Tootfinder

Infrequently Asked Questions About Jeffrey Epstein, Part One: The Money Thing (Greg Olear/PREVAIL)
https://gregolear.substack.com/p/infrequently-asked-questions-about
http://www.memeorandum.com/251202/p57#a251202p57

Quantaloid-enriched categories: Factorization, weak classifiers, and symmetry
Javier Guti\'errez Garc\'ia, Ulrich H\"ohle
https://arxiv.org/abs/2510.10162 https://

Quantaloid-enriched categories: Factorization, weak classifiers, and symmetry
This paper provides a comprehensive overview of some of the foundational properties of categories enriched over quantaloids, along with several new results. We demonstrate that the category whose objects are quantaloid-enriched categories and whose morphisms are left adjoint distributors admits an (epi, extremal mono)--factorization system. Furthermore, we prove that the category of cocomplete quantaloid-enriched categories satisfies the weak subobject classifier axiom, under stability conditio…

On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang
https://arxiv.org/abs/2510.10792 https://arxiv.o…

On function-on-function linear quantile regression
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated …

An efficient algorithm for kernel quantile regression
Shengxiang Deng, Xudong Li, Yangjing Zhang
https://arxiv.org/abs/2510.07929 https://arxiv.org/pdf/251…

An efficient algorithm for kernel quantile regression
Kernel Quantile Regression (KQR) extends classical quantile regression to nonlinear settings using kernel methods, offering powerful tools for modeling conditional distributions. However, its application to large-scale datasets is severely limited by the computational burden of the large, dense kernel matrix and the need to efficiently solve large, often ill-conditioned linear systems. Existing state-of-the-art solvers usually struggle with scalability. In this paper, we propose a novel and hig…

Auch das noch
https://nrw.social/@scinexx/115421903119616149

$\mathsf{P} \neq \mathsf{NP}$: A Non-Relativizing Proof via Quantale Weakness and Geometric Complexity
Ben Goertzel
https://arxiv.org/abs/2510.08814 https://

$\mathsf{P} \neq \mathsf{NP}$: A Non-Relativizing Proof via Quantale Weakness and Geometric Complexity
We give a compositional, information-theoretic framework that turns short programs into locality on many independent blocks, and combine it with symmetry and sparsity of masked random Unique-SAT to obtain distributional lower bounds that contradict the self-reduction upper bound under $\mathsf{P}=\mathsf{NP}$. We work in the weakness quantale $w_Q=K_{\mathrm{poly}}(\cdot\mid\cdot)$. For an efficiently samplable ensemble $D_m$ made by masking random $3$-CNFs with fresh $S_m\ltimes(\mathbb{Z}_2)^…

Morita Equivalence for Quantales
Moacyr Rodrigues, Ciro Russo
https://arxiv.org/abs/2510.07565 https://arxiv.org/pdf/2510.07565

Morita Equivalence for Quantales
We study Morita equivalence in the context of quantales with identity, in the wake of Katsov and Nam's analogous work on semirings. Among a number of other results, we prove a characterization of Morita equivalence and an Eilenberg-Watts-type Theorem for quantales.

US-Wachstum ist eine KI-Blase.
https://fed.brid.gy/r/https://bsky.app/profile/did:plc:qnd2lqn52ernybdfonq4mc43/post/3m6hqm23rd22q

Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong, Gabriele Steidl
https://arxiv.org/abs/2510.12636 https://

Adapting Noise to Data: Generative Flows from 1D Processes
We introduce a general framework for constructing generative models using one-dimensional noising processes. Beyond diffusion processes, we outline examples that demonstrate the flexibility of our approach. Motivated by this, we propose a novel framework in which the 1D processes themselves are learnable, achieved by parameterizing the noise distribution through quantile functions that adapt to the data. Our construction integrates seamlessly with standard objectives, including Flow Matching an…

Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Yunzhen Yao, Lie He, Michael Gastpar
https://arxiv.org/abs/2510.07093 https://arxiv.org/p…

Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Conformal prediction provides prediction sets with coverage guarantees. The informativeness of conformal prediction depends on its efficiency, typically quantified by the expected size of the prediction set. Prior work on the efficiency of conformalized regression commonly treats the miscoverage level $α$ as a fixed constant. In this work, we establish non-asymptotic bounds on the deviation of the prediction set length from the oracle interval length for conformalized quantile and median regre…