A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
FUNSD dataset tackles noisy scanned forms, offering comprehensive annotations.
Introducing Nijenhuis forms on Lie-infinity algebras gives a general frame to understand deformations of the latter. We give here a Nijenhuis interpretation of a deformation of an arbitrary Lie algebroid into a Lie-infinity algebra. Then we show that Nijenhuis forms on Lie-infinity algebras also give a short and effici…
Lectures explore how differential methods improve understanding of algebraic group orbit spaces.
Lyapunov 1-forms on orbifolds help understand flows on compact spaces.
The paper derives optimal inequalities for bi-slant submanifolds in metallic Riemannian space forms.
This paper explores 4-planes in Spin(7) manifolds with symplectic structures.
Proposes a supervised VAE to reveal model invariances for interpretability.
Diffeology explores -forms and bundles with more information than traditional differential forms.
We prove an analogue of Thurston's h-principle for -dimensional foliations on manifolds of dimension bigger or equal to , in the presence of a fiber-wise non-degenerate -form. This helps us understand the flexibility of rank regular Poisson structures on open manifolds with dimension bigger or equal to …
Dunkl connections on complex plane don't preserve metrics.
The paper explores criteria for positivity of forms and proves their strong positivity in specific cases.
Revisits and applies a formula for hypersurface Euler characteristics.
We develop a new geometric method of understanding principal G-Higgs bundles through their spectral data, for G a real form of a complex Lie group. In particular, we consider the case of G a split real form, as well as G = SL(2,R), U(p,p), SU(p,p), and Sp(2p,2p). Further, we give some applications of our results, and d…
Motivated by our attempt to recast Cartan's work on Lie pseudogroups in a more global and modern language, we are brought back to the question of understanding the linearization of multiplicative forms on groupoids and the corresponding integrability problem. From this point of view, the novelty of this paper is that w…
We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning. We show that initial representations generated by common random initializations are sufficiently rich to express all functions in the dual kernel space. Hence, though the training ob…
We derive and approximate the conjugate prior of Dirichlet and beta distributions.
Linear models like EASE and SLIM are competitive in recommendation, and this work explores their theoretical relationship.
Tangent categories provide an axiomatic framework for understanding various tangent bundles and differential operations that occur in differential geometry, algebraic geometry, abstract homotopy theory, and computer science. Previous work has shown that one can formulate and prove a wide variety of definitions and resu…
Scene understanding remains a significant challenge in the computer vision community. The visual psychophysics literature has demonstrated the importance of interdependence among parts of the scene. Yet, the majority of methods in computer vision remain local. Pictorial structures have arisen as a fundamental parts-bas…
The paper constructs Markov partitions for geodesic flow on hyperbolic surfaces.
The aim of the paper is to understand the local forms of conformal vector fields in the neighborhood of a singularity. We begin a general study in this direction, for any pseudo-Riemannian type, and give a complete answer in the Riemannian case. This is done using geometric methods, and studying local dynamics of seque…
Real-time scene understanding solved using Approximate Bayesian Computation.
Contrastive Code Representation Learning improves code summarization and type inference.
Bayesian interpolants explain neural network inferences concisely.
We predict generalization error across model and dataset sizes.
New Heintze-Karcher inequality helps understand droplet shapes.
We build a connection between topology of smooth 4-manifolds and the theory of topological modular forms by considering topologically twisted compactification of 6d (1,0) theories on 4-manifolds with flavor symmetry backgrounds. The effective 2d theory has (0,1) supersymmetry and, possibly, a residual flavor symmetry. …
The first part of this paper exposits a simple geometric description of the Kirby-Siebenmann invariant of a 4--manifold in terms of a quadratic refinement of its intersection form. This is the first in a sequence of higher-order intersection invariants of Whitney towers studied by the authors, particularly for the 4--b…
Motivated by understanding the limiting case of a certain systolic inequality we study compact Riemannian manifolds having all harmonic 1-forms of constant length. We give complete characterizations as far as Kähler and hyperbolic geometries are concerned. In the second part of the paper, we give algebraic and topologi…
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
Study vector fields on hyperbolic spaces to create Ricci-Bourguignon solitons.
Modeling market makers' quoting strategies to understand price impact.
Group lattices (Cayley digraphs) of a discrete group are in natural correspondence with differential calculi on the group. On such a differential calculus geometric structures can be introduced following general recipes of noncommutative differential geometry. Despite of the non-commutativity between functions and (gen…
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations. We explore ParVIs from the perspective of Wasserstein gradient flows, and make both theoretical and practical contributions. We unify variou…
We study almost Kaehler manifolds whose curvature tensor satisfies the third curvature condition of Gray. We show that the study of manifolds within this class reduces to the study of a subclass having the property that the torsion of the first canonical Hermitian connection has the simplest possible algebraic form. Th…
Extract class-specific subnetworks from neural models for better understanding and improved explanations.
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
Automating the customer analytics process is crucial for companies that manage distinct customer bases. In such data-rich and dynamic environments, visualization plays a key role in understanding events of interest. These ideas have led to the popularity of analytics dashboards, yet academic research has paid scant att…
The present work has as principal objective analyze the evolution of the process of privatization, mergers and acquisitions of the big companies in the country in the last decades, to understand the conductive threads that formed the structural changes of the economy, in order world oligopólicas to insert it to the glo…
This is a survey article on the recent progress in understanding the Strominger-Yau-Zaslow (SYZ) mirror symmetry conjecture, especially on the effect of quantum corrections, via Witten-Morse theory using the program first depicted by Fukaya to obtain an explicit relation between differential geometric operations, e.g. …
Visualizes movement control optimization landscapes to understand why it's hard and how to make it easier.
Lyapunov exponents help understand RNN stability.
Closed-form flow matching yields similar performance to stochastic version, improving model performance.
In this paper, we study Riemannian functionals defined by -norms of Ricci curvature, scalar curvature, Weyl curvature, and Riemannian curvature. We try to understand stability of their critical points that are products of Einstein metrics. In particular, we prove that the product of a spherical space form and a co…
Decomposes prime alternating links into prime tangles.
Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine …
This work uses tropical geometry to understand neural network decision boundaries.
The paper examines stability of Minkowski inequalities in warped product spaces.