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 …

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…

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 …

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 …

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…

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 …

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…

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…

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…

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…

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…

ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
Alexander Hoyle, Lorena Calvo-Bartolom\'e, Jordan Boyd-Graber, Philip Resnik
https://arxiv.org/abs/2507.00828

ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this p…

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…

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.02337

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, such as convergence plots, trajectory mapping, and fitness landscape analysis, often fall short in illustrating the structural dynamics of the search process, especially in high-dimensional or complex solution spaces. To address this, we propose a novel representation and visualization methodology that clusters solution …

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 …

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…

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 …

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Eloy Pe\~na-Asensio, Fabio Ferrari
https://arxiv.org/abs/2507.01501 https://…

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Accurate identification of meteoroid streams is central to understanding their origins and evolution. However, overlapping clusters and background noise hinder classification, an issue amplified for missions such as ESA's LUMIO that rely on meteor shower observations to infer lunar meteoroid impact parameters. This study evaluates the performance of the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm for unsupervised meteoroid stream identification, …

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…

This https://arxiv.org/abs/2212.05053 has been replaced.
link: https://scholar.google.com/scholar?q=a

Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Modern network datasets are often composed of multiple layers, either as different views, time-varying observations, or independent sample units, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks, while at the same time being parsimonious and tractable to yield computationally efficient a…

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 …

This https://arxiv.org/abs/2312.13416 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Novel Criterion for Interpreting Acoustic Emission Damage Signals Based on Cluster Onset Distribution
Structural health monitoring (SHM) relies on non-destructive techniques such as acoustic emission (AE) that generate large amounts of data over the lifespan of systems. Clustering methods are used to interpret these data and gain insights into damage progression and mechanisms. Conventional methods for evaluating clustering results utilise clustering validity indices (CVI) that prioritise compact and separable clusters. This paper introduces a novel approach based on the temporal sequence of cl…

Finsler Metric Clustering in Weighted Projective Spaces
Tony Shaska
https://arxiv.org/abs/2507.00001 https://arxiv.org/pdf/2507.00001…

Finsler Metric Clustering in Weighted Projective Spaces
This paper develops a hierarchical clustering algorithm for weighted projective spaces $\mathbb{P}_{\mathbf{q}}$, utilizing a Finsler metric $ d_F([z], [w]) $ and its rational analogue $ d_{F,\mathbb{Q}}([z], [w])$ to define distances that preserve the non-Euclidean geometry of these quotient manifolds. Defined via geodesic integrals of a scaling invariant Finsler norm weighted by the grades $\mathbf{q} = (q_0, q_1, \dots, q_n)$, these metrics satisfy true metric properties including the triang…

Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystr\v{c}ilov\'a, Shashwat Sridhar, Tim Gollisch, Andreas S. Tolias, Alexander S. Ecker
https://arxiv.org/abs/2506.03293

Learning to cluster neuronal function
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types. This raises the question whether th…

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…

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…

Cosmological constraints from galaxy clustering and galaxy-galaxy lensing with extended SubHalo Abundance Matching
Constance Mahony, Sergio Contreras, Raul E. Angulo, David Alonso, Christos Georgiou, Andrej Dvornik
https://arxiv.org/abs/2507.01601

Cosmological constraints from galaxy clustering and galaxy-galaxy lensing with extended SubHalo Abundance Matching
We present the first cosmological constraints from a joint analysis of galaxy clustering and galaxy-galaxy lensing using extended SubHalo Abundance Matching (SHAMe). We analyse stellar mass-selected Galaxy And Mass Assembly (GAMA) galaxy clustering and Kilo-Degree Survey (KiDS-1000) galaxy-galaxy lensing and find constraints on $S_8\equivσ_8\sqrt{Ω_{\rm m}/0.3}=0.793^{+0.025}_{-0.024}$, in agreement with Planck at 1.7$σ$, and consistent with previous results. We are able to constrain all 5 S…

This https://arxiv.org/abs/2409.14644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models
The advent of large language models (LLMs) has significantly advanced artificial intelligence (AI) in software engineering (SE), with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering. However, existing methods for source code embedding, including those based on LLMs, often rely on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel approach to embedding source code by combining large la…

Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
Zhaoyang Li, Jie Wang, XiaoXiao Li, Wangjie Li, Longjie Luo, Lin Li, Qingyang Hong
https://arxiv.org/abs/2506.02610

Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segments. To address these limitations, we propose an Overlapping Community Detection method based on Graph Attention networks and the Label Propagation Algorithm (OCDGALP). The proposed framework comprises two key components: (1) a graph attention network that refines speaker embedd…

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…

SPARSE-PIVOT: Dynamic correlation clustering for node insertions
Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovi\'c
https://arxiv.org/abs/2507.01830

SPARSE-PIVOT: Dynamic correlation clustering for node insertions
We present a new Correlation Clustering algorithm for a dynamic setting where nodes are added one at a time. In this model, proposed by Cohen-Addad, Lattanzi, Maggiori, and Parotsidis (ICML 2024), the algorithm uses database queries to access the input graph and updates the clustering as each new node is added. Our algorithm has the amortized update time of $O_ε(\log^{O(1)}(n))$. Its approximation factor is $20+\varepsilon$, which is a substantial improvement over the approximation factor of t…

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Christian Franssen, Iman van Lelyveld, Bernd Heidergott
https://arxiv.org/abs/2507.00600

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm sel…

DESI DR2 reference mocks: clustering results from Uchuu-BGS and LRG
E. Fern\'andez-Garc\'ia, F. Prada, A. Smith, J. DeRose, A. J. Ross, S. Bailey, M. S. Wang, Z. Ding, C. Guandalin, C. Lamman, R. Vaisakh, R. Kehoe, J. Lasker, T. Ishiyama, S. M. Moore, S. Cole, M. Siudek, A. Amalbert, A. Salcedo, A. Hearin, B. Joachimi, A. Rocher, S. Saito, A. Krolewski, Z. Slepian, Q. Li, K. S. Dawson, E. Jullo, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doe…

DESI DR2 reference mocks: clustering results from Uchuu-BGS and LRG
The aim of this work is to construct mock galaxy catalogues that accurately reproduce the redshift evolution of galaxy number density, clustering statistics, and baryonic properties, such as stellar mass for luminous red galaxies (LRGs) and absolute magnitude in the $r$-band for the bright galaxy sample (BGS), based on the first three years of observations from the Dark Energy Spectroscopic Instrument (DESI). To achieve this, we applied the subhalo abundance matching (SHAM) technique to the Uch…

OLi3-decorated Irida-graphene for High-capacity Hydrogen Storage: A First-principles Study
Jose A. S. Laranjeira, Warda Elaggoune, Nicolas F. Martins, Xihao Chen, Julio R. Sambrano
https://arxiv.org/abs/2506.02375

OLi3-decorated Irida-graphene for High-capacity Hydrogen Storage: A First-principles Study
Efficient hydrogen storage in solid-state materials is essential for next-generation energy systems, yet achieving a high gravimetric capacity with optimal adsorption characteristics remains a critical challenge. Although Li-decorated irida-graphene (IG) has shown promising hydrogen storage potential, its capacity is limited to $\sim$ 7wt\%, which, despite exceeding the U.S. DOE target, remains inadequate for large-scale applications. Additionally, Li clustering over extended cycles may comprom…

This https://arxiv.org/abs/2011.10153 has been replaced.
link: https://scholar.google.com/scholar?q=a

Enriched functional limit theorems for dynamical systems
We prove functional limit theorems for dynamical systems in the presence of clusters of large values which, when summed and suitably normalised, get collapsed in a jump of the limiting process observed at the same time point. To keep track of the clustering information, which gets lost in the usual Skorohod topologies in the space of càdlàg functions, we introduce a new space which generalises the already more general spaces introduced by Whitt. Our main applications are to hyperbolic and non…

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…

Clustering-based accelerometer measures to model relationships between physical activity and key outcomes
Hyatt Moore IV, Thomas N. Robinson, Alexandria Jensen, Fatma Gunturkun, K. Farish Haydel, Kristopher I Kapphahn, Manisha Desai
https://arxiv.org/abs/2507.00484

