Papers

While optical injection locking (OIL) of vertical-cavity surface-emitting lasers (VCSELs) has been widely studied in the past, the polarization dynamics of OIL have received far less attention. Recent studies suggest that polarization locking via OIL could enable novel computational applications such as polarization-encoded Ising computers. However, the inherent polarization preference and limited polarization switchability of VCSELs hinder their use for such purposes. To address these challenges, we fabricate VCSELs with tailored oxide aperture designs and combine these with bias current tuning to study the overall impact on polarization locking. Experimental results demonstrate that this approach reduces the required injection power (to as low as 3.6 {\mu}W) and expands the locking range. To investigate the impact of the approach, the spin-flip model (SFM) is used to analyze the effects of amplitude anisotropy and bias current on polarization locking, demonstrating strong coherence with experimental results.

A core tenet underpinning the conception of contemporary information networks, such as social media platforms, is that users should not be constrained in the amount of information they can freely and willingly exchange with one another about a given topic. By means of a computational agent-based model, we show how even in groups of truth-seeking and cooperative agents with perfect information-processing abilities, unconstrained information exchange may lead to detrimental effects on the correctness of the group's beliefs. If unconstrained information exchange can be detrimental even among such idealized agents, it is prudent to assume it can also be so in practice. We therefore argue that constraints on information flow should be carefully considered in the design of communication networks with substantial societal impact, such as social media platforms.

Spectral vision task plays a pivotal role in extracting discriminative spectral-spatial features from high-dimensional data, enabling fine-grained identification beyond human vision. Traditional methods usually involve first collecting rich spectral-spatial information and then using complex algorithms to digitally process it into scene classification and recognition. However, the complexity of processing massive three-dimensional (3D) hyperspectral datasets poses challenges for algorithms. Here, we demonstrate a compressive Hyperspectral Phasor Imaging with Single-pixel detection (HyPIS) that leverages highly compressed spatial-spectral data to achieve spectral task. Two optical encoders are used for wavelength-dependent sine- and cosine-encoding that transforms spectral signals into a two-dimensional (2D) phasor plot. By applying spatial-temporal illumination patterns, a single-pixel detector is enough to reconstruct the phasor image of the object. This allows to directly generate pixel-wise spectral task, bypassing 3D hyperspectral data. Our experiments show that HyPIS can perform real-time classification and recognition tasks of different scenes, reducing the required amount of data by two orders of magnitude, and it can still accurately classify under low light and uneven lighting conditions. This work develops a completely new spectral technology that enables spectral tasks to be performed without obtaining high-resolution hyperspectral datasets, holding promise for spectral applications in mobile devices, robotics, and satellite technologies.

In incompressible flow simulations, non-uniform grids efficiently capture localized flow features; however, their spatially varying resolutions severely exacerbate computational complexity. The pressure Poisson equation (PPE) formulated on these grids yields highly complex linear systems, forming the primary computational bottleneck in fractional step method. To address this, we develop an extended hybrid framework tailored for non-uniform Cartesian grids, integrating deep learning with classical iterative solvers to accelerate PPE solutions. Specifically, the framework employs a deep operator network with a U-Net-based branch network. To effectively capture spatially varying resolutions, we propose a multi-level distance vector map construction strategy that computes discrete grid-spacing information corresponding to each hierarchical level of the U-Net. This grid-spacing information is explicitly fused into feature maps prior to convolution operations. Empowered by this grid-spacing-aware architecture, the framework seamlessly extends to simulate flows interacting with solid structures using a decoupled immersed boundary projection method. By training exclusively on fabricated linear systems rather than conventional flow-dependent datasets, the model generalizes effortlessly across diverse immersed obstacle geometries with fixed network weights. Benchmark results demonstrate that the framework significantly outperforms standalone preconditioned conjugate gradient methods and its standard convolution counterpart, underscoring its exceptional potential for real-world computational fluid dynamics applications.

