Tootfinder

New York fines auto insurers $19M over major data breaches
https://www.fingerlakes1.com/2025/10/14/auto-insurance-data-breach-ny-fines/

New York fines auto insurers $19M over major data breaches | Fingerlakes1.com
New York State has fined eight major auto insurance companies over $19 million after an investigation uncovered major cybersecurity failures that exposed the personal data of residents, including driver’s license numbers and birthdates. The settlement—announced October 14, 2025—follows a wide-ranging investigation led by Superintendent Adrienne Harris of the New York Department of Financial Services (DFS).… MoreNew York fines auto insurers $19M over major data breaches

Hydrocarbon Hazes on Temperate sub-Neptune K2-18b supported by data from the James Webb Space Telescope
Ruohan Liu, Panayotis Lavvas, Giovanna Tinetti, Jesus Maldonado, Sushuang Ma, Arianna Saba
https://arxiv.org/abs/2509.10947

Hydrocarbon Hazes on Temperate sub-Neptune K2-18b supported by data from the James Webb Space Telescope
K2-18b, a sub-Neptune orbiting in the habitable zone of an M dwarf, has attracted significant interest following observations with the Hubble Space Telescope (HST) and, more recently, with the James Webb Space Telescope (JWST) that reveal detectable atmospheric features. Previous studies have examined a wide range of possible compositions, focusing primarily in the near-infrared (0.8-5.2 $μ$m) or mid-infrared (5-12 $μ$m) wavelengths. We present a new interpretation of K2-18b's JWST transit sp…

Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
Bingchen Wang, Zi-Yu Khoo, Bryan Kian Hsiang Low
https://arxiv.org/abs/2509.11311 https://

Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
Large language models (LLMs) have demonstrated promise in emulating human-like responses across a wide range of tasks. In this paper, we propose a novel alignment framework that treats LLMs as agent proxies for human survey respondents, affording a cost-effective and steerable solution to two pressing challenges in the social sciences: the rising cost of survey deployment and the growing demographic imbalance in survey response data. Drawing inspiration from the theory of revealed preference, w…

RAID-0e: A Resilient Striping Array Architecture for Balanced Performance and Availability
Yanzhao Jia, Zhaobo Wu, Zheyi Cao, Shihao Ji, Xu Tianhao, Zihui Song
https://arxiv.org/abs/2510.12139

RAID-0e: A Resilient Striping Array Architecture for Balanced Performance and Availability
This paper introduces a novel disk array architecture, designated RAID-0e (Resilient Striping Array), designed to superimpose a low-overhead fault tolerance layer upon traditional RAID 0 (striping). By employing a logically and physically separate parity domain to protect a primary data domain, RAID-0e mitigates the risk of array-wide data loss from common, non-catastrophic media failures, such as isolated bad blocks, transient read errors, or sector-level corruption. The architecture is engine…

Transformer Networks for Continuous Gravitational-wave Searches
Prasanna. M. Joshi, Reinhard Prix
https://arxiv.org/abs/2509.10912 https://arxiv.org/pdf/25…

Transformer Networks for Continuous Gravitational-wave Searches
Wide-parameter-space searches for continuous gravitational waves (CWs) using semi-coherent matched-filter methods require enormous computing power, which limits their achievable sensitivity. Here we explore an alternative search method based on training neural networks as classifiers on detector strain data with minimal pre-processing. Contrary to previous studies using convolutional neural networks (CNNs), we investigate the suitability of the transformer architecture, specifically the Vision …

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
Huifa Li, Feilong Tang, Haochen Xue, Yulong Li, Xinlin Zhuang, Bin Zhang, Eran Segal, Imran Razzak
https://arxiv.org/abs/2510.12384

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 12,000 adults aged 30--70 years, with extensive longitudinal prof…

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Lichang Zhu, Laura Schaefer, Leitao Chen, Ben Xu
https://arxiv.org/abs/2509.11863

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Predicting and interpreting thermal performance under oscillating flow in porous structures remains a critical challenge due to the complex coupling between fluid dynamics and geometric features. This study introduces a data-driven wGAN-LBM-Nested_CV framework that integrates generative deep learning, numerical simulation based on the lattice Boltzmann method (LBM), and interpretable machine learning to predict and explain the thermal behavior in such systems. A wide range of porous structures …

A New Population of Point-like, Narrow-line Objects Revealed by the James Webb Space Telescope
Haojing Yan (University of Missouri-Columbia), Bangzheng Sun (University of Missouri-Columbia), Riley Shive (University of Missouri-Columbia)
https://arxiv.org/abs/2509.12177

A New Population of Point-like, Narrow-line Objects Revealed by the James Webb Space Telescope
We report a new population of objects discovered using the data from the James Webb Space Telescope, which are characterized by their point-like morphology and narrow permitted emission lines. Our sample includes nine objects found in three JWST wide survey fields, which have z=3.624 to 5.378 and M_B ~ -18.3 to -21.4 mag. Their light distributions follow gaussian profiles, with the full-width-at-half-maximum (FWHM) values only 3.7%--34.6% larger than those of the point spread functions. Their s…

