Papers

Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Materials (PCM) closes this gap through three stages: adversarial falsification across compositional space, bootstrap envelope refinement with 95% confidence intervals, and Lean 4 formal certification. Auditing CHGNet, TensorNet and MACE reveals architecture-specific blind spots with near-zero pairwise error correlations (r <= 0.13; n = 5,000), confirmed by independent Quantum ESPRESSO validation (20/20 converged; median DFT/CHGNet force ratio 12x). A risk model trained on PCM-discovered features predicts failures on unseen materials (AUC-ROC = 0.938 +/- 0.004) and transfers across architectures (cross-MLIP AUC-ROC ~ 0.70; feature importance r = 0.877). In a thermoelectric screening case study, PCM-audited protocols discover 62 additional stable materials missed by single-MLIP screening - a 25% improvement in discovery yield.

In this work, we report coherent perfect absorption (CPA) of anti-modes in an indirectly coupled magnon–polariton system. By examining both single and indirectly coupled cases, we experimentally distinguish the modal decay rate $\gamma$ from the effective decay rate $\gamma_{\rm{eff}}$. At CPA, $\gamma_{\rm{eff}} = 0$, leading to a vanishing output and a visually narrow spectrum in the dB-scale, while the intrinsic linewidth set by $2\gamma$ remains unchanged, demonstrating that the effective decay rate dictates the spectral amplitude rather than the physical loss. Furthermore, in the indirectly coupled system, CPA persists over a broad, magnetically tunable detuning range, in contrast to the single-detuning CPA observed in the directly coupled case, thereby enabling magnetically reconfigurable and frequency-selective microwave absorbers.

Driven nonlinear oscillators constitute a universal paradigm for understanding synchronization, frequency pulling, and frequency comb formation in nonequilibrium systems. Here, we realize such an emergent nonlinear oscillator in strongly interacting cesium Rydberg vapor, where coherent optical excitation, dissipation, and long-range interactions give rise to a driven-dissipative time crystal phase with intrinsic oscillation frequencies. Applying a radio-frequency (RF) field allows controlled tuning of the intrinsic oscillation frequency. Under RF heterodyne conditions, we observe intermodulation, frequency pulling, and, at strong drive, the emergence of a comb-like spectrum in the atomic coherence. We quantitatively capture these observations using a four-level mean-field model and demonstrate a classical analogue with a driven Van der Pol oscillator. Our results establish interacting Rydberg ensembles as a tunable platform for exploring nonequilibrium time-crystalline order, nonlinear synchronization, and frequency comb generation in many-body atomic systems.

Developing quantum algorithms to simulate fluid dynamics has become an active area of research, as accelerating fluid simulations could have significant impact in both industry and fundamental science. While many approaches have been proposed for simulating fluid dynamics on quantum computers, it is largely unclear whether these algorithms will provide speedup over existing classical approaches. In this paper we give evidence that quantum computers cannot significantly outperform classical simulations of fluid dynamics in general. We study two models of fluids: the Korteweg-de Vries (KdV) equation, which models shallow water waves, and the incompressible Euler equations, which model ideal, inviscid fluids. We show that any quantum algorithm simulating the KdV equation or the Euler equations for time $T$ requires $\Omega(T^2)$ and $e^{\Omega(T)}$ copies of the initial state in the worst case, respectively. These lower bounds hold for the task of preparing the final state, and similar bounds hold for history state preparation. We prove the lower bound for the KdV equation by investigating divergence of solitons. For the Euler equations, we show that instabilities enable fast state discrimination.

The coordination complexes of Yb(III) exhibit some of the longest spin coherence times among 4f compounds, making them a promising platform for molecular quantum technologies. While spin-phonon relaxation remains a limiting factor for coherence times even at low temperature, its control through chemical design has the potential to push these spin qubits prototypes beyond current limits. With the aim of providing insights on how to chemically control spin-phonon relaxation, we here present a full ab initio study of spin-phonon dynamics for three Yb(III) molecules exhibiting minimal chemical differences, yet quantitatively different spin relaxation times. Results show that low-temperature relaxation is governed by Raman processes triggered by a small group of largely delocalized low-energy phonons. The analysis of these contributions highlights that the modulation of spin-phonon coupling by molecular structure modifications beyond the first coordination shell are highly non-trivial in nature and hard to rationalize in simple chemical terms. These findings call for a conceptual step change from the attempt to use simple magneto-structural correlations to interpret the effect of molecular structural modifications on spin-phonon relaxation, and present predictive first-principles frameworks as a potential driving force of future chemical design strategies

Computational physics is a key part of what it means to do physics in the twenty-first century. However, upper division computational physics remains a largely understudied area. We set out to understand the experiences of students in an upper division computational physics lab course. To that end we conducted semi-structured interviews with five students at the end of their second of three terms in the course sequence. We then analyzed these interviews utilizing the emerging framework of Physics Computational Literacy. We found that the way students express how they learn computational physics implies they are making tradeoffs between their development of the different aspects of computational literacy. Additionally, we found that how students approach developing social computational literacy varies across individuals, and is driven by unspoken assumptions.

