Papers

We provide the first compact examples of type-changing generalized Calabi–Yau manifolds using Fei's construction of non-Kähler Calabi–Yau manifolds on branched covers of twistor spaces \cite{Fei16}. The structure, which is generically of type 1 and changes to type 3, is constructed on the product of a hyperelliptic surface of genus three and a four-torus, $\Sigma_3 \times \mathbb{T}^4$, with starting point a non-Kähler Calabi–Yau structure.

This contribution presents the new phase-2 CMS baseline tracking strategy currently proposed for online event reconstruction, which can be similarly useful in offline reconstruction under the extreme pileup conditions of the High-Luminosity LHC (HL-LHC). By unifying diverse algorithmic paradigms and integrating machine learning (ML) techniques into a coherent sequence, CMS can maintain high-efficiency and high-resolution tracking while improving the fake track rate and the computational performance, a crucial aspect especially for the HL-LHC. The approach combines GPU-optimized algorithms (Patatrack, LST) with vectorized CPU methods (mkFit), exploiting modern hardware to maximize throughput. This heterogeneous strategy reduces the resource requirements and enhances the physics reach by incorporating a global displaced track reconstruction and extending sensitivity to rare, interesting signatures, such as long-lived particles.

X-ray polarization models of accreting compact objects often assume the so-called Chandrasekhar-Sobolev polarization prescription for elastic Thomson scattering in electron-scattering semi-infinite atmospheres. In this letter, we examine the effects of inelastic Compton scattering. We assume homogeneous slabs of cold and warm (up to 1 keV) electrons. We use a Monte Carlo approach. We discuss the polarization difference arising in the X-rays between the elastic and inelastic scattering assumptions for plane-parallel transmission and reflection, and the role it can play in the interpretation of X-ray polarimetric observations of accreting compact objects.

Motivated by the recent observation of the $B_c^*$ meson by the ATLAS Collaboration and its promising discovery potential at future high-luminosity colliders, we investigate the semileptonic decays $\bar{B}^*_{c} \to (P, V) \ell^- \bar{\nu}_{\ell}$ ($P=D$, $B_{d,s}$, $\eta_c(1S,\,2S,\,3S)$; $V=D^*$, $B^*_{d,s}$, $\psi(1S,\,2S,\,3S)$) in the Standard Model. Using the relevant form factors obtained in the self-consistent covariant light-front quark model, we present theoretical predictions for several physical observables, including the branching fraction, the ratios of branching fractions $R_{(L)}$, the longitudinal polarization fraction of the daughter vector meson, the $\tau$ lepton spin asymmetry and the forward-backward asymmetry. It is found that the branching fractions for the Cabibbo-favored $\bar{B}^*_{c}$ $\to$ $\bar{B}^*_{s} e^- \bar{\nu}_e$ and $\bar{B}^*_{s} \mu^- \bar{\nu}_\mu$ decays reach the order of ${\cal O}(10^{-6})$, which might be firstly observed at the future high-luminosity Large Hadron Collider.

Charged particle track reconstruction is one of the heaviest computational tasks in the event reconstruction chain at LHC experiments. Furthermore, projections for the High Luminosity LHC (HL-LHC) show that the required computing resources for single-threaded CPU algorithms will exceed those that are expected to be available. It follows that experiments at the HL-LHC will need to employ novel and evolved track reconstruction algorithms, within heterogeneous computing systems that include many-core CPUs as well as GPUs, in the attempt to maximize the computational performance while retaining the best possible reconstruction efficiency. In the context of the CMS High Level Trigger (HLT) at the HL-LHC, the mkFit algorithm, already in use for the CMS track reconstruction during the LHC Run 3, will exploit its parallelized and vectorized nature on CPUs to perform pattern recognition using seed tracks produced with algorithms that are designed to be fully parallelizable and hardware agnostic, thus suitable for heterogeneous systems: the Patatrack and the Line Segment Tracking (LST) algorithms. Patatrack is an established algorithm, already used for the CMS pixel track reconstruction at HLT during the Run 3 of the LHC, while LST is a novel algorithm, recently integrated in the CMS software, targeting the reconstruction of tracks in the outer tracker of the HL-LHC CMS detector. The state-of-the-art performance for the CMS HLT track reconstruction at the HL-LHC is presented, obtained using the combination of the mkFit, Patatrack, and LST algorithms, that in turn use machine-learning (ML) techniques such as deep neural networks and multi-objective particle swarm optimization to suppress duplicate and misreconstructed tracks. Prospects of further improvements are also presented, with a focus on the usage of ML techniques for track reconstruction at CMS.