📈 #LogsQL query language provides fast full-text search, advanced analytics, and data extraction/transformation at query time. Can be combined with Unix tools like grep, less, sort, and jq for log analysis.
🎯 Optimized for high cardinality fields like trace_id, user_id, and ip addresses. Supports logs with hundreds of fields (wide events), multitenancy, out-of-order ingestion, live taili…

Selecting Clusters and Protoclusters via Stellar Mass Density: II. Application to HSC-SSP Observations
Marcelo C. Vicentin, Laerte Sodr\'e Jr., Michael A. Strauss, Erik V. R. de Lima, Pablo Araya-Araya
https://arxiv.org/abs/2510.10736

Selecting Clusters and Protoclusters via Stellar Mass Density: II. Application to HSC-SSP Observations
We present a selection of candidates of clusters and protoclusters of galaxies identified in the photometric data of the HSC-SSP Wide Public Data Release 3 (PDR3), spanning the redshift range $\rm 0.1 \leq z \leq 2$. The selection method, detailed in Vicentin et al. (2025), involves detecting massive galaxies located in high-density regions of matter, identified as potential central dominant galaxies, i.e., (proto)BCGs. Probabilistic criteria based on proximity to the candidate central galaxy a…

TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
Jiahao Liu, Bonan Ruan, Xianglin Yang, Zhiwei Lin, Yan Liu, Yang Wang, Tao Wei, Zhenkai Liang
https://arxiv.org/abs/2510.11203

TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and malicious instructions, that compromise their execution flow and can lead to serious consequences like data breaches and financial loss. Existing studies typically attempt to mitigate such anomalies by predefining specific rules and enforcing them at runtime to enhance safety. Yet, designing comprehensive rules is di…

TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
Christopher Kolberg, Katharina Eggensperger, Nico Pfeifer
https://arxiv.org/abs/2510.06162 https://

TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
Revealing novel insights from the relationship between molecular measurements and pathology remains a very impactful application of machine learning in biomedicine. Data in this domain typically contain only a few observations but thousands of potentially noisy features, posing challenges for conventional machine learning approaches. While prior-data fitted networks emerge as foundation models for tabular data, they are currently not suited to handle large feature counts (>500). Although featur…

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

The dependence of the Type Ia Supernova colour-luminosity relation on their host galaxy properties
S. Ramaiya, M. Vincenzi, M. J. Jarvis, P. Wiseman, M. Sullivan
https://arxiv.org/abs/2509.12035

The dependence of the Type Ia Supernova colour-luminosity relation on their host galaxy properties
Using the Dark Energy Survey 5-year sample, we determine the properties of type Ia supernova (SN Ia) host galaxies across a wide multi-wavelength range - from the optical to far-infrared - including data from the $Herschel$ and $Spitzer$ space telescopes. We categorise the SNe Ia into three distinct groups according to the distribution of their host galaxies on the star-formation rate (SFR) - stellar mass ($M_\star$) plane. Each region comprises host galaxies at distinct stages in their evoluti…

Are LLMs ready to help non-expert users to make charts of official statistics data?
Gadir Suleymanli, Alexander Rogiers, Lucas Lageweg, Jefrey Lijffijt
https://arxiv.org/abs/2510.01197

Are LLMs ready to help non-expert users to make charts of official statistics data?
In this time when biased information, deep fakes, and propaganda proliferate, the accessibility of reliable data sources is more important than ever. National statistical institutes provide curated data that contain quantitative information on a wide range of topics. However, that information is typically spread across many tables and the plain numbers may be arduous to process. Hence, this open data may be practically inaccessible. We ask the question "Are current Generative AI models capable …

The Internet is broken and the inventor of the World Wide Web wants to fix it
Tim Berners-Lee is calling for a return to a decentralized Internet and stronger data privacy, but his arguments could be more inspiring.
😡 https://www.nature.com/articles/d41586-025-03859-y

The Internet is broken and the inventor of the World Wide Web wants to fix it
Tim Berners-Lee is calling for a return to a decentralized Internet and stronger data privacy, but his arguments could be more inspiring.

Looks like a threat actor got massive and wide-scale access to UPenn systems.
The threat actor behind the attack contacted BleepingComputer, claiming the intrusion was far broader and that they had gained access to multiple university systems.
https://www.

Penn hacker claims to have stolen 1.2 million donor records in data breach
A hacker has taken responsibility for last week's University of Pennsylvania "We got hacked" email incident, saying it was a far more extensive breach that exposed data on 1.2 million donors and internal documents.

Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Christian Bluethgen, Dave Van Veen, Daniel Truhn, Jakob Nikolas Kather, Michael Moor, Malgorzata Polacin, Akshay Chaudhari, Thomas Frauenfelder, Curtis P. Langlotz, Michael Krauthammer, Farhad Nooralahzadeh
https://arxiv.org/abs/2510.09404

Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Building agents, systems that perceive and act upon their environment with a degree of autonomy, has long been a focus of AI research. This pursuit has recently become vastly more practical with the emergence of large language models (LLMs) capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and planning across a wide range of tasks. With its multimodal data streams and orchestrated workflows spanning multiple systems, radiology is u…

Automated Gating for Flow Cytometry Data Using a Kernel-Smoothed EM Algorithm
Farhad de Sousa, Fran\c{c}ois Ribalet, Jacob Bien
https://arxiv.org/abs/2510.06051 https://

