Papers

We investigate the pseudoconvexity of level sets of the distance function. First, we show the formula of \cite{Li17} for the Diederich–Fornæss index can be extended to characterize pseudoconvexity. This extension implies that the rate of change of the Levi form is nonincreasing at weakly pseudoconvex points. Additionally, we demonstrate that perturbing the distance function by a factor of $e^{-t|z|^2}$ ensures all level sets sufficiently close to the boundary are strongly pseudoconvex for all $t>0$.

We introduce a censored version of the stochastic six-vertex model. We show that for parameters $b_1 < b_2$, this model started from the initial condition ${1}_{x>0}$ is stochastically dominated at any time by the blocking measure. This is a partial analog of the censoring inequality for monotone spin systems. In particular, this result allows us to control the behavior of second-class particles. The proof uses parabolic Kazhdan–Lusztig $R$-polynomials, whose appearance is explained using a connection between the stochastic six-vertex model and the Iwahori–Hecke algebras of symmetric groups. Furthermore, we find an intertwining relation for this process using normalized parabolic Kazhdan–Lusztig $R$-polynomials as an intertwining kernel.

In this paper, we extend the recent Ionescu–Wainger multiplier theorem for the set of canonical fractions by Kosz, Mirek, Peluse, Wan, and Wright in several directions. First, we prove its weighted version, which allows us to combine a multifrequency setting with appropriate arithmetic weights. Second, we establish useful seminorm variants of the theorem. Third, we improve the norm upper bounds and, surprisingly, show that these bounds cannot be uniform in the size of the family of canonical fractions. Finally, we demonstrate how these refinements (especially handling arithmetic weights) can be applied by giving a short proof of Bourgain's pointwise ergodic theorem for polynomial iterates.

We extend the theory of root clusters from perfect fields to general fields which are not necessarily perfect. We introduce the following notions for field extensions over any given base field and study their interesting properties: root cluster size, multicluster size and their generalizations root capacity, multiroot capacity; ascending index, ascending normal index and their generalizations intersection indicium, intersection normal indicium; compositum indicium and compositum normal indicium. We establish our results on the Inverse problems for these generalized notions over Hilbertian fields which generalizes our earlier results which were over number fields. In particular, we show over a given Hilbertian field, the existence of a polynomial for given degree, cluster size and multicluster size and existence of an extension for given root capacity and multiroot capacity with respect to that polynomial.

We prove that the only CROSSes that admit a (not necessarily homogeneous) almost quaternionic structure are ${\mathbb{H}} {\mathbb{P}}^n$ and ${\mathbb{C}} {\mathbb{P}}^2$.

We introduce a general theoretical approach to hierarchical spline spaces that replaces the classical constructive definition - based on basis selection - with a descriptive formulation in terms of regularity constraints. Specifically, we define generalized hierarchical spline spaces on multi-level domains as collections of piecewise functions satisfying hierarchical contact conditions across interfaces between refinement levels. The proposed framework applies to a broad class of local function spaces and relies on a minimal abstract requirement, the extension assumption, rather than on specific polynomial properties. Within this framework, we identify rules under which the hierarchical selection mechanism yields a complete basis, in the sense that it spans exactly the space characterized by the contact conditions. As an application, we consider Tchebycheffian spline spaces. We show that spaces generated by extended complete Tchebycheff (ECT) systems fit in this framework, thereby establishing the completeness of hierarchical Tchebycheffian splines. This demonstrates that the proposed theory naturally extends beyond the polynomial setting and provides a unified foundation for hierarchical constructions in more general spline spaces.

The standard taxonomy of predictive uncertainty defines epistemic uncertainty as the part removable by collecting more data, while the standard measure identifies it with a mutual-information term. We prove the definition and the measure are extensionally inconsistent. On an explicit construction, the measure assigns all uncertainty to the epistemic class, yet no quantity of training data reduces it. Reducibility is instead a property of the pair (uncertainty, acquisition class), and the dichotomy resolves into three parts: aleatoric, sample-reducible epistemic, and mechanism-reducible epistemic uncertainty. An exact identity for the value of an observation shows that in-distribution data never reduces mechanism-irreducible uncertainty and generically increases it. Ensemble disagreement, the deployed epistemic estimate, tracks the training procedure rather than the epistemic term. It collapses to zero beneath a positive truth under consistent training, and equals hyperparameter-scaled initialization noise under interpolation. A finite-sample falsification test and seed-swept experiments confirm the theory.

