116 research outputs found

    Topological restrictions on Anosov representations

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    We characterize groups admitting Anosov representations into SL(3,R)\mathsf{SL}(3,\mathbb R), projective Anosov representations into SL(4,R)\mathsf{SL}(4,\mathbb R), and Borel Anosov representations into SL(4,R)\mathsf{SL}(4,\mathbb R). More generally, we obtain bounds on the cohomological dimension of groups admitting PkP_k-Anosov representations into SL(d,R)\mathsf{SL}(d,\mathbb R) and offer several characterizations of Benoist representations

    Anosov representations, strongly convex cocompact groups and weak eigenvalue gaps

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    We provide characterizations of Anosov representations of word hyperbolic groups into real semisimple Lie groups in terms of equivariant limit maps, the Cartan property and the uniform gap summation property of \cite{GGKW}. As an application we obtain a characterization of strongly convex cocompact subgroups of the projective linear group PGL(d,R)\mathsf{PGL}(d, \mathbb{R}). We also compute the H\"older exponent of the Anosov limit maps of an Anosov representation in terms of the Cartan and Lyapunov projection of the image.Comment: 45 pages, Comments welcom

    The H\"older exponent of Anosov limit maps

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    Let Γ\Gamma be a non-elementary word hyperbolic group and da,a>1,d_{a}, a>1, a visual metric on its Gromov boundary Γ\partial_{\infty}\Gamma. For an 11-Anosov representation ρ:ΓGLd(K)\rho:\Gamma \rightarrow \mathsf{GL}_{d}(\mathbb{K}), where K=R\mathbb{K}=\mathbb{R} or C\mathbb{C}, we calculate the H\"older exponent of the Anosov limit map ξρ1:(Γ,da)(P(Kd),dP)\xi_{\rho}^1:(\partial_{\infty}\Gamma, d_{a})\rightarrow (\mathbb{P}(\mathbb{K}^d),d_{\mathbb{P}}) of ρ\rho in terms of the moduli of eigenvalues of elements in ρ(Γ)\rho(\Gamma) and the stable translation length on Γ\Gamma. If ρ\rho is either irreducible or ξρ1(Γ)\xi_{\rho}^1(\partial_{\infty}\Gamma) spans Kd\mathbb{K}^d and ρ\rho is {1,2}\{1,2\}-Anosov, then ξρ1\xi_{\rho}^1 attains its H\"older exponent. We also provide an analogous calculation for the exponent of the inverse limit map of (1,1,2)(1,1,2)-hyperconvex representations. Finally, we exhibit examples of non semisimple 11-Anosov representations of surface groups in SL4(R)\mathsf{SL}_4(\mathbb{R}) whose Anosov limit map in P(R4)\mathbb{P}(\mathbb{R}^4) does not attain its H\"older exponent.Comment: 24 page

    Federated Fine-Tuning of Foundation Models via Probabilistic Masking

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    Foundation Models (FMs) have revolutionized machine learning with their adaptability and high performance across tasks; yet, their integration into Federated Learning (FL) is challenging due to substantial communication overhead from their extensive parameterization. Current communication-efficient FL strategies, such as gradient compression, reduce bitrates to around 11 bit-per-parameter (bpp). However, these approaches fail to harness the characteristics of FMs, with their large number of parameters still posing a challenge to communication efficiency, even at these bitrate regimes. In this work, we present DeltaMask, a novel method that efficiently fine-tunes FMs in FL at an ultra-low bitrate, well below 1 bpp. DeltaMask employs stochastic masking to detect highly effective subnetworks within FMs and leverage stochasticity and sparsity in client masks to compress updates into a compact grayscale image using probabilistic filters, deviating from traditional weight training approaches. Our comprehensive evaluations across various datasets and architectures demonstrate DeltaMask efficiently achieves bitrates as low as 0.09 bpp, enhancing communication efficiency while maintaining FMs performance, as measured on 8 datasets and 5 pre-trained models of various network architectures.Comment: 19 pages, 9 figure

    New nonlinear hyperbolic groups

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    We construct nonlinear hyperbolic groups which are large, torsion‐free, one‐ended, and admit a finite K(π,1). Our examples are built from superrigid cocompact rank one lattices via amalgamated free products and HNN extensions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149490/1/blms12248.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149490/2/blms12248_am.pd
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