Papers

The fluctuation theorem and detailed fluctuation theorem are formulated for classical systems whose governing equations can be written in Schrödinger-type equations and which possess either a unitary or an antiunitary time-reversal operator. The initial state vector is treated as a random variable drawn from a time-reversal-symmetric probability distribution, and a stochastic relative entropy defined from its probability density is used to formulate these theorems. The framework is applied to two collisionless plasma systems: the linear Vlasov-Poisson and linear gyrokinetic systems. For the linear Vlasov-Poisson system, the governing equations are recast into Schrödinger form, and Hamiltonian eigenvectors corresponding to Case-Van Kampen modes are derived to construct explicit solutions. The stochastic relative entropy is interpreted as entropy generation associated with Landau damping, in which electric-field energy is transferred from the lowest Hermite state to higher-order Hermite states acting as thermal reservoirs. For a specific class of initial distributions, a new analytical expression for the probability density function of the stochastic relative entropy is derived and validated numerically. For the linear gyrokinetic system in a uniform magnetic field, the governing equations are likewise transformed into Schrödinger form, and the corresponding time-reversal operators are identified. The state-vector space is constructed as a tensor product of species, perpendicular-velocity, and parallel-velocity spaces. The resulting state vectors decompose into two orthogonal components: one coupled to electromagnetic fluctuations and the other corresponding to ballistic modes. These results establish a nonequilibrium statistical-mechanical framework for collisionless plasma dynamics and provide useful examples for future quantum-computing applications to plasma simulations.

A quantum-enhanced approach for fast temperature diagnostics in ultrashort laser surface processing is introduced. The goal is to overcome limitations of existing methods, such as plasma emission, emissivity changes during ablation, and the need for time-consuming pump-probe measurements. The proposed method exploits polarization anisotropy in entangled photon pairs. The idler photon interacts with the laser-affected material surface, while its entangled counterpart is detected in a remote optical arm. Temperature-dependent changes in the complex refractive index modify the reflectance of p- and s-polarizations on the idler path, altering the coincidence-resolved polarization statistics of the signal photons. Using a Qiskit-based model incorporating experimental pump-probe reflectometry data, remote reconstruction of rapid thermal dynamics during femtosecond laser ablation is demonstrated. Although based on simulation, the model employs literature data to represent realistic material behavior. Due to the discrete nature of single-photon events, classical sliding-window analysis suffers from shot noise and temporal inertia. To overcome this limitation, a multilayer perceptron (MLP) regression network is applied to extract implicit anisotropy information from the photon bitstream. Compared with the classical approach, the neural-network method improves reconstruction robustness, reduces temperature noise, enhances the signal-to-noise ratio (SNR), and enables nanosecond-scale tracking of thermal dynamics. The results indicate that entangled-photon polarization anisotropy combined with machine-learning analysis is a promising approach for remote, background-rejected, high-speed temperature diagnostics in laser-matter interaction studies.

Photon anti-bunching is the direct evidence for the existence of photons without having a classical counterpart. Unlike bunching of photons, which can have a semi-classical description, the effect of photon anti-bunching can only be understood with quantized electromagnetic fields. However, for the process of high harmonic generation (HHG), where many photons of the driving field are upconverted to a single photon of higher energy, there is yet no clear evidence for the presence of individual photon emission. The key result of this work is the prediction of photon anti-bunching in the process of HHG, marking it the first theoretical discovery of non-classicality in the temporal correlations of HHG photons. While other non-classical signatures in HHG, such as sub-Poissonian statistics or squeezing, have been discussed for an ensemble of photons, the anti-bunching signature reported here is a signature of a single photon. This is achieved by using the recently developed Heisenberg picture approach for quantum optical HHG, revealing clear anti-bunching signatures in the intensity correlation function across the entire harmonic spectrum.

