Papers

Additive codes over $\mathbb{F}_{q^h}$ generalize linear codes by relaxing linearity over the alphabet while retaining linearity over the subfield $\mathbb{F}_q$. In this paper, we introduce minimal additive codes and we initiate their study from a geometric perspective. We define the concept of additive strong blocking sets, a class of $h$-projective systems whose union forms a strong blocking set. We establish a one-to-one correspondence between equivalence classes of nondegenerate minimal additive codes and equivalence classes of additive strong blocking sets. We also compare this framework with the theory of outer strong blocking sets, showing that the latter arises as a special case. Finally, we provide constructions and existence results for minimal additive codes, and derive upper, lower, and asymptotic bounds on their minimum length.

In this paper, we study the $\alpha$-Ricci curvature and the Lin-Lu-Yau Ricci curvature on simple, connected, and locally finite graphs. For regular graphs, we introduce a combinatorial construction of optimal transport plans realizing the 1-Wasserstein distance and use it to derive exact formulas for the $\alpha$-Ricci curvature and the Lin-Lu-Yau Ricci curvature. This yields a combinatorial proof of the known curvature formulas. Furthermore, for non-regular graphs, we characterize conditions on the size of common neighborhoods that guarantee either non-negative or vanishing Lin-Lu-Yau Ricci curvature.

We extend the study of brane quantization via SYZ mirror symmetry to the setting of singular fibers, building on recent joint work with Chan, Leung, and Li in the semi-flat case. We consider a crepant resolution $X\to\mathbb{C}^2/\mathbb{Z}_{n+1}$ of the $A_n$-singularity, whose mirror $\check{X}$ is also realized as a resolution of $\mathbb{C}^2/\mathbb{Z}_{n+1}$. For each level $k\in\mathbb{Z}_{>0}$, we construct a space filling coisotropic A-brane $\mathcal{B}_{cc}^{(k)}$ of $(X,k\omega)$ and determine its mirror B-brane $\check{\mathcal{B}}_{cc}^{(k)}$ via fiberwise geometric quantization. We then define the endomorphism algebra $Hom_A(\mathcal{B}_{cc}^{(k)},\mathcal{B}_{cc}^{(k)})$ by gluing analytic quantum tori using wall-crossing formulas and establish a mirror isomorphism $Hom_A(\mathcal{B}_{cc}^{(k)},\mathcal{B}_{cc}^{(k)})\cong Hom_B(\check{\mathcal{B}}_{cc}^{(k)},\check{\mathcal{B}}_{cc}^{(k)})$.

In 2012, Andrews and Merca proved a truncated theorem on Euler's pentagonal number theorem. Since then, a number of results on truncated theta series have been proved, including truncated Jacobi triple product identity. In this paper, we provide partition-theoretic interpretations for the bilateral truncated Jacobi triple product identity in terms of the minimal excludant integer.

This manuscript develops material from the internal French research notes Traite de Topologie Algebrique Fractale into an academic manuscript. The editorial rule is strict: implementation names are not used as mathematical proofs, analogies are not promoted to theorems, and every formal result is either proved from explicit assumptions or downgraded to a model law, conjecture, or empirical claim. The central object is T n , a finite heterogeneous concept container formalized as a section of a product bundle whose slots include an empirical sensorimotor base R 14 , grammatical fibres, polarity, intensity, vision and audition slots, an SSTD spectral slot, a refined compositional fibre, and auxiliary tool/metric/axis/hint slots. We prove elementary structural results about product-bundle representation, heterogeneous GCM metrics, and continuity of componentwise operations. We then give conditional results for Frobenius-inspired crystal composition, Gamma/CNS curvature-Hopf modelling, Kalman convergence, SSTD bundle morphisms, and SpiderR flat-connection idealizations. Each conditional result includes its assumptions, proof status, implementation correspondence, and the boundary between mathematics, model assumptions, and empirical evidence.