Generative Artificial Intelligence (gen-AI) is rapidly becoming more integrated into today's classrooms in all ranges of education. In higher education, Gen-AI is often seen as a resource for students, aiding them in drafting outlines, solving simple mathematical problems, or even decoding or constructing code. In this paper, we analyze essay-based interviews (N=6) from an upper-division computational physics course, in which physics majors addressed their views and attitudes towards Gen-AI and how it affects their learning. We analyzed the concepts of creativity and gen-AI using the Four C Model, a framework encompassing four types of creativity. Our analysis of the data involved coding and characterizing students' definitions of creativity and generative AI. Our findings revealed two main observations: first, students conceptualized their creativity primarily within mini-c and little-c; second, students perceived gen-AI as a resource and learning tool but expressed skepticism regarding its accuracy and creativity.

Binary fluid turbulence distinguishes itself from ordinary fluid turbulence by virtue of interfacial dynamics. Whether Kolmogorov-like scaling laws also exist for binary fluid turbulence is a fundamental question to explore. Starting from tensor formalism à la von Kármán and Howarth, here we derive exact scaling laws for isotropic Cahn-Hilliard-Navier-Stokes (CHNS) turbulence both in terms of two point correlators and increments. In particular, we derive the CHNS analogs for $1/3$, $4/3$, $2/15$ and $4/5$ laws known for isotropic hydrodynamic turbulence and show that the new scaling laws contain contributions both from the bulk flow and interface. The $2/15$ and $4/5$ laws of CHNS turbulence are found to be expressed purely in terms of two-point correlators and structure functions and their derivatives, respectively. However, unlike their hydrodynamic counterparts, these relations involve additional contributions from non-longitudinal directions. By means of direct numerical simulations with up to $1024^3$ grid points, all the derived exact laws are numerically verified and the scale dependence of the cascade rates obtained from different exact laws are thoroughly compared. As one moves from the homogeneous (but not necessarily isotropic) divergence form to the isotropic $4/5$ form, the inertial range is found to shift towards larger scales with a comparatively flatter cascade rate profile as a result of successive integrations over the small scales.

We present a method for optimizing stellarator configurations that combine omnigenity and piecewise omnigenity (pwO). Within the OOPS optimization framework [Liu et al., arXiv:2502.09350 (2025)], we introduce a mapping technique that can “squeeze” general omnigenous fields to approximate pwO in the high-field side. Using this approach, we obtain a range of optimized configurations that combine poloidal omnigenity (PO) and pwO, spanning different field periods and aspect ratios. We further show that these configurations are compatible with a magnetic well. The resulting configurations exhibit favorable neoclassical transport and bootstrap current properties while partially relaxing the strict constraints of omnigenity. These results suggest that such configurations are promising candidates for future stellarator reactors.

The detection of single particles or molecules represents a critical milestone in the development of biosensing technologies. Recently developed optical sensors based on quasi-bound states in the continuum (qBICs) have primarily focused on detecting global refractive index changes, aiming to simultaneously enhance both refractive index sensitivity and quality ($Q$) factors. However, sensors capable of resolving local refractive index perturbations, such as the binding of a nanometer-sized molecule on a surface, remain elusive and have not yet been demonstrated in BIC geometries due to the limited $Q$ factors and relatively large mode volumes. Here, we demonstrate low-contrast BIC metasurfaces that can perform sensing with a virus-sized single-nanoparticle resolution. The qBIC resonance operating at the critical coupling condition exhibits an experimental $Q$ factor of 4.5 x 10$^4$ in heavy water. The strong interaction between the localized electric field and polystyrene nanoparticles with a diameter of 100 nm enable the experimental observation of step-like resonance wavelength shifts, serving as signatures of individual particle binding events. Furthermore, binding-induced modifications to the qBIC resonance alter the optical confinement and asymmetry factor, inducing changes not only in the resonance wavelength but also in the linewidth and amplitude with single-particle sensitivity. Combined with position-insensitive response and free-space accessible features, low-contrast BIC metasurfaces provide a user-friendly platform for next-generation single-molecule sensing integrated with microfluidic systems.