Topological spin textures have attracted considerable attention through their emergent electromagnetic properties, including the topological Hall effect and the topological Nernst effect. Among them, magnetic hedgehog lattices (HLs) are three-dimensional topological spin textures that host emergent magnetic monopoles and antimonopoles. HLs have been identified in metallic compounds, such as $\mathrm{MnSi}_{1-x}\mathrm{Ge}_x$ and $\mathrm{SrFeO_3}$. Various theoretical models have been proposed, with either short-range or long-range interactions, which are typically regarded as effective descriptions of insulating and metallic systems, respectively. However, the role of the interaction range in stabilizing the HLs remains only partially understood. To address this gap, we investigate the stability of HLs by systematically varying the range and spatial decay of the exchange interactions, thereby interpolating between interaction profiles commonly associated with localized-spin insulating models and itinerant-electron metallic models. Based on extensive Monte Carlo simulations, we find that HLs are stabilized over a broad interaction range. Intriguingly, among the interaction profiles examined, intermediate-range interactions are particularly favorable for stabilizing HLs. They give rise to two distinct types of $3Q$-HLs that differ in the number of monopole-antimonopole pairs per magnetic unit cell, and their relative stability depends sensitively on the interaction range. Our results provide a unified framework for tuning phase diagrams from microscopic interaction models, serving as a guideline for interpreting experiments in candidate materials.

Simulating the dynamics of dissipative quantum many-body systems governed by local Lindbladians is a fundamental task in quantum computation. While the near-optimal gate count has been achieved for Hamiltonian simulation, comparable results for Lindbladian simulation remain elusive. In this work, we develop quantum algorithms for lattice Lindbladians by exploiting their locality. First, we consider sparsely dissipative systems, in which the dissipation is sparsely located, including boundary-driven systems. We establish a near-optimal quantum algorithm for their dynamics with gate count $O(Nt \, \mathrm{polylog}(Nt/\varepsilon))$, where $N$ is the system size, $t$ is the evolution time, and $\varepsilon$ is the allowable error. We then consider generic lattice Lindbladians with finite-range interactions and dissipation, and develop an algorithm for simulating time-evolved observables with gate count $O((Nt)^{4/3} \, \mathrm{polylog}(Nt/\varepsilon))$ per sample with the sampling complexity $\Theta(\varepsilon^{-2})$. The gate count has the smallest known dependence on the system size among algorithms retaining polylogarithmic dependence on $1/\varepsilon$. Our algorithms are based on two techniques that exploit locality: patching and merging. Patching decomposes dissipative dynamics into dynamics on subsystems with exponentially small error, generalizing a key idea underlying the Haah-Hastings-Kothari-Low algorithm for near-optimal Hamiltonian simulation. Merging absorbs reversed dissipative dynamics into other parts of the evolution, substantially reducing the overhead associated with quasi-probabilistic sampling. These results demonstrate that locality can be fully exploited to achieve fast quantum simulation of dissipative many-body dynamics, opening the way to applications such as predicting nonequilibrium phenomena and preparing desirable quantum states.

