Papers

We study the nucleon spectra in two holographic set-ups: Einstein-dilaton and Starobinsky-dilaton gravity models. The Einstein-dilaton holographic model, also known as improved holographic QCD, have been proposed some time ago to describe confinement and glueball spectra. Recently, it has been applied to the case of mesons and nucleons. In this work, we reconsider the Einstein-dilaton holographic model to discuss the nucleon spectra introducing new parameters that allow us to improve the comparison with experimental data. Then, we extend this idea to the context of Starobinsky-dilaton gravity defining another improved holographic model. We use this new holographic model to reanalyse the nucleon spectra and also compare them with soft-wall model and experimental data.

This article reviews a Hamiltonian framework for describing Dirac fermions propagating on quantum cosmological spacetimes within loop quantum cosmology. Expanding the fermionic field in spinor harmonics on a closed Friedmann–Lemaître–Robertson–Walker background reduces the dynamics to a collection of time-dependent Fermi oscillators, providing a Schrödinger-picture description of fermionic perturbations on a quantum geometry. We discuss the emergence of dressed metrics in the test-field approximation, showing that massive fermions probe both temporal and spatial quantum-geometry corrections, whereas massless fermions, owing to conformal invariance, are affected only through a reparametrization of time. We further review the incorporation of fermionic backreaction within a Born–Oppenheimer framework, where the finite-dimensional Hilbert space of each fermionic mode gives rise to two distinct backreaction channels that naturally generate mode-dependent dressed (rainbow) metrics. Finally, we discuss the cosmological implications of fermionic backreaction, including state-dependent modifications of the quantum bounce and the emergence of an effective cosmological constant in the semiclassical regime. These results highlight the distinctive role of fermionic matter in loop quantum cosmology and outline open directions for understanding quantum fields on quantum spacetimes.

This work presents the demonstration of a superconducting niobium nitride (NbN) nanowire surface acoustic wave transducer, enabling a cryogenic acoustic delay line in scandium aluminum nitride (ScAlN) on silicon carbide (SiC). The superconducting nanowire slows the effective electromagnetic phase velocity along the acoustic wave propagation axis to match that of the surface acoustic wave, such that the co-propagating electrical signal continuously and coherently drives the acoustic wave along the length of the wire. The operating principle is validated through coupled full-wave electromagnetic and piezoelectric finite element method simulations and then experimentally confirmed through demonstration of a 500 {\mu}m delay line exhibiting a 125 ns time delay, with broadband transduction characterized across 0.5-20 GHz at 0.9 K, establishing superconducting nanowires as an emerging class of acoustic transducers with direct implications for cryogenic signal processing and integration with superconducting quantum circuits.

Bulk-Rashba spin splitting is forbidden in tetradymite topological insulators like Bi$_2$Se$_3$ or Bi$_2$Te$_3$, since their quintuple-layer stacking preserves inversion symmetry. We show that BiSbTeSe$_2$ escapes this restriction: in the Se-Bi-Se-Sb-Te sequence, the structure loses its inversion center, reducing the point group symmetry at $\Gamma$ from $D_{3d}$ to $C_{3v}$. First-principles density functional calculations with spin-orbit coupling show that this ordered structure retains bulk band inversion and a linearly dispersive surface state of a strong topological insulator. Additionally, its bulk bands acquire a pronounced linear-in-$k$ spin splitting away from $\Gamma$. Fitting the conduction- and valence-band doublets to symmetry-constrained two-band $k\cdot p$ Hamiltonians, we extract intrinsic linear Rashba coefficients of $\alpha_{\mathrm{CB}}\approx2.66~\mathrm{eV\,\text{\AA}}$ and $\alpha_{\mathrm{VB}}\approx0.35~\mathrm{eV\,\text{\AA}}$. The conduction-band value places ordered BiSbTeSe$_2$ among the strongest bulk-Rashba topological-insulator systems reported to date and approaches the coupling found in the benchmark polar Rashba semiconductor BiTeI. Sublattice ordering thus provides a route to giant bulk spin–momentum locking that coexists with protected topological surface states, offering a platform in which bulk-Rashba and topological surface contributions to spin and charge transport can be investigated within the same material.

