Tootfinder

This https://arxiv.org/abs/2502.12316 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Large-scale clustering of inertial particles in a rotating, stratified and inhomogeneous turbulence
We develop a theory of various kinds of large-scale clustering of inertial particles in a rotating density stratified or inhomogeneous turbulent fluid flows. The large-scale particle clustering occurs in scales which are much larger than the integral scale of turbulence, and it is described in terms of the effective pumping velocity in a turbulent flux of particles. We show that for a fast rotating strongly anisotropic turbulence, the large-scale clustering occurs in the plane perpendicular to …

Join Dennis Berger, Marco Petris, and Volker Carlguth to explore 'Intent-Based Clustering.' An approach to overcome some limitations of modern hybrid search systems. Discover how upfront LLM-supported in-depth query understanding can be applied in various steps, including retrieval, clustering, validation, and presentation. Learn about the process of moving from prototype to production for large-scale, high-volume e-commerce searches.

What you see is what you mean; intent based ecommerce search
In today's e-commerce landscape, hybrid search systems represent the market standard, yet they often struggle with highly inspirational queries, lack of precision in semantic recall, and presentation of diversity. Ambiguity in search terms can lead to disorganized results or overshadowed relevant products. This session will demonstrate how an upfront LLM-supported in depth query understanding can increase recall meaningfully without sacrificing precision. We show how LLM-driven query understand…

Clustering scientific publications: lessons learned through experiments with a real citation network
Vu Thi Huong, Thorsten Koch
https://arxiv.org/abs/2505.18180

Clustering scientific publications: lessons learned through experiments with a real citation network
Clustering scientific publications can reveal underlying research structures within bibliographic databases. Graph-based clustering methods, such as spectral, Louvain, and Leiden algorithms, are frequently utilized due to their capacity to effectively model citation networks. However, their performance may degrade when applied to real-world data. This study evaluates the performance of these clustering algorithms on a citation graph comprising approx. 700,000 papers and 4.6 million citations ex…

Classifying and Clustering Trading Agents
Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis, Juho Kanniainen
https://arxiv.org/abs/2505.21662 https://

Classifying and Clustering Trading Agents
The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is fina…

Analytic inference with two-way clustering
Laurent Davezies, Xavier D'Haultf{\oe}uille, Yannick Guyonvarch
https://arxiv.org/abs/2506.20749 https://

Analytic inference with two-way clustering
This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inference is invalid in non-Gaussian regimes, namely when the estimator of the parameter of interest is not asymptotically Gaussian. We consider a simple fix that addresses both issues. In Gaussian regimes, the corresponding tests are asymptotically exact and equivalent to usual on…

Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering
Susovan Pal, Praneeth Vepakomma
https://arxiv.org/abs/2506.19837

Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering
The mean shift (MS) is a non-parametric, density-based, iterative algorithm that has prominent usage in clustering and image segmentation. A rigorous proof for its convergence in full generality remains unknown. Two significant steps in this direction were taken in the paper \cite{Gh1}, which proved that for \textit{sufficiently large bandwidth}, the MS algorithm with the Gaussian kernel always converges in any dimension, and also by the same author in \cite{Gh2}, proved that MS always converge…

Electric field-induced clustering in nanocomposite films of highly polarizable inclusions
Elshad Allahyarov, Hartmut L\"owen
https://arxiv.org/abs/2506.20903

Electric field-induced clustering in nanocomposite films of highly polarizable inclusions
A nanocomposite film containing highly polarizable inclusions in a fluid background is explored when an external electric field is applied perpendicular to the planar film. For small electric fields, the induced dipole moments of the inclusions are all polarized in field direction, resulting in a mutual repulsion between the inclusions. Here we show that this becomes qualitatively different for high fields: the total system self-organizes into a state which contains both polarizations, parallel…

This https://arxiv.org/abs/2402.13117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Faster, Deterministic and Space Efficient Subtrajectory Clustering
Given a trajectory $T$ and a distance $Δ$, we wish to find a set $C$ of curves of complexity at most $\ell$, such that we can cover $T$ with subcurves that each are within Fréchet distance $Δ$ to at least one curve in $C$. We call $C$ an $(\ell,Δ)$-clustering and aim to find an $(\ell,Δ)$-clustering of minimum cardinality. This problem variant was introduced by Akitaya $et$ $al.$ (2021) and shown to be NP-complete. The main focus has therefore been on bicriteria approximation algorithms, a…

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Quinten De Man, Laxman Dhulipala, Kishen N Gowda
https://arxiv.org/abs/2506.18384 https://

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Single-linkage clustering is a popular form of hierarchical agglomerative clustering (HAC) where the distance between two clusters is defined as the minimum distance between any pair of points across the two clusters. In single-linkage HAC, the output is typically the single-linkage dendrogram (SLD), which is the binary tree representing the hierarchy of clusters formed by iteratively contracting the two closest clusters. In the dynamic setting, prior work has only studied maintaining a minimum…

Clustering Tails in High Dimension
Liujun Chen, Marco Oesting, Chen Zhou
https://arxiv.org/abs/2506.19414 https://arxiv.org/pdf/2506.…

Clustering Tails in High Dimension
One potential solution to combat the scarcity of tail observations in extreme value analysis is to integrate information from multiple datasets sharing similar tail properties, for instance, a common extreme value index. In other words, for a multivariate dataset, we intend to group dimensions into clusters first, before applying any pooling techniques. This paper addresses the clustering problem for a high dimensional dataset, according to their extreme value indices. We propose an iterative…