We study metric entropy and uniform sampling for classes of signals on ${\mathbb Z}_N$ with prescribed Fourier ratio. The Fourier ratio measures how spread out the Fourier transform of a signal is, interpolating between sparse spectral support and nearly uniform spectral distribution. Our main result gives upper and lower bounds for the metric entropy of a Fourier-ratio layer of size $r.$ At any sufficiently small fixed covering scale, these bounds match in their dependence on $r$ and $N$ and show that $FR(f)^2$ acts as an effective dimension parameter governing the size of the class. We use the entropy estimate to obtain uniform bounds for empirical approximation over Fourier-ratio classes. We also establish a phase-orbit packing result. If a single signal has a flat spectral block of size $k,$ then phase perturbations of that signal generate an exponentially large family with the same Fourier ratio and positive $\ell^2$ separation. Together, these results show that the Fourier ratio governs not only approximation properties of individual signals, but also the geometric size and uniform sampling behavior of entire signal classes.

In this paper, we explore links of Mazur manifolds in simple 4-manifolds. We construct non-split 2-component links in $S^4$. These are used to produce links in $\#^n \mathbb{C} \mathbb{P}^2$ which are split topologically but not smoothly. As a consequence, we obtain exotic pairs of simply connected, definite 4-manifolds with boundary, as well as exotic embeddings of various Mazur manifolds in $S^4$.

A Bayesian investor learns an unknown asset drift by Kalman–Bucy filtering and trades using the resulting estimate, but the observation model used for learning may itself be misspecified. We make the portfolio robust to an adversary who distorts the law of observed prices and pays a relative-entropy penalty. A single distortion affects both trading returns and learning, since wealth and the drift estimate respond to the same observed prices. The observation market is complete, so the fixed-target problem becomes an optimization over terminal tracking errors. Entropy duality removes the adversary's inner optimization, and the pricing budget reduces the investor's problem to one scalar equation for the budget multiplier. We show that the resulting payoff is replicable, and together with its worst-case measure forms an attained saddle. The robust position has the same sign as the non-robust one but a smaller size, and it tends to zero far from the target. A robust mean–variance investor also aims lower and starts with a smaller position. The non-robust strategy can fail badly — with a known nonzero drift, its worst-case cost is infinite at every level of ambiguity. Learning can make this cost finite, but whether it does depends on the shape of the prior, not only on its mean and variance. The robust payoff remains worst-case optimal for any prior: it limits the error on price paths that a driftless model explains better than the reference model, which is where the non-robust error is largest.

In this paper, we investigate $\varphi$-normal functions and normal families of holomorphic functions concerning total derivatives in $\mathbb{C}^{n}.$ More precisely, we prove a sufficient condition for a holomorphic function defined on an open unit ball in $\mathbb{C}^{n}$ satisfying certain conditions involving higher order partial derivatives to be $\varphi$-normal. Furthermore, by using differential inequalities involving total differential polynomials in $\mathbb{C}^{n}$, we establish some normality criteria for holomorphic functions in $\mathbb{C}^{n}$ which generalize some known results.

We investigate the numerical simulation of general stochastic differential equations (SDEs) using Quasi-Monte Carlo (QMC) methods. First, we provide a rigorous theoretical analysis of the QMC method applied to the Euler-Maruyama (EM) scheme, establishing that it significantly accelerates the decay of the sampling error and achieves an asymptotically superior convergence rate over the classical Monte Carlo method. Second, the traditional EM scheme exhibits a slow polynomial decay of the discretization error, which necessitates a large number of time steps and leads to a significantly high integration dimension. To address this issue, we propose a Multilevel Stochastic Time Grid (MSTG) method based on Exact Simulation techniques, and we rigorously establish its convergence rate under randomized QMC sampling, proving that it preserves the high-order convergence of the sampling error. In terms of the overall error, the truncation error of the proposed MSTG method exhibits a remarkably fast super-exponential decay. Consequently, to achieve a given accuracy level, our approach requires significantly fewer discretization steps than the EM scheme, thereby drastically reducing the actual integration dimension of the QMC method. This substantial dimensionality reduction strategy greatly enhances the practical efficiency of the QMC algorithm. Numerical experiments fully corroborate the superiority of the proposed approach.

