Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla score matching has shown to be inefficient relative to maximum likelihood estimation for multimodal distributions with well-separated modes, which are commonly encountered in practical applications. We compare a novel diffusion-based denoising score matching estimator (DDSME) to the vanilla score matching estimator (SME) in this scenario. In particular, we prove statistical guarantees for both estimators, showing that the error bound for the vanilla SME worsens when the separation between the modes increases, which can be avoided in case of the DDSME with suitable hyperparameter tuning. This provides a novel theoretical explanation for the superior behavior of diffusion-based score matching over the vanilla version.

In this paper, we propose a tri-domain reconfigurable multiuser multiple-input multiple-output (MIMO) communication system that integrates the electromagnetic (EM) reconfigurable antenna (EMRA) with the spatially movable antenna (SMA), termed the spatial-EM reconfigurable antenna (SEMRA). The proposed system offers EM, spatial, and digital domain degrees of freedom (DoFs) for joint channel reconfiguration, yet introduces new challenges in channel estimation (CE) and precoding optimization. Specifically, for multiuser orthogonal frequency division multiplexing (OFDM) downlink, the precoding design is formulated as a tri-domain optimization problem over antenna positions, EM-domain radiation-pattern weights, and digital precoders. We first develop a zero-forcing (ZF)-based baseline algorithm to decouple the design of spatial reconfiguration, and then propose a weighted minimum mean square error (WMMSE)-based tri-domain joint optimization algorithm for further improving the spectral efficiency (SE). Furthermore, we propose a low-overhead movement-aided channel estimation scheme in which coordinated antenna repositioning across pilot slots synthesizes a denser virtual array, enabling more accurate angle-of-departure (AoD) estimation and EM-domain channel state information (eCSI) reconstruction under the same per-user pilot overhead as the EMRA baseline. The resulting parametric representation enables eCSI assembly at desired antenna positions without additional pilots. Simulation results show that the proposed CE scheme improves eCSI estimation accuracy and the proposed SEMRA achieves higher SE than the EMRA baseline under the same pilot overhead.

In this paper we completely classify semifields of order $2^8=256$ containing a nucleus of order $2^4=16$. We introduce new invariants for semifields, and apply new computational techniques for calculating old invariants. Together these make the computational classification significantly quicker.

We perform light-scattering numerical simulations for two dust populations: (i) consolidated porous particles computed with the discrete dipole approximation (ADDA) and (ii) highly porous aggregate models, including fractal and hierarchical aggregates, computed with the multiple-sphere T-matrix method (MSTM). Using DSHARP optical constants, we compute scattering matrices, cross sections, and effective albedo omega_eff for a size distribution n(a) proportional to a^q, with q = -3.5, amin = 0.1 micron, and ten wavelengths from 0.87 to 10 mm. We find that increasing porosity strengthens forward scattering and enhances polarization near theta approximately 90 degrees. For compact spheres, P(90 degrees) times omega_eff peaks near amax approximately lambda divided by 2 pi and then declines, whereas porous particles show a broader peak extending to larger sizes, keeping polarization-based constraints compatible with amax approximately 1 mm. Porosity also lowers kappa_abs at fixed dust mass relative to compact spheres, implying larger inferred dust masses for a given continuum flux.

The geometrisation theorem of 3-manifolds was conjectured by Thurston the 1980s and proved by Perelman in the 2000s. This is an overview on the subject. We explain the content of the theorem and describe its effects in various situations.

The moment-sum-of-squares hierarchy provides a powerful framework for solving non-convex optimal control problems by constructing a sequence of convex semidefinite relaxations. However, when extending these methods to nonlinear partial differential equations (PDEs), a fundamental challenge is the potential existence of a relaxation gap, where the solution to the linear measure formulation using occupation measures fails to correspond to a classical physical solution of the original PDE. In this paper, we prove the absence of a relaxation gap for scalar semilinear parabolic PDEs of the reaction-diffusion type. We do so by showing that each solution to the linear measure equation gives rise to an energy measure-valued (emv) solution in the space of Young measures satisfying suitable energy identities. We then prove that any such emv solution concentrates on the solution to the nonlinear PDE, provided the latter exists and is unique. To the best of our knowledge, this is the first concentration result of this kind for measure-valued solutions of reaction-diffusion PDEs.

