Tootfinder

SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization
Beilong Tang, Xiaoxiao Miao, Xin Wang, Ming Li
https://arxiv.org/abs/2508.07086 https://

SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization
Voice anonymization protects speaker privacy by concealing identity while preserving linguistic and paralinguistic content. Self-supervised learning (SSL) representations encode linguistic features but preserve speaker traits. We propose a novel speaker-embedding-free framework called SEF-MK. Instead of using a single k-means model trained on the entire dataset, SEF-MK anonymizes SSL representations for each utterance by randomly selecting one of multiple k-means models, each trained on a diffe…

A Simple PTAS for Weighted $k$-means and Sensor Coverage
Akash Pareek, Supratim Shit
https://arxiv.org/abs/2508.06460 https://arxiv.org/pdf/2508.06460

A Simple PTAS for Weighted $k$-means and Sensor Coverage
Clustering is a fundamental technique in data analysis, with the $k$-means being one of the widely studied objectives due to its simplicity and broad applicability. In many practical scenarios, data points come with associated weights that reflect their importance, frequency, or confidence. Given a weighted point set $P \subset R^d$, where each point $p \in P$ has a positive weight $w_p$, the goal is to compute a set of $k$ centers $C = \{ c_1, c_2, \ldots, c_k \} \subset R^d$ that minimizes th…

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

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

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- KLLM: Fast LLM Inference with K-Means Quantization
Xueying Wu, Baijun Zhou, Zhihui Gao, Yuzhe Fu, Qilin Zheng, Yintao He, Hai Li

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

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

Current problems of studying relativistic dissociation of light nuclei in nuclear emulsion
D. A. Artemenkov, N. K. Kornegrutsa, N. Marimuthu, N. G. Peresadko, V. V. Rusakova, A. A. Zaitsev, P. I. Zarubin, I. G. Zarubina
https://arxiv.org/abs/2509.09636

Current problems of studying relativistic dissociation of light nuclei in nuclear emulsion
The progress of the study of unstable states in relativistic dissociation events of light nuclei in nuclear emulsion is presented. Identification of these states is possible by means of the invariant mass determined from the most accurate and complete measurements of relativistic fragment emission angles in the approximation of conservation of momentum per nucleon of the parent nucleus. It is established that excitations $^{12}\mathrm{C}(0^{+}_{2})$ and $^{12}\mathrm{C}(3^{-})$ lead in the diss…

Simplicial clustering using the $\alpha$--transformation
Michail Tsagris, Nikolaos Kontemeniotis
https://arxiv.org/abs/2509.05945 https://arxiv.org/pdf/250…

Simplicial clustering using the $α$--transformation
We introduce two simplicial clustering approaches for compositional data, that are adaptations of the $K$--means and of the Gaussian mixture models algorithms, by employing the $α$--transformation. By utilizing clustering validation indices we can decide on the number of clusters and choose the value of $α$ for the $K$--means, while for the model-based clustering approach information criteria complete this task. extensive simulation studies compare the performance of these two approaches and …

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

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

An analysis of Wigner's friend in the framework of quantum mechanics based on the principle of typicality
Kohtaro Tadaki
https://arxiv.org/abs/2509.07828 https://

An analysis of Wigner's friend in the framework of quantum mechanics based on the principle of typicality
The notion of probability plays a crucial role in quantum mechanics. It appears in quantum mechanics as the Born rule. In modern mathematics which describes quantum mechanics, however, probability theory means nothing other than measure theory, and therefore any operational characterization of the notion of probability is still missing in quantum mechanics. In our former works [K. Tadaki, arXiv:1804.10174], based on the toolkit of algorithmic randomness, we presented a refinement of the Born ru…

Measuring Information Richness in Product Images: Implications for Online Sales
Zhu Yuting, Cao Xinyu, Su Yuzhuo, Ma Yongbin
https://arxiv.org/abs/2508.04541 https://

