Papers

We formulate the Generalized Constraint-Controlled Rheological Model (GCCRM) for entangled linear and long-arm-branched polymer melts under sinusoidal periodic shear deformation (SPSD), spanning the linear and strongly nonlinear regimes. Molecular architecture, conformation, chain stretch, flow-active constraints, convective constraint release, dynamic tube dilation, and activated arm retraction define a common upstream molecular state. This state feeds two time-domain constitutive pathways (a configurational stress and a dissipative internal-variable stress) which are recombined before Fourier analysis. For branched polymers, the architecture is described by an entire backbone of molecular weight $M_{bb}$ and $N_a$ long arms of molecular weight $M_a$, with a compliance-weighted representative-constitutive-cell partition. The linear limit is normalized so that the configurational moduli sum to the entanglement modulus, while dynamic tube dilation and stretch drive distinct contributions to dissipative activation. Under reduced closures, GCCRM generates a smooth nonlinear elastic response with an onset of nonlinearity, an approximately logarithmic growth regime, an architecture-dependent maximum, and distinct high-deformation asymptotes. Fourier harmonics, ELR, VLR, and Lissajous curves are treated as post-processing observables of the total stress. The framework establishes a consistent constitutive architecture whose quantitative validation requires one parameter set per material across SPSD and, prospectively, transient shear histories.

The timing of BOLD-fMRI is typically chosen such that TE matches T2*. This assumes contrast being exclusively elicited by signal differences at TE. While sensible for short or TE-symmetric readouts, such as EPIs, it oversimplifies contrast generation for longer or TE-asymmetric trajectories, such as spirals. We propose the concept of a BOLD PSF for a more comprehensive perspective on the imaging characteristics of a functional experiment. Our findings indicate that TE can be reduced for spiral-out without sacrificing BOLD-sensitivity when compared to EPI. Furthermore, we characterize the intrinsic trade-off between specificity and resolution of the BOLD response under varying TE.

We demonstrate a compact and robust soliton microcomb source designed for field-deployable operation. A directly fiber-coupled SiN microresonator was integrated with precise thermal control into a palm-sized module, reducing the footprint and volume by 16- and 81-fold, respectively. The module, together with the laser sources and optical amplifiers required for microcomb generation, was further integrated into a 19-inch rack-mounted enclosure, achieving system-level footprint and volume reductions of 2.3- and 3.7-fold, respectively. The packaged system achieved a 90% manual startup success rate and maintained continuous soliton operation for 18.8 days, while remaining operational under substantial temperature variations. A state-based automated startup scheme using discrete microcomb-state evaluation achieved single-soliton generation without manual intervention with success rates of 50% within 2 min and 60% within 3 min. Moreover, automated soliton initiation and sustained operation were demonstrated while the packaged microresonator module was directly subjected to random mechanical vibration. These results provide a practical platform for compact, robust, and field-deployable microcomb sources.

Local measurements of gas flow in microscale geometries remain challenging due to the difficulty of micromechanical sensors implantation. Here, we use a levitated nanoparticle as a minimally invasive probe to map, with high spatial resolution, both the local pressure and velocity gas flow for a canonical example - a micropipe subjected to a pressure gradient. This technique enables characterization of nonequilibrium gas transport across several flow regimes, from slip flow to free-molecular flow. We directly observe the emergence of a nonlinear pressure distribution at large pressure ratios and obtain agreement with kinetic-theory predictions and Direct Simulation Monte Carlo calculations. These results introduce levitated nanoparticles as powerful and reconfigurable probes for microchannel gas sensing, with applications in microtechnology, micro-chemistry, and bioengineering.

Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiological-model-based neural network (PMB–NN) framework to glucose forecasting. A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model. The main PMB–NN combined forecast and neural–physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose. Data comprised 6–8 weeks of continuous glucose monitoring, pump, and meal records from 25 AZT1D participants. Person-specific models used chronological 70/10/20 train/validation/test splits and 30-, 60-, and 120-min horizons. Median mean absolute errors were 16.24, 25.00, and 30.20 mg/dL with basal insulin records and 15.38, 24.21, and 31.79 mg/dL before these records became available. In the latter setting, PMB–NN had the lowest median error at 30 and 60 min; it outperformed the physiological model alone in all six settings. In input-increase tests, glucose predictions changed in the expected direction for 85% of correction boluses and 100% of carbohydrate rescues, versus 57% for active insulin and 56% for active carbohydrates. Most standalone physiological fits showed locally distinguishable parameter effects, with glucose-related coefficients more stable under resampling than insulin- or carbohydrate-related coefficients. The three-term variant improved hyperglycaemia accuracy with basal records, but did not significantly change overall MAE and reduced directional plausibility in three tests. PMB–NN combines personalized forecasting with an explicit physiological model, allowing assessment of predictive accuracy, physiological response behavior, and participant-specific coefficients.

Photonic matrix multipliers can accelerate neural-network inference, but their practical utility depends on how optical weight elements respond to source noise, thermal variation, and calibration error. This paper compares Mach-Zehnder modulator (MZM) and microring-resonator (MRR) implementations under laser relative-intensity noise (RIN), column-wise thermal/phase drift, and their joint action. Two CIFAR-10 classifiers - a compact four-layer CNN and ResNet-8 - are mapped to matched photonic multiply-accumulate chains using common source-distribution, detection, numerical, and calibration assumptions. Both architectures reproduce the same zero-perturbation baseline for each workload: 79.5% classification accuracy for the Small CNN and 70.0% for ResNet-8. Robustness is then evaluated using separate RIN-only and drift-only sweeps and a full two-dimensional RIN-drift grid, with dimensionless RMS perturbation levels extending from 0.5% to 20%. The analysis reports classification accuracy, class normalized confusion matrices, rolling-accuracy traces, one-dimensional robustness curves, and multidimensional response surfaces. Device equations and parameter definitions follow the models implemented in the QPI Labs Photonic Inference Simulator, with shared laser fluctuations propagated as correlated multiplicative noise and slow drift represented as a per-output-column perturbation of calibrated weights. All numerical results are generated from recorded experiment configurations and random seeds. The study provides a controlled system-level framework for comparing interferometric and resonant photonic weight elements while limiting conclusions to the implemented device models, perturbation ranges, calibration policy, and neural-network workloads.

For the first time, we study the effects of mass transfer on alloy droplet impact, solidification, and solute non-homogeneity or MacroSegregation (MS). For this purpose, a multi-physics model, including fully-coupled hydrodynamics of multiphase flow, wetting, conjugate heat transfer, phase change, and mass transfer, is established and validated carefully across several benchmarks. In addition, a concentration-flux-correction algorithm is proposed to conserve the solute mass within the droplet at the numerical discretization level. The validated model is subsequently used to explore the impact and solidification of an alloy droplet. The results show that accounting for mass transfer and solute transport improves the prediction of the dynamics of a solidifying splat, in a consistent manner with experimental observations, while this effect has been neglected in all previous studies. The analysis demonstrates that this improvement results from the flow sensitivity to the mushy region thickness, which is regulated by local solute concentration through the solidus and liquidus temperatures. Furthermore, analyses of the MS map identify several critical segregation regions most susceptible to defect initiation, such as cracking, including a long negatively segregated vein within the lamella, formed by the severe advective flow through the mush in the middle stages of the impact, a positively segregated region near the rim of the splat, appearing as a result of hook formation, and a major positively segregated zone at the top of the rim, which develops following the accumulation of the solute-rich melt sustained by washout from the lamella.

In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to traverse dense clutter, limbless vertical body bending to propel through uneven terrain, and rapid spider leg crouching to help vibration sensing of prey on a web. Across these systems, correspondence between robots and animals supports the biological plausibility of these candidate physical mechanisms and motivates better measurements and models to determine how the relevant mechanical information is sensed and used to modulate behavior.