Clustering-based accelerometer measures to model relationships between physical activity and key outcomes
Accelerometers produce enormous amounts of data. Research that incorporates such data often involves a derived summary metric to describe physical activity. Traditional metrics have often ignored the temporal nature of the data. We build on previous work that applies unsupervised machine learning techniques to describe physical activity patterns over time. Specifically, we evaluate a summary measure of accelerometer data derived from unsupervised clustering in a regression framework through com…

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…

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…

This https://arxiv.org/abs/2401.01473 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning
Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses the…

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…

Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Qian Hui, Sidney I. Resnick, Tiandong Wang
https://arxiv.org/abs/2506.04656

Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Accurately identifying the extremal dependence structure in multivariate heavy-tailed data is a fundamental yet challenging task, particularly in financial applications. Following a recently proposed bootstrap-based testing procedure, we apply the methodology to absolute log returns of U.S. S&P 500 and Chinese A-share stocks over a time period well before the U.S. election in 2024. The procedure reveals more isolated clustering of dependent assets in the U.S. economy compared with China which e…

Modelling Galaxy Clustering and Tomographic Galaxy-Galaxy Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model and Redshift Self-Calibration
Tianqing Zhang, Sunao Sugiyama, Surhud More, Rachel Mandelbaum, Xiangchong Li, Roohi Dalal, Hironao Miyatake, Atsushi J. Nishizawa, Takahiro Nishimichi, Masamune Oguri, Ken Osato, Markus M. Rau, Masato Shirasaki, Tomomi Sunayama, Masahiro Takada

Modelling Galaxy Clustering and Tomographic Galaxy-Galaxy Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model and Redshift Self-Calibration
The combination of galaxy-galaxy weak lensing and galaxy clustering is a powerful probe of the cosmological model, and exploration of how to best model and extract this information from the signals is essential. We present the measurement of the galaxy-galaxy weak lensing signals using the SDSS DR11 spectroscopic galaxies as lens galaxies, and the HSC Y3 shear catalog as source galaxies, binned into four tomographic bins by their photometric redshift. The SDSS DR11 galaxies, with a redshift ran…

This https://arxiv.org/abs/2503.09977 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Quadratic Transform for Fractional Programming in Signal Processing and Machine Learning
Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cramér-Rao bound (CRB) in radar sensing, the normalized cut in graph clustering, and the margin in support vector machine (SVM). This article provides a comp…

This https://arxiv.org/abs/2503.04672 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to…

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…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis
Zan Ahmad, et al.

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…

This https://arxiv.org/abs/2503.23809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Nuclear clustering process in heavy-ion collisions : experimental constraints on the low-temperature region of the QCD phase diagram
In this article, we study the production of Hydrogen and Helium isotopes in heavy-ion collisions in the incident energy range between 80 and 150 MeV/nucleon. We compare their inclusive multiplicities emitted in the transverse plane of the reaction with the predictions given by the thermal model. As a first step, we validate the choice of this approach to describe the experimental measurements. We also show that the transient states have to be explicitly taken into account for a good statistical…

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha
https://arxiv.org/abs/2505.24831

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under unc…

Connected k-Median with Disjoint and Non-disjoint Clusters
Jan Eube, Kelin Luo, Dorian Reineccius, Heiko R\"oglin, Melanie Schmidt
https://arxiv.org/abs/2507.02774

Connected k-Median with Disjoint and Non-disjoint Clusters
The connected $k$-median problem is a constrained clustering problem that combines distance-based $k$-clustering with connectivity information. The problem allows to input a metric space and an unweighted undirected connectivity graph that is completely unrelated to the metric space. The goal is to compute $k$ centers and corresponding clusters such that each cluster forms a connected subgraph of $G$, and such that the $k$-median cost is minimized. The problem has applications in very differe…

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…

A novel test of gravity: Does spacetime geometry track matter density?
Camille Bonvin, Nastassia Grimm, Isaac Tutusaus
https://arxiv.org/abs/2506.04387 htt…

A novel test of gravity: Does spacetime geometry track matter density?
We propose a novel test of gravity that combines galaxy clustering with gravitational lensing. In general relativity, the evolution of matter density fluctuations and of the Weyl potential -- the sum of spatial and temporal distortions of the geometry -- are governed by the same growth function. In contrast, alternative theories of gravity that modify the relation between geometry and matter content can lead to differences in these two growths. Exploiting a recent method to directly measure the…

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/2409.19057 has been replaced.
link: https://scholar.google.com/scholar?q=a

Hölder regularity for the fractional p-Laplacian, revisited
We present an alternative proof for local Hölder regularity of the solutions of the fractional p-Laplace equations, based on clustering and expansion (more precisely, recentering) of positivity.