Automated Gating for Flow Cytometry Data Using a Kernel-Smoothed EM Algorithm
Phytoplankton are microscopic algae responsible for roughly half of the world's photosynthesis that play a critical role in global carbon cycles and oxygen production, and measuring the abundance of their subtypes across a wide range of spatiotemporal scales is of great relevance to oceanography. High-frequency flow cytometry is a powerful technique in which oceanographers at sea can rapidly record the optical properties of tens of thousands of individual phytoplankton cells every few minutes. …

Can multimodal representation learning by alignment preserve modality-specific information?
Romain Thoreau, Jessie Levillain, Dawa Derksen
https://arxiv.org/abs/2509.17943 https…

Can multimodal representation learning by alignment preserve modality-specific information?
Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acqu…

Donald Trump’s hateful, falsehood-filled rant on Thursday
blaming immigrants for crime, “social dysfunction” and economic hardship
is refuted by a wide range of immigration statistics,
which show clearly that immigrants dramatically bolster the US economy
and commit crimes at far lower rates than people born in the US.

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Vincent Ochs, Florentin Bieder, Sidaty el Hadramy, Paul Friedrich, Stephanie Taha-Mehlitz, Anas Taha, Philippe C. Cattin
https://arxiv.org/abs/2510.01136

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Tabular data builds the basis for a wide range of applications, yet real-world datasets are frequently incomplete due to collection errors, privacy restrictions, or sensor failures. As missing values degrade the performance or hinder the applicability of downstream models, and while simple imputing strategies tend to introduce bias or distort the underlying data distribution, we require imputers that provide high-quality imputations, are robust across dataset sizes and yield fast inference. We …

Anomalous proteinaceous shells with octagonal local order
Sergei B. Rochal, Aleksey S. Roshal, Olga V. Konevtsova, Rudolf Podgornik
https://arxiv.org/abs/2510.07555 https://

Anomalous proteinaceous shells with octagonal local order
Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the Protein Data Bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicrystals. As we show, these numerous shells have cubic nets cut from short-period approximants of an octagonal tiling composed of square and r…

President Trump signs an EO establishing the "Genesis Mission" to boost AI research and shorten discovery timelines "from years to days or even hours" (Axios)
https://www.axios.com/2025/11/24/trump-ai-genesis-mission-doe-chris-wright<…

Trump orders wide-ranging "Genesis Mission" to boost AI research
It seeks to encourage government data sharing with industry, academia and other scientific institutions.

On the attainment of boundary data in variational problems with linear growth
David Meyer
https://arxiv.org/abs/2510.01515 https://arxiv.org/pdf/2510.01515…

On the attainment of boundary data in variational problems with linear growth
It is well-known that convex variational problems with linear growth and Dirichlet boundary conditions might not have minimizers if the boundary condition is not suitably relaxed. We show that for a wide range of integrands, including the least gradient problem and the non-parametric Plateau problem, and under suitable mean-convexity conditions of the boundary, minimizers of the relaxed problem attain the boundary data in the trace sense if it lies in $BV$ or $W^{α,p}$ with $αp\geq 2$ witho…

Another casino hack although this one doesn't seem so bad.
Boyd Gaming Suffers Cyberattack, Data Breach
https://www.casino.org/news/boyd-gaming-suffers-cyberattack-data-breach/

Boyd Gaming Suffers Cyberattack, Data Breach
Boyd Gaming hit by cyberattack stealing employee data, joins rising Las Vegas casino cybercrime trend amid industry-wide vulnerabilities.

Improving Runtime Performance of Tensor Computations using Rust From Python
Kimmie Harding, Daniel M. Dunlavy
https://arxiv.org/abs/2510.01495 https://arxi…

Improving Runtime Performance of Tensor Computations using Rust From Python
In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API -- e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor …

Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving
Yue Pan, Zihan Xia, Po-Kai Hsu, Lanxiang Hu, Hyungyo Kim, Janak Sharda, Minxuan Zhou, Nam Sung Kim, Shimeng Yu, Tajana Rosing, Mingu Kang
https://arxiv.org/abs/2510.05245

Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving
As Large Language Models (LLMs) continue to evolve, Mixture of Experts (MoE) architecture has emerged as a prevailing design for achieving state-of-the-art performance across a wide range of tasks. MoE models use sparse gating to activate only a handful of expert sub-networks per input, achieving billion-parameter capacity with inference costs akin to much smaller models. However, such models often pose challenges for hardware deployment due to the massive data volume introduced by the MoE laye…

Data-efficient Kernel Methods for Learning Hamiltonian Systems
Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi, Peyman Tavallali, Houman Owhadi
https://arxiv.org/abs/2509.17154

Data-efficient Kernel Methods for Learning Hamiltonian Systems
Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many scientific and engineering problems. In this work, we propose kernel-based methods for identifying and forecasting Hamiltonian systems directly from data. We present two approaches: a two-step method that reconstructs trajectories before learning the Hamiltonian, and a one-step method that jointly infers both. Across several benchmark systems, including …

FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption
Nges Brian Njungle, Eric Jahns, Michel A. Kinsy
https://arxiv.org/abs/2510.03996

FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption
The widespread adoption of Machine Learning as a Service raises critical privacy and security concerns, particularly about data confidentiality and trust in both cloud providers and the machine learning models. Homomorphic Encryption (HE) has emerged as a promising solution to this problems, allowing computations on encrypted data without decryption. Despite its potential, existing approaches to integrate HE into neural networks are often limited to specific architectures, leaving a wide gap in…

Identifying birdsong syllables without labelled data
M\'elisande Teng, Julien Boussard, David Rolnick, Hugo Larochelle
https://arxiv.org/abs/2509.18412 https://

Exploring Polyglot Harmony: On Multilingual Data Allocation for Large Language Models Pretraining
Ping Guo, Yubing Ren, Binbin Liu, Fengze Liu, Haobin Lin, Yifan Zhang, Bingni Zhang, Taifeng Wang, Yin Zheng
https://arxiv.org/abs/2509.15556

Exploring Polyglot Harmony: On Multilingual Data Allocation for Large Language Models Pretraining
Large language models (LLMs) have become integral to a wide range of applications worldwide, driving an unprecedented global demand for effective multilingual capabilities. Central to achieving robust multilingual performance is the strategic allocation of language proportions within training corpora. However, determining optimal language ratios is highly challenging due to intricate cross-lingual interactions and sensitivity to dataset scale. This paper introduces Climb (Cross-Lingual Interact…

Inverse statistics of active matter trajectories to distinguish interaction kernel anisotropy from emergent correlations
Simon F. Martina-Perez
https://arxiv.org/abs/2510.04193 …

Inverse statistics of active matter trajectories to distinguish interaction kernel anisotropy from emergent correlations
High-resolution imaging provides dense trajectories of migrating cells, flocking animals, and synthetic active particles, from which interaction laws can be determined with a wide variety of methods. Yet, distinguishing whether front-back or lateral biases seen in such data reflect intrinsic anisotropy in the interaction kernel or emergent correlations that are nevertheless produced by isotropic pairwise interaction forces remains an open challenge. We resolve this ambiguity by deriving a linea…

SAG-SCI: the Real-time, High-level Analysis Software for Array Control and Data Acquisition of the Cherenkov Telescope Array Observatory
Gabriele Panebianco (INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Bologna, Italy), Nicol\`o Parmiggiani (INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Bologna, Italy), Andrea Bulgarelli (INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Bologna, Italy), Ambra Di Piano (INAF - Osserva…

SAG-SCI: the Real-time, High-level Analysis Software for Array Control and Data Acquisition of the Cherenkov Telescope Array Observatory
The Cherenkov Telescope Array Observatory (CTAO) is going to be the leading observatory for very-high-energy gamma-rays over the next decades. Its unique sensitivity, wide field of view, and rapid slewing capability make the CTAO especially suited to study transient astrophysical phenomena. The CTAO will analyse its data in real-time, responding to external science alerts on transient events and issuing its own. The Science Alert Generation (SAG) automated pipeline, a component of the Array Con…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
Kazuumi Fujioka, Rui Sun
https://arxiv.org/abs/2510.05398 https://arxiv.o…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wide variety of published reactions. The inputs and algorithm guiding it are explained. The code is open-source and can be downloaded at: gith…

Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow, Zeyu Shangguan, Tianheng Shi, Rong Xue, Yuxi Zheng, Yijia Weng, Yang You, Daniel Seita, Leonidas Guibas, Sergey Zakharov, Vitor Guizilini, Yue Wang
https://arxiv.org/abs/2509.22970

Robot Learning from Any Images
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a single image without requiring additional hardware or digital assets. Our framework democratizes robotic data generation by producing massive visuomotor robotic demonstrations within minutes from a wide range of image sources, including camera captures, robotic datasets, and Internet images. At its core, our approach …

Modeling discrete lattice data using the Potts and tapered Potts models
Maria Paula Duenas-Herrera, Stephen Berg, Murali Haran
https://arxiv.org/abs/2509.21478 https://

Modeling discrete lattice data using the Potts and tapered Potts models
The Ising and Potts models, among the most important models in statistical physics, have been used for modeling binary and multinomial data on lattices in a wide variety of disciplines such as psychology, image analysis, biology, and forestry. However, these models have several well known shortcomings: (i) they can result in poorly fitting models, that is, simulations from fitted models often do not produce realizations that look like the observed data; (ii) phase transitions and the presence o…

NeuSO: Neural Optimizer for Subgraph Queries
Linglin Yang, Lei Zou, Chunshan Zhao
https://arxiv.org/abs/2509.23775 https://arxiv.org/pdf/2509.23775

NeuSO: Neural Optimizer for Subgraph Queries
Subgraph query is a critical task in graph analysis with a wide range of applications across various domains. Most existing methods rely on heuristic vertex matching orderings, which may significantly degrade enumeration performance for certain queries. While learning-based optimizers have recently gained attention in the context of relational databases, they cannot be directly applied to subgraph queries due to the heterogeneous and schema-flexible nature of graph data, as well as the large nu…

Wide-field GMRT imaging of X-shaped Radio-Galaxies: Spectral properties of 4C32.25 and 4C61.23
E. Retana-Montenegro
https://arxiv.org/abs/2510.02753 https://