Blockchain systems that settle financial transactions face a structural tension: the single validator that assembles each block holds unilateral power over transaction inclusion and ordering. Traditional markets curb this very power through front-running and market-manipulation laws. Regulators have flagged the absence of such rules as a first-order concern for blockchain-based financial infrastructure. In response, we introduce AMP, a multi-proposer protocol, on top of the Tendermint consensus algorithm, where no validator can control the flow of transactions into blocks. Instead, dedicated nodes called proposers sit between users and validators. They collect user transactions, group them into payloads, and broadcast the payloads to all validators. Consequently, there is no mempool, and AMP applies the design principle of separating dissemination from agreement, which can lead to higher throughput. Validators publicly attest to receiving payloads and run consensus to decide the set of payloads to include in the next block. When all correct validators attest to a given payload, AMP guarantees that payload will be included in the next block; a block thus contains payloads from multiple proposers, allowing for bulk finalization. This bounded inclusion guarantee along with a deterministic ordering algorithm which is run over all payloads included in a block, curbs the power of any single validator. Validators no longer control what is included in a block, nor can they arbitrarily order the contents of blocks.

Illegal content reporting mechanisms are a key technical and organizational measure through which online platforms address illegal content under the European Union Digital Services Act (DSA). Article 16 requires user notices to be sufficiently substantiated and submitted in good faith, placing users in the difficult position of interpreting legal and procedural language and translating ambiguous content into legally meaningful categories and reasons. We investigate how large language model (LLM)-based assistants can support this reporting process. In a controlled user study (N = 450) using an interface modeled on a major platform reporting workflow, we compare three conditions: unaided reporting, a conventional explainable AI assistant (XAI) that suggests a single legal category with a rationale, and an evaluative AI assistant (EvalAI) that presents balanced pro and con arguments across candidate legal provisions. We further examine these assistance forms under systematically varied AI error regimes. Our results show that EvalAI improves provision-level accuracy under AI error and reduces misclassification distance relative to conventional XAI, particularly for near-miss and overbreadth errors. When AI output is correct, conventional XAI enables faster decisions, but neither AI assistance form reliably improves the quality of users' substantiated explanations relative to unaided reporting. We discuss design implications for compliance-oriented reporting interfaces, highlighting trade-offs between accuracy, deliberation, explanation quality, and vulnerability to misleading AI output.

J=1--0 SiO masers at 43 GHz have a well-established distinctive signature in asymptotic giant branch (AGB) stars. 28SiO transitions typically dominate these spectra with the v=1 and v=2 emission being especially prominent and ubiquitous. Several predictions about enhanced 29SiO abundances in exotic stars prompt us to catalog the cases where 29SiO maser emission is enhanced compared to 28SiO. Our purpose is to catalog the known cases of 43 GHz spectra dominated by emission from isotopologue transitions (iso-dom spectra), to explore the commonalities in these sources, and to explain the cause of such maser signatures. Our catalog is drawn from SiO maser line ratios in the infrared-color-selected BAaDE survey and supplemented with a literature detection. The BAaDE catalog has cemented the typical signature of 43 GHz SiO masers, showing it is dominated by the v=1 and v=2 lines. Thirty-five iso-dom spectra are identified, meaning that this signature is seen in about 0.2% of our SiO maser-bearing stars. Their infrared colors are blue compared to other sources of the same period, similar to all sources displaying isotopologue masers. It is clear that the iso-dom nature of sources is variable, but unclear whether this is tied to stellar phase. A large abundance abnormality among the iso-dom sources is disfavored as the iso-dom sources do not appear significantly different from other stars which host isotopologue masers. Maser pumping, affecting the population inversions of specific transitions, can instead explain the enhanced signatures. We posit that iso-dom spectra appear in AGB stars which have undergone third-dredge up (enhancing the 29SiO and 30SiO abundance slightly) and which, in addition, are experiencing very low turbulence velocity (<1 km/s), creating a line overlap which pumps the maser transitions very efficiently.

