We construct static, spherically symmetric, charged traversable wormhole solutions to the Einstein--Maxwell equations, supported by bidirectional (ingoing and outgoing) null dust with negative energy, and discuss a scenario for their dynamical formation from a black hole. Our solution contains a traversable throat, where the areal radius takes a minimum, although the spacetime is not asymptotically flat. In our formation scenario, the spacetime evolves sequentially from a black hole to Vaidya regions and finally to a wormhole, with each transition mediated by an impulsive null shell. We find that the radius of the wormhole throat is determined by the mass and charge of the initial black hole as well as those of the injected shell.

The $μ$Hz gravitational wave band holds crucial insights into coalescing supermassive black hole binaries and stochastic backgrounds but remains inaccessible due to technical challenges. We demonstrate that geocentric space-based GW detectors (e.g., TianQin, gLISA, GADFLI) can bridge this gap by considering orbital resonance effects, circumventing the need for prohibitively long baselines. When GW frequencies match with integer multiples of a satellite's orbital frequency, sustained tidal forces induce cumulative orbital deviations through resonant effects, which, combined with orbital modulation, improve detector sensitivity by 1-2 orders of magnitude in the $μ$Hz band. Consequently, geocentric missions can detect SMBHBs across significantly expanded mass-redshift parameter space. Crucially, such observations could synergize with pulsar timing array data of the same binaries at earlier inspiral stages, enabling unprecedented joint tests of strong-field gravity and binary evolution. Our findings establish geocentric antennas as a cost-effective, near-term precursor for unlocking the $μ$Hz GW astronomy.

Bayesian inference provides a principled framework for probabilistic reasoning. If inference is performed in two steps, uncertainty propagation plays a crucial role in accounting for all sources of uncertainty and variability. This becomes particularly important when both aleatoric uncertainty, caused by data variability, and epistemic uncertainty, arising from incomplete knowledge or missing data, are present. Examples include surrogate models and missing data problems. In surrogate modeling, the surrogate is used as a simplified approximation of a resource-heavy and costly simulation. The uncertainty from the surrogate-fitting process can be propagated using a two-step procedure. For modeling with missing data, methods like Multivariate Imputation by Chained Equations (MICE) generate multiple datasets to account for imputation uncertainty. These approaches, however, are computationally expensive, as multiple models must be fitted separately to surrogate parameters respectively imputed datasets. To address these challenges, we propose an efficient two-step approach that reduces computational overhead while maintaining accuracy. By selecting a representative subset of draws or imputations, we construct a mixture distribution to approximate the desired posteriors using Pareto smoothed importance sampling. For more complex scenarios, this is further refined with importance weighted moment matching and an iterative procedure that broadens the mixture distribution to better capture diverse posterior distributions.

NASA's TESS mission has identified at least 158,000 oscillating red giants, increasing the known sample by roughly an order of magnitude. After validating that these measurements are reliable to 5% for up to 90% of red giants (Theodoridis & Tayar 2023), we make custom stellar evolution models using MESA in order to estimate ages for ~132,794 of these stars to an average uncertainty of 23%. We show that these ages follow similar distributions to those observed in other samples such as Kepler with small differences likely resulting in the galactic volume probed. We provide these ages to the community to enable future galactic archaeology analyses.

We propose a functional calculus which allows one to apply functions to the matrix anti-commutator/commutator operator. The calculus is introduced in a straightforward manner if the operators act on symmetric matrices, and it leads to a coordinate-free version of Daleckii--Krein formula. In this sense, the proposed calculus provides symbolic formulae for the derivatives of matrix-valued functions that are explicit and easy to use. We discuss several applications of the newly introduced calculus in continuum mechanics (Hencky logarithmic strain, objective rates, spin tensors, viscoelastic fluids) and in the theory of partial differential equations.

We construct and analyze a projection-free linearly implicit method for the approximation of flows of harmonic maps into spheres. The proposed method is unconditionally energy stable and, under a sharp discrete regularity condition, achieves second-order accuracy with respect to the constraint violation. Furthermore, the method accommodates variable step sizes to speed up the convergence to stationary points and to improve the accuracy of the numerical solutions near singularities, without affecting the unconditional energy stability and the constraint violation property. We illustrate the accuracy in approximating the unit-length constraint and the performance of the method through a series of numerical experiments, and compare it with the linearly implicit Euler and two-step BDF methods.