Interpretable Text-Guided Image Clustering via Iterative Search
Bingchen Zhao, Oisin Mac Aodha
https://arxiv.org/abs/2506.12514 https://

Interpretable Text-Guided Image Clustering via Iterative Search
Traditional clustering methods aim to group unlabeled data points based on their similarity to each other. However, clustering, in the absence of additional information, is an ill-posed problem as there may be many different, yet equally valid, ways to partition a dataset. Distinct users may want to use different criteria to form clusters in the same data, e.g. shape v.s. color. Recently introduced text-guided image clustering methods aim to address this ambiguity by allowing users to specify t…

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Ang Jia, He Jiang, Zhilei Ren, Xiaochen Li, Ming Fan, Ting Liu
https://arxiv.org/abs/2506.19425

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Binary decomposition, which decomposes binary files into modules, plays a critical role in binary reuse detection. Existing binary decomposition works either apply anchor-based methods by extending anchor functions to generate modules, or apply clustering-based methods by using clustering algorithms to group binary functions, which all rely on that reused code shares similar function call relationships. However, we find that function call graphs (FCGs) vary a lot when using different compilatio…

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…

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…

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Caden Sweeney, Du Yong Kim, Branko Ristic, Brian Cheung
https://arxiv.org/abs/2506.20319

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to the detection and tracking of low SIR targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled mulit- Bernoulli (LMB) filter. A measurement driven birth density design based on…

Impact of pion tensor force on alpha clustering in $^{20}$Ne
Zhao Jing Chen, Bao Yuan Sun
https://arxiv.org/abs/2506.16947 https://ar…

Impact of pion tensor force on alpha clustering in $^{20}$Ne
The nuclear clustering, as a quantum phase transition phenomenon governed by strong interactions, exhibits characteristics that are highly sensitive to the specific features of nuclear forces. Here, we examine how nuclear deformation and tensor forces influence $α$-cluster formation in light nuclei. The axially deformed relativistic Hartree-Fock-Bogoliubov model is utilized to investigate the clustering structure of the $^{20}$Ne nucleus, at both the ground state and the excited state with a s…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Pola Bereta, Ioannis Diamantis
https://arxiv.org/abs/2506.19759 https://…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time-series data present challenges for effective clustering. This study evaluates three unsupervised clustering approaches, Symbolic Aggregate approXimation (SAX), enhanced SAX (eSAX), and Topological Data Analysis (TDA), applied to 20 Google Trends keywords representing major cons…

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
Tobias Cord-Landwehr, Tobias Gburrek, Marc Deegen, Reinhold Haeb-Umbach
https://arxiv.org/abs/2506.16228

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline …

A clusterability test for directed graphs
Mario R. Guarracino, Pierre Miasnikof, Alexander Y. Shestopaloff, Houyem Demni, Cristi\'an Bravo, Yuri Lawryshyn
https://arxiv.org/abs/2506.20111

A clusterability test for directed graphs
In this article, we extend a statistical test of graph clusterability, the $δ$ test, to directed graphs with no self loops. The $δ$ test, originally designed for undirected graphs, is based on the premise that graphs with a clustered structure display a mean local density that is statistically higher than the graph's global density. We posit that graphs that do not meet this necessary (but not sufficient) condition for clusterability can be considered unsuited to clustering. In such cases, ve…

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish
https://arxiv.org/abs/2506.19942

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
We present a machine learning (ML) framework for the detection of wide binary star systems and the identification of deviations from standard gravity using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. We recast the problem of identifying systematic deviations from standard gravity as a supervised anomaly detection. Our approach incorporate…

Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
V\'ictor Blanco, Alfredo Mar\'in, Justo Puerto
https://arxiv.org/abs/2506.19821

Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
Matrix seriation, the problem of permuting the rows and columns of a matrix to uncover latent structure, is a fundamental technique in data science, particularly in the visualization and analysis of relational data. Applications span clustering, anomaly detection, and beyond. In this work, we present a unified framework grounded in mathematical optimization to address matrix seriation from a rigorous, model-based perspective. Our approach leverages combinatorial and mixed-integer optimization t…

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Alexis Plaquet, Naohiro Tawara, Marc Delcroix, Shota Horiguchi, Atsushi Ando, Shoko Araki, Herv\'e Bredin
https://arxiv.org/abs/2506.11605

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentation model of these pipelines, but their synergy had not been thoroughly evaluated. In this work, we provide an in-depth analysis on the impact of major architecture choices on the performance of the pipeline. We investigate different encoders (SincNet, pretrained and finetuned WavLM), different decoders (LSTM, Mamba, …

scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Ping Xu, Zhiyuan Ning, Pengjiang Li, Wenhao Liu, Pengyang Wang, Jiaxu Cui, Yuanchun Zhou, Pengfei Wang
https://arxiv.org/abs/2505.12626

scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially graph neural networks (GNNs)-based methods, have significantly improved clustering performance. However, the analysis of scRNA-seq data remains challenging due to noise, sparsity, and high dimensionality. Compounding these challenges, GNNs often suffer from over-smoothing, limiting their ability to capture complex biol…

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, Fran\c{c}ois Th\'eberge
https://arxiv.org/abs/2506.03105 ht…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph…

Paper & Viz: Analysis of Denmark’s whole bicycle network
https://nerds.itu.dk/2025/05/20/paper-viz-analysis-of-denmarks-whole-bicycle-network/