Wide-field GMRT imaging of X-shaped Radio-Galaxies: Spectral properties of 4C32.25 and 4C61.23
We present wide-field upgraded Giant Metrewave Radio Telescope (uGMRT) images of the fields around the X-shaped radio-galaxies (XRGs) 4C32.25, 4C61.23, and MRC 2011-298 obtained at 400 MHz. The observations are calibrated using the extreme peeling method to account for direction-dependent effects across the field of view, as previously applied to Low-frequency array (LOFAR) data. Our 400 MHz images capture in fine detail the radio-morphology of the XRGs, as well as other serendipitous radio-sou…

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
Lingyu Kong, Jaeheon Shim, Guoxiang Hu, Victor Fung
https://arxiv.org/abs/2509.21694

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanical accuracy. However, AFMs remain markedly slower at inference and are far more memory-intensive than conventional interatomic potentials, due to the need to capture a wide range of chemical and structural motifs in pre-training datasets requiring deep, parameter-rich model architectures. These deficiencies currently l…

Fast-dLLM v2: Efficient Block-Diffusion LLM
Chengyue Wu, Hao Zhang, Shuchen Xue, Shizhe Diao, Yonggan Fu, Zhijian Liu, Pavlo Molchanov, Ping Luo, Song Han, Enze Xie
https://arxiv.org/abs/2509.26328

Fast-dLLM v2: Efficient Block-Diffusion LLM
Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation, requiring only approximately 1B tokens of fine-tuning. This represents a 500x reduction in training data compared …

On Multi-entity, Multivariate Quickest Change Point Detection
Bahar Kor, Bipin Gaikwad, Abani Patra, Eric L. Miller
https://arxiv.org/abs/2509.18310 https://

On Multi-entity, Multivariate Quickest Change Point Detection
We propose a framework for online Change Point Detection (CPD) from multi-entity, multivariate time series data, motivated by applications in crowd monitoring where traditional sensing methods (e.g., video surveillance) may be infeasible. Our approach addresses the challenge of detecting system-wide behavioral shifts in complex, dynamic environments where the number and behavior of individual entities may be uncertain or evolve. We introduce the concept of Individual Deviation from Normality (I…

Recovering Parametric Scenes from Very Few Time-of-Flight Pixels
Carter Sifferman, Yiquan Li, Yiming Li, Fangzhou Mu, Michael Gleicher, Mohit Gupta, Yin Li
https://arxiv.org/abs/2509.16132

Recovering Parametric Scenes from Very Few Time-of-Flight Pixels
We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available time-of-flight sensors. These sensors offer very low spatial resolution (i.e., a single pixel), but image a wide field-of-view per pixel and capture detailed time-of-flight data in the form of time-resolved photon counts. This time-of-flight data encodes rich scene information and thus enables recovery of simple scenes from sparse measurements. We investigate the feasib…

CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
Kaixin Wang, Tianlin Li, Xiaoyu Zhang, Aishan Liu, Xianglong Liu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, and Bin Shi
https://arxiv.org/abs/2510.00501

CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
Code Large Language Models (CodeLLMs) are increasingly used in code generation tasks across a wide range of applications. However, their performance is often inconsistent across different programming languages (PLs), with low-resource PLs suffering the most due to limited training data. In this paper, we present CodeChemist, a novel and efficient framework for test-time scaling that enables functional knowledge transfer from high-resource to low-resource PLs using generated test cases. CodeChem…

Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang, Bryan T. Grenfell, Adji Bousso Dieng
https://arxiv.org/abs/2509.22520 …

Applications of the Vendi score in genomic epidemiology
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the metrics commonly used in ecology. It is classification-independent, incorporates abundance information, and has a tunable sensitivity to rare/abundant types. Using rich COVID-19 sequence data as a paradigm, we develop methods for applying the VS to time-resolved sequence data. We show how the VS allows for characteriz…

SpeechOp: Inference-Time Task Composition for Generative Speech Processing
Justin Lovelace, Rithesh Kumar, Jiaqi Su, Ke Chen, Kilian Q Weinberger, Zeyu Jin
https://arxiv.org/abs/2509.14298

SpeechOp: Inference-Time Task Composition for Generative Speech Processing
While generative Text-to-Speech (TTS) systems leverage vast ``in-the-wild" data to achieve remarkable success, speech-to-speech processing tasks like enhancement face data limitations, which lead data-hungry generative approaches to distort speech content and speaker identity. To bridge this gap, we present SpeechOp, a multi-task latent diffusion model that transforms pre-trained TTS models into a universal speech processor capable of performing a wide range of speech tasks and composing them i…

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Roman Jacome, Romario Gualdr\'on-Hurtado, Leon Suarez-Rodriguez, Henry Arguello
https://arxiv.org/abs/2509.14394

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Imaging Inverse problems aim to reconstruct an underlying image from undersampled, coded, and noisy observations. Within the wide range of reconstruction frameworks, the unrolling algorithm is one of the most popular due to the synergistic integration of traditional model-based reconstruction methods and modern neural networks, providing an interpretable and highly accurate reconstruction. However, when the sensing operator is highly ill-posed, gradient steps on the data-fidelity term can hinde…

Modeling the Carbon Footprint of HPC: The Top 500 and EasyC
Varsha Rao, Andrew A. Chien
https://arxiv.org/abs/2509.13583 https://arxiv.org/pdf/2509.13583…