The realization of entanglement distribution (ED) in large-scale quantum networks (QNs), as a fundamental theoretical framework of quantum communication, faces significant challenges. Percolation theory, drawn from statistical physics, has been identified as a potential solution to this problem. Nevertheless, when addressing ED, percolation theory primarily relies on numerical simulations and approximate algorithms. This approach often lacks interpretability and controllability for many phenomena occurring during the ED process in large-scale networks, including emergent phenomena near the threshold. To address these limitations, in this paper, we {focus on} leveraging operator theory to construct a mathematical framework for entanglement percolation in series-parallel QNs. Through the application of operator-theoretic methodologies, we analyze several critical issues within the entanglement percolation process.

Entanglement embezzlement asks how much entanglement can be borrowed from a many-body state by local operations and classical communication while returning that state with a small error. We uncover a conservation-law mechanism that redistributes the dominant probability mass of the Schmidt spectrum in the logarithmic Schmidt-rank coordinate and thereby controls this operational resource. For typical random pure states at fixed U(1) charge, we prove a finite-error conversion law: away from half filling, the charge bias converts $O(\sqrt L)$ charge fluctuations into $O(\sqrt L)$ borrowable entanglement, whereas particle–hole symmetry at half filling removes this contribution entirely. We then show how the resource develops dynamically in charge-conserving random circuits. Combining hydrodynamic analysis with large-scale replica tensor-network calculations, we find that diffusion broadens the operationally relevant distribution in the logarithmic Schmidt-rank coordinate on the scale $t^{1/4}$ and increases the amount of entanglement that can be borrowed. Charge transport therefore continues to reorganize the entanglement spectrum and activate embezzlement after the leading volume-law entropy has saturated.

We study the thermal equilibrium of a classical system of identical point charges moving between two walls with parallel surfaces at a distance $d$, charged symmetrically with position-dependent surface charge densities. Specifically, we study the effect of surface charge modulation on the effective force (pressure) between the walls in the Poisson-Boltzmann limit. Based on Monte Carlo simulations at high temperatures, it is predicted that surface charge modulation reduces the pressure between the walls compared to uniformly charged wall surfaces with the same average surface charge density. We restrict ourselves to surface charge modulations in only one direction and, using a general solution of the two-dimensional Liouville equation, we construct exact solutions for the electrostatic potential. The surface charge density on the walls is generated inversely from this potential, which means that our exact results apply to a limited set of models with a specific variation of the surface charge density with distance $d$. Explicit analytical results show that surface charge modulation can both increase (small distances $d$) and decrease (large $d$) the pressure between the walls.

In the development of next-generation tracking detectors for gamma-ray space observations, the integration of advanced deep learning techniques into event reconstruction algorithms is a promising approach for improving the performance of the mission, reducing systematic uncertainties and maximizing the scientific output. In this study, we investigate the application of deep learning techniques such as Graph Neural Networks (GNNs) for the identification and reconstruction of particle tracks resulting from pair production events within the tracker of the AMEGO-X proposed concept. The goal is improving the angular resolution and the detection efficiency of the telescope, especially in the soft energy range (below 100 MeV) where multiple Coulomb scattering significantly degrades track reconstruction and consequently limits the sensitivity in the so-called MeV gap. We use the Geant4-based MEGAlib framework and a Python-based dedicated read-out and data handler processor to format the input datasets as close as possible to the real data. The simulated datasets are then used to train and evaluate two graph neural network architectures, GraphSAGE and Interaction Networks, and to compare their performance in pair-production event reconstruction in terms of Point Spread Function 68% containment radius and effective area, also with that obtained using standard reconstruction techniques. The results of this study indicate that graph neural network-based reconstruction is a promising approach for pair-production event reconstruction below 100 MeV when compared with standard reconstruction methods, with significant potential for further optimization.

