We investigate quarkyonic matter within a relativistic quark model by combining the dual quarkyonic picture with the quark-meson coupling (QMC) model. Using relativistic gaussian quark wavefunctions for the nucleon, we construct the quarkyonic QMC (QQMC) model and study the properties of symmetric nuclear matter and pure neutron matter. We find that the quark saturation density depends sensitively on the nucleon size parameter and that nuclear interactions quantitatively modify the high-density behavior of the equation of state (EoS) and the sound velocity. In particular, the QQMC model yields an earlier onset of quark saturation than the noninteracting gaussian quarkyonic (GQ) model, indicating that nuclear interactions enhance the stiffening of the EoS in the quarkyonic regime.

Pulse-to-pulse fluctuations in mode-locked lasers fundamentally limit applications from optical frequency combs to supercontinuum generation. While timing jitter has been extensively characterized, pulse amplitude and width fluctuations remain less accessible experimentally. We present a statistical autocorrelation method that demonstrates pulse breathing dynamics through Fano factor analysis of second-harmonic generation autocorrelation. This reveals a characteristic W-shape in the enhanced Fano profile, a signature of pulse shape dynamics that is invisible to time-averaged fluctuations. Applying this method to two commercially available passively mode-locked oscillators operating at 1030 nm and 1045 nm, with different performance specifications, we measure pulse width fluctuations of 3.2(1)\,fs and 2.86(2)\,fs respectively. The two independent instruments serve as a cross-validation of the technique across different laser platforms. This diagnostic capability opens the door to identifying and suppressing specific breathing mechanisms, paving the way for the design of ultra-stable oscillators required for precision frequency metrology.

Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture of different species. However, quantum effects may play a role in the dynamics of these permeation processes when light molecules are the ones involved in the crossing of the GDY subnanometric pores. In this work we report rigorous quantum-mechanical calculations together with equivalent MD simulations of the transport of H2 molecules through a static GDY membrane, as a case study for the validity of the application to these problems of classical dynamics. The force fields employed are based on an improved Lennard-Jones formulation, with parameters optimized by means of accurate ab initio calculations. It is found that, although quantum effects are still significant at the temperatures of interest (between 250 and 350 K), MD simulations are able to reasonably reproduce the dependence of the quantum permeances with the temperature. Moreover, MD permeances computed with quantum corrections through Feynman-Hibbs effective potentials provide a lower bound to quantum permeances, while the pure classical counterpart gives an upper bound, thus leading to a well delimited range of confidence of the permeation results. Furthermore, within MD simulations it is possible to incorporate the thermal motion of the GDY layer and in this situation it is observed an enhancement of the permeances with respect to the fixed membrane case, due to a significant reduction of the permeation barriers when the GDY atoms are allowed to vibrate. It seems apparent therefore, that modeling the membrane motion is crucial to provide reliable simulations of the gas transport features.

Classical results of Hausmann and Latschev show that Vietoris-Rips complexes can recover the homotopy type of a manifold, even from finite metric spaces that are nearby in Gromov-Hausdorff distance. We prove persistent homotopical versions of these theorems for metric spaces equipped with filtration functions. The central object of study is the so-called persistent homotopy type of the function-Rips complex, a filtered simplicial complex that combines a fixed Rips scale with the filtration data on the underlying space. Using techniques from CAT($κ$)-geometry and persistent simplicial homotopy theory, we generalize Latschev's and Hausmann's theorems to the setting of spaces with filtration functions and homotopical interleavings. A fundamental ingredient is a new homotopical stability theorem. The fixed-scale function-Rips construction is known not to be globally stable with respect to function Gromov-Hausdorff distance and homotopical interleaving distance. Here, we show that it is nevertheless stable for appropriate choices of the Rips parameter at such pairs $(M,f)$ for which $M$ is a complete metric space of curvature bounded above, and $f$ is a Lipschitz continuous multivariate function.