Full-waveform inversion (FWI) is a powerful approach for reconstructing subsurface velocity models, but its practical performance is limited by ill-conditioning, sensitivity to the update direction, and the high cost of curvature-based methods. In this project, we investigate whether a neural operator trained on reverse-time-migration (RTM) images can provide a useful learned preconditioner for FWI. We compare projected FWI against neural-preconditioned FWI using either a standard Fourier neural operator (FNO) or a Jacobian-informed FNO (JI-FNO). The neural preconditioner applies the normal operator of the learned RTM map to modify the true Devito FWI gradient. Our experiments show that the JI-FNO preconditioner achieves the lowest model error while maintaining data-residual decay comparable to baseline projected FWI. In contrast, the standard FNO preconditioner is unstable and gives worse model recovery, despite using the same neural-preconditioning framework. We also identify a failure mode of a nonlinear neural Gauss Newton-type inner solve: directly matching the neural RTM output to the FWI gradient creates an objective-space mismatch and does not yield a reliable descent direction. These results suggest that forward RTM-image prediction alone is insufficient for learned preconditioning; accurate learned derivatives and objective-consistent use of the neural operator are critical for producing useful inversion updates.

Rydberg atomic quantum receivers (RAQR) exhibit exceptional sensitivity to radio-frequency (RF) signals, positioning them as promising candidates for next-generation wireless systems. Extending their capabilities to orthogonal frequency-division multiplexing (OFDM), however, requires atomic responses that go beyond broadband reception and are tailored to the structured interference of multicarrier signals. To bridge this gap, we develop an atomic-comb Rydberg receiver based on six-wave mixing (SWM) transduction. In particular, injecting a multi-tone local oscillator (LO) into the RAQR generates frequency-shifted replicas of the intrinsic SWM response. By subtly tuning the amplitude and phase of each LO tone, constructive and destructive interference can be selectively induced to preserve desired response components while attenuating harmful frequency offsets. Furthermore, we harness the response-reshaping capability of the proposed atomic comb to proactively mitigate OFDM interference. More explicitly, by exploiting the discrete difference-frequency structure of both carrier-frequency-offset (CFO)-induced and phase-noise (PN)-induced inter-carrier interference (ICI), we optimize the atomic-comb response to suppress dominant ICI components directly. We further reveal an intrinsic coupling between spectral shaping and the time-domain effective channel memory, motivating a joint optimization of both ICI and inter-symbol interference (ISI), which simultaneously suppresses inter-carrier leakage and post-cyclic-prefix (CP) tail energy. Extensive simulation results validate our atomic-comb design, unveiling that the proposed atomic comb can effectively reshape the receiver response over the OFDM, while mitigating ICI, shortening the effective channel memory, and substantially lowering CP overhead.

Three-dimensional (3D) atomic-structure analysis is of central importance in (scanning) transmission electron microscopy (STEM). STEM techniques to reconstruct 3D structure include tilt-series or defocus-series annular dark-field, best suited to heavier atomic species, and dynamical-scattering-model inversion applied to single-defocus four-dimensional (4D) STEM data, requiring computationally expensive post-experiment analysis. In this study, we propose a 3D-structure linear inversion (3D LINV) STEM algorithm, which enables a fast and direct reconstruction of 3D structures from a single-defocus 4D-STEM dataset. This method is based on solving linear equations relating the measured 4D-STEM data and the unknown 3D specimen potential under the thick weak phase object approximation. We show the capabilities of 3D LINV STEM through systematic 4D-STEM simulations across various observation parameters such as specimen thickness, partial coherence, and shot noise, and through an experimental demonstration of 3D structure reconstruction of a stacked freestanding membrane with atomic-scale lateral resolution and nanometre-scale depth resolution.

