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.
Adaptive BO improves solder joint reliability by 3% with half the computational cost.
In an earlier paper we discussed soldered forms, multivector fields and Riemannian metrics. In particular, we showed that a Riemannian submanifold is totally geodesic iff the metric is soldered to the submanifold. In the present note we discuss general, soldered tensor fields. In particular, we prove that the almost co…
Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder paste inspector (SPI). If SPP malfunctions due to the printer defec…
Optimizes chip component placement with self-alignment for SMT technology.
The aim of this article is to proof a necessary and sufficient condition for the existence of a Cartan connection on a principal bundle. After collecting the essentially well known facts to fix the terminology, soldering forms and geometrizable principal bundles are defined to finally prove the existence criterion.
The notion of a Dirac submanifold of a Poisson manifold was studied by Xu (arXiv:math.SG/0110326). We give an interpretation of Xu's definition in terms of a general notion of tensor fields soldered to a normalized submanifold. Then, this interpretation is used to define Dirac submanifolds of a Jacobi manifold. Several…
We study special Lagrangian fibrations of -manifolds, not necessarily torsion-free. In the case where the fiber is a unimodular Lie group , we decompose such -structures into triples of solder 1-forms, connection 1-forms and equivariant positive-definite symmetric matrix-va…
Study develops machine learning model to predict component movement during reflow in SMT.
Simply-connected homogeneous spacetimes for kinematical and aristotelian Lie algebras (with space isotropy) have recently been classified in all dimensions. In this paper, we continue the study of these "maximally symmetric" spacetimes by investigating their local geometry. For each such spacetime and relative to expon…
Local generalization of frame bundles using a weakened Maurer-Cartan equation.
The geometric content of the MacDowell-Mansouri formulation of general relativity is best understood in terms of Cartan geometry. In particular, Cartan geometry gives clear geometric meaning to the MacDowell-Mansouri trick of combining the Levi-Civita connection and coframe field, or soldering form, into a single physi…
Develops a unified theory of Yang-Mills and GR using generalized principal bundles.
Proposes a new variational principle for Einstein gravity.
The paper explores the relationship between joint mixability and negative dependence structures.
We discuss the general properties of the theory of joint invariants of a smooth Lie group action in a manifold. Many of the known results about differential invariants, including Lie's finiteness theorem, have simpler versions in the context of joint invariants. We explore the relation between joint and differential in…
FJS method improves multinomial classification accuracy.
Paper proposes a new method to evaluate joint risk under uncertainty.
Introduces joint Shapley values to measure feature importance in models.
Study proposes a new model for joint survival annuity valuation.
Estimates joint causal effects using single-variable interventions on nonlinear models.
Study joint invariants on symplectic spaces, extending group and space variations.
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Proposes joint LCA for multiview data to identify shared and view-specific components.
Joint diffusion models improve data representation for both generation and prediction.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition.…
New Bayesian method for joint sparse parameter inference.
We discuss possible extensions of the classical Chern-Weil formalism to an infinite dimensional setup. This is based on joint work with Steven Rosenberg, joint work with Simon Scott and joint work with Jouko Mickelsson.
Unified theorem for deep and shallow joint-equivariant machines.
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i…
Paper uses non-Euclidean analysis to classify brain structure variations.
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We…
We employ the language of Cartan's geometry to present a model for studying vector spaces of Killing two-tensors defined in pseudo-Riemannian spaces of constant curvature under the action of the corresponding isometry group. We also discuss geometric properties of joint invariants of Killing two-tensors defined in the …
This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional dist…
We analyze a Lagrangian for spacetime connections in Loop Quantum Gravity.
The thesis explores kinematical symmetries beyond Lorentzian spacetime.
Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the condi…
Proposes a new method for handling domain shift in samples with biases in both covariates and labels.
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
Develops a new framework for estimating joint probability distributions.
Researchers derived formulas for joint moments of elliptical distributions.
Surveying joint Gaussian graphical models to identify shared structures across domains.
Better signal detection in undersampled data using joint and cross covariances.
New method combines score lists using joint CDFs, improving computation.
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work l…