We give two combinatorial presentations for the algebraic cobordism ring $\Omega^*(M)$ of the toric variety of the Bergman fan of any loopless matroid $M$. As a consequence of our presentations, we obtain an $\Omega^*(\mathrm{pt})$-algebra isomorphism $\Omega^*(M) \simeq CH^*(M) \otimes_{\mathbb{Z}} \Omega^*(\mathrm{pt})$, where $CH^*(M)$ is the Chow ring of $M$ and $\Omega^*(\mathrm{pt})$ is the algebraic cobordism ring of the point. This isomorphism generalizes, in part, the exceptional integral isomorphism between the Chow ring and $K$-ring of a matroid, studied in the recent works of Berget–Eur–Spink–Tseng and Larson–Li–Payne–Proudfoot. For a complex hyperplane arrangement $\mathcal{H}$, we prove that the algebraic cobordism ring of the wonderful variety $W_\mathcal{H}$ of $\mathcal{H}$ and the algebraic cobordism ring of the toric variety of the matroid underlying $\mathcal{H}$ are isomorphic, and that both rings coincide with the complex cobordism ring of $W_\mathcal{H}$.

In this paper, we continue our study, started in~\cite{CDS1}, of a two-dimensional variational model for ferronematics – composite materials formed by dispersing magnetic nanoparticles into a liquid crystal matrix. The model features two coupled order parameters: a Landau-de Gennes~$\Q$-tensor for the liquid crystal component and a magnetisation vector field~$\M$, both of them governed by a Ginzburg-Landau-type energy. The energy includes a singular coupling term favouring alignment between~$\Q$ and~$\M$. We analyse the asymptotic behaviour of (not necessarily minimizing) critical points as a small parameter~$\eps$ tends to zero. While in~\cite{CDS1} we showed that the (rescaled) energy density for the~$\Q$-component concentrates, to leading order, on a finite number of singular points, in this paper we prove the energy density for the~$\M$-component concentrates along a one-dimensional rectifiable set. Moreover, we prove that the curvature of the singular set for the $\M$-component (technically, the first variation of the associated varifold) is concentrated on a finite number of points, i.e.~the singular set for the~$\Q$-component. Crucial to our arguments will be the energy estimates and compactness results proved in~\cite{CDS1}.

Stochastic Galerkin discretizations of nonlinear hyperbolic conservation laws may lose hyperbolicity because the standard pseudospectral product is generally nonassociative, leading to non-commuting blocks in the flux Jacobian matrix. We develop a novel framework for constructing hyperbolicity-preserving stochastic Galerkin systems based on associative truncated products on polynomial spaces. In one stochastic dimension, we characterize associative truncated products through a single polynomial datum and identify examples with useful symmetry, positivity, and spectral properties, including collocation products and an associative symmetric product based on Gaussian quadrature nodes. We prove a consistency result showing that, under suitable projection-error assumptions, these products converge to the classical product as the polynomial degree grows. For systems with rational fluxes, we derive sufficient conditions under which the resulting stochastic Galerkin flux remains hyperbolic on the corresponding admissible set. Applications to the one-dimensional isothermal and compressible Euler equations show accurate statistical approximation and robust hyperbolicity preservation of the computed stochastic Galerkin states.

This work investigates the impact of severe class imbalance on the performance of automated machine learning (AutoML) frameworks for multiclass network intrusion detection using the NSL-KDD dataset. Unlike previous studies that simplify the problem through binary classification or minority-class removal, we preserve the original five-class distribution, including highly underrepresented attacks such as R2L and U2R, enabling a realistic evaluation of imbalance-sensitive learning behavior. Nine open-source AutoML frameworks were analyzed under a unified and reproducible experimental protocol, considering differences in architectural design, ensemble strategies, validation procedures, hyperparameter optimization, and imbalance-handling mechanisms. The results demonstrate that frameworks incorporating ensemble learning and imbalance-aware optimization achieve better minority-class discrimination. PyCaret obtained the best overall performance, reaching 66% macro-F1, followed by AutoGluon with 55%, whereas frameworks lacking native balancing support exhibited significant degradation in minority-class detection capability. The analysis further shows that accuracy-oriented optimization alone is insufficient for highly imbalanced IDS scenarios, since high-weighted metrics may coexist with poor generalization on rare attack categories. As a contribution, this work establishes a standardized benchmark for AutoML-based intrusion detection under severe multiclass imbalance, highlighting current architectural limitations and the need for native integration of imbalance-aware optimization, resampling, and stratified evaluation strategies into automated learning pipelines. The source code is publicly available.