In this short note, we present a rigorous derivation of the one-body double-hybrid density functional (OBDHF) theory, a self-consistent double-hybrid density functional framework that unifies the generalized Kohn-Sham (GKS) formalism with one-body Møller-Plesset second-order perturbation (OBMP2) theory. Conventional double-hybrid density functionals suffer from a fundamental theoretical inconsistency arising from the non-self-consistent treatment of the perturbative MP2 correlation, in which the orbitals entering the correlation energy expression are not variationally optimized with respect to the full double-hybrid energy functional. To address this deficiency, we construct a model energy functional as a linear combination of semilocal density functional approximation XC, a fraction $\alpha_x$ of exact Hartree-Fock (HF) exchange, and a fraction $\alpha_c$ of OBMP2 correlation. By virtue of the one-body operator structure of OBMP2, the perturbative correlation contribution is embedded directly and self-consistently into the GKS effective Hamiltonian, without recourse to the optimized effective potential (OEP) construction or perturbative orbital relaxation corrections. Through functional differentiation of the total OBDHF energy with respect to the orbitals, we derive the OBDHF effective Hamiltonian and the associated self-consistent field equations in a rigorous and transparent manner. This formulation provides a theoretically well-founded and practically tractable pathway to fully self-consistent double-hybrid density functional theory calculations within the GKS framework, resolving the self-consistency problem inherent in conventional double-hybrid functionals.

When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices – from phones and medical devices to battlefield drones and cars – and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number – the capacity-to-population ratio – that is knowable before any AI-agent ships.

The Bernstein-Vazirani (BV) algorithm is frequently taught as a canonical example of quantum parallelism, yet the standard interference-based explanation often obscures its underlying simplicity. We present a geometric reframing in which the Hadamard gate "wrapping" acts as a global basis rotation rather than a generator of computational complexity. This perspective reveals that the algorithm is effectively a classical linear computation over GF(2) performed in the conjugate Fourier basis, with the apparent parallelism arising from coordinate transformation. Building on Mermin's earlier pedagogical shortcut, which presented a 'classical' circuit equivalent but stopped short of explicitly labeling it as such, we elevate this to a formal geometric framework. In the extension, we distinguish between globally rotated circuits – which we reveal as classical linear computations – and topologically twisted circuits that generate quantum entanglement. We introduce a pedagogical taxonomy distinguishing (1) pure computational-basis circuits, (2) globally rotated circuits (exemplified by Bernstein-Vazirani), and (3) topologically twisted circuits involving non-aligned subsystem bases. This framework allows viewing the Gottesman-Knill theorem from a new angle, extends students' understanding of phase kickback and the 'Ricochet Property'. Furthermore, it provides a more intuitive starting point for explaining Bell-pair extensions through concrete circuit derivations and Qiskit simulations suitable for undergraduate quantum information courses. The outlook explores how this geometric view paves the way for understanding entanglement as topological twists.

The `15-minute city' has emerged as a central paradigm in urban planning, promoting universal access to work and essential services within short travel times. Its feasibility-particularly for commuting to work-has however rarely been examined quantitatively. Here, we show that proximity to employment is fundamentally constrained by the internal structure of urban economies. Combining urban geometry with empirically observed firm-size distributions, we derive a lower bound on commuting times that holds independently of planning choices or transport technologies. This bound reveals a sharp transition: when employment is sufficiently concentrated, no spatial rearrangement of workplaces can ensure uniformly short commutes, even under optimal placement. Applied to Paris and its near suburbs, we find that achieving universal 15-minute commutes would require substantial economic restructuring or differentiated mobility strategies. The relevant question is therefore not whether an $x$-minute city is achievable, but what the minimal feasible $x$ is given a city's economic structure and spatial scale.

