleneck, we formulate a unified four-variable operator framework and develop a comprehensive benchmark suite for two-dimensional steady EHD shock-like problems. The benchmark comprises eight rigorously designed cases featuring diverse front geometries, such as oblique, curved, and intersecting layers, alongside complex multiscale patterns. Under strictly identical configurations, including governing equations, source terms, sampling strategies, and loss formulations, we evaluate a Standard MLP-based PINN, a Residual Attention PINN (ResAtt-PINN), and an LSTM-PINN that leverages pseudo-sequential spatial encoding. Extensive numerical experiments demonstrate that the LSTM-PINN consistently achieves the highest predictive accuracy across all eight cases. It successfully reconstructs sharp gradients and intricate multiscale structures where other architectures fail or over-smooth. Furthermore, the LSTM backbone efficiently captures long-range spatial correlations while maintaining an exceptionally low computational overhead and GPU memory footprint. These findings not only establish the LSTM-PINN as a robust and efficient solver for strongly coupled PDEs with shock-like features, but also provide the computational physics community with a standardized, reproducible benchmark for future algorithmic evaluations.

Fundamental questions related to the roles of fuel type, combustion parameters, and turbulence transport interactions in the inception and growth of contrails have remained intractable in remote sensing and in-flight measurements. Consequently, we developed a novel laboratory-scale facility for studying the inception, growth and persistence of contrails for aircraft-relevant conditions. The set of exhaust conditions, generated using an inverted co-flow soot generator at a set of global equivalence ratio for two fuels - ethylene and propane, is supplied to the contrail tunnel which then mixes with an ambient flow emulating long-haul aircraft cruise conditions (\SI{20.8}{kPa} and \SI{190}{K}). Detailed soot characterization using a scanning mobility particle sizer and transmission electron microscopy is coupled with measurements of instantaneous and averaged scattering intensities from the generated contrails. The experimental results are complemented by numerical simulations of the contrail tunnel using solutions of the Favre-averaged Navier-Stokes (FANS) equation and a two-equation model for handling particulate matter, including soot and ice. Results show the first experimental snapshots of a contrail cross section, highlighting the interaction of turbulent mixing and microphysical growth scales involved in ice nucleation across the shear layers. As expected, the average scattering intensities of contrails increase with soot number concentrations and water vapor content. Comparisons between ethylene and propane exhausts indicate that the scattering propensity of contrails is more sensitive to exhaust water vapor content than to soot concentrations. Finally, depolarization measurements are used to show asphericity in ice crystal habits. Thus, our study present a unique window into contrail formation, theoretical modeling and simulation.

Microswimmers in turbulent flows often navigate complex, heterogeneous, and obstacle-rich environments, where they exhibit intricate behaviors such as trapping at and escape from obstacles. We generalize recent $\mathcal{Q}-$learning methods of J.K. Alageshan et al. [Phys.Rev.E 101, 043110 (2020)] and A. Gupta et al. [Physics of Fluids 37, 045107 (2025)] developed for non-interacting microswimmers that aim to move optimally from an initial position to a target, to account for the additional complication of an obstacle in the flow. We begin by considering one circular obstacle in forced two-dimensional (2D) Navier-Stokes turbulence in which the energy spectrum displays a forward cascade. We employ the volume-penalization method to introduce this obstacle within our doubly periodic simulation domain. We augment our adversarial $\mathcal{Q}-$learning Refs.~\cite{Alageshan_2020,Akanksha_2025} by suppressing the tendency of microswimmers to get trapped in stagnation points in the vicinity of the obstacle. We demonstrate that smart microswimmers ($SS$), which adopt our obstacle-aware adversarial $\mathcal{Q}-$learning strategy, outperform both naïve swimmers ($NS$) and surfers ($SuS$).

The shape of the turbulent energy spectrum in the dissipation range, where viscous effects dominate, remains an open question despite decades of work. We report an experimental investigation of intermediate dissipation range energy spectra in turbulent shear layers at Taylor-scale Reynolds numbers, $Re_\lambda$, ranging from approximately 450 to 1500, which are among the highest achieved in shear flow experiments that resolved small scales. We generated turbulent shear layers in a wind tunnel and measured using nanoscale hot-wire probes with a sensing length $l_w \approx (0.2-0.5)\eta$ that was smaller than the Kolmogorov scale $\eta$ at all $Re_\lambda$. The measurements resolved wavenumbers up to $k_{max} \eta$ $\approx 17$ at the lowest $Re_\lambda$ and $k_{max} \eta$ $\approx 1$ at the highest $Re_\lambda$, where $k_{max}$ is the highest resolved wave number. In the range $0.1 \lesssim k \eta \lesssim 0.5$, the spectra collapse onto a universal stretched-exponential form, $E(k\eta) \sim $ exp$(-k\eta)^{\gamma} $, with $\gamma \approx 0.5$ independent of $Re_\lambda$. This value of stretching exponent, $\gamma$, is consistent with recent empirical and computational studies. The Reynolds-number invariance of $\gamma$ is strong evidence for universal scaling in the intermediate dissipation range of high-Reynolds-number shear turbulence.

This paper presents the first experimental observation and investigation of a lineshape-asymmetry-caused shift (LACS) in a short-baseline atomic interferometer-gravimeter. It is shown that this shift scales inversely with the cube of the free evolution time, $\propto T^{-3}$, and can lead to a noticeable systematic error in the measured value of the gravitational acceleration g at the level of 0.1-1 mGal ($T\approx$ milliseconds). The obtained results are in good agreement with our previous theoretical studies and highlight the importance of accounting for LACS in high-precision absolute measurements of g in compact atomic gravimeters.

We report the first plasma atomic layer etching (ALE) process for diamond using a cyclic plasma sequence composed of two separated steps: oxygen surface modification and krypton ion removal. The process is implemented in an inductively coupled plasma reactor using alternating O$_2$ plasma exposure and low-energy Kr ion bombardment. This cyclic process exhibits the characteristic self-limiting behavior of ALE and enables controlled material removal with atomic-scale precision. An etch depth per cycle of \SI{6.85}{\angstrom} was achieved. Surface analysis reveals that the etched diamond surfaces exhibit lower roughness than the pristine material, while XPS confirms the preservation of the diamond bonding structure and indicates essentially damage-free etching. These results demonstrate that plasma ALE based on O$_2$/Kr chemistry provides a viable route toward damage-controlled nanoscale processing of diamond, opening new opportunities for advanced device fabrication in power electronics, photonics, quantum sensing and quantum computing technologies.

Modern seismic networks resolve earthquake sequences in unprecedented detail, yet explaining how large earthquakes emerge from evolving fault systems remains difficult. We introduce TRACE, a seismology-guided artificial intelligence agent that plans and executes workflows while preserving auditable evidence chains from observations to physical interpretation. We evaluated TRACE through 104 benchmark tasks and two complementary earthquake sequences. For the well-studied 2019 Ridgecrest sequence, TRACE constructed a high-resolution catalog from continuous waveforms and retrospectively recovered delayed cascading activation between the Mw 6.4 and Mw 7.1 earthquakes without a prescribed target interpretation. In the less-understood 2025-2026 Sanriku sequence off northeastern Japan, TRACE developed a testable interpretation of progressive destabilization within a segmented megathrust. Its synthesis linked coupled seismic-aseismic activation around the MJ 6.9 sequence and subsequent persistent, spatially segmented shallow-interface activity to a megathrust patch that lay between regions of past large coseismic slip and later hosted the MJ 7.7 rupture. These results open a path from seismic observations to testable physical insight.