Adaptive networks couple the evolution of node states to the evolution of the interactions between them. In fast-adapting phase oscillator networks, a slow-manifold reduction of a pairwise microscopic model can generate effective higher-order terms in the phase dynamics. We ask whether this higher-order structure survives the dense-graph continuum limit, and whether it matters if one first reduces and then passes to the continuum, or first passes to the continuum and then reduces. We prove well-posedness and discrete-to-continuum convergence for the unreduced and first-order reduced models, and we construct the continuum slow manifold directly in a Banach-space setting. Along admissible equal-cell step approximations, the two routes give the same first-order continuum vector field, including the same pairwise correction and triplet operator, up to controlled $O(\varepsilon^2)$ remainders. A continuum mixed-derivative criterion then shows that, for suitable coupling functions, the resulting triplet operator is genuinely nonpairwise in the smooth bounded-kernel class. Thus the higher-order term is not a finite-network artefact, but persists in the macroscopic continuum description considered here.

In this paper, we introduce generalized phase retrieval (briefly, g-phase retrieval) and generalized norm retrieval (briefly, g-norm retrieval). Then, we present some properties of these concepts in Hilbert spaces, with special emphasis on R^n. Finally, the stability of g-norm retrieval for R^n is proved.

The analytic integration of single-scale Feynman integrals emerging in perturbative calculations in quantum field theories can be performed within special classes of functions, which appear as consecutive generalizations of the polylogarithm in form of Kummer-Poincaré iterative integrals over special alphabets and extensions thereof. These are the polylogarithms, Nielsen integrals, the iterated integrals over linear denominator terms, cyclotomic letters, letters induced by quadratic forms, and square-root valued letters. These integrals are solutions of first-order factorizing differential equations. They are related to specific nested sums via the Mellin transform and their expansions around $x=0$. We construct the $\mu$-extensions of these iterated integrals and the associated nested sums. We present closed form solutions or provide algorithms in the case of more involved cases to derive the respective $\mu$-extensions and study the algebras of the $\mu$-extended function spaces. Except for the case of square-root valued alphabets, the $\mu$-extension maps into the same function space polynomially in $\mu$. This is also the case for the associated nested sums. For square-root valued alphabets or sums containing central binomials, the $\mu$-extension leads to higher transcendental functions. In all other cases the $\mu$-extension preserves the Hopf algebra structure implied by the (quasi)shuffle product, by supplementing $\mu$ to the ground field.

Scalar fields provide perhaps the simplest playground in which to develop our understanding of quantum field theory. In this lecture, we consider what it means to write down a scalar quantum field theory and how we can give geometrical interpretations to the space of such theories: the theory space.

Multigrid methods are among the most effective frameworks for solving large-scale sparse systems. However, achieving their hallmark linear scaling and rapid convergence crucially depends on an effective smoother algorithm, whose design is often highly problem-dependent. This paper develops a new approach, referred to as cascading smoothers due to their operation as an ordered sequence of single-step block-diagonal smoothers. Each level in the cascade is optimised to maximally damp the output of prior steps via a Frobenius norm minimisation of the corresponding error propagators. In particular, we develop an additive (resp., multiplicative) formulation analogous to Jacobi (resp., Gauss-Seidel). Applied within a standard multigrid V-cycle, we show they are remarkably effective across a wide array of problems, including finite difference, finite element, and discontinuous Galerkin discretisations applied to Poisson, elliptic interface, and Stokes systems as well as multiphase variants. In every case, cascading smoothers closely match or significantly outperform their optimally-damped classical counterparts, yet require no parameter tuning apart from a few discrete solver choices. Additionally, the approach is highly parallelisable and robust to geometric and operator complexities such as unstructured meshes and high-contrast coefficients.

