Papers

Vishaps – dragon stones – discovered in the Armenian Highlands convey a remarkable message about the spiritual and social character of their epoch, c. 4000 BC. The unexpected bimodal distribution of their elevations indicates the deliberate, labor-intensive placement of these massive stones – some weighing up to 7–9 tons – in locations where the period suitable for construction activities at high altitudes was extremely limited. Their positions, correlated with nodes of previously identified prehistoric irrigation systems, support the interpretation that they were dedicated to a cult of water. This evidence points to the existence of an organized and unified society capable of sustaining and maintaining such a resource-intensive cult.

We present an analytical framework for the probability of individual success based on a single structural asymmetry between the capacity of an entity to explore possibilities, $k$, and the size of the possibility space offered by the environment, $n$, where $k \ll n$. We introduce an effective density $\rho_{\rm eff}$ of favorable possibilities accessible to a given entity, derive the probability of success as $P \approx 1-(1-\rho_{\rm eff})^k$, and decompose its variance across a population. We show that while the elasticities $\varepsilon_\rho$ and $\varepsilon_k$ are comparable, the variance of outcomes is dominated by ${\rm Var}(\ln \rho_{\rm eff})$ whenever it exceeds ${\rm Var}(\ln k)$. A back-of-envelope calibration based on published inequality and productivity data indicates this condition holds by two to three orders of magnitude. The framework provides an analytical complement to the simulation result of Pluchino, Biondo and Rapisarda (2018), and offers a unified structural account of geographic inequality, intergenerational mobility and accessibility-based discrimination as special cases of the narrowing of the accessible set $A(E,P)$.

We propose a physically explicit sensing application of a time-reversed Young (TRY) interferometer: simultaneous monitoring of beam tilt and focus drift with a fixed detector. The task is relevant to compact optical relays, free-space links, fiber-coupling stages, and micro-optical alignment modules, where continuous tracking of pointing and focus is needed but downstream wavefront cameras or multiport analyzers are undesirable. Using a finite-width double-slit Fresnel model, we derive the exact local TRY response functions for tilt-like and defocus-like phase perturbations and compute the corresponding optimal upstream source codes numerically. The physical optimal codes are fringe-locked and differ qualitatively from the simple odd/even modes suggested by Gaussian toy models. Two source-coded scalar channels recover essentially all local Fisher information in the full source-resolved TRY record for the physical model considered here. Compared with downstream direct intensity sensing, TRY provides first-order access to the mixed tilt–defocus task with fixed detection; compared with ideal downstream matched-mode sorting, its advantage is architectural rather than fundamental.

Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing history. This intrinsic complexity makes predictive modeling of composites expensive, because repeated experiments and high-fidelity simulations are needed to cover large design spaces of material, structure, and manufacturing. Multi-fidelity surrogate modeling addresses this challenge by combining abundant, less expensive data with limited high-accuracy data to recover reliable high-fidelity predictions. This review presents a structured overview of multi-fidelity modeling for composite mechanics, covering Gaussian-process or Kriging-based methods, including co-Kriging, coregionalization models, autoregressive formulations, nonlinear autoregressive Gaussian processes, multi-fidelity deep Gaussian processes, and multi-fidelity neural networks. Their distinctions are examined in terms of cross-fidelity correlation, discrepancy representation, uncertainty quantification, and scalability. Selected examples of their applications to composites are introduced according to the roles that multi-fidelity surrogates play in engineering problems, including forward prediction for rapid exploration of material design spaces, inverse optimization for composite parameter identification and design search under limited high-fidelity access, and workflow integration, where heterogeneous data sources, constraints, and validation requirements determine model utility. Open question discussions highlight recurring challenges specific to composites, such as regime-dependent fidelity gaps associated with nonlinear damage and manufacturing history, mismatches between simulations and experiments, and uncertainty propagation across multi-fidelity models.