Genetic analyses indicate minimal gene flow across the so-called Eastern Pacific Barrier (EPB) in larvae of the reef-building coral Porites lobata. Notably, Clipperton Atoll, situated on the eastern side of the EPB, is the only site that exhibits detectable genetic connectivity with the Line Islands, which lie to the west of the EPB. To elucidate the relationship between this genetic signal and large-scale Pacific Ocean circulation, we analyze historical trajectories of surface-drifting buoys from the Global Drifter Program (GDP). We first discretize the GDP drifter trajectories into a Markov chain representation and subsequently apply transition path theory (TPT) in combination with Bayesian inference. The TPT analysis identifies reactive trajectories – pathways that connect the Line Islands to Clipperton Atoll with minimal detours – whose travel times do not exceed 5 months, which is taken as an upper bound for the larval survival time of P. lobata. Consistently, the posterior distribution of transport from Pacific islands west of the EPB to Clipperton Atoll attains a local maximum in the Line Islands at a travel time of approximately 2.5 months. Our probabilistic characterization of the Lagrangian dynamics therefore supports a scenario of weak, but non-negligible, permeability of the EPB, in agreement with the genetic evidence, and it motivates a refined dynamical definition of the EPB based on the remaining duration of reactive trajectories. Furthermore, our results indicate that the connectivity between the Line Islands and Clipperton Atoll is governed primarily by the seasonal modulation of the North Equatorial Countercurrent, rather than by the phase of the El Niño–Southern Oscillation (ENSO). Finally, Clipperton Atoll's role as a terminal sink for trajectories is relevant to the planned mining operations.

Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UniCAC, a large-scale benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors – prior utilization, network architecture, and training strategy – that most significantly influence CAC performance, and further investigate their respective effects. We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations. Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC.

Wavelength-tunable ultra-intense femtosecond lasers may enable breakthroughs in diverse areas of science spanning attosecond science, particle acceleration and beyond. Conventional crystal-based methods are limited by gain bandwidth and damage thresholds, which restrict their wavelength tunability. Plasma-based frequency conversion, unconstrained by material damage, offers a promising alternative. Here, a novel scheme named Frequency Downshifting Stair (FDS) based on plasma bubble filling control is presented. The FDS enables arbitrary frequency down-conversion of ultra-intense femtosecond pulses and yields chirp-free laser pulses. It can achieve near-100% photon conversion efficiency, approaching the physical limit. This is attributed to the linear control by the FDS of laser chirp evolution during the photon deceleration in the plasma wake bubble. For a laser pulse with an arbitrary wavelength {\lambda}_0 (e.g., {\lambda}_0=800nm), proof-of-concept PIC simulations demonstrate that a single-stage FDS enables continuous wavelength tuning from {\lambda}_0 to {2{\lambda}}_0 (800-1600nm). Moreover, a three-stage cascaded FDS achieves more than tenfold frequency (10{\lambda}_0) downshifting to a central wavelength of 8.5{\mu}m. The FDS scheme thus provides a universal pathway for generating high-energy, few-cycle pulses across the broad infrared regime, offering a powerful new tool for wavelength-dependent ultrafast science.

At lower energies, the resonances in scattering experiments are often isolated. In quantum chaotic many-body, disordered or generically stochastic systems, the resonances overlap at larger energies. Eventually, the Ericson regime is reached in which the cross section behaves like a random function. The scattering-matrix elements then follow a universal Gaussian distribution. For more than sixty years, the emergence of this robust additional universal behavior on top of the universal system stochasticity has awaited a concise analytical treatment. We derive the transition to the Ericson regime in the universal Heidelberg approach and prove the universal Gaussian distribution by a proper asymptotic expansion. We also obtain explicit formulae for the moments of the distributions. We compare with microwave experiments and numerical simulations.

Valence-bond wavefunctions such as spin-coupled generalized valence bond (SCGVB) provide compact and chemically interpretable descriptions of strong correlation, but their nonorthogonal determinant structure makes quantum evaluation of overlaps and Hamiltonian matrix elements difficult. Here we introduce an ancilla-free, measurement-driven framework that reformulates these quantities as vacuum expectation values of Pauli-string operators, enabling evaluation with shallow circuits, local Clifford rotations, and computational-basis measurements, without controlled operations. We validate the method for H$_4$ along a dissociation pathway and for a 70-determinant C$_2$ active-valence benchmark. The reconstructed matrices agree with the classical Löwdin-based reference, while Chirgwin-Coulson weights remain chemically consistent. These results establish the proposed estimators as a low-depth, circuit-compatible formulation for evaluating SCGVB matrix elements, while making no claim of quantum computational advantage.