We show that the nonlinear Schrödinger equation on the Sierpinski gasket with a power nonlinearity of order $2k{+}1$ is not locally well-posed for initial data just below the regularity threshold for the Sobolev embedding $H^s\subseteq L^\infty$. More precisely, the flow map fails to be $C^{2k+1}$-continuous in any Sobolev space $H^s$ below that threshold, and the threshold is independent of the power nonlinearity. This novel behavior significantly differs from other compact spaces such as the torus or the sphere, and it is directly connected to the existence of localized eigenfunctions.

We present an analytical solution for the evolution of parton distributions incorporating mixed-order QCD $\otimes$ QED corrections, addressing both polarized and unpolarized cases. Using the Altarelli-Parisi kernels extended to mixed order, we solve the DGLAP equations exactly in Mellin $N$-space and derive the associated Wilson coefficients for the polarized structure function $g_1$. Our analytical approach improves computational efficiency and enhances the precision of theoretical predictions in observables sensitive to QED corrections, with relevance for current and future phenomenological applications.

We address the channel estimation (CE) problem in reconfigurable intelligent surface (RIS) aided orthogonal frequency-division multiplexing (OFDM) systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm.The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing CE performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multi-dimensional transformations to isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods.

This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the warm dense matter physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations \& experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments and contemporary challenges that are faced by theoretical methods, and experimental techniques needed to create and diagnose warm dense matter. A large part of this roadmap is dedicated to specific warm dense matter systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.

While multi-agent reinforcement learning (MARL) has been proven effective across both collaborative and competitive tasks, existing algorithms often struggle to scale to large populations of agents. Recent advancements in mean-field (MF) theory provide scalable solutions by approximating population interactions as a continuum, yet most existing frameworks focus exclusively on either fully cooperative or purely competitive settings. To bridge this gap, we introduce MF-MAPPO, a mean-field extension of PPO designed for zero-sum team games that integrate intra-team cooperation with inter-team competition. MF-MAPPO employs a shared actor and a minimally informed critic per team and is trained directly on finite-population simulators, thereby enabling deployment to realistic scenarios with thousands of agents. We further show that MF-MAPPO naturally extends to partially observable settings through a simple gradient-regularized training scheme. Our evaluation utilizes large-scale benchmark scenarios using our own testing simulation platform for MF team games (MFEnv), including offense-defense battlefield tasks as well as variants of population-based rock-paper-scissors games that admit analytical solutions, for benchmarking. Across these benchmarks, MF-MAPPO outperforms existing methods and exhibits complex, heterogeneous behaviors, demonstrating the effectiveness of combining mean-field theory and MARL techniques at scale.

Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints.

The theory of open quantum systems plays a fundamental role in several scientific and technological disciplines, from quantum computing and information science to molecular electronics and quantum thermodynamics. Despite its widespread relevance, a rigorous formulation of quantum dissipation in conjunction with thermal noise remains a topic of active research. In this work, we establish a formal correspondence between classical stochastic thermodynamics, in particular the Fokker-Planck and Klein-Kramers equations, and the quantum master equation. Building on prior studies of multiplicative noise in classical stochastic differential equations, we demonstrate that thermal noise at the quantum level manifests as a multidimensional geometric stochastic process. By applying canonical quantization, we introduce a novel Hermitian dissipation operator that serves as a quantum analogue of classical viscous friction. This operator allows for a well-defined expression of heat exchange between a system and its environment, enabling the formulation of an alternative quantum equipartition theorem. Our framework ensures a precise energy balance that aligns with the first law of thermodynamics and an entropy balance consistent with the second law. The theoretical formalism is applied to two prototypical quantum systems, the harmonic oscillator and a particle in an infinite potential well, for which it provides new insights into nonequilibrium thermodynamics at the quantum scale. Our results advance the understanding of dissipation in quantum systems and establish a foundation for future studies on stochastic thermodynamics in the quantum domain.

This paper investigates the local boundedness of weak solutions to a direction-dependent double-phase nonlocal elliptic equation. By employing refined energy estimates and De Giorgi-type techniques, we establish the local boundedness of these solutions.

Spin-$1/2$ kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings $J_2$ and Dzyaloshinskii-Moriya interactions $D$, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of $J_2$ and $D$. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.