We study a Cahn-Hilliard equation with anisotropic traction flux arising as a reduced continuum model of mechanically biased cell interactions in digit-forming organoids. For a regularized problem with strictly positive bounded mobility, we introduce a mixed finite element discretization based on an implicit-explicit treatment of the chemical potential. We prove existence of discrete solutions, establish exact mass conservation and a discrete energy inequality, and show convergence of the fully discrete approximations to a weak solution of the regularized problem. Numerical experiments illustrate the resulting dynamics and show the transition from classical coarsening to traction-induced fingering and protrusive growth. The computational study is complemented by mass and energy diagnostics, an energy-balance residual, fingering-onset and protrusion-count diagnostics, and a manufactured-solution convergence study.

Statistical inference is a fundamental component of data science. In this work, we focus on two classical inference techniques: regression and interpolation. We propose a reaction-network-based framework for implementing linear regression, including both univariate and multivariate settings, as well as linear interpolation. Our approach encodes the outputs of these inference techniques in the steady-state concentrations of species within the reaction network. A key ingredient of the construction is a novel generalized division module capable of handling division involving negative numbers. We validate the proposed framework through in silico implementations on standard synthetic datasets and obtain the expected regression and interpolation outputs.

We develop a four-compartment susceptible–casual–addicted–resistant (SCAR) model for adolescent substance use in a high-school setting. The model divides students into susceptible non-users, casual or experimental users, students with sustained or substance-use-disorder (SUD)-level involvement, and resistant students in protective anti-use environments. It includes peer-driven initiation, escalation from casual to problematic use, protective peer influence, school disengagement, and partial re-entry after rehabilitation. Qualitative analysis and bifurcation diagrams show three main results. First, the return parameter \(\phi\) separates two regimes: when \(\phi=1\), the total population is conserved and interior equilibria may exist; when \(\phi<1\), problematic use causes net school-population loss, so positive scaled equilibria may not represent true endemic equilibria. Second, initiation and escalation are governed by distinct thresholds, meaning first use and progression to problematic use are dynamically different. Third, the model can exhibit multistability, including bistability between a substance-free state and a stable high-use state, so long-term outcomes may depend on initial conditions. These findings suggest that effective school policy should combine universal prevention, early intervention for casual users, targeted support for students at risk of problematic use, recovery-supportive environments, and strong school re-engagement pathways.

Let $A\geq0$ be self-adjoint on a Hilbert space $H$, let $T_{t}=e^{-tA}$, and let $P$ be an orthogonal projection. Relative to the decomposition $H=PH\oplus P^{\perp}H$, write \[ T_{t}=\begin{pmatrix}C_{t} & V^{*}_{t}\\ V_{t} & D_{t} \end{pmatrix}, \] where $C_{t}=PT_{t}P|_{PH}$, $V_{t}=P^{\perp}T_{t}P|_{PH}$, and $D_{t}=P^{\perp}T_{t}P^{\perp}|_{P^{\perp}H}$. The compressed family $\left(C_{t}\right)$ consists of positive contractions but need not form a semigroup. Its defect is given by \[ C_{s+t}-C_{s}C_{t}=V^{*}_{s}V_{t} \] while the complementary block satisfies \[ D_{s+t}-D_{s}D_{t}=V_{s}V^{*}_{t}. \] Thus the failure of $\left\{ C_{t}\right\} $ and $\left\{ D_{t}\right\} $ to be semigroups gives two Gram kernels associated with the same off-diagonal maps. We treat these covariance defects as positive definite operator-valued kernels and use their Kolmogorov spaces to recover the hidden dynamics they encode. We then study short-time rescalings of $E_{s,t}:=V^{*}_{s}V_{t}$. The tangent kernel \[ F\left(s,t\right):=\lim_{\varepsilon\downarrow0}a\left(\varepsilon\right)^{-1}E_{\varepsilon s,\varepsilon t} \] has its own Kolmogorov space, and the lower-right block dynamics induces a positive self-adjoint contraction semigroup on it. The representing vectors of $F$ then satisfy an additive cocycle identity for this semigroup. This gives an intrinsic restriction on the positive kernels that can arise as short-time compression covariance tangents.