This study investigates Thermal-Large Eddy Simulations (T-LES) of anisothermal and turbulent channel flows under physical conditions representative of solar receivers. Solving the low-Mach number Navier-Stokes equations, T-LES results are evaluated a posteriori against Direct Numerical Simulation (DNS) data. We assess 12 subgrid-scale models. All models are based on the Anisotropic Minimum Dissipation (AMD) model. After computing a global error rate to evaluate all models, we select four for a detailed analysis regarding the effects of mesh resolution, numerical schemes, and model formulations. Results demonstrate that a two-layered mixed model combining the AMD/AMD-scalar with the Gradient model yields the best agreement with DNS.

Collective stress generation in cellular monolayers is a key phenomenological process governing coordinated migration and emergent multicellular dynamics. We employ a generic active nematics model to investigate stress generation and its associated properties. By analyzing the maximal principal stress and its correlation with the nematic director across different activity strengths, we find that the principal stress aligns perpendicular (parallel) to the nematic director for extensile (contractile) activity. In the turbulent regime, we identify a rotation-invariant scalar measure of the in-plane deviatoric stress whose zero-level contour coincides with the locations of all $\pm 1/2$ topological defects (both nematic and principal stress defects) are localized. This feature is robust and remains unchanged with variations in both the magnitude and nature (extensile or contractile) of activity. Our findings thus open up a new route to probe the spatial alignment from the mechanical and rheological properties of confluent cell layers, where stress measurements are more accessible than detailed cell shape or size characterisation.

Magneto-active polymers (MAPs) are field-responsive soft composites whose mechanical behaviour can be actively modified by external magnetic fields. Their ability to exhibit field-induced stiffening, magnetostriction, anisotropy and rate-dependent response makes them attractive for sensors, actuators, adaptive structures, vibration-control devices and soft robotic systems. This article presents a structured survey of constitutive modelling approaches for MAPs at finite strains. The review traces the development from early semi-empirical descriptions to thermodynamically consistent nonlinear continuum theories, with particular attention to isotropic and anisotropic magnetoelastic constitutive models, invariant-based and spectral representations, variational and polyconvex frameworks, microstructurally motivated models, dispersed-chain descriptions and magneto-viscoelastic theories. The principal constitutive variables, energy functions, coupling mechanisms and physical assumptions underlying the main models are discussed. The survey shows that constitutive modelling of MAPs has developed into a broad family of nonlinear, anisotropic and dissipative frameworks capable of describing versatile behaviours observed experimentally. Important challenges remain in resolving thermo-magneto-mechanical coupling and microstructure-sensitive effects, identification of parameters, validation of models as well as robust and efficient implementation on computers.

Recent studies on Brownian gyration (BG) have focused primarily on spherically symmetric particles under overdamped conditions. To explore BG in the underdamped regime with a spherically asymmetric particle, we investigate the inertial dynamics of a microscopic ellipsoid in a dissipative medium. The particle is confined in a spherically asymmetric trap and simultaneously coupled to two distinct thermal reservoirs. This configuration drives the system into a nonequilibrium steady state (NESS) characterised by BG, which is quantified by the mean and fluctuation of the particle's specific angular momentum. Using inertial Langevin dynamics, we systematically analyze how this microscopic gyration depends not merely on the trap asymmetry and temperature difference, but also on the particle's intrinsic physical properties like shape and axial orientation, besides inertia. Our study uncovers fundamental differences between the gyration of spherical and non-spherical particles in overdamped as well as underdamped conditions, at microscopic scales. These findings provide key insights for optimizing Brownian gyration across a broader landscape of experimentally tuneable parameters.

Cryogenic complementary metal-oxide-semiconductor (cryo-CMOS) technology is essential for quantum computing interfaces, which require precise modeling of dynamic device behavior. The output impedance of MOS field-effect transistors (MOSFETs) is frequency dependent, which has been conventionally attributed to extrinsic parasitics. Here, we report an intrinsic frequency dispersion in the channel impedance of cryogenic MOSFETs that persists deep into the strong-inversion region. Through a Cole-Cole analysis, we characterize this dispersion as a depressed semicircle in the impedance plane and attribute its behavior to variable-range hopping through band-tail localized states. Unlike conventional models where band-tail states are confined to the oxide interface, we demonstrate that in MOSFETs with high channel doping the band-tail states are induced by ionized impurities and distributed throughout the depletion region. Our paradigm accounts for frequency dispersion under strong inversion. This work demonstrates that ionized-impurities-induced hopping governs the dynamic response of cryo-MOSFETs channel impedance even when drift conduction dominates, offering critical insights for accurate small-signal modeling and high-frequency cryo-CMOS circuit design.

