Papers

NLO simulations are essential for LHC physics analyses but are expensive, as they are not only slow but also lead to negative weights, which imply the need to simulate much larger samples of events. Folding is a powerful technique to reduce negative weights but is itself expensive. In this paper I propose “vectorized folding” as a new idea to speed up these calculations using SIMD and GPUs, and I present its CUDACPP-based implementation for MG5aMC in MadVfold, including its extension for unfolded NLO event generation. Preliminary results show overall speedups around 6x to 9x with folding and 3x without it. This work is based on a test-centric, LLM-assisted software development process.

We present a physics–informed neural solver for the generalized Falkner–Skan boundary-value problem that combines an exact boundary–admissible trial representation with adaptive regional residual balancing (ARRB). The three prescribed boundary conditions are embedded analytically, eliminating boundary-condition penalty terms while allowing the finite–domain streamfunction value and wall shear to be determined by the governing equation. The residual is divided into wall, middle, and tail regions, and a physical global residual is reconstructed from regional mean-squared errors using region-length fractions. Safeguarded exponential–moving–average inverse–gradient coefficients adaptively balance the regional contributions during Adam optimization. A deterministic 800-point residual monitor is used for checkpoint selection, followed by two deterministic L–BFGS stages minimizing the physical global residual. The method is tested on the Blasius, favourable-pressure–gradient Falkner–Skan, and Pohlhausen cases. For \((\beta_0,\beta_1)=(0.75,0.50)\), the predicted wall shear is \(f''(0)=0.8997161394\), compared with the finite-domain reference \(0.8997168085\), giving an absolute error of \(6.691\times10^{-7}\). The residual MSE is \(6.357\times10^{-9}\), while the relative \(L_2\) errors in \(f'\) and \(f''\) are \(2.848\times10^{-6}\) and \(4.188\times10^{-5}\). A matched single–seed ablation shows that ARRB reduces residual MSE by 52.81% relative to global–residual training and by 50.24% relative to equal-regional weighting. Wall–shear errors are reduced by 77.30% and 72.37%, respectively. These results support ARRB as an accuracy-oriented residual-conditioning strategy, while multi–seed experiments remain necessary to quantify optimization variability.

Social influence can preserve errors that individuals would correct on their own. We study this possibility by representing opinions as directions in a semantic space. The formulation connects established models of personal preference, social agreement, and external influence. A common factual signal pulls every opinion toward a reference direction, individuals remain attached to their initial preferences, and connected individuals imitate one another. When the factual signal exceeds the preference strength, each isolated individual corrects, apart from an exactly opposite initial opinion. We prove that every finite undirected network then has a unique state of minimum total energy in which all opinions favor the factual direction. This state is the best overall compromise between factual alignment, personal preferences, and agreement with neighbors. Yet on a ring with planar opinions, sufficiently strong imitation can trap the same individuals in a different stable state that retains misinformation. We prove convergence to that state from the specified initial opinions. We also derive a network spectral condition that excludes competing stable states, and show how an additional semantic direction can destabilize the constructed planar pattern. Numerical maps reveal persistent error, history dependence, and coexisting outcomes, while comparisons across networks show differences that a single spectral scale does not remove. These results distinguish individual correctability from collective correction and identify how attractive social influence can obstruct the latter.

We investigate whether the plasma disturbances generated by a charged debris object can be used to infer its position without directly sampling the object or its immediate sheath. A one-dimensional open-boundary electrostatic particle-in-cell (PIC) framework is used to model a continuously flowing electron-ion plasma containing an initially uncharged debris object. The debris charges self-consistently and produces an ion-ion counter-streaming instability (IICSI), which is sustained and reaches a statistically stationary state. A matching simulation without debris provides a controlled background against which debris-induced changes in different plasma fields are identified. The precursor and wake are examined through their spatial extent, fluctuation power, and frequency–wavenumber spectra. Weak-form sparse regression is then used as a supporting tool to recover reduced fluid and kinetic residuals and to learn an empirical relation connecting the precursor and wake disturbance envelopes to the equilibrium plasma flow, debris charge, and distance from the source. The predictive capability is examined by successively treating each simulation as an independent case. For each evaluation, the model is constructed from the remaining simulations, while the excluded simulation is used only to infer the debris position from its plasma response. For the idealized cold-ion regime considered here, the procedure successfully localizes the debris in transonic and supersonic flows, whereas the subsonic cases remain unresolved. These results demonstrate that debris-induced plasma disturbances possess a learnable spatial structure and provide a proof-of-principle route toward future remote localization using more realistic multidimensional models; they do not constitute an operational debris-detection scheme.

Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is limited by three bottlenecks: acquiring room geometry without costly scanning hardware or manual CAD modeling, decomposing the scene into individually manipulable objects, and reconfiguring those objects for alternative layouts without re-capturing the room. We present a semi-automated workflow that converts a single 360-degree video of a room into individually editable, simulation-ready geometry assets. A dense point cloud is reconstructed using Neural Radiance Fields (NeRF), and 2D instance masks from text-prompted SAM 3 segmentation are lifted to 3D using multi-view consensus and depth-band filtering. Points are separated into object instances with an octree, and occlusion gaps are healed with a connectivity graph. Chair templates are fitted by Iterative Closest Point (ICP) alignment, and table geometry is generated procedurally. A browser-based editor supports quality assurance and rapid construction of alternative layout configurations. A steady Reynolds-averaged OpenFOAM solution then drives transient passive-scalar transport; the setup is verified using a mesh-sensitivity study and validated against an IEA Annex 20 benchmark. We apply the workflow to two university classrooms and a tiered lecture-hall auditorium. The capture-to-geometry pass takes two to five hours per room on a consumer workstation. In a controlled obstruction sequence in one classroom, the modeled half-clearance time varies non-monotonically as furniture is added, and a cross-room comparison indicates that clearance behavior cannot be reliably extrapolated between rooms, motivating per-room geometry acquisition. By making that acquisition low-cost, the workflow makes geometry-resolved comparative ventilation studies practical for spaces such as classrooms.

We introduce a self-adaptive Floquet system based on a quantum kicked rotor, in which the kicking strength itself becomes a dynamical variable generated self-consistently through cavity-mediated feedback. A superradiant transition gives rise to cavity-mediated kicking and two competing instability channels, symmetric and antisymmetric, which provide a unified organizing principle for the nonequilibrium Floquet phases. For resonant kicking, their competition produces double-kick dynamics that support resonant ballistic transport and an emergent antiresonance with period-quadrupled rotor evolution, arising from a balance between the two instability channels. Remarkably, for incommensurate kicking, the antisymmetric instability stabilizes a robust period-doubled localized phase with persistent subharmonic dynamics despite the underlying incommensurate driving, revealing localized temporal order absent in conventional kicked rotors. As the feedback strength increases, correlated temporal fluctuations progressively suppress quantum interference, driving crossovers from period-doubled localization to irregular localization and eventually to subdiffusive transport. Our results establish a general framework for self-adaptive quantum-chaotic dynamics and demonstrate how dynamical feedback can fundamentally reshape transport, localization, and temporal order in driven quantum systems.

Penning ionization allows in situ generation of ionic complexes to launch vibrational relaxation and dissociation dynamics for rare gas–H$_2^+$/HD$^+$ complexes. The ensuing translational kinetic energy release (TKER) spectra characterizing the diatomic cation are a particularly sensitive way to probe the long range part of the intermolecular interactions. TKER distributions from quasi-classical trajectory (QCT) simulations for the Ne–H$_2^+$ and Ne–HD$^+$ complexes quantitatively match measured positions and relative intensities of most experimentally observed features and are also on par with time-dependent quantum simulations. Specifically, the QCT-simulations allow to separate signatures arising from H$_2^+$/HD$^+$ relaxation into different final vibrational states, such as for $(v=2) \rightarrow (v' = 0/1 )$. Trajectory-resolved QCT analysis also provides mechanistic interpretations of the spatial and time-dependent dynamics underlying the TKER distributions. Rare gas–H$_2^+$/HD$^+$ complex lifetimes directly correlate with the final rotational quantum number $j'$ of the diatomic. Low-$j'$ products are associated primarily with an initial axial approach of the rare gas atom, whereas formation of higher-$j'$ states predominantly results from an initial T-shaped approach geometry.

Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature extraction algorithms, and small-scale on-detector deployments can enable real-time intelligent data handling. This work provides the first fine-tuning of an industrial foundation model for particle physics DAQ. Starting from the backbone of Google Research's TimesFM (Time Series Foundation Model), we demonstrate fine-tuning on real-time regression tasks for drift chamber trackers and dual-readout calorimeters. Furthermore, the fine-tuned TimesFM model is distilled into a student and co-designed with FPGA implementation to enable these models to run in real-time at future colliders. The fine-tuned distillations meet or exceed the performance of previously published AI/ML solutions for each task. Further, the pipeline of distillation and model compression from TimesFM is generic and can be easily adapted to a variety of 1D waveform tasks across domains.

We introduce the temporal quadrupole moment and leverage its nonreciprocal frequency-converting topological corner states to experimentally realize a magnet-free transceiving circulator in a topological time-modulated microwave metasurface. Under periodic driving, the temporal quadrupole moment evolves the corner charge of a bulk-gapped lattice over the drive cycle, generating two distinct families of topologically protected corner states: a corner mode at quasienergy $\varepsilon=0$ and a purely dynamical period-doubled mode at $\varepsilon=\pi/T$ that exists only under periodic driving. We show that the relative phase between the two time-modulated coupling channels that generate it acts as a synthetic gauge field, breaking time-reversal symmetry while leaving the corner-mode topological protection intact. Specifically, a signal launched into the metasurface at TX port at frequency is up-converted and radiated into free space, while a free-space wave at is preferentially captured and routed — at the same frequency, unconverted — to RX port rather than back to TX port; the reverse itinerary is forbidden by the same broken symmetry. We confirm the temporal quadrupole moment, its associated Floquet corner states, and the resulting nonreciprocal frequency conversion through spatial field-intensity and angle-resolved radiation measurements of the time-modulated array on a fabricated microwave prototype.

Changing solvent inertia changes the kinetic part of a molecular Hamiltonian and hence its flow in phase space, without requiring a change in the potential energy surface (PES). We study how this affects product probability in a solute-solvent model with two degrees of freedom. Initial conditions are chosen at the same total energy for each solvent mass. For this family, the deterministic outcome changes from reaching products to returning to reactants at a threshold mass. The initial condition at that mass belongs to the stable manifold of the transition-state periodic orbit. Weak Gaussian forcing of the solvent momentum replaces the abrupt change in outcome by a continuous change in product probability over a mass interval proportional to the square root of the noise strength. The deterministic threshold and a linear response calculation predict the position and width of this probability change without fitting the stochastic simulations. Monte Carlo results at three noise strengths agree with the prediction within sampling uncertainty. The calculation connects solvent inertia to product probability through the geometry of the full Hamiltonian flow, rather than through a change in the PES.

We present a machine-learning-accelerated plasma simulation scheme that combines learned time jumps with ordinary physics evolution. A neural network corrects a low-cost physics forecast over multiple time steps, while the original solver reconstructs the state and advances it between jumps through short reanchoring intervals. The scheme is implemented in PASCHEN-1D, a one-dimensional drift-diffusion-Poisson solver, achieving up to $4.70 \times$ CPU rollout speedup relative to the ordinary solver for nanosecond pulsed nitrogen discharges. The simulations capture pre-breakdown evolution, rapid sheath formation, expansion and collapse under pulsed-voltage excitation, and post-pulse afterglow. Bulk densities and electric fields agree closely with the ordinary-solver reference, while cathode sheath width, gap voltage, and discharge current reproduce the principal temporal features. The largest spatial discrepancies occur in the cathode sheath during fast transients. A sweep of the physics-reanchoring duration reveals a trade-off between accuracy, acceleration, and numerical stability over the tested 500 ns simulation interval. A model trained on simulations with 1000 grid points also shows useful transfer to 500- and 1500-point meshes without retraining. These results demonstrate a practical route to accelerating plasma simulations by augmenting an established physics solver with machine learning.

MARVEL's first direct observation of Migdal ionization in neutron scattering marks an experimental milestone and opens a new avenue for probing electronic response to nuclear recoil. Hydrogen, both the simplest atom and a constituent of its molecular target, provides a controlled benchmark. Within the nonrelativistic sudden approximation, we compute its energy-differential and integrated ionization probabilities with the full recoil phase. At the recoil parameter $\beta\equiv v_N/(\alpha c)=1.04$, the full-to-dipole spectral ratio rises from $0.43$ when the emitted-electron energy is $1\%$ of the hydrogen binding energy to $5.0$ when it is $2.3$ times that energy. Near $\beta=10$, partial waves with orbital angular momentum $\ell\ge3$ carry more than $90\%$ of the calculated continuum probability. An independent bound-state-closure evaluation verifies the absolute normalization: at eight recoil values, its ionization probabilities agree with the continuum-integrated results to relative discrepancies below $4.4\times10^{-7}$. These results extend the hydrogen dipole response to the fast-neutron regime and establish energy and angular-momentum redistribution as linked consequences of resolving the recoil phase across an atom.

