Papers

We explore the dynamical response of the free surface of an ultra-soft solid driven by a localized moving pressure disturbance. Experiments reveal a steady V-shaped wake analogous to a surface Mach wedge. A simple geometric argument provides a qualitative explanation consistent with observations. A theoretical framework combining elastodynamic, capillary, and gravitational effects yields a generalized dispersion relation that smoothly interpolates between Kelvin's theory of liquid interface wakes and Rayleigh's theory of elastic surface waves. Together, our experiments and theory reveal the existence of a soft wake regime that bridges fluid and solid surface wave physics, offering new routes for probing the dynamics of soft surfaces.

Objective: To assess the accuracy and computational performance of a stochastic differential equation (SDE)–based model for proton beam dose calculation by benchmarking against Geant4 in simplified phantom geometries. Approach: Building on Crossley et al. (2025), we implemented the SDE model using standard approximations to interaction cross sections and mean excitation energies, enabling straightforward adaptation to new materials and configurations. The model was benchmarked against Geant4 in homogeneous, longitudinally heterogeneous and laterally heterogeneous phantoms to assess depth–dose behaviour, lateral transport and material heterogeneities. Main results: Across all phantoms and beam energies, the SDE model reproduced the main depth–dose characteristics predicted by Geant4, with proton range agreement within 0.2 mm for 100 MeV beams and 0.6 mm for 150 MeV beams. Voxel–wise comparisons yielded gamma pass rates exceeding 95% under 2%/0.5 mm criteria with a 1% dose threshold. Differences were localised to steep dose gradients or material interfaces, while overall lateral beam dispersion was well reproduced. The SDE model achieved speed-up factors of about 2.5–3 relative to single-threaded Geant4. Significance: The SDE approach reproduces key dosimetric features with good accuracy at lower computational cost and is amenable to parallel and GPU implementations, supporting fast proton therapy dose calculations.

Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and substantial labeled data ($>$10,000) and lack adaptability. Here we present MatInvent, a general and efficient reinforcement learning workflow that optimizes diffusion models for goal-directed crystal generation. For single-objective designs, MatInvent rapidly converges to target values within 60 iterations ($\sim$ 1,000 property evaluations) across electronic, magnetic, mechanical, thermal, and physicochemical properties. Furthermore, MatInvent achieves robust optimization in design tasks with multiple conflicting properties, successfully proposing low-supply-chain-risk magnets and high-$\kappa$ dielectrics. Compared to state-of-the-art methods, MatInvent exhibits superior generation performance under specified property constraints while dramatically reducing the demand for property computation by up to 378-fold. Compatible with diverse diffusion model architectures and property constraints, MatInvent could offer broad applicability in materials discovery.

Large magnetic fields, either imposed externally or produced spontaneously, are often present in laser-driven high-energy-density systems. In addition to changing plasma conditions, magnetic fields also directly modify laser-plasma interactions (LPI) by changing participating waves and their nonlonear interactions. In this paper, we use two-dimensional particle-in-cell (PIC) simulations to investigate how magnetic fields directly affect crossbeam energy transfer (CBET) from a pump to a seed laser beam, when the transfer is mediated by the ion-acoustic wave (IAW) quasimode. Our simulations are performed in the parameter space where CBET is the dominant process, and in a linear regime where pump depletion, distribution function evolution, and secondary instabilities are insignificant. We use a Fourier filter to separate out the seed signal, and project the seed fields to two electromagnetic eigenmodes, which become nondegenerate in magnetized plasmas. By comparing the seed energy before CBET occurs and after CBET reaches quasi-steady state, we extract CBET energy gains of both eigenmodes for lasers that are initially linearly polarized. Our simulations reveal that starting from a few MG fields, the two eigenmodes have different gains, and magnetization alters how the gains depend on laser detuning. The overall gain decreases with magnetization when the laser polarizations are initially parallel, while a nonzero gain becomes allowed when the laser polarizations are initially orthogonal. These findings qualitatively agree with theoretical expectations.