Measuring Information Richness in Product Images: Implications for Online Sales
A common challenge for e-commerce sellers is to decide what product images to display on online shopping sites. In this paper, we propose and validate a novel metric, k-value, to quantify the information richness of an image set, and we further investigate its effect on consumers' purchase decisions. We leverage patch-level embeddings from Vision Transformers (ViT) and apply k-means clustering to identify distinct visual features, defining k-value as the number of clusters. An online experiment…

Production of the $ X(3872)$ state via the $B^0 \to K^{\ast 0} X(3872)$ decay
Luciano M. Abreu
https://arxiv.org/abs/2508.21223 https://arxiv.org/pdf/2508.…

Production of the $ X(3872)$ state via the $B^0 \to K^{\ast 0} X(3872)$ decay
In this work the production of the state $X(3872)$ is estimated via the reaction $B^0 \to K^{\ast 0} X(3872)$ through triangle mechanisms described by the sequence $B^0 \to D_s^{(*)+} (\to K^{\ast 0} D^{(*)+} ) \ D^{(*)-} \to K^{\ast 0} \ ( D^{(*)+} D^{(*)-} ) \to K^{\ast 0} X(3872) $. The molecular configuration $(D\bar D^* - c.c. )$ of the $X(3872)$ is considered. By means of the effective Lagrangian approach, the branching ratio $\mathcal{B}(B^0 \to K^{\ast 0} X(3872))$ is calculated as a fu…

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…

Clustering Wind data at 1 AU to contextualize magnetic reconnection in the solar wind
Francesco Carella, Giovanni Lapenta, Alessandro Bemporad, Stefan Eriksson, Maria Elena Innocenti, Sophia K\"ohne, Jasmina Magdalenic
https://arxiv.org/abs/2508.05579

Clustering Wind data at 1 AU to contextualize magnetic reconnection in the solar wind
Context. Magnetic reconnection events are frequently observed in the solar wind. Understanding the patterns and structures within the solar wind is crucial to put observed magnetic reconnection events into context, since their occurrence rate and properties are likely influenced by solar wind conditions. Aims. We employed unsupervised learning techniques such as self-organizing maps (SOM) and K-Means to cluster and interpret solar wind data at 1 AU for an improved understanding of the conditi…

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

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

Coclass of the second 3-class group
Siham Aouissi, Daniel C. Mayer
https://arxiv.org/abs/2508.17510 https://arxiv.org/pdf/2508.17510

Coclass of the second 3-class group
By means of parametrized presentations of finite metabelian 3-groups, it is proved that the coclass cc(M) of the second 3-class group M=Gal(F_3^2(K)/K) of any algebraic number field K with elementary bicyclic 3-class group Cl_3(K)=(3,3) is determined unambiguously by the second largest order ord(Cl_3(E_2))=3^{cc(M)+1} among the four 3-class groups of the unramified cyclic cubic extensions E_i (i=1,..,4) of K. Minimal discriminants of quadratic and cubic fields K with assigned coclass cc(M) are …

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

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

What Raiders' First Injury Report Means For Week 1 https://www.si.com/nfl/raiders/las-vegas-kyu-blu-kelly-kenny-pickett-pete-carroll

The next time some know-it-all informs you that The Slippery Slope Is A Fallacy, just remember that it only took a generation for the UK to slide from the heights of Monty Python to Ofcom wanting to ban SpongeBob clips to protect the children

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

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

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

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

Estimating the Effective Topics of Articles and journals Abstract Using LDA And K-Means Clustering Algorithm
Shadikur Rahman, Umme Ayman Koana, Aras M. Ismael, Karmand Hussein Abdalla
https://arxiv.org/abs/2508.16046

Estimating the Effective Topics of Articles and journals Abstract Using LDA And K-Means Clustering Algorithm
Analyzing journals and articles abstract text or documents using topic modelling and text clustering has become a modern solution for the increasing number of text documents. Topic modelling and text clustering are both intensely involved tasks that can benefit one another. Text clustering and topic modelling algorithms are used to maintain massive amounts of text documents. In this study, we have used LDA, K-Means cluster and also lexical database WordNet for keyphrases extraction in our text …