New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions. Because such policies generate spillovers across modes and locations, credible control groups are difficult to construct. We address this challenge using time series foundation models to generate probabilistic counterfactual demand forecasts with calibrated uncertainty. Applying this framework to bus, subway, and aggregate trip volume data, we find that post-policy bus and subway ridership increased significantly relative to expected no-policy demand, while overall travel demand decreased modestly. The effects are spatially heterogeneous: while reductions in overall travel demand are concentrated within the Congestion Relief Zone, transit gains extend beyond Manhattan's core. Socio-demographic analyses further reveal uneven adaptation across neighborhoods, highlighting spatial equity implications. Our framework provides a scalable approach for the uncertainty-aware evaluation of system-wide urban interventions when clean control groups are unavailable.

The rotational dynamics induced by the non-frontal binary collisions of quantum droplets composed of ultracold alkali atoms are analyzed. A theoretical study is presented within the extended Gross-Pitaevskii equation framework, using experimentally feasible conditions. Numerical experiments elucidate a rich landscape of possible topological excitations in the system that are robust towards measurements. The collision of heteronuclear quantum droplets composed of $^{41}$K and $^{87}$Rb atoms in the incompressible regime gives rise to dynamical instabilities that spontaneously generate topological defects: vortex rings, dislocation lines, and vortices in one species. Their presence depends on the Weber number and the impact parameter. An experimental proposal for vortex detection in both real and Fourier space using interaction ramps is described.

Submesoscale fronts, occurring at intermediate scales between mesoscale eddies and boundary layer turbulence, play a crucial role in driving vertical transport from the ocean surface into the interior. Their dynamics involve complex interactions between submesoscale currents and turbulence. However, the mechanisms by which these multiscale processes combine to transport tracers such as pollutants or nutrients remain less well understood. This study uses large-eddy simulation to investigate passive tracer transport associated with submesoscale fronts. Intense turbulence develops during frontogenesis, leading to strong diapycnal tracer transport into the ocean interior. While part of this transport arises from the direct turbulent flux, represented by the covariance between turbulent velocity and tracer concentration fluctuations, a substantial portion is due to an advective diapycnal flux driven by the mean diapycnal velocity. The mean diapycnal velocity results from the evolving secondary circulation in the presence of turbulent density mixing. These findings reveal an underexplored diapycnal transport pathway in submesoscale frontal zones, with implications for improved representation of vertical exchange in ocean models.

Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows. This work introduces Operator Boosting, a stagewise residual-learning framework for constructing compact neural-operator surrogates directly, rather than training a large model and compressing it afterward. Starting from the empirical mean predictor in normalized output coordinates, the method trains a sequence of tiny same-family neural operators on residual fields and incorporates each correction through validation-selected shrinkage. We instantiate the framework with Fourier neural operators (FNOs), DeepONets, and convolutional neural operators (CNOs), and compare boosted tiny stacks against full-size monolithic baselines across one-, two-, and three-dimensional PDE benchmarks from PDEBench, APEBench, and The Well. Across 30 dataset-architecture pairs, 21 show positive mean accuracy gains and 17 have positive confidence intervals, while all boosted stacks reduce trainable parameter count by approximately 72-95%. Best-model comparisons show empirical Pareto improvements on 7 of 10 completed PDE benchmarks, including two-dimensional Navier-Stokes, shallow-water dynamics, Darcy flow, one-dimensional transport and reaction systems, and three-dimensional compressible Navier-Stokes. These results show that Operator Boosting often improves the empirical accuracy-parameter Pareto frontier of neural PDE surrogates, while also exposing PDE- and architecture-dependent regimes where residual boosting fails to offset compression.