AI applications in fusion is a maturing field, playing a key role as surrogate models and digital twins to overcome computational expense limitations and insufficiently characterised phenomena, and expanding the horizon for real-time applications. The IAEA is supporting this activity through the AI for Fusion Coordinated Research Project (CRP), a five-year initiative launched in 2022, which involves 24 institutions across 11 countries. A key goal is to support the development of modern data infrastructure required to enable the development of agnostic AI models that can be safely extrapolate into the parameter space of future fusion power plants. The IAEA is playing an active role in contributing to the data infrastructure with the Fusion Data Lake project. A modern data platform to enable the development of AI workflows in line with FAIR data principles. The platform comprises three major components: 1. An international data catalogue; 2. A centralised medium-term storage; and 3. A data federation of the various fusion data platforms around the world. The current proof of concept (PoC) demonstrates the data cataloguing and federation capacity by integrating with the UKAEA's MAST Data Catalog. Currently, the second phase of the PoC will demonstrate scalability by integrating two additional experimental fusion device catalogues. This report presents a high-level project overview, including: - Technical architecture and design, collaborations and contributions, and the PoC solution; - Data and metadata model development and ontological concepts; and - The approach to data governance and terms of service. This illustrates the approach, results, and direction of the work, highlighting the high potential value to the fusion community of increasing the visibility and accessibility of the numerous international experimental data sets.

Purpose: This study compares the hierarchical structure of scientific teams across countries and investigates factors associated with the observed cross-national differences. Design/methodology/approach: Drawing on 150,817 publications with author contribution statements, we focus on the 15 countries with the largest volume of scientific publications, examine cross-country variations in the proportion of tall teams, and analyze how this proportion correlates with other factors. Findings: Scientific output from East Asia is dominated by tall teams, which persist after controlling for team size, indicating that this pattern cannot be fully accounted for by the prevalence of larger teams in these countries. Cultural factors, measured by Power Distance, as well as the observed funding patterns of major basic science agencies, are associated with the dominance of tall teams in East Asia. Research limitations: This study is limited by its reliance on publications with author contribution statements, which may introduce selection bias; its focus on cultural and funding factors, while leaving other institutional contexts unexamined; and its use of a leadership concentration measure that does not capture other dimensions of hierarchy. Practical implications: Understanding cross-national differences in research team structures and their associated cultural and institutional factors can inform science policy and team management. Originality/value: This study provides a systematic cross-national comparison of team hierarchy and offers a mechanistic understanding of the dominance of tall teams in East Asia, highlighting associations with cultural and funding factors.

Gas diffusion layers (GDLs) play a crucial role for the performance of proton exchange membrane fuel cells (PEMFCs). The utilization of composite foam-rib flow fields (CFRFFs) can alter the reactant gas transfer pattern, hence improving the efficiency of under-rib reactant gas transfer and water drainage. The impact of the cathode and anode GDL thicknesses (h_{c,GDL} and h_{a,GDL}) on the performance of CFRFF design is investigated by three-dimensional multiphase non-isothermal numerical simulation in this study. The results indicate that for the conventional rib flow field (CRFF) design, there is an optimal h_{c,GDL} for optimal cell performance, while for the CFRFF design, as h_{c,GDL} becomes thinner, the cell performance increases, and the trend is dominated by the variation of the oxygen concentration. Under a thin GDL, the rib width of the CRFF design should be as small as possible to minimize concentration polarization loss, while the rib width of the CFRFF design can be slightly larger. Furthermore, by decreasing the thickness of h_{a,GDL} in both the CRFF and CFRFF designs, there is an increase in the dissolved water content in the ionomer of the cathode CL and a subsequent decrease in the Ohmic polarization loss.

Multiple x-ray beams generated by interference processes in perfect crystals were imaged with a resolution of about 100nm using tele-ptychography in the diffraction direction. These multiple wave-fields, also known as x-ray diffraction echoes, are related to the process known as the Pendelloesung effect and are described by dynamical diffraction theory. The echoes are produced by the constructive interference of diffracted x-rays at the exit surface of the crystal sample. In the imaged diffraction peak, we observed 10 echoes maxima with a total signal length of 78 um. Which translates into a total temporal delay in the signal of less than 108 fs.This makes the echoes of high importance for x-ray optics at x-ray Free Electron Laser sources, as the effect could be used for future ultrafast x-ray beam splitters. In addition to this application, echoes can be exploited to follow ultrafast processes in single crystal micro-structures such as melting or strain propagation.

