Papers

We present a novel Exchange Monte Carlo (EMC) method designed for application in continuous-space Path Integral Monte Carlo (PIMC) simulations at finite temperature. Traditional PIMC methods for bosonic systems suffer from long autocorrelation times, particularly when measuring observables affected by particle permutations, such as the winding number. To address this issue, we introduce an exchange update scheme that facilitates replica transitions between different interaction regimes, significantly accelerating Monte Carlo dynamics-especially for global observables sensitive to permutation effects. Furthermore, we incorporate Stochastic Potential Switching (SPS) to efficiently decompose interactions, substantially enhancing computational efficiency for long-range interatomic pair potentials such as the Lennard-Jones and Aziz potentials.

Hybrid integration of InAs/GaAs quantum dot (QD) single-photon sources (SPSs) is a promising approach for introducing quantum light into SiN photonic integrated circuits. However, the large refractive-index mismatch between GaAs and SiN poses a challenge for efficient optical coupling. Here, we propose and experimentally demonstrate hybrid integration of an InAs/GaAs QD-SPS on SiN using a coupled crossover waveguide structure. A photonic crystal nanocavity is employed for coupling QD emission into a GaAs waveguide, which efficiently transfers photons to a SiN waveguide at the crossover section. We observed Purcell-enhanced single-photon emission, on-chip propagation, and outcoupling through a SiN grating coupler.

This report documents the program of the second Toulouse Tensor Workshop which took place at the University of Toulouse on September 17-19, 2025, and summarizes the main points of discussion. This workshop follows the first Workshop (CECAM workshop on Tensor Contraction Library Standardization), which took place in Toulouse one year earlier, on May 24-25, 2024 and led to the formation of a tensor standardization working group, which has since specified a low-level standard interface for tensor operations available freely on GitHub. The 2025 workshop brought together developers of applications which rely extensively on tensor computations such as quantum many-body simulations in chemistry and physics (material science and electronic structure calculations), as well as developers and experts of tensor software who have the know-how to provide the technical support for such applications. The workshop enabled the community to provide feedback on the specified low-level interface and how it can be further refined. It also initiated a discussion on how the standardization efforts should be oriented in the near feature, in particular on what should be higher-level interfaces and how to tackle other requirements of the community such as tensor decompositions, symmetric tensors and structured sparsity support.

While energy band topology in spatial photonic crystals (PCs) and momentum-band topology in temporal crystals have each served as powerful probes of topological phases in their respective domains, their unification in a static platform remains unexplored. In this Letter, we bridge this gap by introducing a waveguide assisted non-Hermitian SSH model, in which controlled tuning of loss and coupling drives PT-symmetry breaking and enables a continuous transition between energy- and momentum-gap regimes. This allows us to construct a complete spatiotemporal topological phase diagram in a unified parameter space. By mapping this phase diagram onto a spatially graded PC, we experimentally observe multiple Bloch momentum-band gaps and a continuous spatiotemporal topological transition via translating across the static sample, enabling real-time control over the evolution pathway of the band topology. Our work creates a versatile, bias-free platform for exploring synthetic spacetime physics and opens new avenues for controlling light via non-Hermitian band engineering.

Characterizing the ground-state properties of disordered systems, such as spin glasses and combinatorial optimization problems, is fundamental to science and engineering. However, computing exact ground states and counting their degeneracies are generally NP-hard and #P-hard problems, respectively, posing a formidable challenge for exact algorithms. Recently, Tensor Networks methods, which utilize high-dimensional linear algebra and achieve massive hardware parallelization, have emerged as a rapidly developing paradigm for efficiently solving these tasks. Despite their success, these methods are fundamentally constrained by the exponential growth of space complexity, which severely limits their scalability. To address this bottleneck, we introduce the Branch-and-Bound Tensor Network (BBTN) method, which seamlessly integrates the adaptive search framework of branch-and-bound with the efficient contraction of tropical tensor networks, significantly extending the reach of exact algorithms. We show that BBTN significantly surpasses existing state-of-the-art solvers, setting new benchmarks for exact computation. It pushes the boundaries of tractability to previously unreachable scales, enabling exact ground-state counting for $\pm J$ spin glasses up to $64 \times 64$ and solving Maximum Independent Set problems on King's subgraphs up to $100 \times 100$. For hard instances, BBTN dramatically reduces the computational cost of standard Tropical Tensor Networks, compressing years of runtime into minutes. Furthermore, it outperforms leading integer-programming solvers by over 30$\times$, establishing a versatile and scalable framework for solving hard problems in statistical physics and combinatorial optimization.

