Papers

We develop a partial differential equation formulation of the Behrens-Fisher problem for two independent normal samples with unknown and unequal variances. An orthogonal decomposition separates mean and residual components (corresponding to the centered within-sample variation left after removal of the mean directions) and recasts the studentized difference of sample means as a scale-invariant geometric constraint. This reduction transforms the distributional problem into the evaluation of spherical wedge probabilities, which are identified with harmonic measure and with the value at the origin of a Laplace-Dirichlet boundary value problem. From this framework, we derive exact finite-sample representations for the cumulative distribution function and the probability density function in terms of beta functions, with dependence only on the sample sizes and the variance ratio. These representations place the Behrens-Fisher law in a standard special-function form that is directly accessible in widely available commercial software – including Microsoft Excel – thereby facilitating distributional evaluation and quantile computation. We also obtain a Gegenbauer separation-of-variables expansion for the associated harmonic extension and its threshold derivative, with coefficients in closed Beta-Gamma form, and derive sharp tail expansions with explicit leading constants and higher-order corrections.

For a family of graphs $\mathcal F$, a graph $G$ is said to be $\mathcal F$-free if it contains no member of $\mathcal F$ as a subgraph. A wheel graph $W_k$ is a graph on $k+1$ vertices formed by joining a new vertex to all vertices of a $k$-cycle. Given an integer $k\ge 3$, we consider the problem of determining the maximum number of triangles in a $W_{\geq k}$-free graph, where $W_{\geq k}=\{W_\ell: \ell \geq k\}$. The case $k=3$ was raised by Gallai, who proposed a conjecture for this case (see Erdős [5]. Gallai's conjecture was disproved by Zhou [17] and independently by Füredi, Goemans, and Kleitman [9]. In this paper, we study the case $k=4$. Namely, for every integer $n\ge 3$, we determine the maximum number of triangles in an $n$-vertex $W_{\geq 4}$-free graph and characterize all extremal graphs.

We propose and analyze a structure-preserving numerical method for the $2\tfrac{1}{2}$-dimensional (2.5D) electron magnetohydrodynamics system with fractional dissipation on the periodic torus. The method works directly with the magnetic field components and combines this component formulation with the gradient recovery operator of [T. Chu, H. Guo, and Z. Zhang, SIAM J. Numer. Anal., 63 (2025), pp. 23–53]. We establish discrete energy stability for a semi-implicit structure-preserving formulation and use an explicit-Hall integrating-factor implementation for efficient computation on periodic grids. The fractional dissipation is treated exactly in Fourier space, and the in-plane divergence constraint is enforced by a spectral Hodge projection. Numerical experiments demonstrate second-order spatial convergence and stable Hall-driven dynamics across several benchmark tests.

This paper investigates radio stripes (RSs) as a practical realization of generalized pinching antennas and proposes an RS-based generalized pinching-antenna (RS-GPA) framework. Unlike dielectric-waveguide-based passive pinching antennas that rely on passive coupling from a guided wave into free space, RSs employ active antenna processing units (APUs) deployed along a shared cable for local transmission, reception, and signal processing. This cable-like active architecture offers flexible installation and broad frequency applicability, while allowing selected APUs to act as discrete and controllable radiation or reception points for location-flexible wireless access. Based on the proposed RS-GPA framework, we establish the system and channel models by accounting for the distance-dependent APU-user channels. For downlink transmission, we formulate a circuit-power-aware sparse APU activation and beamforming problem and develop a reweighted group-sparse beamforming algorithm. To reveal the activation principle, we analyze the single-user downlink case and characterize when an additional APU should be activated by balancing transmit-power saving and circuit-power cost. Inspired by this insight, a geometry-guided low-complexity multiuser algorithm is proposed. For uplink transmission, we formulate a joint APU activation and user power control problem and develop a geometry-guided sparse activation design. Numerical results show that the proposed RS-GPA framework substantially reduces the total consumed power compared with benchmark schemes, while the geometry-guided algorithm achieves near-identical consumed-power performance to the group-sparse design with significantly lower runtime.

