Results 31 to 40 of about 1,868 (172)
On computing local monodromy and the numerical local irreducible decomposition
Abstract Similarly to the global case, the local structure of a holomorphic subvariety at a given point is described by its local irreducible decomposition. Geometrically, the key requirement for obtaining a local irreducible decomposition is to compute the local monodromy action of a generic linear projection at the given point, which is always well ...
Parker B. Edwards +1 more
wiley +1 more source
On the additive image of zeroth persistent homology
Abstract For a category X$X$ and a finite field F$F$, we study the additive image of the functor H0(−;F)∗:rep(X,Top)→rep(X,VectF)$\operatorname{H}_0(-;F)_* \colon \operatorname{rep}(X, \mathbf {Top}) \rightarrow \operatorname{rep}(X, \mathbf {Vect}_F)$, or equivalently, of the free functor rep(X,Set)→rep(X,VectF)$\operatorname{rep}(X, \mathbf {Set ...
Ulrich Bauer +3 more
wiley +1 more source
Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Tate modules as condensed modules
Abstract We prove that the category of countable Tate modules over an arbitrary discrete ring embeds fully faithfully into that of condensed modules. If the base ring is of finite type, we characterize the essential image as generated by the free module of infinite countable rank under direct sums, duals and retracts.
Valerio Melani +2 more
wiley +1 more source
Random Diophantine equations in the primes
Abstract We consider equations of the form a1x1k+⋯+asxsk=0$a_{1}x_{1}^{k}+\cdots +a_{s}x_{s}^{k}=0$ where the variables xi$x_{i}$ are all taken to be primes. We define an analogue of the Hasse principle for solubility in the primes (which we call the prime Hasse principle), and prove that, whenever k⩾2$k\geqslant 2$, s⩾3k+2$s\geqslant 3k+2$, this holds
Philippa Holdridge
wiley +1 more source
Quantitative asymptotics for polynomial patterns in the primes
Abstract We prove quantitative estimates for averages of the von Mangoldt and Möbius functions along polynomial progressions n+P1(m),…,n+Pk(m)$n+P_1(m),\ldots, n+P_k(m)$ for a large class of polynomials Pi$P_i$. The error terms obtained save an arbitrary power of logarithm, matching the classical Siegel–Walfisz error term.
Lilian Matthiesen +2 more
wiley +1 more source
Sections and projections of the outer and inner regularizations of a convex body
Abstract We establish new geometric inequalities comparing the volumes of sections and projections of a convex body, whose barycenter or Santaló point is at the origin, with those of its inner and outer regularizations. We also provide functional extensions of these inequalities to the setting of log‐concave functions. Our approach relies on the recent
Natalia Tziotziou
wiley +1 more source
On the Lang–Trotter conjecture for Siegel modular forms
Abstract Let f$f$ be a genus‐two cuspidal Siegel eigenform. We prove an adelic open image theorem for the compatible system of Galois representations associated with f$f$, generalizing the results of Ribet and Momose for elliptic modular forms. Using this result, we investigate the distribution of the Hecke eigenvalues ap$a_p$ of f$f$, and obtain upper
Arvind Kumar, Moni Kumari, Ariel Weiss
wiley +1 more source
Topological Graph Neural Networks: A Novel Approach for Geometric Deep Learning
This graphical abstract illustrates the Topological Graph Neural Network (TopGNN) architecture. It demonstrates a parallel processing approach where an input graph is simultaneously analyzed by a standard GNN Encoder to capture local node features and by Persistent Homology to extract global topological features (like cycles and voids), visualized as a
Amarjeet +7 more
wiley +1 more source