Orbital localization methods have a long history in quantum chemistry, as they provide a convenient way to describe individual chemical bonds and local properties. However, only a handful of localization functionals are known up to date, and most localization methods do not separate $\sigma$- and $\pi$-orbitals, which is important to describe chemical bonding patterns and reactivity. In this work, we present a new class of localization functionals, based on nucleus-electron physical observables, that preserve $\sigma$- and $\pi$-orbitals separation in molecular systems. We also present a general mechanism behind $\sigma/\pi$-separating variational localization which can be used for constructing a broader class of $\sigma/\pi$-separating localization functionals from other local physical quantities.

In a rotated frame the biaxial spin Hamiltonian $k_1S_x^2+k_2S_y^2-\mathbf{h}\cdot\mathbf{S}$ is a finite tight-binding chain whose hopping amplitudes are tuned by the applied field. A chain with no vanishing hopping has a nondegenerate spectrum, so a degeneracy can occur only where the field severs the chain. We show that at every point of the exact diabolical-point lattice found by Kececioglu and Garg the chain is severed twice over, in two different rotated frames and at two bonds that are fixed independently. The two severings are carried by projectors that commute with the Hamiltonian but not with each other. In that form they realize the hidden symmetry anticipated by Garg. A single operator built from them pairs the degenerate levels; its rank gives the multiplicity of every lattice point, replacing an earlier continuity and topological argument. Because the two partners of a doublet occupy disjoint stretches of the chain, an exact and manifestly negative determinant fixes the orientation of every cone. At every degeneracy of the model the lower level therefore carries Chern charge -1 in the convention used here.

Reducing the electrical current required to excite magnetization dynamics is a central challenge, e.g. for energy-efficient magnonic devices or oscillator-based computing. Spin Hall oscillators typically rely on large current densities to compensate intrinsic magnetic damping, so these systems are usually studied on the nanoscale (Spin Hall Nano Oscillators, SHNOs) to work with moderate currents and a favorable heat dissipation geometry. Here, we demonstrate that engineering a near-zero effective magnetization ($M_\mathrm{eff}$) enables a drastic reduction of the magnetization oscillation threshold current density for Spin Hall oscillators. This makes it possible to excite even comparably large systems with micrometer lateral sizes, so-called "Spin Hall Micro-Oscillators" (SHMOs). Using micro-focused Brillouin light scattering spectroscopy, we quantify the threshold current density in SHMOs based on W/CoFeB/MgO/Ta with near-zero $M_\mathrm{eff}$. We observe threshold current densities as low as $J_{\mathrm{th}}$ = (0.292 $\pm$ 0.025) $\times 10^{10}$ A/m$^{2}$, representing a reduction of more than two orders of magnitude compared with most recent reported SHNOs. Using systematic micromagnetic simulations, we investigate the breaking down of the macrospin approximation and underline the high influence of $M_\mathrm{eff}$ on magnetization dynamics under applied spin currents. Our results establish $M_\mathrm{eff}$ engineering as a powerful strategy for realizing ultra-low-power spin Hall oscillators and energy-efficient magnetization control.

Machine learning (ML) is transforming materials discovery by enabling rapid prediction of properties that previously required computationally expensive first-principles calculations. Yet most current ML models remain fundamentally limited to zero-temperature descriptions, learning static lattice energies while neglecting the thermodynamic effects that govern materials behaviour at finite temperature. Because phase stability, functional response, and performance are governed by free-energy landscapes rather than static energies alone, this limitation represents a major barrier to predictive materials design under realistic operating conditions. In this Perspective, we argue that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery. We examine the fundamental shortcomings of energy-based models, highlighting the essential roles of entropy and anharmonicity in determining free energies and materials functionality. We review emerging strategies, including machine-learned interatomic potentials and hybrid ML-statistical mechanics frameworks, while identifying key challenges related to data availability, transferability, and thermodynamic consistency. Building on these advances, we outline a roadmap for thermodynamics-informed ML centred on direct free-energy learning, entropy-aware representations, and adaptive sampling across temperature. We highlight the transformative opportunities this paradigm offers for energy materials and argue that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.

