Tootfinder

Clustering in Varying Metrics
Deeparnab Chakrabarty, Jonathan Conroy, Ankita Sarkar
https://arxiv.org/abs/2510.07860 https://arxiv.org/pdf/2510.07860

Clustering in Varying Metrics
We introduce the aggregated clustering problem, where one is given $T$ instances of a center-based clustering task over the same $n$ points, but under different metrics. The goal is to open $k$ centers to minimize an aggregate of the clustering costs -- e.g., the average or maximum -- where the cost is measured via $k$-center/median/means objectives. More generally, we minimize a norm $Ψ$ over the $T$ cost values. We show that for $T \geq 3$, the problem is inapproximable to any finite facto…

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…

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 …

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

Testing the equality of estimable parameters across many populations
Marcos Romero-Madro\~nal, Mar\'ia de los Remedios Sillero-Denamiel, Mar\'ia Dolores Jim\'enez-Gamero
https://arxiv.org/abs/2510.06763

Testing the equality of estimable parameters across many populations
The comparison of a parameter in $k$ populations is a classical problem in statistics. Testing for the equality of means or variances are typical examples. Most procedures designed to deal with this problem assume that $k$ is fixed and that samples with increasing sample sizes are available from each population. This paper introduces and studies a test for the comparison of an estimable parameter across $k$ populations, when $k$ is large and the sample sizes from each population are small when …

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Dead Pioneers:
🎵 Freedom Means Something
#DeadPioneers
https://deadpioneers.bandcamp.com/track/freedom-means-something
https://open.spotify.com/track/2G0CoMhydSCIE20eSYBUEO

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…

Always wondered why people complain so much about dynamic ads in podcasts. 🤔
Until I realized my podcast proxy (Audiobookshelf on a server in 🇦🇹) means I either get few Austrian ads or mostly just no ads at all in #Pocketcasts. 😅
#Podcast

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…

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…

Kernel K-means clustering of distributional data
Amparo Ba\'illo, Jose R. Berrendero, Mart\'in S\'anchez-Signorini
https://arxiv.org/abs/2509.18037 https://

Kernel K-means clustering of distributional data
We consider the problem of clustering a sample of probability distributions from a random distribution on $\mathbb R^p$. Our proposed partitioning method makes use of a symmetric, positive-definite kernel $k$ and its associated reproducing kernel Hilbert space (RKHS) $\mathcal H$. By mapping each distribution to its corresponding kernel mean embedding in $\mathcal H$, we obtain a sample in this RKHS where we carry out the $K$-means clustering procedure, which provides an unsupervised classifica…

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 …

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means
Jiten Oswal, Saumya Biswas
https://arxiv.org/abs/2509.19395 https://

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means
We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. With classical simulation results, we demonstrate a quantum performance, on and above par, with the classical k-means algorithm. We present benchmarks of its accuracy for test cases of both well-known and experimental datasets. Despite extensive research into quantum k-means algorithms, our approach reveals previously unexplored methodological improvements. The encoding step can be minimalisti…

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…

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

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 …

Characterizing failure morphologies in fiber-reinforced composites via k-means clustering based multiscale framework
Harpreet Singh
https://arxiv.org/abs/2509.20011 https://

Characterizing failure morphologies in fiber-reinforced composites via k-means clustering based multiscale framework
A novel homogenization methodology is proposed for analyzing the failure of fiber-reinforced composite materials, utilizing elastic and eigen influence tensors within a damage informed transformation field analysis (D-TFA) framework. This approach includes a technique for calculating macroscopic damage under uniform stress and strain conditions, offering more realistic simulations. Computational efficiency is enhanced through a reduced-order modeling strategy, while elastic and eigen strain dis…

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…

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…

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…

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 …

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…

Geographic Characterisation of Children's Health and Wellbeing through Vulnerability Indices Using Principal Component Analysis (PCA) and K-Means Clustering
Harriette Phillips, Aiden Price, Kerrie Mengersen
https://arxiv.org/abs/2509.14236

Geographic Characterisation of Children's Health and Wellbeing through Vulnerability Indices Using Principal Component Analysis (PCA) and K-Means Clustering
Using Australian children as a case study, this paper aims to develop vulnerability indices to characterise the health and well-being of children aged 0-5 years old. The indices are used to identify differences in children's health and well-being across geographic regions and identify clusters of regions with similar characteristics. The approach is underpinned by two well-known statistical methods, namely Principal Component Analysis and K-Means Clustering. The identification of these regions …

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

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.