Every particle-physics experiment builds detector-health monitoring, and many end up rebuilding it, because a first system written under commissioning pressure often proves too rigid to maintain. ARGUS (Automated Anomaly-detection, Run-quality and General Unified Surveillance) is a configuration-driven framework that separates what is generic in this problem (the health monitors, their validation, and the reporting) from what is not (the data schema and the thresholds). Standing it up for a new detector requires a data adapter and a configuration file, not a rewrite. Detector health is judged by four independent monitors: expert cuts, which are authoritative; a machine-learning second opinion that is additive and off by default; a trend forecast that gives early warning before a threshold is crossed; and a reference-histogram comparison with pluggable statistical tests. The monitors are compared and never merged, and an alerting layer turns their flags and warnings into deduplicated notifications. We demonstrate the full chain on a synthetic reference detector with planted pathologies and a fault-injection campaign that manufactures ground truth: the cuts recover every planted pathology with no false positives, the machine-learning monitor reaches a ROC AUC of 0.896 on injected faults and extends coverage to fault classes no cut was written for, the forecast flags a drifting channel before its cut fires, and the histogram comparison catches every planted shape distortion while holding its configured false-positive rate on clean pairs. A second demonstration shows the framework adapting to a reorganized file layout with no change to code or configuration. Every result and figure in this paper regenerates from the code alone. The framework has been implemented for two particle-physics experiments, where it is under evaluation; adoption decisions rest with the collaborations.

Heterogeneous integration combines complementary material properties in a single photonic platform. Here, we demonstrate on-chip $f$-2$f$ self-referencing in a heterogeneously integrated Si$_3$N$_4$/LiNbO$_3$ platform, in which supercontinuum generation in a Si$_3$N$_4$ waveguide and second-harmonic generation in a LiNbO$_3$ layer are spatially separated and linked by adiabatic escalator couplers. Pumped by a 1560 nm mode-locked laser with 75 pJ on-chip pulse energy, we detect the carrier-envelope offset frequency with 30 dB signal-to-noise ratio in 300 kHz resolution bandwidth. A comparative measurement confirms that the second harmonic originates in the LiNbO$_3$ layer and indicates opportunity for further improvements through periodic poling. These results establish heterogeneous Si$_3$N$_4$/LiNbO$_3$ integration as a viable building block for self-referenced ultrafast pulse sources in low-loss Si$_3$N$_4$ photonics.

Unstable thermal stratification modifies near-wall momentum and heat transport, causing the mean velocity profile to depart from the classical logarithmic law and complicating wall modelling for turbulent mixed convection. We develop a buoyancy-modified logarithmic-quadratic wall model for incompressible Poiseuille–Rayleigh–Bénard flow. The model combines an approximately linear relation between the near-wall mean temperature and mean streamwise velocity with a thermally modified mean-gradient representation inspired by mixing-length scaling. A priori assessments using wall quantities from the direct numerical simulation (DNS) database show that the calibrated wall law reconstructs near-wall velocity and wall-function-equivalent eddy-viscosity profiles. We implement the wall model in wall-modelled large-eddy simulations (WMLES) at friction Reynolds numbers up to $Re_\tau\approx6000$ and Rayleigh numbers up to $Ra=10^{10}$. For cases with DNS reference profiles at $Ra=10^8$ and $10^9$, the maximum pointwise absolute relative errors are $3.6\%$ for the mean velocity and $1.9\%$ for the mean temperature. For cases with available DNS global-transport data, the maximum relative deviations in the Nusselt number $Nu$ and skin-friction coefficient $C_f$ are $11.7\%$ and $15.9\%$, respectively. The WMLES reduces the mesh count by factors of approximately $195$–$542$ relative to the corresponding DNS meshes. For the $Ra=10^{10}$ and $Ri_b=0.1$ case, extrapolation of reference DNS resolution strategies gives a mesh count of order $10^{11}$, approximately three orders of magnitude larger than the present WMLES mesh count. We also examine how the balance between shear and buoyancy reorganises flow structure, and we identify signatures consistent with the coexistence of streamwise-elongated motions resembling very-large-scale motions and buoyancy-associated streamwise rolls.