We study the Cauchy–Dirichlet problem for parabolic complex Hessian equations on Hermitian manifolds and on bounded strictly m-pseudoconvex domains. In the smooth setting, we prove global existence and uniqueness of classical solutions under the presence of an admissible parabolic subsolution, by establishing a priori estimates up to the parabolic boundary. The estimates combine parabolic boundary techniques for complex Hessian equations with interior second order estimates and a blow-up argument. We then develop a general pluripotential framework for degenerate right-hand sides with L^p densities, p>n/m, and bounded Cauchy–Dirichlet data. Since the usual automorphism and Walsh-type arguments do not directly apply in a variable Hermitian background, we use approximation by smooth data, balayage, parabolic Perron envelopes, and a continuous obstacle approximation based on Harvey–Lawson–Plis subequation theory. The resulting solution is continuous for positive time, locally uniformly Lipschitz and semi-concave in time, and continuous up to the initial slice when the initial datum is continuous. We also prove a parabolic comparison principle via time regularization, Riemann sum approximations, and mixed Hessian inequalities.

In this paper, we study the transition matrix between the PBW basis and the canonical basis for the negative part of the quantized enveloping algebra of the Kronecker quiver from a geometric viewpoint. Building on Lusztig's geometric construction of the canonical basis, we construct sheaf-complex realizations of PBW basis elements by means of flag sheaf complexes over the strata $X(\alpha,m)$ of representation varieties. Our first goal is to give a geometric description of the simple constituents appearing in the restrictions of these flag sheaf complexes to the strata $X(\alpha,m)$. This allows us to compare the PBW-type sheaf complexes with the simple perverse sheaves $IC(X(\alpha),L_\chi)$ arising in Lusztig's construction. Using this description together with a purity result for the relevant $\mathbb{F}_q$-structures, we obtain another proof that the elements defined by Lusztig's perverse sheaves indeed form a basis of the composition algebra.Our second goal is to make the transition coefficients between the PBW basis and the canonical basis geometrically explicit. More precisely, we show that these coefficients are governed by the multiplicities of local systems in the restrictions of intersection cohomology complexes to smaller strata. As a consequence, the transition matrix from the canonical basis to the PBW basis is upper triangular with diagonal entries equal to $1$, and its coefficients admit a direct geometric interpretation. In particular, in the Kronecker quiver case we recover the triangularity of the transition matrix and obtain positivity properties of the corresponding coefficient polynomials.

Working within Constructive Morse Set Theory (CMST), we introduce axioms for a new notion, jst Pp, intended to capture what it means for P to prove, or justify, p under the BHK interpretation of intuitionistic logic. Since it makes no distinction between terms and formulae – every term is also a formula, and vice versa – CMST is well suited to our axiomatic development of justification theory within set theory itself. After stating our axioms for jst Pp, we derive many consequences thereof. In particular, we show that (with certain restrictions) our axioms for jst Pp align with the intended BHK interpretations of the axioms of intuitionistic logic.

In this paper, we study probabilistic representations for stationary HJBI-type variational inequalities with bilateral constraints. We provide two complementary stochastic representations.The first representation is obtained through an augmented infinite-horizon two-player zero-sum stochastic differential game (SDG). By enlarging the control spaces with two additional stopping symbols, the obstacle terms are incorporated into the running payoff. Using the framework of infinite-horizon stochastic recursive differential games, we show that the resulting lower and upper value functions are the unique bounded viscosity solutions of the corresponding HJBI variational inequalities. The second representation is given by a two-player zero-sum mixed control–stopping SDG. In this formulation, each player chooses both a continuous control and a stopping decision, and the payoff is defined by a BSDE with a random terminal time. To make the stopping component compatible with the Elliott–Kalton strategy framework, we introduce nonanticipative stopping strategies depending on the opponent's control process. The proof is based on penalized infinite-horizon SDGs coupled with their own value functions, together with dynamic programming arguments and stability estimates for backward semigroups. We prove that the value functions of the mixed control–stopping game coincide with the unique bounded viscosity solutions of the bilateral HJBI variational inequalities.

Tight equalities between symmetric information and entropy production in driven steady states remain elusive. We show that they are forbidden by a parity selection rule for rotation-driven linear nonequilibrium steady states. Whenever the relaxation and diffusion matrices commute, the snapshot mutual information between two time slices is exactly even under drive reversal, and parity violation rises linearly in the commutator norm when alignment is broken. Full isotropy strengthens this to drive-independence, and the planar mutual information takes the closed-form value of about 0.145 nats. Under the same alignment, the entropy production is exactly quadratic in the drive, and its prefactor admits an explicit closed form in the traces and determinant of the two matrices. The orthogonality of even and odd sectors leaves only one-sided thermodynamic-uncertainty bounds. The rule rests on the rotational symmetry of the drift alone and survives heavy-tailed isotropic stable noise with tail index below two, where variance-based bounds become vacuous. A falsifiable test is proposed on an electrical Brownian gyrator augmented for independent drive control with circuit-level stable-noise injection.

