Tootfinder

$\alpha_i$-Metric Graphs: Hyperbolicity
Feodor F. Dragan, Guillaume Ducoffe
https://arxiv.org/abs/2404.14792 https://arxiv.org/pdf/24…

$α_i$-Metric Graphs: Hyperbolicity
A graph is called $α_i$-metric ($i \in {\cal N}$) if it satisfies the following $α_i$-metric property for every vertices $u, w, v$ and $x$: if a shortest path between $u$ and $w$ and a shortest path between $x$ and $v$ share a terminal edge $vw$, then $d(u,x) \ge d(u,v) + d(v,x) - i$. The latter is a discrete relaxation of the property that in Euclidean spaces the union of two geodesics sharing a terminal segment must be also a geodesic. Recently in (Dragan & Ducoffe, WG'23) we initiated the …

On pseudo-Riemannian Ricci-parallel Lie groups which are not Einstein
Huihui An, Zaili Yan
https://arxiv.org/abs/2404.13797 https://a…

On pseudo-Riemannian Ricci-parallel Lie groups which are not Einstein
In this paper, we mainly study left invariant pseudo-Riemannian Ricci-parallel metrics on connected Lie groups which are not Einstein. Following a result of Boubel and Bérard Bergery, there are two typical types of such metrics, which are characterized by the minimal polynomial of the Ricci operator. Namely, its form is either $(X-α)(X-\barα)$ (type I), where $α\in \mathbb{C}\setminus \mathbb{R}$, or $X^{2}$ (type II). Firstly, we obtain a complete description of Ricci-parallel metrics of t…

I enjoyed "The lure of [[CGA]] graphics for indie games":
https://indiegamesworld.com/the-lure-of-cga-graphics-for-indie-games/

The lure of CGA graphics for indie games - Indie Games World
Games often try to tout great graphics, but what if poor graphics like CGA actually bring out the quality of the game better? Let's explore.

This https://arxiv.org/abs/2311.00182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Local Max-Cut on Sparse Graphs
We bound the smoothed running time of the FLIP algorithm for local Max-Cut as a function of $α$, the arboricity of the input graph. We show that, with high probability, the following holds (where $n$ is the number of nodes and $ϕ$ is the smoothing parameter): 1) When $α= O(\log^{1-ε} n)$ FLIP terminates in $ϕpoly(n)$ iterations, where $ε\in (0,1]$ is an arbitrarily small constant. Previous to our results the only graph families for which FLIP was known to achieve a smoothed polynomial r…

Trump’s legal bills drain millions more from his political committees
Donald Trump is picking up his fundraising pace
even as he fights criminal charges in four cases and appeals a nearly half-billion dollar civil fraud judgment against him in New York.
But his legal expenses continue to be a tremendous burden on his campaign and its allied groups,
the latest campaign finance records show,
accounting for 26 percent of the spending in March by his political com…

Trump’s legal bills drain millions more from his political committees
The former president’s legal bills account for more than a quarter of the spending by his political committees in March, new campaign finance filings show.

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
Tong Xu, Armeen Taeb, Simge K\"u\c{c}\"ukyavuz, Ali Shojaie
https://arxiv.org/abs/2404.12592

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: i) they cannot provide optimality guarantees and can suffer from learning sub-optimal models; ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We ov…

This https://arxiv.org/abs/2301.12143 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The endoscopic classification of representations of non-quasi-split odd special orthogonal groups
In an earlier book of Arthur, the endoscopic classification of representations of quasi-split orthogonal and symplectic groups was established. Later Mok gave that of quasi-split unitary groups. After that, Kaletha, Minguez, Shin, and White gave that of non-quasi-split unitary groups for generic parameters. In this paper we prove the endoscopic classification of representations of non-quasi-split odd special orthogonal groups for generic parameters, following Kaletha, Minguez, Shin, and White.

Broadcom tinkers with VMware licencing terms as EU gets involved
Trade groups concerned over unilateral changes to licencing conditions
https://www.computing.co.uk/news/4197637/broadcom-tinkers-vmware-licencing-terms-eu-involved…

Broadcom tinkers with VMware licencing terms as EU gets involved
US chipmaker Broadcom is under scrutiny by EU antitrust regulators over alterations to the licencing conditions of its recently acquired cloud computing company VMware, following complaints from several EU business users and trade groups. On the same day Broadcom announced licencing changes.

Monotone parameters on Cayley graphs of finitely generated groups
Martin Kassabov, Igor Pak
https://arxiv.org/abs/2404.10870 https://…

Monotone parameters on Cayley graphs of finitely generated groups
We construct a new large family of finitely generated groups with continuum many values of the following monotone parameters: spectral radius, critical percolation, and asymptotic entropy. We also present several open problems on other monotone parameters.

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
Tong Xu, Armeen Taeb, Simge K\"u\c{c}\"ukyavuz, Ali Shojaie
https://arxiv.org/abs/2404.12592

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: i) they cannot provide optimality guarantees and can suffer from learning sub-optimal models; ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We ov…