Papers

Transmission through temporary groups does not necessarily preserve complete information about the group-size distribution in aggregate epidemic data. We show that, in finite-population SIS and SIR models, the group-size distribution enters the dynamics only through a finite set of moments selected by the order of the nonlinear transmission kernel. Consequently, markedly different distributions can generate identical stochastic dynamics when their relevant moments coincide. This equivalence extends to transient evolution, fluctuations, extinction-time statistics, and final outbreak sizes. In the deterministic limit, the first moment sets the invasion threshold, whereas the second controls the nature of the transition and the emergence of bistability and hysteresis. The two distributions become distinguishable only when a higher-order transmission mechanism activates their first unmatched moment; even a weak additional channel can shift the phase boundary and place the systems in different dynamical regimes. Solutions of the master equation and stochastic simulations support these analytical predictions. These results establish an intrinsic limit on epidemic inference: a single aggregate dynamical protocol can identify only an equivalence class of group-size distributions, rather than uniquely reconstructing the full distribution.

CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. CheMLFlow provides modular workflow components, ready-to-run reference pipelines, standardized artifacts, and evaluation outputs that reduce orchestration overhead and support benchmarking across methods and datasets. The platform is designed to be extensible, reproducible, and automation friendly, with pluggable representations and models, deterministic splits, explicit run artifacts, batch execution, and report generation. As scientific software increasingly moves toward agent assisted experimentation, CheMLFlow's configuration driven workflows and structured outputs also provide a practical interface for coding agents to help users construct experiments, inspect results, and summarize findings under human supervision. This article describes the system architecture, core workflows, and benchmarks that reach literature performance for quantum mechanical, physicochemical and bioactivity property prediction, and use cases involving time series datasets demonstrating applications beyond molecular chemistry datasets.

Quantum computing is a deep technology whose progress cannot be driven effectively from one direction alone. While the field has developed a growing catalogue of mathematically grounded algorithmic speedups, industrial impact will depend just as much on starting from real industrial decision contexts and working downward to what must be computed, validated and integrated. In this Perspective, I argue that sustained progress requires treating these two directions: bottom-up development from physics, hardware and algorithms, and top-down development from industrial needs and constraints. Equally primary and continuously coupled. This dual-viewpoint is not a matter of balance for its own sake. Quantum computers cannot solve arbitrary problems, so engagement with industry must remain anchored in algorithmic tractability. Yet tractable computations are rarely valuable unless they connect to decision points in established workflows such as candidate selection in drug discovery or the design of a new aircraft shape with improved aerodynamics. I analyse how historical narratives and structural separations of expertise slowed the formation of this coupling and outline what it takes to build it: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation. Framed this way, quantum computing's opportunity is clearest where deep physical modelling meets high-value decisions. Provided the field co-designs both sides from the outset.

Spin-polarized electron beams are essential tools for probing fundamental symmetries and for the search beyond the Standard Model. While plasma-based accelerators are a promising pathway towards higher-energy frontiers, they have so far failed to deliver a competitive polarized source: existing proposals are challenging to realize and achievable polarizations remain far below conventional sources. Here, we introduce a photocathode-like scheme, applied to a gas of pre-polarized hydrogen and halogen atoms. A VUV and a visible laser pulse excite the halogen atoms to create a two-component ionization medium, consisting of low-threshold excited halogen atoms and high-threshold polarized hydrogen. Particle-in-cell simulations show witness beams with tens of pC charge retaining up to 97% of the initial polarization, rivaling state-of-the-art conventional sources.

Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounced environmental sensitivity. Meanwhile, driven by advances in photonic integration, chip-scale approaches have sought to realize miniaturized pulse sources. However, simultaneously achieving sub-100-fs duration, ideal pulse shape, and a broadband flat-topped spectrum remains a significant challenge. Here, we address these challenges by combining two key photonic chip technologies: TFLN EO modulators for picosecond seed pulse generation, and highly nonlinear optical loop mirrors (NOLM) based on AlGaAsOI nanowaveguides for efficient temporal pulse cleaning and spectral broadening. In theoretical simulation and experiment, we show that for an input seed pulse centred at ~1550nm, a single-stage AlGaAs NOLM with a loop length of 1cm can produce flat-topped, nearly tenfold spectral broadening and over tenfold compression of pulse width, and more than 10dB suppression of pulse pedestals. Using initial EO comb pulses with ps-level durations at repetition rates of 10-20GHz, we demonstrate photonic-chip-enabled pulses with an unprecedented duration of 55fs and a flat-topped comb spectrum whose 10dB optical bandwidth exceeds 90nm. Our results highlight the remarkable potential of photonic chip technologies to realize high-repetition-rate, miniaturized sub-100-fs optical pulse generators with the prospect of superior stability and operability. The demonstrated photonic-chip-based sub-100-fs optical frequency comb sources may establish a new paradigm for both scientific research and practical applications.

We investigate dynamical, time-evolving dark-energy models exhibiting phantom behaviour within Einstein gravity by analysing the evolution of matter perturbations. In particular, we consider five dark-energy parameterisations that predict a slowing down of the current cosmic acceleration. We examine the evolution of the growth index and its first derivative, as well as the combination parameter and the statefinder diagnostics, and compare their behaviour with that of the concordance $\Lambda$CDM model. For the parameter choices considered, the present-day values of the growth index and its first derivative are found to be compatible with ranges reported in previous phenomenological studies. The combination parameter is also found to differ from its $\Lambda$CDM value, while the statefinder parameters provide additional means of distinguishing the considered dynamical dark-energy scenarios from the concordance model. Our results illustrate the phenomenological signatures associated with these models and identify observables that may be useful for future observational tests. We stress, however, that the present analysis does not constitute a statistical model comparison or establish an observational preference for any of the considered dynamical dark-energy models over $\Lambda$CDM.

Transport in fluids is generally reduced to continuum laws parametrized by bulk coefficients and effective interfacial parameters, such as viscosities, diffusivities, slip lengths, and interfacial resistances. This description becomes incomplete at the nanoscale, where spatial heterogeneity, molecular structure, and finite relaxation times are inseparable from the transport process. Here we formulate coupled transport in nanoconfined fluids as a space–time-resolved Onsager response matrix and extract it from equilibrium molecular dynamics simulations. Applied to a confined charged fluid, the framework resolves the nonlocal and transient pathways coupling particle, solute, heat, and charge transport. Momentum transport appears as a long-lived, nonlocal hydrodynamic mode, whereas charge transport relaxes rapidly through localized ionic friction. Off-diagonal responses reveal distinct projected dynamics, providing a microscopic basis for nonlocal, history-dependent transport laws.

Extracting total correlations from a quantum system usually requires reconstructing its state, whereas many experiments access only a few measurement settings. A possible shortcut is to add the mutual informations obtained from complementary measurements; in dimensions above two, however, this procedure can count the same classical correlation twice. We establish that qubits are protected from such overcounting. For every two-qubit state, the correlations observed in two complementary local bases are bounded by the premeasurement quantum mutual information. The proof traces this protection to binary-entropy curvature on the Bloch ball and combines a qubit information-exclusion tradeoff with data processing under local dephasing. Consequently, two correlation tables give a tomography-free lower bound on total correlation. A score above one bit also certifies a quantitative one-way entanglement-distillation rate; when applied to the Choi state of a qubit channel, the same data lower bound its quantum capacity. The theorem therefore identifies both an operational use of complementarity and the trusted two-dimensional setting in which its correlation accounting is valid.