Context. Type Ibn supernovae (SNe) are characterized by narrow helium emission lines arising from ejecta-circumstellar medium interaction, yet their progenitors remain debated, with both massive Wolf-Rayet stars and low-mass helium stars in binaries proposed. Aims. We aim to directly identify the progenitor of the Type Ibn SN 2023fyq and to characterize its environment in order to constrain the progenitor's nature and evolutionary channel. Methods. We search for the SN progenitor based on pre-explosion and late-time HST and JWST images and derive its properties by fitting the spectral energy distribution. We investigate the SN environment by probing the stars, dust, ionized gas and molecular gas with a multi-wavelength dataset including HST and JWST imaging, VLT/MUSE integral-field-unit spectroscopy and ALMA CO (2--1) radio interferometry. Results. We discover a pre-explosion source at the SN position, which is consistent with a hot ($T>$15000 K) and luminous (log($L$/$L_\odot$) $\gtrsim$ 5.5) SN progenitor and a possible host star cluster. The progenitor is confirmed to have disappeared after explosion. Analysis of the SN environment implies that the progenitor likely has an age of log($t$/yr) = 7.1--7.2. These phenomena disfavor a very massive single-star progenitor and instead support a binary scenario involving a low-mass helium star and a compact object; the observed progenitor emission likely arises from binary interaction that began at least $\sim$12 yr before the explosion. Conclusions. SN 2023fyq is the first Type Ibn SN with a directly detected progenitor and a possible host star cluster. It adds to the diversity of Type Ibn SNe in terms of their progenitor channels and mass-loss mechanisms.

Numerical approximations to Ricci-flat Calabi--Yau metrics make it possible to move beyond the topological and holomorphic data that have traditionally dominated explicit string compactifications. This article explains what new physics and mathematics become accessible once the metric, and eventually the associated Hermitian Yang--Mills bundle data, can be computed. In heterotic compactifications, such data are needed to determine matter Kähler metrics, canonically normalized Yukawa couplings, Kaluza--Klein spectra, threshold effects, soft terms, and other non-holomorphic ingredients of the four-dimensional effective action. More broadly, numerical Calabi--Yau geometry provides quantitative input for moduli stabilization, $α'$-corrected backgrounds, de~Sitter model building, axion physics, swampland distance tests, and compactifications in which the internal geometry varies over spacetime. Geometric data permit a computational approach to long-standing mathematical questions involving special Lagrangian submanifolds, SYZ fibrations, mirror symmetry, calibrated geometry, metric degeneration, restrictions of Ricci-flat metrics to fibers, and the search for analytic or semi-analytic structures. We present these directions as a roadmap for future work.

This paper develops a two-branch multiplicative-coset construction for regular Calderbank-Shor-Steane (CSS) quantum low-density parity-check base matrices. For a target column weight \(J\) and an even row weight \(L\), the method reduces regularity, CSS orthogonality, and same-type 4-cycle exclusion to explicit quotient-coset conditions over a finite field. A normalized exhaustive search for these conditions produces base matrices for several \((J,L)\) pairs, so the construction is not tied to a single degree distribution. The construction separates the finite-length design into two stages: the base matrix fixes the degree distribution and the first girth constraints, and a cyclic lift randomizes edge connections subject to exact algebraic checks. As a detailed example, we carry one \((3,10)\)-regular base through the lift and decoding stages. For this example, the selected 64-fold lift gives a code whose same-type Tanner graphs have girth at least eight, and it also excludes a specified weight-16 nondegenerate logical-support orbit. The resulting instance is a \([[10240,4108,\,10\le d\le32]]\) CSS code. For decoding, we use joint log-domain belief propagation together with low-complexity deterministic post-processing rules for small residual syndromes, including repairs for residual patterns with two unsatisfied checks. The frame error rate (FER) measurements provide finite-length decoding data for this detailed example; at depolarizing probability \(p=0.058\), the post-processing FER is \(1.0\times10^{-7}\).

Argumentative dialogues across political divides can reduce polarization, yet opportunities for citizens to engage with opposing views in accessible and structured ways remain limited. AI dialogue partners offer a scalable framework for such open-mindedness exercises, but how the format of human-AI dialogues shapes their benefits remains unclear. In a two-session online experiment, 469 US participants were assigned to argue either for or against their own attitude on a contested political issue with an AI chatbot. Our experimental findings show attitude-congruent dialogues produced greater immediate reduction in both affective and opinion polarization than attitude-incongruent dialogues. By contrast, attitude-incongruent dialogues elicited weaker cognitive state empathy than the non-AI reference task but increased cognitive trait empathy in the two-week period between sessions, suggesting the effects of active generation of attitude-incongruent arguments may emerge over time. These findings highlight dialogue design as a key determinant of effective AI-mediated behavioral interventions.