Generalized nearest-neighbor distance and Hawkes point process modeling applied to mining induced seismicity
Mohammadamin Sedghizadeh, Robert Shcherbakov, Matthew van den Berghe
https://arxiv.org/abs/2506.00768

Generalized nearest-neighbor distance and Hawkes point process modeling applied to mining induced seismicity
Modeling seismic activity rates and clustering plays an important role in studies of induced seismicity associated with mining and other resource extraction operations. This is critical for understanding the physical and statistical characteristics of seismicity and assessing the associated hazard. In this work, we introduce the generalization of the Nearest-Neighbor Distance (NND) method by incorporating an arbitrary distribution function for the frequency-magnitude statistics of seismic event…

This https://arxiv.org/abs/2411.04846 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

On the Complexity of 2-club Cluster Editing with Vertex Splitting
Editing a graph to obtain a disjoint union of s-clubs is one of the models for correlation clustering, which seeks a partition of the vertex set of a graph so that elements of each resulting set are close enough according to some given criterion. For example, in the case of editing into s-clubs, the criterion is proximity since any pair of vertices (in an s-club) are within a distance of s from each other. In this work we consider the vertex splitting operation, which allows a vertex to belong …

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…

Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors
Shu-Fan Chen, Mikhail M. Ivanov
https://arxiv.org/abs/2507.00118 htt…

Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors
The effective-field theory based full-shape analysis with simulation-based priors (EFT-SBP) is the novel analysis of galaxy clustering data that allows one to combine merits of perturbation theory and simulation-based modeling in a unified framework. In this paper we use EFT-SBP with the galaxy clustering power spectrum and bispectrum data from BOSS in order to test the recent preference for dynamical dark energy reported by the DESI collaboration. While dynamical dark energy is preferred by th…

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…

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…

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…

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/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…

Random Hyperbolic Graphs with Arbitrary Mesoscale Structures
Stefano Guarino, Davide Torre, Enrico Mastrostefano
https://arxiv.org/abs/2506.02686 https://

Random Hyperbolic Graphs with Arbitrary Mesoscale Structures
Real-world networks exhibit universal structural properties such as sparsity, small-worldness, heterogeneous degree distributions, high clustering, and community structures. Geometric network models, particularly Random Hyperbolic Graphs (RHGs), effectively capture many of these features by embedding nodes in a latent similarity space. However, networks are often characterized by specific connectivity patterns between groups of nodes -- i.e. communities -- that are not geometric, in the sense t…

This https://arxiv.org/abs/2412.08151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Improving cosmological constraints via galaxy intrinsic alignment in full-shape analysis
The intrinsic alignment (IA) of galaxy shapes probes the underlying gravitational tidal field, thus offering cosmological information complementary to galaxy clustering. In this paper, we perform a Fisher forecast to assess the benefit of IA in improving cosmological parameter constraints, for the first time, leveraging the full-shape (FS) information of IA statistics. Our forecast is based on PFS-like and Euclid-like surveys as examples of deep and wide galaxy surveys, respectively. We explore…

This https://arxiv.org/abs/2408.10868 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Inconsistency Removal of Reduced Bases in Parametric Model Order Reduction by Matrix Interpolation using Adaptive Sampling and Clustering
Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are first sampled and then made consistent with each other by transforming the underlying reduced bases. Finally, the transformed reduced operators can be interpolated to predict reduced models for queried parameter points. However, the accuracy of the predicted red…

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
G. Zacharegkas, C. Chang, J. Prat, W. Hartley, S. Mucesh, A. Alarcon, O. Alves, A. Amon, K. Bechtol, M. R. Becker, G. Bernstein, J. Blazek, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, R. Chen, A. Choi, J. Cordero, C. Davis, J. Derose, H. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, X. Fang, A. Ferte, M. Gatti,…

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from $\sim 100 \; {\rm kpc}$ to $\sim 100 \; {\rm Mpc}$, using a theoretical framework based on the Halo Occupation Distribution and data from Year 3 of the Dark Energy Survey (DES) dataset. The new advances of this work include: 1) the generation and validation o…