A LOFAR search for coherent radio emission accompanying prompt engine activity in gamma-ray bursts
A. Hennessy, R. L. C. Starling, A. Rowlinson, I. de Ruiter, A. J. van der Horst, G. E. Anderson, N. R. Tanvir, S. ter Veen, K. Wiersema, R. A. M. J. Wijers
https://arxiv.org/abs/2509.21142

A LOFAR search for coherent radio emission accompanying prompt engine activity in gamma-ray bursts
Relativistic jets generated in gamma-ray bursts (GRBs) produce luminous transient events, yet the fundamentals of jet composition and radiation mechanisms remain unclear. One means of identifying a magnetically-dominated outflow would be detection of prompt, coherent radio emission at low frequencies, and we are able to search for this using the LOw Frequency ARray (LOFAR) coupled with modelling of high energy pulses detected by the Neil Gehrels Swift Observatory (Swift). We present the rapid r…

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…

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 …

Generation of C-band entangled photon pairs by biexciton-exciton cascade from symmetric InAs/InP quantum dots
Anna Musia{\l}, Maja Wasiluk, Katarzyna Roszak, Micha{\l} Gawe{\l}czyk, Wojciech Rudno-Rudzi\'nski, Pawe{\l} Wyborski, Johann P. Reithmaier, Mohamed Benyoucef, Grzegorz S\k{e}k
https://arxiv.org/abs/2509.24500

Generation of C-band entangled photon pairs by biexciton-exciton cascade from symmetric InAs/InP quantum dots
Hereby, we study the generation of pairs of polarization-entangled photons at telecom C-band by biexciton-exciton cascade from non-resonantly excited epitaxial InAs/InP quantum dots (QDs). It is realized without external tuning of the fine structure splitting (FSS), which does not exceed 10 $μ$eV in as-grown nanostructures, due to their high in-plane symmetry. Excitonic complexes are identified by means of excitation power-dependent and polarization-resolved magneto)microphotoluminescence. The…

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…

Nematic Fluctuations and Electronic Correlations in Heavily Hole-Doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ Probed by Elastoresistance
Franz Eckelt, Steffen Sykora, Xiaochen Hong, Vilmos Koscis, Vadim Grinenko, Bernd B\"uchner, Kunihiro Kihou, Chu-Ho Lee, Christian Hess
https://arxiv.org/abs/2510.09151…

Nematic Fluctuations and Electronic Correlations in Heavily Hole-Doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ Probed by Elastoresistance
This work investigates nematic fluctuations and electronic correlations in the hole-doped iron pnictide superconductor Ba$_{1-x}$K$_x$Fe$_2$As$_2$ by means of longitudinal and transverse elastoresistance measurements over a wide doping range ($0.63 < x < 0.98$). For this purpose, the orbital character of the electronic response was revealed by decomposition of the elastoresistance into the $A_{1g}$ and $B_{2g}$ symmetry channels. It was shown that at lower doping levels nematic fluctuations in …

Do Large Language Models Respect Contracts? Evaluating and Enforcing Contract-Adherence in Code Generation
Soohan Lim, Joonghyuk Hahn, Hyunwoo Park, Sang-Ki Ko, Yo-Sub Han
https://arxiv.org/abs/2510.12047

Do Large Language Models Respect Contracts? Evaluating and Enforcing Contract-Adherence in Code Generation
Prevailing code generation benchmarks, such as HumanEval+ and MBPP+, primarily evaluate large language models (LLMs) with pass@k on functional correctness using well-formed inputs. However, they ignore a crucial aspect of real-world software: adherence to contracts-the preconditions and validity constraints that dictate how ill-formed inputs must be rejected. This critical oversight means that existing benchmarks fail to measure, and models consequently fail to generate, truly robust and reliab…

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…

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…

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…

On the fully analytical cumulative distribution of product of correlated Gaussian random Variables with zero means
Erdinc Akyildirim, Alper Hekimoglu
https://arxiv.org/abs/2509.09866

On the fully analytical cumulative distribution of product of correlated Gaussian random Variables with zero means
We derive a fully analytical, one-line closed-form expression for the cumulative distribution function (CDF) of the product of two correlated zero-mean normal random variables, avoiding any series representation. This result complements the well-known compact density formula with an equally compact and computationally practical CDF representation. Our main formula expresses the CDF in terms of Humbert's confluent hypergeometric function $Φ_1$ and modified Bessel functions $K_ν$, offering both…

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement
J. D. R. Pierel, E. E. Hayes, M. Millon, C. Larison, E. Mamuzic, A. Acebron, A. Agrawal, P. Bergamini, S. Cha, S. Dhawan, J. M. Diego, B. L. Frye, D. Gilman, G. Granata, C. Grillo, M. J. Jee, P. S. Kamieneski, A M. Koekemoer, A. K. Meena, A. B. Newman, M. Oguri, E. Padilla-Gonzalez, F. Poidevin, P. Rosati, S. Schuldt, L. G. Strolger, S. H. Suyu, S. Thorp, A. Zitr…