The temporal dynamics of ultrashort pulses are a fundamental feature in ultrafast optics. These dynamics can often be extracted from a two-dimensional trace consisting of a set of nonlinear spectra, using an iterative algorithm. Typically, the measurement of this trace requires integrating the signal of many pulses, which implies that the trace does not correspond to a single pulse when shot-to-shot variations occur. In this case, the pulse train can be characterized by a base pulse and a metric that quantifies its instabilities. Here, we demonstrate that the amplitude swing technique is highly sensitive to pulse-train instabilities, allowing not only for their detection but, crucially, for their quantification. First, we examine the analytical components of the amplitude swing trace. We then introduce a robust parameter designed to assess the instabilities, evaluating its performance in the presence of noise. Finally, we validate this approach using simulated unstable pulse trains, introducing random spectral phase fluctuations, establishing amplitude swing as a simple yet powerful diagnostic tool for full pulse-train characterization.

Hydrogen electrocatalysis (HER/HOR) exhibits an anomalous, non-Nernstian pH dependence that has motivated competing mechanistic narratives yet still lacks a unified, transferable, and quantitatively predictive description across conditions. Here, we introduce a theory-neutral waveguide kinetics framework that reinterprets the polarization curve as a power-flow-like response, enabling a compact modal representation of interfacial kinetics. Without presuming any specific mechanism, the model quantitatively fits four representative polarization datasets historically explained by divergent theories. From each fit, we extract interpretable diagnostics, including a reflection amplitude and a useful-output density, that provide transferable metrics for interfacial efficiency. The framework thus establishes a computational experiment platform for mechanistic triangulation, operating-regime diagnosis, and the rational design of hydrogen electrocatalysts across the pH spectrum.

Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT due to minimal contrast between pathological and healthy tissue. This work investigates the feasibility of cross-modality weak supervision for scenarios where pathology is visible in one modality (MRI) but absent in another (CT). We present a hybrid registration-segmentation framework that combines MSCGUNet for inter-modal image registration with a UNet-based segmentation module, enabling registration-assisted pseudo-label generation for CT images. Our evaluation on the CHAOS dataset demonstrates that the pipeline can successfully register and segment healthy liver anatomy, achieving a Dice score of 0.72. However, when applied to clinical data containing tumours, performance degrades substantially (Dice score of 0.16), revealing the fundamental limitations of current registration methods when the target pathology lacks corresponding visual features in the target modality. We analyse the "domain gap" and "feature absence" problems, demonstrating that whilst spatial propagation of labels via registration is feasible for visible structures, segmenting truly invisible pathology remains an open challenge. Our findings highlight that registration-based label transfer cannot compensate for the absence of discriminative features in the target modality, providing important insights for future research in cross-modality medical image analysis. Code an weights are available at: https://github.com/BudhaTronix/Weakly-Supervised-Tumour-Detection