Tokamak equilibria are commonly exchanged as gridded G-EQDSK files whose conventions, resolutions, and machine-specific formats impede cross-device comparisons and data-driven modeling. Here, we present VEQDB, an open, compact, and reconstructible fixed-boundary equilibrium database built on continuous MXH–Chebyshev geometry and independent physical-profile roots. By decoupling authoritative equilibrium physics from rectangular meshes, VEQDB enables continuous evaluation and metric differentiation at arbitrary application-demanded resolutions. Backed by an automated numerical validation pipeline, VEQDB is structured as an extensible repository for ongoing community expansion. Its inaugural release provides 13,291 accepted equilibria across 267 conventional and spherical tokamaks, encompassing parameter-sampled Grad–Shafranov solutions, G-EQDSK projections spanning EAST, MAST-U, and ITER scales, and controlled variation families with explicit provenance. Benchmark projections reproduce normalized flux maps with RMS errors between $1.09 \times 10^{-3}$ and $1.45 \times 10^{-3}$, while compact JSON representations achieve an 89–96-fold size reduction relative to standard $129 \times 129$ G-EQDSK files. The complete initial release occupies 41~MB in raw JSON and 18~MB in compressed archives, and all records successfully passed independent reload and evaluation tests. VEQDB establishes an extensible, provenance-preserving foundation for equilibrium studies, reduced-order surrogate modeling, and cross-machine workflows.

In decaying turbulence, the turbulent cascade is not merely a passive background for breakup but an evolving stability boundary that dynamically selects the fragmentation-aggregation pathway. As turbulence decays, the Hinze scale sweeps through the bubble-size distribution, progressively eliminating the breakable population and driving a transition from a mixed breakup-coalescence regime to a self-sustaining pure-coalescence state. Our model predicts a critical turbulence decay exponent $m=5/3$, a transient mixed breakup-coalescence regime, and a late-stage pure-coalescence state with a universal coalescence hazard. Interestingly, the transient breakup acts as a catalyst that amplifies mixed-regime bubble growth relative to pure coalescence. Results from our direct numerical simulations of decaying homogeneous isotropic turbulence as well as high-Reynolds-number duct-flow experiments collapse onto the model predictions when expressed in terms of the measured decay rate. These results replace the conventional instantaneous Hinze criterion with a non-equilibrium, cascade-controlled framework for turbulent fragmentation and aggregation.

This work continues the study of the real vector field theory.It shows how the potentials can be used to derive the field and dynamical variables of the wave electrojeitonic field generated by an electrically charged point particle.The derived expressions provide the angular distributions of the instantaneous electrojeitonic radiated power emitted by an charged particle in arbitrary motion. The results predict the dominance of longitudinal electrojeitonic radiation from ultrarelativistic charged particles.Keywords: electrojeitonic field, longitudinal electrojeitonic waves, electrojeitonic radiation.

Gravitational generation of quantum entanglement on squeezed coherent states in a system of two quantum mechanical particles is explored. The particles are modeled as harmonic oscillators, and their gravitational interaction is described by an interaction potential. Dynamics of the gravi- tationally induced entanglement is explored using the linear entropy of entanglement. An analytical expression for the linear entropy is obtained via Gaussian integration and covariance method. In the short-time weakly coupled regime, it is observed that the entanglement is proportional to the square of the time interval, square of the interaction strength, and proportional to the squeezing parameters of the modes. These results provide insights into the effects of quantum squeezing on entanglement generation in systems with gravitational interactions. Analytically and numerically it is shown that momentum squeezing can enhance gravitationally induced quantum entanglement.