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Chia-Heng Yu, Yen-Lung Tsai
https://arxiv.org/abs/2506.13607

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant c…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Stable and consistent density-based clustering via multiparameter persistence
Alexander Rolle, Luis Scoccola

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…

FuGa3D: Fast full-sky analysis of Galaxy catalogs in 3D
Elina Keih\"anen, Jani Haapala, Valtteri Lindholm, Martin Reinecke, Susan Rissanen, Jussi Valiviita, Akke Viitanen
https://arxiv.org/abs/2506.18676

FuGa3D: Fast full-sky analysis of Galaxy catalogs in 3D
We present FuGa3D, a code for fast computation of correlation functions and power spectra for galaxy survey observables, including galaxy clustering and cosmic shear. We define the redshift-space correlation function (RCF) as the correlation function defined in the parameter space of two redshifts and an angular separation angle. Assuming that there is no preferred direction in the sky, these parameters fully define the relative position of two galaxies, independently of the assumed cosmologica…

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
Zahra Mobini, Ahmet Hasim Gokceoglu, Li Wang, Gunnar Peters, Hien Quoc Ngo
https://arxiv.org/abs/2506.14494

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral e…

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Sijia Geng, Thomas Lee, Dharik Mallapragada, Audun Botterud
https://arxiv.org/abs/2506.09388

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispat…

Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
https://arxiv.org/abs/2506.12287

Relative Error Fair Clustering in the Weak-Strong Oracle Model
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak oracle providing potentially inaccurate distance estimates at a low cost. The goal is to produce a near-optimal fair clustering on $n$ input points with a minimum number of strong oracle queries. This models the increasingly common trade-off between accurate but expensive similarity measures (e.g., large-scale embedding…

Understanding uncertainty in Bayesian cluster analysis
Cecilia Balocchi, Sara Wade
https://arxiv.org/abs/2506.16295 https://arxiv.org…

Understanding uncertainty in Bayesian cluster analysis
The Bayesian approach to clustering is often appreciated for its ability to provide uncertainty in the partition structure. However, summarizing the posterior distribution over the clustering structure can be challenging, due the discrete, unordered nature and massive dimension of the space. While recent advancements provide a single clustering estimate to represent the posterior, this ignores uncertainty and may even be unrepresentative in instances where the posterior is multimodal. To enhanc…

This https://arxiv.org/abs/2407.09056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments
Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known as jets. Accurate jet clustering is crucial as it retains the information of the originating quark or gluon and forms the basis for studying properties of the Higgs boson, which underlies teh mechanism of mass generation for subatomic particles. For …

This https://arxiv.org/abs/2410.00023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Self-Tuning Spectral Clustering for Speaker Diarization
Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity matrix called spectral clustering on p-neighborhood retained affinity matrix (SC-pNA). Our method improves on node-specific fixed neighbor selection by allowing a variable number …

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

Machine learning based prediction of dynamical clustering in granular gases
Sai Preetham Sata, Ralf Stannarius, Benjamin Noack, Dmitry Puzyrev
https://arxiv.org/abs/2506.15657

Machine learning based prediction of dynamical clustering in granular gases
When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called clusters. This dynamical clustering, or gas-cluster transition, is caused by a complex interplay and balance between the energy influx and dissipation in particle collisions. Particle number density, container geometry, and excitation strength influence this tran…

In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration
Jiajie Fu, Haitong Tang, Arijit Khan, Sharad Mehrotra, Xiangyu Ke, Yunjun Gao
https://arxiv.org/abs/2506.02509

In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration
Entity Resolution (ER) is a fundamental data quality improvement task that identifies and links records referring to the same real-world entity. Traditional ER approaches often rely on pairwise comparisons, which can be costly in terms of time and monetary resources, especially with large datasets. Recently, Large Language Models (LLMs) have shown promising results in ER tasks. However, existing methods typically focus on pairwise matching, missing the potential of LLMs to perform clustering di…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
Olivier Saidi
https://arxiv.org/abs/2506.13810 https:…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
NP hard optimization problems like the Traveling Salesman Problem (TSP) defy efficient solutions in the worst case, yet real-world instances often exhibit exploitable patterns. We propose a novel patternaware complexity framework that quantifies and leverages structural regularities e.g., clustering, symmetry to reduce effective computational complexity across domains, including financial forecasting and LLM optimization. With rigorous definitions, theorems, and a meta learning driven solver pi…

A Robust Nonparametric Framework for Detecting Repeated Spatial Patterns
Rajitha Senanayake, Pratheepa Jeganathan
https://arxiv.org/abs/2506.14103 https://…

A Robust Nonparametric Framework for Detecting Repeated Spatial Patterns
Identifying spatially contiguous clusters and repeated spatial patterns (RSP) characterized by similar underlying distributions that are spatially apart is a key challenge in modern spatial statistics. Existing constrained clustering methods enforce spatial contiguity but are limited in their ability to identify RSP. We propose a novel nonparametric framework that addresses this limitation by combining constrained clustering with a post-clustering reassigment step based on the maximum mean disc…

End-to-End Diarization utilizing Attractor Deep Clustering
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
https://arxiv.org/abs/2506.11090 https://…

End-to-End Diarization utilizing Attractor Deep Clustering
Speaker diarization remains challenging due to the need for structured speaker representations, efficient modeling, and robustness to varying conditions. We propose a performant, compact diarization framework that integrates conformer decoders, transformer-updated attractors, and a deep clustering style angle loss. Our approach refines speaker representations with an enhanced conformer structure, incorporating cross-attention to attractors and an additional convolution module. To enforce struct…