Buried pipelines transporting oil and gas across geohazard-prone regions are exposed to potential ground movement, leading to the risk of significant strain demand and structural failure. Reliability analysis, which determines the probability of failure after accounting for pertinent uncertainties, is essential for ensuring the safety of pipeline systems. However, traditional reliability analysis methods involving computationally intensive numerical models, such as finite element simulations of pipeline subjected to ground movement, have limited applications; this is partly because stochastic sampling approaches require repeated simulations over a large number of samples for the uncertain variables when estimating low probabilities. This study introduces Physics-Informed Neural Network for Reliability Analysis (PINN-RA) for buried pipelines subjected to ground movement, which integrates PINN-based surrogate model with Monte Carlo Simulation (MCS) to achieve efficient reliability assessment. To enable its application under uncertain variables associated with soil properties and ground movement, the PINN-based surrogate model is extended to solve a parametric differential equation system, namely the governing equation of pipelines embedded in soil with different properties. The findings demonstrate that PINN-RA significantly reduces the computational effort required and thus accelerates reliability analysis. By eliminating the need for repetitive numerical evaluations of pipeline subjected to permanent ground movement, the proposed approach provides an efficient and scalable tool for pipeline reliability assessment, enabling rapid decision-making in geohazard-prone regions.

The definition of a reference frame in General Relativity is achieved through the construction of a congruence of time-like world-lines. In this framework, splitting techniques enable us to express physical phenomena in analogy with Special Relativity, thereby realizing the local description in terms of Minkowski spacetime in accordance with the equivalence principle. This approach holds promise for elucidating the foundational principles of relativistic gravitational physics, as it illustrates how its 4-dimensional mathematical model manifests in practical measurement processes conducted in both space and time. In addition, we show how, within this framework, the Newtonian gravitational force naturally emerges as an effect of the non-geodesic path of the reference frame.

The problem of reconstructing the time dependence of the dynamic pressure of a plasma jet impinging on one end of a solid rod based on the measured displacement of the opposite end has been solved. This solution allows for a reduction in the size of the dynamic pressure sensor proposed and later improved in the works [1, 2].

The small-scale turbulent dynamo is a key mechanism for amplifying galactic magnetic fields, yet the resulting field morphology remains poorly understood. Using 3D driven turbulence simulations across a range of compressibilities, characterised by Mach number, and Minkowski functionals, we quantitatively investigate the morphology of magnetic fields generated by the small-scale turbulent dynamo in both the exponentially growing kinematic stage and the statistically steady saturated stage. In both stages and across all Mach numbers, we find that the magnetic field departs significantly from a Gaussian random field. Magnetic structures are statistically less curved and more interconnected in the saturated stage than in the kinematic stage, with these morphological differences decreasing as compressibility increases. Our work provides a quantitative description of how density fluctuations in turbulence and the back-reaction of amplified magnetic fields via the Lorentz force together shape complex, non-Gaussian magnetic structures, and offers a valuable framework for comparing simulations with both analytical models and polarisation observations.

We propose a particle acceleration mechanism driven by large-amplitude Alfvén waves in a strong magnetic field. The acceleration process proceeds through multiple stages triggered by counterpropagating wave-particle resonant acceleration (CWRA) via decay instability. Initially, parent and daughter Alfvén waves resonantly accelerate particles perpendicular to the ambient magnetic field. The resultant modulational instability generates electrostatic fields within the wave packet, which are locally amplified by the ponderomotive force of the Alfvén wave packet. These fields subsequently drive further acceleration within a few relativistic gyroperiods via gyroresonant surfing acceleration (GRSA). During this, the v*B force facilitates momentum transfer from the perpendicular to the parallel direction. In the later stage, particles become trapped by the parent wave and gain additional energy through single wave resonant acceleration (SWRA). Furthermore, the accumulation of accelerated particles induces electrostatic trailing fields behind and at the tail of the wave packet, which drive trailing-field acceleration (TFA) of other electrons. The combined effects of these mechanisms, CWRA followed by GRSA and SWRA, result in highly relativistic electron energy. The electron energy accelerated through the above process is higher than that accelerated through TFA. This multistage acceleration process provides insights into the generation of high energy cosmic rays in astrophysical environments.

Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet, this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants has been shown to be a powerful ansatz for capturing strong correlation. By contrast, the analogue for bosons, backflow permanents, is unpractical due to the steep cost of computing the matrix permanent and due to the lack of particle conservation in common bosonic problems. To circumvent these obstacles, we introduce a modal backflow (MBF) NQS design and demonstrate its efficacy by solving the anharmonic vibrational problem. To accommodate the demand of high accuracy in spectroscopic calculations, we implement a selected-configuration scheme for evaluating physical observables and gradients, replacing the standard stochastic approach based on Monte Carlo sampling. A vibrational self-consistent field calculation is conveniently carried out within the MBF network, which serves as a pretraining step to accelerate and stabilize the optimization. In applications to both artificial and ab initio Hamiltonians, we find that the MBF network is capable of delivering spectroscopically accurate zero-point energies and vibrational transitions in all anharmonic regimes.