Interactive fixed effects are routinely controlled for in linear panel models. While an analogous fixed effects (FE) estimator for nonlinear models has been available in the literature (Chen, Fernandez-Val and Weidner, 2021), it sees much more limited use in applied research because its implementation involves solving a high-dimensional non-convex problem. In this paper, we complement the theoretical analysis of Chen, Fernandez-Val and Weidner (2021) by providing a new computationally efficient estimator that is asymptotically equivalent to their estimator. Unlike the previously proposed FE estimator, our estimator avoids solving a high-dimensional non-convex optimization problem and can be feasibly computed in large nonlinear panels. Our proposed method involves two steps. In the first step, we convexify the optimization problem using nuclear norm regularization (NNR) and obtain preliminary NNR estimators of the parameters, including the fixed effects. Then, we find the global solution of the original optimization problem using a standard gradient descent method initialized at these preliminary estimates. To make our method readily applicable in practice, we also propose specific numerical algorithms for solving the involved optimization problems, establish their convergence, and provide their efficient implementation in our R package NNRPanel.

For a normal algebraic variety we generalise the relation between reflexive rank one sheaves and Weil divisors to reflexive sheaves of arbitrary rank and so-called Weil decorations. As an application, we define and study a natural generalisation of the celebrated Horrocks-Mumford bundle.

System outputs such as eigenfrequencies or strain data, often used in structural health monitoring (SHM), not only react to damage but also depend on environmental conditions. When trying to correct for these confounding effects, it is often (at least implicitly) assumed that only the expected, i.e., mean, output values are affected by environmental conditions. However, the evaluation of real-world SHM data indicates that environmental conditions may influence not only the mean output but also higher-order statistical moments, particularly the variances of and the covariances and correlations between the output quantities, such as eigenfrequencies of different modes or strain sensors at different locations. To address these issues, we discuss two approaches for identifying and quantifying multivariate confounding effects on output covariances and correlations: a random forest and a nonparametric, kernel-based approach. We compare the two competing methods on both artificial and real-world SHM data, finding that the kernel-based approach achieves higher accuracy, but the random forest produces estimates that are more robust and sometimes easier to interpret.

We review recent theoretical and experimental developments in magnetic Weyl semimetals, focusing on the electromagnetic responses emerging from the interplay of their electronic band topology and magnetism. We begin by introducing the fundamental topological properties of the electrons in Weyl semimetals, and provide an overview of the characteristic phenomena arising from their band topology, such as the anomalous Hall effect and chiral magnetic effect. The materials exhibiting the magnetic Weyl semimetal state, with ferromagnetic ordering, antiferromagnetic ordering, etc., are listed. The possible mechanisms for their magnetism are discussed in connection with the Weyl electrons. Non-uniform magnetic textures and magnetization dynamics are expected to exhibit a topological interplay with the Weyl electrons, manifesting as spinmotive force and spin torques. We also review the magnetotransport phenomena such as domain wall magnetoresistance, studied by mesoscopic scale calculations. Finally, we mention the spin transport properties studied in magnetic Weyl semimetals. The topological nature of Weyl electrons reviewed here is important not only for fundamental physics, but also for the potential application to low-dissipative electronics and spintronics devices.

We show that the leading non-analytic terms in the small-t expansion of the energy momentum tensor (EMT) form factors of an electrically charged particle in QED can be correctly derived in a classical model of the electron by Bialynicki-Birula. Based on the lucidity of the employed exactly solvable model, we comment also on the recently proposed concept of a regularized proton D-term.

If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model of a transitioning economy, each firm captures the full cost saving from automation but bears only a fraction of the demand loss it creates in the product market; the rest falls on rivals. This demand externality traps rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and ``better'' AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity, universal basic income, upskilling, or Coasean bargaining. A Pigouvian automation tax can. The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.

We characterize boundedness and compactness of Toeplitz operators on quaternionic Fock spaces with positive measure symbols and slice-function symbols in \(\mathrm{BMO}^1\). For positive measure symbols, we derive criteria using normalized reproducing kernels and symmetric box averages, while for slice \(\mathrm{BMO}^1\) symbols, the characterizations rely on the Berezin transform. We further introduce a global quaternionic Fock space \(F_α^p\) to define Toeplitz operators with real-valued measure symbols; this space is built by integrating slice regular functions over all complex slices of \(\mathbb{H}\) and is norm-equivalent to the standard slice-based quaternionic Fock space. In the Hilbert space case \(p=2\), a slice-independent orthogonal projection exists, which allows us to define Toeplitz operators with real-valued measure symbols and slice-function symbols in a unified way.