The terahertz (THz) spectral regime offers unique opportunities for next-generation biochemical sensing due to its non-destructive, label-free probing capability and strong sensitivity to molecular vibrations. However, conventional THz biosensors remain hampered by intrinsically low-quality factors and limited sensitivity, severely restricting their utility for trace-level biochemical and chemical detection. Here, we report an ultrasensitive THz metasurface biosensor that harnesses quasi-bound states in the continuum (QBICs) with sharp resonances and enhanced light-matter interactions to overcome these limitations. As a proof of concept, the device achieves label-free detection of a sulfur-containing amino acid cysteine, with an ultrahigh sensitivity of 492 GHz/RIU and an ultralow detection limit down to 0.00025 mg/mL. The synergy between QBIC-induced field confinement and meticulous structural optimization of the metasurface underpins this performance, marking a significant advance over conventional THz metasurface biosensing schemes. These results establish QBIC-based metasurfaces as a promising platform for ultrasensitive and high-precision biochemical and chemical sensing, with broad implications for medical diagnostics, food safety, and environmental monitoring.

We theoretically investigate the control of the Goos-Hänchen shift (GHS) of a reflected weak probe field in both parity-time ($\mathcal{PT}$)-symmetric and conventional optomechanical systems. The proposed scheme consists of a single optomechanical platform where a passive optical cavity is coupled to an active mechanical resonator, in contrast to standard passive-passive configurations. Analysis of the eigenfrequency spectrum reveals the emergence of an exceptional point under balanced gain-loss conditions at a tunable effective optomechanical coupling strength. Using the transfer-matrix method combined with stationary-phase analysis, we examine the GHS across broken and unbroken $\mathcal{PT}$ phases and compare it with that in the conventional system. The lateral shift exhibits strong phase dependence: it is markedly enhanced in the unbroken regime relative to both the broken phase and the passive configuration. We further show that the GHS can be actively tuned through the cavity detuning and the intracavity medium length. These results provide a controlled means for manipulating beam shifts in optomechanical systems and suggest pathways toward tunable photonic components and precision optical sensing.

In this paper, we propose a method to analyze correlations in pandemic-related data across different geographical regions, relying on the analysis of correlations for non-stationary time series, which are typical of pandemic data. Unlike traditional epidemiological approaches focused on medical and modeling perspectives during a pandemic, our method emphasizes post-pandemic analysis to assess how societal responses; such as lockdowns, travel restrictions, mobility patterns, and vaccination campaigns, manifest in the collective behavior of regions. These insights can inform future public health strategies and enhance understanding of the complex dynamics underlying pandemic spread and control.

Magnetic Resonance Spectroscopy (MRS) offers a unique non-invasive window into metabolic processes, yet its potential remains strictly constrained by severe spectral congestion and intrinsic insensitivity. Traditional pulse sequence design, tethered to human intuition, predominantly targets simple quantum states, thereby overlooking the vast majority of the exponentially scaling operator space which consists of complex spin superpositions. Here, we introduce a spectrum-driven, end-to-end differentiable physical framework that transcends these heuristic limitations. By integrating physical laws with automatic differentiation algorithm, our approach directly navigates the high-dimensional spin dynamics space, bypassing the intractable inverse problem of state preparation. This enables the discovery of non-intuitive, complex mixed states that simultaneously satisfy the dual objectives of selective excitation and interferometric signal enhancement. We validate this paradigm by achieving the robust separation of Glutamate and Glutamine, which is a longstanding neuroimaging challenge, in the human brain at 3T, demonstrating spectral fidelity superior to conventional methods. By unlocking the "dark" informational content of nuclear spin ensembles, our work establishes a generalizable paradigm for goal-driven quantum state engineering in magnetic resonance and beyond.

A method is proposed for computing coefficients in the Kazantsev equation of small-scale dynamo for the full spectrum of hydromagnetic turbulence comprising the inertial range together with the range of viscous dissipation. The dynamo equation with so-defined coefficients is solved numerically for magnetic (Rm) and hydrodynamic (Re) Reynolds numbers from $10^2$ to $10^8$. The threshold value ${\rm Rm}_c$ for onset of dynamo increases initially with Re but then saturates at a constant value of ${\rm Rm}_c \simeq 300$ for ${\rm Re}\geq 10^5$. In the case of small Prandtl number Pm = Rm/Re << 1, the field growth rate is also small and depends logarithmically on Rm. In this case, the magnetic energy spectrum peaks around the scale of Ohmic dissipation, which decreases with increasing Pm. The decrease stops at the scale of viscous dissipation while the growth rate increases sharply when Pm approaches the value of one. The increase in the growth rate proceeds to ${\rm Pm} > 1$ but slows down and then saturates at a value somewhat below the inverse lifetime of most short-living eddies. An explanation of the results is proposed.