Let $\cpsi_{2,a}(n)$ denote the number of $(2,a)$-colored Frobenius partitions of weight $n$, where the two rows have prescribed length difference. We study the two cases $a=0$ and $a=1$ and connect them with minimal-excludant statistics on bipartitions. Let $\sigma\mex_2(n)$ be the sum of the Lin–Liu bipartition minimal excludants over all bipartitions of $n$, and let $E_2(n)$ be the number of bipartitions whose two component minimal excludants are equal. For all $n\geq 0$, we give a combinatorial proof of \[ \cpsi_{2,0}(n)=2\sigma\mex_2(n) \qquad\text{and}\qquad \cpsi_{2,1}(n)=2\sigma\mex_2(n)-E_2(n). \] These identities give direct combinatorial interpretations of two-colored Frobenius partition functions in terms of bipartition minimal-excludant sums.

We consider an intrusion detection task in which a defender must jointly optimize sensor placement locations and orientations to minimize the probability of missed detection of an intruder traversing a protected environment. We decompose this problem into a meta problem, termed SensorPlacement, and an embedded subproblem, termed OrientationScheduling. The OrientationScheduling subproblem, for a fixed sensor placement, is modeled as a 2-player zero-sum game between the defender and the intruder, where the defender seeks an orientation strategy for the deployed sensors to minimize the probability of missed detection, while the intruder seeks a path selection strategy to maximize it. Since the defender's strategy space grows combinatorially with the number of sensors and orientations, solving the game via standard linear programming becomes prohibitive. To this end, we develop an iterative and efficient equilibrium-seeking algorithm that exploits the structure of the game's payoff function and establishes theoretical guarantees for convergence to the Nash equilibrium (NE) of the game. This NE value is then used as a utility measure in the SensorPlacement meta problem. We show that this game-value-based utility function is weakly submodular over the set of sensor placements and propose a greedy placement algorithm with near-optimality guarantees. To our knowledge, this is the first unified framework to integrate game-theoretic utility design with (weak) submodular optimization, enabling principled joint optimization of sensor placement and orientation scheduling. Through extensive simulations, we demonstrate that the proposed approach achieves near-optimal detection performance while significantly reducing computation time compared to baselines.

Twisted quantum loop algebras are a generalization of twisted quantum affine algebras in Drinfeld new presentation. The Hall algebras of Geigle–Lenzing's weighted projective lines are used to realize (untwisted) quantum loop algebras of simply-laced type associated to star-shaped graphs by Schiffmann and Dou–Jiang–Xiao. In this paper, we use the semi-derived Ringel-Hall algebras of more general weighted projective lines to realize the twisted quantum loop algebras associated to the valued star-shaped graphs, including the twisted quantum affine algebras in Drinfeld new presentation.

Variance-based finite-time Lyapunov exponents (vFTLEs) provide a stochastic analogue of deterministic FTLE by measuring the covariance of stochastic arrival locations. Existing PDF-based formulations compute this covariance by solving a Fokker–Planck equation for each initial point, which becomes expensive when the diagnostic is required on a dense grid. In this work, we develop a moment-based Eulerian approximation to vFTLE in the small-noise regime. Starting from a stochastic trajectory expansion about the deterministic flow, we derive a closed covariance equation for the leading stochastic displacement. By embedding this trajectory-wise covariance dynamics into physical space, we obtain an Eulerian transport–reaction equation for a symmetric covariance tensor field. The covariance associated with each initial point is recovered by evaluating this tensor field at the deterministic arrival location, and a moment-based vFTLE is then defined from its largest eigenvalue. The proposed method replaces a family of Fokker–Planck solves by the evolution of a single covariance tensor field, requiring only $d(d+1)/2$ scalar fields in $d$ dimensions. It also retains directional information through the eigenvectors of the covariance tensor, allowing the dominant directions of stochastic spreading to be visualized. We establish the leading-order consistency of the method with PDF-based vFTLE in the small-noise limit, clarify its relation to scalar stochastic sensitivity, and show how the same covariance equation connects process-noise spreading with deterministic deformation. In particular, deterministic FTLE is recovered, up to an additive constant, from an isotropic initial covariance when no process noise is present, while continuous process noise produces a time-integrated deformation covariance.