The Ramsey number $r_k(s,m)$ is the smallest $N$ such that any red/blue coloring of the $k$-subsets of $[N]$ contains a red $s$-set or a blue $m$-set. For fixed $k$ and $s$, and for sufficiently large $m$, the tower growth rate is determined by the stepping-up lemma, but for $s=m=k+1$ the available stepping-up lemmas do not apply. Fox asked for estimates of $r_k(k+1,k+1)$. Pudlák, Rödl, and Wesley gave the first tower-type bound: $r_k(k+1,k+1)\ge s_3(\lfloor k/4\rfloor)\ge 4\operatorname{twr}_{\lfloor k/4\rfloor-4}(2)$, where $s_3(k)$ is the $3$-color shift number and $\operatorname{twr}_1(2)=2$, $\operatorname{twr}_{i+1}(2)=2^{\operatorname{twr}_i(2)}$. In this paper, for $k\ge 6$, we improve the lower bound to $r_k(k+1,k+1)> s_3\bigl(\lfloor k/2\rfloor-2\bigr)$ by overcoming an obstruction in their construction. In addition, we give an exact characterization of $s_3(k)$ and, for $k\ge 5$, obtain a new explicit lower bound $s_3(k)\ge(\operatorname{twr}_{k-2}(2))^2$, which improves the result of Pudlák and Rödl. Consequently, for $k\ge 14$, $r_k(k+1,k+1)>(\operatorname{twr}_{\lfloor k/2\rfloor-4}(2))^2$.

This paper studies tractability versus the curse of dimensionality for several geometric $L_p$-discrepancies through a unified discrepancy–integration duality framework, where worst case integration errors in suitable function spaces equal the corresponding discrepancies. A general lower bound method for non-negative linear rules in spaces under broad tensor-product assumptions establishes exponential information complexity in the dimension $d$, yielding the curse of dimensionality for the respective discrepancy. We complement this overview by new results on discrepancy–integration duality and the curse of dimensionality for the periodic $L_p$-discrepancy. The current state of research on this general problem is summarized in a clearly laid out table, which also highlights the remaining open questions.

Recent advances in dynamical low-rank approximation (DLRA) have demonstrated its effectiveness in high-dimensional simulations. However, existing DLRA algorithms still face significant challenges when handling systems that involve complex collision terms, including the pseudo-differential operator (${\rm \Psi}$) in the Wigner equation, a representative operator characterized by nonlocality. It is deserving to carry out a series of works to develop the DLRA algorithms for solving the Wigner equation. As the first step in this series of works, we propose an efficient DLRA algorithm for the Wigner equation, using a separable decomposition of the difference potential. We combine this separable assumption with two often-used truncations of ${\rm \Psi}$, namely $\mathcal{K}$-truncation and $\mathcal{Y}$-truncation, to obtain a kind of separated representation of ${\rm \Psi}$. Complexity analysis and several challenging experiments, including harmonic oscillators, Gaussian barrier scattering, electron-electron scattering, and a Helium-like system, all of which satisfy the separable assumption, confirm that the proposed DLRA algorithm has significant advantages, achieving a reduction in computational effort by one to two orders of magnitude in both runtime and memory requirements compared to the full-grid approach. It is worth noting that, even in the absence of a predetermined low-rank structure for the solution, DLRA can still serve as a numerical scheme that balances efficiency and accuracy.

This paper revisits the E scheme of Osher \cite{Osher-SINUM1984} and the flux-limiter scheme of Sweby for quasi-linear hyperbolic conservation laws \cite{Sweby-SINUM1984}. Part of existing results will be re-understood and some new results will be presented. For a scalar conservation law, except for the conservative monotone schemes, the E scheme is a type of numerical methods that satisfy the discrete entropy condition for any convex entropy, but numerical entropy flux is not unique. Two-point monotone flux is E flux, but conversely it may not necessarily be correct. Moreover, multi-point (three or more points) E flux may not necessarily be monotone flux, and multi-point monotone flux may not necessarily be E flux. Sweby's flux-limiter scheme for the quasi-linear conservation laws was built on the E flux-based splitting $f_{j+1}-f_j=f_{j+1} { -\hat{f}^{\text{\tiny E}}_{j+\frac12}+\hat{f}^{\text{\tiny E}}_{j+\frac12}}-f_j$ and the LW scheme. It may not be second-order accurate in both space and time.