As organisms navigate the environment to locate critical resources, their behavioral actions must be tightly coupled to their sensory inputs. Here, we introduce an information-theoretic framework that quantifies this coupling using transfer entropy, which measures information flow between sensory inputs and behavioral outputs. Information flow from sensory inputs to behavior defines a "reactive" component of a navigational strategy, whereas information flow from behavior to sensory inputs defines an "active" component, whereby actions shape subsequent sensory experiences. Analyzing these bidirectional information flows enables us to both predict navigational performance and dissect navigation strategies from trajectories. Using a minimal model that captures the active and reactive components, we connect macroscopic performance to microscopic information flows. We then apply the framework to experimentally measured trajectories of bacteria, worms, and flies, as well as to machine learning agents navigating sensory landscapes. Across systems, bidirectional information flow reliably predicts navigation efficiency, revealing a common behavioral-environment feedback loop. Decomposing active and reactive information flows further exposes distinct strategies underlying bacterial chemotaxis, the spatial dependency of the navigation strategy in fly olfactory navigation, and the learned policies of a reinforcement-trained agent. Together, these results establish bidirectional information flow as a unifying principle for understanding navigation in biological and artificial systems.

Interaction-free measurement (IFM) certifies the presence of an absorbing object without a photon ever being absorbed by it. When several candidate locations are available, existing protocols can also identify which one holds the absorber, but they do so by testing paths sequentially through two-path interferometers, resolving a binary presence question at each step. We propose a different approach: probing all candidate locations at once, with the photon prepared in a coherent superposition across every path before a single measurement resolves the outcome. We prove that a three-stage protocol built on a $d$-path interferometer attains the exact one-shot optimum for this task, and we extend it to $k$ absorbers among $d$ paths, where the no-absorption branch encodes the entire absorber subset coherently rather than revealing individual locations one by one. The dark port therefore ceases to be a mere witness of presence and becomes a location-resolving signal. We then move beyond single-pass strategies using the quantum-comb formalism, casting the problem as an exact optimization over all multi-pass strategies and showing that adaptive protocols surpass the one-shot ceiling. Enriching the interferometer geometry with one additional path guaranteed to be empty, we show that sequential scanning, bright-port recycling, and Zeno-type interrogation all become particular feasible strategies within this same optimization, rather than separate benchmarks to compare against. This unified formulation identifies the optimal interaction-free localization strategy for any given set of resources, opening a route toward loss-resilient quantum imaging protocols for the study of fragile, absorption-sensitive samples.

We derive the method of expansion by regions from the Mellin transform for integrals involving polynomials with positive coefficients. This method is widely used to obtain asymptotic expansions of Feynman integrals.

Dipolar magnetic skyrmions can assemble into chains with alternating helicity that act as one-dimensional polymers, yet their statistical mechanics violates the universal harmonic scaling observed in actin, DNA, and microtubules. From first principles, we compute the inter-skyrmion pair potential and find a bi-exponential form of competing interactions with two characteristic decay lengths that encode the distinct microscopic mechanisms of repulsion and attraction. Multiscale simulations reveal a power-law temperature dependence with exponent $1$ in the worm-chain limit of a single bond, and exponent $1/2$ in the three-bond limit. We find that the power-law behavior is remarkably independent of magnetic field strength, and the crossover is due to competing radial interactions responsible for the bonds, resulting in a quartic transverse confinement. We show that the precise form of the competing interactions (e.g., Morse or double-Yukawa) does not affect the temperature dependence.

Topology governs finite-size violations of local electroneutrality in confined electrolytes. Within Poisson-Boltzmann theory, we show that this topological control gives rise to a universal hierarchy of deviations in spherical, cylindrical, and planar confinement. We quantify this effect through an electroneutrality deviation ratio that captures the global electrostatic constraints associated with compactness and boundary multiplicity in the three topological classes corresponding to slit, cylindrical, and spherical cavities. Although local electroneutrality is asymptotically restored in the limit of infinite cavity size, finite-size deviations follow a robust topological hierarchy, being strongest in spherical cavities, weaker in cylindrical confinement, and weakest in planar slits. These results prove that topology is the organizing principle underlying confinement-induced charge redistribution and that violations of local electroneutrality constitute a general electrostatic constraint governing overcharging, charge reversal, and long-range charge correlations. More fundamentally, they demonstrate that changing the topology modifies the global electrostatic constraints without altering the local Poisson-Boltzmann equations. As a counterintuitive manifestation of nonlinear confinement in a point-ion model, we report the existence of inside confinement charge reversal (ICCR) in charged hollow cylindrical and spherical nanoparticles. Since physically consistent, more general electrolyte theories must recover the Poisson-Boltzmann description in the appropriate limiting case, the present results establish a benchmark against which topological effects should be assessed beyond the Poisson-Boltzmann description.

Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability. Medical image-based foundation models have recently been developed for specific imaging modalities to streamline down-stream prediction tasks by extracting modality-relevant features. Purpose: In this study, we evaluate the effectiveness of using a foundation model as the feature extractor to predict DM risk in HNC patients and compare its performance with traditional approaches that require prior knowledge on the regions of interest. Methods: Preoperative CT images of 2327 patients from the RADCURE dataset were used. Three features-sets were created including radiomics, deep-learning based features, and CT Foundation derived features. The feature-sets were used individually in a multi-layer perceptron (MLP) to predict DM risk. Results: The model using CT Foundation embeddings outperformed the radiomics and deep learning-based models, achieving a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.791, compared to AUC values of 0.772 and 0.753 for the radiomics and deep learning-based models, respectively. The CT Foundation based model had similar performance to a model that combined the use of radiomics and deep learning-based features that achieved an AUC of 0.794. Conclusions: Features based on foundation models offer a promising alternative to traditional radiomics while reducing the need for domain expertise and extensively annotated datasets. Their minimal preprocessing requirements also make them a more accessible and scalable option.

Fracture and calving of glaciers are key contributors to ice-mass loss and sea-level rise, yet predictive modeling remains challenging. Fracture in grounded glaciers is driven by gravitational forces and is typically studied within the framework of quasi-static linear elastic fracture mechanics. In this work, we show that purely quasistatic brittle fracture simulations within the phase field fracture framework under fixed self-weight can become strongly overdriven after crevasse initiation, producing unphysical thickening of the diffusive crack band and diffuse damage patterns. This pathology arises because gravity drives a growing region ahead of the crack tip beyond the strength surface. To resolve this, we show that the post-nucleation propagation is fundamentally a dynamic instability rather than a quasistatic process and propose the use of dynamic formulations of fracture. We demonstrate that accounting for inertia results in sharp, localized cracks that propagate through the ice thickness. As a second objective, this paper introduces a new dynamic formulation of the phase-field fracture model of Kumar et al. (J. Mech. Phys. Solids 2018) in which elastic, inertial, and gravitational contributions are degraded consistently in fractured regions.

We present the statistical properties of H$\alpha$ and H$\beta$ line absorption in little red dots (LRDs) at $z\simeq2.5$–7.2 using archival JWST/NIRSpec spectra from the DAWN JWST Archive and complementary NIRSpec/IFU observations. Among 40 LRDs with broad H$\alpha$ and [O~iii] obtained with medium- or high-resolution gratings, 14 objects exhibit H$\alpha$ absorption. We find that the incidence of Balmer line absorption is $\sim35$% ($=14/40$), significantly higher than that in SDSS low-$z$ type 1 AGNs ($\sim0.04$%), demonstrating that Balmer line absorption occurs approximately 850 times more frequently in LRDs than in type 1 AGNs. We combine our 14 detections with 32 additional LRD Balmer absorbers from the literature, yielding a census of 46 absorbers. Their velocities span $\Delta v_\mathrm{abs}(\mathrm{H\alpha})\sim-430$ to $+140\ {\rm km\,s^{-1}}$, markedly narrower than the $-800$ to $+1600\ {\rm km\,s^{-1}}$ range of Balmer absorption in SDSS type~1 AGNs, for which our simulations confirm that the velocity difference is too large to be explained by detection incompleteness. The lower absolute absorber velocities in LRDs may partly reflect the shallower gravitational potential at their characteristic BLR radii. We also find that 38 of the 46 absorbers (83%) are blueshifted, with only eight redshifted, indicating that most of Balmer absorbers are moving outward. An analytic model with radiation pressure suggests that most absorbers with $N_{\rm H}\gtrsim10^{24}\ {\rm cm^{-2}}$ remains gravitationally bound. The smaller number of redshifted (i.e., infalling) absorbers may indicate that outbound absorbers lose density: some return to the BLR, whereas others undergo stronger radiative acceleration and escape.