A variational principle for quantum-state distinguishability is established in many-body systems with generalized symmetries, including noninvertible cases described by fusion categories. Standard fidelity and symmetry-resolved diagnostics emerge as coarse-grained limits of a more refined operational structure. When symmetry actions terminate at entanglement cuts, distinguishability is governed by boundary tube algebras within a symmetry-constrained measurement resource theory. The physically admissible instruments are characterized by complete positivity, entanglement-cut locality, boundary-module covariance, and sequential stability. The resulting optimal measurement structure is uniquely fixed by the center of the boundary tube algebra, $\mathcal{A}_{\mathrm{phys}} = Z\!\left(\mathrm{Tube}_{\mathcal{C}}(\mathcal{M}_A)\right)$, whose primitive dempotents define tube-sector probabilities that refine fidelity-based and symmetry-resolved descriptions. The associated tube positive-operator-valued measures (POVMs) are extremal and yield optimal one-shot hypothesis-testing distinguishability under symmetry constraints. The categorical selection rule is universal across fusion categories and independent of microscopic realization; microscopic models enter by choosing the boundary module and the protocol family used to witness a nontrivial tube fiber.

We show, for example, that if one samples \[\frac{\log p}{2\log(1+(p-1)^{-1})} \cdot n^2(1 + o_{n\to \infty}(1))\] points in $\mathbb{F}_p^n$ at random then asymptotically almost surely this set intersects every quadratic hypersurface. We furthermore show that this is tight in that sampling $o_{n\to\infty}(n^2)$ fewer points almost surely fails to intersect some quadratic hypersurface. Our main result is a sharp threshold for the following problem: how many points in $\mathbb{F}_p^n$ does one need to randomly sample to almost surely intersect every algebraic set defined by at most $s$ polynomials each of degree at most $k$? As an application we improve lower bounds in the random Szemerédi theorem in $\mathbb{F}_p^n$, in particular obtaining a leading constant which grows as the threshold for what is considered a `dense' set in Szemerédi's theorem shrinks.

Establishing stability guarantees for dynamical systems on Lie groups is a fundamental challenge, as classical Lyapunov methods developed for Euclidean spaces do not directly transfer to curved geometries. In this paper, we propose a framework for learning maximal Lyapunov functions for systems evolving on the special orthogonal group $\mathsf{SO}(n)$. Theoretically, we introduce a neural Lyapunov architecture based on the logarithmic map with proven approximation capabilities, and we formulate the learning problem via a Zubov-type characterization of the maximal region of attraction. A key technical contribution is the derivation of explicit, numerically tractable formulas for the derivative of the logarithmic map, enabling training through a two-phase algorithm that balances computational efficiency and accuracy. Empirically, we validate the approach on a low-dimensional nonlinear system.

We reconstruct unitary quantum dynamics from a minimal axiomatic foundation built on Hilbert-space observables and isospectral evolution. The only dynamical assumption is that physical time evolution is a continuous one-parameter flow of Hermitian observables that preserves their spectra, i.e. the possible outcomes of measurement. We show that this assumption is already sufficient to force the Lax form of quantum dynamics. The Heisenberg equation, the time-dependent and time-independent Schrödinger equations, conservation laws, and good quantum numbers then follow as theorems rather than postulates. In this formulation, Lax pair theory supplies the missing dynamical bridge between the measurement structure of a Hilbert space and standard quantum evolution: the Hamiltonian is not assumed, but emerges as the generator required for an isospectral observable flow.

Aided by the AI tool GPT5.5 Pro, we provide a counterexample to a conjecture made by Ramachandra and Natarajan (2025) [Pairwise independent correlation gap, Operations Research Letters, 107255, 6040].