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

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

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer
Yin Huang, Yongqi Dong, Youhua Tang, Li Li
https://arxiv.org/abs/2509.04362

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer
The rapid growth of private car ownership has worsened the urban parking predicament, underscoring the need for accurate and effective parking availability prediction to support urban planning and management. To address key limitations in modeling spatio-temporal dependencies and exploiting multi-source data for parking availability prediction, this study proposes a novel approach with SST-iTransformer. The methodology leverages K-means clustering to establish parking cluster zones (PCZs), extr…

Off-lattice Microscopic Monte Carlo Modeling of Molecular Hydrogen Formation on Carbonaceous Dust Grains
N. A. Satonkin (Research Laboratory for Astrochemistry, Ural Federal University, Ekaterinburg, Russia), A. B. Ostrovskii (Research Laboratory for Astrochemistry, Ural Federal University, Ekaterinburg, Russia), A. A. Mozhegorov (Research Laboratory for Astrochemistry, Ural Federal University, Ekaterinburg, Russia), A. F. Punanova (Onsala Space Observatory, Ra\"o, Onsala, Sweden)…

Off-lattice Microscopic Monte Carlo Modeling of Molecular Hydrogen Formation on Carbonaceous Dust Grains
In this work, we present an off-lattice Monte Carlo model of accretion and migration of hydrogen atoms on a rough surface of carbon dust grain. The migration of physisorbed atoms by means of thermal diffusion and quantum tunnelling through barriers between the surface potential minima is considered. The model is applied to simulations of molecular hydrogen formation in a cold interstellar medium for a temperature range 5-35 K. Eley-Rideal and Langmuir-Hinshelwood mechanisms for the formation of…

On $2$-connected graphs avoiding cycles of length $0$ modulo $4$
Hojin Chu, Boram Park, Homoon Ryu
https://arxiv.org/abs/2507.12798 https://

On $2$-connected graphs avoiding cycles of length $0$ modulo $4$
For two integers $k$ and $\ell$, an $(\ell \text{ mod }k)$-cycle means a cycle of length $m$ such that $m\equiv \ell\pmod{k}$. In 1977, Bollobás proved a conjecture of Burr and Erdős by showing that if $\ell$ is even or $k$ is odd, then every $n$-vertex graph containing no $(\ell \text{ mod }k)$-cycles has at most a linear number of edges in terms of $n$. Since then, determining the exact extremal bounds for graphs without $(\ell \text{ mod }k)$-cycles has emerged as an interesting question i…

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

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

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

Visual Execution and Validation of Finite-State Machines and Pushdown Automata
Marco T. Moraz\'an (Seton Hall University), David Anthony K. Fields (Seton Hall University), Andr\'es M. Garced (Seton Hall University), Tijana Mini\'c (University of Washington)
https://arxiv.org/abs/2508.03641

Visual Execution and Validation of Finite-State Machines and Pushdown Automata
In Formal Languages and Automata Theory courses, students find understanding nondeterministic finite-state and pushdown automata difficult. In many cases, this means that it is challenging for them to comprehend the operational semantics of such machines and, as a consequence, determine why a word is accepted or rejected. This is not entirely surprising, because students are mostly trained to design and implement deterministic programs. Comprehension of pushdown automata is further complicated,…

Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
Jiahe Ling, Wei Biao Wu
https://arxiv.org/abs/2509.08214 htt…

Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
This study evaluates three probabilistic forecasting strategies using LightGBM: global pooling, cluster-level pooling, and station-level modeling across a range of scenarios, from fully homogeneous simulated data to highly heterogeneous real-world Divvy bike-share demand observed during 2023 to 2024. Clustering was performed using the K-means algorithm applied to principal component analysis transformed covariates, which included time series features, counts of nearby transportation infrastruct…

Regularized k-POD: Sparse k-means clustering for high-dimensional missing data
Xin Guan, Yoshikazu Terada
https://arxiv.org/abs/2507.11884 https://

Regularized k-POD: Sparse k-means clustering for high-dimensional missing data
The classical k-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values. A natural extension of k-means to missing data, namely k-POD, uses only the observed entries for clustering and is both computationally efficient and flexible. However, for high-dimensional missing data including features irrelevant to the underlying cluster structure, the presence of such irrelevant features leads to the bias of k-POD in estim…

Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation
Shiyao Zhang, Omar Faruk, Robert Porzel, Dennis K\"uster, Tanja Schultz, Hui Liu
https://arxiv.org/abs/2508.01743

Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation
An increasing number of online interaction settings now provide the possibility to visually represent oneself via an animated avatar instead of a video stream. Benefits include protecting the communicator's privacy while still providing a means to express their individuality. In consequence, there has been a surge in means for avatar-based personalization, ranging from classic human representations to animals, food items, and more. However, using avatars also has drawbacks. Depending on the hum…

Random field reconstruction of inhomogeneous turbulence. Part II: Numerical approximation and simulation
Markus Antoni, Quinten K\"urpick, Felix Lindner, Nicole Marheineke, Raimund Wegener
https://arxiv.org/abs/2509.02570

Random field reconstruction of inhomogeneous turbulence. Part II: Numerical approximation and simulation
A novel random field model for the reconstruction of inhomogeneous turbulence from characteristic flow quantities has been recently introduced and analyzed by the authors. This article concerns the numerical approximation and implementation of the model and discusses its key features by means of numerical simulations. We present an effective discretization scheme based on a randomized quadrature method for stochastic integrals. The convergence of the scheme is verified analytically and its algo…

An Efficient Massively Parallel Constant-Factor Approximation Algorithm for the $k$-Means Problem
Vincent Cohen-Addad, Fabian Kuhn, Zahra Parsaeian
https://arxiv.org/abs/2507.14089

An Efficient Massively Parallel Constant-Factor Approximation Algorithm for the $k$-Means Problem
In this paper, we present an efficient massively parallel approximation algorithm for the $k$-means problem. Specifically, we provide an MPC algorithm that computes a constant-factor approximation to an arbitrary $k$-means instance in $O(\log\log n \cdot \log\log\log n)$ rounds. The algorithm uses $O(n^σ)$ bits of memory per machine, where $σ> 0$ is a constant that can be made arbitrarily small. The global memory usage is $O(n^{1+\varepsilon})$ bits for an arbitrarily small constant $\varepsi…

Experimental electronic structure of the mineral superconductor covellite CuS
Alexandre Antezak, Takemi Kato, Pedro Rezende Gon\c{c}alves, Franck Fortuna, Marcin Rosmus, Natalia Olszowska, Andr\'es Felipe Santander-Syro, Emmanouil Frantzeskakis
https://arxiv.org/abs/2509.02468

Experimental electronic structure of the mineral superconductor covellite CuS
Covellite (CuS) is the first known natural mineral superconductor. Despite its simple chemical formula, covellite exhibits a rich crystal structure at the origin of several remarkable properties. The ionic arrangement in CuS crystals leads to a mixed valence of Cu and a second-order structural transition at 55 K. Despite the abundance of structural studies and theoretical reports on its electronic structure, there are scarce references on its experimental band structure. By means of Angle Resol…

Quadratic estimators for unwindowed power spectrum of galaxy-galaxy weak lensing and its application to $P_{\rm gm}(k)$ estimation
Taisei Terawaki, Masahiro Takada, Takanori Taniguchi
https://arxiv.org/abs/2507.18789

Quadratic estimators for unwindowed power spectrum of galaxy-galaxy weak lensing and its application to $P_{\rm gm}(k)$ estimation
Galaxy-galaxy weak lensing provides a powerful means of measuring the average matter distribution around lens galaxies -- i.e., the galaxy bias relation. Properly accounting for the spin-2 nature of weak lensing distortions, we develop a quadratic estimator for measuring the $E$- and $B$-mode angular power spectra from galaxy-galaxy weak lensing, correcting for survey window effects arising from, for example, survey geometry and bright star masks. The estimator can be implemented efficiently by…

K-Means Panel Data Clustering in the Presence of Small Groups
Mikihito Nishi
https://arxiv.org/abs/2508.15408 https://arxiv.org/pdf/2508.15408