A bibliometric analysis on the current situation and hot trends of the impact of microplastics on soil based on CiteSpace
Yiran Zheng, Yue Quan, Su Yan, Xinting Lv, Yuguanmin Cao, Minjie Fu, Mingji Jin
https://arxiv.org/abs/2507.01520

A bibliometric analysis on the current situation and hot trends of the impact of microplastics on soil based on CiteSpace
This paper aims to comprehensively grasp the research status and development trends of soil microplastics (MPs). It collects studies from the Web of Science Core Collection covering the period from 2013 to 2024. Employing CiteSpace and VOSviewer, the paper conducts in - depth analyses of literature regarding the environmental impacts of microplastics. These analyses involve keyword co - occurrence, clustering, burst term identification, as well as co - occurrence analysis of authors and institu…

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…

On the influence of language similarity in non-target speaker verification trials
Paul M. Reuter, Michael Jessen
https://arxiv.org/abs/2506.02777 https://

On the influence of language similarity in non-target speaker verification trials
In this paper, we investigate the influence of language similarity in cross-lingual non-target speaker verification trials using a state-of-the-art speaker verification system, ECAPA-TDNN, trained on multilingual and monolingual variants of the VoxCeleb dataset. Our analysis of the score distribution patterns on multilingual Globalphone and LDC CTS reveals a clustering effect in speaker comparisons involving a training language, whereby the choice of comparison language only minimally impacts s…

FuzzCoh: Robust Canonical Coherence-Based Fuzzy Clustering of Multivariate Time Series
Ziling Ma, Mara Sherlin Talento, Ying Sun, Hernando Ombao
https://arxiv.org/abs/2506.22861

FuzzCoh: Robust Canonical Coherence-Based Fuzzy Clustering of Multivariate Time Series
Brain cognitive and sensory functions are often associated with electrophysiological activity at specific frequency bands. Clustering multivariate time series (MTS) data like EEGs is important for understanding brain functions but challenging due to complex non-stationary cross-dependencies, gradual transitions between cognitive states, noisy measurements, and ambiguous cluster boundaries. To address these issues, we develop a robust fuzzy clustering framework in the spectral domain. Our method…

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…

This https://arxiv.org/abs/2503.01604 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

The $α$-particle condensation in diluted $^{16}\text{O}$ at finite temperature
We investigate the effect of temperature on $α$-particle clustering in the diluted nucleus $^{16}\text{O}$ using the multi-constrained finite-temperature relativistic Hartree-Bogoliubov model with the DD-ME2 interaction. At a critical density the nucleus undergoes a Mott-like transition from a homogeneous to a localised configuration characterised by $α$-particle clustering and the emergence of a finite non-axial octupole deformation. We study the interplay between the onset of localisation u…

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…

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, …

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Fitting Sparse Markov Models to Categorical Time Series Using Convex Clustering
Tuhin Majumder, Soumendra Lahiri, Donald Martin

Angular correlation functions of bright Lyman-break galaxies at $\mathbf{3 \lesssim z \lesssim 5}$
Isabelle Ye, Philip Bull, Rebecca A. A. Bowler, Rachel K. Cochrane, Nathan J. Adams, Matt J. Jarvis
https://arxiv.org/abs/2507.02515

Angular correlation functions of bright Lyman-break galaxies at $\mathbf{3 \lesssim z \lesssim 5}$
We investigate the clustering of Lyman-break galaxies at redshifts of 3 $\lesssim z \lesssim$ 5 within the COSMOS field by measuring the angular two-point correlation function. Our robust sample of $\sim$60,000 bright ($m_{\rm UV}\lesssim 27$) Lyman-break galaxies was selected based on spectral energy distribution fitting across 14 photometric bands spanning optical and near-infrared wavelengths. We constrained both the 1- and 2-halo terms at separations up to 300 arcsec, finding an excess in t…

On-device Streaming Discrete Speech Units
Kwanghee Choi, Masao Someki, Emma Strubell, Shinji Watanabe
https://arxiv.org/abs/2506.01845 https://