Fish display remarkable swimming capabilities through the coordinated interaction of the body and caudal fin, yet the potential role of a passively pitching tail in enhancing hydrodynamic performance remains unresolved. In this work, we evaluate the performance of a carangiform swimmer equipped with either an actively pitching tail or a passively pitching tail. Fluid-structure interactions-based simulations are employed to asses how variations in joint stiffness, damping, and inertia influence thrust generation, power demand, and overall stability at two representative Reynolds numbers of 500 and 5000. The results reveal that actively pitching tails tend to generate greater thrust, while passively pitching tails deliver improved outcomes in terms of the power demand at the lower Reynolds number. Larger pitching amplitudes contribute positively only when associated with higher swimming frequency, when produced by reduced inertia for more flexible joints, they lead to unfavorable effects. At the higher Reynolds number, active tails consistently outperform passive ones, although a small subset of passive cases still achieve favorable performance. Across all cases, a recurring balance emerges, with thrust production and power expenditure varying inversely. These findings clarify the hydrodynamic consequences of passive versus active tail motion and establish design principles for bio-inspired underwater vehicles, where smaller swimmers may benefit from passive tail pitching, while larger swimmers are better served by active control

Voluntary carbon-free electricity (CFE) procurement has the potential to accelerate electric sector decarbonization, but procurement strategies vary widely, leading to uncertainty about emissions, investments, and costs. This study assesses the system-wide effects of voluntary CFE procurement on U.S. regional power systems using a detailed energy systems model across a range of program designs, eligible technologies, policy environments, and modeling assumptions. Results suggest that hourly matching, where clean electricity procurement aligns with hourly load, combined with new and local generation could maximize emissions reductions from CFE procurement, particularly under existing Inflation Reduction Act incentives and state policies. However, regional costs vary significantly, with a CFE cost premium ranging from \$11-63/MWh nationally across scenarios and \$1-130/MWh across regions, broader than previous estimates. Expanding the eligible technology portfolio to include renewables, nuclear, carbon capture, and energy storage reduces costs, particularly in regions with lower wind and solar resource quality, though variable renewables and battery storage remain the dominant resources in many scenarios. Additionally, we show that the future policy environment strongly influences the effectiveness of voluntary CFE programs, with more stringent emissions policies or subsidies potentially limiting the incremental benefits of procurement. The analysis also quantifies how features of the model framework can shape insights about CFE procurement strategies.

Second-order nonlinear optical materials enable frequency doubling of light (second-harmonic generation, SHG), which is essential for optoelectronic applications ranging from materials characterization to quantum technologies. However, comparing SHG performance across materials remains challenging as the second-order nonlinear susceptibility $\chi^{(2)}$ spans several orders of magnitude and strongly depends on the band gap $E_g$. To address this, we empirically validate a theoretical upper bound on $\chi^{(2)}$ using new databases of ab initio-computed nonlinear optical (NLO) properties. We then formulate a normalized descriptor, $\hat{d}$, which expresses the NLO response of a material relative to the band gap-dependent physical limit. We show that $\hat{d}$ exhibits a similar distribution across a wide range of band gap energies. This universality supports the use of $\hat{d}$ as a robust, generalizable descriptor for data-driven and chemistry-informed machine learning models of NLO response, enabling accelerated materials discovery and optimization across broad application frequencies.