This paper presents a novel disturbance-torque-estimation-augmented model predictive control (MPC) framework to perform momentum management on NASA's Solar Cruiser solar sail mission. Solar Cruiser represents a critical step in the advancement of large-scale solar sail technology and includes the innovative use of an active mass translator (AMT) and reflectivity control devices (RCDs) as momentum management actuators. The coupled nature of these actuators has proven challenging in the development of a robust momentum management controller. Recent literature has explored the use of MPC for solar sail momentum management with promising results, although exact knowledge of the disturbance torques acting on the solar sail was required. This paper amends this issue through the use of a Kalman filter to provide real-time estimation of unmodeled disturbance torques. Furthermore, the dynamics model used in this paper incorporates key fidelity enhancements compared to prior work, including Solar Cruiser's four-reaction-wheel assembly and the offset between its center of mass and center of pressure. More realistic operation scenarios involving the tracking of large angle slew maneuvers under attitude-dependent solar radiation force and torque are also performed to further validate the proposed method compared to prior work. Simulation results demonstrate that the proposed policy successfully manages angular momentum growth under slew maneuvers that exceed the operational envelope of the current state-of-the-art method. The inclusion of the disturbance torque estimate is shown to greatly improve the reliability and performance of the proposed MPC approach. This work establishes a new benchmark for Solar Cruiser's momentum management capabilities and paves the way for MPC-based momentum management of other solar sails making use of an AMT and/or RCDs.

In many choice settings the decision maker (DM) adopts a criterion which is a mediation between her preference, and its opposite. According to such compromise, the first i alternatives on top of the DM's taste are moved, in reverse order, to the bottom. This pattern allows to define the compromise-based degree of irrationality, which quantifies the extent of the mediation embraced by the DM in her choice. Necessary and sufficient conditions characterizing this index are singled out. I investigate non rationalizable choices that display the lowest degree of irrationality, and I fully identify the preferences that explain the DM's picks by a minimal mediation between opposite judgments. These datasets account for some well known selection biases, such as second-best procedures, and the handicapped avoidance. I offer a simple characterization of the choice behavior that exhibits the most severe compromise, and I show that this subclass comprises almost all choices. Finally, some alternative measures of compromise are characterized, and compared with the score previously determined.

The three-flavor framework of neutrino oscillations successfully explains most experimental results, but persistent anomalies at short- and long-baseline experiments hint at the existence of additional light sterile states. In particular, eV-scale sterile neutrinos are motivated by LSND and MiniBooNE results, while sub-eV sterile states with mass-squared differences at the $10^{-2}$ and $10^{-5}$~eV$^2$ scales have been proposed to address the T2K--NO$ν$A tension and the absence of the expected upturn in the solar neutrino energy spectrum, respectively. Such sterile states are singlets under the Standard Model gauge group and mix only through their admixture with active neutrinos. In this work, we investigate the phenomenology of the $3+2$ scenario, incorporating one eV-scale sterile neutrino together with a sub-eV state, and analyze their impact on absolute-mass related observables: the sum of neutrino masses $Σ$ constrained by cosmology, the effective electron neutrino mass $m_β$ from beta decay, and the effective Majorana mass $m_{ββ}$ probed in neutrinoless double beta decay. We demonstrate that the presence of two sterile states can significantly modify the allowed parameter space compared to the three-flavor and $3+1$ frameworks, with some mass-ordering schemes already disfavored by current cosmological and laboratory limits. Finally, we assess the implications of upcoming sensitivities from KATRIN, Project~8, and LEGEND-1000, highlighting the complementary role of sub-eV sterile neutrinos in probing physics beyond the minimal three-flavor paradigm.

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

Recursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using CART-type recursive partitioning, with splitting criteria designed to identify variation in treatment effects across covariate-defined subgroups. We study causal tree estimators based on adaptive recursive partitioning and establish lower bounds on their estimation accuracy. The class we analyze includes versions with and without sample splitting, based on common treatment effect and squared-error splitting criteria. Even in a constant-effect benchmark with randomized treatment assignment, causal trees constructed via standard CART-type splitting rules can have uniform-norm errors that decrease more slowly than any power of the sample size. The underlying mechanism is that greedy recursive partitioning selects highly imbalanced splits with nonvanishing probability, producing terminal nodes containing very few observations and leading to large estimation variance. We further show that sample splitting, often called ``honesty,'' does not remove this limitation. As a consequence, causal tree estimators may converge arbitrarily slowly uniformly over the covariate space. At the same time, these estimators can have small integrated mean squared error, showing that average accuracy can mask local inaccuracy. Our results also clarify the role of balanced partition assumptions in existing theoretical guarantees for causal forests and related ensemble methods.