The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting model. Secondly, the vibration predictions are compared to the measured ones to detect large deviations. Finally, the identified cases are used as an early-warning indicator of structural change. The artificial intelligence-based model outperforms approaches for response forecasting as no assumption on wind stationarity or on structural normal vibration behavior is needed. Specifically, wind-excited dynamic behavior suffers from uncertainty related to obtaining poor predictions when the environmental or traffic conditions change. This results in a hard distinction of what constitutes normal vibration behavior. To this end, a framework is rigorously examined on real-world measurements from the Hardanger Bridge monitored by the Norwegian University of Science and Technology. The approach captures accurate structural behavior in realistic conditions, and with respect to the changes in the system excitation. The results, importantly, highlight the potential of transformer-based digital twin components to serve as next-generation tools for resilient infrastructure management, continuous learning, and adaptive monitoring over the system's lifecycle with respect to temporal characteristics.

Ultra-low-field (ULF, <0.1 T) magnetic resonance imaging (MRI) systems offer advantages in cost, portability, and accessibility, but their current utility is still limited by low signal-to-noise ratio (SNR). Deep learning (DL)-based denoising has emerged as a potential strategy to mitigate this limitation. In this work, we present a systematic evaluation of a high-performance DL denoising model trained using the SNRAware framework and applied to 88 mT and 72 mT data. Using a series of controlled experiments, we assessed model performance as a function of spatial resolution, coil impedance matching, readout bandwidth, input noise level, k-space undersampling, anatomy, image contrast, and scanner platform, and compared against analytical denoising algorithms. The model consistently increased the effective SNR of ULF acquisitions, enabling images with nominal spatial resolutions comparable to those commonly used in clinical 3 T protocols. Residual analyses indicated that the model predominantly removed stochastic noise while preserving underlying signal structure. At the same time, the results highlight some constraints: denoising performance remains dependent on the starting SNR of the acquisition, and training-domain mismatch influences behavior under certain artifact conditions. These findings suggest that DL-based denoising can significantly expand the practical capabilities of ULF MRI, while emphasizing potential benefits from hardware-software co-optimization and the need for rigorous clinical validation to determine the diagnostic value of denoised images.

High precision Low-level RF (LLRF) control and monitoring systems for future particle accelerators will be a significant technical challenge as the requirements in performance, flexibility and affordability become increasingly stringent. We have developed an RF system-on-chip (RFSoC) based next-generation LLRF (NG-LLRF) for S-band accelerating structures, which samples and synthesizes the RF pulses directly without the analog mixers used for traditional LLRF systems. The platform delivered considerably better performance than the requirements of the targeted applications, such as the upgrades for Next Linear Collider Test Accelerator (NLCTA) and test facilities at SLAC. As part of the upgrade program, we also developed a custom solid-state amplifier (SSA) to deliver RF pulses at the desired power level of the klystron. Integration of the LLRF with the SSA and the high-power test facility could be challenging. The power levels and RF pulse stability at each stage of the high-power RF drive system must be optimized to deliver the desired RF performance. In this paper, the integration procedure and the test and characterization results at each stage of integration will be summarized, analyzed and discussed. This integration is an essential step for the full deployment of the NG-LLRF system to test facilities and accelerators in different frequency bands.

The p-type surface conductivity of H-terminated diamond (HD, H-diamond) has created new path ways for developing diamond based electronic devices as well as chemical and bio-sensors. However, the hydrophobic nature of the HD surface can negatively impact device performance due to its low wettability. Herein, we report the study on polymer electrolyte-gated field effect transistors (EGFETs) fabricated using pristine and partially O-terminated HD films. The HD surface is transformed from hydrophobic to moderate hydrophilic by partial O-termination. Also, the sheet resistance of the HD surface increases from 7.6 to 18.7 k-Ohms per sq. while the sheet hole density decreases from 10.5 to 4.8 x 10^12 cm^-2 upon partial O-termination. Consequently, the ON - OFF ratio of the EGFET devices decreases from ~ 40 to 14 and the maximum transconductance declines from of -150 to -7.9 micro-seimens per V, but the areal capacitance increases from ~ 7.8 to 27.1 microFarad per cm^2 with partial ozonation on HD surface. In addition the in situ Raman measurements in HD EGFET provide direct experimental evidence of a gating-induced blue shift and linewidth broadening of the diamond Raman band which are associated with strong electron phonon coupling. This work highlights the significant impact of the partial O-termination on the performance of the HD EGFET devices and effect of electrochemical gating on the phonon behaviour of the H-diamond.