Fluorescence response matrix imaging enables separate computational correction of excitation and emission aberrations through dual deconvolution, but single-focus acquisition requires a large number of camera frames, limiting its practical use. Multifocal excitation can accelerate acquisition by recording many illumination positions in parallel, but denser foci also increase inter-focus crosstalk, which can bias the estimated excitation and emission optical transfer functions and degrade aberration correction. Here, we introduce high-throughput aberration-free fluorescence response matrix imaging with adaptive multifocal illumination, in which the inter-focus spacing of the multifocal lattice is adjusted according to specimen-dependent crosstalk. A programmable digital micromirror device generates multifocal excitation lattices, and the resulting detector-resolved fluorescence responses are assembled into a two-coordinate fluorescence response matrix. Dual deconvolution separately estimates and corrects the excitation and emission optical transfer functions, and the aberration-corrected matrix is then combined in the spatial-frequency domain to reconstruct the aberration-free image. This strategy reduces the number of required camera frames by approximately three orders of magnitude, allowing response matrices with $\sim 10^6$ illumination positions to be acquired within seconds to a few minutes. The accelerated acquisition preserves the aberration-correction capability of dual deconvolution and achieves an estimated lateral resolution of $\sim$100 nm. Applied to heterogeneous mouse brain and kidney tissue, the method enables high-resolution, depth-resolved volumetric imaging while correcting specimen-dependent aberrations. Together, these results establish adaptive multifocal illumination as a practical route to high-throughput aberration-free fluorescence response matrix imaging.

In this work we present a model of a neuroglivascular unit associated with a neuronal ensemble. We combine calcium dynamics in the astrocyte, the cascade of synthesis of arachidonic acid and its derivatives, as well as the activity of the network of inhibitory and excitatory neurons with changes in local blood flow. We add equations to the model for extracellular neurotransmitters such as GABA, glutamate, and norepinephrine. We show that the resulting model can exhibit different types of behavior, and also shows a significant connection between the functioning of the neural network and its supporting components.

Low-energy electrons in graphene obey an emergent, rotationally invariant Dirac equation, while the honeycomb lattice enters only through higher-order, symmetry-breaking corrections, the leading one being trigonal warping. This provides a unique solid-state arena to address a cornerstone challenge in quantum gravity: how to detect ultraviolet symmetry-breaking granular corrections, suppressed by a fundamental scale, atop an emergent infrared relativistic continuum. We show that intense counter-rotating $\omega$-$2\omega$ fields and valley-resolved high-harmonic generation (HHG) provide such a probe. Within a family of models that interpolates continuously between the isotropic Dirac cone and nearest-neighbour graphene at fixed Fermi velocity, the valley contrast of the harmonic yield vanishes identically in the symmetric theory, grows linearly with the warping coupling, and its valley-even counterpart appears only at second order, as expected from effective-field-theory power counting. The stronger the laser, the farther it drives electrons from the Dirac points, so the laser intensity plays the role of the collision energy. For the fourth harmonic, the leading warping correction alone accounts for the valley contrast of real graphene up to fundamental intensities of about $10^{11}$ W/cm$^2$, below the damage threshold of monolayer graphene, whereas the fifth harmonic already reveals higher-order lattice corrections at intensities around $10^{10}$ W/cm$^2$. The two harmonics, which rotate in opposite directions, are emitted by opposite valleys. Valley-resolved harmonics are far more sensitive than the residual valley populations, making them a practical detector of lattice-scale physics hidden beneath an emergent Dirac theory.

Objective. To develop an automated method for segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET while accounting for partial-volume and tissue-fraction effects. Approach. Synthetic carotid PET images with known fractional-volume ground truth were generated by combining MR-derived high-resolution activity maps with GATE Monte Carlo simulation incorporating major PET physical processes and matched to the Biograph mMR acquisition and reconstruction. A two-stage network first localized the carotid vessel wall from T1-weighted MR and then estimated voxel-wise inflammation fraction from masked PET. Both stages used a multiscale residual (MSR) convolutional backbone. Performance was compared with U-Net, Transformer U-Net, DUCK-Net, and iterative Yang partial-volume correction. Main results. In a two-alternative forced-choice study, five nuclear-medicine physicians identified real images with 54% accuracy (54/100; Wilson 95% CI 0.44-0.63; exact binomial p = 0.48). On 184 simulated test slices from five held-out patients, the proposed method achieved a weighted DSC of 0.79, compared with 0.73 for U-Net, 0.69 for Transformer U-Net, and 0.75 for DUCK-Net, with corresponding HD values of 1.85, 2.29, 2.32, and 2.23 voxels. Patient-cluster bootstrap comparisons favored the proposed method for DSC and HD against all three baselines (Holm-corrected p <= 0.031). Overall ENRMSE was 0.24 versus 0.59, 0.68, and 0.59 for the three DL baselines and 0.57 for iterative Yang. In seven patients, corrected SUV showed a positive association with MR-derived lipid-rich/necrotic core with hemorrhage. Significance. The proposed framework improved carotid PET segmentation and uptake quantification under simulated conditions. The patient analysis is preliminary and requires external validation with biological reference standards before clinical deployment.

