Tootfinder

Enbee at Den Gamle By in Aarhus.

Sample-Efficient Omniprediction for Proper Losses
Isaac Gibbs, Ryan J. Tibshirani
https://arxiv.org/abs/2510.12769 https://arxiv.org/pdf/2510.12769

Sample-Efficient Omniprediction for Proper Losses
We consider the problem of constructing probabilistic predictions that lead to accurate decisions when employed by downstream users to inform actions. For a single decision maker, designing an optimal predictor is equivalent to minimizing a proper loss function corresponding to the negative utility of that individual. For multiple decision makers, our problem can be viewed as a variant of omniprediction in which the goal is to design a single predictor that simultaneously minimizes multiple los…

As usual we watched the "Stoertebekker" stage play at Ralswiek on Rügen Island. The stage setups and wardrobes were as awesome as always. They really try to improve on that every year and come up with clever mechanisms to transform the building for the various acts of the play. The script this year left room for desire ;) Nevertheless a happing worth attending
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Infinite-time ruin probability of a multivariate renewal risk model with Brownian perturbations
Dimitrios G. Konstantinides
https://arxiv.org/abs/2510.11229 https://

Infinite-time ruin probability of a multivariate renewal risk model with Brownian perturbations
We consider the multivariate risk model with common renewal process among the lines of business, and Brownian perturbations. Assuming that the integrated tail distribution of claims is multivariate subexponential, we establish an asymptotic relation for the infinite-time ruin probability. A more explicit expression is given in case of claim distribution from multivariate regular variation. The results indicate the insensitivity of the asymptotic behavior of the ruin probability with respect to …

Scale-by-scale kinetic energy budgets in multiphase turbulence
Fabien Thiesset, Jonathan Vah\'e
https://arxiv.org/abs/2509.11843 https://arxiv.org/pdf/…

Scale-by-scale kinetic energy budgets in multiphase turbulence
The present work aims at exploring the scale-by-scale kinetic energy exchanges in multiphase turbulence. For this purpose, we derive the Kármán-Howarth-Monin equation which accounts for the variations of density and viscosity across the two phases together with the effect of surface tension. We consider both conventional and phase conditional averaging operators. This framework is applied to numerical data from detailed simulations of forced homogeneous and isotropic turbulence covering diffe…

OpenAI agrees to acquire 10GW of chips from Broadcom and co-design custom GPUs with Broadcom to run its AI models; sources: the deal is worth multiple billions (Wall Street Journal)
https://www.wsj.com/tech/ai/openai-broad…

Upper bounds for Erd\'{e}lyi's multivariate Laguerre polynomials
Min-Jie Luo, Ravinder Krishna Raina
https://arxiv.org/abs/2507.11024 https://

Upper bounds for Erdélyi's multivariate Laguerre polynomials
We establish in this paper two inequalities for the multivariate Laguerre polynomials introduced and studied by Arthur Erdélyi [Sitzungsber. Akad. Wiss. Wien, Math.-Naturw. Kl., Abt. IIa 146 (1937), 431--467]. These inequalities generalize the well-known Szegö's inequality for the Laguerre polynomials $L_n^{(α)}(x)$. We also mention briefly few insightful remarks giving a comparative analysis concerning the upper bounds of the derived inequalities in the concluding section.

Pair-Bid Auction Model for Optimized Network Slicing in 5G RAN
Mengyao Li, Sebastian Troia, Yingqian Zhang, Guido Maier
https://arxiv.org/abs/2509.10533 https://

Pair-Bid Auction Model for Optimized Network Slicing in 5G RAN
Network slicing is a key 5G technology that enables multiple virtual networks to share physical infrastructure, optimizing flexibility and resource allocation. This involves Mobile Network Operators (MNO), Mobile Virtual Network Operators (MVNOs), and end users, where MNO leases network slices to MVNOs, and then provides customized services. This work considers end-to-end network slicing with a focus on fair sharing and financial-related power efficiency, modeled as a two level hierarchical com…

Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
Chuyuan Li, Austin Xu, Shafiq Joty, Giuseppe Carenini
https://arxiv.org/abs/2509.09852 h…

Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
A key challenge in Multi-Document Summarization (MDS) is effectively integrating information from multiple sources while maintaining coherence and topical relevance. While Large Language Models have shown impressive results in single-document summarization, their performance on MDS still leaves room for improvement. In this paper, we propose a topic-guided reinforcement learning approach to improve content selection in MDS. We first show that explicitly prompting models with topic labels enhanc…

DiffAero: A GPU-Accelerated Differentiable Simulation Framework for Efficient Quadrotor Policy Learning
Xinhong Zhang, Runqing Wang, Yunfan Ren, Jian Sun, Hao Fang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.10247

DiffAero: A GPU-Accelerated Differentiable Simulation Framework for Efficient Quadrotor Policy Learning
This letter introduces DiffAero, a lightweight, GPU-accelerated, and fully differentiable simulation framework designed for efficient quadrotor control policy learning. DiffAero supports both environment-level and agent-level parallelism and integrates multiple dynamics models, customizable sensor stacks (IMU, depth camera, and LiDAR), and diverse flight tasks within a unified, GPU-native training interface. By fully parallelizing both physics and rendering on the GPU, DiffAero eliminates CPU-G…