Modeling the Carbon Footprint of HPC: The Top 500 and EasyC
Climate change is a critical concern for HPC systems, but GHG protocol carbon-emission accounting methodologies are difficult for a single system, and effectively infeasible for a collection of systems. As a result, there is no HPC-wide carbon reporting, and even the largest HPC sites do not do GHG protocol reporting. We assess the carbon footprint of HPC, focusing on the Top 500 systems. The key challenge lies in modeling the carbon footprint with limited data availability. With the disclo…

Hector Galaxy Survey: Data Processing, Quality Control and Early Science
S. Oh, M. L. P. Gunawardhana, S. M. Croom, G. Quattropani, S. Tuntipong, J. J. Bryant, P. Corcho- Caballero, P. K. Das, O. \c{C}ak{\i}r, J. H. Lee, A. Ristea, S. Barsanti, M. Pak, S. M. Sweet, T. J. Woodrow, T. Rutherford, Y. Mai, M. S. Owers, M. Colless, L. S. J. Stuart, H. R. M. Zovaro, S. P. Vaughan, J. van de Sande, T. Farrell, M. Beom, J. Bland-Hawthorn, J. Chung, C. Foster, K. Grasha, H. Jeong, J. C. Lee, A.…

Hector Galaxy Survey: Data Processing, Quality Control and Early Science
The Hector Galaxy Survey is a new optical integral field spectroscopy (IFS) survey currently using the AAT to observe up to 15,000 galaxies at low redshift ($z < 0.1$). The Hector instrument employs 21 optical fibre bundles feeding into two double-beam spectrographs to enable wide-field multi-object IFS observations of galaxies. To efficiently process the survey data, we adopt the data reduction pipeline developed for the SAMI Galaxy Survey, with significant updates to accommodate Hector's dual…

Impact Plasma Amplification of the Ancient Mercury Magnetic Field
Isaac S. Narrett, Rona Oran, Yuxi Chen, Katarina Miljkovi\'c, G\'abor T\'oth, Catherine L. Johnson, Benjamin P. Weiss
https://arxiv.org/abs/2510.01975

Impact Plasma Amplification of the Ancient Mercury Magnetic Field
Spacecraft measurements of Mercury indicate it has a core dynamo with a surface field of 200-800 nT. These data also indicate that the crust contains remanent magnetization likely produced by an ancient magnetic field. The inferred magnetization intensity is consistent with a wide range of paleofield strengths (0.2-50 uT), possibly indicating that Mercury once had a dynamo field much stronger than today. Recent modeling of ancient lunar impacts has demonstrated that plasma generated during basi…

Modi linear failure rate distribution with application to survival time data
Lazhar Benkhelifa
https://arxiv.org/abs/2509.20831 https://arxiv.org/pdf/2509.…

Modi linear failure rate distribution with application to survival time data
A new lifetime model, named the Modi linear failure rate distribution, is suggested. This flexible model is capable of accommodating a wide range of hazard rate shapes, including decreasing, increasing, bathtub, upside-down bathtub, and modified bathtub forms, making it particularly suitable for modeling diverse survival and reliability data. Our proposed model contains the Modi exponential distribution and the Modi Rayleigh distribution as sub-models. Numerous mathematical and reliability prop…

On the Ultra-Long Gamma-Ray Transient GRB 250702B/EP250702
Jin-Peng Zhang, Chen-Wei Wang, Zheng-Hang Yu, Shao-Lin Xiong, Shu-Xu Yi, Jia-Cong Liu, Wang-Chen Xue, Wen-Jun Tan, Zi-Rui Zhang, Rahim Moradi, Hao-Xuan Guo, Chao Zheng, Yan-Qiu Zhang, Yue Wang, Sheng-Lun Xie, Peng Zhang, Yang-Zhao Ren, Cheng-Kui Li, Xiao-Bo Li, Ce Cai, Shuo Xiao, Li-Ming Song, Shuang-Nan Zhang

On the Ultra-Long Gamma-Ray Transient GRB 250702B/EP250702
GRB 250702B/EP250702a is an interesting long-duration gamma-ray transient whose nature is in debate. To obtain a full picture in gamma-ray band, we implement a comprehensive targeted search of burst emission in a wide window of 30 days jointly with Insight-HXMT, GECAM and Fermi/GBM data within the ETJASMIN framework. In gamma-ray band, we find there is a 50-second precursor about 25 hours before the 4-hour main burst, which generally consists of 4 emission episodes. Remarkably, we find that the…

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents
Hongze Mi, Yibo Feng, Wenjie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Naiqiang Tan, Gang Pan
https://arxiv.org/abs/2509.21799

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents
Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis -- a novel deliberative framework in this paper. D-Artemis leverages a fine-gra…

Is Liller 1 a building block of the Galactic bulge? - Evidence with APOGEE
Anna Liptrott, Ricardo P. Schiavon, Andrew C. Mason, Sebastian Kamann, Borja Anguiano, Roger E. Cohen, Jos\'e G. Fern\'andez-Trincado, Danny Horta, Steven R. Majewski, Dante Minniti, David M. Nataf, Michael J. W. O'Connor, Dominic Wearne
https://arxiv.or…