This paper presents a non-dissipative, high-order, moving overset method for curvilinear grids to simulate unsteady compressible flows in complex geometries with moving components. Centered finite-difference schemes that are up to sixth-order accurate in the interior are used with a weak moving overset interface treatment. The novel aspects of the proposed approach compared to conventional overset methods are: (i) instead of overwriting all conservative or primitive variables at the interface (or fringe) points with the interpolated values, a characteristic decomposition is performed and only the incoming characteristic variables are imposed for inviscid flows, consistent with the hyperbolic character of the Euler equations; for viscous flows, the viscous fluxes are imposed in addition to the incoming characteristics variables, (ii) instead of using multiple layers of fringe points at the interface, the proposed approach ensures high-order accuracy and stability with a single layer, thus minimizing the parallel communication costs at each timestep, and (iii) the proposed approach ensures long time stability with non-dissipative schemes without introducing artificial dissipation explicitly (using numerical filters) or implicitly (using upwind schemes). The stability is demonstrated by an eigenvalue analysis of the time-dependent (semi-discrete) system matrix for moving grids, proving the eigenvalue spectra remains confined to the left half of the complex plane with grid motion. The proposed approach is validated over a range of canonical and practical unsteady flow problems involving moving grids: 1-D scalar advection, 2-D isentropic vortex convection, flow past rotating 2-D circular cylinder, pitching 2-D and 3-D airfoil/wing flow, and flow past 2-D and 3-D oscillating circular cylinder, demonstrating high-order accuracy and long time stability for inviscid/viscous flows.

Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across a vast chemical space that is difficult to explore efficiently through conventional screening alone. Although machine learning-based high-throughput screening has accelerated catalyst discovery, its efficiency inevitably declines as the search space grows, motivating the development of generative models that can directly construct catalysts with target properties. Here, we present a conditional catalyst generative model based on the Generative Pretrained Transformer architecture with a numerical embedding layer that enables the generation of catalyst structures conditioned on both categorical and continuous properties within a single autoregressive framework. The model was pretrained on 133 million catalyst structures and subsequently fine-tuned on approximately 460,000 optimized structures with associated categorical properties and binding energies for conditional generation. The resulting model achieved 98% structural validity, 95% optimization validity, and high categorical condition fidelity, with a 93 % joint match rate for adsorbate type and composition. For binding energy conditioning, the match rate of approximately 20% represents a four-fold improvement over the baseline training distribution, and the generated distributions shift systematically toward the target values, enabling a 1.5 to 4-fold improvement in screening efficiency for reaction-targeted catalyst discovery without additional fine-tuning. These results show that large-scale autoregressive pre-training, combined with explicit property conditioning, provides a practical route toward controllable catalyst generation and accelerated catalysts discovery.

We demonstrate broadband phase-sensitive amplification (PSA) measurement of squeezed light generated by a waveguide optical parametric amplifier (OPA) with external dispersion compensation. In broadband systems, group velocity dispersion (GVD) induces a frequency-dependent rotation of the squeezing axis, which limits the observable bandwidth in PSA measurements. To overcome this limitation, we introduce external dispersion compensation between two OPAs and suppress the quadrature rotation over a wide frequency range. As a result, we observe a maximum squeezing of 5.9 dB near the carrier frequency and more than 5 dB of squeezing up to a frequency offset of 4.5 THz from the carrier. Furthermore, squeezing below the shot-noise level is confirmed up to a frequency offset of 6 THz from the carrier, corresponding to the accessible phase-matching bandwidth of the waveguide OPA. Our results establish a practical method for broadband characterization of squeezed light and provide a key step toward ultrafast continuous-variable quantum information processing.