The ensemble Kalman filter (EnKF) is widely adopted for sequential data assimilation, but fails for solutions with discontinuities, such as shocks in compressible flows. For ensembles with uncertainty in discontinuity location, the EnKF analysis can lose physical consistency, which can be viewed through three lenses: (i) bimodal pointwise statistics in the forecast ensemble, (ii) mixed-sign analysis weights in the affine superposition of forecast members, and (iii) spatially distributed Kalman-gain profiles that modify the state around a discontinuity rather than translating it coherently. In order to preserve flow features in the analysis state and, as such, maintain physical consistency, a feature-preserving latent-EnKF is introduced. The proposed method performs the ensemble update in a low-dimensional latent space learned by a coordinate-conditioned auto-decoder. The latent representation retains near-Gaussian ensemble statistics and provides coordinates in which flow features vary coherently across the forecast ensemble. The EnKF updates performed on the latent codes are decoded into coherent changes in the represented flow features, translating shocks through coordinate morphing rather than superposing them at displaced locations. The corresponding Kalman-gain profiles generated by the latent-EnKF exhibit an approximately step-like variation across the discontinuities, allowing the fronts to translate while preserving sharp features and monotonicity. Sod shock-tube, Mach~2 shock-cylinder, and immersed-body shape-inference tests with sparse, noisy observations demonstrate accurate recovery of shocks and contact discontinuities without spurious oscillations.

This is an expository note. We take the dyadic Cauchy-kernel identity of Castillo-Costin-Costin, a global rational/factorial decomposition built on the polylogarithm, and follow it down several specializations. In each direction it returns to a classical landmark: the polylogarithm duplication formula and Hurwitz's Fourier-series formula; representations of the zeta function at the special argument pi and at rational arguments, in the neighborhood of Hurwitz's multiplication theorem; the Hasse-Sondow globally convergent series; and, through its discrete scale invariance, the extra zeros of the Dirichlet eta function together with the harmonic-sum asymptotics of Flajolet-Gourdon-Dumas, with Dirichlet L-values emerging as the amplitudes of a log-periodic oscillation. The aim is unification: to exhibit one compact identity as an organizing center from which these classical results may be read off. We claim no new theorems; where an identity may not previously have been displayed in exactly this form, we say so and explain why it is nonetheless a recombination of known ingredients.

We prove that the riso-stratifications of singularities of affine algebraic sets introduced by Bradley-Williams–Halupczok are embedding independent and can be computed étale locally. Building on this, we upgrade their procedure, originally formulated in non-archimedean and model-theoretic language, into a sharp canonical and functorial stratification process for schemes of finite type over fields of characteristic 0. Our work enables comparisons with classical approaches to singularities and makes these stratifications available without requiring familiarity with the original methods.

Recent efforts to formulate quantum mechanics in a way that treats space and time on a more equal footing have led to a large variety of spacetime-oriented approaches. In this work we present a detailed study of spacetime states, the objects that play the role of quantum states in the recently introduced framework of spacetime quantum mechanics, and show that the main proposals in the literature are different manifestations of the same underlying object. Path integrals, quantum states over time, pseudo-density matrices, the Page and Wootters mechanism, superdensity operators, and timelike-entanglement proposals all arise from spacetime states through particular evaluations, reduced information, linear maps, or quantum channels. This unification provides explicit mathematical representations of these formalisms, reveals relations among them, and clarifies the spacetime information each one captures. We also study the broader relevance of the spacetime-state point of view for Leggett-Garg inequalities, OTOCs, temporal tensor networks, fermionic systems, relativistic QFTs, quantum reference frames, and classical physics, together with additional insights and perspectives revealed by the common unifying framework.

We construct Wigner-like equations of motion for symmetric continuous-spin fields in anti-de Sitter space of arbitrary dimension, treating both the bosonic and fermionic cases. We generalise the classical flat-space Wigner constraints for bosonic continuous-spin fields, and for the fermionic case we adopt the equations proposed by Bekaert and Mourad as our starting point. This is achieved by covariantising the ordinary derivatives and deforming the resulting constraints so that they form a closed algebra. The construction is carried out in a metric-like formalism and yields a system of first-class constraints that define a representation of the $\mathfrak{so}(2,D-1)$ isometry algebra, realised via the Lie-Lorentz derivative. Using these constraints, we compute the eigenvalues of the quadratic and quartic Casimir operators and compare the obtained values with Metsaev's classification of continuous-spin representations for both the bosonic and fermionic cases. A crucial algebraic role is played by special operators of the (super)algebra Howe-dual to the Lorentz subalgebra $\mathfrak{so}(1,D-1)$: the $\mathfrak{sl}(2)$ Casimir operator in the bosonic case, and the Casimir's ghost of the $\mathfrak{osp}(1|2)$ superalgebra in the fermionic case. Both objects naturally organise the constraint algebra and ensure its consistency.