The large-scale limit or outer scale of turbulence in the solar wind is associated with the correlation length of the magnetic field. Determining correlation lengths from magnetic field time series in magnetic clouds is complicated by the presence of the global flux rope: without removal of the flux rope trend, correlation length measurements will be sensitive to the flux rope as well as the turbulence, and give overestimates of the outer scale when turbulence amplitudes at the outer scale are small relative to the flux rope amplitude. We have used force-free flux rope fits to detrend magnetic field time series measured by Parker Solar Probe in two magnetic clouds and calculated the turbulence correlation length in the clouds using the detrended data. The detrended correlation length in terms of the proton inertial length, $d_p$, was $2.7\times10^{4} d_p$ in one cloud (observed at 0.77 au) and $1.6\times10^{4}d_p$ in the other (observed at 0.39 au), significantly smaller than the values obtained without detrending. Increments in the flux rope fits scaled equivalently to a $k^{-3}$ wavenumber power spectrum; this contribution from the flux rope considerably steepened the total spectrum at the largest scales but had a negligible effect in the inertial range, where scaling in both clouds equivalent to $\sim$$k^{-5/3}$ was observed. Finally, we discuss the possible relation of turbulence correlation lengths to mesoscale structure in magnetic clouds.

Noisy quantum annealing experiments on Rydberg atom arrays produce measurement outcomes that deviate from ideal distributions, complicating performance evaluation. To enable a data-driven benchmarking methodology for quantum devices that accounts for both solution quality and the classical computational cost of inference from noisy measurements, we introduce deterministic error mitigation (DEM), a shot-level inference procedure informed by experimentally characterized noise. We demonstrate this approach using the decision version of the $k$-independent set problem. Within a Hamming-shell framework, the DEM candidate volume is governed by the binary entropy of the bit-flip error rate, yielding an entropy-controlled classical postprocessing cost. Using experimental measurement data, DEM reduces postprocessing overhead relative to classical inference baselines. Numerical simulations and experimental results from neutral atom devices validate the predicted scaling with system size and error rate. These scalings indicate that one hour of classical computation on an Intel i9 processor corresponds to neutral atom experiments with up to $N=250-450$ atoms at effective error rates, enabling a direct, cost-based comparison between noisy quantum experiments and classical algorithms.

Geometric phases are widely used in modern optics, yet their meaning and underlying geometry depend on the actual physical settings, which can substantially differ from one another. This tutorial article introduces geometric phases in nanophotonic systems, focusing on the interaction of structured light with nanostructures or metaatoms. We compare the present setting with conventional geometric phases of structured-light optics and show that similar phase laws may correspond to genuinely different underlying geometries. Our aim is to provide a pedagogical bridge between the mathematical language of geometric phases and experimentally relevant examples from nanophotonics.

While proper orthogonal decomposition (POD)-based surrogates are widely explored for hydrodynamic applications, the use of Koopman autoencoders for real-world coastal-ocean modelling remains relatively limited. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compares its performance against POD-based surrogates. The Koopman autoencoder employs a learned linear temporal operator in latent space, enabling eigenvalue regularization to promote temporal stability. This strategy is evaluated alongside temporal unrolling techniques for achieving stable and accurate long-term predictions. The models are assessed on three test cases spanning distinct dynamical regimes, with prediction horizons up to one year at 30-minute temporal resolution. Across all cases, the reduced order surrogates with temporal unrolling achieve high accuracy with relative root-mean-squared-errors of 0.0068-0.14 and $R^2$-values of 0.61-0.995, where prediction errors are largest for current velocities, and smallest for water surface elevations. In two of the three cases, the Koopman Autoencoder have higher accuracy than the POD-based surrogates. Comparing to in-situ observations, the surrogate yields -0.64% to 12% increase in water surface elevation prediction error when compared to prediction errors of the physics-based model. These error levels, corresponding to a few centimeters, are acceptable for many practical applications, while inference speed-ups of 300-1400x enables workflows such as ensemble forecasting and long climate simulations for coastal-ocean modelling.