Meyer defined crystalline measures as tempered distributions $μ$ such that both $μ$ and its Fourier transform $\widehatμ$ are pure-point Radon measures of locally finite support. He conjectured that every crystalline measure is almost periodic as a tempered distribution. Favorov constructed a counterexample and asked whether crystalline measures are at least almost periodic as general distributions. To resolve Favorov's question, we first show that the almost periodicity of a crystalline measure is characterised in terms of its translation boundedness, in any class of Radon measures, tempered distributions, or general distributions. We then construct a crystalline Fourier eigenmeasure that fails to be translation bounded even as a distribution. We finally construct a crystalline measure that fails to be a~Fourier quasicrystal (in particular, it fails to be slowly increasing), but it is an almost periodic tempered distribution whose Fourier transform is even a norm almost periodic measure. Our examples fully resolve the questions of Meyer and Favorov and sharply delineate the class boundary of translation boundedness. They also demonstrate the unusual behaviour of crystalline measures beyond the class of Fourier quasicrystals.

The Talbot effect describes the emergence of periodic patterns in perturbed propagating wave fields. The effect is well studied for perturbations from structurally coherent optics such as diffraction gratings. The emergence of freeform and metaoptical designs raises the question of whether comparable behavior can also be observed from complex, non-periodic structures. Here we demonstrate that stochastic structures inspired by recent metamaterial designs, display a strong Talbot-like behavior. Re-emergence of projected wavefronts through stochastic spinodal architectures at distinct propagation distances are proven theoretically and experimentally in the visible and hard X-ray regimes. A direct application of this phenomenon is X-ray dark-field imaging for characterizing artificial and natural meso-structured materials. Our work shows that spinodal X-ray optics effectively bridge the gap between the two opposing approaches in dark-field X-ray imaging that advocate for either spatially fully coherent (i.e gratings) or incoherent (i.e diffusers) optics. This opens opportunities for exploring a new dimension in the implementation of X-ray imaging methods. Given the impact and universality of the classical Talbot effect, we expect our work to enable new opportunities for characterizing and manipulating matter.

The understanding of light-induced dynamical states continues to be a challenging and fruitful pursuit of science. This pursuit is supported by quantum simulation of dynamical phenomena, e.g., in ultracold atom systems. Typically, ultracold atom dynamics are read out destructively, via time-of-flight imaging, limiting a detailed analysis. However, atom-cavity systems provide a real-time readout of the photonic state via photon emission from the cavity, making the system ideally suited for the simulation of dynamical phenomena. Here, we review three distinct time crystalline states, predicted and realized in a cavity-BEC system. We give an example for each of them, based on minimal few-mode models. We characterize the time crystalline states via correlation functions of the cavity mode, and characteristic momentum modes of the condensate. This supports a clear distinction between these time crystals. More generally, the sequence of studies reviewed here, serves as a blueprint for setting up minimal models and their characterization, for dynamical phenomena.

We study the decoherence induced by near-extremal charged black holes on quantum systems in their exterior. Specifically, we analyze a thought experiment recently discussed in the literature, where the quantum system is a charged particle prepared in a spatial superposition. Near-extremal black holes are known to exhibit large quantum metric fluctuations of the near-horizon geometry at low temperatures. We show that, at late times, if the black hole is sufficiently close to extremality, these quantum gravity effects make the decoherence rate vanish. This phenomenon is due to a spin-induced energy gap in the quantum black hole spectrum. For energies above the gap, the decoherence rate becomes nonzero, but is still suppressed relative to semiclassical expectations, so these quantum gravity effects always enhance the coherence of the superposition.

Altered hemodynamics play a key role in cerebrovascular diseases such as aneurysms and stenosis. However, in vivo imaging lacks the spatial resolution required to resolve flow dynamics in small vessels. This study presents an experimental framework to investigate microscale hemodynamics using transparent 3D printed vascular models and particle image velocimetry (PIV). Optically transparent microfluidic models with straight and pathological (aneurysmal and stenotic) geometries were fabricated via additive manufacturing up to a minimum diameter size of 500 microns and characterized using optical microscopy. Flow experiments were conducted under steady laminar conditions, and local velocity fields and wall shear stress (WSS) were measured using microPIV. Measured velocities have been compared with analytical Hagen Poiseuille predictions, obtaining mean relative errors of 5 to 17 percent. The platform reliably captured key flow features and spatial variations in velocity. Overall, the results demonstrate that transparent 3D printed vascular models combined with microPIV provide a robust experimental approach for studying microscale cerebrovascular hemodynamics.