This paper develops a data-driven framework for long-term prediction of fluid–structure interaction (FSI) dynamics, focusing on the flow-induced vibration (FIV) of a flexible plate. A stiffness-conditioned neural evolution operator jointly represents the Eulerian flow field and Lagrangian structural state. The plate is represented by 101 ordered structural tokens carrying nodal coordinates and velocities, with nondimensional bending stiffness as a global conditioning variable. Bidirectional cross-attention couples fluid and structural representations within a hybrid CNN-Transformer architecture. Trained with staged multi-step autoregressive rollouts and symmetry-reflected trajectories, a single operator captures three stiffness-dependent response regimes: deflected–flapping, deflected, and flapping. The predicted trajectories preserve the principal flow structures, structural oscillations, and dominant frequencies, while blind 1000-step rollouts remain bounded. The operator also interpolates to stiffness values excluded from training. To reduce sensitivity to under-resolved near-wall gradients in force reconstruction, we develop a differentiable aerodynamic-force module based on the derivative-moment transformation (DMT). Conventional wall-stress surface integrals are replaced by an enclosed 2D curve integral around the core vortex region, enabling accurate reconstruction of lift and drag. A Signed Distance Function (SDF) and smoothed Dirac-delta formulation make the integration fully differentiable while preserving gradient flow. The proposed framework provides an accurate and differentiable surrogate for stiffness-dependent FSI dynamics, enabling efficient parameter studies and future stiffness optimization for flow-energy-harvesting applications.

We propose a new model for describing the excitation of atoms by short symmetric pulses. The theory is based on the impulse approximation and incorporates a confining potential approximated by a harmonic oscillator. Results are presented for a wide variety of parameters, including field intensity and laser frequency. Permitted and forbidden transitions are analyzed in detail. The photon energy spectrum presents peaks attributed to multiphoton absorption. Our model demonstrates robustness and reliability, paving the way for addressing more complex scenarios.

This work presents a comprehensive radiation hardness characterization of SiPM devices for space-based applications: FBK NUV-HD-MT and NUV-HD-LowCT (1$\times$1, 3$\times$3, and 6$\times$6~mm$^2$) and Hamamatsu S14160 MPPCs with equivalent active areas. Total Ionizing Dose (TID) tests were performed at the ESA/ESTEC Co-60 facility with a level of dose up to 20~krad in silicon, while displacement damage effects were evaluated using 100~MeV protons with a fluence up to $1.12\times10^{11}$~p/cm$^2$ at the Paul Scherrer Institute. Radiation damage in SiPMs manifests through two main mechanisms: ionizing energy loss and non-ionizing energy loss (NIEL), which increases dark current and dark count rate. In this work, breakdown voltage, dark current, and quenching resistance were systematically measured as functions of accumulated dose and fluence. Gain and dark count rates were indirectly measured from the characterized electrical parameters. Both technologies exhibit remarkable stability of most of the functional parameters across all dose levels, while dark current shows predictable increases significantly more pronounced under proton irradiation due to displacement damage. Based on these results, both FBK and Hamamatsu devices are confirmed suitable for Low Earth Orbit (LEO) mission environment.

We investigate late-time cosmic acceleration in mass-varying neutrino (MaVaN) dark energy scenarios, where the neutrino mass is coupled to a dynamical quintessence field through a conformal interaction. Starting from the coupled conservation equations, we derive the corresponding background evolution equations for three related scenarios: a constant dark-energy equation of state with a constant coupling (Case-I), an exponential neutrino mass linked to an exponential potential under an adiabatic minimum-tracking approximation (Case-II), and a CPL-type time-varying equation of state with constant coupling (Case-III). For each case we obtain the neutrino and dark energy density evolution and the corresponding Hubble parameter, and use an MCMC analysis (emcee) to test the models against DESI DR2 BAO data, the compressed Planck CMB likelihood, and three supernova compilations (Pantheon+, DES-SN5YR, Union3), taken one at a time. Across the three cases, $H_0$ and $\Omega_{m0}$ move together in a way set mainly by which supernova sample is used, following the pattern already seen in other DESI DR2 analyses. A nonzero coupling stays consistent with the data in most fits, though its size, and sometimes its sign, depend on how it enters the model and on the priors chosen for the underlying parameters. The resulting neutrino mass bounds range from about $0.06$ to $0.3\,\mathrm{eV}$, depending on the case and dataset combination. Comparing the Bayesian evidence of each case with flat $\Lambda$CDM gives a mixed picture, with no case preferred throughout, so we regard these results as suggestive rather than firm evidence for a coupling between neutrinos and dark energy.

