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. …

Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours
Iniakpokeikiye Peter Thompson, Yi Dewei, Reiter Ehud
https://arxiv.org/abs/2509.05358 …

Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours
This paper presents a unique driving dataset collected in Nigeria via mobile phone sensors to support a machine learning model for detecting alcohol-influenced driving behaviours, with the long-term aim of integrating this model into a mobile application that encourages safer driving behaviours. Driving under the influence of alcohol is a major public safety concern, particularly in low-income countries like Nigeria, where traditional enforcement mechanisms may be limited. The proposed model le…

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.

Attention as an Adaptive Filter
Peter Racioppo
https://arxiv.org/abs/2509.04154 https://arxiv.org/pdf/2509.04154 …

Attention as an Adaptive Filter
We introduce Adaptive Filter Attention (AFA), a novel attention mechanism that incorporates a learnable dynamics model directly into the computation of attention weights. Rather than comparing queries and keys directly, we model the input sequence as discrete observations of a linear stochastic differential equation (SDE). By imposing a linear dynamics model with simultaneously diagonalizable state matrices and noise covariances, we can make use of a closed-form solution to the differential Lya…

Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Bret Nestor, Oumaima Amezgar, Francesca Dominici
https://arxiv.org/abs/2508.19149 http…

Algorithmic Collective Action with Multiple Collectives
As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to regulator-side policy and firm-side model design. Although real-world actions have been traditionally decentralized and fragmented into multiple collectives despite sharing overarching objectives-with each collective differing in size, strategy, and actionable goals, most of the ACA literature focused on single collec…

Size-Dependent Structural Motifs in Ag$_n$Mo (n = 2-13) Clusters: From Planar to Icosahedral Architectures
Samantha Ortega-Flores, Peter Ludwig Rodr\'iguez-Kessler
https://arxiv.org/abs/2509.02984 …

Size-Dependent Structural Motifs in Ag$_n$Mo (n = 2-13) Clusters: From Planar to Icosahedral Architectures
We present a comprehensive density functional theory (DFT) study of Mo-doped silver clusters Ag$_n$Mo ($n=1$-14), focusing on their structural, electronic, and bonding properties. Global optimization reveals an evolution from planar and low-symmetry isomers in small clusters to compact three-dimensional geometries with higher symmetry, culminating in a highly stable icosahedral structure at $n=12$. Binding energy and second-order energy difference analyses identify $n=12$ as a "magic number" cl…

What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videos
Qiyue Sun, Qiming Huang, Yang Yang, Hongjun Wang, Jianbo Jiao
https://arxiv.org/abs/2508.21770

What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videos
Humans usually show exceptional generalisation and discovery ability in the open world, when being shown uncommon new concepts. Whereas most existing studies in the literature focus on common typical data from closed sets, open-world novel discovery is under-explored in videos. In this paper, we are interested in asking: \textit{What if atypical unusual videos are exposed in the learning process?} To this end, we collect a new video dataset consisting of various types of unusual atypical data (…

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…

An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
Sergio Consoli, Pietro Coletti, Peter V. Markov, Lia Orfei, Indaco Biazzo, Lea Schuh, Nicolas Stefanovitch, Lorenzo Bertolini, Mario Ceresa, Nikolaos I. Stilianakis
https://arxiv.org/abs/2509.02258

An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, are marking a pivotal moment in epidemiology and public health research. Leveraging the power of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract valuable actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs). DONs is a collectio…