In this study, we report the synthesis and characterization of a novel tetragonal polymorph of Tb-doped Pb2P2O7. Single-crystal X-ray diffraction confirms the stabilization of the P41 and P43 enantiomorphs at room temperature due to the incorporation of Tb3+ ions. Optical investigations reveal green photoluminescence from the characteristic 5D4 -> 7Fj (J = 1-5) transitions of Tb3+, with each emission split due to the crystal field effect, indicating the presence of Tb3+ in multiple coordination environments. The power dependence of the PL intensity follows a linear power-law behavior, suggesting a one-photon excitation process. Temperature-dependent PL measurements show an initial increase in intensity up to 125C, attributed to energy transfer from structural defects, followed by thermal quenching above this temperature. Structural stability at elevated temperatures is confirmed via high-temperature X-ray diffraction (XRD), showing no phase transitions before melting at approximately 800C. These findings highlight the potential of tetragonal Tb-doped Pb2P2O7 as a new class of photoluminescent material.

Earth's atmosphere operates a steam cycle in which water vapor evaporates from the surface, expands, condenses, and returns as precipitation. The Clausius-Clapeyron law relates the incremental expansion work of saturated water vapor to latent heat converted at a Carnot efficiency corresponding to the temperature difference between evaporation and condensation. We generalize this relation to an atmospheric column with condensation occurring over a range of heights and derive the expansion work per mole of precipitated water. This includes the gravitational work associated with lifting moist air to the mean condensation height, the expansion work generated by condensation, and a correction for incomplete condensation. Using GPCP v3.3 precipitation and observational constraints on condensation height, we estimate the global steam-engine power as $W_v=4.4\pm0.9$ W/m2, close to an independent estimate of total atmospheric power, $W=W_P+W_K\simeq4.3\pm0.6$ W/m2, obtained from the gravitational power of precipitation and kinetic energy generation by horizontal pressure gradients diagnosed from MERRA-2. Kinetic energy generation is $W_K\simeq3.2\pm0.3$ W/m2, of which at least two thirds is generated in the lower atmosphere. The smaller upper-atmospheric contribution, dominated by temperature-related pressure gradients, is comparable to Lorenz available potential energy generation. The agreement between steam-engine and atmospheric power is linked to condensation and precipitation fallout. By removing water from the atmospheric gas phase and enabling column-mass redistribution, precipitation maintains surface pressure gradients that drive cross-isobaric flow in the frictional lower atmosphere. The steam-engine framework thus provides a thermodynamic basis for condensation-induced atmospheric dynamics and identifies a major lower-atmospheric power pathway associated with water phase transitions.

Generic quantum many-body systems are expected to thermalize, scrambling initial coherence while local observables relax to equilibrium values. Weak ergodicity breaking, often associated with quantum many-body scarring of homogeneous states, provides rare exceptions with long-lived coherence. We introduce a family of local spin-1/2 chains with a structured subspace that hosts a much broader range of nonthermal phenomena, such as scars, chirally propagating quasiparticles or approximate topological edge modes. These nonthermal phenomena happening at high energy densities can be understood via structured subspaces that are protected by destructive interference. We develop a quantitative leakage theory predicting which states retain coherence and suggest ways to improve the stability by inducing fast oscillations in the complement subspace. Our framework connects asymptotic scars, quantum cages, and parent-Hamiltonian constructions, and shows that weak ergodicity breaking in chaotic systems extends well beyond revivals of homogeneous initial states.

Electromagnetic scattering effects of a vapor cell on electric-field measurements using Rydberg atom-based sensors are analyzed with the aid of the volume integral equation method. In a manner similar to measurement, this computational approach determines the electric field over grid points within the vapor cell. Its relatively high computational efficiency makes it suitable for use in optimization routines and statistical uncertainty studies. We apply this method to compare uncertainty contributions arising due to the presence of the vapor cell, such as uncertainty in the glass relative permittivity or standing wave formation inside the cell, to those arising from the atomic spectroscopic measurement, such as uncertainty in the atomic dipole moment. For vapor cell dimensions less than half a wavelength, the dominant uncertainty source arises from uncertainty in the glass relative permittivity, resulting in a total uncertainty of $\sim$3.5\% -- comparable to the best uncertainties obtained with traditional field generation methods at national metrology institutes. Precise permittivity measurements have the potential to further reduce measurement uncertainty to $<1$\%.