On-device Streaming Discrete Speech Units
Discrete speech units (DSUs) are derived from clustering the features of self-supervised speech models (S3Ms). DSUs offer significant advantages for on-device streaming speech applications due to their rich phonetic information, high transmission efficiency, and seamless integration with large language models. However, conventional DSU-based approaches are impractical as they require full-length speech input and computationally expensive S3Ms. In this work, we reduce both the attention window a…

This https://arxiv.org/abs/2412.12187 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Random walk based snapshot clustering for detecting community dynamics in temporal networks
The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk-based approach that can identify clusters of time-snapshots in which network community structures are stable. This allows us to detect significant structural shifts over time, such as the splitting or merging of communities or …

Mixtures of Neural Network Experts with Application to Phytoplankton Flow Cytometry Data
Ethan Pawl, Fran\c{c}ois Ribalet, Paul A. Parker, Sangwon Hyun
https://arxiv.org/abs/2507.01375

Mixtures of Neural Network Experts with Application to Phytoplankton Flow Cytometry Data
Flow cytometry is a valuable technique that measures the optical properties of particles at a single-cell resolution. When deployed in the ocean, flow cytometry allows oceanographers to study different types of photosynthetic microbes called phytoplankton. It is of great interest to study how phytoplankton properties change in response to environmental conditions. In our work, we develop a nonlinear mixture of experts model for simultaneous clustering and regression utilizing random-weight neur…

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
https://arxiv.org/abs/2506.22064

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
This work serves two-fold purpose. Firstly, we provide an alternative to the traditional method of determining the growth rate of density perturbations $f(z)$. In usual practice, $f(z)$ can not be directly measured from tracer clustering at some redshift without knowledge of the bias. While the bayron acoustic oscillation (BAO) imprint allows the determination of $(D_A(z), H(z))$, redshift space anisotropy (RSD) allows the measurement of a quantity $f_8(z) = f(z) σ_{8,0} D_{+}(z)$. To extract …

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…

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/2410.11470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Almost Optimal Fully Dynamic $k$-Center Clustering with Recourse
In this paper, we consider the \emph{metric $k$-center} problem in the fully dynamic setting, where we are given a metric space $(V,d)$ evolving via a sequence of point insertions and deletions and our task is to maintain a subset $S \subseteq V$ of at most $k$ points that minimizes the objective $\max_{x \in V} \min_{y \in S}d(x, y)$. We want to design our algorithm so that we minimize its \emph{approximation ratio}, \emph{recourse} (the number of changes it makes to the solution $S$), and \em…

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…

DNCASR: End-to-End Training for Speaker-Attributed ASR
Xianrui Zheng, Chao Zhang, Philip C. Woodland
https://arxiv.org/abs/2506.01916 https://

DNCASR: End-to-End Training for Speaker-Attributed ASR
This paper introduces DNCASR, a novel end-to-end trainable system designed for joint neural speaker clustering and automatic speech recognition (ASR), enabling speaker-attributed transcription of long multi-party meetings. DNCASR uses two separate encoders to independently encode global speaker characteristics and local waveform information, along with two linked decoders to generate speaker-attributed transcriptions. The use of linked decoders allows the entire system to be jointly trained und…

Revealing a transitional epoch of large-scale cosmic anisotropy in the quasar distribution
Amit Mondal, Biswajit Pandey, Krishna Ghosh
https://arxiv.org/abs/2507.02835

Revealing a transitional epoch of large-scale cosmic anisotropy in the quasar distribution
The Cosmological Principle posits that the Universe is isotropic on the largest scales. While widely supported, this foundational assumption remains testable. We analyse the angular distribution of over one million quasars from the Gaia-unWISE catalogue using Renyi entropy, a multiscale statistical measure sensitive to higher-order clustering. Dividing the sample into three redshift bins, we find that both the low- and high-redshift distributions are statistically consistent with isotropy. Howe…

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…

This https://arxiv.org/abs/2404.07249 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

PolyBin3D: A Suite of Optimal and Efficient Power Spectrum and Bispectrum Estimators for Large-Scale Structure
By measuring, modeling and interpreting cosmological datasets, one can place strong constraints on models of the Universe. Central to this effort are summary statistics such as power spectra and bispectra, which condense the high-dimensional data into low-dimensional representations. In this work, we introduce a modern set of estimators for computing such statistics from three-dimensional clustering data, and provide a flexible Python/Cython implementation; PolyBin3D. Working in a maximum-likel…