A lowering of the post-inflation reheating temperature $T_{\rm RH}$ has attracted much attention as a means to facilitate obtaining the correct Dark Matter (DM) relic abundance. We take a relook at the paradigm under the assumption that the inflaton decay is essentially instantaneous, with the thermal bath coming into existence only at a relatively low temperature. We demonstrate that, contrary to the popular misconception, the relevant scale is not necessarily the DM mass itself, but the characteristic temperature at which the dominant DM production processes are thermally efficient. With $T_{\rm RH}$ setting the maximum temperature available for thermal production, and thereby determining the accessible stages of the production history, lowering $T_{\rm RH}$ tends to reduce the relic abundance. While this effect could, in principle, be offset by altering the couplings defining the theory, there exists a minimum $T_{\rm RH}$—determined by the mass spectrum—below which the relic abundance falls to minuscule levels, whether the mechanism be freeze-in or freeze-out.

The phenomenon of bias-driven circular charge and spin currents in a ring nanojunction is investigated in the presence of electron-electron (e-e) and electron-phonon (e-ph) interactions within a tight-binding framework based on the non-equilibrium Green's function formalism. The Lang-Firsov transformation is employed to map the interacting system onto an effective electronic model, which is subsequently treated within the Hartree-Fock mean-field scheme. By exploring the interplay among e-e interaction, e-ph coupling, and electrode-ring interface sensitivity, several intriguing features emerge in both circular charge and spin currents that, to the best of our knowledge, have not been reported previously. In addition to bias-driven circular currents, charge and spin-dependent junction currents through the nanojunction are also analyzed. Selective spin transport is achieved, leading to a high degree of spin polarization. All four current components, two associated with circular currents and two with transport currents, are systematically inspected over a wide range of parameter regimes to assess the sensitivity of the results to the relevant system parameters. Our findings provide useful insights into charge and spin transport phenomena in interacting nanojunctions with single- and multi-loop geometries.

In-depth characterization of heterogeneous materials has long been a challenge in non-destructive testing. Here, a method is proposed to determine the elastic constants of metallic polycrystalline materials using back-scattered ultrasound. The waves scattered by the microstructure are analyzed to image the effective bulk velocities. To this end, a reflection matrix is acquired with an array of transducers. The projection of this matrix onto a focused basis is used to estimate an average point spread function. Optimizing this function with respect to the propagation model leads to an estimation of the longitudinal velocity. Additional treatments are developed to adapt the method to map the shear wave velocity. The local Poisson's ratio is then deduced from the ratio between those two velocities. Young's modulus and shear modulus can also be obtained assuming known densities. This matrix approach is experimentally validated on different polycrystalline materials. A sample displaying heterogeneous mechanical properties is then simulated to assess the accuracy and the resolution of the method. Its strengths and limitations are discussed, demonstrating its potential for quantitative non-destructive material characterization.

We theoretically investigate the role played by non-Markovianity in the charging of a quantum battery. We focus on the case of a Micromaser, where the energy is stored in a resonant cavity mode and memory effects are introduced and controlled by coupling this mode to a memory ancilla, described as a two-level system, that in turn sequentially interacts with a stream of two-level chargers through pairwise exchange interactions leading to a partial swap. Through numerical analysis, we show that non-Markovian memory effects act as a resource that enhances both the charging capacity and the stability of this device with respect to the Markovian counterpart. We conclude our study showing that the proposed scheme can be implemented in state-of-the-art solid-state platforms for circuit quantum electrodynamics.