This work is devoted to the mechanisms of dielectric response and electric conductivity of suspensions consisting of the nematic liquid crystal 5CB with different concentrations (from 0 to 10 wt.%) of ferroelectric BaTiO_3 nanoparticles with an average size of 24 nm. We revealed that the incorporation of nanoparticles influences significantly the dielectric permittivity magnitude and anisotropy, as well as dielectric losses of the suspension. A pronounced temperature dependence of the anisotropic dielectric permittivity of the suspensions was found at lower temperatures corresponding to the mesophase state; but it is also present at higher temperatures corresponding to the isophase. The dependence of the mesophase-isophase transition temperature on the concentration of BaTiO_3 nanoparticles appeared nonmonotonic. With increasing temperature, both the capacitance and the electrical resistance of the pure liquid crystal increase, as well as it increases in the suspensions with small concentration of BaTiO_3 nanoparticles. Due to space charge accumulation in the shells of nanoparticles, larger concentrations of BaTiO_3 nanoparticles influence strongly the ionic transport by promoting the formation of ionic-electronic screening. This effect modifies the dielectric properties and conduction mechanisms of the suspension, leading to the nonmonotonic dependence of the mesophase - isophase transition temperature versus the nanoparticle concentration.

We introduce the multispecies version of the harmonic process on a one-dimensional chain in contact with boundary reservoirs. This process is a continuous-time Markov chain where each site can host an unbounded number of colored particles. The symmetric bulk dynamics is put in contact with reservoirs, which inject and remove particles driving the system out-of-equilibrium. The Markov generator of the process is identified with the integrable Hamiltonian of an open rational Heisenberg spin chain of higher rank. We derive the underlying R- and K-matrices in operator form and construct the double-row transfer matrix following Sklyanin. Similar to the monospecies case the R-matrix factorises into two R-operators, each factor corresponding to left and right moving particles. We further define three dual models: an absorbing particle model, a hidden parameter model and a heat conduction model.

In reciprocating flapping-like motions, wing-wake interaction plays a crucial role in fluid force generation. While this effect's existence has been acknowledged, particularly in explaining discrepancies between measured forces and quasi-steady approximations, fundamental research on the mechanism underlying this interaction and its scaling remains limited. To address this, we investigate the excess drag force, relative to quasi-steady estimates, acting on a flat plate during the reversal phase of a forward and back translational motion. The flow produced by this motion, studied at insect-flight-relevant Reynolds numbers, serves as a simplified analogue to biological flapping. We demonstrate that interaction with pre-existing wake flow indeed generates excess drag. The main parameter governing this interaction is the distance travelled before reversal, which influences both magnitude and temporal dynamics of the peak drag. We link our observations to optimal vortex formation, as the time trace of the additional drag during reversal is qualitatively altered by the detachment of the starting vortex ring: vortex detachment and re-formation lead to two distinct wake-force peaks. Furthermore, as the pre-reversal distance traversed increases, the wake interaction force post-reversal decays more slowly. Flow observations reveal a similar spatial decay of the streamwise velocity in the wake at the moment of reversal, suggesting a direct link. Representing the starting vortex as a point vortex indicates that the wake's spatial scaling, and commensurately the temporal scaling of the wing-wake interaction effect, is primarily governed by the vortex ring position, shape, and circulation. This simplification reveals a dependence on the pre-reversal translation distance that can be described by a combined fourth-root and linear scaling.

Hydrogen atmosphere white dwarf KUV03442+0719 was first reported as a pulsator by Gianninas et al. in 2006. Follow up campaigns by Su et al. (2014) revealed more periods. Some spectroscopic results suggest that KUV03442+0719 has a slightly below average mass and an effective temperature of 11000 K. But Gaia data (parallax and magnitude) suggest that it may be a low mass white dwarf. Such an object would have a helium core. We perform the asteroseismic fitting of KUV03442+0719, modeling it both as a carbon/oxygen normal mass white dwarf, and a helium core, low mass white dwarf. To perform the study, we perfom a grid search with WDEC models, refined by simplex minimization of the best fits. Both analyses result in best fit models that are comparable in terms of quality of fit. More pulsation data would be required to allow us to distinguish between the two scenarios. We present and constrast our results with expectations from stellar evolution. We also provide analytic formulae for a temperature dependent mass-radius relationship for helium core white dwarfs.