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement
Multiply-imaged supernovae (SNe) provide a novel means of constraining the Hubble constant ($H_0$). Such measurements require a combination of precise models of the lensing mass distribution and an accurate estimate of the relative time delays between arrival of the multiple images. Only two multiply-imaged SNe, Refsdal and H0pe, have enabled measurements of $H_0$ thus far. Here we detail the third such measurement for SN Encore, a $z=1.95$ SNIa discovered in JWST/NIRCam imaging. We measure the…

Sublinear Time Quantum Sensitivity Sampling
Zhao Song, David P. Woodruff, Lichen Zhang
https://arxiv.org/abs/2509.16801 https://arxiv.org/pdf/2509.16801

Sublinear Time Quantum Sensitivity Sampling
We present a unified framework for quantum sensitivity sampling, extending the advantages of quantum computing to a broad class of classical approximation problems. Our unified framework provides a streamlined approach for constructing coresets and offers significant runtime improvements in applications such as clustering, regression, and low-rank approximation. Our contributions include: * $k$-median and $k$-means clustering: For $n$ points in $d$-dimensional Euclidean space, we give an algo…

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
Bertrand Cloez, Adrien Cotil, Jean-Baptiste Menassol, Nicolas Verzelen
https://arxiv.org/abs/2509.15989

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from the Lloyd algorithm for the $k$-means problem, our approach extends to SBMs with general edge weight distributions. We establish the consistency of our estimator under a natural identifiability condition. Through extensive numerical experiments, we benchmark our methods a…

Hybrid Quantum-Classical Optimisation of Traveling Salesperson Problem
Christos Lytrosyngounis, Ioannis Lytrosyngounis
https://arxiv.org/abs/2509.26229 https://

Hybrid Quantum-Classical Optimisation of Traveling Salesperson Problem
The Traveling Salesperson Problem (TSP), a quintessential NP-hard combinatorial optimisation challenge, is vital for logistics and network design but limited by exponential complexity in large instances. We propose a hybrid quantum-classical framework integrating variational quantum eigensolver (VQE) optimisation with classical machine learning, using K-means clustering for problem decomposition and a RandomForestRegressor for path refinement. Evaluated on 80 European cities (from 4 to 80 citie…

Cu2XSiS4 (X = Ge, Sn, and Pb) materials for solar-cell applications: A DFT SCAPS-1D simulation
H. Laltlanmawii, L. Celestine, R. Zosiamliana, B. Chettri, S. Bhattarai, K. C. Bhamu, D. P. Rai
https://arxiv.org/abs/2509.20845

Cu2XSiS4 (X = Ge, Sn, and Pb) materials for solar-cell applications: A DFT+SCAPS-1D simulation
By means of the first-principles density functional theory (DFT), I2-II-IV-VI4 type Cu-based quaternary chalcogenides Cu 2 XSiS 4 (X = Ge, Sn, and Pb) have been thoroughly investigated. We report the study of Ge and Sn substitution in the divalent cation site for their potential applications in photovoltaics for the first time. The structural, electronic, optical, and mechanical properties have been calculated. The structural and thermal stability is verified by calculating the elastic constant…

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…

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…

Reinforcement learning for spin torque oscillator tasks
Jakub Mojsiejuk, S{\l}awomir Zi\k{e}tek, Witold Skowro\'nski
https://arxiv.org/abs/2509.10057 https://

Reinforcement learning for spin torque oscillator tasks
We address the problem of automatic synchronisation of the spintronic oscillator (STO) by means of reinforcement learning (RL). A numerical solution of the macrospin Landau-Lifschitz-Gilbert-Slonczewski equation is used to simulate the STO and we train the two types of RL agents to synchronise with a target frequency within a fixed number of steps. We explore modifications to this base task and show an improvement in both convergence and energy efficiency of the synchronisation that can be easi…

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…

Sublinear Metric Steiner Forest via Maximal Independent Set
Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski, Ali Vakilian
https://arxiv.org/abs/2510.11627 https://

Sublinear Metric Steiner Forest via Maximal Independent Set
In this work we consider the Metric Steiner Forest problem in the sublinear time model. Given a set $V$ of $n$ points in a metric space where distances are provided by means of query access to an $n\times n$ distance matrix, along with a set of $k$ terminal pairs $(s_1,t_1), \dots, (s_k,t_k)\in V\times V$, the goal is to find a minimum-weight subset of edges that connects each terminal pair. Although sublinear time algorithms have been studied for estimating the weight of a minimum spanning tre…

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https://deadpioneers.bandcamp.com/track/freedom-means-something
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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…

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…