Granular light bridges in sunspots are a manifestation of magnetoconvection in an approximately vertical magnetic field that is weaker than the field of the umbra. We study the relations between observed properties of granules and magnetic field in five strong granular light bridges located in a complex sunspot. Thermal parameters, magnetic field vector, and line-of-sight velocity were obtained from an inversion of the GREGOR spectropolarimetric scan in the lines Si I 1082.71 nm and Ca I 1083.90 nm. Sizes and lifetimes of granules were measured in a simultaneous sequence of images in the TiO band. The light bridges under study are comprised of granules spanning 0.2"-1.4" with approximately 6-minute lifetimes. The largest granular structures (> 1") are spatially correlated with 1 km/s upflows and weak, inclined magnetic fields (300 G) near the equipartition threshold. Statistical comparisons demonstrate an inverse relationship between magnetic field strength and continuum intensities, line-of-sight velocity dispersion, and effective granular diameters. These relationships vary among individual light bridges, depending on the large-scale magnetic field topology. Large expanding granules similar to the photospheric ones appear in light bridges only when the local magnetic field strength approaches the equipartition value.

We study the prediction of plastic activity in the confined channel flow of two-dimensional amor- phous soft particles around a circular obstacle. Using datasets generated with a particle-based bubble model, we formulate the prediction problem within two supervised-learning frameworks: re- gression of the non-affine displacement and binary classification of neighbor change events. A key technical challenge is that the obstacle and the confining walls explicitly break translational and rotational symmetries. We address this issue by introducing additional structural descriptors that encode the positions of particles relative to these boundaries. Starting from simple linear models, we systematically increase the complexity of the learning framework by considering a logarithmic transformation of the target variable, the incorporation of particle-size information, the addition of symmetry-breaking obstacle and wall descriptors, the coarse-graining of local structural descrip- tors, and nonlinear neural-network models. We find that the obstacle and wall descriptors provide the largest improvement in predictive performance. Nevertheless, the models considered here cap- ture mainly the overall localization of plastic activity near the obstacle and do not fully reproduce its detailed heterogeneous pattern in individual configurations. A perturbation analysis indicates that this heterogeneity is robustly encoded in the initial structure, suggesting that further progress requires more expressive structural representations and machine-learning architectures.

We study local minimizers of a sextic-potential Landau–de Gennes energy for nematic liquid crystals in the small-elastic-constant limit. Under the uniform energy and $L^\infty$ bounds, these minimizers converge to a locally energy-minimizing harmonic map $\mathbf{Q}_0$ into a biaxial vacuum manifold. The main result of this paper is that such a limiting map $\mathbf{Q}_0$ has no interior point singularities. The proof relies on a geometric identification of the lifted Frobenius metric on the universal cover $\mathbb{S}^3$ with a rescaled Berger metric. For a hypothetical tangent cone at a point singularity, its link is a nonconstant harmonic two-sphere into the Berger sphere. We construct a smooth variation field adapted to the Hopf direction and show that it induces a quantitative instability estimate, in contradiction with the shifted stability inequality inherited from local minimality. This replaces the usual round-sphere test fields by a target-specific construction and rules out interior point defects in the biaxial setting. The result is sharp in view of the well-known existence of point defects in the uniaxial theory.

Learned spectral models fail at symmetry forced degenerate sectors for two distinct reasons. Where symmetry forces levels to coincide exactly, the per level observable one would normally fit is not well defined, since every unit vector of that shared space is an eigenvector; and near a symmetry protected crossing, the eigenvector observable gradient carries a factor 1/(lambda_i - lambda_j) that is genuinely singular as the gap closes. The usual response is to regularize the divergence or threshold the gap, and both carry a real cost: a fixed gap cannot both protect a forced multiplet and keep two genuinely distinct levels apart. We show a different fix, on synthetic operator families with a known symmetry answer key. A gate reads the symmetry structure directly from the observed operators, as the linear commutant of the family, one singular value decomposition nullspace, following the simultaneous block diagonalization of Maehara and Murota. Block identity is read from the centre of that commutant rather than from eigenvalue clustering, which makes the forced versus accidental distinction structural rather than metric, and the objective switches between a projector trace through forced blocks and a per level target elsewhere. Under operator estimation noise the gate classifies correctly to epsilon of about 0.3, where energy clustering already fails by 0.02. Gated fitting reaches the truth at machine precision in both the symmetric and the symmetry breaking regime, in the latter converging to a truth that is a singularity of the ungated objective, and observable bias sits at the noise floor. Validation off the regular representation, where multiplicity and dimension separate, eliminated two defective estimators that all regular representation tests had passed. The demonstrated object is a gated estimator; a full parametric matrix model, with the matrices learned, is the next experiment.