K-Means Panel Data Clustering in the Presence of Small Groups
We consider panel data models with group structure. We study the asymptotic behavior of least-squares estimators and information criterion for the number of groups, allowing for the presence of small groups that have an asymptotically negligible relative size. Our contributions are threefold. First, we derive sufficient conditions under which the least-squares estimators are consistent and asymptotically normal. One of the conditions implies that a longer sample period is required as there are …

Automated Code Review Using Large Language Models at Ericsson: An Experience Report
Shweta Ramesh, Joy Bose, Hamender Singh, A K Raghavan, Sujoy Roychowdhury, Giriprasad Sridhara, Nishrith Saini, Ricardo Britto
https://arxiv.org/abs/2507.19115

Automated Code Review Using Large Language Models at Ericsson: An Experience Report
Code review is one of the primary means of assuring the quality of released software along with testing and static analysis. However, code review requires experienced developers who may not always have the time to perform an in-depth review of code. Thus, automating code review can help alleviate the cognitive burden on experienced software developers allowing them to focus on their primary activities of writing code to add new features and fix bugs. In this paper, we describe our experience in…

What Raiders' Kolton Miller Means to the New Regime https://www.si.com/nfl/raiders/las-vegas-kolton-miller-pete-carroll-john-spytek-geno-smith

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…

Congruences modulo powers of $7$ for $k$-elongated plane partitions
Dandan Chen, Tianjian Xu, Siyu Yin
https://arxiv.org/abs/2508.09723 https://arxiv.org/p…

Congruences modulo powers of $7$ for $k$-elongated plane partitions
The enumeration $d_k(n)$ of $k$-elongated plane partition diamonds has emerged as a generalization of the classical integer partition function $p(n)$. Congruences for $d_k(n)$ modulo certain powers of primes have been proven via elementary means and modular forms by many authors. Recently, Banerjee and Smoot established an infinite family of congruences for $d_5(n)$ modulo powers of 5. In this paper we have discovered an infinite congruence family for $d_3(n)$ and $d_5(n)$ modulo powers of 7.

Word Meanings in Transformer Language Models
Jumbly Grindrod, Peter Grindrod
https://arxiv.org/abs/2508.12863 https://arxiv.org/pdf/2508.12863

Word Meanings in Transformer Language Models
We investigate how word meanings are represented in the transformer language models. Specifically, we focus on whether transformer models employ something analogous to a lexical store - where each word has an entry that contains semantic information. To do this, we extracted the token embedding space of RoBERTa-base and k-means clustered it into 200 clusters. In our first study, we then manually inspected the resultant clusters to consider whether they are sensitive to semantic information. In …

Comparing Visual Qubit Representations in Quantum Education: The Bloch sphere Enhances Task Efficiency
Linda Qerimi, Sarah Malone, Eva Rexigel, Jochen Kuhn, Stefan K\"uchemann
https://arxiv.org/abs/2507.21721

Comparing Visual Qubit Representations in Quantum Education: The Bloch sphere Enhances Task Efficiency
Visual-graphical qubit representations offer a means to introduce abstract quantum concepts - such as quantum state, superposition, or measurement - in an accessible manner, particularly for learners with low prior knowledge. Building on a previous expert rating of mechanisms of qubit representations, this study compared two representations - Bloch sphere and Quantum Bead - in terms of learning outcomes, task performance, cognitive load, and mid-term retention. The study was conducted with N=14…

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

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

Titanium for rare-event searches: Hydrofluoric acid-free etching
P. Knights, K. Nikolopoulos, G. Rogers, D. Spathara, P. Walters
https://arxiv.org/abs/2508.21029 https://…

Titanium for rare-event searches: Hydrofluoric acid-free etching
Rare-event search experiments require construction materials with high radiopurity to minimise background contributions. Thanks to its high mechanical strength, low density, machinability, and commercial availability in relatively radio-pure forms, titanium is a suitable material for structural elements in rare event searches. To remove surface deposits on materials used, a chemical etching stage is usually performed. However, the chemical resistance of titanium means that, conventionally, such…