This is a streamlined introduction to the global (infrared) completion of Maxwell-type higher gauge fields (as in the higher gauge sectors of higher dimensional supergravity and its brane probes) by electromagnetic flux quantization in differential nonabelian cohomology, using cohesive homotopy theory. Applications include D/NS brane charge in (unstable) K-theory, M-brane charge in unstable Cohomotopy and geometric engineering of topological quantum order on probe M5-branes.

We study perturbations of Yang-Mills plasma, represented by scalar quasinormal modes of AdS black branes, as functions of the wave number $q$ in the entire range from zero to infinity. At finite $q$, these modes can be computed by classical spectral methods based on truncating the boundary value problem. We show that this truncation admits a natural analytic interpretation in terms of quantum Seiberg–Witten periods in the Nekrasov–Shatashvili limit, with the spectral condition organised as an instanton expansion around small values of the counting parameter. The physical black-brane problem corresponds to evaluating this series at a finite value of the counting parameter, and the Seiberg–Witten formulation provides a systematic way to analyse when the truncation is under control. In particular, it reveals that, as $q$ or the mode number $N$ increases, the physical point approaches the boundary of the domain of convergence of the instanton expansion, limiting the validity of the truncation approach. We overcome this limitation through an exact WKB analysis in which $q^{-1}$ acts as the expansion parameter. The resulting exact quantisation conditions, expressed in terms of period integrals and the associated Stokes geometry, incorporate both perturbative and non-perturbative corrections. The resummed quasinormal modes remain accurate far beyond the strict large-$q$ regime and can be analytically continued all the way to $q=0$, notably by using the Seiberg–Witten approach, providing a consistent description of the QNM spectrum.

The universal commuting dilation constant $C_d$ is the smallest constant $\alpha$ such that every $d$-tuple of contractions dilates to a commuting $d$-tuple of normal operators with norm at most $\alpha$. The work of several authors shows that $1.5438 \lesssim C_2 \leq 2$, and it has been asked on a few accounts whether $C_2 < 2$. We provide a positive answer that, in fact, produces a near optimal upper bound of $C_2 \leq \frac{2}{\sqrt{\phi}}$ where $\phi$ is the golden ratio. This tightens the gap on the universal commuting dilation constant to $1.5438 \lesssim C_2 \lesssim 1.5724$. We also tighten the known upper and lower bounds on $C_d$ for arbitrary $d$-tuples.

Molecular communication (MC) suffers from severe diffusion memory because molecules released for one symbol may arrive during later symbol intervals. Neural sequence detectors, especially sliding bidirectional recurrent neural networks (SBRNNs), substantially outperform threshold detection in such channels. This raises a central question for MC channel coding: does a code whose superiority was established under threshold detection retain it when both coded and uncoded transmission are evaluated with neural detection? This letter answers this question for run-length-limited ISI-mitigation (RLIM) codes by proposing a decoder-aware training mask that removes the positions the RLIM decoder has a high probability of deterministically overwriting, steering compact-SBRNN capacity toward the information-bearing positions. The masked RLIM$_2$-SBRNN beats the best uncoded receiver (threshold or SBRNN) at 40 of 57 operating points; gains peak at 43$\times$ under favorable channels, while losses, confined to the most adverse, never exceed 2.7$\times$. Masking improves the unmasked RLIM$_2$-SBRNN in 56 of 57 matched comparisons. Finally, with storage counted equally in SBRNN weights and MLSE table entries, the masked RLIM$_2$-SBRNN is the more accurate receiver up to a few thousand stored values despite using no channel knowledge; channel-state-aware uncoded MLSE moves ahead only beyond tens of thousands.

This work is concerned with the study of lower bounds for various expressions related to the arithmetic functions $\varphi(n)$, $\psi(n)$ and $\sigma(n)$. Several explicit estimates are established.