We present a phase-space formulation of elastic full-waveform inversion (FWI) based on local-angle-domain (LAD) representations of seismic scattering. Instead of minimizing acquisition-domain waveform misfits, the method minimizes image-domain inconsistencies in a phase-space domain parameterized by position, propagation direction, scattering angle, azimuth, and time. The formulation is motivated by the observation that velocity and impedance perturbations occupy largely distinct regions of phase space. For anisotropic elastic media, we derive a generalized objective function together with the corresponding adjoint-state gradient and Hessian operators. Representing LAD images through directional and opening wavenumbers provides a unified description of propagation and scattering effects. The approach is independent of the forward-modeling engine and can be implemented with finite-difference, finite-element, spectral-element, ray-based, or beam-based methods. Under Born linearization, ray/beam formulations allow explicit Hessian computation. Owing to the near-orthogonality of directional and opening-angle sensitivities, the Hessian is more diagonally dominant than in conventional FWI, improving parameter separation, reducing velocity-reflectivity crosstalk, and enhancing inversion stability. By organizing seismic information in a physically meaningful phase-space representation, LAD-FWI supports adaptive, resolution-driven inversion and may reduce cycle skipping through attributes that vary more smoothly than waveform residuals. The framework naturally integrates directional focusing, reflector-consistency analysis, anisotropic parameter estimation, converted-wave imaging, and diffraction-based characterization, providing a unified workflow for elastic FWI, migration-velocity analysis, impedance inversion, and high-resolution subsurface imaging.

The gauge structure of the Weyl Gravitoelectromagnetic (GEM) formalism has been investigated, showing that although the theory admits additional propagating modes, only an effective spin-2 sector contributes to physical observables. Building on this result, the formalism is applied to Bhabha scattering mediated by the tensor field $A_{\mu\nu}$, and the Newtonian gravitational potential is derived in the zero-temperature limit. Finite-temperature effects are incorporated through the Thermo Field Dynamics (TFD) formalism. While the Newtonian interaction is recovered at low temperatures, it becomes progressively suppressed in the high-temperature regime, revealing a thermal screening mechanism. The physical origin of this behavior and its possible dependence on the underlying scattering process are briefly discussed.

Modified Newtonian Dynamics (MOND) generally resolves the need for dark matter in galaxy rotation curves introducing a single new constant of acceleration $a_0$. It is well known that increasing $a_0$ by a factor of a few can alleviate the residual mass discrepancies that MOND leaves in galaxy clusters. Within a parameter-free Machian interpretation of MOND motivated by $a_0\sim GM_u/R_u^2$ arising from the scalar sum of inverse-square distance gravitational mass contributions in the universe, we promote $a_0$ to a variable in spherically symmetric mass distributions, which is sensitive to mass external to the locally enclosed spherical region. Instead of a boost of $a_0$ in terms of gravitational potentials as in EMOND, we show that a boost in terms of this directionless inverse-square field roughly amounts to the boost needed to accommodate the mass discrepancies of MOND in galaxy clusters, while retaining MOND predictions in galaxies. We begin to generalize this approach by defining a new $a'_0=G\sum_{i}m_i/|\mathbf{r}-\mathbf{r_i}|^2-a_N$, where $a_N$ is the magnitude of the usual Newtonian acceleration.