Predicting how a deformable body moves and deforms in a viscous flow underlies problems ranging from microorganism locomotion to soft microrobotics, yet existing frameworks are either problem-specific or ill-suited to inverse design. We propose the soft mobility theory: applying the principle of virtual power and the Lorentz reciprocal theorem to a hyperelastic body in a background Stokes flow yields a configuration-dependent ordinary differential equation for the generalized coordinates of the body. This soft mobility equation extends classical rigid-body mobility theory in that the mobility, elastic, body-force, and flow-coupling tensors all depend explicitly on the instantaneous deformation. We specialize the framework to assemblies of hydrodynamically interacting spheres connected by elastic springs, using the Rotne-Prager-Yamakawa approximation to compute the mobility, and validate it on canonical problems spanning rigid and flexible bodies in quiescent and shear flows. An open-source JAX implementation makes entire simulations end-to-end differentiable. This allows efficient gradient-based inverse design: as proofs of concept, we recover the asymptotic optimum of a three-sphere swimmer and design a soft gyrotactic "surfer" that exploits passive deformation to ascend faster than its rigid counterpart in a Taylor-Green flow.

A zonotope is a linear image of the cube $[-1,1]^m$ for some $m \in \mathbb{N}$. We show that there is a universal constant $C$ such that, for every zonotope $Z\subset \mathbb{R}^d$ and vectors $v_1,\dots,v_n\in Z$, there are signs $x_1,\dots,x_n\in\{-1,1\}$ with \[ \sum_{i=1}^n x_i v_i \in C\sqrt d\, Z. \] This resolves a 2002 question of Schechtman and generalizes Spencer's six standard deviations theorem, which corresponds to the case $Z=[-1,1]^d$.

Let $\mathbb{F}$ be a field and let $M_2(\mathbb{F})$ be the algebra of $2\times 2$ matrices endowed with an involution of the first kind. We study the image of multilinear $*$-polynomials evaluated on $M_2(\mathbb{F})$. For the transpose involution over $\mathbb{R}$, we show that the image is either a proper vector subspace or contains a basis of $M_2(\mathbb{R})$. For the symplectic involution over quadratically closed fields or over $\mathbb{R}$, we prove that the image is always a vector space, namely one of $\{0\}$, $\mathbb{F}$, $sl_2(\mathbb{F})$ or $M_2(\mathbb{F})$. As a byproduct, we complete a theorem of Brešar and Klep describing the linear span of the image of a $*$-polynomial on finite dimensional central simple algebras with involution of the first kind. Their result excluded algebras of dimensions 4 and 16; we settle both cases, extending the description to all dimensions greater than 1 (over $\mathbb{R}$ for the transpose involution, and over quadratically closed fields or $\mathbb{R}$ for the symplectic involution). We also classify all Lie skew-ideals of $M_4(\mathbb{F})$ over fields of characteristic zero.

In industrial scenarios involving multi-agent collective decision-making, centralized decision-making may not be admissible due to restrictive access to individual local information, while the conflicts between participants' self-interest and global performance may also impede collaborative distributed decision-making. This paper proposes a mechanism-driven distributed decision-making method, wherein incentives are employed and designed to motivate participants to collaborate in a distributed fashion even though each participant's decision is driven primarily by self-interest. Focusing on optimization problems with coupled objective functions and coupled constraints, we design a distributed optimization algorithm tailored for this class of problems and provide guarantees for its convergence. Furthermore, we design two incentive mechanisms, the shadow pricing mechanism and the Vickrey-Clarke-Groves mechanism, and demonstrate that participants are willing to engage in distributed collaboration under these mechanisms. The mechanism drives the execution of the distributed algorithm, and the optimal result of distributed computation guides the determination of incentives in the mechanism, both of which are interrelated to form a closed loop. Finally, numerical experiments illustrate the effectiveness of the proposed algorithm and mechanisms.