Surfactant adsorption at solid-fluid interfaces strongly influences contact line dynamics, yet most surfactant-laden phase-field models neglect solid adsorption and rely on Cahn-Hilliard-type equations. We develop a thermodynamically consistent and conservative Allen-Cahn-Navier-Stokes model for surfactant-laden contact line dynamics with solid adsorption. A unified free-energy formulation accounts for surfactant transport, adsorption, and wetting dynamics, yielding consistent wetting and adsorption boundary conditions. Second-order Allen-Cahn-type equations govern both the phase field and surfactant concentration, avoiding the numerical complexity of fourth-order formulations. A classical linear constraint ensures exact conservation of total surfactant mass, whereas a generalized nonlinear conserved quantity is introduced for the phase field to improve geometric volume conservation. The resulting weighted Lagrange-multiplier correction vanishes in the bulk phases and is confined to the diffuse interface, suppressing the leading-order curvature-induced bulk shift associated with the linear constraint while preserving the energy-dissipation structure. Matched asymptotic analysis shows that the nonlinear formulation can achieve formal second-order geometric-volume accuracy. A lattice Boltzmann method is developed to solve the coupled system. Numerical results confirm the predicted second-order convergence, demonstrate enhanced volume preservation for high-curvature droplets, and reproduce wetting responses induced by preferential solid adsorption, including hydrophilization and autophobing. Under shear flow, the results also show that solid adsorption can significantly influence contact line recession and droplet detachment.

Differential equations can be encoded as ground-state problems by constructing a positive-semidefinite effective Hamiltonian whose minimum-energy state represents the solution. We extend this framework in several directions. First, we show how multiple nonzero initial, boundary, and data conditions can be incorporated into the homogeneous formulation $A | f\rangle=0$ using a known nonzero reference condition. We then analyse an alternative formulation based on $A |f\rangle = | b\rangle$, with Hamiltonian $H_b=A^\dagger(I-|b\rangle\langle b|)A$, which incorporates source terms and nonzero constraints through an augmented system. For nonlinear differential equations, we examine the unphysical ground-state degeneracy introduced by tensor-product representations and consider both Hamiltonian constraints and ansatz-level restrictions for targeting physically valid product states. Finally, we develop grid-value amplitude-encoded versions of both Hamiltonian constructions and compare them with spectral coefficient encoding. Through linear and nonlinear examples, we assess how the choice of Hamiltonian formulation and function representation affects solution recovery, spectral gap, degeneracy, and readout. These results broaden the applicability of ground-state-based quantum differential-equation solvers while clarifying their principal practical trade-offs.

With advances in high-speed imaging there is a growing need for robust high-speed deconvolution algorithms. Wiener deconvolution remains one of the fastest and simplest algorithms available, however it suffers from ringing artifacts that can limit its applicability. In this work, we develop two complementary, computationally efficient heuristic methods to suppress these ringing artifacts by using physical non-negativity and sparsity priors.

Black-hole movies provide a unique venue to study the time evolution of accreting plasma. Modeling this evolution with slow-light radiative transfer requires closely spaced simulation outputs, because the plasma changes as light propagates through it. Saving these outputs in full is costly, whereas sparse sampling introduces time-interpolation errors. We present the Dense Dump Codec (DDC), a compression method for dense GRMHD time series. DDC stores exact anchor states and compactly represents the intermediate evolution, while allowing direct access to selected times and variables. Applied to eight production sequences spanning SANE and MAD simulations at several black-hole spins with 0.1M output, DDC archives are about 10 times smaller than the original dense data and even about 50% smaller than the original 0.5M output in aggregate. We further integrate a native DDC reader into polarized slow-light radiative transfer. Tests at 86, 230, and 345 GHz show that DDC may reduce aggregate Stokes-image errors by 54-79% relative to slow-light calculations using original 0.5M input. These results indicate that DDC enables high-cadence slow-light calculations without the prohibitive cost of saving every dense GRMHD state in its original form.

Polarized black-hole images carry information about both the magnetized plasma and the curved spacetime through which the radiation propagates. We present KPolaris, an open-source GPU-accelerated general-relativistic polarized radiative-transfer code built on the Kokkos framework. It supports analytic plasma models and GRMHD simulation data, with both fast-light and slow-light radiative transfer. The code also provides diagnostics that connect image structure to the emitting plasma and to propagation effects along the rays. These include emissivity-weighted plasma maps, emission contributions from selected regions, selected-ray histories, and sensitivities of the Stokes parameters to plasma parameters. Numerical convergence tests and comparisons with an independent radiative-transfer code assess the accuracy of the calculations. GPU acceleration reduces the computational cost of image generation, facilitating multi-frequency and time-dependent studies of polarized black-hole images.