Parasitic interference is a common limitation in laser interferometers, arising from unwanted beams that corrupt the phase measurement and degrade displacement sensitivity. In this work, we present a unified framework for the characterization and mitigation of parasitic interference in heterodyne interferometers. Parasitic beams are classified into two types based on the orientation of their corresponding phase vectors, and their noise contribution is modeled as a function of polarization, relative amplitude, and phase of the parasitic beam. Central to this framework is the concept of coupling coefficients, which quantify the interferometer's susceptibility to parasitic interference and can be readily computed using Jones calculus for any optical configuration. The two types of parasitic interference motivate distinct mitigation strategies: differential interferometry and balanced detection, complemented by polarization control and high-quality beam splitters. The models and mitigation strategies are validated experimentally in both a simplified Mach-Zehnder interferometer and a differential interferometer used for optomechanical inertial sensing, demonstrating reductions in parasitic phase noise by up to four orders of magnitude and achieving sub-picometer displacement sensitivity at frequencies as low as 9 mHz under ambient atmospheric pressure.

The Square Kilometre Array will transform studies of the cosmic web by tracing radio galaxies (RGs) and star-forming systems across cosmic time with unprecedented sensitivity, angular resolution, frequency coverage, and survey speed. Powered by accreting supermassive black holes, RGs are not only signposts of AGN feedback but also incisive probes of their environments, from dense clusters to the low-density intergalactic medium. Their lobes, magnetic fields, and energy outflows encode the thermal and non-thermal histories of the surrounding gas, offering diagnostics of IGM pressure, particle ageing, and magnetisation over megaparsec scales. With its broad frequency coverage (50 MHz-15 GHz), microJy to sub-microJy continuum sensitivity, and wide field of view, the SKA will detect vast radio-source populations across broad ranges of redshift and environment. Measurements of source size, morphology, spectral ageing, radio power, polarisation, and Faraday rotation will reveal how the environment regulates jet propagation and lobe evolution, how radio plasma heats and magnetises the intracluster and intergalactic media, and how early AGN activity influences galaxy growth and star formation in protoclusters. Combined with host identifications, spectroscopic redshifts, and optical, infrared, X-ray, Sunyaev–Zel'dovich, and cosmic-web catalogues, SKA observations will place RGs within their three-dimensional large-scale environments. This chapter presents a framework for using RGs to trace and probe the cosmic web, from nearby filaments and clusters to high-redshift protoclusters, and to test how environment, magnetic fields, feedback, and gas dynamics shape radio-galaxy evolution, protocluster assembly, and star formation across cosmic time.

Achieving significant energy gain in laser-driven relativistic electron beams remains challenging due to dephasing between the accelerating laser field and the electrons. We show that transverse electric fields, when aligned with the plane of laser polarization, can mitigate dephasing and enable substantial energy gain without compromising beam directionality. As a practical realization, we propose a two-laser scheme in which one laser generates the transverse field while the other drives electron acceleration. By tailoring the interaction geometry, this configuration sustains phase locking, enhances energy transfer, and opens a pathway toward compact, high-efficiency electron accelerators.

The energy gap is one of the defining properties of a superconductor and appears because the quasiparticle excitations radically change once a metallic system enters the superconducting state. Unconventional superconductors typically exhibit a non-uniform gapping and therefore show physical effects that are subject to intense research. In this review, we provide an overview of concepts needed to understand how unconventional superconductors are different from conventional ones, where the gapping comes from and how it expresses itself in experimentally accessible quantities. This should give the basis to understand current open research questions and navigate the recent literature that tend to contain contradictory conclusions.

The imposition of boundary velocities in finite element models of port-Hamiltonian elastodynamics typically relies on Lagrange multipliers, yielding Differential-Algebraic Equations (DAEs). Alternatively, weak imposition methods that maintain an Ordinary Differential Equation (ODE) structure often exhibit poor accuracy at Dirichlet boundaries. To address these limitations, this paper introduces an additive kinematic decomposition at the continuous level, splitting the displacement and velocity fields into a relative dynamic component that vanishes on the boundary and a prescribed lifting function extending into the interior domain. This decomposition induces a distributed port that maps the effects of the boundary actuation inside the domain. By incorporating this mapping into suitable virtual power principles, we derive lifted port-Hamiltonian system (PHS) models that, upon finite element discretization, reduce to ODE systems in which Dirichlet boundary velocities are strongly imposed. The framework is applied to derive 2-field and 4-field formulations suited to distinct PHS geometric representations. Furthermore, we show that under specific shape functions, standard FEM schemes are recovered, demonstrating that the lifting framework in the discrete models is equivalent to the classic algebraic matrix partitioning in computational mechanics practice. The energy-balance properties and computational performance of the proposed methodology are verified through numerical simulations.

