Tootfinder

Trump’s tariffs are running up against the limits of nature
The deeper you delve into the weird and wonderful world of aluminum,
the more you realize there are physical limits that make a resurgence of U.S. production unlikely.

Raw supply isn’t the problem.
There is more aluminum in the Earth’s crust than any other metal.
But finding a way of extracting it and turning it into a usable form was something we achieved surprisingly recently. -- Until then, it was the mo…

Trump’s tariffs are running up against the limits of nature
U.S. geography makes it difficult to produce more aluminum — even with a 50 percent tariff for motivation.

Hydrodynamic theory of wetting by active particles
Noah Grodzinski, Robert L. Jack, Michael E. Cates
https://arxiv.org/abs/2506.14559 https://

Hydrodynamic theory of wetting by active particles
The accumulation of self-propelled particles on repulsive barriers is a widely observed feature in active matter. Despite being implicated in a broad range of biological processes, from biofilm formation to cytoskeletal movement, wetting of surfaces by active particles remains poorly understood. In this work, we study this active wetting by considering a model comprising an active lattice gas, interacting with a permeable barrier under periodic boundary conditions, for which an exact hydrodynam…

A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare
Ivania Donoso-Guzm\'an, Krist\'yna Sirka Kacaf\'irkov\'a, Maxwell Szymanski, An Jacobs, Denis Parra, Katrien Verbert
https://arxiv.org/abs/2506.13904

A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare
Despite promising developments in Explainable Artificial Intelligence, the practical value of XAI methods remains under-explored and insufficiently validated in real-world settings. Robust and context-aware evaluation is essential, not only to produce understandable explanations but also to ensure their trustworthiness and usability for intended users, but tends to be overlooked because of no clear guidelines on how to design an evaluation with users. This study addresses this gap with two ma…

sitting traditional #radio shiva for brian wilson tonight, concluding (or not!) a week of @… festivities with 3 hours of faves, rarities, outtakes, covers, productions, weird-outs, & more, from early demos to the most recent unfinished album, from now ’til midnight ET.

Effect of Signal Quantization on Performance Measures of a 1st Order One Dimensional Differential Microphone Array
Shweta Pal, Arun Kumar, Monika Agrawal
https://arxiv.org/abs/2506.15463

Effect of Signal Quantization on Performance Measures of a 1st Order One Dimensional Differential Microphone Array
In practical systems, recorded analog signals must be digitized for processing, introducing quantization as a critical aspect of data acquisition. While prior studies have examined quantization effects in various signal processing contexts, its impact on differential microphone arrays (DMAs), particularly in one-dimensional (1D) first-order configurations, remains unexplored. This paper investigates the influence of signal quantization on performance of first-order 1D DMAs across various beampa…

Continued domain-specific pre-training of protein language models for pMHC-I binding prediction
Sergio E. Mares, Ariel Espinoza Weinberger, Nilah M. Ioannidis
https://arxiv.org/abs/2507.13077

Continued domain-specific pre-training of protein language models for pMHC-I binding prediction
Predicting peptide--major histocompatibility complex I (pMHC-I) binding affinity remains challenging due to extreme allelic diversity ($\sim$30,000 HLA alleles), severe data scarcity for most alleles, and noisy experimental measurements. Current methods particularly struggle with underrepresented alleles and quantitative binding prediction. We test whether domain-specific continued pre-training of protein language models is beneficial for their application to pMHC-I binding affinity prediction.…

From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns
Bernardo Leite, Henrique Lopes Cardoso, Pedro Pinto, Abel Ferreira, Lu\'is Abreu, Isabel Rangel, Sandra Monteiro
https://arxiv.org/abs/2506.15598

From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns
While MCQs are valuable for learning and evaluation, manually creating them with varying difficulty levels and targeted reading skills remains a time-consuming and costly task. Recent advances in generative AI provide an opportunity to automate MCQ generation efficiently. However, assessing the actual quality and reliability of generated MCQs has received limited attention -- particularly regarding cases where generation fails. This aspect becomes particularly important when the generated MCQs …

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Zhenyao Fang, Ting-Wei Hsu, Qimin Yan
https://arxiv.org/abs/2506.15652

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. I…

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Jeongyong Yang, KwangBin Lee, SooJean Han
https://arxiv.org/abs/2507.11920 …

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Real-time path planning in dense, uncertain environments remains a challenging problem, as predicting the future motions of numerous dynamic obstacles is computationally burdensome and unrealistic. To address this, we introduce Hybrid Prediction-based Risk-Aware Planning (HyPRAP), a prediction-based risk-aware path-planning framework which uses a hybrid combination of models to predict local obstacle movement. HyPRAP uses a novel Prediction-based Collision Risk Index (P-CRI) to evaluate the ris…

Impact of a Deployed LLM Survey Creation Tool through the IS Success Model
Peng Jiang, Vinicius Cezar Monteiro de Lira, Antonio Maiorino
https://arxiv.org/abs/2506.14809

Impact of a Deployed LLM Survey Creation Tool through the IS Success Model
Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complex…