Recently, a hybrid qudit-qubit algorithm [1] was presented for solving the integer programming problem with a polynomial quantum advantage. In this work, we investigate the algorithm [1] to understand the role of qudits ($d$-dimensional quantum system) by conducting a comparative resource analysis with its qubit-only implementation and the classical simulability of the algorithm by exploring the entanglement structure. The resource analysis part is performed in terms of logical gate counts, and fault-tolerant physical resources, including non-Clifford gates. The hybrid qudit-qubit implementation has a more compact structure of the unitary operators as opposed to its qubit-only simulation which reduces the logical and fault-tolerant resource requirements. Two example problems, one with qutrits ($d=3$) and the other with ququint ($d=5$), when contrasted with their qubit-only implementation, within a simplified fault-tolerant resource model, showed $\sim 180 \times $ and $\sim 2220\times$ fewer total resource count, respectively. For the entanglement study, volume-law-like entropy growth and signatures of multi-partite entanglement is observed leading to increasing difficulty in the classical simulation of the algorithm. The algorithm generates synergistic tri-partite entanglement even for a quadratic problem, revealing that the algorithmic structure itself can generate higher-order entanglement in the system independent of the problem.

Purpose: Free-breathing multi-echo MRI enables hepatic PDFF/R2* quantification without breath-holds, but respiratory motion can affect R2* estimation through motion-induced field variations. We investigated whether respiratory motion sensitivity is reduced at 0.55T versus 3T and developed a time-resolved free-breathing 4D MRI approach without respiratory binning. Methods: A navigator-embedded multi-echo stack-of-stars sequence was implemented. Ten subjects were scanned during free breathing and deliberate bulk motion at both 3T and 0.55T. Data were reconstructed using motion-averaged, motion-resolved, and time-resolved approaches. Quantitative performance was also evaluated in a PDFF/R2* phantom using Cartesian and radial acquisitions. Results: Phantom experiments showed close radial-Cartesian agreement for R2* and PDFF at 3T. At 0.55T, R2* showed good agreement over the low-to-moderate range but higher uncertainty at high R2*, where larger radial-Cartesian PDFF differences were also observed. R2* was substantially less sensitive to respiratory motion at 0.55T than at 3T, whereas PDFF was relatively insensitive at both field strengths. Under bulk motion, time-resolved reconstruction provided robust R2* and PDFF measurements, with particularly high consistency between free-breathing and bulk-motion acquisitions at 0.55T. Conclusion: R2* quantification was less sensitive to respiratory motion at 0.55T than at 3T, while PDFF remained relatively insensitive. Time-resolved reconstruction provided additional robustness to bulk motion. Reduced susceptibility effects at 0.55T and time-resolved reconstruction may provide complementary advantages for motion-robust free-breathing hepatic R2* and PDFF quantification.

Hybrid physics-machine-learning solvers improve under-resolved simulations by embedding trainable corrections into the time integration. During autoregressive inference, repeated solver-model interactions can amplify small errors, motivating multi-timestep solver-in-the-loop training. However, production solvers rarely expose the derivatives needed to backpropagate through such rollouts. We introduce Diff-NekRS, a scalable differentiable framework that embeds neural corrections directly in the GPU-accelerated NekRS incompressible-flow solver. NekRS computes the authoritative forward trajectory, a manually implemented exact discrete adjoint differentiates the supported fully discrete timestep, and LibTorch supplies neural vector-Jacobian products and parameter gradients. End-to-end Taylor and centered finite-difference tests verify the assembled gradient for two-dimensional cylinder flow (2Dcyl) and the three-dimensional Taylor-Green vortex (3DTGV) across five horizons and 12-1,020 MPI ranks. At 1,020 ranks, optimizer-enabled post-setup training updates retain 54.5%-78.0% and 80.7%-81.9% weak-scaling efficiency for 2Dcyl and 3DTGV, respectively. In 200-step autoregressive inference, the M = 50 model reduces the three-seed median terminal relative L2 velocity error by 59.2% for 2Dcyl and 12.1% for 3DTGV relative to the uncorrected coarse-grid P = 2 baseline, and retains wall-clock speedups of 5.38x and 2.49x, respectively, relative to the corresponding P = 7 configurations for equal simulated-time intervals. These results establish a verified and scalable path for multi-timestep solver-in-the-loop training that improves coarse-grid trajectory accuracy while retaining a speed advantage over the high-order reference