The bulk-boundary correspondence predicts the existence of boundary modes localized at the edges of topologically nontrivial systems. The wavefunctions of hermitian boundary modes can be obtained as the eigenmodes of a modified Jackiw-Rebbi equation. The bulk-boundary correspondence has also been extended to nonhermitian systems, which describe physical phenomena such as gain and loss in open and non-equilibrium systems. Nonhermitian energy spectra can be complex-valued and exhibit point gaps or line gaps in the complex plane, whether the gaps can be continuously deformed into points or lines, respectively. Specifically, line-gapped nonhermitian systems can be continuously deformed into hermitian gapped spectra. Here, we find the analytical form of the wavefunctions of nonhermitian boundary modes with zero energy localized at smooth domain boundaries between topologically distinct phases by solving the generalized Jackiw-Rebbi equation in the nonhermitian regime. Moreover, we unveil a universal relation between the scalar fields and the decay rate and oscillation wavelength of the boundary modes. This relation quantifies the bulk-boundary correspondence in nonhermitian line-gapped systems through physical quantities that are experimentally measurable. Furthermore, this relation is not affected by the specific spatial variations of the scalar fields. These results offer new insights into the localization properties of boundary modes in nonhermitian and topologically nontrivial states of matter.

Bi2O2Se belongs to a group of quasi-2D semiconductors that can replace silicon in future high-speed/low-power electronics. However, the correlation between crystal/band structure and other physical properties still eludes understanding: carrier mobility increases non-intuitively with carrier concentration; the observed $T^2$ temperature dependence of resistivity lacks explanation. Moreover, a very high relative out-of-plane permittivity of about 150 has been reported in the literature. A proper explanation for such a high permittivity is still lacking. We have performed infrared (IR) reflectivity and Raman scattering experiments on a large perfect single crystal with defined mosaicity, carrier concentration and mobility. Five of the eight phonons allowed by factor group theory have been observed and their symmetries determined. The IR spectra show that the permittivity measured in the tetragonal plane is as high as $ε_r{\approx}500$, and this high value is due to a strong polar phonon with a low frequency of ~34 $cm^{-1}$ (~1 THz). Such an unusually high permittivity allows the screening of charge defects, leading to the observation of high electron mobility at low temperatures. It also allows effective modulation doping providing a platform for high performance 2D electronics. DFT calculations suggest the existence of a very low frequency acoustic phonon ~14 $cm^{-1}$ (~0.4 THz). Both the low frequency phonons cause anomalous phonon DOS, which is reflected in the unconventional temperature dependence of the heat capacity, $c_M{\approx}T^{3.5}$. The temperature-dependent, two-component group velocity is proposed to explains the unusual temperature dependence of the thermal conductivity, $κ{\approx}T^{1.5}$

High-resolution yield maps for manually harvested crops are impractical to generate on commercial scales because yield monitors are available only for mechanical harvesters. However, precision crop management relies on accurately determining spatial and temporal yield variability. This study presents the development of an integrated system for precision yield estimation and mapping for manually harvested strawberries. Conventional strawberry picking carts were instrumented with a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), and load cells to record real-time geo-tagged harvest data and cart motion. Extensive data were collected in two strawberry fields in California, USA, during a harvest season. To address the inconsistencies and errors caused by the sensors and the manual harvesting process, a robust data processing pipeline was developed by integrating supervised deep learning models with unsupervised algorithms. The pipeline was used to estimate the yield distribution and generate yield maps for season-long harvests at the desired grid resolution. The estimated yield distributions were used to calculate two metrics: the total mass harvested over specific row segments and the total mass of trays harvested. The metrics were compared to ground truth and achieved accuracies of 90.48% and 94.05%, respectively. Additionally, the accuracy of the estimated yield based on the number of trays harvested per cart for season-long harvest was better than 94%. It showed a strong correlation (Pearson r = 0.99) with the actual number of counted trays in both fields. The proposed system provides a scalable and practical solution for specialty crops, assisting in efficient yield estimation and mapping, field management, and labor management for sustainable crop production.

We give an alternative proof of the Kulikov-Persson-Pinkham Theorem for a proper Kähler degeneration of K-trivial smooth surfaces. After running the Minimal Model Program, the obtained minimal dlt model has mild singularities which we resolve via Brieskorn's simultaneous resolutions and toric resolutions.