We present a simple experimental realization of a two-dimensional floating body that can remain in equilibrium in any orientation. This system is based on a class of shapes known as Zindler curves, which possess the remarkable geometric property that all chords dividing their area into equal parts have the same length. Using a multilayer fabrication approach, we construct a heart-shaped floating object with an effective density close to one half of that of the surrounding liquid. We show experimentally that, under these conditions, the object exhibits neutral equilibrium with respect to rotation. When the density is slightly varied, preferred orientations emerge, consistent with a simple energy-based description. Our experiments highlight both the accessibility of this classical problem and the subtle role of physical effects such as density inhomogeneities and capillarity. They provide a simple platform to explore the interplay between geometry and buoyancy, and to test geometric results in a tangible setting.

In arXiv:2507.07171, we demonstrate that the high-redshift supermassive black holes in the so-called "little red dots" discovered by James Webb Space Telescope (JWST) can be explained by the primordial black hole (PBH) clustering on small scales. In this paper, we present a comprehensive simulation of the successive PBH mergers within a cluster by solving the Smoluchowski coagulation equation. We derive the coagulation kernel considering both cases with and without the effects of mass segregation. Then we employ the Monte Carlo method to solve the equation, implementing the full-conditioning scheme using the discrete inverse transformation method. Our simulations determine the runaway timescales of clusters and the mass population evolution of PBHs across a wide range of cosmic redshifts, depending on the number of PBHs within the cluster and the associated density.

To investigate the physics of picosecond gain-switching dynamics in single-mode lasers under femtosecond optical pumping at room temperature, we designed and fabricated first-order surface-grating GaAs distributed-feedback (DFB) lasers with five systematically varied grating periods (120-124 nm), corresponding to lasing wavelengths of 825.7-849.5 nm (1.502-1.459 eV). The 124-nm-period device, closest to the quantum-well gain peak among the investigated devices, exhibited the highest output power and spectral bandwidth. Among all devices, the 122-nm-period DFB laser (838.2 nm, 1.480 eV) generated the shortest pulses, despite lasing at a higher photon energy and lower output power than the device closest to the gain peak. All devices exhibited characteristic down-chirp behavior that increased with excitation power. The shortest pulses had a chirped pulse width of 6.6 ps and a chirp rate of 0.13 meV/ps, whereas spectrally resolved measurements revealed a minimum pulse width of 3.8 ps (2.3 ps after deconvolution of the detection time resolution) near the central photon energy of the pulse spectrum. Numerical simulations revealed temporally and spatially resolved dynamics of photons, carriers, gain, and refractive index, reproducing the experimental results qualitatively and quantitatively. Furthermore, a mechanism for generating the shortest pulses at photon energies above the gain peak was identified and attributed to higher differential gain, saturation gain, and a higher transparency carrier density in the high-energy region of the gain spectrum. These experimental and theoretical results elucidate the intrinsic dynamics of picosecond pulse generation in gain-switched DFB lasers and provide design guidance for short-pulse generation and computational tools applicable to both optical and electrical pumping.

We investigate frequency-switching coherent reception (FSCR), a hardware-efficient technique for offline coherent optical signal characterization in laboratory environments, enabling bandwidth extension without costly and hardware-intensive parallel receiver architectures. By combining signal gating and local-oscillator frequency switching, FSCR captures multiple spectral slices sequentially using a single receiver front-end and oscilloscope. We experimentally demonstrate characterization of signals at symbol rates up to 288 GBaud beyond the receiver bandwidth limit.