We consider ordinary differential equations (ODE) of the form $u''u - (u')^2 = e^{-x}P(u) - 1$, where $P$ is a polynomial. In previous work, necessary conditions on $P$ have been established for certain families of solutions of these ODEs to have asymptotic expansions of the form $u(x) = \sum_{k=0}^{\infty} p_k(x+c)e^{-kx}$ for $Re\,x \to +\infty$, where $c \in \mathbb C$ is an arbitrary constant parameterizing the solution family, and $p_k$ are polynomials, with $p_0(x) = x$. These conditions amount to $P(0) = 0$ and $P'(0) = \frac12P''(0)$. Here we show that these two conditions are also sufficient. The results imply the existence of corresponding expansions for certain degenerate Painlevé III transcendents.

We study the asymptotic stability of rarefaction waves for one-dimensional stochastic viscous conservation laws driven by nonlinear conservative noise. In a critical scaling where stochastic energy injection and viscous dissipation compete at comparable magnitudes, standard kinetic and viscosity frameworks encounter obstructions due to regularity gaps and non-integrable profiles. To address this, we introduce a stochastic area inequality controlling accumulated energy fluctuations, a local $L^1$ contraction principle via stochastic Kružkov doubling-of-variables that yields pathwise uniqueness without global integrability, and a modified Galerkin scheme preserving the $H^2$ energy structure. Assuming local $H^2$ regularity, we prove almost sure algebraic convergence to the rarefaction wave. For sufficiently small initial perturbations, we establish global well-posedness and sharp decay estimates in expectation. The smallness condition identifies a regime where viscous dissipation dominates stochastic injection, reflecting a structural stability threshold rather than a technical artifact. Our approach extends the analytical framework for conservative SPDEs with rough fluxes.

For a finite set $A$ of positive integers, let $H(A)$ be its set of subset sums, including the empty sum, and let $g_k(n)$ be the least $N$ for which some $n$-element set $A\subseteq[N]$ has $H(A)$ free of nonconstant $k$-term arithmetic progressions. The problem of determining $g_k(n)$ was posed by Erdős and Sárkőzy. In the three-term case, we prove a lower bound equal to the exact bandwidth of the ternary grid. If $T_m=[x^m](1+x+x^2)^m$ is the central trinomial coefficient, then \[ g_3(n)\ge \frac{T_n-1}{2}+\sum_{j=0}^{n-1}T_j =\left(\frac{\sqrt{3}}{2\sqrt{\pi}}+o(1)\right)\frac{3^n}{\sqrt{n}}. \] For general $k \ge 4$ we show \[ g_k(n)\gg_k \left(\frac{k-1}{k-2}\right)^n n^{-\log_2((k-1)/(k-2))} \] In the opposite direction, a carry-free digit construction based on nearly-regular graphs gives \[ \limsup_{n\to\infty}g_k(n)^{1/n} \le \min_{p\ \mathrm{prime},\ p\ge3}p^{2/(\min\{p,k\}-1)}. \] Consequently, as $k\to\infty$, the logarithm of the lower exponential rate is at least $(1+o(1))/k$, while the logarithm of the upper exponential rate is at most $(2+o(1))\log k/k$.

We show that when the number of zeros or poles is at least four, every connected component of the strata of differentials in genus one with prescribed zero and pole orders is not an orbifold $K(\pi,1)$. For quadratic differentials, this provides infinitely many counterexamples to a conjecture attributed to Kontsevich, as well as to a folklore conjecture concerning the contractibility of spaces of Bridgeland stability conditions.

Motivated by an application in machine learning optimization, this paper focuses on the challenges of sampling a matrix uniformly from the unit spectral norm ball. It is proven that all singular values of sampled matrices converge to 1 almost surely as the matrix dimensions increase. This result provides the theoretical justification for a proposed simple sampling method applicable for large dimension sizes matching matrices found in modern large language models. Experimental results demonstrate both the convergence of the singular values, as well as the exact and proposed approximate sampling methods.