We study a kinetic multi-agent framework coupling opinion dynamics with epidemic spreading, where individual social behaviour both affects and is affected by disease transmission. Each agent is characterised by an epidemiological state and a continuous opinion variable measuring compliance with non-pharmaceutical interventions. The key mechanism of the model is an asymmetric opinion update driven by epidemic encounters: infection events induce more cautious attitudes, while failed transmissions push individuals toward more extreme opinions. We focus on a prototypical SIS setting, for which we derive a macroscopic kinetic description and, in a fast social-interaction regime, a reduced system of differential equations capturing the feedback between epidemic prevalence and opinion evolution. Convergence of the reduced model is rigorously quantified through a modified Wasserstein distance. Numerical simulations highlight how infection-induced awareness and non-infection-driven extremization jointly shape collective epidemic-opinion dynamics.

Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.

The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&D, where navigating uncharted chemical space to find next-generation materials or drugs is paramount. These models, reliant on structural descriptors or graph neural networks (GNNs), often fail when predicting properties for molecules with novel chemotypes. Here, we introduce a physics-augmented ML framework that overcomes this limitation. Our approach replaces conventional structural inputs with thermodynamic properties such as cohesive energy, heat of vaporization, and density, derived directly from molecular dynamics (MD) simulations. While performing comparably to structure-based models on known organic compounds, our method uniquely maintains low error when extrapolating to dissimilar chemical spaces. Crucially, it accurately predicts boiling points for entire chemical classes absent from the training set, including inorganic compounds, salts, and molecules with elements such as Si, B, and Te. By learning from the intermolecular forces that govern phase transitions, our framework provides a more fundamental and generalizable strategy for molecular property prediction, enabling chemical exploration beyond established structural domains.

Magnetic particle imaging (MPI) is a tracer-based technique that directly detects the distribution of magnetic iron-oxide nanoparticles with millisecond temporal resolution and no tissue background. Despite extensive preclinical work, in-vivo application of MPI in humans has not previously been reported. Here, we report the first in-vivo human MPI angiography, visualizing venous perfusion of the upper extremity using a human-scale scanner and clinically approved ferucarbotran. Under identical procedural conditions, we performed X-ray digital subtraction angiography as the clinical gold standard. MPI visualized major superficial and deep veins, including inflow, branching, valve filling, and clearance dynamics in real time with 2 frames per second. These results establish magnetic particle imaging as a clinically translatable modality for radiation-free vascular imaging in humans and mark the transition of MPI from preclinical research to first clinical application.

Multi-swarm systems, where two or more swarms of mobile agents occupy the same region of space with different parameters and goals, occur in a variety of biological, engineering, and defense applications. Composites of multiple swarms can produce hybrid spatiotemporal patterns, which compared to single swarming systems, are relatively unexplored. In this work, we develop a framework for studying the collision of counter-moving swarms, each with its own preferred, stable velocity before collision. We show that redirection of such swarms after collision occurs when a stable velocity synchronized state of the multi-swarm composite exists. Using a rigid-body approximation, we are able to extract how scatter-redirection transitions scale with swarm parameters in a variety of scenarios from reciprocal and non-reciprocal systems to symmetric and antagonistic parameter values. Our results compare well to simulations of both particle modeled agents and wheeled robots.

High accuracy calculations of atomic properties require using long basis sets. In particular, it is necessary to include large number of partial waves and estimate truncation corrections. The convergence in partial waves is known to be rather slow, so calculations become very costly. We use valence perturbation theory [PRA 105, 052805 (2022)] to calculate contribution of the high partial waves and estimate truncation corrections. These estimates may be useful to make assessment of theoretical error in atomic calculations more reliable.

Achieving efficient three-dimensional feedback cooling of levitated nanoparticles is a key requirement for precision sensing and quantum control in levitated optomechanics. Here we demonstrate three-dimensional optical feedback cooling of a levitated nanoparticle using an interference-enhanced optical force generated within a single beam path. In this scheme, a weak auxiliary field co-propagates with the trapping tweezer and interferes with it to produce a tunable optical force that enables cold damping along all three center-of-mass motional axes without additional beam paths or trap reconfiguration. Using this approach, we cool a 142-nm-diameter silica nanoparticle in high vacuum to effective temperatures of 625.8, 711.6, and 19.9 mK along the $x$, $y$, and $z$ directions, respectively, at a pressure of $8.5\times10^{-6}$ mbar. The cooling dynamics and their dependence on feedback gain and pressure are well described by a cold-damping model. Because the feedback force is generated optically, the scheme does not rely on electrical actuation and is directly compatible with neutral particles. These results establish interference-based optical forces as a simple and broadly applicable mechanism for three-dimensional feedback control in levitated optomechanics, with a clear pathway toward the quantum regime under improved vacuum and detection conditions.

