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48 results for PU(2

We study the arithmeticity of the Couwenberg-Heckman-Looijenga lattices in PU(n,1), and show that they contain a non-arithmetic lattice in PU(3,1) which is not commensurable to the non-arithmetic Deligne-Mostow lattice in PU(3,1).

2017-10-12abs ↗pdf ↗

This paper studies the covolumes of nonuniform arithmetic lattices in PU(n, 1). We determine the smallest covolume nonuniform arithmetic lattices for each n, the number of minimal covolume lattices for each n, and study the growth of the minimal covolume as n varies. In particular, there is a unique lattice (up to conj…

2011-07-26abs ↗pdf ↗

Every element of PU(2,1) can be decomposed into at most 4 special elliptic isometries.

problem Understanding the length of elements in PU(2,1) relative to special elliptic isometries.
method Generalizing the involution length of the complex hyperbolic plane, calculating the αα-length of PU(2,1) and describing decompositions of isometries.
result Every element of PU(2,1) can be decomposed into at most 4 special elliptic isometries.

The goal of this article was the S^1-equivariant transversality-problem and the compactification-problem for the moduli spaces of (perturbed) PU(2)-monopoles. A substantially improved version entitled "Moduli spaces of PU(2)-monopoles (revised version)" which gives simpler, clearer proofs of the transversality results,…

1997-02-07abs ↗pdf ↗

The paper proves residual finiteness of certain lattices and constructs surfaces with specific fundamental groups.

problem Residual finiteness of lattices in PU(2,1)~\widetilde{\mathrm{PU}(2,1)} and existence of smooth projective surfaces.
method Proved residual finiteness of certain lattices and constructed surfaces using central extensions.
result First examples of residually finite lattices in PU(2,1)~\widetilde{\mathrm{PU}(2,1)} and construction of surfaces with specific fundamental groups.

We explore hybrid subgroups of certain non-arithmetic lattices in PU(2,1)\mathrm{PU}(2,1). We show that all of Mostow's lattices are virtually hybrids; moreover, we show that some of these non-arithmetic lattices are hybrids of two non-commensurable arithmetic lattices in PU(1,1)\mathrm{PU}(1,1).

2019-05-29abs ↗pdf ↗

We consider a certain hybridization construction which produces a subgroup of PU(n,1){\rm PU}(n,1) from a pair of lattices in PU(n1,1){\rm PU}(n-1,1). Among the Picard modular groups PU(2,1,Od){\rm PU}(2,1,\mathcal{O}_d), we show that the hybrid of pairs of Fuchsian subgroups PU(1,1,Od){\rm PU}(1,1,\mathcal{O}_d) is a lattice when d=1d=1 and $d=7…

2018-06-04abs ↗pdf ↗

This paper reviews PU learning evaluation methods and provides practical recommendations.

problem Evaluating PU learning methods when only positive and unlabelled data are available.
method Critical review of 51 articles proposing PU classifiers and alternative predictive accuracy measures.
result Practical recommendations for improving PU learning evaluation.

This is the third installment in our series of articles (dg-ga/9712005, dg-ga/9710032) on the application of the PU(2) monopole equations to prove Witten's conjecture (hep-th/9411102) concerning the relation between the Donaldson and Seiberg-Witten invariants of smooth four-manifolds. The moduli space of solutions to t…

1999-07-16abs ↗pdf ↗

Using quantum field-theoretic arguments, Witten has established a relation between the Donaldson and Seiberg-Witten invariants of smooth four-manifolds. In this survey article, we describe the program to prove this relation using a moduli space of PU(2) = SO(3) monopoles as a cobordism between the Donaldson moduli spac…

1997-09-30abs ↗pdf ↗

We study representations of lattices of PU(m,1) into PU(n,1). We show that if a representation is reductive and if m is at least 2, then there exists a finite energy harmonic equivariant map from complex hyperbolic m-space to complex hyperbolic n-space. This allows us to give a differential geometric proof of rigidity …

2003-09-11abs ↗pdf ↗

Improves PU learning for imbalanced data with practical AUL estimation and new training method.

problem Training binary classifiers on datasets with only positive and unlabeled samples.
method Asymptotic unbiased AUL estimation and ProbTagging for imbalanced data.
result ProbTagging increases AUC by up to 10% on industrial and artificial data sets.

