Tootfinder

In the ISE2025 lecture today, our students learned about unsupervised learning on the example of k-Means clustering. One nice hands-on example is image colour reduction based on k-means clustering, as demonstrated in a colab notebook (based on the Python DataScience Handbook by Vanderplus)
colab notebook: https://…

Fully Dynamic Euclidean k-Means
Sayan Bhattacharya, Mart\'in Costa, Ermiya Farokhnejad, Shaofeng H. -C. Jiang, Yaonan Jin, Jianing Lou
https://arxiv.org/abs/2507.11256

Fully Dynamic Euclidean k-Means
We consider the fundamental Euclidean $k$-means clustering problem in a dynamic setting, where the input $X \subseteq \mathbb{R}^d$ evolves over time via a sequence of point insertions/deletions. We have to explicitly maintain a solution (a set of $k$ centers) $S \subseteq \mathbb{R}^d$ throughout these updates, while minimizing the approximation ratio, the update time (time taken to handle a point insertion/deletion) and the recourse (number of changes made to the solution $S$) of the algorith…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

MIGHTEE-HI: The direct detection of neutral hydrogen in galaxies at $z>0.25$
Matt J. Jarvis, Madalina N. Tudorache, I. Heywood, Anastasia A. Ponomareva, M. Baes, Natasha Maddox, Kristine Spekkens, Andreea Varasteanu, C. L. Hale, Mario G. Santos, R. G. Varadaraj, Elizabeth A. K. Adams, Alessandro Bianchetti, Barbara Catinella, Jacinta Delhaize, M. Maksymowicz-Maciata, Pavel E. Mancera Pi\~na, Hengxing Pan, Am\'elie Saintonge, Gauri Sharma, O. Ivy Wong

MIGHTEE-HI: The direct detection of neutral hydrogen in galaxies at $z>0.25$
Atomic hydrogen constitutes the gas reservoir from which molecular gas and star formation in galaxies emerges. However, the weakness of the line means it has been difficult to directly detect in all but the very local Universe. Here we present results from the first search using the MeerKAT International Tiered Extragalactic Exploration (MIGHTEE) Survey for high-redshift ($z>0.25$) H{\sc i} emission from individual galaxies. By searching for 21-cm emission centered on the position and redshift …

Time reversal symmetry breaking and s-wave superconductivity in CaPd2Ge2: A $\mu$SR study
V. K. Anand, A. Bhattacharyya, D. T. Adroja, K. Panda, P. K. Biswas, A. D. Hillier, B. Lake
https://arxiv.org/abs/2506.10432

Time reversal symmetry breaking and s-wave superconductivity in CaPd2Ge2: A $μ$SR study
${\rm CaPd_2Ge_2}$ which crystallizes in ${\rm ThCr_2Si_2}$-type body-centered tetragonal structure exhibits superconductivity below the critical temperature $T_{\rm c} = 1.69$~K\@. We have investigated the superconducting gap structure and time reversal symmetry of the ground state in ${\rm CaPd_2Ge_2}$ by means of muon spin relaxation and rotation ($μ$SR) measurements. Our analysis of $μ$SR data collected in transverse magnetic field reveals BCS superconductivity with a single-band $s$-wave…

New kickoff rules forcing Raiders kicker Daniel Carlson put his multi-sport skills to work https://raiderswire.usatoday.com/story/sports/nfl/raiders/2025/06/10/raiders-k-daniel-carlso…

Consistency and Inconsistency in $K$-Means Clustering
Mo\"ise Blanchard, Adam Quinn Jaffe, Nikita Zhivotovskiy
https://arxiv.org/abs/2507.06226 https:…

Consistency and Inconsistency in $K$-Means Clustering
A celebrated result of Pollard proves asymptotic consistency for $k$-means clustering when the population distribution has finite variance. In this work, we point out that the population-level $k$-means clustering problem is, in fact, well-posed under the weaker assumption of a finite expectation, and we investigate whether some form of asymptotic consistency holds in this setting. As we illustrate in a variety of negative results, the complete story is quite subtle; for example, the empirical …

Combinatorial relative algebraic $K$-theory
Jane Turner
https://arxiv.org/abs/2506.09905 https://arxiv.org/pdf/2506.09905

Combinatorial relative algebraic $K$-theory
In a preprint released in 2016, Daniel Grayson introduces a conjectural presentation of the (higher) relative algebraic $K$-groups using purely combinatorial means. In this paper, we will show that this presentation is isomorphic to the classically defined higher relative algebraic $K$-groups, and provide some reasons to believe the conjecture posed in this 2016 preprint to be true.