We analyze the computational complexity of solving a sequence of related second-order cone programs (SOCPs) whose right-hand-side data $b_t$ varies between rounds. The standard primal-dual interior-point algorithm solves each round at cost $\tilde{O}(n^{2.5}\log(1/\epsilon))$ from a cold start. We show that when the per-round perturbation $\|b_t - b_{t-1}\|_2$ is bounded by a problem-specific threshold $\delta$, Newton's method warm-started at the previous round's solution $x^*_{t-1}$ converges to $x^*_t$ to accuracy $\epsilon$ in $O(\log\log(1/\epsilon))$ iterations. Over $T$ rounds the total cost is $\tilde{O}(n^{2.5}\log(1/\epsilon) + T n^2 \log\log(1/\epsilon))$, compared to $\tilde{O}(T n^{2.5}\log(1/\epsilon))$ for cold start at each round; the per-round speedup for large $T$ is $\Theta(\sqrt{n}\,\log(1/\epsilon)/\log\log(1/\epsilon))$. The argument combines an infinitesimal local-norm sensitivity bound on the central-path optimum, a self-concordant finite-difference corollary, and the standard quadratic-convergence basin of Newton's method on a self-concordant barrier. The local-norm formulation circumvents the rank-deficiency issues of Euclidean sensitivity bounds for fat constraint matrices. A multi-seed experiment on bounded SOCPs with $n=50$, $p=100$ confirms a 30-70x per-round speedup across the predicted regime.

Electrical power and computing power are highly interdependent especially due to the AI revolution. This dependency is largely underestimated during normal and recovery operation. To address this issue, an Electricity-Computing Interdependency System (ECIS) is developed in this paper, which first quantifies the interdependencies between ECIS and other critical infrastructures. The data center (DC) is modeled as a facility that transforms electrical power into computing power, and a recovery optimization strategy for ECIS is then proposed considering electrical power constraints, computing power supply, and infrastructure interdependencies. The case study demonstrates that the proposed strategy dynamically adjusts the DC electrical power quota according to system states, improving system resilience by more than 20% during power shortage.

We develop a framework for the information discarded by machine learning models whose inputs carry a Lie group action. Given a representation $\pi$ of a Lie group $G$ on a space $V$ and a learned function $f\colon V \to \mathbb{R}$, we define two objects measuring the symmetry invisible to $f$. The null fiber at a point $x \in V$ is the set $N_G(f,x) = \{g \in G : f(\pi(g^{-1}) \cdot x) = f(x)\}$ of group elements whose inverse action on $x$ is undetectable by $f$. When $N_G(f,x)$ is independent of $x$, it coincides with the stabilizer $\mathrm{Stab}_G(f)$, the largest subgroup of $G$ under which $f$ is invariant. For smooth maps to $\mathbb{R}$, the preimage theorem guarantees that null fibers have dimension at least $\dim G - 1$ at generic inputs, regardless of architecture. For compact groups acting on themselves, the Peter–Weyl theorem yields a spectral characterization of both objects in terms of the Fourier coefficient matrices of $f$. We show that null fiber elements can be computed efficiently via Newton iteration on the orbit map, at a cost comparable to a few gradient evaluations. Applications to data masking, model fingerprinting, and privacy-preserving computation are developed and tested experimentally on molecular property prediction under $\mathrm{SO}(3)$ and spherical image classification under the Möbius group $\mathrm{PSL}(2, \mathbb{C})$. The framework applies uniformly to classical neural networks and variational quantum circuits.

Snapshot-backed sandboxes make branching cheap while leaving evidence dependence unchanged. Branches can reuse a model, prompt, repository, tests, observations, or execution ancestor, so counting outputs can amplify one repeated error into high-confidence consensus. We introduce an evidence-aware reduction contract: each worker reports an estimate, estimated information, evidence identifiers, fork lineage, and execution metadata. For independent workers estimating one common parameter, we use standard inverse-information pooling in its Gaussian/Wald form. The fixed-dimensional numeric summary can merge in any tree order; evidence IDs and lineage follow separate rules. The residual $\Delta$ measures disagreement, becomes Cochran's $Q$ in the scalar inverse-variance case, and appears in the product integral. A reference implementation validates serialized records, rejects repeated nonempty evidence identifiers, carries evidence and lineage through tree reduction, and uses Cholesky-based numerical linear algebra. Unit tests and seeded synthetic checks exercise the algebra, unequal information, and forged precision; one four-worker named-snapshot trace exercises the end-to-end path. Platform logs document the exercised execution paths. A central open systems challenge is to turn evidence identity and fork lineage into a dependence model for correlated and adaptively selected AI branches.