Protostellar outflows are thought to be responsible for the low star formation efficiency of protostellar cores. However, whether outflows can disperse a significant fraction of the gas in the core depends on the outflow opening angle. It is established that the outflow opening angle increases during the early stages of the protostellar evolution, but the underlying mechanism is poorly understood. Observations of HOPS 198, a Class 0 protostar in the Orion A molecular cloud, provide insights into this question. HOPS 198 exhibits a strong east-west asymmetry in its outflow and its core. The opening angle of the eastern lobe ($\sim80^{\circ}$) is more than twice wider than that of the western lobe ($\sim30^{\circ}$), while the surface density of the west side of the core is $1.5-2.8$ times higher than the east side. Using an analytical model in which the molecular outflow morphology is shaped by interactions between the wide-angle protostellar wind ($\gtrsim 80^{\circ}$) and surrounding material in the core, we find that the difference in opening angle for the two lobes can be explained by the difference in core density on the two sides. This result supports the hypothesis that the evolution of the outflow opening angle is driven by the evolution in the density of the protostellar core.

All-two-dimensional magnetic tunnel junctions promise atomically sharp interfaces, yet the role of interface-induced states in their spin transport is not fully understood. Here, we theoretically investigate spin-dependent transport in van der Waals magnetic tunnel junctions of the structure Cr$_2$C/$MY_2$/Cr$_2$C ($M$ = Mo, W; $Y$ = S, Se) with barrier thicknesses of 3, 5, 7, and 9 layers. The broad features of the $\mathbf{k}_{\parallel}$-resolved conductances, namely suppression near the $\Gamma$ point and enhancement at six off-$\Gamma$ hot spots, are consistent with the decay of evanescent states in the barrier. However, trilayer WS$_2$, MoSe$_2$, and WSe$_2$ barriers exhibit conductances of the order of $e^2/h$ at $\mathbf{k}_{\parallel}$ points within the hot spots. We attribute these near-unity transmission channels to resonant coupling between the interfacial states at the two electrode–barrier interfaces, as evidenced by their weak but finite residual weight at the barrier center. For thicker barriers, this coupling weakens, which suppresses the residual weight, thereby reducing the tunnel magnetoresistance (TMR) ratio of the MoS$_2$ junction while enhancing those of the other junctions. To exploit the interfacial states for spin-selective tunneling, we further examine biaxial tensile strain applied to the trilayer junctions. At 4% strain, the TMR ratio increases from 176% to 540% for MoS$_2$ and from 98% to 496% for WS$_2$, whereas MoSe$_2$ and WSe$_2$ exhibit comparatively weaker enhancement. Our results establish interfacial-state engineering via strain and barrier thickness as effective routes for enhancing the TMR effect in all-two-dimensional magnetic tunnel junctions.

We introduce a variational approach to a finite-temperature continuous-spin perceptron trained on a Gaussian mixture. The model allows for a broad class of concave utilities and log-concave separable prior measures on the spins. By combining the interpolation method with log-concavity and concentration estimates, we derive lower and upper minimax variational bounds for the limiting quenched pressure. Remarkably, the two bounds differ only in the order of optimization of two variational parameters, while all remaining extrema are controlled by the concave–convex structure of the variational potential. Whenever the two optimizations commute, the two bounds match and identify the solution of the model. The same potential yields the fixed-point equations as stationarity conditions and provides a unified route to the computation of the ground-state energy, training loss, and generalization error.

Quantum superresolution reveals that the vanishing of separation sensitivity in conventional imaging below the Rayleigh limit does not necessarily indicate a fundamental loss of information in the optical field. However, the quantum limit for estimating the physical distance between two incoherent point sources in three-dimensional imaging systems and its dependence on the spatial structure of the point-spread function remains largely unknown. In this work, we derive the quantum-limited precision for estimating the full distance between two incoherent point sources with arbitrary intensity imbalance in a three-dimensional spatially invariant imaging system. We show that the distance information remains finite in the sub-Rayleigh regime and is governed by the second-order displacement-response tensor of the point-spread function. The eigensystem of this tensor determines the optimal relative orientation between the two sources, and reflection symmetries of the point-spread function can further provide a simplified means of identifying the optimal orientation. This geometric structure is coordinate invariant and provides a direct strategy for improving resolution by physically rotating an anisotropic imaging system to align its optimal principal response direction with the source displacement. For a general three-dimensional Gaussian point-spread function, the response tensor is proportional to the inverse spatial covariance, establishing a direct connection between quantum-limited distance precision and the geometry of Gaussian distribution.