Evaluating the Effectiveness of Transformer Layers in Wav2Vec 2.0, XLS-R, and Whisper for Speaker Identification Tasks
Linus Stuhlmann, Michael Alexander Saxer
https://arxiv.org/abs/2509.00230

Evaluating the Effectiveness of Transformer Layers in Wav2Vec 2.0, XLS-R, and Whisper for Speaker Identification Tasks
This study evaluates the performance of three advanced speech encoder models, Wav2Vec 2.0, XLS-R, and Whisper, in speaker identification tasks. By fine-tuning these models and analyzing their layer-wise representations using SVCCA, k-means clustering, and t-SNE visualizations, we found that Wav2Vec 2.0 and XLS-R capture speaker-specific features effectively in their early layers, with fine-tuning improving stability and performance. Whisper showed better performance in deeper layers. Additional…

Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data
Sebastian Kot
https://arxiv.org/abs/2508.09243

Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data
This paper examines Modern Mercantilism, characterized by rising economic nationalism, strategic technological decoupling, and geopolitical fragmentation, as a disruptive shift from the post-1945 globalization paradigm. It applies Principal Component Analysis (PCA) to 768-dimensional SBERT-generated semantic embeddings of curated news articles to extract orthogonal latent factors that discriminate binary event outcomes linked to protectionism, technological sovereignty, and bloc realignments. A…

Destructive Interference induced constraints in Floquet systems
Somsubhra Ghosh, Indranil Paul, K. Sengupta, Lev Vidmar
https://arxiv.org/abs/2508.18368 https://

Destructive Interference induced constraints in Floquet systems
We introduce the paradigm of destructive many-body interference between quantum trajectories as a means to systematically generate prethermal kinetically constrained dynamics in Floquet systems driven at special frequencies. Depending on the processes that are suppressed by interference, the constraint may or may not be associated with an emergent global conservation; the latter kind having no mechanism of generation in time-independent settings. As an example, we construct an one-dimensional i…

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

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

On the chain of commuting operators on Banach spaces and Lomonosov's invariant subspace theorem
Tomasz Szczepanski
https://arxiv.org/abs/2507.14297 htt…

On the chain of commuting operators on Banach spaces and Lomonosov's invariant subspace theorem
An operator $T$ on a Banach space is said to be of chain $N$ if there exist non-scalar operators $S_1,...,S_{N-1}$ and a non-zero compact $K$ such that $$T \leftrightarrow S_1 \leftrightarrow S_2 \leftrightarrow ...\leftrightarrow S_{N-1} \leftrightarrow K,$$ where $A\leftrightarrow B$ means $AB=BA$. A connection of this theory to the Lomonosov's Invariant Subspace Theorem is highlighted. It is shown that for every weighted shift $T$ it is of chain $3$. In particular, every non-Lomonosov operat…

Self-consistent clustering analysis for homogenisation of heterogeneous plates
Menglei Li, Haolin Li, Bing Wang, Bing Wang
https://arxiv.org/abs/2508.20446 https://

Self-consistent clustering analysis for homogenisation of heterogeneous plates
This work introduces a reduced-order model for plate structures with periodic micro-structures by coupling self-consistent clustering analysis (SCA) with the Lippmann-Schwinger equation, enabling rapid multiscale homogenisation of heterogeneous plates. A plate-specific SCA scheme is derived for the first time and features two key elements: (i) an offline-online strategy that combines Green's functions with k-means data compression, and (ii) an online self-consistent update that exploits the wea…

Mapped $spdf$ interacting boson model for quadrupole-octupole collective states in nuclei
K. Nomura
https://arxiv.org/abs/2507.23282 https://arxiv.org/pdf/…

Mapped $spdf$ interacting boson model for quadrupole-octupole collective states in nuclei
Dipole bosons are introduced in the interacting boson model (IBM) by means of the self-consistent mean-field method. The constrained mean-field calculations employing a given nuclear energy density functional yield the potential energy surfaces in terms of the axially-symmetric quadrupole-octupole, dipole-quadrupole, and dipole-octupole deformations. By mapping these energy surfaces onto the expectation values of the IBM Hamiltonian in the coherent state of the interacting $s$, $p$, $d$, and $f…