Conflict events in Middle Eastern countries exhibit complex temporal fluctuations. As a simple baseline, we examine whether their conflict-frequency time series contain measurable linear temporal structure using the autoregressive integrated moving average (ARIMA) framework, which assumes linear dependence on past observations and errors. Despite this simple linear assumption, the temporal dependence is largely captured for most countries by ARIMA. Predictions based on the selected ARIMA models reproduce the overall temporal behavior to some extent, although abrupt changes remain difficult to predict. We further examine the short-term persistence of changes in conflict activity using the first zero-crossing point $k^*$, which characterizes the duration of the initial positive temporal correlation, and the integrated correlation time $\tau_{\mathrm{int}}$, which additionally reflects its strength and decay. The resulting $k^*$ values are approximately 2–6 days, while $\tau_{\mathrm{int}}$ ranges from approximately 0.6 to 1.7 days. The significant negative association of $\tau_{\mathrm{int}}$ with mean GDP per capita suggests a possible relation between short-term conflict persistence and broader socioeconomic conditions. These results reveal measurable linear temporal structure and country-dependent short-term persistence in the temporal patterns of conflict activity across the Middle East.

Objective. This work aims to accelerate our four-dimensional (4D) proton pencil beam delivery strategy, which incorporates respiratory motion into a dynamic treatment plan, to improve dose conformity and treatment efficiency for gantry-less and magnet-free scanner designs. Approach. To accelerate the generation and adaptation of 4D treatment plans, we propose a conditional hybrid ResNet18-Transformer model for predicting pencil beam weights. The model predicts either the full set of pencil beam weights or a subset thereof. Main Results. The conditional model can successfully predict the remaining beam weights of a treatment, and the results suggest that extending this approach to full treatment prediction is feasible but requires further investigation. Significance. The acceleration of 4D treatment generation via pencil beam weight prediction takes us one step closer to the feasibility of treating mobile targets with simplified, gantry-less, and magnet-free scanner designs. Reducing system complexity while preserving dosimetric conformity may offer a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours. Our code is available here: https://github.com/NairVonMuehlenen/Pencil-Beam-Weight-Prediction

Single-beam ptychography (SBP) requires overlapping scan positions for reliable reconstruction, and imaging larger areas demands more measurements. Multi-beam ptychography (MBP) illuminates several regions simultaneously, but its measurement gain at a fixed resolution remains unclear when diffraction intensities from different beams overlap on the detector. Using numerical simulations, we determined the minimum number of patterns needed to reach a target resolution by varying the scan step size for SBP and fully overlapped MBP with up to 15 beams. We define gain as the pattern count for repeated SBP scans divided by that for MBP at the same total imaged area and achieved resolution. Although the required MBP pattern count increased approximately linearly with beam number, it remained below that of repeated SBP scans. At 67% of the spatial Nyquist frequency using a one-bit threshold, fully overlapped MBP achieved a gain of approximately two. Detector cropping and lower-resolution targets reduced this gain, showing its dependence on usable diffraction information and the reconstruction criterion. Because overlapping intensities provide no spatial identification of individual beam contributions, this gain is a conservative benchmark; separation on the detector offers a route to larger gains.

Symmetry breaking at crystalline boundaries can enable electronic responses that are forbidden in the bulk. Here, we investigate the Berry curvature dipole (BCD) at GaAs(110) surfaces using first-principles calculations. While the BCD vanishes in bulk GaAs due to symmetry constraints, the reduced surface symmetry permits a finite dipole. We show that surface relaxation promotes hybridization between bulk and surface states, which strongly influences the surface BCD. Specifically, in unrelaxed slabs, the BCD arises predominantly from interband coupling among exponentially localized surface states lying within the projected bulk band gap. Surface relaxation shifts these states into the energy range of the bulk continuum, transforming them into surface resonances and promoting hybridization with bulk-like states. This hybridization generates pronounced Berry-curvature hot spots, resulting in an overall enhancement of the BCD. Our results highlight the importance of surface relaxation for accurately describing surface–bulk hybridization, which in turn plays a key role in Berry-curvature-driven responses at crystalline boundaries.

