Tootfinder

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters
Si-Yuan Zhu, Lang Shao, Pak-Hin Thomas Tam, Fu-Wen Zhang
https://arxiv.org/abs/2509.08224

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters
Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, compact binary mergers and massive collapsars. Traditional and some recent machine learning-based classification schemes predominantly rely on observer-frame physical parameters, which are significantly affected by the redshift effects and may not accurately represent the intrinsic properties of GRBs. In particular, the progenitors usually could only be decided by successful detection of the multi-band lo…

Anomaly detection in network flows using unsupervised online machine learning
Alberto Miguel-Diez, Adri\'an Campazas-Vega, \'Angel Manuel Guerrero-Higueras, Claudia \'Alvarez-Aparicio, Vicente Matell\'an-Olivera
https://arxiv.org/abs/2509.01375

Anomaly detection in network flows using unsupervised online machine learning
Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection model for network flows using unsupervised machine learning with online learning capabilities. This approach allows the system to dynamically learn the normal behavior of the network and detect deviations …

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Ariel Basso Madjoukeng, J\'er\^ome Fink, Pierre Poitier, Edith Belise Kenmogne, Benoit Frenay
https://arxiv.org/abs/2509.05188

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing negative pairs (different from the positive pairs) apart. In SLR, in a sign video, only certain parts provide information that is truly useful for its r…

Crosslisted article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fi...
Hong Sun, Joshua A. Vita, Amit Samanta, Vincenzo Lordi

Comparing unsupervised learning methods for local structural identification in colloidal systems
Alptu\u{g} Ulug\"ol, Jessi B\"uckmann, Ruizhi Yang, Roy Hoitink, Alfons van Blaaderen, Frank Smallenburg, Laura Filion
https://arxiv.org/abs/2509.07186

Comparing unsupervised learning methods for local structural identification in colloidal systems
Quantifying local structures in self-assembled systems is a central challenge in soft matter and materials science. When no a priori knowledge of the relevant structures is available, traditional order parameters often fall short. Unsupervised machine learning provides a convenient route to autonomously uncover structural motifs directly from particle configurations. In this work, we systematically compare three popular dimensionality reduction techniques; Principal Component Analysis (PCA), Au…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
- Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang

Revised classification of the CHIME fast radio bursts with machine learning
Liang Liu, Hai-Nan Lin, Li Tang
https://arxiv.org/abs/2509.02645 https://arxiv.…

Revised classification of the CHIME fast radio bursts with machine learning
Fast radio bursts (FRBs) are short-duration and energetic radio transients of unknown origin. Observationally, they are commonly categorized into repeaters and non-repeaters. However, this binary classification may be influenced by observational limitations such as sensitivity and time coverage of telescopes. In this work, we employ unsupervised machine learning techniques to re-examine the CHIME/FRB catalog, with the goal of identifying intrinsic groupings in the FRB population without relying…

Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning
Ang-Kun Wu, Louis Primeau, Jingtao Zhang, Kai Sun, Yang Zhang, Shi-Zeng Lin
https://arxiv.org/abs/2510.03018

Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning
Fractional Chern insulators (FCIs) in moire materials present a unique platform for exploring strongly correlated topological phases beyond the paradigm of ideal quantum geometry. While analytical approaches to FCIs and fractional quantum Hall states (FQHS) often rely on idealized Bloch wavefunctions, realistic moire models lack direct tunability of quantum metric and Berry curvature, limiting theoretical and numerical exploration. Here, we introduce an unsupervised machine learning framework t…

Tidal Tails and Their Dynamics in Open Clusters Using Gaia DR3
Ira Sharma, Vikrant V. Jadhav, Annapurni Subramaniam
https://arxiv.org/abs/2509.09279 https://

Tidal Tails and Their Dynamics in Open Clusters Using Gaia DR3
This research presents unsupervised machine learning and statistical techniques to detect and analyze tidal tails in five open clusters, BH 164, Alessi 2, NGC 2281, NGC 2354, and M67, with ages ranging from 60 Myr to 4 Gyr, using Gaia DR3 data. Color-magnitude diagram (CMD) matching, principal component analysis (PCA), and density-based clustering were used to detect the tails. Tidal tails were found in all clusters, extending 40 to 100 pc and containing 100 to 200 stars. Other statistical meth…

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Marcelo Guzman, Simone Ciarella, Andrea J. Liu
https://arxiv.org/abs/2509.15842

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they enjoy distributed and energy-efficient learning due to their physical dynamics. In this paper, we introduce a self-learning resistor network, the Restricted Kirchhoff Machine, capable of solving unsupervised learning tasks akin to the Restricted Boltzmann Machine algorithm. The circuit relies on existing technology b…