Humans often predict physical outcomes from only a few observations, a capability known as physical intuition. The mechanisms underlying this efficient learning remain elusive. Here, we introduce a variational learning framework in which small neural networks learn the mapping from observational parameters to optimal physical states from merely two or three similar examples. Demonstrating across classical and quantum regimes including strongly correlated molecules, networks trained this way generalize far beyond the training data. This generalization is explained by a unified theory: it arises when the network approximates a solution manifold where the Euler-Lagrange operator is stationary with respect to observation features. The theory predicts the existence of a critical network size below which robust generalization fails to emerge. Our work establishes variational learning as a principled route to acquiring artificial physical intuition and offers a theoretical perspective for understanding similar capabilities in biological intelligence.

Elliptically symmetric distributions are a classic example of a semiparametric model where the location vector and the scatter matrix (or a parameterization of them) are the two finite-dimensional parameters of interest, while the density generator represents an \textit{infinite-dimensional nuisance} term. This basic representation of the elliptic model can be made more accurate, rich, and flexible by considering additional \textit{finite-dimensional nuisance} parameters. Our aim is therefore to investigate the deep and counter-intuitive links between statistical efficiency in estimating the parameters of interest in the presence of both finite and infinite-dimensional nuisance parameters. Previous seminal works have addressed this problem by leveraging a general result: if the statistical model has a specific group invariance, then the projection operator onto the semiparametric nuisance tangent space can be asymptotically expressed as a conditional expectation with respect to the maximal invariant sub-$σ$ algebra. In this article, we show that, for the statistical model of elliptical distributions, the projection operator can be explicitly computed without relying on the above-mentioned asymptotic approximation. This allows us to obtain original results also for the case in which the location vector and the scatter matrix are parameterized by a finite-dimensional vector that can be partitioned in two sub-vectors: one containing the parameters of interest and the other containing the nuisance parameters. As an example, we illustrate how the obtained results can be applied to the well-known \virg{low-rank} parameterization. Furthermore, while the theoretical analysis will be developed for Real Elliptically Symmetric (RES) distributions, we show how to extend our results to the case of Circular and Non-Circular Complex Elliptically Symmetric (C-CES and NC-CES) distributions.

The Mpemba effect, in which a system initially farther from equilibrium relaxes faster than a closer one, has been observed in a wide variety of linear and nonlinear systems. Here we develop a unified framework for the Mpemba effect in many-body systems near equilibrium based on the spectral geometry of the relaxation operator. We distinguish a non-uniform Mpemba effect, associated with a crossing of global distances to equilibrium, from a strict componentwise Mpemba effect, in which the initially hotter state remains larger in every degree of freedom yet relaxes faster. We show that reciprocal systems admit only the former, whereas reciprocity breaking renders the relaxation operator non-normal and can enable the latter. These results identify reciprocity and non-normality as key ingredients governing anomalous relaxation in linear many-body systems.

This article offers a gentle introduction to the axiom of choice. We introduce the axiom, discuss some common objections to it, and present three kinds of reasons to accept it. Although the exposition is aimed at non-experts in set theory, we also include some lesser-known results.

A recently identified class of collinear magnetic order, characterized by vanishing net magnetization yet unconventional spin splitting, known as altermagnets (AMs), has attracted significant research interest. Controlling the unconventional spin splitting and the associated band topology in AMs offers opportunities for realizing novel spin and topological transport phenomena. In this work, using Floquet engineering with periodically driven linearly polarized light (LPL), we explore light-induced control of an AM topological insulator. Remarkably, we find that AMs and conventional antiferromagnets (AFMs) exhibit distinct responses under LPL irradiation. Specifically, since LPL breaks neither time-reversal ($\mathcal{T}$) symmetry nor parity-time-reversal ($\mathcal{PT}$) symmetry, it is incapable of generating spin splitting or inducing an anomalous Hall effect (AHE) in conventional AFMs. In contrast, AMs intrinsically lack both $\mathcal{T}$ and $\mathcal{PT}$ symmetries. Their spin-up and spin-down bands are related by the combined symmetry of time reversal $\mathcal{T}$ and a crystal rotation. We show that LPL readily breaks these symmetries, thereby triggering a finite AHE exclusively in AMs. Furthermore, LPL can drive the AM topological insulator into a fully spin-polarized Chern insulating phase. Our findings not only provide a robust experimental scheme to distinguish AMs from conventional AFMs, but also establish a promising pathway toward dissipationless spintronic applications.