A mathematical model for the evolution of, and deposition from, a thin particle-laden droplet on an infinitely thick, isotropic, flooded, porous substrate with interconnected pores undergoing simultaneous evaporation and imbibition is formulated and analysed. In particular, analytical expressions for the evolution of the droplet, as well as for the flow within the droplet and the substrate, and for the transport and deposition onto the substrate of the particles are obtained for droplets evolving in four different modes. While the physical mechanisms driving evaporation and imbibition are rather different, perhaps rather unexpectedly, it is found that there are a number of qualitative and quantitative similarities as well as differences in the resulting behaviour of the droplet as it loses mass to its environment. For example, it is shown that a droplet undergoing pure imbibition in the constant contact radius mode never completely imbibes, but if it evaporates (either with or without imbibition also occurring) then it has a finite lifetime, and increasing the strength of evaporation and/or imbibition shortens its lifetime. It is also shown that in the regime in which diffusion of particles is faster than axial advection but slower than radial advection of particles, the final deposit of particles left behind on the substrate after the droplet has completely evaporated and/or imbibed is independent of both the nature and the strength of the physical mechanism(s) driving the mass loss from the droplet. Not only are these results of theoretical interest, but they are also relevant to a wide variety of practical applications, including ink-jet printing, DNA chip manufacturing, and disease diagnostics, that would benefit from an improved ability to predict and/or control the final deposit pattern from a droplet undergoing simultaneous evaporation and imbibition.

Neural network observers (NNOs) are proposed for real-time estimation of fluid flows, addressing a key challenge in flow control: obtaining real-time flow states from a limited set of sparse and noisy sensor data. For this task, we propose a generalization of the classical Luenberger observer. In the present framework, the estimation loop is composed of subsystems modeled as neural networks (NNs). By combining flow information from selected probes and an NN surrogate model (NNSM) of the flow system, we train NNOs capable of fusing information to provide the best estimation of the states, that can in turn be fed back to an NN controller (NNC). The NNO capabilities are demonstrated for three nonlinear dynamical systems. First, a variation of the Kuramoto-Sivashinsky (KS) equation with control inputs is studied, where variables are sparsely probed. We show that the NNO is able to track states even when probes are contaminated with random noise or with sensors at insufficient sample rates to match the control time step. Then, a confined cylinder flow is investigated, where velocity signals along the cylinder wake are estimated by using a small set of wall pressure sensors. In both the KS and cylinder problems, we show that the estimated states can be used to enable closed-loop control, taking advantage of stabilizing NNCs. Finally, we present a legacy dataset of a turbulent boundary layer experiment, where convolutional NNs (CNNs) are employed to implement the models required for the estimation loop. We show that, by combining low-resolution noise-corrupted sensor data with an imperfect NNSM, it is possible to produce more accurate estimates, outperforming both the direct reconstructions via specialized super-resolution NNs and the direct model propagation from initial conditions.

We investigate the self-consistent formation and long-term evolution of proton beams in the expanding solar wind using an ensemble of one-dimensional hybrid expanding box simulations. Initial conditions are chosen to represent a range of plasma states observed by the Helios spacecraft at 0.3 AU, including an amplitude-modulated Alfvén wave that nonlinearly drives a proton beam aligned with the magnetic field. We compare simulation results with solar wind data out to 1.5 AU and show that our model reproduces key observed features of proton beams on average, such as the radial evolution of the drift and the relative core-to-beam density ratio. These findings support the theory that the observed evolution of the proton beam drift in the solar wind is determined by kinetic instabilities. More broadly, our results indicate that the interplay between nonlinear Alfvén wave dynamics, expansion effects and kinetic instabilities plays a fundamental role in solar wind dynamics, with implications for interpreting solar wind heating rate estimates.

Low-Gain Avalanche Detectors (LGADs) are characterized by a fast rise time (500 ps) and extremely good time resolution (down to 17 ps). The intrinsic low granularity of LGADs and the large power consumption of readout chips for precise timing are problematic in near-future experiments such as e+e- Higgs factories (FCC-ee) and the ePIC detector at the Electron-Ion Collider. AC-coupled LGADs, where the readout metal is AC-coupled through an insulating oxide layer, could solve both issues at the same time thanks to the 100% fill factor and charge-sharing capabilities. Charge sharing between electrodes allows a hit position resolution well below the pitch/$\sqrt12$ of standard segmented detectors. At the same time, it relaxes the channel density and power consumption requirements of readout chips. Extensive laboratory characterization of AC-LGAD devices from the first full-size (up to 3x4 cm) production from HPK for ePIC will be shown in this contribution. Both pixel and strip geometry was produced and tested. This study was conducted within the scope of the ePIC detector time of flight (TOF) layer R&D program at the EIC.