Let $\mathcal{D}_n$ denote the average number of passes of the stack-sorting map $s$ required to sort a permutation in $S_n$. We use the recently introduced framework of stack-sorting diagrams and tableaux to prove that the limit $\lim_{n\to\infty}\mathcal{D}_n/n$ exists. This resolves a longstanding conjecture of West originally proposed in $1990$. As a consequence, we also provide a monotonically increasing sequence that converges to $\lim_{n\to\infty}\mathcal{D}_n/n$, improving upon Defant's lower bound of $\lambda\approx 0.62433$.

Variational principles specify the dynamics of a physical system via the extremization of associated functional data. Onsager's variational principle (OVP), which characterizes dissipation-dominated phenomena such as phase separation, admits an unconditionally energy-stable time discretization through the minimization of a Rayleighan functional combining free energy and dissipative effects. The present work considers the case where one or more parts of this functional are empirically approximated or otherwise uncertain. To address this, a novel variational discretization of OVP is introduced which recovers previous work as a special case, and a learning problem is formulated which identifies uncertain terms in the free energy and dissipation potential from observable data. It is shown that the resulting OVP-based models connect directly to previous work in terms of proximal methods, Sobolev and Wasserstein gradient flows, while remaining provably energy-stable under arbitrarily long rollouts. The approach is illustrated on examples including Allen-Cahn, Fokker-Planck, and Cahn-Hilliard system models, where the effects of bulk free-energy densities, nonlocal potentials, and nonstandard boundary conditions are effectively learned with model classes consisting of polynomials, shallow neural networks, and spectral convolution kernels.

Let $f: X \to X$ be a polarized endomorphism of a smooth projective surface which is birationally ruled. We answer a question of Kollár and Zhuang, in the affirmative, on the incompressibility of $f$, under the assumption that $f$ is Galois and an explicit lower bound of deg$(f)$ depending only on $X$. We also give examples showing the optimality of such a lower bound.

We prove concentration inequalities for polynomials of independent, sparse $\alpha$-sub-exponential random variables. Specifically, we consider $X_i=\delta_i\xi_i$, where the Bernoulli selectors $\delta_i$ are independent with parameters $p_i$, and the variables $\xi_i$ are independent \(\alpha\)-sub-exponential random variables (not necessarily centered). For any polynomial $f:\mathbb R^n\to\mathbb R $ of degree at most $D$ and any $0<\alpha \le 1 $, we establish an $L_r$-moment bound for \(f(X)-\mathbb E f(X)\) in terms of partition norms of sparsity-weighted expected derivative tensors. The weights count distinct coordinates rather than multiplicities and therefore distinguish diagonal, partially diagonal, and off-diagonal contributions. This captures the sparse scaling in both collective fluctuation regimes and extreme-coordinate regimes. When all sparsity parameters are equal to one, our result recovers the polynomial concentration inequality of Götze, Sambale, and Sinulis. In degree two, it recovers sparse Hanson-Wright bounds. As applications, we derive deviation inequalities for the distance between a sparse simple random tensor and a fixed subspace, and obtain lower bounds for the smallest singular value of matrices whose columns are independent sparse simple random tensors.

Wang~\cite{Wang2026} introduced the Stochastic-Oracle Turing Machine (SOTM) framework and defined token complexity as the minimum expected cost of interacting with a stochastic oracle needed to attain a specified solution quality for a task. This paper develops an analogous notion for certifying the reliability of a stochastic oracle on a given domain. Certification token complexity is the minimum expected token cost required, with controlled error probability, to distinguish oracles that meet a target reliability level from those that fall below a lower reliability threshold. We construct an SPRT-based certification SOTM that queries the oracle, computes binary correctness scores, and stops when the accumulated log-likelihood evidence crosses a decision threshold. The SOTM halts almost surely, satisfies the desired two-sided error guarantee over the reliability regions to be certified, and yields an explicit upper bound on certification token complexity in terms of the reliability thresholds, the error bound, and the expected per-turn token cost. We then establish a matching information-theoretic lower bound: even with adaptive queries, every error-bounded certification SOTM must incur the same leading-order expected token cost as the SPRT-based construction as the prescribed error bound tends to zero. Together, these bounds characterize the leading-order certification token complexity in the small-error regime.