Scalable unsupervised feature selection via weight stability
Xudong Zhang, Renato Cordeiro de Amorim
https://arxiv.org/abs/2506.06114 https://

Scalable unsupervised feature selection via weight stability
Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we introduce the Minkowski weighted $k$-means++, a novel initialisation strategy for the Minkowski Weighted $k$-means. Our initialisation selects centroids probabilistically using feature relevance estimates derived from the data itself. Building on this, we propose two new feature selection algorithms, FS-MWK++, whic…

Constrained Pareto Set Identification with Bandit Feedback
Cyrille Kone, Emilie Kaufmann, Laura Richert
https://arxiv.org/abs/2506.08127 https://

Constrained Pareto Set Identification with Bandit Feedback
In this paper, we address the problem of identifying the Pareto Set under feasibility constraints in a multivariate bandit setting. Specifically, given a $K$-armed bandit with unknown means $μ_1, \dots, μ_K \in \mathbb{R}^d$, the goal is to identify the set of arms whose mean is not uniformly worse than that of another arm (i.e., not smaller for all objectives), while satisfying some known set of linear constraints, expressing, for example, some minimal performance on each objective. Our focu…

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
Zhengqing Wang, Yuefan Wu, Jiacheng Chen, Fuyang Zhang, Yasutaka Furukawa
https://arxiv.org/abs/2507.08776

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
This paper proposes a neural rendering approach that represents a scene as "compressed light-field tokens (CLiFTs)", retaining rich appearance and geometric information of a scene. CLiFT enables compute-efficient rendering by compressed tokens, while being capable of changing the number of tokens to represent a scene or render a novel view with one trained network. Concretely, given a set of images, multi-view encoder tokenizes the images with the camera poses. Latent-space K-means selects a re…

$k$-means considered harmful: On arbitrary topological changes in Mapper complexes
Mikael Vejdemo-Johansson
https://arxiv.org/abs/2507.06212 https://

$k$-means considered harmful: On arbitrary topological changes in Mapper complexes
The Mapper construction is one of the most widespread tools from Topological Data Analysis. There is an unfortunate trend as the construction has gained traction to use clustering methods with properties that end up distorting any analysis results from the construction. In this paper we will see a few ways in which widespread choices of clustering algorithms have arbitrarily large distortions of the features visible in the final Mapper complex.

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new/
[1/1]:
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy

This https://arxiv.org/abs/2504.20349 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Closing objectivity loophole in Bell tests on a public quantum computer
Adam Bednorz, Josep Batle, Tomasz Bia{\l}ecki, Jaros{\l}aw K. Korbicz
https://arxiv.org/abs/2506.08940

Closing objectivity loophole in Bell tests on a public quantum computer
We have constructed and run a Bell test of local realism focusing on the objectivity criterion. The objectivity means that the outcomes are confirmed macroscopically by a few observers at each of party. The IBM Quantum devices turn out to be sufficiently accurate to pass such an extended Bell-type test, although at the price of communication loopholes and residual but statistically significant signaling. The test serves also as the benchmark of entanglement spread across larger sets of qubits.

This https://arxiv.org/abs/2505.22984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

A Computational Approach to Improving Fairness in K-means Clustering
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster …

Quantum k-SAT Related Hypergraph Problems
Simon-Luca Kremer, Dorian Rudolph, Sevag Gharibian
https://arxiv.org/abs/2506.17066 https://

Quantum k-SAT Related Hypergraph Problems
The Quantum k-SAT problem is the quantum generalization of the k-SAT problem. It is the problem whether a given local Hamiltonian is frustration-free. Frustration-free means that the ground state of the k-local Hamiltonian minimizes the energy of every local interaction term simultaneously. This is a central question in quantum physics and a canonical QMA_1-complete problem. The Quantum k-SAT problem is not as well studied as the classical k-SAT problem in terms of special tractable cases, appr…

Stealthy-Hyperuniform Wave Dynamics in Two-Dimensional Photonic Crystals
Maria Barsukova, Zeyu Zhang, Brian Gould, Koorosh Sadri, Christian Rosiek, S{\o}ren Stobbe, Jonas Karcher, Mikael C. Rechtsman
https://arxiv.org/abs/2507.05253