Einstein, Podolsky, and Rosen discussed their paradox in terms of measuring the positions or momenta of two particles. These degrees of freedom can become entangled upon scattering, but how much entanglement can be created in this process? Here we address this question using fully coherent calculations of bipartite scattering in three-dimensional space, quantifying entanglement by the inverse of the single particle purity. We show that the standard plane-wave description of scattering fails to capture the entanglement properties, due to the essential role of quantum uncertainty in the initial state. For a more realistic description of a scattering setup, we find that the entanglement scales linearly with the scattering cross section, including strong enhancement near shape resonances, for sufficiently narrow initial momentum dispersion. We highlight the differences between scattering in one and higher spatial dimensions and discuss how the generation of motional entanglement can be detected in experiments. Our results open the way to probing, controlling, and eventually using entanglement in quantum collisions.

While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized by a society with a growing presence of autonomous machines is high. Autonomous AI agents are already deployed and active across several industries and digital environments and alongside human-human and human-machine interactions, machine-machine interactions are poised to become increasingly prevalent. Given these developments, I argue that criminology must begin to address the implications of this transition for crime and social control. Drawing on Actor-Network Theory and Woolgar's decades-old call for a sociology of machines – frameworks that acquire renewed relevance with the rise of generative AI agents – I contend that criminologists should move beyond conceiving AI solely as a tool. Instead, AI agents should be recognized as entities with agency encompassing computational, social, and legal dimensions. Building on the literature on AI safety, I thus examine the risks associated with the rise of multi-agent AI systems, proposing a dual taxonomy to characterize the channels through which interactions among AI agents may generate deviant, unlawful, or criminal outcomes. I then advance and discuss four key questions that warrant theoretical and empirical attention: (1) Can we assume that machines will simply mimic humans? (2) Will crime theories developed for humans suffice to explain deviant or criminal behaviors emerging from interactions between autonomous AI agents? (3) What types of criminal behaviors will be affected first? (4) How might this unprecedented societal shift impact policing? These questions underscore the urgent need for criminologists to theoretically and empirically engage with the implications of multi-agent AI systems for the study of crime and play a more active role in debates on AI safety and governance.

This is the manuscript of my "Habilitation à diriger des recherches", where I present the research work that I have done after my PhD, defended in 2009. The manuscript is divided in two parts. The first one is dedicated to atomic-structure calculations with neutral and trivalent lanthanides, in the contexts of ultracold gases and rare-earth doped solids. The second part deals with long-range interactions in ultracold gases of alkali-metal atoms and diatomic molecules, as well as lanthanide atoms. The detailed description of long-range interactions serves to characterize ultralow-temperature phenomena, like photoassociation and collisional shielding.

We present a method for solving the positron diffusion equation in multi-layer systems. Our approach incorporates material-specific implantation profiles, diffusion parameters, and positron affinities. It utilizes a Markov chain approach to model annihilation probabilities and provides fitting capabilities for experimental S (lineshape) parameter data. We have implemented this algorithm in Python and made it available for free under the name LIMPID. To demonstrate its performance, we analyze depth-resolved Doppler-Broadening Spectroscopy measurements of a Cu layer on a Si substrate, achieving excellent agreement with the experimental profiles. The LIMPID tool enhances the reproducibility and comparability of positron defect characterization measurements across different research groups.

We show that transmutation driven by high-energy neutrons from deuterium-tritium (D-T) fusion reactions can produce many important medical radioisotopes - including $^{32}$P, $^{60}$Co, $^{64}$Cu, $^{89}$Sr, $^{90}$Y, $^{89}$Zr, $^{99}$Mo/$^{99\mathrm{m}}$Tc, $^{103}$Pd, $^{111}$In, $^{117}$In/$^{117\mathrm{m}1}$Sn, $^{123}$I, $^{125}$I, $^{131}$I, $^{133}$Xe, $^{153}$Sm, $^{166}$Ho, $^{177}$Lu, $^{188}$Re, and $^{192}$Ir-and emerging isotopes such as $^{47}$Sc, $^{67}$Cu, $^{103}$Ru/$^{103\mathrm{m}}$Rh, $^{103}$Pd/$^{103\mathrm{m}}$Rh, $^{119}$Sb, $^{124}$I, $^{155}$Tb, $^{161}$Tb, $^{195\mathrm{m}1}$Ir/$^{195\mathrm{m}}$Pt, and $^{225}$Ac with high specific activity and in large quantities. These reactions involve stable, abundant feedstocks and non-fission transmutation channels that change the proton number, enabling chemical separation of the product. Fusion-based transmutation could provide a flexible and proliferation-resistant platform for supply of high-purity isotopes. A D-T neutron source operating at a few megawatts of fusion power could meet or exceed global demand for most major radioisotopes. Further research is required to develop tailored approaches for feedstock processing and product extraction.

