arXiv research

The asymptotic concentration of the Fr{é}chet mean of IID random variables on a Rieman-nian manifold was established with a central limit theorem by Bhattacharya \& Patrangenaru (BP-CLT) [6]. This asymptotic result shows that the Fr{é}chet mean behaves almost as the usual Euclidean case for sufficiently concentrated di…

The paper introduces new geometric methods to analyze radar electromagnetic wave statistics.

In [16], a new family of vector-valued risk measures called multivariate expectiles is introduced. In this paper, we focus on the asymptotic behavior of these measures in a multivariate regular variations context. For models with equivalent tails, we propose an estimator of these multivariate asymptotic expectiles, in …

Study on geometry of Dirichlet distributions using Fisher-Rao metric.

New metrics defined for full-rank correlation matrices, ensuring unique operations.

Constructs supermartingale couplings with full marginals constraints.

New formulas for geodesics on Stiefel and flag manifolds using trust-region method.

Let $G$ be a compact connected simple Lie group and let $M=G^{\bb{C}}/P=G/K$ be a generalized flag manifold. In this article we focus on an important invariant of $G/K$, the so called $\fr{t}$-root system $R_{\fr{t}}$, and we introduce the notion of symmetric $\fr{t}$-triples, that is triples of $\fr{t}$-roots $ξ, ζ, η…

We consider in this paper the $FRS$-deformations of a family of space curves with codimension $\leq 3$. Some geometric aspects of a space curve such as flattenings, vertices and twistings points has been studied.

A new method for estimating large-scale linear models with improved precision.

Study shows splitting schemes can approximate WFR flows faster than the exact flow.

Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features …

FR-Train improves fair and robust AI training by detecting and reducing poisoned data.

Proves FR-NGD optimally approximates evolutionary dynamics and continuous Bayesian inference.

FR-LUX optimizes portfolio management by learning cost-aware policies robust to market conditions.

Consider an anchored bundle $(E,ρ)$, i.e. a vector bundle $E\to M$ equipped with a bundle map $ρ\colon E \to TM$ covering the identity. M.~Kapranov showed in the context of Lie-Rinehard algebras that there exists an extension of this anchored bundle to an infinite rank universal free Lie algebroid $FR(E)\supset E$. We …

Introduces a new geometric framework for field theories.

We recall an extension of Kirby's Calculus on non-simply connected 3-manifolds given in [FR], and the surgery calculus of bridged links from [Ke], which involves only local moves. We give a short combinatorial proof that the two calculi are equivalent, and thus describe the same classes of 3-manifolds. This makes the p…

This study provides an explicit expansion of KL divergence's gradient flow in Fisher-Rao geometry.

We study geodesics of the form $γ(t)=π(\exp(tX)\exp(tY))$, $X,Y\in \fr{g}=\operatorname{Lie}(G)$, in homogeneous spaces $G/K$, where $π:G\rightarrow G/K$ is the natural projection. These curves naturally generalise homogeneous geodesics, that is orbits of one-parameter subgroups of $G$ (i.e. $γ(t)=π(\exp (tX))$, $X\in …

Paper proposes FR algorithm to solve minimax optimization locally.

We study geodesics in generalized Wallach spaces which are expressed as orbits of products of three exponential terms. These are homogeneous spaces $M=G/K$ whose isotropy representation decomposes into a direct sum of three submodules $\frak{m}=\frak{m}_1\oplus\frak{m}_2\oplus\frak{m}_3$, satisfying the relations $[\fr…

This article follow the article {http://hal.archives-ouvertes.fr/hal-00361030/fr/} in which the author characterize the fact of being of finite volume for a convex projective surface. We show here that the moduli space $β_f(Σ_{g,p})$ of the convex projective structure on the surface $Σ_{g,p}$ of genius $g$ with $p$ pun…

A new category generates 1D tangle invariants.

New method improves fairness of facial recognition systems.

NukeBERT improves performance on nuclear domain Q&A with less training data.

Bayesian analysis predicts properties of proton-emitting nuclei beyond the proton drip line.

Quantified limits of nuclear stability beyond drip lines.

Study on tensor nuclear norm's decomposability and subdifferential.

Efficiently regularizes deep learning models using Jacobian nuclear norm.

Low-rank matrix recovery has found many applications in science and engineering such as machine learning, signal processing, collaborative filtering, system identification, and Euclidean embedding. But the low-rank matrix recovery problem is an NP hard problem and thus challenging. A commonly used heuristic approach is…

This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great a…

We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Pytho…

Study reveals centralization in Bitcoin transactions involving retail users.

Paper tackles robust prediction of nuclear reactor materials under scarce data.

Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of nuclear norms of matricizations of a tensor has been shown to be very efficient in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to recover…

Deep learning predicts nuclear equation of state from rotating core collapse GW signals.

DeepONet accelerates nuclear DT inference with high accuracy and efficiency.

Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep …

Deep learning detects corrosion in nuclear fuel canisters.

We construct examples of finitely generated groups L that have non-trivial actions on $\mathbb{R}$-trees but which cannot act, without fixing a vertex, on any simplicial tree. Moreover, any finitely presented group mapping onto L does have a fixed point-free action on some simplicial tree.

Improved action recognition in live videos with hybrid FR-DL method.

The study uses statistical methods to analyze nuclear mass models.

Recently theoretical guarantees have been obtained for matrix completion in the non-uniform sampling regime. In particular, if the sampling distribution aligns with the underlying matrix's leverage scores, then with high probability nuclear norm minimization will exactly recover the low rank matrix. In this article, we…

New method speeds up nuclear-norm constrained learning over multiple machines.

Unified framework for discrete diffusion modeling with flexible noising processes.

This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.

Motivation: Prediction of ligands for proteins of known 3D structure is important to understand structure-function relationship, predict molecular function, or design new drugs. Results: We explore a new approach for ligand prediction in which binding pockets are represented by atom clouds. Each target pocket is compar…