The separation of acoustic and vortical fluctuations in compressible turbulence becomes ambiguous in thermodynamically inhomogeneous media, where refraction by entropy gradients and shocks can be misclassified as solenoidal content by Euclidean post-processing. We introduce a covariant Helmholtz–Hodge decomposition (CHHD) with respect to an effective acoustic metric, which identifies the irrotational (potential) component with the exact part of the metric-dual velocity one-form. Thermal refraction and shock-induced bending are absorbed into the induced curvature, ensuring that such geometric variations are not misidentified as physical vorticity. For canonical entropy-spot refraction and normal-shock discontinuities, Euclidean Helmholtz–Hodge and momentum-potential post-processing produce significant leakage in the refracting/discontinuous region, whereas the covariant splitting remains at the numerical noise floor (typically $\lesssim 10^{-12}$) throughout the domain, demonstrating robustness even at the sonic horizon, where the Euclidean metric singularity typically causes catastrophic error amplification. This geometric framework for velocity fields resolves the ambiguity of irrotational motion in inhomogeneous media and establishes a necessary foundation for future generalizations to full thermodynamic state vectors.

Density functional theory (DFT) and linear-response time-dependent density functional theory (LR-TDDFT) rely on an exchange-correlation (xc) approximation that provides not only energy but also its functional derivatives that enter the self-consistent potential and the response kernel. Here, we present an end-to-end differentiable workflow to optimize a single deep-learned energy functional using targets from both Kohn-Sham DFT and adiabatic LR-TDDFT. To enable this training in a computationally efficient and differentiable manner, we developed a JAX-based two-component quantum chemistry package (IQC), in which the learned functional provides a self-consistent potential and linear-response kernel via automatic differentiation. This construction permits gradient-based optimization through both the self-consistent-field (SCF) fixed-point equations and the Casida eigenvalue problem. We learn an exchange-correlation functional on excitation energies and ground-state properties (noncovalent interactions, thermochemistry, bond dissociation, ionization potentials, electron affinities, isomerization energies, and reaction barriers) while incorporating one-electron self-interaction cancelation as penalty terms, and we assess its possible transfer to molecular test cases.

Quantum metrology harnesses quantum entanglement to improve measurement precision beyond standard quantum limit. Although nonlinear interaction is essential for generating entanglement, during signal accumulation, it becomes detrimental and therefore must be suppressed. To address this challenge, we propose an alternating in-phase and quadrature modulation (AIQM) scheme, designed to operate under a fixed nonlinear interaction. During signal accumulation, our time-interleaved approach sequentially applies the in-phase and quadrature driving fields, thereby eliminating the effects of nonlinear interaction on signal accumulation. Our AIQM scheme achieves better metrological performance than conventional schemes, particularly under strong nonlinear interaction and prolonged signal accumulation, with pronounced robustness against parameter variations. By selectively eliminating and utilizing nonlinear interactions via AIQM, our work enables high-precision and high-accuracy entanglement-enhanced sensing without the need for active control of the nonlinear interaction.

Radio observations provide powerful diagnostics of energy release, particle acceleration, and transport processes in solar flares. However, despite recent progress in radio interferometric imaging spectroscopy, current instruments still face limitations in image fidelity and resolution, restricting detailed spectroscopic studies of flaring regions. Here we present high-fidelity imaging spectroscopy of a M1.3 GOES class flare with MeerKAT, a precursor to the future-generation array SKA-Mid. Radio emissions at the observed frequencies typically originate in the low corona, offering valuable insights into magnetic reconnection and primary energy-release sites. The obtained images achieve an unprecedented dynamic range exceeding 10^3, enabling simultaneous analysis of bright coherent bursts and faint incoherent emission from the active region. Multiple spatially distinct coherent sources are identified, implying contributions from different populations of accelerated electrons. The incoherent emission extends beyond AIA structures, highlighting MeerKAT's ability to detect dilute but hot plasma invisible to Extreme Ultraviolet instruments. Combined with co-temporal Hard X-ray images and magnetic field extrapolations, the radio sources are located within distinct magnetic structures, further revealing their association with different populations of accelerated electrons. These results demonstrate MeerKAT imaging spectroscopy as powerful diagnostics of solar flares and pave the way for future solar flare studies with SKA-Mid.

