The paper presents a series representation for European option pricing driven by fractional diffusion.
problem Pricing European options under space-time fractional diffusion.
method Uses Mellin-Barnes representation and residue summation in the complex plane.
result Derives a rapidly convergent double-series formula for option pricing.
Quantum dilogarithm function proven from a linear difference equation.
problem Proving Faddeev's quantum dilogarithm from a linear difference equation.
method Proved Faddeev's quantum dilogarithm using Borel summation of a formal power series solution of a linear difference equation.
result Borel summation of a formal power series solution produces Faddeev's quantum dilogarithm.
RDL-Net improves speech enhancement with fewer parameters and better performance.
problem Improving speech enhancement with fewer parameters and better performance.
method Proposes RDL-Net, a CNN combining residual and dense aggregations without over-allocating parameters.
result RDL-Net achieves higher speech enhancement performance with fewer parameters and lower computational requirements.
Wave trace singularity formula for fibre bundles generalizes Poisson summation.
problem Wave trace singularity formula for fibre bundles.
method Proved a wave trace singularity formula for a family of generalised Laplacians defined by a Riemannian fibre bundle.
result Generalizes Poisson summation formulae for families.
Study geodesics on flat tori, focusing on convex bodies.
problem Analyze geodesics orthogonal to convex subsets on flat tori.
method Define anisotropic Sobolev spaces and study properties of geodesics.
result Compute residues of geometric Epstein function in terms of intrinsic volumes.
Paper extends trigonometric summation formula with weights.
problem Trigonometric summation formula by Grigor'yan, Lin and Yau.
method Weighted trigonometric summation formula derivation.
result Extension of trigonometric summation formula.
Improved privacy-preserving summation protocol with fewer messages.
problem Achieving efficient differential privacy in multi-party summation.
method Combining secure shuffling with Laplace mechanism in the shuffle model.
result Protocol with O(1/ε) error and O(log(n/δ)) messages per party. The study shows how certain ODEs and integrals are regular under Borel summation.
problem Analyzing the regularity of solutions to ODEs and integration problems.
method Using geometric perspective on Laplace and Borel transforms, the study examines level 1 ODEs and exponential period integrals over Lefschetz thimbles.
result Solutions of certain ODEs and integration problems are Borel regular.
New theorem shows every integer can be represented by knot summation.
problem Understanding the summation of knot coefficients.
method Analyzing spatial complete graphs and Hamiltonian knots.
result Every integer can be represented by knot summation.
Mathematical structures link Gromov-Witten to Donaldson-Thomas invariants.
problem Understanding non-perturbative topological string theory.
method Borel summation of Gromov-Witten potential and analysis of Stokes phenomena.
result Stokes phenomena encode Donaldson-Thomas invariants of the resolved conifold.
New method solves sparse approximation problem using trimmed lasso and generalized soft-min penalties.
problem Sparse approximation or best subset selection problem.
method Regularized approach with trimmed lasso and generalized soft-min penalties.
result The trimmed lasso provides sparse recovery guarantees and a practical optimization algorithm.
Deep-Gap predicts crowdsourcing supply-demand gaps using deep learning.
problem Balancing supply and demand in mobile crowdsourcing.
method Residual learning-based deep neural networks trained on time series data and external factors.
result Deep-Gap achieves lowest forecasting errors compared to state-of-the-art methods.
Improved greedy 2-coordinate updates for optimization problems with constraints.
problem Minimizing smooth functions subject to constraints.
method Exploiting a connection to steepest descent in the 1-norm, we give faster convergence rates and efficient computation.
result Greedy selection converges faster than random selection and can be computed in O(nlogn) time. Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimation optimality for any input distribution, cross-validating for an optimal parameter requires significant computation do…
A new method slices and sums radial kernels faster.
problem Fast computation of large kernel sums in kernel methods.
method Random projections to 1D subspaces and QMC for selecting projections.
result QMC-slicing outperforms existing methods on test datasets.