We identify a hierarchical symmetry structure underlying the Hilbert space of quantum systems made of identical particles. By studying the linear map between single-body and many-body spectra, we show that the resulting spectral organization admits a natural partition into sectors induced by a symmetry governed by cyclotomic fields and their Galois groups. This approach offers a new perspective on the structure of quantum many-body spectra, defining a controlled coarse-graining of the Hilbert space in which degeneracies and spectral features can be systematically organized through a set of new quantum numbers. The resulting structure induces a hierarchy of spectral resolutions following a renormalization flow, which enables information loss control. We demonstrate that this hierarchical decomposition provides an efficient route to approximate MBDoS calculations for any identical particle system, while preserving physically relevant spectral properties. This approach provides a general symmetry-based framework for organizing and approximating many-body spectra, with potential applications to quantum thermalization, spectral statistics, and large-scale quantum simulations.

We study the parity-violating electron-electron interaction mediated by the axion-like in the hexatomic molecule of a symmetric top type. The rich rovibrational behavior require electronic computations for multiple molecular configurations which can be reduced using Generalized Relativistic Effective Core Potential. To restore the correct behavior in the core region we use a one-center restoration technique generalized by us earlier to the two-electron properties. The property is averaged on the lowest-lying rovibrational states.

We extend our two-scale neural-network method for scalar singularly perturbed problems with one small parameter to dynamical systems with multiple small parameters. To accommodate multiple small parameters, we use a single effective scale parameter defined as the geometric mean of all parameters. We thus augment the network input with a scale-aware feature, enabling it to capture sharp solution transitions intrinsically. Numerical experiments across a range of dynamical systems demonstrate that the proposed framework can handle coupled systems with multiple and high-contrast small parameters and obtain satisfactory accuracy in capturing solution features induced by small parameters.

Axions are promising candidates for the role of Dark matter particles. In this paper, the question of the suitability of the RaOH molecule for the experimental detection of electron-electron interactions through the exchange of axions is studied. To take into account an impact of the rotations and vibrations a computation must be performed for a large number of molecular configurations. In this work we study this problem using a combination of the Generalized relativistic effective core potential and One-center restoration technique of the correct four-component spinors.

The realization of integrated, high-speed ultraviolet (UV) modulation is pivotal for the advancement of quantum information processing, portable atomic clocks, and secure solar-blind communications. While mature photonic platforms have facilitated sophisticated system-level integration across visible and infrared spectra, high-speed active modulation in UV remains with traditional bulk crystals. Consequently, a scalable integrated solution that simultaneously combines low insertion loss and extreme compactness with high modulation efficiency has remained challenging. Here, we report the first integrated UV electro-optic modulator on a thin-film lithium tantalate (TFLT) platform. By employing a compact lumped-electrode design, we achieve a record-low V{\pi}L of 85 mV\cdot cm at 375 nm, providing an up to four orders of magnitude improvement in terms of bandwidth/V{\pi} L over bulk technologies. The device demonstrates a robust extinction ratio of 22.7 dB, a low insertion loss of 1.3 dB, and a V{\pi} of 4.2V. Although the measured 3-dB bandwidth of 922 MHz is currently limited by photodetector performance, the small device footprint of 1.16 mm and electrode design of 200 {\mu}m indicate intrinsic potential for high-speed operation beyond 67 GHz which is confirmed by the electrical-to-electrical response. This work establishes TFLT as a disruptive platform for wafer-scale compatible active UV photonics, enabling the next generation of scalable quantum and communication systems.

The upgraded LHCb detector has started its Run 3 of data taking in 2022, with a completely overhauled DAQ system, reading out and processing the full detector data at every LHC bunch crossing (30 MHz average rate). At the same time, an intense R&D activity is taking place, with the aim of further improving the real-time data processing performance of LHCb, in view of "Upgrade II", where luminosity will be increased. In this work, we describe the experience gained with a prototype device for a 30 MHz real-time tracking in the LHCb VELO detector, implemented in state-of-art PCIe-hosted FPGA cards interconnected by fast optical links. The system has been processing live LHCb data opportunistically during physics data taking, thanks to a dedicated TestBed facility fed by the experiment monitoring system. We describe, amongst other things, the system used to organise and optimise the high-speed distribution of data to the components, and the synchronisation with the most updated alignment constants to be used in track reconstruction.