Magnetic Resonance Imaging (MRI) is crucial in global healthcare, but the traditional receive coils, as a core component of MRI, SNR enhancement is limited due to the optimization of channel number and magnetic field strength faces high cost and complexity challenges. Here, we demonstrate the use of a topological material to enhance MRI signal reception. Designed with a stack of weak couplings, this material forms quasi-two-dimensional dual topological boundary states. High properties are achieved through low-loss signal transmission via these topological states, as well as only enhanced local magnetic fields and increased number of channels. Initial tests demonstrate superior performance and accessibility compared to commercial coils, suggesting significant potential. This concept introduces a transformative paradigm for all MRI coil designs.

We present lrux, a JAX-based software package for fast low-rank updates of determinants and Pfaffians, targeting the dominant computational bottleneck in various quantum Monte Carlo (QMC) algorithms. The package implements efficient low-rank updates that reduce the cost of successive wavefunction evaluations from $\mathcal{O}(n^3)$ to $\mathcal{O}(n^2k)$ when the update rank $k$ is smaller than the dimension $n$ of matrices. Both determinant and Pfaffian updates are supported, together with delayed-update strategies that trade floating-point operations for reduced memory traffic on modern accelerators. lrux natively integrates with JAX transformations such as JIT compilation, vectorization, and automatic differentiation, and supports both real and complex data types. Benchmarks on GPUs demonstrate up to $1000\times$ speedup at large matrix sizes. lrux enables scalable, high-performance evaluation of antisymmetric wavefunctions and is designed as a drop-in component for a wide range of QMC workflows. lrux is available at https://github.com/ChenAo-Phys/lrux.

Phase transitions impose topological constraints on thermodynamic state variables, masking energetic fluctuations at the phase boundary. This constraint is most apparent in melting systems, where temperature remains pinned despite continued energy input. Here we resolve this information loss by introducing a latent temperature-a counterfactual trajectory describing the system's unconstrained thermal evolution. We show that energy conservation alone enforces a rigorous duality between the total latent heat dissipated during phase change and the accumulated exceedance of the latent temperature above the melting point. This duality is mathematically equivalent to the one-dimensional Wasserstein-1 distance between the latent and observed temperature trajectories, with the transport cost set by a characteristic surface dissipation timescale and melting energy. Applied to ice-sheet surface melting, this timescale admits a direct physical interpretation in terms of radiative and turbulent heat loss. The same framework yields a first-principles derivation of the empirical Positive Degree Day law and predicts realistic degree-day factors that emerge from surface energy balance, without ad hoc calibration. More broadly, phase change emerges as an optimal transport process that projects continuous energetic variability onto a constrained thermodynamic boundary.

Semiconductor quantum dots (QDs) have emerged as a premier solid-state platform for the deterministic generation of nonclassical light, offering a compelling pathway toward scalable quantum photonic systems. While single-photon emission from QDs has reached a high level of maturity, the realization of high-fidelity entangled photon-pair sources remains an active and rapidly evolving frontier. In this review, we survey the recent progress in QD-based entangled photon sources, highlighting the conceptual evolution from the established biexciton-exciton cascade to the emerging paradigm of spontaneous two-photon emission. We further examine how advances in nanophotonic architectures and coherent control strategies are redefining fundamental performance limits, enabling concurrent improvements in source brightness, coherence, and entanglement fidelity. Finally, we discuss the key physical and technological challenges that must be addressed to bridge the gap between laboratory demonstrations and large-scale deployment. We conclude by outlining the future opportunities for integrating QD-based entangled photon sources into practical quantum communication, computation, and sensing platforms.