We provide faster algorithms for the problem of Gaussian summation, which occurs in many machine learning methods. We develop two new extensions - an O(Dp) Taylor expansion for the Gaussian kernel with rigorous error bounds and a new error control scheme integrating any arbitrary approximation method - within the best …
We consider fast kernel summations in high dimensions: given a large set of points in d dimensions (with d≫3) and a pair-potential function (the {\em kernel} function), we compute a weighted sum of all pairwise kernel interactions for each point in the set. Direct summation is equivalent to a (dense) matrix-vec…
Paper introduces VSSGD to balance deep learning training with variance suppression.
problem Unbalanced data leads to unbalanced training process in deep learning.
method Adaptively adjusts error variance and mean to balance training.
result VSSGD accelerates training, prevents overfitting, and improves learning from small samples.
Dirichlet-Neumann duality for Riemannian submersions
problem Spectral geometry of Riemannian submersions
method Summation formula for reciprocal of basic Dirichlet eigenvalues
result Supersymmetric duality between basic Dirichlet and Neumann spectra
For an eigenfunction of the Laplacian on a hyperbolic Riemann surface, the coefficients of the Fourier expansion are described as intertwining functionals. All intertwiners are classified. A refined growth estimate for the coefficients is given and a summation formula is proved.
We prove an explicit formula of the Berezin star product on Kaehler manifolds. The formula is expressed as a summation over certain strongly connected digraphs. The proof relies on a combinatorial interpretation of Englis' work on the asymptotic expansion of the Laplace integral.
Transformers without skip connections collapse token representations to a single direction.
problem Rapid convergence of token representations to a single direction in self-attention-only Transformers.
method Analysis of layer normalization, residual connections, and multi-head attention mechanisms.
result Residual connections prevent rank collapse in real Transformers, while MLPs generate new feature directions.
Finet uses FBN for efficient, lightweight neural networks.
problem Building efficient neural networks with limited computational resources.
method Introduces Fine-grained Batch Normalization (FBN) and a novel light-weight network (Finet) that combines FBN with standard convolution.
result Finet achieves state-of-the-art performance on ImageNet classification with reduced computational complexity.
New techniques prove quantum modularity for various functions.
problem Proving quantum modularity of false theta functions and related series.
method Developed techniques including Poisson summation formula and modular series framework.
result Unified approach to proving quantum modularity for various functions.
For the Möbius spheres Sq,p, we give alternative elementary proofs of the recursive formulas for GJMS-operators and Q-curvatures due to the first author [Geom. Funct. Anal. 23, (2013), 1278-1370; arXiv:1108.0273]. These proofs make essential use of the theory of hypergeometric series.
In this paper, we study some vanishing identities for Gromov-Witten invariants conjectured by K. Liu and H. Xu. We will prove these conjectures in the case that the summation range is large compare to genus. In fact, in such cases, we can obtain a vanishing identity which is stronger than their conjectures. Moreover we…
Link quandles are shown to be residually finite.
problem Residual finiteness of link quandles.
method Proving residual finiteness of free products of residually finite quandles with residually finite associated groups.
result Link quandles are residually finite.
This paper is an introduction to Khovanov homology, starting with the Kauffman bracket state summation, emphasizing the Bar-Natan Canopoloy and tangle cobordism approach. The paper discusses a simplicial approach to Khovanov homology and a quantum model for it so that the graded Euler characteristic that produces the J…
Free and knot quandles are residually finite.
problem Residual finiteness of quandles.
method Definition and investigation of residual finiteness; proof for free and knot quandles; discussion on automorphism groups.
result Free and knot quandles are residually finite and Hopfian.
VRSMD improves SMD convergence and has implicit regularization.
problem Efficiently estimating models with large datasets.
method Variance reduction in stochastic mirror descent.
result VRSMD converges to the minimum mirror interpolant.
FNFs model parameter-dependent densities by combining a fixed flow with a polynomial parameter-dependent transformation.
problem Learning a separate flow for every parameter configuration is intractable.
method Factorizable Normalizing Flows (FNFs) represent the parameter-dependent density as a fixed flow for a reference configuration and a learnable polynomial transformation factorized over parameters.
result FNFs enable the recovery of the combined effect of multiple parameters without sampling their joint space, providing a scalable and interpretable solution.