The (unbounded version of the) Lempert function $l_D$ on a domain $D\subset\Bbb C^d$ does not usually satisfy the triangle inequality, but on bounded $\mathcal C^2$-smooth strictly pseudoconvex domains, it satisfies a quasi triangle inequality: $l_D(a,c)\le C( l_D(a,b)+l_D(b,c))$. We show that pseudoconvexity is necessary for this property as soon as $D$ has a $\mathcal C^1$-smooth boundary. We also give estimates of the Lempert function and of other invariants in some domains which are models for local situations, and derive some general local bounds depending on the regularity of the boundary of a domain.

We provide an axiomatization for the variety generated by the $n$-periodic l-pregroup $\mathbf{F}_n(\mathbb{Z})$, for every $n \in \mathbb{Z}^+$, as well as for all possible joins of such varieties; the finite joins form an ideal in the subvariety lattice of l-pregroups and we describe fully its lattice structure. On the way, we characterize all finitely subdirectly irreducible (FSI) algebras in the variety generated by $\mathbf{F}_n(\mathbb{Z})$ as the $n$-periodic l-pregroups that have a totally ordered group skeleton (and are not trivial). The finitely generated FSIs that are not l-groups are further characterized as lexicographic products of a (finitely generated) totally ordered abelian l-group and $\mathbf{F}_k(\mathbb{Z})$, where $k \mid n$.

Quantum many-body scars have received much recent attention for being both intriguing non-ergodic states in otherwise quantum chaotic systems and promising candidates to encode quantum information efficiently. So far, these studies have mostly been restricted to Hermitian systems. Here, we study many-body scars in many-body quantum chaotic systems coupled to a Markovian bath, which we term Lindblad many-body scars. They are defined as simultaneous eigenvectors of the Hamiltonian and dissipative parts of the vectorized Liouvillian. Importantly, because their eigenvalues are purely real, they are not related to revivals. The number and nature of the scars depend on both the symmetry of the Hamiltonian and the choice of jump operators. For a dissipative four-body Sachdev-Ye-Kitaev (SYK) model with $N$ fermions, either Majorana or complex, we construct analytically some of these Lindblad scars while others could only be obtained numerically. As an example of the former, we identify $N/2+1$ scars for complex fermions due to the $U(1)$ symmetry of the model and two scars for Majorana fermions as a consequence of the parity symmetry. Similar results are obtained for a dissipative XXZ spin chain. We also characterize the physical properties of Lindblad scars. First, the operator size is independent of the disorder realization and has a vanishing variance. By contrast, the operator size for non-scarred states, believed to be quantum chaotic, is well described by a distribution centered around a specific size and a finite variance, which could be relevant for a precise definition of the eigenstate thermalization hypothesis in dissipative quantum chaos. Moreover, the entanglement entropy of these scars has distinct features such as a strong dependence on the partition choice and, in certain cases, a large entanglement.

For studies where a reliable neutron source/beam is required and a nuclear reactor is not a viable option (considering their high neutron flux supply, which may not be suitable for research concerning low flux operations), alternative approaches may be sought. We present a comprehensive simulation analysis of 30 MeV proton induced ${}^9\mathrm{Be}\text{(p,n)}{}^9\mathrm{B}$ reaction, which can be utilized as an isotropic neutron source. This energy was chosen as it was the maximum allowed limit of protons supplied by IBA's Cyclone 30 XP proton cyclotron. Due to different underlying physics and transport mechanisms, when it comes to numerical calculations, slight variations in findings between different toolkits may occur. Hence it may require one to have a guide at hand to address the differences and interpret the data more accurately before conducting the actual experiment. In this work present the different numerical results of Geant4 and FLUKA. We conducted preliminary Monte Carlo runs to estimate the resulting neutron fluence and prompt gamma dose equivalents, while also assessing the degree of moderation achieved by different thicknesses of high density polyethylene. Furthermore, this work also presents an examplar modular irradiation station designed for target-moderator configurations, with the capability of generating thermal neutron fields.

This paper describes the $K$-theory structure for three algebra classes. For cyclic $p$-group rings and truncated polynomial rings over $\mathbb{Z}/p^s\mathbb{Z}$, we determine reduced $K_2$-structures via a common algebraic framework. For abelian $p$-group rings over $\widehat{\mathbb{Z}}_p$, we extend the isomorphism between reduced continuous $K_2$ and the first cyclic homology group to all finite abelian $p$-groups. A constructive proof using a generalized Artin-Hasse map yields an explicit splitting. This isomorphism is realized by extending Oliver's $p$-adic logarithm. We also characterize the map from reduced continuous $K_2$ to reduced linearized $K_2$, clarifying the links between $K_2$, cyclic homology, and Kähler differentials.