Harborth conjecture states that every planar graph has a crossing-free straight-line drawing in which every edge has an integer length. Kleber's strengthening asks for the vertices themselves to have integer coordinates. In this series of papers, we make progress towards settling these conjectures. Kleber's conjecture reduces to local rational-distance statements for special polygons with at most five vertices. The triangle case is known from the results of Almering and Berry. In this paper, we prove the existence of a rational-distance point on an interior integer diagonal in all the cases for non-degenerate and degenerate quadrilaterals. In the upcoming papers, we focus on non-degenerate and degenerate pentagons.

In this work, we study a coupled nonlinear thermoelastic system in which the heat capacity, thermal conductivity, and elastic modulus are all temperature-dependent. The thermal conduction equation is formulated in a generalized radial geometry governed by a parameter $\nu$, which encompasses planar, cylindrical, and spherical configurations as special cases. A complete Lie symmetry classification of the admissible constitutive functions is carried out, yielding three principal classes: exponential laws, power laws, and a unified class that contains both as limiting cases. For each class, the admitted symmetry algebras are determined, optimal systems of one-dimensional subalgebras are constructed, and the governing partial differential equations are reduced to coupled ordinary differential equation systems via similarity transformations. Exact closed-form solutions are obtained for each class using a combination of integrating factor methods, flux-variable substitutions, and power-law and logarithmic ansätze. For the exponential class, the thermal field exhibits a logarithmic similarity profile while the displacement field decays algebraically, with the geometry parameter absorbed into the leading-order transformation. For the power-law class, the thermal field follows a self-similar power-law profile and the displacement field decomposes into distinct elastic, self-similar, and thermoelastically driven modes. Travelling-wave solutions are also constructed, with elementary forms recovered in limiting cases. The influence of geometry, particularly the structural distinction between cylindrical and other configurations, is identified and physically interpreted. All solutions are discussed in terms of the interplay between thermal diffusion, elastic stiffness, and thermoelastic coupling.

This article deals with the upper and lower bounds of triangular subnorms generated by continuous, strictly decreasing additive generators. It first establishes necessary and sufficient conditions for the comparability of such triangular subnorms. It then explores the existence of strict (resp. nilpotent) bounds of a finite family of strict (resp. nilpotent) triangular subnorms generated by continuous, strictly decreasing additive generators. By duality, completely analogous results are derived for triangular superconorms generated by continuous, strictly increasing additive generators. In particular, it supplies the corresponding algorithms for computing those bounds, which are illustrated by several examples.

We study Dice Bingo, a game in which players fill a $3\times3$ bingo board whose entries are possible sums of two fair dice. After each roll, a player marks one matching square, and the goal is to complete a row, column, or diagonal. We model optimal play for a fixed board as a finite Markov decision process and derive Bellman equations that compute the exact expected number of rolls required to obtain a bingo. Using this framework, we identify a unique optimal board up to natural symmetries and determine its exact expected completion time. We then investigate head-to-head competition in which two players observe the same sequence of dice rolls. By analyzing a joint Markov chain that tracks both boards simultaneously, we compute (in exact arithmetic) win, loss, and tie probabilities. Surprisingly, a board with a worse expected completion time can nevertheless be favored in head-to-head competition. Motivated by this phenomenon, we exhibit nontransitive triples of bingo boards: board $A$ is favored against board $B$, board $B$ is favored against board $C$, and board $C$ is favored against board $A$. Finally, we consider strategic play in which players adapt their choices to their opponent's board rather than merely minimizing their own completion time. In this setting, optimal decisions depend on the opponent's state, leading naturally to game-theoretic analysis. We present a position with no pure Nash equilibrium and compute an explicit mixed Nash equilibrium.