Unsupervised Machine Learning for Anomaly Detection in LHC Collider Searches
Antonio D'Avanzo (on behalf of the ATLAS Collaboration)
https://arxiv.org/abs/2509.24723 https:/…

Unsupervised Machine Learning for Anomaly Detection in LHC Collider Searches
Searches for new physics at the LHC at CERN traditionally use advanced simulations to model Standard Model and new-physics processes in high-energy collisions and compare them with data. The lack of recent direct discoveries, however, has motivated the development of model-independent approaches in HEP to complement existing hypothesis-driven analyses, particularly Anomaly Detection. A review of the latest efforts in BSM searches with anomaly detection is presented in these proceedings, focusin…

A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
Chi-Ting Ho, Daw-Wei Wang
https://arxiv.org/abs/2508.17688 https://

A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
We provide a general machine learning methodology that integrates classical shadow representations with unsupervised principal component analysis (PCA) to explore various quantum phase transitions. By sampling spin configurations from random Pauli measurements, our approach can effectively analyze hidden statistical patterns in the data, thereby capturing the distinct signatures of quantum criticality through their fluctuations. We benchmark this approach across various spin-1/2 systems, includ…

Machine learning in lattice quantum gravity
Jan Ambjorn, Zbigniew Drogosz, Jakub Gizbert-Studnicki, Andrzej G\"orlich, D\'aniel N\'emeth, Marcus Reitz
https://arxiv.org/abs/2510.02159

Machine learning in lattice quantum gravity
Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters.

Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification
Kun Qiu, Ying Wang, Baoqian Li, Wenjun Zhu
https://arxiv.org/abs/2509.00701 https://

Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification
Traffic classification, a technique for assigning network flows to predefined categories, has been widely deployed in enterprise and carrier networks. With the massive adoption of mobile devices, encryption is increasingly used in mobile applications to address privacy concerns. Consequently, traditional methods such as Deep Packet Inspection (DPI) fail to distinguish encrypted traffic. To tackle this challenge, Artificial Intelligence (AI), in particular Machine Learning (ML), has emerged as a…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- OT Score: An OT based Confidence Score for Source Free Unsupervised Domain Adaptation
Yiming Zhang, Sitong Liu, Alex Cloninger

Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning
Matteo Cerruti
https://arxiv.org/abs/2508.20927 https://arxiv.o…

Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning
Blazars dominate the extragalactic $γ$-ray sky and show pronounced flares. Using public Fermi-LAT light curves for 732 blazars with secure redshifts, I implement an automated pipeline to identify and characterize $γ$-ray bursts from blazars (GRBBLs). Each event is modeled with an exponential rise/decay profile, and spectral variability is quantified via a constant fit. From 679 high-quality GRBBLs, I apply extreme deconvolution for unsupervised classification. The GRBBL population is remarkab…

Tidal Tails in Open Clusters I. Morphology, Binary Fraction, Dynamics, and Rotation
Ira Sharma, Vikrant V. Jadhav, Annapurni Subramaniam, Henriette Wirth
https://arxiv.org/abs/2508.19457

Tidal Tails in Open Clusters I. Morphology, Binary Fraction, Dynamics, and Rotation
Context: This research presents unsupervised machine learning and statistical methods to identify and analyze tidal tails in open star clusters using data from the Gaia-DR3 catalog. Aims: We aim to identify member stars, detect and analyze tidal tails in five open clusters, BH 164, Alessi 2, NGC 2281, NGC 2354, and M67, of ages between 60 Myr and 4 Gyr. These clusters were selected based on the previous evidence of extended tidal structures. Methods: We utilized machine learning algorithms such…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Unsupervised Domain Adaptation with an Unobservable Source Subpopulation
Chao Ying, Jun Jin, Haotian Zhang, Qinglong Tian, Yanyuan Ma, Yixuan Li, Jiwei Zhao

Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses
Mridul Kumar, Yevgeny Rakita
https://arxiv.org/abs/2509.00943 https://arxiv.org/pd…

Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses
Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional techniques. Here, we apply unsupervised machine learning to 4-dimensional scanning transmission electron microscopy (4D-STEM) data to identify…

Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
ATLAS Collaboration
https://arxiv.org/abs/2508.19778

Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
A model-agnostic search for Beyond the Standard Model physics is presented, targeting final states with at least four light leptons (electrons or muons). The search regions are separated by event topology and unsupervised machine learning is used to identify anomalous events in the full 140 fb$^{-1}$ of proton-proton collision data collected with the ATLAS detector during Run 2. No significant excess above the Standard Model background expectation is observed. Model-agnostic limits are presente…