The classical theory of the wind-driven surface layer of a rotating ocean closes the momentum balance with a local flux-gradient law, in which the turbulent stress at a given depth is proportional to the shear at that depth. It predicts a surface current deflected forty-five degrees from the wind, which exceeds most direct measurements. This study asks what follows when locality is relaxed. Beginning from the exact integral relation between turbulent stress and mean shear, and requiring the memory kernel to carry no preferred vertical scale, we obtain a power-law kernel and therefore a stress law of fractional order. The resulting equation cannot be posed on the velocity, because the fractional derivative of a bounded profile vanishes at the surface, so the wind stress cannot be applied, while the alternative definition of the derivative leaves the surface current unbounded. Posed on the stress instead, the problem is solvable in closed form in Mittag-Leffler functions at every order between zero and one. The surface deflection then depends on the closure order alone and is smaller than forty-five degrees throughout, whereas the depth-integrated transport stays exactly normal to the wind, because that constraint follows from the momentum balance and not from the closure. The far field decays as a power law rather than exponentially, and its amplitude vanishes in the local limit, so that limit is singular. Under a suddenly applied stress the surface transient decays algebraically. Three independent algorithms agree closely, and no observational or model data are used.

The dynamics of interacting dipole skyrmion chains in a weak pinning landscape is studied through numerical solutions of the stochastic Thiele equation. Chains of different lengths with periodic boundary conditions are driven across quenched attractive pinning sites under different magnetic fields. A clear threshold spin current is identified as the critical depinning force. Three main results emerge: (i) for chains of 50 skyrmions or more, the depinning threshold is independent of chain length and remains close to one third of that of an isolated skyrmion. (ii) The transition rounding is only partly thermal: the width changes from w = 0.334 +/- 0.004 decades at T = 0 to 0.633 +/- 0.002 at 300 K, while the intrinsic Larkin correlation length remains about 2.6 bonds below kBT ~ V0/25. (iii) The width is nearly unchanged up to kBT ~ 0.14 V0, but increases almost fourfold by kBT ~ 0.23 V0, mainly through broadening of the creep regime. It is independent of chain length, ruling out finite-size rounding. Increasing the field from 30 to 40 mT changes the transition shape, widening its core by 26% while shortening the creep tails. For chains of about 200 skyrmions, depinning proceeds through a sequence of internal deformations - growth of the Larkin length and roughness, transient bond stretching, and subsequent recovery of mobility and Hall angle. These collective effects are relevant for skyrmion-based devices in which chains, rather than isolated textures, act as information carriers.

We extend the Obukhov-Corrsin theory of scalar turbulence to compressible flows with spatially variable, anisotropic diffusivity. Combining density and diffusivity into a single positive matrix field, a transport landscape, permits an exact scale-by-scale balance in which the sign-indefinite commutator between filtering and diffusion is eliminated rather than estimated. If the density-weighted third-order velocity and scalar increments scale as $\ell^\alpha$ and $\ell^\beta$, respectively, we prove that anomalous scalar dissipation is impossible when $\alpha+2\beta>1$. Remarkably, this threshold is independent of the anisotropy and spatial regularity of the diffusivity, provided it remains uniformly elliptic. Codimension-one shocks in both velocity and scalar have $\alpha=\beta=1/3$ and therefore lie exactly at the critical threshold. For statistically stationary turbulence we further obtain an exact density-weighted constant-flux relation, providing a compressible analogue of the relation underlying Yaglom's law. The results apply directly to passive-scalar transport in both gases and magnetized plasmas and provide testable diagnostics for simulations of compressible magnetohydrodynamic turbulence. Because the same operator governs anisotropic heat conduction, the results carry over to the electron temperature of a magnetized plasma, and they imply that gradient statistics in such flows must be contracted with the transport landscape rather than with the density.

Quantum information processing requires efficient storage and manipulation of photonic states. In this work, we harness pre-created macroscopic spin coherence as an additional degree of freedom to control a cavity-assisted quantum memory. We derive equations of motion for such a memory that predict efficient emission and absorption of two-color fields under a generalized nonlocal spectral impedance-matching condition, which reduces to the well-known condition in the single-mode case. These equations reveal a new operating mechanism of cavity-assisted quantum memory wherein the controlled spin coherence couples the emitted spectral modes. As a result, our memory deterministically synthesizes arbitrary frequency-bin photonic qubits using simple radio-frequency rotations, while the temporal reversibility of the protocol provides perfect transformation of the qubit states. Our findings establish a unified platform bridging quantum storage with elementary signal processing for quantum networks based on spectral encoding of photonic qubits.