Among the most intriguing properties of honeycomb lattices is the presence of Dirac points that typically emerge in pairs, which can be destroyed by physical effects breaking certain symmetries of the system and leading to nontrivial band topology. We propose a nonlinear microcavity system supporting condensate of exciton-polaritons, where simultaneous breakup of inversion and time-reversal symmetries results in unusual spectrum with unpaired Dirac cones, profoundly affecting the properties of unidirectional edge states. Realized as an array of microcavity pillars, the inversion symmetry is broken by fission of pillar belonging to one of sublattices of honeycomb array into three pillars, while time-reversal symmetry is broken due to interplay of Zeeman splitting in the external magnetic field and spin-orbit coupling. Despite the absence of complete spectral gap, unidirectional edge states may still emerge that can circumvent array corners. Resonant optical pumping leads to reach bistability effects and allow selective excitation of the edge states. We obtain first example of stable localized dissipative edge soliton that circulates along the periphery of insulator over indefinitely long times without radiation. Our results suggest a new platform for nonlinear topological photonics and reveal nontrivial interplay between unpaired Dirac cones and nonlinear effects.

Dispersive coupling between vibrational modes with different frequencies is a major nonlinear dynamical effect. We show that in $p$-doped semiconductors such coupling is strongly enhanced. Moreover, the coupling parameters increase with the order of the nonlinearity. The doping-induced dispersive coupling becomes much stronger than the intrinsic one already for moderately strong doping. Its dependence on the hole density is nonmonotonic, and the temperature dependence becomes nonmonotonic for higher densities. Relevant mesoscopic frequency fluctuations are briefly discussed. The results are applied to Si resonators, where doping is used to compensate the temperature dependence of a clock mode, whereas another low-frequency mode is used as a thermometer to enable temperature stabilization.

Boron pebble aggregate was tested for the first time as a high-heat-flux granular plasma-facing material in a tokamak divertor. Exposures of up to $q_{\parallel} = \SI{80}{\MW\per\m\squared}$ incident heat flux were conducted in the DIII-D tokamak. Single protruding rods of pebble aggregate composed of sintered amorphous boron pebbles bound with carbon binder were mounted in the Divertor Material Evaluation System (DiMES) sample holders and exposed to L-mode lower single null (LSN) plasmas. Under these heat loads, significant boron dust emission from the boron spheres was observed, and this dust dominates the divertor boron ionization source. Only about half of the released boron was recovered locally as mm-sized particles; with the rest presumably lost mainly as dust into the plasma and vacuum chamber. Preliminary estimates suggest that the rate of surface recession of $\sim$1 cm/s in the pebble conglomerate within the plasma divertor is consistent with the recession rates observed in laser bench tests subjected to normal-incidence heat loads. Although core performance was not adversely affected by the high boron dust emission, future work will need to improve the boron pebble aggregate design to reduce boron dust emission at high heat loads.

During the last decade, the notion of solitons has been mentioned in neuroscience related to the propagation of action potentials (AP). In this paper, based on many studies in mathematical physics and neuroscience, the clear differences between the APs and solitons are summarised. It is stressed that the physics of the emergence of APs and solitons is fundamentally different. The numerical examples collected from earlier studies demonstrate the differences in the process of generation and interaction of corresponding waves explicitly. It is also noted that although the longitudinal mechanical waves in biomembranes can be described by the Boussinesq-type equation, the time-scale of emerging solitons from an arbitrary initial excitation exceeds the time-scale of the usual AP existence considerably.

In this paper we explore and derive an uncertainty principle for an ultraviolet complete nonlocal quantum field theory where under our hypothesises of an induced equal time detector response kernel, we then prove that the observed localization width obeys an exact variance addition law. Then when we combine this with the ordinary Heisenberg inequality and we obtain a nonlocal uncertainty relation. The bound reduces to the usual local relation in the infrared or local limit when $E_M \to \infty$, while in the ultraviolet it implies a minimal localization length of order $L_M$. We go on to explain what this means for locality, microcausality, the interpretation of spacetime points, and the ultraviolet structure of quantum field theory. In this formulation we note and prove that spacetime will remain a Lorentz covariant continuum at the level of the manifold description but pointlike localization ceases to be a physically realizable observable notion below the nonlocality scale.

