Tootfinder

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu, Peter Steenkiste, Srinivasan Seshan
https://arxiv.org/abs/2510.03891

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are typically described using shapes. There is an inherent tension between minimizing network contention and maximizing cluster utilization when placing various-shaped jobs. While existing schedulers typically optimize for one objective at the expense of the other, we demonstrate that both can be achieved simultaneously. …

On algebro-geometric quotients of torus-invariant subvarieties of the flag varietiy
Luis Yair Meza P\'erez, Pedro L. del \'Angel R., Carlos Pompeyo-Guti\'errez, Miguel Angel Dela-Rosa
https://arxiv.org/abs/2510.06074

On algebro-geometric quotients of torus-invariant subvarieties of the flag varietiy
In this paper, we study the subvarieties of a complex flag variety that are invariant under the action of a maximal torus. Using combinatorial techniques derived from matroid theory, we introduce a decomposition of this variety into affine, locally closed subsets, which we refer to as \textit{thin Schubert cells}, each indexed by an element of a Cartesian product of matroids. We also show that the set of orbits for each of these thin Schubert cells under the action of the torus is, in fact, an …

„Policymakers need to set clear emissions targets and stronger financial incentives for use of lower emission materials, while industry must establish common standards for easier adoption of these materials into value chains.“
Jens-Peter Saul
CEO, #Ramboll

Jens-Peter Saul: The 100 Most Influential Climate Leaders of 2025
Find out why Jens-Peter Saul is on TIME's 2025 list of the 100 most influential leaders driving business climate action.

For fellow art-poetry nerds/lovers, Édouard Glissant’s art collection is coming to New York.
https://www.cara-nyc.org/program/exhibitions/the-earth-the-fire-the-water-and-the-winds-for-a-museum-of-errantr…

How can the use of different modes of survey data collection introduce bias? A simple introduction to mode effects using directed acyclic graphs (DAGs)
Georgia D Tomova, Richard J Silverwood, Peter WG Tennant, Liam Wright
https://arxiv.org/abs/2510.00900

How can the use of different modes of survey data collection introduce bias? A simple introduction to mode effects using directed acyclic graphs (DAGs)
Survey data are self-reported data collected directly from respondents by a questionnaire or an interview and are commonly used in epidemiology. Such data are traditionally collected via a single mode (e.g. face-to-face interview alone), but use of mixed-mode designs (e.g. offering face-to-face interview or online survey) has become more common. This introduces two key challenges. First, individuals may respond differently to the same question depending on the mode; these differences due to mea…

Los souvenirs del concierto de Manuel García el sšbado.
El de arriba es «La Casa de Asterión», el segundo disco de Mecšnica Popular, la banda en la que formó parte Manuel García a finales de los '90. El de abajo es «Pšnico» su primer disco como solista que este año cumple 20 años desde su lanzamiento.
#ManuelGarcía

Good morning, Petar

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Pietro Totis, Alberto Pozanco, Daniel Borrajo
https://arxiv.org/abs/2510.00876 https://

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to e…

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
Peter Wauyo, Dalia Bwiza, Alain Murara, Edwin Mugume, Eric Umuhoza
https://arxiv.org/abs/2510.02165

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
This research introduces a multimodal system designed to detect fraud and fare evasion in public transportation by analyzing closed circuit television (CCTV) and audio data. The proposed solution uses the Vision Transformer for Video (ViViT) model for video feature extraction and the Audio Spectrogram Transformer (AST) for audio analysis. The system implements a Tensor Fusion Network (TFN) architecture that explicitly models unimodal and bimodal interactions through a 2-fold Cartesian product. …

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Pietro Totis, Alberto Pozanco, Daniel Borrajo
https://arxiv.org/abs/2510.00876 https://

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to e…