Let ΓΓ be a non-uniform lattice in PU(p,1)PU(p,1) without torsion and with p2p\geq2 . We introduce the notion of volume for a representation ρ:ΓPU(m,1)ρ:Γ\rightarrow PU(m,1) where mpm \geq p. We use this notion to generalize the Mostow--Prasad rigidity theorem. More precisely, we show that given a sequence of representations $ρ_n:…

2017-11-03abs ↗pdf ↗

We describe a set of coordinates on the PU(2,1)-representation variety of the fundamental group of an oriented punctured surface SS with negative Euler characteristic. The main technical tool we use is a set of geometric invariants of a triple of flags in the complex hyperpolic plane. We establish a bijection between …

2007-10-17abs ↗pdf ↗

Paper tackles leveraging unlabeled data for PU classification and robust generation.

problem Scarcity of labeled data in machine learning problems.
method Introduces a novel training framework that simultaneously targets PU classification and conditional generation using extra unlabeled data.
result Proves the effectiveness of a Classifier-Noise-Invariant Conditional GAN (CNI-CGAN) that enhances PU classifier performance and leverages extra data.

New representations for surface groups in PU(2,1) are stable and larger than convex cocompact ones.

problem Characterizing representations of surface groups in PU(2,1).
method Introducing simple-stable representations and proving their properties.
result The set of conjugacy classes of simple-stable representations is a domain of discontinuity strictly larger than convex cocompact representations.

New methods learn from PU data with non-representative positives.

problem Learning from PU data with non-representative positive classes.
method Integrates negative-unlabeled and unlabeled-unlabeled learning, or uses a recursive risk estimator.
result Effective across various real-world datasets and forms of positive bias.

This study finds a special class of representations that dominate others in a complex hyperbolic group.

problem Domination of surface-group representations in complex hyperbolic groups.
method Analysis of TT-bent representations and their domination by discrete and faithful representations.
result A discrete and faithful representation exists that dominates a given TT-bent representation in the Bergman translation length spectrum.

Paper proposes a new loss function for PU learning without negative examples.

problem Traditional machine learning struggles with negative examples, leading to biased predictions.
method Developed a collective loss function (cPU) for positive and unlabeled data.
result The cPU consistently outperforms existing methods in PU learning benchmarks and real-world datasets.

A novel transfer learning framework combines multiple data sources for PU learning.

problem Challenges in PU learning due to lack of negative labels and data scarcity.
method Model averaging of heterogeneous data sources, including binary labeled, semi-supervised, and PU data.
result Method outperforms other methods in predictive accuracy and robustness, especially under limited labeled data.

A new method predicts true classes from positive and unlabeled data with additional labeled observations.

problem Predicting true classes from positive and unlabeled data with selection bias.
method Introduces augmented PU prediction, allowing feature-dependent labeling, and compares various empirical Bayes rules.
result The variational autoencoder-based method performs similarly or better than other methods and improves accuracy for unlabeled samples.

In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in prac…

2018-10-01abs ↗pdf ↗

A new method for PU learning improves classification error on CIFAR-10.

problem Learning from positive and unlabeled data in practical applications.
method A simple yet effective data augmentation method based on consistency regularization.
result Achieves an averaged improvement of 3.40 points in classification error on CIFAR-10.

We consider in this work representations of the of the fundamental group of the 3-punctured sphere in PU(2,1){\rm PU}(2,1) such that the boundary loops are mapped to PU(2,1){\rm PU}(2,1). We provide a system of coordinates on the corresponding representation variety, and analyse more specifically those representations correspond…

2013-12-13abs ↗pdf ↗