We construct new classes of examples of self-similar processes with stationary increments indexed by $\mathbb Q_p$ via stable integrals. Classical constructions arise from the real counterpart and from discounted branching random walks. We discuss a new decoration technique that significantly enlarges these classes. The decoration technique makes use of the special symmetry of $\mathbb{Q}_p$ to obtain self-similarity and stationarity of increments, and it does not have an analogue on the real line. We also show that these enlarged classes of decorated processes are pairwise incomparable under inclusion.

This paper studies algorithms for computing the minimal monomial basis for multivariate Birkhoff interpolation problems. Our approach is built around the notion of a reverse reduced set, which serves as the key tool for bridging the interpolation conditions to the monomial basis, thereby avoiding the construction and evaluation of Vandermonde matrices required in existing algorithms. For the single-node case, we prove that after Gaussian elimination on the incidence matrix, the least monomials of the polynomial set corresponding to the interpolation conditions precisely constitute the minimal monomial basis. To handle the multi-node case, we exploit the one-to-one correspondence between interpolation functionals and formal power series, whereby interpolation conditions at arbitrary nonzero nodes can all be converted to the origin. This provides both a coherent theoretical framework and a constructive algorithm for determining a proper minimal monomial basis for the general multivariate Birkhoff interpolation problem. Numerical examples demonstrate the effectiveness of the proposed algorithm.

We study global hypoellipticity and global solvability for Vekua-type operators associated with diagonal left-invariant operators on compact Lie groups. The conjugation term produces, on the Fourier side, a family of coupled systems whose determinants govern both regularity and solvability. Under a natural non-self-duality assumption, we obtain complete Diophantine-type characterizations of global hypoellipticity and global solvability, the latter on the natural space of admissible data. We then show how the theory must be modified in the self-dual setting by carrying out a complete analysis of the model case \(\mathbb S^3\simeq SU(2)\), where conjugation acts inside each representation block. The resulting criteria exhibit two Fourier-side mechanisms for Vekua-type operators on compact Lie groups: coupling between distinct conjugate representations and coupling inside self-dual representation blocks. We also present examples on product groups illustrating how these mechanisms may coexist and how global solvability may hold even when global hypoellipticity fails.

In this paper, we develop a unified framework to evaluate the pre-Schwarzian norm for the Ma-Minda starlike class. We present a direct, general computational approach. As an application, we streamline and consolidate the results from Ali and Pal (Monatsh. Math., 2023), who obtained sharp estimates for the pre-Schwarzian norm of the Janowski starlike class. Furthermore, we utilize the proposed framework to derive explicit norm formulas for both classical and newly introduced subclasses of starlike functions.

We study a family of Hardy-type inequalities for weighted averages over nested subsets of a measure space. Given a partition of a measure space and a weight function $m$, we consider operators of the form \[ f \mapsto \frac{1}{M_n}\int_{X^{(n)}} m(x)f(x)\,\mathrm{d}\mu(x), \] with additional weights on the resulting sequence of averages. In particular, we generalize an inequality obtained by Vincent and Sohani in \cite{VincentSohani2025} and characterize the boundedness in terms of the finiteness of a single testing quantity $\beta$. We also provide two-sided estimates for the best constant $C_{\mathrm{opt}}$, namely \[ \beta \leq C_{\mathrm{opt}} \leq p^{1/q} (p')^{1/p'}\beta \leq 2\beta. \] Thus the characterization is never off by more than a factor of 2. We also develop a second approach, inspired by Broadbent's proof of Hardy's inequality, which gives a local sufficient condition that often provides sharper constants and recovers several important cases, including the classical weighted Hardy inequality.

The fractional $k$-dimensional measure of a submanifold of $\mathbb{R}^n$ is a generalization of the fractional perimeter and fractional length appearing in the literature and depends on a parameter $\sigma$ between $0$ and $1$. Here its first variation is computed. The resulting formula is used to define a nonlocal version of the mean-curvature vector for embedded submanifolds. It is shown that in the case where $k=n-1$, this agrees with the nonlocal mean-curvature that has been widely studied.