We demonstrate a delay-programmable two-color femtosecond source based on a chirped-seed noncollinear optical parametric amplifier. Introducing controlled dispersion into the seed enables spectral selection through pump-seed delay, allowing flexible generation of two independently tunable pulse components with adjustable relative timing at high repetition rate. The temporal and spectral properties are characterized using nonlinear optical cross-correlation and dispersion-scan measurements. As a benchmark application, the source is employed in a COLTRIMS-based multiphoton ionization experiment on trapped Li atoms, revealing delay-dependent ionization pathways and demonstrating its suitability for bichromatic ultrafast spectroscopy.

Electromagnetic eavesdropping is a well-established attack vector for remotely monitoring a target activity, most notably displays, over considerable ranges. Other targets have been considered resistant to such attacks or do not exhibit sufficient electromagnetic leakage for practical exploitation. Radio-frequency retroreflector attacks (RFRA) were developed to enable covert, active monitoring of a target by implanting a minimal hardware Trojan. These implants, typically implemented using discrete components such as transistors or diodes, do not betray their presence by emitting signals themselves; rather, they modulate the electromagnetic reflectivity of the target depending on the probed signal line data. Prior RFRA work has demonstrated their viability against video links and low-speed peripheral interfaces. In this work, we extend the applicability of RFRA to high-speed targets by presenting a successful attack on the 100BASE-TX Ethernet standard. We describe the design and realization of a compact implant capable of recovering the MLT-3 encoded signaling used in Fast Ethernet, as well as a dedicated demodulation and interpretation pipeline that mitigates errors introduced by the radio channel and maximizes the amount of recovered information. Experimental results validate the feasibility of covertly monitoring Fast Ethernet traffic using RF retroreflection and highlight the viability of such attacks for high-speed links.

Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employed eXplainable Artificial Intelligence (XAI) methods are largely adapted from tabular or generic multivariate domains, assigning relevance to isolated spectral variables rather than to the chemically meaningful spectral zones. Widely adopted tools such as SHapley Additive exPlanations (SHAP), Permutation Feature Importance (PFI), and Variable Importance in Projection scores (VIP) were not designed for the physical continuity and high collinearity of spectral data, and their variable-level outputs require post-hoc aggregation to recover zone-level information. This study introduces the Spectral Model eXplainer (SMX), a post-hoc, global, model-agnostic XAI framework that explains spectral classifiers through expert-informed spectral zones. SMX summarizes each zone via PCA, defines quantile-based logical predicates, estimates predicate relevance with perturbation in stochastic subsamples, and aggregates bag-wise rankings in a directed weighted graph summarized by Local Reaching Centrality. A key component is threshold spectrum reconstruction, which back-projects predicate boundaries to the original spectral domain in natural measurement units, enabling direct visual comparison with measured spectra. The method was evaluated on eight real spectral datasets (six based on X-ray Fluorescence–XRF and two based on Gamma-ray Spectrometry) and one synthetic benchmark with known gr

Optical skyrmionic textures, including Néel-type skyrmions and merons, have attracted significant attention across diverse physical systems for their promising applications in ultra-precise metrology, optical information processing, and quantum technologies. However, the lack of an effective approach for their on-chip directional transport and manipulation impedes their applications in photonic integrated devices. Here, we numerically demonstrate a photonic platform that utilizes topologically protected valley edge state to achieve robust on-chip directional transport of optical spin merons. These merons originate from spin-orbit coupling within the evanescent field at the valley photonic crystal surface and exist as eigenstates of the topologically protected edge state, ensuring their robust propagation even in the interface with defects. Leveraging the valley degree of freedom of topological edge states, we further achieve valley-locked spin merons, enabling flexible control over the polarity of spin merons. By endowing spin merons with topological protection in momentum space, our work provides an approach for robust on-chip transport and manipulation of spin merons, thereby paving the way for expanding their application in photonic systems.