Synaptic transmission delays are ubiquitous in neural circuits and can alter the dynamical repertoire of coupled oscillators quantitatively and qualitatively. Here, we demonstrate that delayed coupling in inhibitory networks introduces an effective slow-fast structure in the phase-difference dynamics, generating low-frequency components that are not due to intrinsic cellular properties, and we show that this behavior is not specific to a particular model structure. The origin of this generic phenomenon is analyzed by numerical continuation and bifurcation analysis, which provides a systematic approach to find such delay-induced slow modulating rhythms. We employ phase reduction based on phase response curves to derive a phase-difference model with delay for mutually inhibitory coupled oscillators, where the individual units are given by the FitzHugh-Nagumo model, the Morris-Lecar model, or a next-generation neural mass model derived from quadratic integrate-and-fire neurons. We use phase planes to study multistability and limit cycles, which correspond to slow modulation of fast oscillations in the full model. Treating the synaptic delay as a bifurcation parameter, we apply numerical continuation to construct delay-dependent bifurcation diagrams. The analysis reveals Hopf, heteroclinic, and saddle-node-of-periodics bifurcations that cause and organize slow rhythmic behavior. Our analysis provides a systematic approach to the search for limit cycles in phase-reduction models corresponding to delay-induced slow rhythms in the original model.

Projective representations arise naturally in physics and representation theory, and determining whether they can be linearized has been a fundamental problem. In this work, we study the analogous problem for quantum cellular automata (QCA) representations, which incorporate locality constraints imposed by a metric space $X$. Over an arbitrary field $\mathbb{F}$, we develop an obstruction theory for the linearization of QCA representations, using the algebraic $K$-theory spectrum of QCA constructed in previous work of the authors. The resulting obstructions are governed by the homotopy type of the QCA spaces, from which we extract universal obstruction classes to linearization. In the complex algebraic and unitary case, we also fully compute the homotopy types of the QCA spaces over a point, a line, and a plane.

Standard MCMC diagnostics ($\hat{R}$, effective sample size, divergence counts) detect whether a chain has mixed, but not why it has not. We ask whether the centring/non-centring obstruction in hierarchical models has a geometric cause beyond the metric. The joint parameter space is a fiber bundle (hyperparameters the base, group-level parameters the fibers), and the Fisher information metric induces an Ehresmann connection $A = -G_{FF}^{-1}G_{BF}$; the natural hypothesis is that the obstruction is its curvature, felt by the sampler as holonomy. We prove this false. The connection is flat for any smooth hierarchical posterior, not only the Gaussian case, because its horizontal leaves are the level sets of the fiber score $\partial_\alpha \log p$: there is no geometric obstruction above the metric. What remains is statistical, not geometric, and the flat connection identifies it as a single quantity: the conditional dependence of fiber on base, governed per group by the prior fraction $\pi_j$, the classical pooling factor. From it the framework recovers the established picture, that prior-dominated groups mix slowly and that the optimal per-group non-centring weight follows in closed form, and a simulation study separates this base-fiber coupling from the funnel, a distinct base-space pathology, by their opposite dependence on the hierarchical variance. A direct attribution test confirms that NUTS does not transport the fiber: the chain-level footprint is excess conditional autocorrelation in prior-dominated groups, exactly as $\pi_j$ predicts. Genuine, even rotational, curvature does appear, but only for connections built from a sampler's working metric (a fixed mass matrix), where holonomy re-enters as an algorithmic rather than geometric phenomenon. The prior-fraction diagnostic is distributed as the R package fibr, with the geometric methods as accompanying reproduction code.

We develop a capacitary approach to semilinear elliptic inequalities on weighted graphs with a potential. More precisely, we study the nonexistence of nontrivial nonnegative solutions of \[ \Delta u+w(x)u+v(x)u^\sigma\le0 \qquad\text{in }V, \] where \((V,\omega,\mu)\) is a connected, locally finite weighted graph, \(\Delta\) is the associated graph Laplacian, \(\sigma>1\), \(v>0\), and \(w\) is a real-valued potential. The potential term is handled by means of a positive solution \(H\) of \(\Delta H+wH=0\), which transforms the operator \(\Delta+w\) into the \(H\)-Laplacian associated with a new weighted graph. Our main nonexistence criterion is formulated directly in terms of cut-off functions and the regions where their \(H\)-Laplacian is controlled. Unlike metric criteria based on pseudo-metric annuli, our formulation determines the capacitary sets from the support of the \(H\)-Laplacian estimates for the cut-off functions. We provide an example showing that our result applies in situations not covered by previous nonexistence criteria based on structural lower bounds or pseudo-metric annular volume estimates. We also show that the growth exponent in our capacitary condition is sharp by constructing an example for which the condition fails by an arbitrary power \(R^\varepsilon\), while a positive nontrivial solution exists.