Tootfinder

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
https://arxiv.org/abs/2510.00831

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
The increasing integration of distributed energy resources (DERs), particularly renewables, poses significant challenges for power system protection, with fault classification (FC) and fault localization (FL) being among the most critical tasks. Conventional protection schemes, based on fixed thresholds, cannot reliably identify and localize short circuits with the increasing complexity of the grid under dynamic conditions. Machine learning (ML) offers a promising alternative; however, systemat…

On minimal product-one sequences of maximal length over the non-abelian group of order $pq$
Danilo Vilela Avelar, Fabio Enrique Brochero Mart\'inez, S\'avio Ribas
https://arxiv.org/abs/2510.00070

On minimal product-one sequences of maximal length over the non-abelian group of order $pq$
Let $G$ be a finite group. A sequence over $G$ is a finite multiset of elements of $G$, and it is called product-one if its terms can be ordered so that their product is the identity of $G$. The large Davenport constant $\D(G)$ is the maximal length of a minimal product-one sequence, that is, a product-one sequence that cannot be partitioned into two nontrivial product-one subsequences. Let $p,q$ be odd prime numbers with $p \mid q-1$ and let $C_q \rtimes C_p$ denote the non-abelian group of or…

Zero Trust-based Decentralized Identity Management System for Autonomous Vehicles
Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari, Mai Abdel-Malek, Soheil Salehi, Pratik Satam
https://arxiv.org/abs/2509.25566

Zero Trust-based Decentralized Identity Management System for Autonomous Vehicles
The rise of autonomous vehicles (AVs) promises to significantly enhance transportation safety and efficiency by mitigating human error, which is responsible for over 90\% of road accidents. However, the increasing connectivity of AVs introduces new cybersecurity challenges, as traditional perimeter-based security models are inadequate for dynamic and untrusted environments. This paper presents a novel Zero Trust-based Decentralized Identity Management (D-IM) protocol for AVs. By integrating the…

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
Huike Zou, Haiyang Yang, Yindu Su, Liyu Chen, Chengbao Lian, Qingheng Zhang, Shuguang Han, Jufeng Chen
https://arxiv.org/abs/2509.23874

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generat…

On Graphical Partitions with Restricted Parts
Gilead Levy
https://arxiv.org/abs/2510.00007 https://arxiv.org/pdf/2510.00007

On Graphical Partitions with Restricted Parts
We study the distributions of parts in random integer partitions subject to general arithmetic restrictions. In particular, we enumerate restricted graphical partitions of an even integer $n$ and identify the conditions under which the fraction of graphical partitions, relative to all restricted partitions, is maximal. We prove that this maximal fraction is asymptotically $O(n^{-1/2})$. Furthermore, for any set of arithmetic restrictions, we establish the existence of a minimal lower bound on t…

Identifying massive black hole binaries via light curve variability in optical time-domain surveys
Alfredo Chiesa, David Izquierdo-Villalba, Alberto Sesana, Fabiola Cocchiararo, Alessia Franchini, Alessandro Lupi, Daniele Spinoso, Silvia Bonoli
https://arxiv.org/abs/2508.21510

Identifying massive black hole binaries via light curve variability in optical time-domain surveys
Accreting massive black hole binaries (MBHBs) often display periodic variations in their emitted radiation, providing a distinctive signature for their identification. In this work, we explore the MBHBs identification via optical variability studies by simulating the observations of the LSST survey. To this end, we generate a population of MBHBs using the L-Galaxies semi-analytical model, focusing on systems with observed orbital periods $\leq$ 5 years. This ensures that at least two complete c…

Event Tokenization and Next-Token Prediction for Anomaly Detection at the Large Hadron Collider
Ambre Visive (Institute of Physics, University of Amsterdam, Amsterdam, The Netherlands, Nikhef, Dutch National Institute for Subatomic Physics, Amsterdam, The Netherlands), Polina Moskvitina (High Energy Physics, Radboud University, Nijmegen, The Netherlands, Nikhef, Dutch National Institute for Subatomic Physics, Amsterdam, The Netherlands), Clara Nellist (Institute of Physics, University …

Event Tokenization and Next-Token Prediction for Anomaly Detection at the Large Hadron Collider
We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures trained to reconstruct background events via masked-token prediction, our method identifies anomalies through deviations in reconstruction performance, without prior knowledge of signal characteristics. Applied to searches for simultaneous four-top-quark production, this token-based approach shows competitive performan…

An Interpolation-based Scheme for Rapid Frequency-Domain System Identification
Jared Jonas, Bassam Bamieh
https://arxiv.org/abs/2510.00525 https://arxiv.or…

An Interpolation-based Scheme for Rapid Frequency-Domain System Identification
We present a frequency-domain system identification scheme based on barycentric interpolation and weight optimization. The scheme is related to the Adaptive Antoulas-Anderson (AAA) algorithm for model reduction, but uses an adaptive algorithm for selection of frequency points for interrogating the system response, as would be required in identification versus model reduction. The scheme is particularly suited for systems in which any one sinusoidal response run is long or expensive, and thus th…

Equal knapsack identities between symmetric group character degrees
David J. Hemmer, Armin Straub, Karlee J. Westrem
https://arxiv.org/abs/2510.00301 https://

Equal knapsack identities between symmetric group character degrees
We prove a series of ``knapsack'' type equalities for irreducible character degrees of symmetric groups. That is, we find disjoint subsets of the partitions of $n$ so that the two corresponding character-degree sums are equal. Our main result refines our recent description of the Riordan numbers as the sum of all character degrees $f^λ$ where $λ$ is a partition of $n$ into three parts of the same parity. In particular, the sum of the ``fat-hook'' degrees $f^{(k,k,1^{n-2k})}+f^{(k+1,k+1,1^{n-2…

Infinitesimal Dilogarithm Satisfies Cluster Identities
Sinan Unver
https://arxiv.org/abs/2510.00016 https://arxiv.org/pdf/2510.00016

Infinitesimal Dilogarithm Satisfies Cluster Identities
In this paper, we show that the infinitesimal dilogarithm and Kontsevich's one-and-a-half logarithm function satisfies the identities which result from periods in cluster patterns. We also prove that these cluster identities are a consequence of the pentagon relation in the infinitesimal case.