Is Liller 1 a building block of the Galactic bulge? - Evidence with APOGEE
Liller 1 is a stellar system orbiting within the inner 0.8kpc of the Galactic centre, characterised by a wide spread in age and metallicity, indicating a high mass. Liller 1 has been proposed to be a major contributor to the stellar mass of the Galactic bulge, yet its origin is subject to debate. We employ Sloan Digital Sky Survey IV (SDSS-IV) data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to test scenarios proposed to explain the nature of Liller 1. Using a rando…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
Ginger Tsueng (The Scripps Research Institute, La Jolla, CA, USA), Emily Bullen (The Scripps Research Institute, La Jolla, CA, USA), Candice Czech (The Scripps Research Institute, La Jolla, CA, USA), Dylan Welzel (The Scripps Research Institute, La Jolla, CA, USA), Leandro Collares (The Scripps Research Institute, La Jolla, CA, USA), Jason Lin (The Scripps Research Institute, La Joll…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
The NIAID Data Ecosystem Discovery Portal (https://data.niaid.nih.gov) provides a unified search interface for over 4 million datasets relevant to infectious and immune-mediated disease (IID) research. Integrating metadata from domain-specific and generalist repositories, the Portal enables researchers to identify and access datasets using user-friendly filters or advanced queries, without requiring technical expertise. The Portal supports discovery of a wide range of resources, including epide…

Probing dust torus radius--luminosity relation: An WISE view
Ashutosh Tomar, Suvendu Rakshit, Amit Kumar Mandal, Shivangi Pandey
https://arxiv.org/abs/2510.04517 https://…

Probing dust torus radius--luminosity relation: An WISE view
We present measurements of the dusty torus sizes of 51 active galactic nuclei (AGNs) with a redshift of $z<$ 0.8. Our analysis utilizes about 16 years of optical photometric data of 146 AGNs from various time-domain surveys, including ASAS-SN, CRTS, and ZTF, along with 14 years of infrared data in the $W$1 ($\sim$ 3.4 $μ$m) and $W$2 ($\sim$ 4.6 $μ$m) bands obtained from the Wide-Field Infrared Survey Explorer (WISE). The estimated dust torus size ranges from 1000 to 3000 days, using both the …

Modelling non-stationary extremal dependence through a geometric approach
C. J. R. Murphy-Barltrop, J. L. Wadsworth, M. de Carvalho, B. D. Youngman
https://arxiv.org/abs/2509.22501

Modelling non-stationary extremal dependence through a geometric approach
Non-stationary extremal dependence, whereby the relationship between the extremes of multiple variables evolves over time, is commonly observed in many environmental and financial data sets. However, most multivariate extreme value models are only suited to stationary data. A recent approach to multivariate extreme value modelling uses a geometric framework, whereby extremal dependence features are inferred through the limiting shapes of scaled sample clouds. This framework can capture a wide r…

FIELDMAPS Data Release: Far-Infrared Polarization in the "Bones" of the Milky Way
Simon Coud\'e, Ian W. Stephens, Philip C. Myers, Nicole Karnath, Howard A. Smith, Andr\'es Guzm\'an, Jessy Marin, Catherine Zucker, B-G. Andersson, Zhi-Yun Li, Leslie W. Looney, Giles Novak, Thushara G. S. Pillai, Sarah I. Sadavoy, Patricio Sanhueza, Archana Soam

FIELDMAPS Data Release: Far-Infrared Polarization in the "Bones" of the Milky Way
Polarization observations of the Milky Way and many other spiral galaxies have found a close correspondence between the orientation of spiral arms and magnetic field lines on scales of hundreds of parsecs. This paper presents polarization measurements at 214 $μ$m toward ten filamentary candidate ``bones" in the Milky Way using the High-resolution Airborne Wide-band Camera (HAWC+) on the Stratospheric Observatory for Infrared Astronomy (SOFIA). These data were taken as part of the Filaments Ext…

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Zefan Liu, Natalia Nolde
https://arxiv.org/abs/2509.15023 https://

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Panel data arise in a wide range of application areas, and developing modelling methods for extreme values under such a setup is essential for reliable risk assessment and management. When choosing to model the marginal distributions of univariate extremes, one may wish to balance the flexibility in capturing the heterogeneity among margins and the efficiency of estimation. This can be achieved through a combination of regression techniques and assuming a latent group structure based on paramet…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
Ioannis Stergakis
https://arxiv.org/abs/2509.13037 https:/…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
This dissertation focuses on the reconstruction of Equations of State (EoSs) describing the interior of compact stars, using modern machine learning and deep learning methods. The pipeline is based on data from mass-radius (M-R) curves, obtained by numerically solving the Tolman-Oppenheimer-Volkoff equations for a wide range of admissible EoSs. The manuscript is divided into a Theoretical Part (Chs. 1-4) and a Computational Part (Chs. 5-7). The theoretical chapters analyze the properties of neu…

A proposal for automated turbulence modelling
Marco Castelletti, Maurizio Quadrio
https://arxiv.org/abs/2509.14140 https://arxiv.org/pdf/2509.14140

A proposal for automated turbulence modelling
Solving the Reynolds-averaged Navier-Stokes equations (RANS) closed with an eddy viscosity computed through a turbulence model is still the leading approach for Computational Fluid Dynamics simulations. Unfortunately, universal models with good predictive capabilities over a wide range of flows are not available. In this work, we propose the use of machine learning to improve existing RANS models. The approach does not require high-fidelity training data. A convolutional neural network is use…