We study how confinement affects topology and conformations in polymer films of varying thickness $h$. The knotting probability exhibits a maximum at intermediate thicknesses near the bulk radius of gyration $h \approx R_\mathrm{g,bulk}$, vanishes at small $h$ and approaches bulk values for large $h$. Close to walls, the entanglement length increases monotonically and conformations become flatter. A layer-resolved analysis of structural and topological properties allows us to reconstruct the explicit thickness dependencies by integrating layer-resolved properties of a thick film.

Near-infrared (NIR and Vis-NIR) spectroscopy is widely used for rapid, non-destructive analysis in food, agriculture, pharmaceuticals, process analytical technology, and bioprocess monitoring. Nevertheless, deep-learning studies in NIR chemometrics often reach conflicting conclusions about convolutional neural network (CNN) design, including kernel size, depth, preprocessing, model complexity, and transfer robustness. This review argues that many apparent contradictions reflect incomplete experimental conditioning rather than incompatible findings. CNN performance depends on interactions among spectral physics, dataset regime, acquisition protocol, validation design, and deployment conditions. We organize the literature around three moderators. First, NIR signals are indirect, highly collinear, and shaped by broad overlapping bands, scattering, temperature, and matrix effects. Second, CNN design should be interpreted through receptive-field reasoning: kernel size, depth, dilation, and multi-scale branches determine the wavelength span available to the model, whereas the effective receptive field indicates which parts are actually used. Third, validation design can behave as a hidden hyperparameter because random splits may reward architectures that exploit shared batch, instrument, season, or process-run structure instead of transferable chemical information. We therefore propose a conditional design framework in which preprocessing, architecture, hyperparameter optimization, transfer evaluation, interpretability, and reproducibility are treated as coupled components. Rather than seeking a universally optimal CNN, the framework aims to support physics-aware, shift-aware, and reproducible model comparison in NIR chemometrics.

A recent article by Lohmiller & Slotine (Proc.\ R.\ Soc.\ A 482: 20250413) claims that the Schrödinger equation can be solved exactly using only classical least action and classical fluid density, asserting that this formulation avoids semiclassical approximations. We show that their mathematical derivation contains a foundational error. By neglecting the spatial derivatives of the probability density amplitude, the authors inadvertently omit the quantum potential – the term originally identified by Madelung and later emphasised by Bohm. Consequently, their proposed equivalence is not exact but rather constitutes the standard semiclassical approximation. We further demonstrate that each of the paper's illustrative examples either belongs to a class where the quantum potential vanishes identically due to the geometry of the problem, or recovers the correct quantum result by importing quantum eigenfunctions through the initial conditions, thereby concealing the error.

The lack of analytical models describing diffusion time dependence at intermediate time scales in complex tissue microstructure limits the accurate quantification of extracellular diffusivity and tissue microstructure. We introduce TRACED, a biophysical model that incorporates diffusion time dependence in cell distributions to quantify pathologically-relevant properties in solid tumors. Neural networks were trained on Monte Carlo diffusion simulations using sphere distribution-based geometries to enable the rapid computation of time-dependent diffusion MRI signals in cell populations of variable cell size. Model sensitivity and fit performance were assessed via simulation. Diffusion data from eight mixed-grade glioma patients was fitted using the TRACED model. Data fitting was performed using a novel physics-informed transfer learning pipeline, Sim2PINN. In two patients, cell size measurements were compared directly with image-localized histology. Simulation results indicate improved parameter estimation compared to the simple two-compartment model. TRACED enabled the simultaneous in vivo quantification of intracellular volume fraction, cell size distribution, extracellular intrinsic diffusivity, and tortuosity in glioma patients. Neural network implementations of diffusion time-dependence and tortuosity showed behavior consistent with coarse-graining and effective medium theory, respectively. Future work will explore the clinical utility of TRACED parameters in additional patients.