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 …

Differentially Private Explanations for Clusters
Amir Gilad, Tova Milo, Kathy Razmadze, Ron Zadicario
https://arxiv.org/abs/2506.05900 https://

Differentially Private Explanations for Clusters
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental tasks of data analysis is clustering , which is meant to unravel hidden patterns within complex datasets. However, interpreting clustering results poses significant challenges, and often necessitates an…

Clustering of large deviations events in heavy-tailed moving average processes: the catastrophe principle in the short-memory case
Jiaqi Wang, Gennady Samorodnitsky
https://arxiv.org/abs/2506.10256

Clustering of large deviations events in heavy-tailed moving average processes: the catastrophe principle in the short-memory case
How do large deviation events in a stationary process cluster? The answer depends not only on the type of large deviations, but also on the length of memory in the process. Somewhat unexpectedly, it may also depend on the tails of the process. In this paper we work in the context of large deviations for partial sums in moving average processes with short memory and regularly varying tails. We show that the structure of the large deviation cluster in this case markedly differs from the correspon…

How universal is the mean-field universality class for percolation in complex networks?
Lorenzo Cirigliano
https://arxiv.org/abs/2506.17175 https://…

How universal is the mean-field universality class for percolation in complex networks?
Clustering and degree correlations are ubiquitous in real-world complex networks. Yet, understanding their role in critical phenomena remains a challenge for theoretical studies. Here, we provide the exact solution of site percolation in a model for strongly clustered random graphs, with many overlapping loops and heterogeneous degree distribution. We systematically compare the exact solution with mean-field predictions obtained from a treelike random rewiring of the network, which preserves on…

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Guillem Pellicer, Carlos Sabater
https://arxiv.org/abs/2506.09608

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasib…

This https://arxiv.org/abs/2504.14978 has been replaced.
link: https://scholar.google.com/scholar?q=a

clusttraj: A Solvent-Informed Clustering Tool for Molecular Modeling
Clustering techniques are consolidated as a powerful strategy for analyzing the extensive data generated from molecular modeling. In particular, some tools have been developed to cluster configurations from classical simulations with a standard focus on individual units, ranging from small molecules to complex proteins. Since the standard approach includes computing the Root Mean Square Deviation (RMSD) of atomic positions, accounting for the permutation between atoms is crucial for optimizing …

Hemisphere mass up to four-loops with generalised $k_t$ algorithms
K. Khelifa-Kerfa, M. Benghanem
https://arxiv.org/abs/2506.14415 https://

Hemisphere mass up to four-loops with generalised $k_t$ algorithms
We compute the fixed-order distribution of the non-global hemisphere mass observable in $e^+ e^-$ annihilation up to four loops for various sequential recombination jet algorithms. In particular, we focus on the $k_t$ and Cambridge/Aachen algorithms. Using eikonal theory and strong-energy ordering of the final-state partons, we determine the complete structure of both abelian (clustering) and non-abelian non-global logarithms through four loops in perturbation theory. We compare the resulting r…

Covariance Decomposition for Distance Based Species Tree Estimation
Georgios Aliatimis, Ruriko Yoshida, Burak Boyak, James Grant
https://arxiv.org/abs/2506.16425

Covariance Decomposition for Distance Based Species Tree Estimation
In phylogenomics, species-tree methods must contend with two major sources of noise; stochastic gene-tree variation under the multispecies coalescent model (MSC) and finite-sequence substitutional noise. Fast agglomerative methods such as GLASS, STEAC, and METAL combine multi-locus information via distance-based clustering. We derive the exact covariance matrix of these pairwise distance estimates under a joint MSC-plus-substitution model and leverage it for reliable confidence estimation, and …

Scale-dependent bias and mode coupling in redshift-space clustering near the BAO scale
Aseem Paranjape (IUCAA), Ravi K. Sheth (UPenn/ICTP)
https://arxiv.org/abs/2506.08082

Scale-dependent bias and mode coupling in redshift-space clustering near the BAO scale
The baryon acoustic oscillation (BAO) feature in the 2-point clustering of biased tracers in redshift space can be described in a model-agnostic manner, relying only on the assumption that nonlinear growth approximately smears this feature with a Gaussian kernel sourced by gravitationally driven bulk flows as in the Zel'dovich approximation. An explicit model that demonstrated this in recent work did not account for two physical effects that are very likely observationally relevant in the conte…

A Far-Infrared Search for Planet Nine Using AKARI All-Sky Survey
Amos Y. -A. Chen, Tomotsugu Goto, Issei Yamamura, Takao Nakagawa, Cossas K. -W. Wu, Terry Long Phan, Tetsuya Hashimoto, Yuri Uno, Simon C. -C. Ho, Seong Jin Kim
https://arxiv.org/abs/2506.12854

A Far-Infrared Search for Planet Nine Using AKARI All-Sky Survey
An unusual orbital element clustering of Kuiper belt objects (KBOs) has been observed. The most promising dynamic solution is the presence of a giant planet in the outer Solar system, Planet Nine. However, due to its extreme distance, intensive searches in optical have not been successful. We aim to find Planet Nine in the far-infrared, where it has the peak of the black body radiation, using the most sensitive all-sky far-infrared survey to date, AKARI. In contrast to optical searches, where t…

Anomalous current fluctuations and mobility-driven clustering
Tanmoy Chakraborty, Punyabrata Pradhan
https://arxiv.org/abs/2506.00949 https://