We investigate the linear and nonlinear Hall response of a moiré watermill lattice, in which stacking and twisting generate a four-band manifold near the Fermi level with suppressed group velocities at discrete magic angles. Including an inversion symmetry breaking onsite mass breaks the interlayer symmetry, opening a gap in this manifold and driving the system into a non-trivial bulk topological phase. We map the resulting phase diagram as a function of the strength of the mass and twist angle $\theta$, revealing several sectors with high Chern numbers. We then introduce strain to break the residual $C_3$ symmetry of the lattice which activates a finite Berry curvature dipole and correspondingly, a nonlinear Hall response. The dipole reverses sign sharply across topological phase boundaries, producing butterfly like features when plotted against the relevant system parameters. Its magnitude substantially exceeds that reported for symmetry-broken transition metal dichalcogenides, consistent with the elevated Wilson-loop winding and enhanced quantum geometry associated with the lattice's pseudospin-$3/2$ character. We conclude by incorporating thermal effects on the Berry curvature dipole, asserting that it is an important tool for discerning topology at low temperatures.

Qualification of quantum states judges which of two sets of quantum states an unknown state lies in, where the two sets are labeled by two distinct parameter regions. We formulate this problem as a composite quantum hypothesis test and uncover universal scaling laws for the minimum error probability for $N$ copies. Taking polarization-direction qualification and purity qualification as examples, we show that the $N$-copy permutation symmetry and the geometric symmetries of the parameter regions identify the optimal measurements and the "worst pairwise states". The minimum error probability scales as $N^{-3/2}\exp(-N\xi)$ for disjoint regions and as $(NF)^{-1/2}$ for adjacent regions, where $\xi$ and $F$ are the quantum Chernoff divergence and quantum Fisher information associated with the "worst pairwise states", respectively. With the minimum error probability serving as an order parameter, the transition between the scaling behaviors becomes a second-order phase transition as $N\to\infty$. Our approach determines whether a quantum state belongs to a given set without full state tomography, thereby enabling qualification of large ensembles using finite samples.

We investigate the cross-correlation of the kinematic Sunyaev-Zeldovich (kSZ) effect with lensing of the cosmic microwave background (CMB) as a probe of the epoch of reionisation. During reionisation, bubbles of ionised electrons form around overdensities, generating temperature perturbations in the CMB via the kSZ effect, which correlate with the projected matter density field probed by CMB lensing. We demonstrate using AMBER simulations that this effect can be probed via cross-correlating the squared kSZ field with the CMB lensing potential, and that the signal is sensitive to the duration and midpoint of reionisation. We forecast that for a Simons Observatory-like experiment with 5$\mu$K-arcmin white noise, covering 40% of the sky, the signal could be marginally detected at $S/N$ = 2 to 3. Meanwhile, a futuristic experiment like CMB-HD could provide a measurement of $S/N \sim 50$, yielding informative constraints on reionisation scenarios. We investigate potential challenges in measuring the signal and explore mitigation for each: contamination from extragalactic foregrounds at low redshift, contamination of the kSZ-squared estimator by lensing, and other estimator biases.