An ultracompact thin-film polarizer for normal-incidence (0{\deg} angle of incidence, AOI) applications is analytically and experimentally investigated. The device is based on Fano resonances in periodically nanostructured dielectric thin films, enabling polarization selective reflection and transmission due to polarization dependent resonance frequencies. The operating principle is analyzed both analytically and numerically, and the optimized structure is fabricated and experimentally characterized. Measurements demonstrate polarization contrast ratios of up to approximately 1:1000 at normal incidence. Laser-induced damage threshold measurements using nanosecond laser pulses further confirm the high damage resistance of the all-dielectric polarizer.

Applicator-specific phase space (PHSP) files recorded at the aperture exit reduce Monte Carlo dose calculation time by 30-50% for electron FLASH radiotherapy. However, positioning PHSP scoring planes coincident with the applicator-air interface introduces boundary sampling artifacts. This study characterizes these artifacts in Geant4-based simulations and demonstrates their mitigation. PHSP files were generated using GAMOS 6.2.0 for a 9 MeV Mobetron UHDR model across twelve clinical aperture configurations (2.5-10 cm diameter). Three scoring plane positions were evaluated relative to the physical aperture exit: coincident with the interface, 0.1 mm downstream, and 1 mm downstream. Scoring at the exact interface produced proximal R50 shifts of up to 2.2 mm and Distance-to-Agreement (DTA) values of 4-6 mm, exceeding clinical acceptance criteria. Artifact severity scaled inversely with aperture diameter, with the smallest configurations most severely affected. A 0.1 mm offset partially restored the primary electron energy spectrum but failed to recover the bremsstrahlung tail. A 1 mm offset fully resolved all artifacts, achieving mean DTA values within 2.0 mm, equivalent to or better than linac-exit references. These artifacts arise from the degenerate behavior of the fUseSafety step-limitation algorithm when safety equals zero at exact material boundaries, producing incomplete secondary electron equilibration and suppressed bremsstrahlung production. Angular distribution analysis revealed a near-forward particle pileup in the 0 mm PHSP and a deficit of large-angle secondaries recovered by the 1 mm offset. A 1 mm downstream offset fully mitigates these artifacts while introducing negligible perturbation to primary beam characteristics. This requirement applies to any Geant4-based framework (including TOPAS and GATE) scoring PHSP files at material exit surfaces.

Shape anisotropy of individual building blocks plays a crucial role in creating exotic structures and controlling phase behavior in equilibrium systems. We present a combined experimental and simulation study in which we used light-driven self-propelled rods to investigate when and how shape-induced alignment and steric and hydrodynamic interactions govern self-organization. Varying rod aspect ratio and area fraction causes the system to evolve from active Brownian motion to swarming, active turbulence, flocking, large clusters, and jamming. A state diagram summarizes emergent behaviors, and spatiotemporal analyses reveal distinct giant-number fluctuations across states. This minimal model offers insight into the self-organization of biological rodlike microswimmers, enabling the decoupling of physical from biological mechanisms. Our results provide design rules for programmable synthetic active materials and highlight parallels with bacterial swarms and other biological assemblies.

Unmanned aerial vehicles (UAVs, or drones) are likely to significantly increase the amount of air traffic. If the skies are full of UAVs, they need to interact with each other, for instance by yielding or other evasive maneuvers. The aggregated movements of drones will create traffic patterns. Just like in current road traffic, the interactions will be very frequent, so a centralized computer managing these interactions is expected not to be possible. There is a long history of traffic flow theory and modeling for 1 dimensional (road) traffic; this has been expanded to 2 dimensional traffic (pedestrians). It is unclear how traffic flow theory works for 3 dimensional traffic. In this paper we show how drone traffic can interact in a decentralized way. For the microscopic description, we add asymmetric interaction rules. We show that without centralized control, we can have efficient and safe traffic. Moreover, we provide a framework that directly links microscopic interactions to macroscopic properties. For the macroscopic description, we formulate and apply a numerical scheme that integrates the competition of space by UAVs for multiple classes, directions and dimensions. We apply both the microscopic and macroscopic descriptions to analyze (emerging) patterns which may arise in 3D traffic flow. The current paper provides background to develop interaction rules for drone traffic. Currently, the drone traffic is taking its first steps, but once the aeronautic technique takes off, the legislation regarding drone interactions should be ready. To support so, and be able to assess traffic consequences of decisions, the traffic flow theory framework developed here is essential.