Anomalous current fluctuations and mobility-driven clustering
We study steady-state current fluctuations in hardcore lattice gases on a ring of $L$ sites, where $N$ particles perform symmetric, {\it extended-ranged} hopping. The hop length is a random variable depending on a length scale $l_0$ (hopping range) and the inter-particle gap. The systems have mass-conserving dynamics with global density $ρ= N/L$ fixed, but violate detailed balance. We consider two analytically tractable cases: (i) $l_0 = 2$ (finite-ranged) and (ii) $l_0 \to \infty$ (infinite-r…

This https://arxiv.org/abs/2502.12316 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Large-scale clustering of inertial particles in a rotating, stratified and inhomogeneous turbulence
We develop a theory of various kinds of large-scale clustering of inertial particles in a rotating density stratified or inhomogeneous turbulent fluid flows. The large-scale particle clustering occurs in scales which are much larger than the integral scale of turbulence, and it is described in terms of the effective pumping velocity in a turbulent flux of particles. We show that for a fast rotating strongly anisotropic turbulence, the large-scale clustering occurs in the plane perpendicular to …

This https://arxiv.org/abs/2305.19965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A clustering theorem in fractional Sobolev spaces
We prove a general clustering result for the fractional Sobolev space $W^{s,p}$: whenever the positivity set of a function $u$ in a square has measure bounded from below by a multiple of the cube's volume, and the $W^{s,p}$-seminorm of $u$ is bounded from above by a convenient power of the cube's side, then $u$ is positive in a universally reduced cube. Our result aims at applications in regularity theory for fractional elliptic and parabolic equations. Also, by means of suitable interpolation …

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Vu Thi Huong, Ida Litzel, Thorsten Koch
https://arxiv.org/abs/2506.04045

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive dat…

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Christian Tinauer, Maximilian Sackl, Stefan Ropele, Christian Langkammer
https://arxiv.org/abs/2506.03890

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural net…

Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
https://arxiv.org/abs/2506.05495 https://

Learning-Augmented Hierarchical Clustering
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong barriers to approximation algorithms with the hardness of approximation. Thus, we consider the problem of hierarchical clustering given auxiliary information from natural oracles. Specifically, we fo…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new/
[1/1]:
Low-Distortion Clustering in Bounded Growth Graphs
https://

This https://arxiv.org/abs/2505.16223 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network
In this paper, we propose MADCluster, a novel model-agnostic anomaly detection framework utilizing self-supervised clustering. MADCluster is applicable to various deep learning architectures and addresses the 'hypersphere collapse' problem inherent in existing deep learning-based anomaly detection methods. The core idea is to cluster normal pattern data into a 'single cluster' while simultaneously learning the cluster center and mapping data close to this center. Also, to improve expressiveness…

Clustering and emergent hyperuniformity by breaking microswimmer shape and actuation symmetries
Anson G. Thambi, William E. Uspal
https://arxiv.org/abs/2506.12293

Clustering and emergent hyperuniformity by breaking microswimmer shape and actuation symmetries
Hydrodynamic interactions driven by particle activity are ubiquitous in active colloidal systems. Although these interactions are strongly influenced by the interfacial actuation mechanism and geometry of the swimming particles, theoretical understanding of how these microscopic design parameters govern collective dynamics remains limited. Here, we investigate the collective dynamics of oblate spheroidal microswimmers. Using an approximate kinetic theory and corroborating boundary element metho…

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
Emy Mons, Vipul Prasad Maranchery, M. S. Suryan Sivadas, Charles Jose
https://arxiv.org/abs/2506.07662

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the linear bias approximation of the halo model is that the halo bias between two halo populations is a separable function of the mass of each population. In this work, we test the validity of the separable bias approximation on quasi-linear scales using both simulations and observational data. Un…

Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Luca Aiello, Raffaele Argiento, Sirio Legramanti, Lucia Paci
https://arxiv.org/abs/2505.24694

Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce …

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
https://arxiv.org/abs/2506.00828

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant tha…

Imprint of $\alpha$-clustering on nucleon-nucleon correlations in relativistic light ion collisions
Hadi Mehrabpour
https://arxiv.org/abs/2506.12673 https:…

Imprint of $α$-clustering on nucleon-nucleon correlations in relativistic light ion collisions
This study investigates the influence of $α$-cluster structures in relativistic light nuclear collisions. Using a cluster framework, the characteristics of the nucleonic configurations of $^{16}$ O and $^{20}$ Ne are extracted as predicted by various \textit{ab initio} models, including Nuclear Lattice Effective Field Theory (NLEFT), Variational Monte Carlo (VMC), and the Projected Generator Coordinate Method (PGCM). Additionally, I analyze configurations derived from a three-parameter Fermi (…

Consistent line clustering using geometric hypergraphs
Kalle Alaluusua, Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskel\"a
https://arxiv.org/abs/2505.24868

Consistent line clustering using geometric hypergraphs
Traditional data analysis often represents data as a weighted graph with pairwise similarities, but many problems do not naturally fit this framework. In line clustering, points in a Euclidean space must be grouped so that each cluster is well approximated by a line segment. Since any two points define a line, pairwise similarities fail to capture the structure of the problem, necessitating the use of higher-order interactions modeled by geometric hypergraphs. We encode geometry into a 3-unifor…

SemanticST: Spatially Informed Semantic Graph Learning for1 Clustering, Integration, and Scalable Analysis of Spatial2 Transcriptomics
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad-Rokny
https://arxiv.org/abs/2506.11491