Use of carbon electrodes integrated with CMOS technology has great promise for lab-on-chip, biomedical, environmental, and industrial applications. One method of developing carbon electrodes is to 3D print polymer nanostructures on a CMOS chip, and use a thermal annealing process to pyrolyze them into carbon at high temperature, approximately 950 {\deg}C. Such high temperature is detrimental to the CMOS sensor circuits, causing severe degradation. We present a novel solution of localized Joule heating of these polymer nanostructures to achieve complete carbonization of the electrodes. Both analytical and COMSOL simulations guided the experiments to characterize the carbonization process conditions. Partial carbonization was achieved at 500-600 {\deg}C of global temperature, and complete carbonization was achieved by localized Joule heating. Pyrolized nanostructures monolithic samples on a silica wafer were fabricated using standard material processing, such as 2-Photon Polymerization. Pyrolysis was achieved by Rapid Thermal Processing (RTP). Observations using optical microscopy, scanning electron microscopy, and Raman spectroscopy demonstrated the feasibility of polymer carbonization within a CMOS compatible temperature window.

In this work, we study a theoretical scheme to investigate the monogamy of quantum steering in a hybrid exciton–cavity optomechanical (EOM) system. Within the Gaussian-state formalism, we quantify the steerability among the exciton, photon, and mechanical modes in both the steady-state and dynamical regimes. We establish and verify CKW-type monogamy inequalities for Gaussian steering and demonstrate that the distribution of bipartite steering among the three modes can be controlled through the system parameters. We further examine the influence of thermal noise and temporal evolution on the steering properties and the corresponding monogamy relations. Our results show that increasing the temperature generally suppresses the strength of Gaussian steering and modifies the degree of steering sharing imposed by the CKW-type monogamy relations. In contrast, in the dynamical regime, the steerability can increase with evolution time over suitable temporal windows, indicating that the transient dynamics can be exploited to enhance quantum correlations. These results highlight the potential of hybrid exciton–optomechanical platforms for controllable quantum correlations and provide a promising route toward the generation and manipulation of multipartite quantum resources.

Veins are commonly subjected to altered mechanical loading in clinical settings such as vein grafting, arteriovenous fistula creation, and tissue-engineered vascular graft implantation, making accurate constitutive models of venous tissue clinically important. Venous mechanical behavior differs characteristically from that of arteries, and existing constitutive models struggle to simultaneously fit the circumferential and axial stress-stretch responses across vein types. Here, we present a constitutive artificial neural network (CANN) framework that discovers a compact, interpretable hyperelastic constitutive model from biaxial mechanical data across three types of veins (saphenous, jugular, and inferior vena cava). The framework incorporates an expanded candidate function library with structurally motivated terms representing progressive fiber recruitment and fiber-family interactions. Sobol sensitivity analysis, correlation-based pruning, and response-level family collapse were then used to reduce the discovered basis to a compact strain-energy density function (SEF). The resulting eight-term SEF achieved significantly higher fitting accuracy than established venous constitutive models across all three types of veins $(R^2>0.9, p<0.01)$. These findings establish a framework for constitutive model discovery in veins and propose a candidate constitutive form for further validation across larger, independent cohorts.

