Tootfinder

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Thomas Barbera, Jacopo Burger, Alessandro D'Amelio, Simone Zini, Simone Bianco, Raffaella Lanzarotti, Paolo Napoletano, Giuseppe Boccignone, Jose Luis Contreras-Vidal
https://arxiv.org/abs/2506.16168

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-…

Vielleicht sollten die Bauern statt für billigen Agrardiesel einfach mal für sinnvolle Klimaschutzmaßnahmen demonstrieren?
https://www.saechsische.de/lokales/dresden/erst-zu-trocken-jetzt-z…

Erst zu trocken, jetzt zu viel Regen: Dresdner Landwirt beklagt Wetterextreme
Für die Ernte von Raps und Weizen brauchen die Bauern im Schönfelder Hochland in Dresden trockene Tage. Doch diese sind erstmal nicht in Sicht. Weshalb sich die Landwirte sorgen.

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
https://arxiv.org/abs/2506.20114

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator…

A deep dive down the broad-line region: permitted OI, CaII and FeII emission in an AGN Little Red Dot at z=5.3
Roberta Tripodi, Maru\v{s}a Brada\v{c}, Francesco D'Eugenio, Nicholas Martis, Gregor Rihtar\v{s}i\v{c}, Chris Willott, Laura Pentericci, Yoshihisa Asada, Antonello Calabr\`o, Giordano Felicioni, Gaia Gaspar, Anthony H. Gonzalez, Anishya Harshan, Xihan Ji, Jon Jude\v{z}, Brian C. Lemaux, Vladan Markov, Rosa M. Merida, Lorenzo Napolitano, G\"ael Noirot, Massimiliano Par…

A deep dive down the broad-line region: permitted OI, CaII and FeII emission in an AGN Little Red Dot at z=5.3
We present a spectroscopic analysis of a broad-line active galactic nucleus (AGN) selected as little red dot at $z = 5.3$ behind the Bullet cluster (Bz5.3), based on JWST/NIRCam and NIRSpec data. The detection of strong FeII, OI, and CaII triplet emission lines, along with the evidence of broad Balmer lines, provides unambiguous evidence of a broad-line region (BLR) and an accreting supermassive black hole. Notably, we report the first detection of the $\lambda1304$ bump (i.e., blend of OI$λ$1…

Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN
Mohammad Saleh Refahi, Gavin Hearne, Harrison Muller, Kieran Lynch, Bahrad A. Sokhansanj, James R. Brown, Gail Rosen
https://arxiv.org/abs/2507.16978

Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN
The exponential growth of DNA sequencing data has outpaced traditional heuristic-based methods, which struggle to scale effectively. Efficient computational approaches are urgently needed to support large-scale similarity search, a foundational task in bioinformatics for detecting homology, functional similarity, and novelty among genomic and proteomic sequences. Although tools like BLAST have been widely used and remain effective in many scenarios, they suffer from limitations such as high com…

A 3D Machine Learning based Volume Of Fluid scheme without explicit interface reconstruction
Moreno Pintore, Bruno Despr\'es
https://arxiv.org/abs/2507.05218

A 3D Machine Learning based Volume Of Fluid scheme without explicit interface reconstruction
We present a machine-learning based Volume Of Fluid method to simulate multi-material flows on three-dimensional domains. One of the novelties of the method is that the flux fraction is computed by evaluating a previously trained neural network and without explicitly reconstructing any local interface approximating the exact one. The network is trained on a purely synthetic dataset generated by randomly sampling numerous local interfaces and which can be adapted to improve the scheme on less re…