SemanticST: Spatially Informed Semantic Graph Learning for1 Clustering, Integration, and Scalable Analysis of Spatial2 Transcriptomics
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogeneity. However, current analysis methods often struggle with noisy data, limited scalability, and inadequate modelling of complex cellular relationships. We present SemanticST, a biologically informed, graph-based deep learning framework that models diverse cellular contexts through multi-semantic graph construction. Sem…

This https://arxiv.org/abs/2502.16212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Clustering-Based Sub-QUBO Extraction for Hybrid QUBO Solvers
Quantum Approximate Optimization Algorithm (QAOA) can be used to solve quadratic unconstrained binary optimization (QUBO) problems. However, the size of the solvable problem is limited by the number of qubits. To leverage noisy intermediate-scale quantum (NISQ) devices to solve large-scale QUBO problems, one possible way is to decompose the full problem into multiple sub-problems, which we refer to as the Sub-QUBO Formalism. In this work, we enhance this formalism by proposing a sub-QUBO extrac…

This https://arxiv.org/abs/2503.16688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Random bipartite graphs with i.i.d. weights and applications to inhomogeneous random intersection graphs
We propose a random bipartite graph with weights assigned to both parts of the vertex sets. Edges are formed independently with probabilities that depend on these weights. This bipartite graph naturally gives rise to a random intersection graph which has nontrivial clustering properties and inhomogeneous vertex degrees. We focus on the situation where the weights are themselves i.i.d. random variables. In the so-called moderate clustering regime, we identify three types of scaling limit for the…

Primordial black holes with mass ratio-modulated initial clustering: merger suppression and projected constraints
Gabriel Luis Dizon
https://arxiv.org/abs/2506.06648

Primordial black holes with mass ratio-modulated initial clustering: merger suppression and projected constraints
We present a modification of the expected local primordial black hole (PBH) count $N(y)$, typically seen in the context of the early PBH binary merger rate as a term in the merger rate suppression. We utilize recent results in small-scale PBH clustering to formulate $N(y)$ in such a way that accounts for variations in the binary mass ratio $q$. We then examine how this change affects the projected constraints on PBH abundance from simulated Einstein Telescope (ET) and LISA mergers. Our results …

Lonely Little Red Dots: Challenges to the AGN-nature of little red dots through their clustering and spectral energy distributions
Mar\'ia de las Mercedes Carranza Escudero, Christopher J. Conselice, Nathan Adams, Thomas Harvey, Duncan Austin, Peter Behroozi, Leonardo Ferreira, Katherine Ormerod, Qiao Duan, James Trussler, Qiong Li, Lewi Westcott, Rogier A. Windhorst, Dan Coe, Seth H. Cohen, Cheng Cheng, Simon P. Driver, Brenda Frye, Lukas J. Furtak, Norman A. Grogin, Nimish P. Hat…

Lonely Little Red Dots: Challenges to the AGN-nature of little red dots through their clustering and spectral energy distributions
Observations with the James Webb Space Telescope (JWST) reveal a previously unseen population of compact red objects, known as ``little red dots`` (LRDs). We study a new photometrically selected sample of 124 LRDs in the redshift range $z$ $\sim$ 3 - 10 selected from NIRCam coverage of the CEERS, NEP-TDF, JADES and JEMS surveys. For JADES, the NEP-TDF and CEERS, we compare SED models with and without AGN components and analyse the impact of an AGN component on the goodness of fit using the Baye…

Unit Commitment with Cost-Oriented Temporal Resolution
Junyi Tao, Ran Li, Salvador Pineda
https://arxiv.org/abs/2506.02707 https://ar…

Unit Commitment with Cost-Oriented Temporal Resolution
Time-adaptive unit commitment (UC) has recently been investigated to reduce the scheduling costs by flexibly varying the temporal resolution, which is usually determined by clustering the net load patterns. However, there exists a misalignment between cost and net load patterns due to the discrete start-up costs and out-of-merit-order dispatch triggered by ramping and other constraints. The optimal time-adaptive resolution cannot be completely captured by clustering-based method. This paper pro…

Dynamic allocation: extremes, tail dependence, and regime Shifts
Yin Luo, Sheng Wang, Javed Jussa
https://arxiv.org/abs/2506.12587 https://

Dynamic allocation: extremes, tail dependence, and regime Shifts
By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity fac…

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

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…

Proximal Operators of Sorted Nonconvex Penalties
Anne Gagneux, Mathurin Massias, Emmanuel Soubies
https://arxiv.org/abs/2506.15315 https://

Proximal Operators of Sorted Nonconvex Penalties
This work studies the problem of sparse signal recovery with automatic grouping of variables. To this end, we investigate sorted nonsmooth penalties as a regularization approach for generalized linear models. We focus on a family of sorted nonconvex penalties which generalizes the Sorted L1 Norm (SLOPE). These penalties are designed to promote clustering of variables due to their sorted nature, while the nonconvexity reduces the shrinkage of coefficients. Our goal is to provide efficient ways t…

Conformal coronary calcification volume estimation with conditional coverage via histogram clustering
Olivier Jaubert, Salman Mohammadi, Keith A. Goatman, Shadia S. Mikhael, Conor Bradley, Rebecca Hughes, Richard Good, John H. Hipwell, Sonia Dahdouh
https://arxiv.org/abs/2506.04030

