6781 papers
This paper provides a more elementary proof of the nonasymptotic universality laws established by Brailovskaya and van Handel, demonstrating that the spectral statistics of independent sums of random matrices mirror those of Gaussian matrices with matching first- and second-order moments, by utilizing a novel implementation of the method of exchangeable pairs.
This paper constructs and analyzes the birational geometry of a Kausz-type compactification of the space of symmetric matrices by realizing it as an evaluation fiber in the Kontsevich space of pointed conics on the Lagrangian Grassmannian, while also presenting analogous results for orthogonal Grassmannians.
This paper investigates a new elementary method by Luo and Lin for solving quartic equations, successfully applying it to provide a straightforward solution for Bumby's equation and proposing a conjecture that could extend this approach to an infinite family of similar equations.
This paper proposes a nonlinear conjugate gradient method with Wolfe line search for solving unconstrained multiobjective interval optimization problems, providing proofs of global convergence for general and specific algorithmic parameters (Fletcher-Reeves, Conjugate Descent, Dai-Yuan, and modified Dai-Yuan) alongside numerical performance evaluations.
This paper computes Donaldson-Thomas invariants and their descendants for the local Calabi-Yau 4-fold over the Mukai-Umemura variety using localization formulas, thereby verifying the predictions of Cao, Maulik, and Toda under the assumption that genus-one Gopakumar-Vafa invariants vanish.
The paper establishes a sharp inequality relating the Alexander-Taylor capacity to the functional capacity within complex Sobolev spaces on compact Kähler manifolds.
This paper presents a simplified extension of Birkhoff's representation theorem to locally-finite distributive lattices, establishing an isomorphism between such a lattice and the order ideals of the poset of its prime filters that differ from a specific ideal by a finite set.
This paper establishes a necessary and sufficient condition for decomposing the complete graph on vertices into isomorphic copies of a Cayley graph on a CI-group , while also providing a constructive method for such factorizations.
This paper resolves two longstanding problems from 1990 regarding indefinite integral ternary quadratic forms, highlighted by Margulis and initiated by Serre, by developing new tools to handle high ramification in sums weighted by diophantine invariants.
This paper investigates the long-time asymptotic behavior of multivariate Hawkes processes with power-law decaying interactions, employing a combination of short-range interaction techniques, -stable law properties, and Tauberian theorems to model realistic systems like neural networks with long-range connections.
This paper constructs a presheaf of Koszul complexes on weighted finite subsets of Euclidean space, utilizing linearized coefficients and change-of-coordinates maps to form a Čech-Koszul bicomplex whose degree-0 cocycles capture homotopical relationships between locally defined least squares solutions on data overlaps.
This paper proves that augmented admissible sets in Iwahori-Weyl groups are dual EL-shellable, thereby resolving a conjecture of Görtz and establishing the Cohen-Macaulayness of special fibers for local models with parahoric level structure—including previously open cases in residue characteristic 2 and non-reduced root systems—through a characteristic-free, intrinsic construction that yields an explicit shelling and inductive building procedure.
This paper demonstrates that while the "spectral density" derived from Gurau's resolvent trace yields valid probability measures on average for certain random tensor ensembles, it fails to correspond to a probability measure for individual deterministic tensors, thereby proving it is not a pointwise-defined probability distribution.
This paper introduces a Bayesian framework for linear programming under learned uncertainty that integrates data-driven posterior updates with robust optimization and scenario-based strategies to provide rigorous, finite-sample feasibility guarantees and improved decision safety compared to naive approaches.
This paper establishes a minimax theory for nonparametric regression under covariate shift by introducing a transfer function to characterize convergence rates that range from classical benchmarks to accelerated regimes involving multiplicative sample size interactions, all achievable by a design-adaptive estimator even with unbounded covariate support.
This paper establishes the existence and nonexistence of ground states for mass-critical N-coupled attractive Fermi nonlinear Schrödinger systems in ring-shaped potentials based on the strength of interactions relative to a critical constant derived from a finite-rank Lieb-Thirring inequality, while also characterizing the mass concentration behavior of these states as the interaction strength approaches this critical threshold.
This paper proposes a joint optimization algorithm for transmit precoding and discrete phase-shift reflection in an extremely large-scale RIS-aided near-field secure communication system with artificial jamming, effectively maximizing secrecy rates even when eavesdroppers are located close to the RIS and in the same direction as legitimate users.
This paper establishes the Morita invariance of several classes of ring extensions and provides a counterexample of a class that is not Morita invariant, building upon previous results by Miyashita and Ikehata.
This paper characterizes weakly separable polynomials in skew polynomial rings and explores the relationship between separability and weak separability in rings of derivation type, building upon the generalization of separable extensions introduced by Hamaguchi and Nakajima.
The paper proposes PriorIDENT, a prior-informed weak-form sparse-regression framework that integrates compact physics priors into dictionary construction to robustly identify governing partial differential equations from noisy spatiotemporal data while outperforming existing baselines in accuracy and stability.