Abstract: Non-residually finite hyperbolic groups imply non-residually finite rigid hyperbolic groups.
problem Existence of non-residually finite hyperbolic groups
method Direct implication
result Existence of non-residually finite rigid hyperbolic groups
Given a measured lamination on a finite area hyperbolic surface we consider a natural measure Mon the real line obtained by taking the push-forward of the volume measure of the unit tangent bundle of the surface under an intersection function associated with the lamination. We show that the measure M gives summation id…
Every non-trivial knot group is fully residually perfect.
problem Understanding the residual properties of knot groups.
method Analyzing the residual properties of knot groups using group theory.
result Every non-trivial knot group is fully residually perfect.
Residual algorithms improve reinforcement learning performance.
problem Distribution mismatch in model-based planning.
method Bidirectional target network technique for residual algorithms.
result Residual reinforcement learning significantly outperforms vanilla methods.
Study of Penner's cocycle on fatgraph complex.
problem Understanding Penner's cocycle on fatgraph complex.
method Explicit cobounding cochain formula involving summation over trivalent vertices.
result Expressed symplectic form of surface underlying fatgraph.
This paper solves the open problem of computing Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
problem Computing the Bayes optimal prediction for decision trees is infeasible due to an infeasible summation over all division patterns of a feature space.
method Solved the open problem using a Markov chain Monte Carlo method with adaptively tuned step size.
result Computed the Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
Residual flows are shown to approximate MMD well.
problem Lack of theoretical understanding of normalizing flows' expressiveness.
method Proved residual flows are universal approximators in MMD.
result Residual flows can approximate MMD with a bounded number of blocks.
Formula calculates residues for maps near holomorphic distributions.
problem Calculating residues for maps near holomorphic distributions.
method Residues formula for maps generically transversal to regular holomorphic distributions.
result Established a residues formula for maps near holomorphic distributions.
Let p be a prime. In this paper, we classify the geometric 3-manifolds whose fundamental groups are virtually residually p. Let M=M3 be a virtually fibered 3-manifold. It is well-known that G=π1(M) is residually solvable and even residually finite solvable. We prove that G is always virtually residually p…
Researchers identify critical protein residues using advanced graph theory.
problem Identifying essential residues in proteins for function.
method Learning Random Geometric Graphs (RGG) with Cramer's V correlation and organic thresholding.
result Advanced RGG methods accurately identify critical residues compared to existing techniques.
Defines Wodzicki residue using groupoids and fibered distributions.
problem Defining and understanding the Wodzicki residue in noncommutative geometry.
method Using groupoid language and filtered manifolds, defining the residue and showing its properties.
result The groupoidal residue is a trace on pseudodifferential operators and matches the usual residue in certain cases.
Holomorphic residue formula for complex supermanifolds.
problem Residue localization on complex supermanifolds.
method Holomorphic residue localization formula for odd vector fields.
result Explicit local residue formula under isolated non-degeneracy hypotheses.
In this work we prove a Baum-Bott type residue theorem for flags of holomorphic foliations. We prove some relations between the residues of the flag and the residues of their correspondent foliations. We define the Nash residue for flags and we give a partial answer to the Baum-Bott type rationality conjecture in this …
Wide residual networks generalize well with uniform convergence to RNTK as width increases.
problem Understanding the generalization ability of wide residual networks.
method Uniform convergence of residual network kernel to residual neural tangent kernel (RNTK).
result Generalization error converges to kernel regression error with respect to RNTK.
Paper analyzes Winograd convolution errors and proposes methods to reduce them.
problem Reduction of floating point error in Winograd convolution for deep neural networks.
method Analysis of worst case FP error, estimation of norm and conditioning, proposed evaluation orderings, sampling points selection, mixed-precision convolution, pairwise summation.
result Proposed methods significantly reduce FP error for a given block size, allowing larger block sizes and reduced computation.
The paper studies residues of manifolds and their applications in geometry.
problem Understanding the residues of manifolds and their geometric implications.
method Analytic continuation and Möbius invariance of residues, introduction of relative and weighted residues.
result Scalar curvature, mean curvature, and Euler characteristic can be expressed in terms of residues.
Given a prime p, a group is called residually p if the intersection of its p-power index normal subgroups is trivial. A group is called virtually residually p if it has a finite index subgroup which is residually p. It is well-known that finitely generated linear groups over fields of characteristic zero are …