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

Smart Contract Intent Detection with Pre-trained Programming Language Model
Youwei Huang, Jianwen Li, Sen Fang, Yao Li, Peng Yang, Bin Hu, Tao Zhang
https://arxiv.org/abs/2508.20086

Smart Contract Intent Detection with Pre-trained Programming Language Model
Malicious intent in smart contract development can lead to substantial economic losses. SmartIntentNN is a deep learning model specifically designed to identify unsafe intents in smart contracts. This model integrates the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory network for multi-label classification, achieving an F1 of 0.8633 in distinguishing ten different intent categories. In this study, we present an up…

On the Properties of Optimal-Decay Control Barrier Functions
Pio Ong, Max H. Cohen, Tamas G. Molnar, Aaron D. Ames
https://arxiv.org/abs/2507.12717 https:/…

On the Properties of Optimal-Decay Control Barrier Functions
Control barrier functions provide a powerful means for synthesizing safety filters that ensure safety framed as forward set invariance. Key to CBFs' effectiveness is the simple inequality on the system dynamics: $\dot{h} \geq - α(h)$. Yet determining the class $\mathcal{K}^e$ function $α$ is a user defined choice that can have a dramatic effect on the resulting system behavior. This paper formalizes the process of choosing $α$ using optimal-decay control barrier functions (OD-CBFs). These mo…

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

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

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

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

Axial-vector molecular structures $B_{c}^{\ast \pm} B_{c}^{\mp}$
S. S. Agaev, K. Azizi, H. Sundu
https://arxiv.org/abs/2507.18735 https://arxiv.org/pdf/250…

Axial-vector molecular structures $B_{c}^{\ast \pm} B_{c}^{\mp}$
The axial-vector molecular structure $\mathcal{M}_{\mathrm{AV}}=(B_{c}^{\ast +}B_{c}^{-} + B_{c}^{\ast - } B_{c}^{+})/2$ is explored in QCD sum rule framework. The mass and current coupling of this compound are found by means of the two-point sum rule method. It turns out that the mass of $\mathcal{M} _{\mathrm{AV}}$ is equal to $m=(12770 \pm 60)~\mathrm{MeV}$ making possible its dissociations to pairs of $J/ψη_b$, $Υη_c$, $ B_{c}^{\ast +} B_{c}^{-}$, and $B_{c}^{\ast - } B_{c}^{+}$ mesons.…

Young Stellar Objects in the Carina nebula: Near-Infrared variability and spectroscopy
Jura Borissova, Radostin Kurtev, Josemanuel Escobar, Javier Alonso-Garc\'ia, Nicolas Medina, Javier Osses, Zhen Guo, Philip Lucas, Michael Kuhn, Dante Minniti, Kevin R. Covey, Roberto Saito, Francisco F\"orster
https://arxiv.org/abs/2507.20086…

Young Stellar Objects in the Carina nebula: Near-Infrared variability and spectroscopy
We present a catalog of 652 young stellar objects (YSOs) in the Carina star-forming region. The catalog was constructed by combining near-infrared Ks-band variability from the VISTA Variables in the Via Lactea eXtended survey and medium-resolution H-band spectroscopy from APOGEE-2, Sloan Digital Sky Survey IV (SDSS-IV). Variability analysis of 6.35 million sources identified 606 variable stars. The classification of the spectral lines by semisupervised K-means clustering of 704 stars, refined t…

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…

Bridging Clarity and Accuracy: A Simple Spectral Longwave Radiation Scheme for Idealized Climate Modeling
Andrew I. L. Williams
https://arxiv.org/abs/2508.09353 https://

Bridging Clarity and Accuracy: A Simple Spectral Longwave Radiation Scheme for Idealized Climate Modeling
Parameterizing radiative transfer in means navigating trade-offs between physical accuracy and conceptual clarity. However, currently available schemes sit at the extremes of this spectrum: correlated-k schemes are fast and accurate but rely on lookup tables which obscure the underlying physics and make such schemes difficult to modify, while gray radiation schemes are conceptually straightforward but introduce significant biases in atmospheric circulation. Here we introduce a Simple Spectral M…