ForMoSA (FORward MOdeling tool for Spectral Analysis) is an open-source Python package to fit spectroscopic and photometric observations using a Bayesian framework. It can utilize different self-consistent atmospheric models to perform robust parameter space exploration. It has been mainly designed for fitting directly imaged young planetary-mass brown dwarfs and exoplanets. The developments within ForMoSA are supported by an international collaboration of several laboratories in France (IPAG, LIRA, LAM, and Lagrange), Germany (MPIA), USA (NASA Goddard), and Chile (FCLA, Universidad Diego Portales, and Universidad de Chile). The evolution of the code and the growing interest from the scientific community has led to the need for this dedicated publication alongside the release of ForMoSA v2.0, which has been refactored into a class architecture with user-friendly features and extended functionalities.

Dielectric loss due to two-level systems is a limiting factor for superconducting qubit relaxation times. These losses arise mostly from nanometer-scale interfacial defect regions in superconducting devices with planar dimensions of microns to millimeters, thus making it resource intensive to accurately simulate the electric field density in these regions with traditional electromagnetic solvers. In this work, we demonstrate a fast boundary integral equation solver that allows precise simulation of electric field density in these thin regions, showing a speedup of around two orders of magnitude over traditional solvers, with relative errors around $10^{-7}$ for a ten-minute solution runtime. By computing participation ratios through Green's first identity without squaring the electric field, our approach is less susceptible to the field singularities near conductor corners. We apply this solver to a basic untrenched coplanar waveguide cross-section, showing that the common assumption of participation ratio linearity with dielectric constant holds well for some interfaces and not others; in particular, while the metal-air (MA) top and corner follow this linear relationship strongly, the MA sidewall does not. We then compare isotropic and anisotropic etching, showing that the MA sidewall and the metal-air-substrate triple junction are the most strongly affected. We are currently leveraging this solver to explore geometries that will uniquely isolate the participation ratios of the different dielectrics. Finally, we are working to combine this solver framework with a full 3D microwave solver to accurately calculate participation ratios for the thin dielectrics that are known sources of loss in superconducting qubits.

We establish a connection between quantum resource theory and probability theory, under repeated application of free operations. We show that strong monotonicity of a resource measure implies that the resource is a supermartingale. We then use the optional stopping and martingale convergence theorems to derive bounds on the efficiency of post-selection, and other free adaptive strategies. We also describe the asymptotic dynamics of the conditional state, where resource fluctuations are necessarily absent, showing one of two distinct phenomena occurs: resource vanishing or resource freezing.

We derive an explicit formula for the pair correlation function of the Sine$_6$ process in terms of Bessel functions of the first kind. This provides the first single-variable special function representation of the pair correlation function for the bulk limit of a beta-ensemble beyond the classical values of $\beta=1,2,$ and $4$.

Human mobility plays a central role in shaping contact patterns that drive infectious disease transmission, yet mobility is often simplified in agent-based models (ABMs) due to data and computational constraints. The effects of these simplifications on model outputs are poorly understood. In this study, we systematically examined how alternative mobility assumptions influence emergent contact networks and epidemic dynamics within a large-scale ABM. Using a synthetic population of one million agents representing an urban environment, we implemented five mobility models varying along two dimensions: activity patterns (empirically derived vs. randomized) and destination choice mechanisms (empirical popularity, distance-based, or random). Holding disease parameters constant, we found that mobility assumptions alone produced substantially different contact network structures and epidemic trajectories, including differences in peak incidence and shifts in outbreak timing. Importantly, these differences could not be attributed to agents simply moving more or less overall since aggregate movement volumes were broadly comparable across models. Instead, the contrasting dynamics arose from how mobility generates contact opportunities: specifically, who meets whom, where, and how often. These results suggest that in models where mobility has not been carefully calibrated, simulated epidemic outcomes and evaluations of interventions may reflect mobility assumptions as much as underlying disease parameters. Our findings underscore the importance of mobility model calibration and validation, particularly in policy-facing applications.