A common impediment in conducting inference for Bayesian nonparametric models is either the need for complex MCMC algorithms and/or computational run-time for large datasets. We propose solutions here for Enriched Dirichlet process mixtures (EDPM). We derive a variational Bayes estimator based on a previously developed truncation approximation for EDPMs. The variational Bayes estimator can be used in two ways: 1) to develop a more efficient truncation approximation; 2) as good initial values for a blocked Gibbs sampler based on this more efficient truncation approximation or for a polya urn sampler. We derive the accuracy of this more efficient truncation approximation and demonstrate how this allows for simple implementation of a blocked Gibbs Sampler EDPMs in Nimble. We confirm the validity of the approximations by simulations and illustrate on a real data set.

Polaris is a highly unusual Cepheid with observed properties that are difficult to reconcile with stellar evolutionary models. Since the initial detection of Polaris' magnetic field in 2020, we have conducted a magnetic monitoring campaign with the ESPaDOnS spectropolarimeter at the Canada-France-Hawaii Telescope. We compute Stokes $V$ least-squares deconvolution profiles and measure the associated mean longitudinal magnetic field strengths $\langle B_{z}\rangle$. The surface magnetic field has remained remarkably stable over five years of observations, with $\langle B_{z}\rangle$ varying between approximately $-3$ G and $+0.6$ G. From the periodic modulation of $\langle B_{z}\rangle$ we infer a stellar rotation period of $P_{\mathrm{rot}}=100.29\pm0.19$ days. This is the first direct measurement of $P_{\mathrm{rot}}$ for a classical Cepheid. Previous interferometric radius measurements and $P_{\mathrm{rot}}$ imply an equatorial rotation velocity of $v_{\mathrm{eq}}=23.3\pm0.2$ km s$^{-1}$. We set a conservative upper bound on the projected equatorial rotational velocity of $v_{\mathrm{eq}}\sin i_{\star} < 13.5$ km s$^{-1}$ and constrain the stellar inclination angle to be $i_{\star}<37^{\circ}$. Using the previously determined orbital solution, we find a high likelihood of a strong spin-orbit misalignment. We determine the lower bound on the obliquity angle between the stellar rotation and orbital axes to be $β>18.7^{\circ}$ at 99% confidence. We discuss the challenges in interpreting the origin and properties of the surface magnetic field in the context of Polaris' uncertain evolutionary history and the merger hypothesis.

Anisotropic image analysis is ubiquitous in medical and scientific imaging, and while the literature on the subject is extensive, the robustness to numerical rotations of numerous methods remains to be studied. Indeed, the principal directions and angular profile of a rotated image are often expected to rotate accordingly. In this work, we propose a new spectral method for the anisotropic analysis of images (EquivAnIA) using two established directional filters, namely cake wavelets, and ridge filters. We show that it is robust to numerical rotations throughout extensive experiments on synthetic and real-world images containing geometric structures or textures, and we also apply it successfully for a task of angular image registration. The code is available at https://github.com/jscanvic/Anisotropic-Analysis

Viscosity is a fundamental property of liquid lubricants, yet it is challenging to determine accurately, especially at high pressures. Although equilibrium molecular dynamics (EMD) simulations are a promising alternative to resource-intensive experiments, practical challenges remain in assessing the sufficiency of simulation time and in controlling uncertainties in the Green-Kubo formalism due to the finite amount of trajectory data. In this work, we extend the STable AutoCorrelation Integral Estimator (STACIE), a recently developed algorithm for estimating transport properties. First, we introduce the Lorentz model to estimate the viscosity and the exponential correlation time from the low-frequency power spectrum of deviatoric pressure fluctuations. Second, we show how to supplement the three conventional off-diagonal elements of the pressure tensor ($P_{xy}$, $P_{yz}$ and $P_{zx}$) with two additional uncorrelated deviatoric pressure components for shear viscosity calculations. Using these improvements, we apply STACIE to calculate the shear viscosity of 2,2,4-trimethylhexane from EMD simulations. We demonstrate STACIE's capability to reliably calculate viscosity under high-pressure conditions, offering a robust and automated solution with validated uncertainty quantification. Our results, when compared to the outcomes of the 10th International Fluid Properties Simulation Challenge, underscore the need for long EMD simulations. Large deviations from experimental viscosities in previous works were primarily due to insufficient simulation times and ad hoc post-processing choices, rather than the limitations of the force fields used. Unlike previous studies, our viscosity estimates agree well with experimental results (relative error < 6%) up to the highest pressure of 1 GPa, highlighting the improved reliability and accuracy of STACIE's systematic approach to viscosity predictions.