Discovering governing equations, whether manually or by data-driven methods, has been central in physics and related areas. Since governing equations are typically constrained by a set of symmetries, using symmetry constraints to restrict terms is usually the first step in manually formulating a governing equation, but it often becomes intractable for complex systems with high-order derivatives or multiple fields. When a data-driven method is used, on the other hand, imposing physical constraints such as symmetries typically requires manual preprocessing or computationally expensive iterative procedures. Here, we propose an algebraic filtering method that enumerates all symmetry-allowed terms for continuum equations within a finite candidate space. By treating symmetry generators as linear operators on the candidate space, we reduce the problem of enforcing both discrete and continuous symmetries to solving a set of linear kernel equations. The solution yields a provably complete list of permitted terms. We demonstrate the method's effectiveness by identifying invariant terms for systems with dihedral symmetry and recovering the governing equations for the Toner–Tu and Kardar–Parisi–Zhang systems, including higher-order terms useful for extending known models. The method provides a systematic way to obtain a symmetry-allowed search space for data-driven equation discovery, e.g., the sparse identification of nonlinear dynamics method.

Thermal imaging is crucial for night vision but fundamentally hampered by the ghosting effect, a loss of detailed texture in cluttered photon streams. While conventional ghosting mitigation has relied on data post-processing, the recent breakthrough in heat-assisted detection and ranging (HADAR) opens a promising frontier for hyperspectral computational thermal imaging that produces night vision with day-like visibility. However, universal anti-ghosting imaging remains elusive, as state-of-the-art HADAR applies only to limited scenes with uniform materials, whereas material non-uniformity is ubiquitous in the real world. Here, we propose a universal computational thermal imaging framework, TAG (thermal anti-ghosting), to address material non-uniformity and overcome ghosting for high-fidelity night vision. TAG takes hyperspectral photon streams for nonparametric texture recovery, enabling our experimental demonstration of unprecedented expression recovery in thus-far-elusive ghostly human faces – the archetypal, long-recognized ghosting phenomenon. Strikingly, TAG not only universally outperforms HADAR across various scenes, but also reveals the influence of material non-uniformity, shedding light on HADAR's effectiveness boundary. We extensively test facial texture and expression recovery across day and night, and demonstrate, for the first time, thermal 3D topological alignment and mood detection. This work establishes a universal foundation for high-fidelity computational night vision, with potential applications in autonomous navigation, reconnaissance, healthcare, and wildlife monitoring.

In networks that grow by isotropic redirection (IR), a new node selects an initial target node uniformly at random and attaches to a randomly chosen neighbor of the target. The emerging networks exhibit leaf proliferation, in which the number of nonleaves scales sublinearly as $N^\mu$ and the degree distribution has an algebraic tail with exponent $1+\mu$. To understand these mysterious properties, we introduce a class of models with redirection to leaves whenever possible. The resulting networks exhibit qualitatively similar phenomenology to IR networks, but avoid the inherent non-locality of the IR growth rule. These networks admit an analytical description of the leaf degree distribution, from which we extract the exponent $\mu$.

We examine precipitate-induced dynamic strain aging in precipitation-hardened Al-Cu alloys by combining atomistic simulations, kinetic Monte Carlo, and analytical rate theory. Atomistic simulations were used to characterize (1) the energetics of nearest neighbour Cu<->Al exchanges at dislocation - precipitate junctions and (2) the subsequent change in obstacle strength. For robustness, the simulations were performed with two distinct interatomic potentials. The resulting catalog of local Cu-Al exchange events was used as input for a kinetic Monte Carlo model of the time-dependent evolution of obstacle strength during dislocation pinning at the precipitate. The predicted strengthening kinetics were then embedded in an analytical dynamic strain aging model to predict the strain-rate sensitivity parameter. On the whole, the modeling predicts a low strain-rate sensitivity across a broad range of intermediate quasi-static strain rates, consistent with experimental observations for precipitate-strengthened alloys. The results therefore identify a mechanistic origin of the low strain-rate sensitivity in precipitation hardened aluminum alloys, emerging directly from the kinetics of dislocation-precipitate interactions when nearest neighbour Cu<->Al exchanges are considered.

In this paper, we present a focused-ion-beam-assisted method for preparing magnet tips for magnetic resonance force microscopy measurements. The method electrostatically transfers prefabricated magnetic nanoparticles to microcantilevers, achieving precise control over the magnet overhang past the cantilever leading edge while minimizing the fabrication damage to the leading edge of the tip magnet. We demonstrate successful fabrication of magnets ranging in size from 460 nm to 2.8 um. These magnets were affixed to two types of cantilevers: silicon cantilevers with a spring constant of 800 uN/m, and single-crystal silicon cantilevers with a spring constant of 30 uN/m. We show that the electrostatic transfer method enables a wide variety of tip shapes, sizes, and materials that were previously not possible with conventional fabrication methods. The transfer procedure allows us to prefabricate the desired particle geometry with minimal ion-beam damage, as confirmed by Monte Carlo simulations. We show that the technique is versatile and can be used to fabricate custom-tipped cantilevers for a broader range of scanning probe techniques.