When does a collection of autonomous cells become a multicellular individual? We propose that coherence provides a physical description of this transition. Coherence is treated as a global property arising when distinguishable constituents admit a physically meaningful collective state-space description. Using the center of mass and an interaction-based construction, we show that such collective states can be defined for classical bodies before dynamics is introduced, with normal modes emerging as a particular dynamical realization. We apply this framework to multicellular organization, where cells retain their identities while their independent individuality is replaced by participation in the organized whole. In Dictyostelium discoideum, cAMP-mediated coupling produces population-level collective modes, while starvation provides an experimentally controlled energetic constraint on the transition to multicellularity. The framework yields direct tests through interaction-derived collective eigenstates and the energetic cost of maintaining autonomous versus collective organization. Coherence may thus provide a general physical description of multicellular individuality without requiring microscopic quantum coherence or intrinsic wave character.

We present a systematic study of semileptonic decays of heavy vector mesons within the Hard-Wall AdS/QCD framework. Semileptonic decays provide a clean environment for testing the Standard Model because the leptonic current is well understood, while the hadronic part is encoded in transition form factors that reflect non-perturbative QCD dynamics. The processes under consideration include: \( \psi \to D^{*-} ( D^{-} ) \ell^{+} {\nu}_{\ell} \), \( D^{*0} \to \pi^{-} \ell^{+} {\nu}_{\ell} \), \( D^{*+}_{s} \to K^{0} \ell^{+} {\nu}_{\ell} \), \( D^{*0} \to \rho^{-} \ell^{+} {\nu}_{\ell} \), \( B_c^{*+} \to B^{*0} \ell^{+} {\nu}_{\ell} \), \( B_c^{*+} \to \psi \,\ell^{+} {\nu}_{\ell} \), \( B^{*-} \to D^{*0} \ell^{-} \bar{\nu}_{\ell} \), \( B^{*0} \to D^{*+}(D^{+}) \ell^{-} \bar{\nu}_{\ell} \), \( B^{*0}_{s} \to D^{*+}_{s} (D^{+}_{s}) \ell^{-} \bar{\nu}_{\ell} \), \( \psi \to D^{*-}_{s}(D^{-}_{s}) \ell^{+} {\nu}_{\ell} \), \( D^{*0} \to K^{*-} (K^{-}) \ell^{+} {\nu}_{\ell} \), \( B_c^{*-} \to D^{*0} \ell^{-} \bar{\nu}_{\ell} \), \( \bar{B_{s}}^{*0} \to K^{*+} \ell^{-} \bar{\nu}_{\ell} \), and \( \bar{B}^{*0} \to \rho^{+} \ell^{-} \bar{\nu}_{\ell} \). These decays correspond to four different quark-level transitions: \(c\to d\), \(b\to c\), \(c\to s\), and \(b\to u\), allowing us to explore a broad range of flavor dynamics.\\ Using the Hard-Wall AdS/QCD model, we compute the relevant transition form factors as functions of the momentum transfer \(q^{2}\). The framework provides analytic expressions for meson wave functions and decay constants, which serve as inputs for the form-factor calculations. From these form factors, we evaluate the differential and integrated branching ratios for each decay mode. Where possible, we compare our predictions with results obtained from other non-perturbative approaches.