Stealthy-Hyperuniform Wave Dynamics in Two-Dimensional Photonic Crystals
Hyperuniform structures are spatial patterns whose fluctuations disappear on long length scales, making them effectively homogeneous when observed from afar. Mathematically, this means that their spectral density, $\tildeρ({\bf k})$, approaches zero for low wavenumber, $|\textbf{k}|$. Crystalline lattices are hyperuniform, as are certain quasicrystals, maximally random jammed packing of spheres, and electrons in the fractional quantum Hall state. Stealthy-hyperuniformity is an even s…

This https://arxiv.org/abs/2403.00909 has been replaced.
link: https://scholar.google.com/scholar?q=a

Atacama Large Aperture Submillimeter Telescope (AtLAST) Science: Resolving the Hot and Ionized Universe through the Sunyaev-Zeldovich effect
An omnipresent feature of the multi-phase ``cosmic web'' is that warm/hot (>$10^5$ K) ionized gas pervades it. This gas constitutes a relevant contribution to the overall universal matter budget across multiple scales, from the several tens of Mpc-scale IGM filaments, to the Mpc ICM, all the way down to the CGM surrounding individual galaxies from ~1 kpc up to their respective virial radii (~100 kpc). The study of the hot baryonic component of cosmic matter density represents a powerful means f…

An Observation on Lloyd's k-Means Algorithm in High Dimensions
David Silva-S\'anchez, Roy R. Lederman
https://arxiv.org/abs/2506.14952 https://

An Observation on Lloyd's k-Means Algorithm in High Dimensions
Clustering and estimating cluster means are core problems in statistics and machine learning, with k-means and Expectation Maximization (EM) being two widely used algorithms. In this work, we provide a theoretical explanation for the failure of k-means in high-dimensional settings with high noise and limited sample sizes, using a simple Gaussian Mixture Model (GMM). We identify regimes where, with high probability, almost every partition of the data becomes a fixed point of the k-means algorith…

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Hao Hu, Xinqi Wang, Simon Shaolei Du
https://arxiv.org/abs/2506.09202 https://

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajectories. By leveraging the connection between the KL-divergence of offline trajectory distributions and a mixture of policy-induced distributions, we formulate a natural clustering objective. To solve this, we propose Policy-Guided K-means (PG-Kmeans) and Centroid-Attracted Autoencoder (CAAE). PG-Kmeans iteratively trains…

Memory- and compute-optimized geometric multigrid GMGPolar for curvilinear coordinate representations -- Applications to fusion plasma
Julian Litz, Philippe Leleux, Carola Kruse, Joscha Gedicke, Martin J. K\"uhn
https://arxiv.org/abs/2507.03812

Memory- and compute-optimized geometric multigrid GMGPolar for curvilinear coordinate representations -- Applications to fusion plasma
Tokamak fusion reactors are actively studied as a means of realizing energy production from plasma fusion. However, due to the substantial cost and time required to construct fusion reactors and run physical experiments, numerical experiments are indispensable for understanding plasma physics inside tokamaks, supporting the design and engineering phase, and optimizing future reactor designs. Geometric multigrid methods are optimal solvers for many problems that arise from the discretization of …

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Donovan Slabbert, Dean Brand, Francesco Petruccione
https://arxiv.org/abs/2507.07018

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
We compare a parameterized quantum kernel (pQK) with a quantum leaky integrate-and-fire (QLIF) neuromorphic computing approach that employs either the Victor-Purpura or van Rossum kernel in a spectral clustering task, as well as the classical radial basis function (RBF) kernel. Performance evaluation includes label-based classification and clustering metrics, as well as optimal number of clusters predictions for each dataset based on an elbow-like curve as is typically used in $K$-means cluster…

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
Mustafa Demirer, Ersin G\"o\u{g}\"u\c{s}, Yuki Kaneko, \"Ozge Keskin, Sinem \c{S}a\c{s}maz, Shotaro Yamasaki
https://arxiv.org/abs/2506.04414

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
We have conducted a time-resolved spectral analysis of magnetar bursts originating from SGR J1550-5418. Our analysis utilizes a two-step methodology for temporal segmentation of the data. We first generated and fitted overlapping time segments. Subsequently, we obtained non-overlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k-means clustering. For the fitting process, we employed three distinct models, namely …

Note on real and imaginary parts of harmonic quasiregular mappings
Suman Das, Antti Rasila
https://arxiv.org/abs/2506.04618 https://a…