We present a method for performing low frequency electric field sensing via ionization detection of Rydberg atoms in a collimated atomic beam. A collimated beam avoids much of the electric field screening effects that are common in warm vapor cells due to the accumulation of alkali-metal atoms on glass surfaces. Further, a beam facilitates a spatially separated region for high signal-to-noise readout via ionization detection. Using this approach, we measure DC Stark shifts from external fields with frequencies as low as 1 Hz. The sensor demonstrates a sensitivity of better than 1 mV/m$\sqrt{\rm {Hz}}$ for frequencies above 20 Hz and $0.14(4)$ mV/m$\sqrt{\rm {Hz}}$ above 500 Hz with a linear dynamic range of over 50 dB.

As language models are deployed as autonomous agents that negotiate, cooperate, and compete on behalf of human principals, their strategic dispositions acquire direct economic consequences. Here we show, across 51,906 game-theoretic trials generating 826,990 strategic decisions from 25 large language models spanning seven developers and 38 canonical games, that models converge on competitive and coordination behaviour (coefficient of variation 0.06 for coordination, 0.11 for strategic depth) while diverging 48-fold on cooperation, from 1.5 per cent (GPT-5 Nano) to 71.5 per cent (Claude Opus 4.6). Provider identity is the dominant predictor of cooperative disposition, and this divergence is generationally unstable: OpenAI cooperation fell from 50.3 to 1.5 per cent across four model generations while Google cooperation rose from 8.3 to 56.8 per cent. Endgame analysis reveals that Anthropic frontier models sustain 57 per cent cooperation in the final round of finitely repeated games, where backward induction predicts zero, while the newest Google models cooperate throughout but universally defect when punishment becomes impossible. These strategic personalities are shaped by training pipelines, shift unpredictably across model versions, and cannot be inferred from capability benchmarks, yet they determine the cooperative outcomes of every economic interaction these models mediate. The complete dataset and an interactive explorer for the data are publicly available at https://felipemaffonso.github.io/strategic-personalities/.

Classical dynamical laws are conventionally formulated as closed evolution equations defined on fixed geometric backgrounds and a global time parameter. We develop a formulation in which neither prescribed evolution laws nor an external clock are assumed a priori. Grounded in the principles of conservation, locality of interaction, and independent local frame freedom, the framework treats spatial geometry as a relational structure that may evolve together with the state. We introduce a notion of local incompatibility defined as the covariant difference between neighboring states under a dynamical transport connection. Because the transport relations are not fixed, restoring compatibility requires the joint adaptation of both state variables and transport geometry. We show that locality, gauge covariance, and coercivity strongly restrict the admissible form of this incompatibility and lead to a simple, globally additive, gauge-invariant quadratic measure of mismatch. Admissible dynamics are then defined as the asynchronous, finite-rate relaxation of this measure, without assuming a predefined action principle. A global time description appears only as an effective coarse-grained limit of this local relaxation process. In appropriate limits, the resulting compatibility-restoration dynamics recovers familiar continuum equations, including diffusion, incompressible Navier–Stokes, and the Ampère–Maxwell relation. In this sense, dynamics arises from the coupled evolution of state and transport geometry toward local gauge consistency. The formulation provides a constructive framework in which effective physical laws emerge from local relational constraints.

SWEEP (Seismic Wave Equation Exploration Platform) is a unified and extensible wave equation solver library designed for wavefield modeling and inversion. It supports a wide range of wave propagation engines, including acoustic, elastic, attenuative, VTI, TTI, and their Born approximations, among others. With a built-in support for automatic differentiation, the framework enables seamless implementation of full-waveform inversion (FWI), least-squares reverse time migration (LSRTM), and other gradient-based optimization methods. It also features a plug-and-play architecture, allowing easy integration and flexible combination of custom loss functions, multi-GPU computation, neural networks, and more. This makes Sweep a powerful and customizable platform for tackling advanced seismic inverse problems.