High-order discontinuous Galerkin (DG) methods equipped with subcell finite-volume (FV) limiters provide an efficient framework for the simulation of nonlinear hyperbolic balance laws featuring shocks and complex flow structures. However, for systems with non-conservative terms, the design of hybrid DG/FV schemes that simultaneously guarantee high-order accuracy, robustness, and well-balancedness remains challenging. In particular, for the shallow water equations with variable bottom topography, standard flux-differencing formulations combined with node-wise subcell limiting generally destroy the well-balanced property, even if both the underlying DG and FV methods are individually well-balanced. In this work, we propose a novel flux-differencing formulation for non-conservative systems that enables node-wise subcell limiting while preserving steady states exactly. The key idea is to construct staggered DG fluxes whose non-conservative contributions are in local-times-jump form and vanish individually at equilibrium. To achieve this, we introduce a reformulation of the shallow water equations in which the source term is proportional to the gradient of the total water height. This reformulation allows the design of staggered fluxes that preserve equilibrium locally at the node level, thereby enabling arbitrary nodal blending with low-order FV fluxes. The resulting DG/FV method is high-order accurate, robust, and exactly well-balanced under node-wise limiting. Numerical experiments, including two-dimensional dam-break configurations with wet/dry fronts and complex obstacle interactions, demonstrate the improved stability and accuracy of the proposed approach. Although this work focuses on the shallow water equations, the well-balanced hybrid DG/FV methods developed here are applicable to a broader class of nonlinear systems of balance laws.

We report a significant enhancement of room-temperature photoluminescence from trivalent erbium-doped (Er3+) silicon nitride (Si3N4) metasurfaces at the telecommunication wavelength prepared via ion implantation. The metasurfaces, consisting of periodic nanocylinder arrays, are designed to support Mie-type resonances that tailor the local density of optical states. By systematically optimizing the nanocylinder radii, we achieve a photoluminescence (PL) enhancement factor of ~18 at a radius of 390 nm after thermal annealing, which is in excellent agreement with our simulations. Time-resolved PL measurements reveal a nearly ten-fold reduction in luminescence lifetime, confirming that the enhancement is primarily driven by the Purcell effect. Furthermore, we demonstrate that the PL intensity is strongly dependent on the Er3+ ion implantation depth, with a four-fold increase in emission observed from 20 nm to 80 nm ion range. These results provide a robust pathway for integrating efficient, active light sources into CMOS-compatible photonic device.

We demonstrate direct time-domain observation of l-doubling contributions in molecular rotational dynamics using shaped femtosecond laser pulses. By imposing a tailored spectral phase on the excitation pulse, we pre-compensate centrifugal distortion, which otherwise leads to temporally broadened, multi-cycle revival structures that obscure fine rotational features. A cubic spectral phase [Phys. Rev. A 107, 053108 (2023)] compresses selected revivals into near single-cycle events, in agreement with an analytic expression derived from molecular rotational constants, enabling predictive pulse design beyond numerical optimization. The resulting distortion-free revivals reveal temporally separated l-doubling contributions that remain unresolved in conventional impulsive alignment experiments. The method proves robust against experimental imperfections, including spatial light modulator discretization. While selective control of individual l-doubling components becomes feasible, here we focus on their direct observation in the time domain.