The interplay between nonlinear and topological physics has led to intriguing emergent phenomena, such as quantized and fractionally quantized Thouless pumping of solitons dictated by the topological invariants of the underlying band structure. Unlike linear Thouless pumping, which requires excitation of a Wannier function of a uniformly filled band, quantized soliton pumping is observed even with localized excitations that do not represent Wannier functions. Here, we show that similar soliton-like quantized pumping can be observed in Aubry-Andre-Harper (AAH) model by introducing a synthetic nonlinearity in the form of a cutoff on the coupling strengths between lattice sites. More importantly, we reveal that the localized excitations driving quantized soliton pumping are precisely the Wannier functions of the uniformly filled bands of the effectively nonlinear lattice, thus restoring consistency with linear Thouless pumping. We extend this approach to multi-band systems and show that the nonlinearity introduces a degeneracy between bands, subsequently leading to the observation of fractionally quantized pumping. Our approach of introducing a synthetic nonlinearity is general and could be extended to reveal soliton dynamics in other nonlinear topological systems.

Understanding regional hydroclimatic variability and its drivers is essential for anticipating the impacts of climate change on water resources and sustainability. Yet, considerable uncertainty remains in the simulation of the coupled land atmosphere water and energy cycles, largely due to structural model limitations, simplified process representations, and insufficient spatial resolution. Within the framework of the Collaborative Research Center 1502 DETECT, this study presents a coordinated intercomparison of regional climate model simulations designed for water cycle process analysis over Europe. We analyze the performance of simulations using the ICON and TSMP1 model systems and covering the period from 1990 to 2020, comparing against reference datasets (E-OBS, GPCC, and GLEAM). We focus on 2 m air temperature, precipitation and evapotranspiration over four representative basins, the Ebro, Po, Rhine, and Tisa, within the EURO CORDEX domain. Our analysis reveals systematic cold biases across all basins and seasons, with ICON generally outperforming TSMP1. Precipitation biases exhibit substantial spread, particularly in summer, reflecting the persistent challenge of accurately simulating precipitation. ICON tends to underestimate evapotranspiration, while TSMP1 performs better some seasons. Sensitivity experiments further indicate that the inclusion of irrigation improves simulation performance in the Po basin, which is intensively irrigated, and that higher-resolution sea surface temperature forcing data improves the overall precipitation representation. This baseline evaluation provides a first assessment of the DETECT multimodel ensemble and highlights key structural differences influencing model skill across hydroclimatic regimes.

Standard visualizations of Extreme Ultraviolet (EUV) solar imagery often fail to convey the full complexity of the Sun's corona, especially in faint off-limb regions. This can leave the misleading impression of the Sun as a bright ball in a dark void, rather than revealing it as the dynamic, structured source of the solar wind and space weather. A variety of enhancement algorithms have been developed to address this challenge, each with its own strengths and tradeoffs. We introduce the Radial Histogram Equalizing Filter (RHEF), a novel hybrid technique that optimizes contrast in high dynamic range solar images. By combining the spatial awareness of radial graded filters with the perceptual benefits of histogram equalization, RHEF reveals faint coronal structures and works out of the box – without requiring careful parameter tuning or prior dataset characterization. RHEF operates independently on each frame, and it enhances on-disk and off-limb features uniformly across the field of view. For additional control, we also present the Upsilon redistribution function – a symmetrized cousin of gamma correction – as an optional post-processing step that provides intuitive programmatic tonal compression. We benchmark RHEF against established methods and offer guidance on filter selection across various applications, with examples from multiple solar instruments provided in an appendix. Implemented and available in both Python sunkit_image and IDL, RHEF enables immediate improvements in solar coronal visualization.