Matter density ($\rho$) and radial pressure ($p$) are often used as the matter Lagrangian density ($\mathcal{L}_{m}$) because both are thermodynamically consistent and produce the same Einstein field equation (EFE) in general relativity (GR). New gravity models with explicit links between matter and geometry instead involve second-order derivatives of $\mathcal{L}_{m}$ relative to the metric tensor. So, picking either $p$ or $-\rho$ for $\mathcal{L}_{m}$ gives different effective EFEs. This confusion appears because one usually treats the four-velocity ($u_\mu$) and the metric tensor ($g_{\mu \nu}$) as independent. Here, we revisit the basics and offer a consistent framework by relaxing that assumption, thereby making the modified gravity theory independent of the choice of $\mathcal{L}_{m}$. Finally, we test this approach on neutron and quark stars (ultraviolet region) and on cosmological situations with radiation-dominated ($p=\rho/3$) equations of state (infrared region), showing how it clarifies the ambiguity in picking $\mathcal{L}_{m}$ for gravity models.

Prior-based Multi-Voltage Threshold (MVT) sampling reconstructs pulse parameters from sparse threshold-crossing times rather than full waveforms, making parameter recovery inherently a model-dependent inverse problem. However, prior-based MVT has lacked a formal mathematical statement, leaving identifiability, stochastic error propagation, and threshold design without a unified theoretical foundation. We formalize prior-based MVT for strictly unimodal pulse families as a structured inverse problem. On that foundation, we develop the first unified theory of prior-based MVT, comprising deterministic identifiability conditions, a stochastic timing-error model with leading-order mismatch bias, and a nuisance-profiled threshold-design theory centered on an effective-information equation for robust single-event and partial-trigger multi-event operation. We instantiate the framework for the bi-exponential pulse model, derive executable design recipes, and validate the resulting predictions on a 10,000-pulse $^{22}$Na/LYSO/SiPM dataset. The experiments confirm that the framework yields useful threshold designs in the photopeak regime while also revealing the regime boundary at which partial triggering and model mismatch limit the predictive power of Fisher-guided optimization. These results provide the first unified mathematical foundation for prior-based MVT and recast it from an empirical threshold heuristic as a principled inferential framework.

Gaia, the 1 billion star, high precision, astrometric satellite will revolutionise our understanding in many areas of astronomy ranging from bodies in our Solar System to the formation and structure of our Galaxy. To fully achieve the ambitious goals of the mission, and to completely eliminate effects such as aberration, we must know the position and velocity vectors of the spacecraft as it orbits the Lagrange point to an accuracy greater than can be obtained by traditional radar techniques, leading to the decision to conduct astrometric observations of the Gaia satellite itself from the ground. Therefore the Ground Based Optical Tracking (GBOT) project was formed and a small worldwide network using 1-2 m telescopes established in order to obtain one measurement per day of a precision/accuracy of 20 mas. We will discuss all aspects of GBOT, setup, feasibility considerations, preliminary tests of observing methods, partner observatories, the pipeline/database

Extreme flows realize the largest possible growth, either instantaneously or in finite time, of certain quantities of interest which is achieved by a suitable choice of the initial condition or the applied forcing. The quantities of interest usually measure some small-scale properties and therefore provide information about the regularity of the flow. Extreme behavior is at the heart of several open problems in fluid mechanics including the dissipation anomaly in turbulence and formation of singularities in various models of fluid flow. In this essay we describe a framework making it possible to study such extreme behavior systematically by combining mathematical analysis, scientific computation and physics. As a first step, one aims to deduce rigorous upper bounds on the growth of the quantities of interest in the solutions of a given model. These inequalities express fundamental limitations on the most extreme behavior possible among all admissible solutions. However, given how they are obtained, these bounds may be conservative and overestimate the growth actually realizable in the system. In order to probe this possibility, as the next step, we set up variational optimization problems where the growth of the quantity of interest is maximized under suitable constraints. Solution of such problems is enabled by modern methods of numerical optimization. When properties of the thus obtained maximizers match the bounds, the bounds are declared sharp and therefore cannot be fundamentally improved. Finally, properties of the solutions saturating the bounds reveal insights about the physical mechanisms realizing the extreme behavior. We survey problems where this research program has produced sharp bounds together with extreme flows saturating these bounds. A collection of open problems is then presented and we close the essay with a discussion of possible methodological improvements.