In this work, a hybrid simulation framework combining TCAD and Allpi$\text{x}^2$ is presented to investigate the sensor properties of MALTA2, a depleted monolithic active pixel sensor designed for future tracking. The study starts from 3D modeling and transient simulations in TCAD, with generic doping profiles and simple well structures. The resulting doping profiles and electric field are extracted and fed into Allpi$\text{x}^2$ for high-statistics Monte Carlo simulations in both DUT-only and full-telescope mode. Simulations reveal a strong dependence of sensor performance, specifically the detection efficiency and cluster size, on the doping concentration of the N-type blanket at the sensor surface. The doping concentration is then optimized by comparing simulations with measurement data. The active depth of the depleted region of the MALTA2 sensor is estimated in both simulations and measurements using a grazing angle method, in which the sensor is positioned at various inclinations relative to the beam, covering angles from 0 to 60 degrees. Excellent agreement on active depth is obtained with the optimal doping concentration, showing a deviation of 2\% from the measured value at a threshold of 450\,$\text{e}^-$. Consequently, the framework offers a generic toolkit for sensor studies without requiring proprietary information.

In this technical report, we describe our submission for the WildSpoof Challenge TTS Track: Text-to-Speech with In-the-Wild Data. We introduce F5-TTS-DPS, a model built upon the F5-TTS architecture. Our approach integrates Exponential Moving Average (EMA) into supervised fine-tuning to stabilize training and improve generalization. To enhance synthesis fidelity, we leverage large language models (LLMs) and large audio language models (LALMs) for dual-scoring prompt selection, filtering reference audio and text prompts to ensure quality while addressing alignment issues in noisy datasets. Experimental evaluation demonstrates that F5-TTS-DPS achieves strong performance with UTMOS of 3.20 and speaker similarity of 0.51 on the development set. More importantly, our model achieves the best a-DCF scores of 0.1582, 0.5233, and 0.2562 across three advanced SASV systems among all submissions, indicating our synthesized speech is the most difficult to detect and exhibits the highest degree of naturalness and authenticity. Combined with competitive WER performance, these results validate the effectiveness of our approach in generating natural-sounding speech with strong spoofing capabilities.

The accelerating shift toward low and ultra-low fertility has intensified the debate over whether countries now undergoing rapid decline are approaching stabilization or entering a more persistent low-fertility regime. Existing projection systems answer that question differently because they embed different assumptions about recovery and about the role of external drivers. To provide an empirical benchmark in this debate, we introduce NeuralTFR, an endogenous global forecasting framework based on a recurrent neural network. Drawing on a harmonized panel of historical fertility series from 196 countries and territories, the model pools cross-country information to learn demographic momentum and generate empirical prediction intervals via multi-quantile regression. Evaluated on a held-out period (2009--2023), NeuralTFR achieves lower point-forecast errors than a Naive Drift baseline and BayesTFR, the United Nations' Bayesian Hierarchical Model, while maintaining competitive uncertainty calibration. In forward projections to 2040, NeuralTFR points to broader exposure to low and very low fertility than BayesTFR, suggesting weaker support for near-term stabilization while still falling short of the most severe decline paths predicted by the Global Burden of Disease project.

Intercalation at the graphene/SiC interface provides a controlled route to stabilize atomically thin layers with properties distinct from their bulk counterparts. In this platform, the structure and stability of the intercalated phase depend sensitively on the defect landscape of the starting substrate. For intercalated two-dimensional silver at the graphene/SiC interface, two phases have been observed: a phase epitaxial to the SiC lattice, Ag$_{(1)}$, readily obtained following the conventional intercalation method under ultra-high-vacuum conditions and extensively characterized, and a more densely packed phase, called Ag$_{(2)}$, which has remained largely unexplored. Here we report an in situ ultra-high-vacuum preparation method of the second phase intercalated at the graphene/SiC interface; this phase previously was prepared via high-pressure confinement heteroepitaxy. Low-energy electron diffraction shows that Ag$_{(2)}$ is rotated by 30 degree relative to the SiC lattice and forms supercells, in contrast to the $(1\times 1)$ epitaxial relation of Ag$_{(1)}$ with SiC. High-resolution angle-resolved photoemission spectroscopy reveals a more rich Ag$_{(2)}$ band dispersion compared to the Ag$_{(1)}$. In density functional theory calculations, by defining the unfolding entropy which, in a quantified way, finds that the band structure of Ag$_{(2)}$ is more suitable to be unfolded to the SiC primitive cell, and the resulting unfolded band dispersion is in great agreement with the experimental data. We further show that the different intercalated Ag phases tune the electronic properties of the overlying quasi-free-standing graphene layer differently: compared with Ag$_{(1)}$, Ag$_{(2)}$ yields an $\sim$1.75 times higher charge carrier density and modifies the charge-plasmon interaction of the graphene layer, indicating a change in effective screening at the interface.