An anomalous flat-band dispersion provided by a degenerate band edge (DBE) of longitudinal optical modes in a double-grating waveguide is used to enhance second-harmonic generation (SHG). The DBE is a fourth-order exceptional point degeneracy (EPD) in a lossless and gainless waveguide, characterized by the coalescence of four eigenmodes that establish a frozen mode in a cavity. At a DBE resonance, the cavity quality factor scales $Q\propto N^5$, where $N$ is the number of unit cells of the grating waveguide. In our numerical experiments, we observe the peak intensity of the fundamental field in the edge-excited cavity scaling as $I_1\propto N^{3.6}$. This leads to a highly efficient SHG process that is radiated vertically from the cavity (i.e., normal to the grating) without requiring collinear phase matching, with a conversion efficiency scaling as $\eta\propto N^{8.27}$. These results establish DBE-based waveguides as promising platforms for miniaturized efficient nonlinear photonic devices.

In the Tokamak à Configuration Variable (TCV), infrared thermography (IR) is currently composed of the horizontal, vertical, and tangential infrared systems (HIR, VIR, TIR), which all use Equus 81k M cameras. The IR diagnostics obtain the surface temperature of TCV's graphite tiles for post-discharge analysis. Target heat flux profiles are inferred from the tile temperature with the THEODOR (Thermal Energy Onto Divertor) code. Fast transient analysis is possible in reduced frame mode, with acquisition frequencies above 10kHz. The main views are the lower inner wall for HIR, the floor for VIR, and the lower outer wall for TIR. The HIR camera can also be moved to view the midplane inner wall, while TIR can be moved to see the midplane inner wall and the upper outer wall, mainly to measure synchrotron radiation and heat deposition due to runaway electrons. Recent developments in TCV's IR systems include (i) tile diffusivity and conductivity measurements to assure the precision of heat flux estimates; (ii) the addition of one new VIR heated valley tile and two rooftop TIR tiles, for measurements of fast heat flux transients; (iii) the implementation of long-pass wavelength filter of 4095 nm, to diminish the measurement of plasma parasitic infrared light, mainly from deuterium 5-4 emission at 4051 nm. Despite these developments, the main sources of uncertainty for IR in TCV are still parasitic infrared light and the determination of the surface layer heat transmission factor, both of which mainly affect the VIR system.

Vertical-cavity surface-emitting laser (VCSEL) structures emitting near 940 nm were grown by solid source molecular beam epitaxy (MBE) on Ge(001) substrates. The VCSEL MBE-growth was realized upon a virtual substrate composed of GaAs on Ge grown by melatorganic vapour phase epitaxy (MOVPE). In situ monitoring during MBE growth employed multispectral reflectometry and magnification-inferred curvature imaging for real-time growth analysis. Curvature measurements revealed progressive compressive stress, while optical reflectivity data confirmed uniform layer growth and accurate stopband formation. Fabricated devices with mesa diameters of 35-40 $\mu$m, corresponding to oxide apertures of approximately 11-16 $\mu$m, exhibited room-temperature lasing under continuous-wave bias with threshold currents below 3 mA. To the best of our knowledge, this is the first demonstration of monolithically integrated 940 nm VCSELs grown on Ge substrates by MBE. These results confirm the viability of MBE-grown VCSELs on Ge with in situ process control for scalable optoelectronic integration.

We present the application of the recently developed one-body Møller–Plesset perturbation theory (OBMP2) to the prediction of K-edge excited states. OBMP2 is a self-consistent perturbation theory in which a canonical transformation followed by a cumulant approximation yields an effective one-body Hamiltonian. This resulting operator augments the standard Fock operator with a one-body correlation potential containing double-excitation MP2 amplitudes, allowing molecular orbitals and orbital energies to be optimized in the presence of correlation. This self-consistent framework mitigates convergence and accuracy issues often encountered in standard non-iterative MP2 for open-shell systems and bond-stretching regimes. In this work, we evaluate the performance of an OBMP2-based approach for the calculation of K-edge excitations. Utilizing benchmark test sets of both closed-shell and open-shell molecules, we demonstrate that our method outperforms established standard techniques, including $\Delta$DFT, EOM-CCSD, and USTEOM-CCSD. Our findings establish the OBMP2-based $\Delta$SCF protocol as a robust and accurate new computational method for the treatment of K-edge excited states.