The recently proposed spontaneous disentanglement hypothesis is formulated using a modified Schrödinger equation having an added nonlinear term. The hypothesis is motivated by some outstanding issues in the foundations of quantum mechanics, including the problem of quantum measurement. Spontaneous disentanglement is explored in the current study for the macroscopic limit. This is done using some many–body models having known exact solutions. For the under–study models, it is found that non–local entanglement becomes unstable in the macroscopic limit. On the other hand, stability in the macroscopic limit of local entanglement is not excluded. These findings demonstrate that the spontaneous disentanglement hypothesis can bridge between the quantumness of the microscopic realm, and the classicalness of the macroscopic one.

Ta$_2$O$_5$ nonvolatile memristors are used as compact, traceable, and well-controllable dynamic reservoir computing layers to perform time-series prediction tasks. The strongly voltage-dependent switching speed is utilized for information processing. It enables the configuration of tailorable programming and forgetting times in response to the positive and negative driving voltage pulses. Benchmarking this framework on time-series prediction problems reveals that the configurable forgetting dynamics enables a high prediction accuracy using a rather small number of memristive input channels. The training is based either on optimizing the output layer using linear regression with fixed forgetting times, or on optimizing the forgetting times as well. In the first case, six memristive channels, while in the second, only two memristive channels are used to demonstrate excellent prediction accuracy for the benchmark tasks. This scheme allows for the tunability of the operating frequency over many orders of magnitude: by adjusting the input voltage levels, the information processing speed of the same memristive dynamic layer can be increased from the kHz to the MHz range while maintaining excellent prediction accuracy. These findings demonstrate the merits of memristor based dynamic networks in the analysis, prediction and recovery of fast temporal signals, approaching telecommunication data rates.

Although Arnoldi reduction of a generally non-Hermitian Hamiltonian yields an upper Hessenberg matrix rather than the tridiagonal form of Hermitian Lanczos theory, we show that a closed Toda sector survives in its diagonal and subdiagonal coefficients. For a fixed finite-dimensional Hamiltonian and a cyclic state vector deformed holomorphically, the Krylov Gram determinants are $\tau$ functions of the finite two-dimensional Toda lattice, whose Flaschka variables coincide exactly with these Arnoldi coefficients. The Toda dynamics therefore closes on this sector without determining the remaining upper Hessenberg entries. The subdiagonal part of the same sector also has a direct geometric meaning: the squared subdiagonal coefficients determine both the Fubini–Study metric and the Berry curvature of holomorphic Krylov subspaces, whereas the geometric quantities associated with subspaces lost at Arnoldi breakdown cease to be defined. Along a smooth real path in the cyclic region, the Arnoldi-frame connection further provides a Hermitian tridiagonal generator of exact isospectral transport. When added to the Arnoldi matrix, this generator cancels transitions between instantaneous eigenspaces and realizes counterdiabatic driving whenever the matrix is diagonalizable with a nondegenerate spectrum.

The main objective of this work is to investigate the traveling wave solution and dynamic characteristics of the (2 + 1)-dimensional seventh-order Caudrey-Dodd-Gibbon-KP (sCDG-KP) equation. Applying the ($\frac{G^{\prime}}{G^{\prime}+G+A}$) method, we examine the exact solution of the (2 + 1)-dimensional seventh-order Caudrey-Dodd-Gibbon-KP (sCDG-KP) equation by altering it into a reduced ODE via a suitable wave transformation. Graphical representations, such as 2D, 3D, and a heat map of the ascertained solution, are present to facilitate comprehension of the empirical relevance of the obtained solutions. As a result, we acquired a bright and anti-kink soliton solution. Next, we alter the ODE into a 2D system of equations to analyze the dynamical behavior of the reduced system via bifurcation analysis, phase portrait, and attractor analysis. During this process, we portray the graphical visualization of the bifurcation phase portrait, 2D phase portrait, 3D phase portrait, time series, chaotic attractor, sensitive analysis, fractal dimension, recurrence plot, and power spectrum of the dynamical system.