This paper deals with the geometric properties of functions belonging to the class $\mathcal{S}^*_{B}$ of starlike functions associated with a balloon-shaped domain, given by \[ \mathcal{S}^{\ast}_{B}= \left\{ f \in \mathcal{A} : \frac{z f'(z)}{f(z)} \prec \frac{1}{1-\log (1+z)} :=B(z), \quad z \in \mathbb{D} \right\}, \] and also derive sharp bounds for the Zalcman functionals, Krushkal inequality, third-order Hankel, Toeplitz and Hermitian-Toeplitz determinant. The sharpness of these results are verified by constructing suitable extremal functions.

This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We treat the design of recommendation systems as a state-feedback infinite-horizon optimal control problem with a performance index that (i) rewards alignment and engagement, (ii) penalizes polarization and large deviations from an uncontrolled baseline, and (iii) regularizes exposure across neighboring users. The recommendation entries are fed to the platform users, who are assumed to follow a networked, multi-topic, continuous-time opinion dynamics. We show that the designed control yields a stabilizing recommendation system under simple algebraic spectral conditions on the weights that encode the platform's preference for engagement, stability of preferences, polarization, and cross-user diversity. Conversely, we show that when ill-posed weights are selected in the optimal control problem (namely, when engagement is excessively rewarded), the closed-loop system can exhibit destabilizing, pathological behaviors that conflict with the design objectives.

In this work, the origin of nonlocal effects is inspected and the contributions of nontrivial topological structures to physical properties are investigated in details for both the 3D Ising model and the Z2 lattice gauge model. Then the exact solution for the 3D Z2 lattice gauge theory is derived by the duality between the two models. Several fundamental issues, such as dimensionality, duality, symmetry, manifold, degenerate states, are investigated for these many-body interacting spin systems. The connections with superfluid, superconductors, etc. are evaluated. Furthermore, physical significances and mathematical aspects of the 3D Z2 lattice gauge theory are discussed with respect to topology, geometry, and algebra.

The central problem in the physics of immiscible two-phase flow in porous media is to find a proper description of the flow at scales large enough so that the medium may be regarded as a continuum: the scale-up problem. So far, the only workable approach to the multiphase flow scale-up problem has been a set of phenomenological equations that have obvious weaknesses. Attempts at going beyond this relative permeability theory have so far not led to practical applications due to exploding complexity. Edwin T. Jaynes proposed in the fifties a generalization of statistical mechanics to non-thermal systems based on the information theoretical entropy of Shannon. This approach is used to construct a description of immiscible two-phase flow in porous media at the continuum scales, which is directly related to the physics at the pore scale, and at a level of complexity that is manageable. The approach leads to a thermodynamics-like formalism at the continuum scale with all the relations between variables that "normal" thermodynamics has to offer. New emergent variables appear. Among these, the co-moving velocity stands out as a key variable with implications for ordinary thermodynamics. We present here a short review of this approach.

Natural language processing (NLP) technologies are rapidly reshaping how language is created, processed, and interpreted by humans. With current and potential applications in hiring, law, healthcare, and other areas that impact people's lives, understanding and mitigating harms towards marginalized groups is critical. In this survey, we examine NLP research papers that explicitly address the relationship between LGBTQIA+ communities and NLP technologies. We systematically review all such papers published in the ACL Anthology up until February 2026 (n=122), to answer the following research questions: (1) What are current research trends? (2) What gaps exist in terms of topics and methods? (3) What areas are open for future work? We find that while the number of papers on queer NLP has grown within the last few years, most papers take a reactive rather than a proactive approach, focusing on shortcomings of existing systems rather than creating new solutions. Our survey uncovers many opportunities for future work, especially regarding stakeholder involvement, intersectionality, interdisciplinarity, and languages other than English. We also offer an outlook from a queer studies perspective, highlighting understudied topics and blind spots in the harms addressed in NLP papers. Beyond being a roadmap of what has been done, this survey is a call to action for work towards more just and inclusive NLP technologies.