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PowerBin: Fast Adaptive Data Binning with Centroidal Power Diagrams
Michele Cappellari (University of Oxford)
https://arxiv.org/abs/2509.06903 https://arxi…

PowerBin: Fast Adaptive Data Binning with Centroidal Power Diagrams
Adaptive binning is a crucial step in the analysis of large astronomical datasets, such as those from integral-field spectroscopy, to ensure a sufficient signal-to-noise ratio (S/N) for reliable model fitting. However, the widely used Voronoi-binning method and its variants suffer from two key limitations: they scale poorly with data size, often as O(N^2), creating a computational bottleneck for modern surveys, and they can produce undesirable non-convex or disconnected bins. I introduce PowerB…

French Prime Minister
Sébastien Lecornu has spoken to reporters,
about an hour after announcing his resignation.
“I was ready to compromise,
but each political party wanted the other political party to adopt its entire program,”
he said from the courtyard of Matignon Palace, the prime minister’s headquarters

EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection
Syed M. Arshad, Lee C. Potter, Yingmin Liu, Christopher Crabtree, Matthew S. Tong, Rizwan Ahmad
https://arxiv.org/abs/2507.23224

EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection
We propose EMORe, an adaptive reconstruction method designed to enhance motion robustness in free-running, free-breathing self-gated 5D cardiac magnetic resonance imaging (MRI). Traditional self-gating-based motion binning for 5D MRI often results in residual motion artifacts due to inaccuracies in cardiac and respiratory signal extraction and sporadic bulk motion, compromising clinical utility. EMORe addresses these issues by integrating adaptive inter-bin correction and explicit outlier rejec…

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Wenhao Li, Selvakumar Manickam, Yung-Wey Chong, Shankar Karuppayah, Priyadarsi Nanda, Binyong Li
https://arxiv.org/abs/2510.05900

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not only incurs substantial annotation costs but also limits adaptability to novel attack patterns. To address these challenges, we propose PhishSSL, a self-supervised contrastive learning framework that eliminates the need for labeled phishing data during trainin…

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Yuanhan Wang, Yifei Chen, Shuo Jiang, Wenjing Yu, Mingxuan Liu, Beining Wu, Jinying Zong, Feiwei Qin, Changmiao Wang, Qiyuan Tian
https://arxiv.org/abs/2509.17925

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Reliable brain tumor segmentation in MRI is indispensable for treatment planning and outcome monitoring, yet models trained on curated benchmarks often fail under domain shifts arising from scanner and protocol variability as well as population heterogeneity. Such gaps are especially severe in low-resource and pediatric cohorts, where conventional test-time or source-free adaptation strategies often suffer from instability and structural inconsistency. We propose SmaRT, a style-modulated robust…

Bananas for Tonks' gas: structural transitions in bent-core molecule bi-layers
David A. King
https://arxiv.org/abs/2509.21503 https://arxiv.org/pdf/250…

Bananas for Tonks' gas: structural transitions in bent-core molecule bi-layers
We study the role of particle geometry in the ordering of bilayers of bent-core particles using a simplified two-dimensional model. Particles are confined to two parallel one-dimensional layers and can adopt only two discrete orientations of their bent cores. By mapping the system exactly onto a pair of coupled Ising chains in the thermodynamic limit, we may obtain closed-form expressions for all correlation functions. The coupling between orientational and layer degrees of freedom produces rem…

Speech Command Recognition Using LogNNet Reservoir Computing for Embedded Systems
Yuriy Izotov, Andrei Velichko
https://arxiv.org/abs/2509.00862 https://ar…

Speech Command Recognition Using LogNNet Reservoir Computing for Embedded Systems
This paper presents a low-resource speech-command recognizer combining energy-based voice activity detection (VAD), an optimized Mel-Frequency Cepstral Coefficients (MFCC) pipeline, and the LogNNet reservoir-computing classifier. Using four commands from the Speech Commands da-taset downsampled to 8 kHz, we evaluate four MFCC aggregation schemes and find that adaptive binning (64-dimensional feature vector) offers the best accuracy-to-compactness trade-off. The LogNNet classifier with architect…

Replaced article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- PowerBin: Fast Adaptive Data Binning with Centroidal Power Diagrams
Michele Cappellari (University of Oxford)

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
Richard Sserujongi, Daniel Ogenrwot, Nicholas Niwamanya, Noah Nsimbe, Martin Bbaale, Benjamin Ssempala, Noble Mutabazi, Raja Fidel Wabinyai, Deo Okure, Engineer Bainomugisha
https://arxiv.org/abs/2508.14451

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper presents the design, implementation, and evaluation of the AirQo data pipeline: a modular, cloud-native Extract-Transform-Load (ETL) system engineered to support both real-time and batch processing of heterogeneous air quality data across urban deployments in Afri…