Optical solitons propagating through a multimode fiber represents one of the most fascinating class of objects exhibiting peculiar properties, with widespread potential for applications. We theoretically investigate the effect of the core refractive index distribution, characterized by the index exponent $\alpha$, on the evolution of multimode (MM) soliton beams and their peculiar properties in graded-index multimode fibers. Our analysis reveals an optimal range $\alpha$ = 2.04-2.08, within which MM solitons with minimum pulsewidth and characteristic energy are formed, owing to reduced modal walk-off and enhanced intermodal nonlinear interactions. Within this regime, the MM soliton undergoes efficient spatial condensation into the fundamental mode, resulting in a well-defined quasi-Gaussian output intensity profile. Notably, for some particular values of $\alpha$, we observe a reversal of conventional energy flow associated with MM soliton condensation, leading to the net transfer of energy toward higher-order modes, akin to the thermalization of MM optical fields into negative-temperature equilibrium states. Furthermore, we show that the characteristic Raman-induced spectral redshift of MM solitons can be controlled by tailoring the refractive index distribution. Our results highlight the refractive index distribution as a key control parameter governing MM soliton dynamics and their condensation behavior and are expected to be relevant for the design and optimization of MM fiber-based systems where controlled spatiotemporal dynamics are desired.

One of the most powerful strategies to address properties of real many-body systems is to incorporate data obtained for models, for example, to use data of the homogeneous electron gas in order to build the Local Density Approximation for the Kohn-Sham exchange-correlation potential. In the present work, we examine to what extent we can use model data to design functionals directly for observables of materials. In particular, we study different approximations for the charge density of real inhomogeneous materials expressed as a simple, explicit functional of a given Kohn-Sham potential, using as central building block the Lindhard density-density response function of the homogeneous electron gas. Our increasingly realistic set of approximations includes a fully nearsighted expression equivalent to the Thomas-Fermi approximation, functional Taylor expansions, and different approximations to the Connector Theory developed in [Aouina et al., npj Computational Materials 11, 242 (2025)]. In all cases, the charge density is obtained without ever solving the Kohn-Sham Schrödinger equation. Results for cubic helium, a prototypical strongly inhomogeneous material, as well as the covalent semiconductor silicon and metallic aluminum, systematically improve with higher levels of approximation. At the present stage, the results may be used for qualitative discussions or as optimized starting point for a self-consistent Kohn-Sham cycle. More generally, their quality indicates that this is a promising route to obtain functional expressions for observables that are relatively simple to calculate and to analyze.

To fulfill the security requirements of quantum cryptography, photon number coherence (PNC) of single photon sources has recently become an important figure of merit. Quantum dots (QDs) embedded in photonic microcavities offer a mature source of single photons, of which many properties can be tuned by the use of different excitation protocols or parameters. We show that the Swing-UP of quantum EmitteR population (SUPER) scheme can significantly decrease the PNC of the emitted photon, compared to resonant excitation. The reason for this is a laser-induced Stark shift, which effectively decouples the QD from the cavity during the SUPER excitation. Our calculations account for environmental effects such as phonons and radiative losses.

Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and rapidly unstable regimes. This framework provides a statistical-physics interpretation of threshold dispersion in single-event burnout of SiC power MOSFETs by linking coarse-grained electrothermal dynamics to probabilistic and time-resolved failure observables.

We present a machine learning framework for testing general relativity (GR) with gravitational wave signals from binary black hole mergers. Using the source parameters of 173 BBH events from the GWTC catalog as a realistic astrophysical population, we generate simulated GR waveforms and construct beyond GR (BGR) waveforms by applying controlled phase deformations. We introduce a response function formalism that provides a systematic framework for quantifying how any observable responds to modifications of GR. We train convolutional neural networks (CNNs) on two input representations: whitened waveforms and a response function type observable derived from the waveform mismatch, which isolates the effect of phase deviations from the bulk signal. Using response functions as the CNN input improves the classification sensitivity by a factor of approximately 33 compared to whitened waveforms, demonstrating that the choice of observable representation is as important as the classifier architecture. We study the fundamental limits of this classification through Bayes optimal error analysis, averaging methods that reveal coherent patterns hidden in noise, and a comparison between CNN accuracy and a single feature classifier as a proxy for human performance. At all deformation scales, the CNN outperforms the best single feature approach. We extend the framework to physically motivated theories using the parameterized post Einsteinian (ppE) formalism and apply it to massive gravity, where the classifier detects deviations for graviton masses of order $m_g \sim 10^{-23}\;\mathrm{eV}/c^2$ with aLIGO design sensitivity.