Relativistic quintuple-zeta basis sets for the p elements are presented. The basis sets for the occupied spinors were optimized at the Dirac-Coulomb self-consistent field (SCF) level on the ground configurations. Valence and core correlating functions were optimized in multireference SDCI calculations on the ground configuration. Diffuse functions optimized on the anion (or derived from neighboring elements for group 18) are also provided. Basic atomic and molecular properties were used to test the newly developed basis sets. A smooth convergence to the basis set limit is observed with increased basis set quality from the previously available double-zeta, triple-zeta, and quadruple-zeta basis sets to the newly developed quintuple-zeta basis sets for the calculated molecular bond lengths and dissociation energies and for atomic ionization potentials and electron affinities. Use of these basis sets in combination with state-of-the-art approaches for treatment of relativity and correlation will allow significantly increased accuracy in calculations on the heavy elements and their compounds. The basis sets are available at https://doi.org/10.5281/zenodo.18351835.

The evaluation of hydrological models is essential for both model selection and reliability assessment. However, simply comparing predictions to observations is insufficient for understanding the global landscape of model behavior. This is especially true for many deep learning models, whose structures are complex. Further, in risk-averse operational settings, water managers require models that are trustworthy and provably safe, as non-robustness can put our critical infrastructure at risk. Motivated by the need to select reliable models for operational deployment, we introduce and explore adversarial robustness analysis in hydrological modeling, evaluating whether small, targeted perturbations to meteorological forcings induce substantial changes in simulated discharge. We compare physical-conceptual and deep learning-based hydrological models across 1,347 German catchments under perturbations of varying magnitudes, using the fast gradient sign method (FGSM). We find that, as expected, the FGSM perturbations systematically reduce KGE and increase MSE. However, catastrophic failure is rare and, surprisingly, LSTMs generally demonstrate greater robustness than HBV models. Further, changes in both the predicted hydrographs and the internal model states often respond approximately linearly (at least locally) as perturbation size increases, providing a compact summary of how errors grow under such perturbations. Similar patterns are also observed for random perturbations, suggesting that small input changes usually introduce approximately proportional changes in model output. Overall, these findings support further consideration of LSTMs for operational deployment (due both to their predictive power and robustness), and motivate future work on both characterizing model responses to input changes and improving robustness through architectural modifications and training design.

The effect of Kolmogorov-size spherical particles on homogeneous and isotropic turbulence is investigated using particle-resolved direct numerical simulations at an unladen Taylor-scale Reynolds number of $150$. Four monodisperse suspensions of particles with identical diameter and volume fraction $10^{-3}$ are considered, while the particle-to-fluid density ratio varies between $100$ and $1500$ and the mass fraction between $0.1$ and $0.6$. As particle inertia increases, the energy spectrum departs from the canonical Kolmogorov $\kappa^{-5/3}$ scaling and approaches a peculiar regime with $\kappa^{-1}$. In this limit, the nonlinear energy transfer is strongly suppressed and the kinetic energy balance is dominated by the fluid-solid interaction and the viscous dissipation. Consistently, the second-order structure function shows logarithmic scaling at separations larger than the particle diameter, indicating velocity decorrelation. Increasing particle inertia promotes axial strain and vortex compression in the vicinity of the particles and enhances the particle-fluid relative velocity. Particle clustering is maximum when the Stokes number based on the Kolmogorov time scale is $O(1)$ and weakens as the density ratio and the Stokes number increase, with the volume and the population of the clusters decreasing when inertia is enhanced. When clustering occurs, particles preferentially sample regions of high strain and low vorticity.

The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations.

Inspired by the dilution refrigerator and magnet monitoring system developed for HAYSTAC at Yale University, Speller Lab at Johns Hopkins University developed the Fridge Real Time Monitoring System (FRTMS). The FRTMS accesses logs saved locally by the dilution refrigerator, saves these logs to various backup locations, edits the logs into a format for upload to a MySQL database, and allows the logs to be remotely monitored in near-real time.