Although a number of studies have investigated human mobility patterns during natural hazards, mechanistic models that capture mobility dynamics under large-scale perturbations, such as extreme floods, remain scarce. Leveraging mobile phone data and building upon recent insights into universal mobility patterns, we assess whether the general structure of population flows persists during the extreme floods that struck Emilia-Romagna, Italy, in 2023. Our analysis reveals that the relationship between visitor density, distance, and visitation frequency remains robust even under extreme flooding conditions. To disentangle the effects of distance and visitation frequency, we define two aggregated visitor densities: the marginal density over frequency and the aggregated density over distance. We find that the marginal density over frequency exhibits a time-invariant power-law exponent, indicating resilience to flooding disturbances. In contrast, the aggregated density over distance displays more complex behavior: an exponential decay over biweekly periods and a power-law decay over a monthly interval. We propose that the observed power law emerges from the superposition of exponential distributions across shorter timescales. These findings provide new insights into human mobility scaling laws under extreme perturbations, highlighting the robustness of visitation patterns and suggesting avenues for improved mechanistic modeling during natural disasters.

Using the Kohn-Sham (KS) inversion method of Hollins et al. [J. Phys.: Condens. Matter 29, 04LT01 (2017)], we invert densities from variational and diffusion quantum Monte Carlo (QMC) calculations to obtain benchmark QMC-KS potentials for a range of insulators and semiconductors, which we then compare to the KS potentials of popular density functional approximations (DFAs). Our results show that different DFAs yield similar electron densities, despite differences in their KS potentials, which originate primarily from the exchange and correlation contribution. We also find that the KS gap from the QMC density is typically larger than the KS gaps of most DFAs, with the exception of Hartree-Fock. Finally, the KS gap is sensitive to the inclusion of semicore states in the pseudopotentials, such that comparison with experiment should be done with caution.

Full waveform inversion (FWI) can be expressed in a Bayesian framework, where the associated uncertainties are captured by the posterior probability distribution (PPD). In practice, solving Bayesian FWI with sampling-based methods such as Markov chain Monte Carlo (MCMC) is computationally demanding because of the extremely high dimensionality of the model space. To alleviate this difficulty, we develop a deep convolutional autoencoder (CAE) that serves as a learned prior for the inversion. The CAE compresses detailed subsurface velocity models into a low-dimensional latent representation, achieving more effective and geologically consistent model reduction than conventional dimension reduction approaches. The inversion procedure employs an adaptive gradient-based MCMC algorithm enhanced by automatic differentiation-based FWI to compute gradients efficiently in the latent space. In addition, we implement a transfer learning strategy through online fine-tuning during inversion, enabling the framework to adapt to velocity structures not represented in the original training set. Numerical experiments with synthetic data show that the method can reconstruct velocity models and assess uncertainty with improved efficiency compared to traditional MCMC methods.

This paper explores the implementation of a low-cost high-precision microwave oscillator sensor with an adjustable input resistance to enhance its limit of detection (LoD). To achieve this, we introduce a \textit{Z$_{2}$} branch in the input network, comprising a transmission line, a capacitor (\textit{C$_{B}$}) and a resistor (\textit{R$_{V}$}). The sensor is tested with eight different liquids with different dielectric constants, including water, IV fluid, milk, ethanol, acetone, petrol, olive oil, and Vaseline. By fine-tuning the \textit{Z$_{2}$} branch, a clear relationship is found between $\varepsilon_{r}$ of materials and R$_{V}$.Our experimental results demonstrate outstanding characteristics, including remarkable linearity (nonlinearity < 2.44%), high accuracy with an average sensitivity of 21 kHz/$\mu$m, and an excellent limit of detection (LoD < 0.05 mm). The sensor also exhibits good stability across a range of liquid temperatures and shows robust and repeatable behavior. Considering the strong absorption of microwave energy in liquids with high dielectric constants, this oscillator sensor is a superior choice over capacitive sensors for such applications. We validate the performance of the oscillator sensor using water as a representative liquid. Additionally, we substantiate the sensor's improvement through both experimental results and theoretical analysis. Its advantages, including affordability, compatibility with CMOS and MEMS technologies, and ease of fabrication, make it an excellent choice for small-scale liquid detection applications.

We derive formulas and perform calculations of nonadiabatic corrections to rates of electric quadrupole transitions in the hydrogen molecule. These corrections can be represented in terms of the quadrupole moment curve $D^{(1)}(R)$, similarly to the Born-Oppenheimer one, $D^{(0)}(R)$, derived originally by Wolniewicz. Numerical results change E2 transition rates for the fundamental band by as much as 0.4 - 12% depending on rotational quantum numbers.