Tootfinder

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@robpike@hachyderm.io
2026-03-02 04:18:44

Over the holidays last last year I was building an Airfix model (good way to ignore the outside world) and discovered two pieces had fallen off the sprues and were not in the box. I considered constructing them myself but I didn't have a good reference image to go by. 99% certain it wasn't my fault, I looked up how to contact Airfix, followed the instructions, and never heard back.
Until Friday, when two tiny pieces of plastic arrived in an adorable little pouch and the modelmaking can continue.
I love good customer service. I just love it.

@tinoeberl@mastodon.online
2026-02-02 06:07:02

KI-Stimmen verändern die #Hörbuchbranche.
Neue Tools ermöglichen günstige, KI-generierte #Hörbücher. #Audioplattform springen auf den Zug auf.

Hörbuchproduktion zum Spartarif: Professionelle Sprecher in Gefahr
ElevenLabs greift mit KI-Stimmen in den Hörbuchmarkt ein. Klingt gut, ist aber schlecht für alle, die vom Sprechen leben. Mehr Hintergründe.

@arXiv_csGR_bot@mastoxiv.page
2026-02-03 07:43:07

Fast Sparse Matrix Permutation for Mesh-Based Direct Solvers
Behrooz Zarebavami, Ahmed H. Mahmoud, Ana Dodik, Changcheng Yuan, Serban D. Porumbescu, John D. Owens, Maryam Mehri Dehnavi, Justin Solomon
https://arxiv.org/abs/2602.00898 https://arxiv.org/pdf/2602.00898 https://arxiv.org/html/2602.00898
arXiv:2602.00898v1 Announce Type: new
Abstract: We present a fast sparse matrix permutation algorithm tailored to linear systems arising from triangle meshes. Our approach produces nested-dissection-style permutations while significantly reducing permutation runtime overhead. Rather than enforcing strict balance and separator optimality, the algorithm deliberately relaxes these design decisions to favor fast partitioning and efficient elimination-tree construction. Our method decomposes permutation into patch-level local orderings and a compact quotient-graph ordering of separators, preserving the essential structure required by sparse Cholesky factorization while avoiding its most expensive components. We integrate our algorithm into vendor-maintained sparse Cholesky solvers on both CPUs and GPUs. Across a range of graphics applications, including single factorizations, repeated factorizations, our method reduces permutation time and improves the sparse Cholesky solve performance by up to 6.27x.
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@arXiv_csDS_bot@mastoxiv.page
2026-02-03 09:22:28

A polynomial-time algorithm for recognizing high-bandwidth graphs
Luis M. B. Varona
https://arxiv.org/abs/2602.01755 https://arxiv.org/pdf/2602.01755 https://arxiv.org/html/2602.01755
arXiv:2602.01755v1 Announce Type: new
Abstract: An unweighted, undirected graph $G$ on $n$ nodes is said to have \emph{bandwidth} at most $k$ if its nodes can be labelled from $0$ to $n - 1$ such that no two adjacent nodes have labels that differ by more than $k$. It is known that one can decide whether the bandwidth of $G$ is at most $k$ in $O(n^k)$ time and $O(n^k)$ space using dynamic programming techniques. For small $k$ close to $0$, this approach is effectively polynomial, but as $k$ scales with $n$, it becomes superexponential, requiring up to $O(n^{n - 1})$ time (where $n - 1$ is the maximum possible bandwidth). In this paper, we reformulate the problem in terms of bipartite matching for sufficiently large $k \ge \lfloor (n - 1)/2 \rfloor$, allowing us to use Hall's marriage theorem to develop an algorithm that runs in $O(n^{n - k 1})$ time and $O(n)$ auxiliary space (beyond storage of the input graph). This yields polynomial complexity for large $k$ close to $n - 1$, demonstrating that the bandwidth recognition problem is solvable in polynomial time whenever either $k$ or $n - k$ remains small.
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