Conformal coronary calcification volume estimation with conditional coverage via histogram clustering
Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. However, over-reporting could negatively affect patient wellbeing and unnecessarily burden the medical system. Therefore, careful considerations should be taken when automatically reporting coronary calcium scores. A cluster-based conditional conformal prediction framework is proposed to provide score intervals with calibrated coverage from trained s…

This https://arxiv.org/abs/2312.03940 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

PECANN: Parallel Efficient Clustering with Graph-Based Approximate Nearest Neighbor Search
This paper studies density-based clustering of point sets. These methods use dense regions of points to detect clusters of arbitrary shapes. In particular, we study variants of density peaks clustering, a popular type of algorithm that has been shown to work well in practice. Our goal is to cluster large high-dimensional datasets, which are prevalent in practice. Prior solutions are either sequential, and cannot scale to large data, or are specialized for low-dimensional data. This paper unif…

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
Madan Krishnamurthy, Daniel Korn, Melissa A Haendel, Christopher J Mungall, Anne E Thessen
https://arxiv.org/abs/2506.02160

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
This research aims to develop a dynamic and scalable framework to facilitate harmonization of Common Data Elements (CDEs) across heterogeneous biomedical datasets by addressing challenges such as semantic heterogeneity, structural variability, and context dependence to streamline integration, enhance interoperability, and accelerate scientific discovery. Our methodology leverages Large Language Models (LLMs) for context-aware text embeddings that convert CDEs into dense vectors capturing semant…

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Zhongyuan Lyu, Yuqi Gu
https://arxiv.org/abs/2506.07167 https://…

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Latent class models are widely used for identifying unobserved subgroups from multivariate categorical data in social sciences, with binary data as a particularly popular example. However, accurately recovering individual latent class memberships and determining the number of classes remains challenging, especially when handling large-scale datasets with many items. This paper proposes a novel two-stage algorithm for latent class models with high-dimensional binary responses. Our method first i…

This https://arxiv.org/abs/2412.04325 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Clustering-induced localization of quantum walks on networks
Quantum walks on networks are a paradigmatic model in quantum information theory. Quantum-walk algorithms have been developed for various applications, including spatial-search problems, element-distinctness problems, and node centrality analysis. Unlike their classical counterparts, the evolution of quantum walks is unitary, so they do not converge to a stationary distribution. However, for many applications, it is important to understand the long-time behavior of quantum walks and the impact …

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…

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Shuren Zhou, Zhao Chen, Yu Yu
https://arxiv.org/abs/2506.04671

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum …

Extracting Information About Publication Venues Using Citation-Informed Transformers
Brian D. Zimmerman, Joshua Folkins, Olga Vechtomova
https://arxiv.org/abs/2506.08199

Extracting Information About Publication Venues Using Citation-Informed Transformers
Scientific document embeddings contain a variety of rich features which can be harnessed for downstream tasks such as recommendation, ranking, and clustering. We explore which tangible insights can be drawn from scientific document embeddings to understand trends in computer science research featured across nine well-known venues. We collect approximately 60,000 scientific documents published between 2015 and 2023 and analyze their embeddings, which we produce with the SPECTER pre-trained langu…

This https://arxiv.org/abs/2412.03238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Dynamic Consistent $k$-Center Clustering with Optimal Recourse
Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a $k$-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM '17] and Fichtenberger, Lattanzi, Norouzi-Fard, and…

Constraining the lensing dispersion from the angular clustering of binary black hole mergers
Fumihiro Chuman, Masamune Oguri
https://arxiv.org/abs/2506.10469

Constraining the lensing dispersion from the angular clustering of binary black hole mergers
Gravitational waves from inspiraling compact binaries provide direct measurements of luminosity distances and serve as a powerful probe of the high-redshift Universe. In addition to their role as standard sirens, they offer an opportunity to constrain small-scale density fluctuations through the dispersion in the distance-redshift relation induced by gravitational lensing. We propose a method to constrain this lensing dispersion without requiring the redshift information by analyzing the angula…

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
Dorsa Sadat Hosseini, Amir Dehghani, J. Leo Kim, Alex Krolewski, Suvodip Mukherjee, Ghazal Geshnizjani
https://arxiv.org/abs/2506.11201

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
We present a framework for relating gravitational wave (GW) sources to the astrophysical properties of spectroscopic galaxy samples. We show how this can enable using clustering measurements of gravitational wave (GW) sources to infer the relationship between the GW sources and the astrophysical properties of their host galaxies. We accomplish this by creating mock GW catalogs from the spectroscopic Sloan Digital Sky Survey (SDSS) DR7 galaxy survey. We populate the GWs using a joint host-galaxy…

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
Okemawo Obadofin, Joao Barros
https://arxiv.org/abs/2506.05601 https://

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
A critical requirement for modern-day Intelligent Transportation Systems (ITS) is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol (LISP) and the Hierarchical Hexagonal Clustering (H3) geo-spatial indexing system, offers a promising framework for dynamic, privacy-preserving data aggregation. Seeking to address the critical security and privacy v…

This https://arxiv.org/abs/2412.03238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Dynamic Consistent $k$-Center Clustering with Optimal Recourse
Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a $k$-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM '17] and Fichtenberger, Lattanzi, Norouzi-Fard, and…