Note on real and imaginary parts of harmonic quasiregular mappings
If $f=u+iv$ is analytic in the unit disk $\mathbb{D}$, it is known that the integral means $M_p(r,u)$ and $M_p(r,v)$ have the same order of growth. This is false if $f$ is a (complex-valued) harmonic function. However, we prove that the same principle holds if we assume, in addition, that $f$ is $K$-quasiregular in $\mathbb{D}$. The case $0

This https://arxiv.org/abs/2312.02965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Conditional constrained and unconstrained quantization for probability distributions
In this paper, we introduce and develop the concept of conditional quantization for Borel probability measures on $\mathbb{R}^k,$ considering both constrained and unconstrained frameworks. For each setting, we define the associated quantization errors, dimensions, and coefficients, and provide explicit computations for specific classes of probability distributions. A key result in the unconstrained case is that the union of all optimal sets of $ n$-means is dense in the support of the measure. …

Dark states of electrons in a quantum system with two pairs of sublattices
Yoonah Chung, Minsu Kim, Yeryn Kim, Seyeong Cha, Joon Woo Park, Jeehong Park, Yeonjin Yi, Dongjoon Song, Jung Hyun Ryu, Kimoon Lee, Timur K. Kim, Cephise Cacho, Jonathan Denlinger, Chris Jozwiak, Eli Rotenberg, Aaron Bostwick, Keun Su Kim
https://arxiv.or…

Dark states of electrons in a quantum system with two pairs of sublattices
A quantum state of matter that is forbidden to interact with photons and is therefore undetectable by spectroscopic means is called a dark state. This basic concept can be applied to condensed matter where it suggests that a whole band of quantum states could be undetectable across a full Brillouin zone. Here we report the discovery of such condensed matter dark states in palladium diselenide as a model system that has two pairs of sublattices in the primitive cell. By using angle-resolved phot…

This https://arxiv.org/abs/2412.15168 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Dynamic structure factor of a spin-1/2 Heisenberg chain with long-range interactions
We study the dynamic structure factor $S(k,ω)$ of the spin-1/2 chain with long-range, power-law decaying unfrustrated (sign alternating) Heisenberg interactions $J_r \sim (-1)^{r-1} r^{-α}$ by means of stochastic analytic continuation (SAC) of imaginary-time correlations computed by quantum Monte Carlo calculations. We do so in both the long-range antiferromagnetic (AFM, for $α\lesssim 2.23$) and quasi-long-range-ordered (QLRO, for $α\gtrsim 2.23$) ground-state phases, employing diffe…

Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization
Duong Bach
https://arxiv.org/abs/2506.21601

Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization
Multi-vector document retrieval systems, such as ColPali, excel in fine-grained matching for complex queries but incur significant storage and computational costs due to their reliance on high-dimensional patch embeddings and late-interaction scoring. To address these challenges, we propose HPC-ColPali, a Hierarchical Patch Compression framework that enhances the efficiency of ColPali while preserving its retrieval accuracy. Our approach integrates three innovative techniques: (1) K-Means quant…

Reduced and mixed precision turbulent flow simulations using explicit finite difference schemes
B\'alint Sikl\'osi, Pushpender K. Sharma, David J. Lusher, Istv\'an Z. Reguly, Neil D. Sandham
https://arxiv.org/abs/2505.20911

Reduced and mixed precision turbulent flow simulations using explicit finite difference schemes
The use of reduced and mixed precision computing has gained increasing attention in high-performance computing (HPC) as a means to improve computational efficiency, particularly on modern hardware architectures like GPUs. In this work, we explore the application of mixed precision arithmetic in compressible turbulent flow simulations using explicit finite difference schemes. We extend the OPS and OpenSBLI frameworks to support customizable precision levels, enabling fine-grained control over pr…

clustra: A multi-platform k-means clustering algorithm for analysis of longitudinal trajectories in large electronic health records data
Nimish Adhikari, Hanna Gerlovin, George Ostrouchov, Rachel Ehrbar, Alyssa B. Dufour, Brian R. Ferolito, Serkalem Demissie, Lauren Costa, Yuk-Lam Ho, Laura Tarko, Edmon Begoli, Kelly Cho, David R. Gagnon
https://

clustra: A multi-platform k-means clustering algorithm for analysis of longitudinal trajectories in large electronic health records data
Background and Objective: Variables collected over time, or longitudinally, such as biologic measurements in electronic health records data, are not simple to summarize with a single time-point, and thus can be more holistically conceptualized as trajectories over time. Cluster analysis with longitudinal data further allows for clinical representation of groups of subjects with similar trajectories and identification of unique characteristics, or phenotypes, that can be investigated as risk fac…