When a wine glass is dragged on a table along a circular path, a spontaneous rotation about its vertical axis can develop even if the applied hand force does not directly introduce a yaw torque. This document provides a structured formal derivation of the governing equations that are responsible for this behavior. The analysis shows that the mechanism responsible for this effect is the redistribution of pressure onto the table when applying the force with your hand. This causes an uneven frictional force distribution which exerts a torque on the glass, causing it to spin.

Modern microscopy systems integrate heterogeneous hardware devices that require dedicated software for coordination. However, high-performance C++ implementations of microscopy control software remain scarce. We present ScopeOne, a C++ and Qt-based microscopy control software built on the MicroManager hardware abstraction layer. Through process isolation and shared memory, ScopeOne achieves simultaneous multi-camera preview with real-time image processing, while maintaining full compatibility with the {\mu}Manager device ecosystem.

A predictive physics-based model of human respiratory, phonatory, and articulatory subsystems is developed to simulate voice production. Representing lungs, compressible airways, and vocal folds as spring-damper-mass controlled piston-cylinder systems, our mathematical model robustly captures the intricate dynamics of airways during phonation. The nonlinear viscoelastic properties of lung tissues and compressible airways were investigated, yielding a responsive and expressive baseline respiratory model with the capability to further extend into a patient-specific model for both respiration and phonation. The resulting framework was subsequently integrated with a mechanical representation of the vocal tract, governed by the glottal area waveform (GAW) capturing the motion of vocal folds during sustained phonation. The GAW is extracted from laryngeal high-speed videoendoscopy data of a normophonic participant using deep learning. Our novel paradigm transcends beyond modeling the respiratory system, enabling AI-driven modeling of vocalization, including vocal fold dynamics, interactions with flow aerodynamics, and flow resistances, induced by the oscillatory behavior of vocal folds. Our investigation leads to the first-ever simulation of respiratory dynamics for vocalization, directly advancing the prediction of subglottal pressure distributions, impossible to measure directly and noninvasively in humans, dynamic resistances, and energy transfer mechanisms during phonation in voice mechanics.

We report on a vision AI agent pipeline for non-contact material strain and property extraction from video data, demonstrated on video taken during hydrostatic testing of four OFHC copper cylinders conducted as part of the LEGEND-1000 hardware validation campaign. Traditional strain gauge measurements proved unreliable, motivating a fully-automated agentic alternative. The agent was built on the LangChain framework with Claude Haiku 4.5 as its central reasoning engine, integrating a specialized suite of computer vision tools: FFmpeg for video preprocessing and rotation correction via Hough Line Transform, the Segment Anything Model 2 (SAM2) for spatiotemporal segmentation with automated memory-informed dynamic chunking, and a hybrid EasyOCR and LLM-based timestamp validation pipeline. Three specialized sub-agents were developed to process the video data and obtain cylinder diameters and timestamps while autonomously handling obstacles such as corrupted frames and memory limits. From the diameter profiles synchronized to pressure data, hoop stress–strain curves were reconstructed and yield strengths were calculated using the 0.2% offset, 0.5% EUL, and Johnson-Cook methods across two independent tests. Cross-validation against a non-agentic pipeline confirmed agreement for the diameter extraction at the $\pm$5 pixel level. The material properties and testing results were further compared to Ansys mechanical simulations performed as part of the LEGEND-1000 reentrant tube design campaign. This work showcases the power of agentic pipelines to extract materials data from video alone.

Vortex stretching is a fundamental aspect of Navier-Stokes turbulence and is commonly understood in analogy to the stretching of infinitesimal material lines. However, the parallel alignment of material lines and vorticity cannot be maintained due to the role of viscosity in the directional realignment of vorticity. In Navier-Stokes turbulence, the result is relatively modest quantitative differences in the alignment and stretching rates of vorticity compared to material lines. In this study, the qualitative effect of viscous tilting of vorticity is demonstrated directly by surgically removing it from direct numerical simulations of isotropic turbulence. The result is a drastic change to the fundamental structure of the flow, including a substantial deviation from the -5/3 inertial range scaling of the energy spectrum brought about by the infiltration and prevalence of viscous effects beyond the smallest scales. These observations demonstrate that the viscous tilting of vorticity is an essential characteristic of fluid turbulence. By extension, the same may be said of the difference in orientation and stretching rates for vorticity and infinitesimal material lines.