Constraints on the Orbit of the Young Substellar Companion GQ Lup B from High-Resolution Spectroscopy and VLTI/GRAVITY Astrometry
Vidya Venkatesan, S. Blunt, J. J. Wang, S. Lacour, G. -D. Marleau, G. A. L. Coleman, L. Guerrero, W. O. Balmer, L. Pueyo, T. Stolker, J. Kammerer, N. Pourr\'e, M. Nowak, E. Rickman, A. Sivaramakrishnan, D. Sing, K. Wagner, A. -M. Lagrange, R. Abuter, A. Amorim, R. Asensio-Torres, J. -P. Berger, H. Beust, A. Boccaletti, M. Bonnefoy, H. Bonnet, M. S. Bordo…

Constraints on the Orbit of the Young Substellar Companion GQ Lup B from High-Resolution Spectroscopy and VLTI/GRAVITY Astrometry
Understanding the orbits of giant planets is critical for testing planet formation models, particularly at wide separations greater than 10 au where traditional core accretion becomes inefficient. However, constraining orbits at these separations has been challenging because of sparse orbital coverage and degeneracies in the orbital parameters. We use existing high-resolution spectroscopic measurements from CRIRES+ (R ~ 100000), astrometric data from SPHERE, NACO, and ALMA, and new high-precisi…

SHAPE. I. A SOM-SED hybrid approach for efficient galaxy parameter estimation leveraging JWST
Zihao Wang, Tao Wang, Ke Xu, Hanwen Sun, Ruining Tian, Qi Hao
https://arxiv.org/abs/2510.00187

SHAPE. I. A SOM-SED hybrid approach for efficient galaxy parameter estimation leveraging JWST
With the launch and application of next-generation ground- and space-based telescopes, astronomy has entered the era of big data, necessitating more efficient and robust data analysis methods. Most traditional parameter estimation methods are unable to reconcile differences between photometric systems. Ideally, we would like to optimally rely on high-quality observation data provided by, e.g., JWST, for calibrating and improving upcoming wide-field surveys such as the China Space Station Telesc…

Shocked, Heated, and Now Resolved: H$_2$ excitation in the low-luminosity AGN at M58 core with JWST
I. E. L\'opez, E. Bertola, V. Reynaldi, P. Ogle, R. D. Baldi, M. Brusa, S. Garc\'ia-Burillo, B. Sebastian, M. V. Zanchettin, G. Cresci, J. A. Fern\'andez-Ontiveros, A. Marconi, R. M. Rich, T. M. Rodriguez
https://arxiv.org/abs/25…

Shocked, Heated, and Now Resolved: H$_2$ excitation in the low-luminosity AGN at M58 core with JWST
We present \textit{JWST} NIRSpec and MIRI MRS observations of the central kiloparsec of M58 (NGC 4579), a nearby LINER galaxy hosting a low-luminosity AGN (LLAGN; $L_\mathrm{bol} \sim 10^{42}$ erg s$^{-1}$) with a low-power jet. These data provide an unprecedented view of the warm molecular gas phase and reveal clear signatures of feedback. We detect 44 H$_2$ lines, including bright pure rotational lines (S(1)-S(18)) and rovibrational lines up to $ν=2$, probing a wide range of excitation condi…

Photometric Redshift Forecast for 7-Dimensional Sky Survey
Eunhee Ko, Myungshin Im
https://arxiv.org/abs/2509.22165 https://arxiv.org/pdf/2509.22165…

Photometric Redshift Forecast for 7-Dimensional Sky Survey
We investigate the expected accuracy of redshifts that can be obtained using low-resolution spectroscopic (medium-band) data from the 7-Dimensional Sky Survey (7DS). By leveraging 40 densely sampled filters with widths of full width at half maximum (FWHM) = 25 nm, we create 7DS mock catalogs and estimate the redshift accuracy for three 7DS main surveys: Wide-field Time-Domain Survey (WTS), Intensive Monitoring Survey (IMS), and Reference Image Survey (RIS). Using photometric redshifts calculate…

The Extended Stellar Distribution in the Outskirts of the Ursa Minor Dwarf Spheroidal Galaxy
Kyosuke S. Sato, Sakurako Okamoto, Masafumi Yagi, Yutaka Komiyama, Nobuo Arimoto, Rosemary F. G. Wyse, Evan N. Kirby, Masashi Chiba, Itsuki Ogami, Mikito Tanaka
https://arxiv.org/abs/2509.20914

The Extended Stellar Distribution in the Outskirts of the Ursa Minor Dwarf Spheroidal Galaxy
We discover an extended distribution of main-sequence (MS) stars along the minor axis of the Ursa Minor (UMi) dwarf spheroidal galaxy (dSph). This study is enabled by deep, wide Subaru/Hyper Suprime-Cam data, reaching photometric uncertainties below 0.1 mag at $g,i \sim 26$ mag. Color-magnitude diagrams along the major and minor axes reveal a clear excess of MS stars beyond the nominal tidal radius along the minor axis. To characterize this structure, we derive radial number density profiles in…