Optical frequency combs (OFCs) are frequency rulers essential for precision metrology, next generation navigation, and testing of fundamental physics. Despite intense efforts, chip-integrated OFCs remain laboratory-bound, unable to fulfill their promise of compact and cost-effective deployment. While improvements in fabrication and integration are important, a conceptual limitation has stymied progress: on-chip OFC architectures have aimed to miniaturize their table-top counterparts and relied on cascading outward from a single pump. In integrated platforms, this approach does not readily generate the strong, low-noise, octave-spaced signals crucial for robust zero-frequency offset detection. Here, we overcome this limitation via an architectural inversion where an optical microcomb fills the spectrum between two octave-separated pumps. The two pumps generate a parametrically driven cavity soliton (PDCS) in an integrated $\chi^{(3)}$ resonator, which robustly self-aligns to both pumps across multiple foundry-fabricated devices and operating configurations. This produces a single octave-spanning comb from telecom to visible wavelengths, whose zero-frequency offset is defined by the pumps' harmonic offset and can therefore be reliably detected and stabilized. We showcase our platform's capabilities by executing all of the three core tasks of OFC metrology: optical frequency synthesis, low-noise millimeter-wave generation, and integrated optical clock readout, using the same self-aligned microcomb with only its input locks changed.

An active dissipative process organizes auditory frequency analysis in the mammalian cochlea. A minimal active beam model reveals that a spatially varying viscous coupling operator, $\partial_{xx}\kappa\partial_{xx}$, generates dissipative forces with wave–like propagation. Local energy injection and spatial redistribution compete to govern the dynamics. This balance enables the quantitative reproduction of four key features: sharp tuning, high gain, compression, and spontaneous otoacoustic emissions. Hearing thereby belongs to a broad class of nonequilibrium pattern-forming systems.

This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. We benchmark three distinct planning algorithms Greedy heuristic, Monte Carlo Tree Search (MCTS), and deep reinforcement learning (RL) using Masked Proximal Policy Optimization (PPO) within a realistic orbital simulation environment featuring randomized debris fields, keep out zones, and delta V constraints. Experimental results over 100 test scenarios demonstrate that Masked PPO achieves superior mission efficiency and computational performance, visiting up to twice as many debris as Greedy and significantly outperforming MCTS in runtime. These findings underscore the promise of modern RL methods for scalable, safe, and resource efficient space mission planning, paving the way for future advancements in ADR autonomy.

Objective collapse models are often implemented so that collapse acts only on the fermionic (matter) sector, while bosonic fields do not undergo fundamental collapse. In generalized trace dynamics (GTD), spontaneous localization is expected to arise when the trace Hamiltonian has a significant anti-self-adjoint component. In this note we show, starting from the STM-atom (spacetime-matter atom) trace Lagrangian written in terms of two inequivalent matrix velocities $\dot Q_1$ and $\dot Q_2$, that the purely bosonic subsector admits a self-adjoint Hamiltonian, whereas the fermionic sector carries an intrinsic anti-self-adjoint contribution. The key structural input is that making the trace Lagrangian bosonic requires insertion of two unequal odd-grade Grassmann elements $\beta_1\neq \beta_2$. Assuming natural adjoint properties for these elements, we compute the trace Hamiltonian explicitly via trace-derivative canonical momenta (with bosonic and fermionic variations treated separately) and isolate the resulting anti-self-adjoint term. This provides a first-principles mechanism, within GTD, for why only fermionic degrees of freedom act as collapse channels.

We extend the recently developed entropic and conservative variance reduction framework [M. Sadr, N. G. Hadjiconstantinou, A variance-reduced direct Monte Carlo simulation method for solving the Boltzmann equation over a wide range of rarefaction, Journal of Computational Physics 472 (2023) 111677.] to the particle-in-cell (PIC) method of solving Vlasov-Poisson equation. We show that a zeroth-order approximation that freezes the importance weights during the velocity-space kick is stable at the expense of introducing bias. Then, we propose a correction for the weight distribution using maximum cross-entropy formulation to ensure conservation laws while minimizing the introduced bias. In several test cases including Sod's shock tube and Landau damping we show that the proposed method maintains the substantial speed-up of variance reduction method compared to the PIC simulations in the low signal regime with minimal changes to the simulation code.