Unsupervised Document and Template Clustering using Multimodal Embeddings
Phillipe R. Sampaio, Helene Maxcici
https://arxiv.org/abs/2506.12116 https://

Unsupervised Document and Template Clustering using Multimodal Embeddings
This paper investigates a novel approach to unsupervised document clustering by leveraging multimodal embeddings as input to traditional clustering algorithms such as $k$-Means and DBSCAN. Our method aims to achieve a finer-grained document understanding by not only grouping documents at the type level (e.g., invoices, purchase orders), but also distinguishing between different templates within the same document category. This is achieved by using embeddings that capture textual content, layout…

This https://arxiv.org/abs/2505.14449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach
While subgroup disparities and performance bias are increasingly studied in computational research, fairness in categorical Speech Emotion Recognition (SER) remains underexplored. Existing methods often rely on explicit demographic labels, which are difficult to obtain due to privacy concerns. To address this limitation, we introduce an Implicit Demography Inference (IDI) module that leverages pseudo-labeling from a pre-trained model and unsupervised learning using k-means clustering to mitigat…

Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
Mikael M{\o}ller H{\o}gsgaard, Andrea Paudice
https://arxiv.org/abs/2506.14673 h…

Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
The Median of Means (MoM) is a mean estimator that has gained popularity in the context of heavy-tailed data. In this work, we analyze its performance in the task of simultaneously estimating the mean of each function in a class $\mathcal{F}$ when the data distribution possesses only the first $p$ moments for $p \in (1,2]$. We prove a new sample complexity bound using a novel symmetrization technique that may be of independent interest. Additionally, we present applications of our result to $k$…

Aluminium fast neutron leakage spectrum validation
Martin Schulc, Tom\'a\v{s} Czakoj, Ev\v{z}en Nov\'ak, Alena Krechlerov\'a, Adam Gre\v{s}, Jan \v{S}imon, Bohumil J\'ansk\'y, Ji\v{r}\'i Rejchrt, Michal Ko\v{s}\v{t}\'al, Zden\v{e}k Mat\v{e}j
https://arxiv.org/abs/2506.19861

Aluminium fast neutron leakage spectrum validation
Aluminum is a crucial material in the nuclear industry, valued for its ability to perform reliably over time. The manuscript focuses on validating aluminum neutron transport libraries. The validation was conducted by activating samples in different positions, and measuring the fast neutron spectrum in the energy range of 0.1 MeV to 1.3 MeV by means of a proportional detector filled with hydrogen and, in the energy range of 1-12 MeV using a scintillation stilbene detector. Validation experiments…

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Tami C. Meyer, Gesa-R. Siemann, Paulina Majchrzak, Thomas Seyller, Jennifer Rigden, Yu Zhang, Emma Springate, Charlotte Sanders, Philip Hofmann
https://arxiv.org/abs/2506.02137

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in T…

This https://arxiv.org/abs/2505.17917 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
We propose a novel method, termed the M-learner, for estimating heterogeneous indirect and total treatment effects and identifying relevant subgroups within a mediation framework. The procedure comprises four key steps. First, we compute individual-level conditional average indirect/total treatment effect Second, we construct a distance matrix based on pairwise differences. Third, we apply tSNE to project this matrix into a low-dimensional Euclidean space, followed by K-means clustering to iden…

Resonant Island Trapping in a Fourth-Generation Synchrotron Light Source
E. C. Cort\'es Garc\'ia, N. Carmignani, F. Ewald, S. A. Antipov, K. Scheidt, S. White, I. V. Agapov
https://arxiv.org/abs/2505.12469

Resonant Island Trapping in a Fourth-Generation Synchrotron Light Source
We report the first direct observation of nonlinear resonance island trapping in a fourth-generation light source with working points far from the excited resonance and examine the nonlinear dynamics and properties of the trapped beam. The discovered dynamics of island trapping may help understand bunch purity and halo formation issues, create additional experimental capabilities for photon science applications, and present means of nonlinear characterization of the machine optics.

Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
https://arxiv.org/abs/2506.12839 https://arxiv.org/pdf/2506.…

Fair Bayesian Model-Based Clustering
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based cl…