We validate Leonid Space's space weather forecasts through comprehensive cross-validation of retroforecasts scored against the entire F10.7 and Ap historical record. We built a unified forecast model that jointly forecasts F10.7 and Ap on all timescales from 3 hours up to multi-decade chains of future solar cycles. This was used to generate an archive of historical 15-year retroforecasts, after performing a leave-one-out fit of the model on held-out data and conditioning only on historical data available at the time. The accuracy of this archive was scored against measured space weather indices at all lookahead horizons from 1 day to 15 years, using the continuous ranked probability score (CRPS) as the primary quality metric. All other publicly available F10.7 and Ap forecast archives were scored using the same approach, including NASA's MSAFE and Schatten forecasts, SWPC's 27-day, 45-day, and Solar Cycle predictions, SWPC/USAF's RSGA, SIDC's Ursigram, CLS, Athens, KSO, BGS, SEPC's 3-day and 27-day forecasts, AFRL/NSO's SIFT, and BoM. Taken together, these 15 flux forecasts and 12 Ap forecasts compared on common lookahead metrics make up the most comprehensive cross-comparison of space weather forecasts we are aware of. Over a common forecast interval since 2000, Leonid had the lowest CRPS prediction error of any F10.7 or Ap prediction model: strictly lowest for Ap at all lookaheads and for F10.7 up to 5 years, and parity with MSAFE after 5 years. Much of this performance comes from our well-calibrated uncertainty bands, and we urge space weather forecasters to include calibrated uncertainties with their predictions and to issue retroforecast archives that can be used to rigorously evaluate their generating models.

The practicality of realizing high-directivity sources is studied using a generalized sensitivity metrics. These measures are based on the error model adopted in previous studies on array tolerance, but account for heterogeneous array characteristics in an arbitrary source-expansion basis, making it applicable to the study of continuous sources, ideal uncoupled arrays, and heterogeneous arrays with mutual coupling. The formulation provides the ensemble average of realized directivity under the selected error model, making it a fast predictor of anticipated directivity reduction due to source perturbation. The data required to compute these predictions are straightforward to obtain via simulation or measurement, with a significant simplification when the system can be approximated as lossless. The directivity and the proposed tolerance measures allow for the formulation of optimization problems illustrating the tradeoff between these measures. Several examples demonstrate these tradeoffs, special cases, comparison to previous estimates of maximum directivity, and the accuracy of sensitivity as a predictor of statistical performance over large ensembles of design realizations.

Van der Waals heterostructures offer a direct route to couple an optically active layer to a two-dimensional spin sensor, yet their coherent nonlinear response at interfaces remains poorly resolved. Here, we use polarization-resolved second harmonic-generation imaging to resolve four distinct regions of a hexagonal boron nitride (hBN) -based heterostructure, native hBN, neutron-irradiated hBN containing negatively charged boron vacancies (VB-), PbI2 and their complete overlap. The detected intensity scales quadratically with incident power, consistent with a dominant second-order optical process. Polarization scans show that adding PbI2 transforms the distorted VB- SHG anisotropy into a predominantly fourfold response with a sixfold stronger SHG than native hBN alone. This non-additive nonlinear response occurs in the same layer architecture also shown to enhance defect photoluminescence, linking spin-defect sensitization to an interface-modified effective second-order susceptibility.

We investigate whether a physics foundation model (PFM) pretrained for PDE forecasting can be repurposed for closed-loop fluid-flow control. We construct a PFM-based controller by attaching lightweight Gaussian actor and critic heads to frozen pretrained representations and train the policy using proximal policy optimization (PPO). We evaluate the approach on HydroGym's two-dimensional Kolmogorov-flow environment using vorticity observations at \(64\times64\) and \(128\times128\) resolutions and Reynolds numbers \(\mathrm{Re}=100\) and \(200\). Across these settings, we compare the frozen PFM controller against a baseline MLP policy trained with the same PPO framework. The PFM controller achieves consistently higher reward while requiring smaller executed action magnitudes over training, indicating improved control performance with reduced control effort. These results provide initial evidence that representations learned through self-supervised next-frame prediction can be transferred directly to closed-loop fluid flow control. Project page: https://mahindrautela.github.io/morph.

In this work, we demonstrate submicron-gate $\beta-Ga_{2}O_{3}$ FinFETs that integrate Si delta-doped channels with selectively regrown n++ source/drain contacts. The devices employ 100 nm-wide fins to improve electrostatic control of a high sheet charge density channel of $3.3\times 10^{13}$ cm-2. A 150 nm-gate-length FinFET exhibits a maximum drain current density of 764 mA/mm at VGS = 1 V and VDS = 10 V, an on-resistance of 9.1 $\Omega$.mm, and an Ion/Ioff ratio exceeding $10^9$. The device also shows a peak transconductance of 43 mS/mm and de-embedded $f_{T}/f_{MAX}$ values of 6.3/2.0 GHz. Pulsed I-V measurements indicate ~20 % current collapse, attributed primarily to trapping at etched fin sidewalls and the $Al_{2} O_{3}$/ $\beta-Ga_{2}O_{3}$ interface. These results show that delta-doped $\beta-Ga_{2}O_{3}$ FinFETs can simultaneously achieve high drive current, strong current modulation, and GHz-frequency operation, providing a scalable device platform for future power and RF electronics.