We study variable selection (also called support recovery) in high-dimensional sparse linear regression when one has external information on which variables are likely to be associated with the response. Consistent recovery is only possible under somewhat restrictive conditions on sample size, dimension, signal strength, and sparsity. We investigate how these conditions can be relaxed by incorporating said external information. A key application that we consider is structural transfer learning, where variables selected in one or more source datasets are used to guide variable selection in a target dataset. We introduce a family of likelihood penalties that depend on the external information, motivated by connections to Bayesian variable selection. We show that these methods achieve variable selection consistency in regimes where any method ignoring external information fails, and that they achieve consistency at faster rates. We first quantify the potential gains under ideal, oracle-chosen, penalties. We then propose computationally efficient empirical Bayes procedures that learn suitable penalties from the data. We prove that these procedures have improved variable selection properties compared to methods that do not use external information. We illustrate our approach using simulations and a genomics application, where results from mouse experiments are used to inform variable selection for gene expression data in humans.

Some of the issues that make sampling parameter spaces of various beyond the Standard Model (BSM) scenarios computationally expensive are the high dimensionality of the input parameter space, complex likelihoods, and stringent experimental constraints. In this work, we explore likelihood-free approaches, leveraging neural network-aided Simulation-Based Inference (SBI) to alleviate this issue. We focus on three amortized SBI methods: Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE) and perform a comparative analysis through the validation test known as the \textit{ Test of Accuracy with Random Points} (TARP), as well as through posterior sample efficiency and computational time. As an example, we focus on the scalar sector of the phenomenological minimal supersymmetric SM (pMSSM) and observe that the NPE method outperforms the others and generates correct posterior distributions of the parameters with a minimal number of samples. The efficacy of this framework is tested on 5 parameter pMSSM with Higgs and flavor physics data and its performance is compared with the MCMC method. We further add dark matter (DM) observables to make the task more challenging and consider a 9 parameter pMSSM. We observe that even though the efficiency factor drops, the amortized SBI method still produces faithful posterior distributions. SBI predicted points satisfying DM constraints are mostly bino-dominated upto $\sim$ 1.5 TeV, and are mostly wino-dominated within the 1.5 - 2 TeV range.

In this paper we study substitutions on $A^\mathbb{Z}$ where $A$ is a finite alphabet. We precisely characterize the minimal components of substitution subshifts, give an optimal bound for their number and describe their dynamics. The explicitness of these results provides a method to algorithmically compute and count the minimal components of a given substitution subshift.

Histopathology, the study and diagnosis of disease through analysis of tissue samples, is an indispensable part of modern medicine. However, the practice is time consuming and labor intensive, compelling efforts to improve the process and develop new approaches. One perspective technique involves mapping changes in the polarization state of light scattered by the tissue, but the conventional implementation requires bulky polarization optics and is slow. We report the design, fabrication and characterization of a compact metasurface polarimeter operating at 640 nm enabling simultaneous determination of Stokes parameters and degree of polarization with $\pm$2% accuracy. To validate its use for histopathology we map polarization state changes in a tissue phantom mimicking a biopsy with a cancerous inclusion, comparing it to a commercial polarimeter. The results indicate a great potential and suggest several improvements with which we believe metasurface polarimeter based devices will be ready for practical histopathology application in clinical environment.

An analog of Picard's little theorem for entire functions of matrices is proved.

For a set $P$ of $n$ points in $\mathbb R^d$, for any $d\ge 2$, a hyperplane $h$ is called $k$-rich with respect to $P$ if it contains at least $k$ points of $P$. Answering and generalizing a question asked by Peyman Afshani, we show that if the number of $k$-rich hyperplanes in $\mathbb R^d$, $d \geq 3$, is at least $Ω(n^d/k^α+ n/k)$, with a sufficiently large constant of proportionality and with $d\le α< 2d-1$, then there exists a $(d-2)$-flat that contains $Ω(k^{(2d-1-α)/(d-1)})$ points of $P$. We also present upper bound constructions that give instances in which the above lower bound is tight. An extension of our analysis yields similar lower bounds for $k$-rich spheres or $k$-rich flats.