This https://arxiv.org/abs/2411.07502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Ab initio informed 20Ne(p, p$α$)16O reaction elucidates the emergence of alpha clustering from chiral potentials
We report on the first \textit{ab initio} informed $α$ knock-out reaction in the intermediate-mass region, with the aim to probe the underlying chiral potential and its impact on the emergence of alpha clustering in this mass region. The theoretical predictions of the $α+^{16}$O clustering in the $^{20}$Ne ground state, based on the \textit{ab initio} symmetry-adapted no-core shell model, yield a triple differential cross section for $^{20}$Ne(p, p$α$)$^{16}$O that is in a remarkable agreeme…

Strongly Consistent Community Detection in Popularity Adjusted Block Models
Quan Yuan, Binghui Liu, Danning Li, Lingzhou Xue
https://arxiv.org/abs/2506.07224

Strongly Consistent Community Detection in Popularity Adjusted Block Models
The Popularity Adjusted Block Model (PABM) provides a flexible framework for community detection in network data by allowing heterogeneous node popularity across communities. However, this flexibility increases model complexity and raises key unresolved challenges, particularly in effectively adapting spectral clustering techniques and efficiently achieving strong consistency in label recovery. To address these challenges, we first propose the Thresholded Cosine Spectral Clustering (TCSC) algor…

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

This https://arxiv.org/abs/2410.21225 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Boosting HI-Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations
After reionization, neutral hydrogen (HI) traces the large-scale structure (LSS) of the Universe, enabling HI intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study HI clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of $k$-nearest neighbor ($k$NN) optical galaxies and the HI brightness tempe…

This https://arxiv.org/abs/2408.00651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Dirichlet stochastic block model for composition-weighted networks
Network data are observed in various applications where the individual entities of the system interact with or are connected to each other, and often these interactions are defined by their associated strength or importance. Clustering is a common task in network analysis that involves finding groups of nodes displaying similarities in the way they interact with the rest of the network. However, most clustering methods use the strengths of connections between entities in their original form, ig…

Cosmic curl -- Features and convergence of the vorticity power spectrum in $N$-body simulations
Camilla T. G. S{\o}rensen, Steen Hannestad, Thomas Tram
https://arxiv.org/abs/2506.15486

Cosmic curl -- Features and convergence of the vorticity power spectrum in $N$-body simulations
Observations of the cosmic velocity field could become an important cosmological probe in the near future. To take advantage of future velocity-flow surveys we must however have the theoretical predictions under control. In many respects, the velocity field is easier to simulate than the density field because it is less severely affected by small-scale clustering. Therefore, as we also show in this paper, a particle-mesh (PM) based simulation approach is usually sufficient, yielding results wit…

The dynamics of background evolution and structure formation in phase space: a semi-cosmographic reconstruction
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
https://arxiv.org/abs/2506.14275

The dynamics of background evolution and structure formation in phase space: a semi-cosmographic reconstruction
The Baryon Acoustic Oscillation (BAO) feature, imprinted in the transverse and radial clustering of dark matter tracers, enables the simultaneous measurement of the angular diameter distance $D_A(z)$ and the Hubble parameter $H(z)$ at a given redshift. Further, measuring the redshift space anisotropy (RSD) allows us to measure the combination $f_8\equiv fσ_8(z)$. Motivated by this, we simultaneously study the dynamics of background evolution and structure formation in an abstract phase space o…

MeerKAT HI observations of Low Surface Brightness/Ultradiffuse Galaxy Candidates Projected around Two Southern Loose Groups
Chandreyee Sengupta, Tom C. Scott, Hao Chen, Hyein Yoon, Yogesh Chandola, Mengtian Li, Gyula I. G. J\'ozsa, O. Ivy Wong, Yin-Zhe Ma, Patricio Lagos, Ruta Kale, Denis Tramonte
https://arxiv.org/abs/2506.…

MeerKAT HI observations of Low Surface Brightness/Ultradiffuse Galaxy Candidates Projected around Two Southern Loose Groups
A large catalogue of low surface brightness galaxies (LSBGs) from the Dark Energy Survey showed significant clustering around nearby galaxy groups and clusters. Using the HIPASS survey, we tried to determine the redshift of a sub-sample of these LSBGs and determine whether they were members of the groups they were projected near, but this was hampered by HIPASS's high spectral rms. This letter reports on MeerKAT H I observations to determine the redshifts of 52 LSBG candidates projected in the …

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new/
[1/1]:
Improved Combinatorial Approximations for Weighted Correlation Clustering

Merger History of Clustered Primordial Black Holes
Viktor Stasenko
https://arxiv.org/abs/2506.11290 https://arxiv.org/pdf/2506.11290

Merger History of Clustered Primordial Black Holes
Primordial black hole (PBH) binaries experience strong gravitational perturbations in the case of their initial clustering, which significantly affects the dynamics of their mergers. In this work, we develop a new formalism to account for these perturbations and track the evolution of the binary orbital parameters distribution. Based on this approach, we calculate the merger rate of PBH binaries and demonstrate that its temporal evolution differs greatly from that of isolated binary systems. Mo…

This https://arxiv.org/abs/2505.20656 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A joint analysis of 3D clustering and galaxy \times CMB-lensing cross-correlations with DESI DR1 galaxies
The spectroscopic data from DESI Data Release 1 (DR1) galaxies enables the analysis of 3D clustering by fitting galaxy power spectra and reconstructed correlation functions in redshift space. Given low measurements of the amplitude of structure from cosmic shear at $z\sim1$, redshift space distortions (RSD) + Baryon Acoustic Oscillation (BAO) signals from DESI galaxies combined with weak lensing can break degeneracies and provide a tight alternative constraint on the $z\sim1$ amplitude of struc…