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"Philippines protects huge coral hotspot off the coast of Panaon Island"
#Philippines #CoralReef #Environment

Philippines protects huge coral hotspot off the coast of Panaon Island
The corals around Panaon Island in the southeastern Philippines form some of the healthiest and most climate-resilient reefs in the world, and they’re now a legally protected seascape. Philippine President Ferdinand Marcos Jr. approved the Panaon Island Protected Seascape law on Aug. 29. It creates a 61,204-hectare (151,200-acre) marine protected area within the Pacific Coral […]

Dallas Cowboys predicted to trade Micah Parsons to Arizona Cardinals for massive haul https://www.sportingnews.com/us/nfl/arizona-cardinals/news/dallas-cowboys-trade-micah-parsons-arizona-cardinals-nf…

Learning Microswimmer Collision Dynamics and Predicting Diffusivities using a Neural-Network-Assisted Boltzmann Approach
Haruki Hayano, Akira Furukawa, Kang Kim
https://arxiv.org/abs/2510.02559

Learning Microswimmer Collision Dynamics and Predicting Diffusivities using a Neural-Network-Assisted Boltzmann Approach
We present a neural-network--assisted Boltzmann framework that learns the binary-collision map of microswimmers from data and uses it to evaluate collision integrals efficiently. Using a representative model swimmer, the learned map provides quantitative predictions for rotational and translational diffusivities and allows a linear-stability analysis of the isotropic state against polar ordering in dilute suspensions. The resulting predictions closely match direct simulations in the dilute regi…

Self-lensing binaries in globular clusters - predictions for ELT
Grzegorz Wiktorowicz, Matthew Middleton, Mirek Giersz, Adam Ingram, Adam McMaster, Abbas Askar, Lucas Hellstr\"om
https://arxiv.org/abs/2510.02751

Self-lensing binaries in globular clusters - predictions for ELT
Self-lensing (SL) represents a powerful technique for detecting compact objects in binary systems through gravitational microlensing effects, when a compact companion transits in front of its luminous partner. We present the first comprehensive study of SL probability within globular cluster (GC) environments, utilizing synthetic stellar populations from MOCCA simulations to predict detection rates for the Extremely Large Telescope (ELT). Our analysis incorporates finite-size lens effects for w…

Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
Dror Aiger, Bingyi Cao, Kaifeng Chen, Andre Araujo
https://arxiv.org/abs/2509.04351

Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching techniques. This design, dubbed global-to-local, stems from the computational cost of local matching approaches, which can only be afforded for a small number of retrieved images. However, emerging efficient local feature search approaches have opened up new possibilities, in particular enabling detailed retrieval at…

TriAlignXA: An Explainable Trilemma Alignment Framework for Trustworthy Agri-product Grading
Jianfei Xie, Ziyang Li
https://arxiv.org/abs/2510.01990 https://

TriAlignXA: An Explainable Trilemma Alignment Framework for Trustworthy Agri-product Grading
The 'trust deficit' in online fruit and vegetable e-commerce stems from the inability of digital transactions to provide direct sensory perception of product quality. This paper constructs a 'Trust Pyramid' model through 'dual-source verification' of consumer trust. Experiments confirm that quality is the cornerstone of trust. The study reveals an 'impossible triangle' in agricultural product grading, comprising biological characteristics, timeliness, and economic viability, highlighting the li…

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas
J. B. Lestz, G. Avdeeva, T. F. Neiser, M. V. Gorelenkova, F. D. Halpern, S. M. Kaye, J. McClenaghan, A. Y. Pankin, K. E. Thome
https://arxiv.org/abs/2509.04359

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas
Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those us…

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
https://arxiv.org/abs/2510.00831

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
The increasing integration of distributed energy resources (DERs), particularly renewables, poses significant challenges for power system protection, with fault classification (FC) and fault localization (FL) being among the most critical tasks. Conventional protection schemes, based on fixed thresholds, cannot reliably identify and localize short circuits with the increasing complexity of the grid under dynamic conditions. Machine learning (ML) offers a promising alternative; however, systemat…

SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation
M Zafir Sadik Khan, Nowfel Mashnoor, Mohammad Akyash, Kimia Azar, Hadi Kamali
https://arxiv.org/abs/2508.03558

SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation
In today's rapidly evolving field of electronic design automation (EDA), the complexity of hardware designs is increasing, necessitating more sophisticated automation solutions. High-level synthesis (HLS), as a pivotal solution, automates hardware designs from high-level abstractions (e.g., C/C++). However, it faces significant challenges, particularly in design space exploration and optimization. While large language models (LLMs) have shown notable capabilities in code generation, their appli…

A fast machine learning tool to predict the composition of astronomical ices from infrared absorption spectra
Andr\'es Meg\'ias, Izaskun Jim\'enez-Serra, Fran\c{c}ois Dulieu, Julie Vitorino, Bel\'en Mat\'e, David Ciudad, Will R. M. Rocha, Marcos Mart\'inez Jim\'enez, Jacobo Aguirre
https://arxiv.org/abs/2509.043…

A fast machine learning tool to predict the composition of astronomical ices from infrared absorption spectra
Current observations taken by James Webb Space Telescope (JWST) allow us to observe the absorption features of icy mantles that cover interstellar dust grains, which are mainly composed of $\mathrm{H_2O}$, $\mathrm{CO}$, and $\mathrm{CO_2}$, along with other minor species. Thanks to its sensitivity and spectral resolution, JWST has the potential to observe ice features towards hundreds of sources at different stages along the process of star formation. However, identifying the spectral features…