The stellar binary systems are classical tools for studying dynamics, and tidal circularization is one of the processes taking place in these. During the last few years, several dozen systems have been published supposedly having orbital periods shorter than 1 d, and have also been published as having eccentric orbits. Such a configuration is due to their spatial closeness, and the tides very improbable. Therefore, we performed a thorough analysis of all these eclipsing binaries (>140), showing that the vast majority of the published systems (> 98%) are in fact circular. Especially, the eclipsing binaries identified and published from the Gaia DR3 catalogue suffer from a lack of data, and about 50 per cent had incorrectly derived periods. This mostly incorrect identification of the eccentric orbits is due to automatic pipelines and routines running without human checking/validating, and also due to the large number of systems with (evolving) spots on the surfaces of either of the stars. Only two eccentric orbits were found below the 1-d period limit, while the star TIC 1190662 with an orbital period of about 0.888 d has a surprisingly large value of eccentricity about 0.21.

We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entanglement. Across a 512-fold increase in the Hilbert space dimension corresponding to a system size increase from $N=14$ to $N=24$, HRBM NQS robustly outperforms its Euclidean version, the RBM NQS, in terms of better ground state energy optimization as well as lower Renyi-2 $S_2$ and von Neumann $S_{vN}$ absolute entanglement entropy reconstruction errors. More importantly, for all tested QSK system sizes, HRBM NQS demonstrates a superior expressivity in faithfully reproducing the entire entanglement spectrum of the QSK model from the top eigenvalues down to the tail end across 15 orders of magnitude, while RBM NQS consistently overestimates the sub-dominant modes. This work furnishes a proof-of-concept demonstrating that hyperbolic non-autoregressive NQS ansatze, thanks to the exponential volume of the hyperbolic geometry underlying their constructions, might be more natural at representing volume-law quantum systems than conventional Euclidean NQS. Furthermore, an interesting byproduct of this work is the polynomial scaling result of RBM-type NQS ansatze in the QSK volume-law system as the Hilbert space dimension increases exponentially.

The debate over pairwise and higher-order models is often framed as a contest of expressive power. This framing is misleading. A graph with arbitrary multivariate node functions can reproduce the node-level dynamics of many hypergraph models, but the hypergraph's grouping information does not thereby disappear: it may simply move from the structure to the rule. We separate four notions frequently conflated here: structural representation, functional representability, statistical identifiability, and mechanistic adequacy. We then prove that interaction order, the number of variables that must act jointly in some term of any additive decomposition of a node's update, is the same in every exact representation: no change of structural language can lower it. As a description-length problem, at the unrestricted algorithmic level a fixed compiler redistributes information between structure and rule at constant overhead, so expressiveness alone cannot privilege either language. Preferences arise only relative to explicit model classes, code families, and data. This yields an operational minimum-description-length criterion combining structural cost, conditional rule cost, and imperfect fit. Within it, for $M$ disjoint groups of size $k$, the clique projection's edge list is asymptotically $k-1$ times longer than the hyperedge list it replaces: the projection encodes the same grouping at higher cost. Examples from diffusion, Boolean dynamics, ecology, and ambiguous projections give graph-preferred, hypergraph-preferred, and unresolved cases; for bipartite and multilayer lifts, cost, fit, and identifiability tie, and the choice turns on which entities are posited as primitive. The position is symmetric: higher-order structure should not be inferred from phenomenology alone, nor does a graph's ability to emulate a system make it the most parsimonious or adequate description.

Conventional ferroelectrics exhibit well-defined polarization states linked through electric switching. In fluorite-structured ferroelectrics like hafnia, though, switching and oxygen diffusion seem to coexist, enriching the nature of ferroelectricity. Here we address the intrinsic, room-temperature switching and diffusion kinetics of hafnia ferroelectrics using machine-learning molecular dynamics. We identify two distinct switching mechanisms that are both active at realistic time scales. Critically, our simulations reveal that, because the lattice does not dissipate fast enough the heat originating from localized switching events, these two processes concatenate in an avalanche-like manner leading to oxygen conduction. Our results thus show that intrinsic switching leads to breakdown in hafnia ferroelectrics. They also suggest how this outcome might be avoided through suitably designed field pulses.