Exploiting Discriminative Codebook Prior for Autoregressive Image Generation
Longxiang Tang, Ruihang Chu, Xiang Wang, Yujin Han, Pingyu Wu, Chunming He, Yingya Zhang, Shiwei Zhang, Jiaya Jia
https://arxiv.org/abs/2508.10719

Exploiting Discriminative Codebook Prior for Autoregressive Image Generation
Advanced discrete token-based autoregressive image generation systems first tokenize images into sequences of token indices with a codebook, and then model these sequences in an autoregressive paradigm. While autoregressive generative models are trained only on index values, the prior encoded in the codebook, which contains rich token similarity information, is not exploited. Recent studies have attempted to incorporate this prior by performing naive k-means clustering on the tokens, helping to…

Informal Education is Essential to Physics: Findings of the 2024 JNIPER Summit and Recommendations for Action
Alexandra C. Lau, Jessica R. Hoehn, Michael B. Bennett, Claudia Fracchiolla, Kathleen Hinko, Noah Finkelstein, Jacqueline Acres, Lindsey D. Anderson, Shane D. Bergin, Cherie Bornhorst, Turhan K. Carroll, Michael Gregory, Cameron Hares, E. L. Hazlett, Meghan Healy, Erik A Herman, Lindsay R. House, Michele W. McColgan, Brad McLain, Azar Panah, Sarah A. Perdue, Jonathan D. Perry, …

Informal Education is Essential to Physics: Findings of the 2024 JNIPER Summit and Recommendations for Action
In order to reach the full civic and scientific potential of physics, this white paper calls for a culture change in physics to recognize informal physics education (also referred to as public engagement or outreach) as an essential disciplinary practice. That is, engaging in informal physics education (IPE) is part of what it means to ''do physics.'' In June 2024, we hosted a summit with forty-two members of the Joint Network for Informal Physics Education and Research (JNIPER) to discuss conc…

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
Alesanmi Richmond Rerelope Odufisan
https://arxiv.org/abs/2508.16590

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
We present FDTRImageEnhancer, an open-source computational framework that improves thermal conductivity mapping from Frequency Domain ThermoReflectance (FDTR) phase data by integrating a physics-based Gaussian convolution abstraction with microstructure-aware deep learning. The Gaussian kernel models the spatial averaging effects of pump and probe beams, while k-means clustering of high-resolution structural images reduces the parameter space for inverse modeling. A physics-informed neural netw…

Unbiased Estimation in One-Parameter Exponential Families of the Natural Parameter with Extensions
Pankaj Bhagwat, Eric Marchand
https://arxiv.org/abs/2507.15077

Unbiased Estimation in One-Parameter Exponential Families of the Natural Parameter with Extensions
For one-parameter continuous exponential families, we identify an unbiased estimator of the inverse of the natural parameter $θ$ for cases where $θ> 0$, extending an earlier result of \cite{voinov1985unbiased} applicable to a normal model. We provide various applications for Gamma models, Inverse Gaussian models, distributions obtained by truncation, and ratios of normal means. Moreover, we extend the findings to estimating negative powers $θ^{-k}$, and more generally to complete monotone fu…

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…

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

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

Benchmarking Prosody Encoding in Discrete Speech Tokens
Kentaro Onda, Satoru Fukayama, Daisuke Saito, Nobuaki Minematsu
https://arxiv.org/abs/2508.11224 https://

Benchmarking Prosody Encoding in Discrete Speech Tokens
Recently, discrete tokens derived from self-supervised learning (SSL) models via k-means clustering have been actively studied as pseudo-text in speech language models and as efficient intermediate representations for various tasks. However, these discrete tokens are typically learned in advance, separately from the training of language models or downstream tasks. As a result, choices related to discretization, such as the SSL model used or the number of clusters, must be made heuristically. In…

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

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