Cosmology and Source Redshift Constraints from Galaxy Clustering and Tomographic Weak Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model
Tianqing Zhang, Xiangchong Li, Sunao Sugiyama, Rachel Mandelbaum, Surhud More, Roohi Dalal, Hironao Miyatake, Atsushi J. Nishizawa, Takahiro Nishimichi, Masamune Oguri, Ken Osato, Markus M. Rau, Masato Shirasaki, Tomomi Sunayama, Masahiro Takada

Lensing Without Borders: Measurements of galaxy-galaxy lensing and projected galaxy clustering in DESI DR1
S. Heydenreich, A. Leauthaud, C. Blake, Z. Sun, J. U. Lange, T. Zhang, M. DeMartino, A. J. Ross, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, F. J. Castander, T. Claybaugh, A. Cuceu, A. de la Macorra, J. DeRose, Arjun Dey, Biprateep Dey, P. Doel, N. Emas, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, B. Had…

Lensing Without Borders: Measurements of galaxy-galaxy lensing and projected galaxy clustering in DESI DR1
We present Galaxy-Galaxy Lensing measurements obtained by cross-correlating spectroscopically observed galaxies from the first data release of the Dark Energy Spectroscopic Instrument (DESI) with source galaxies from the Hyper Suprime-Cam Subaru Strategic Survey, the Kilo-Degree Survey, the Sloan Digital Sky Survey, and the Dark Energy Survey. Specifically, we measure the excess surface mass density $ΔΣ$ and tangential shear $γ_\mathrm{t}$ for the Bright Galaxy Sample and Luminous Red Galaxi…

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…

Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis
Tianqing Zhang, Husni Almoubayyed, Rachel Mandelbaum, Markus Michael Rau, Nikolina \v{S}ar\v{c}evi\'c, C. Danielle Leonard, Jeffrey A. Newman, Brett Andrews, Shuang Liang, The LSST Dark Energy Science Collaboration
https://

Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This paper introduces a Fisher forecast code FisherA2Z for the LSST Year 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FisherA2Z employ…

Anisotropic Counts-in-Cells in Redshift Space: A New Route to Cosmological Constraints from Galaxy Surveys
Pawe{\l} Drozda, Wojciech A. Hellwing, Maciej Bilicki
https://arxiv.org/abs/2506.01762

Anisotropic Counts-in-Cells in Redshift Space: A New Route to Cosmological Constraints from Galaxy Surveys
We introduce a novel extension of the volume-averaged correlation function (VACF) framework by replacing the traditional spherical smoothing kernels with anisotropic, ellipsoidal windows. This generalized approach enables the study of shape-dependent clustering statistics and captures directional information encoded in large-scale structure, particularly in redshift space where galaxy distribution is distorted by peculiar velocities. We define and compute ellipsoidal VACFs $\barξ_J (r_{\parall…

This https://arxiv.org/abs/2502.02636 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Bounds on Velocity-Dependent Dark Matter-Baryon Scattering from Large-Scale Structure
We explore interacting dark matter (DM) models that allow DM and baryons to scatter off of each other with a cross section that scales with relative particle velocity. Using the effective field theory of large-scale structure, we perform the first analysis of BOSS full-shape galaxy clustering data for velocity-dependent DM-baryon interactions. We determine that while the addition of BOSS full-shape data visibly modifies the shape of the posterior distribution, it does not significantly alter th…

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…

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…

Gravitational redshift from large-scale structure: nonlinearities, anti-symmetries, and the dipole
Lawrence Dam, Camille Bonvin
https://arxiv.org/abs/2506.22431

Gravitational redshift from large-scale structure: nonlinearities, anti-symmetries, and the dipole
Gravitational redshift imprints a slight asymmetry in the observed clustering of galaxies, producing odd multipoles (e.g.\ the dipole) in the cross-correlation function. But there are other sources of asymmetry which must also be considered in any model which aims to measure gravitational redshift from large-scale structure. In this work we develop a nonlinear model of the redshift-space correlation function complete down to these subleading, asymmetric effects. In addition to gravitational red…