The mirror symmetries of a periodic unit cell are exploited to decompose the standing-wave eigenproblem at the high-symmetry vertices of the Brillouin zone into four independent sub-problems on a quarter-cell, each governed by Neumann (sound-hard) or Dirichlet (sound-soft) boundary conditions. Sorting and pairing the resulting eigenfrequencies by index along each segment of the irreducible Brillouin zone boundary yields an explicit formula for the stop-band intervals without computing the full dispersion diagram. The decomposition is exact, following directly from the representation theory of the little group at each high-symmetry point. It applies to any unit cell whose material distribution is invariant under the mirrors normal to the cell faces. The method is validated on two configurations: a phononic crystal of lead cylinders in an epoxy matrix, analyzed using the plane-wave expansion, and a lattice of coupled C-shaped Helmholtz resonators, analyzed using finite-element analysis. For both systems, the reconstructed stop-band boundaries agree with the full Floquet dispersion calculation to within 1% for the lowest bands, requiring eigenvalue solutions at only three discrete wavevectors. Avoided crossings within a Brillouin zone segment can cause bands to exhibit non-monotone behavior, rendering the pairing rule approximate; the spectral conditions for this are identified. Flat bands common to both boundary-condition types are identified as bound states in the continuum.

The coupled-mode equations (CMEs) have proven very successful in describing parametric processes in nonlinear optics. More recently, the same formulation has been used to model microwave superconducting parametric amplifiers and frequency multipliers. However, when applied to the microwave regime, not all assumptions remain valid and losses play a more dramatic role. Here, we revisit the CMEs applied to traveling-wave superconducting amplifiers to include losses and provide a formulation that enables their systematic derivation for any combination of traveling waves. As examples, we discuss the impact of unwanted harmonics and intermodulation products on parametric amplification, as well as harmonic generation. We verify that, if not properly accounted for, device performance can deviate considerably from the ideal case. Furthermore, using a superconducting CPW-based artificial transmission line and combining an independent experimental determination of its nonlinear parameter $I'_*$ with simulations of its linear properties, we obtain a parameter-free validation of this formulation. The nonlinear parameter was determined to be $I'_* \approx 27$ mA which, surprisingly, scales with the theoretical depairing current and not with the much smaller critical current of the device. For the validation, we measured multiple-harmonic generation and found excellent agreement between theory and experiment. The fact that $I'_* \gg I_C$ has direct implications for device design.

Physics sets the information contained in the signals that cells sense. But cells are active, not passive, sensors. They shape and reshape both the environment and themselves. These active behaviors allow cells to amplify, redistribute, share, and prioritize sensory information, often surpassing or obviating passive physical limits. Here, we review recent results on active sensing. After describing classic and more recent limits to sensory precision, we focus on four ways that cells implement active sensing: coordinating with other cells, reshaping their environment, dynamically updating themselves, and discriminating signals. We conclude with potential future directions.

We investigate the nonlinear dynamics of a single-component plasma confined in a time-dependent harmonic trap regarding aspects of symmetry and invariant functions. The system is described as a fluid in an isentropic adiabatic regime by a system of partial differential equations. A convenient change of variables, with a Gaussian ansatz for the number density distribution, allows a consistent mathematical description in terms of ordinary differential equations, from which we follow up with an analysis concerning the corresponding differential operators algebraic structure and Noether symmetries in specific physical regimes. For each studied case, proper invariants are identified. The obtained conserved quantities capture an interplay between the internal plasma dynamics and the time modulation of the trap, resulting in a sharp restriction for the system evolution.

We use temporal unitary transforms to generate 16-QAM up to 220 GBd using only 50-GHz electrical bandwidth. The technique is theoretically lossless and can generate arbitrary optical waveforms beyond the bandwidth of the constituent modulators.