Unidirectional transport of light is a central capability in photonics, enabling critical functionalities such as isolation and routing. Ordinarily, this directionality depends on the mode of light, but not its phase. In this work, we experimentally demonstrate phase-dependent unidirectional transport of light for the first time. Specifically, we realize a system where conjugate quadratures of an optical field decouple and propagate in opposite directions. Our all-optical approach uses multiple pump frequencies in a high-$Q$ nanophotonic micro-resonator to achieve Hamiltonian programming through Kerr nonlinearity. Even though we rely on optical nonlinearity, our approach is not limited to any minimal power requirement on the signal. We demonstrate the ultimate limit of this by measuring the effect of quadrature-dependent transport on vacuum fluctuations. Our approach unlocks a fundamental new capability for controlling light, and can enable previously inaccessible regimes of phase-sensitive directional amplification, sensing, and routing.

Intense ultrafast lasers are the backbone of strong-field physics and enable table-top scale applications of coherent high-photon energy radiation through high-harmonic generation (HHG). Using short-wavelength drivers in HHG promises highly efficient conversion into the extreme ultraviolet, but short pulse durations are essential to this approach. We generate multi-GW peak power, sub-10 fs pulses in the visible with broad spectral tunability (220–752 nm) by harnessing soliton dynamics in large-core, gas-filled capillary fibers for high-energy resonant dispersive wave emission. This establishes the potential of resonant dispersive waves as a new class of driving lasers for HHG and strong-field physics in addition to widespread applications in ultrafast spectroscopy.

We present BSMScanner, a framework in which a beyond-the-Standard-Model (BSM) parameter scan is specified as a set of declarative YAML documents rather than written as a program. The user declares the free parameters and their priors, the mass matrices and the diagonalisations that act on them, the observables, the theory-validity conditions and the likelihood terms. The framework validates this specification, resolves it into a dependency graph and compiles it once into a typed C++ evaluation kernel, which is then driven by an independently chosen scanning engine: uniform sampling, two differential-evolution engines, or a staged basin-discovery orchestrator, optionally followed by Markov-chain posterior refinement. Flavour physics matrix diagonalisation, PMNS and CKM construction in fixed conventions, tabulated oscillation likelihoods and a library of common flavour observables are provided by the framework and imported by name, so that a model file contains only model-specific definitions; quantities without a closed form, such as a relic density, enter through a C++ or Python plugin interface. We describe the specification schema, the observable and likelihood library, the scanning engines and their configuration, and demonstrate the framework on six benchmark models with $4$ to $26$ free parameters, giving for each the specification, the scan configuration, the resulting best fit and analytic cross-checks of the result.

Plasma-neutral interactions critically affect the edge dynamics of magnetically confined fusion devices. Neutral atoms modify ion and electron velocity distributions, potentially affecting local instabilities Standard fluid models simplify these processes (namely charge exchange, ionization and recombination) using Maxwellian assumptions and linearized source terms. We revisit these assumptions for a hydrogen plasma interacting with hydrogen atoms. We derive plasma-neutral interaction terms in the kinetic and gyrokinetic frameworks. Accounting for velocity-dependent differential cross-sections, the model captures the kinetic impact of inelastic collisions on ions and electrons beyond energy and momentum exchange. We demonstrate that the fluid moments of the kinetic model reduce to standard fluid source terms under the appropriate assumptions of Maxwellianity and linearity. We compare the kinetic model with fluid models through their effects on distribution functions and fluid moments. Substantial discrepancies are observed, even for Maxwellian distributions. For charge exchange, the kinetic model yields momentum and energy exchange rates up to twice those predicted by fluid descriptions. We show that a fluid treatment of ionization greatly overestimates the mean kinetic energy of the produced electrons. These results highlight the limitations of fluid descriptions and the importance of kinetic effects in edge-plasma momentum and energy exchange.

Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic motion. Recent work has shown that relaxing physical inductive biases can improve computational efficiency and predictive accuracy, raising the question of whether energy-and-force matching alone is sufficient to ensure accurate sampling. We show that, for unconstrained hypothesis classes, the relationship between prediction error and sampling accuracy is exponentially ill-conditioned in dimension. In particular, prediction errors that decay exponentially with dimension can coexist with nearly maximal total variation distance between the learned and target distributions. We identify two failure mechanisms. The first is kinetic trapping, where relaxation to an accurate learned equilibrium requires exponentially long times, even when simulations are initialized from training configurations at which energies and forces are matched exactly. The second is equilibrium mismatch, where the learned stationary distribution itself remains far from the target. Under explicit assumptions, we show that equilibrium mismatch can persist even in local, symmetry-preserving models, where small per-atom errors coexist with a distributional discrepancy that approaches its maximum as system size increases. Finally, we establish global energy-control conditions that guarantee equilibrium accuracy and identify additional conditions required for rapid relaxation.

Plasma experiments observe coupled physical states through sparse, heterogeneous, and incomplete diagnostics. We present a proof of concept toward a multimodal foundation model for plasma state reconstruction during magnetic reconnection, motivated by the Facility for Laboratory Reconnection Experiments (FLARE). Using 250 synthetic VPIC simulations at fixed reference electron beta $\beta_{e0}=0.2$, with 225 training runs and 25 disjoint validation runs, we train a 5.5M-parameter residual 3D U-Net to reconstruct three magnetic-field components and electron density from incomplete observations. The model combines spatial-only pooling, which preserves all 24 temporal frames, with global spatiotemporal self-attention and rotary positional encoding. Independently masking magnetic and density modalities across random, grid, block, and temporal patterns enables sparse sensing, cross-modal completion, density super-resolution, and temporal completion with a single set of weights and no task-specific retraining. With density fully hidden, just 0.1% magnetic spatial visibility reduces median density NRMSE from approximately 0.9 to 0.1. Conversely, observed density improves current-density reconstruction when magnetic data are absent or extremely sparse. With no magnetic observations, roughly 1% density visibility approaches the density reconstruction accuracy achieved with fully observed magnetic fields, although current structure remains more dependent on direct magnetic measurements. Full magnetic conditioning also stabilizes completion of missing future density frames. These results demonstrate bidirectional cross-modal predictive coupling within this simulation regime. Generalization across plasma regimes or to experimental measurements remains untested.

Learning agents, whether humans or algorithms, adapt to one another rather than to a fixed environment. Selfish learning need not produce a good outcome for the group, especially when learners face a social dilemma. The iterated prisoner's dilemma (IPD) is the classical example, but it is usually studied in populations of agents who imitate successful strategies. Here, we instead study selfish gradient ascent learners on the two-dimensional space of reactive strategies. A reactive strategy is described by two continuous traits: reciprocity, the chance of cooperating after the co-player cooperates, and forgiveness, the chance of cooperating after the co-player defects. Each learner updates its strategy in the direction that most rapidly improves its payoff against all other learners, by an amount governed by its learning rate. We show that heterogeneity in learning rates can qualitatively change the long-term outcome: a population with uniform learning rates converges to always-defect (ALLD), whereas a population with variable learning rates approaches generous-tit-for-tat (GTFT), which produces nearly optimal collective payoffs. In simulations, every population passes through a transient state of minimal forgiveness, in which the distribution of reciprocity determines the outcome. Variable learning rates promote cooperation because slow learners preserve high reciprocity until the population reaches this state. We prove sufficient conditions on the distribution of reciprocity for convergence to ALLD or to tit-for-tat (TFT). Variable learning rates also allow cooperation to recover from perturbation, which is provably impossible under uniform learning. These results suggest that purely selfish and myopic learning can produce persistent cooperation provided the population has intrinsic variability in learning rates.