Cosmic-Ray Syntactic Pool

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity
Sep 14, 2026Shuo Zhang, Xin Su, Wei Wang +8Visual Geometry Grounded Transformer Positional Encoding
Wuhan University, Wuhan, China · Rongyun Robot (Guizhou) Co., Ltd. · Institute of Automation, Chinese Academy of Sciences, Beijing, China
Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods
Jul 30, 2026J. Balibrea-Correa, E. N{á}cher, C. Fonseca-Vargas +1Cosmic-Ray Syntactic Pool Laser-Induced Breakdown Spectroscopy
Instituto de Física Corpuscular, CSIC - Universidad de Valencia, Spain, 46980, Valencia, Spain
Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector
Jul 8, 2026Raiff H. Santos, Amilcar R. Queiroz, Tharcisyo S. S. Duarte +2Recent Vision-Language Models Cosmic-Ray Syntactic Pool
Universidade Federal de Campina Grande, R. Apr´ıgio Veloso 882, Bairro Universit´ario, 58429-900, Campina Grande, PB, Brazil · Instituto de Forma¸c˜ao de Educadores, Universidade Federal do Cariri, Rua Oleg´ario Em´ıdio de Ara´ujo, s/n, Aldeota, 63260-000, Brejo Santo, CE, Brazil
Computer vision-based neural networks for radioisotope identification in urban environments
Jun 30, 2026Masen Bachleda, Peter LalorCosmic-Ray Syntactic Pool Unsupervised Detection
Pacific Northwest National Laboratory, Richland, WA 99354 USA
Peak-Based Nuclide Identification in HPGe $γ$ -Spectrometry with Machine Learning and SHAP
Jun 12, 2026Samuel Emmons, Kelly Truax, Maurice Lonsway +2Cosmic-Ray Syntactic Pool
Pacific Northwest National Laboratory, Richland, 99354, WA, USA
A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts
May 28, 2026Soumita ModakCosmic-Ray Syntactic Pool Clustering
Department of Statistics, Presidency University 86/1 College Street, Kolkata-700073, India
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
May 26, 2026Siddharth Chaini, Federica B. Bianco, Ashish MahabalCosmic-Ray Syntactic Pool Gaussian Process
Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA · University of Delaware, Data Science Institute, Newark, DE 19716, USA · Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware, DE 19716, USA +3
Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection
May 21, 2026Alberto Garinei, Stefano Speziali, Alessandro Vispa +11Cosmic-Ray Syntactic Pool Unsupervised Detection
Idea-Re S.r.l., 06125, Perugia, Italy · bDipartimento Scienze Ingegneristiche, Università Guglielmo Marconi, 00193, Rome, Italy · cIstituto Nazionale Fisica Nucleare, sezione di Perugia, 06123, Perugia, Italy +1
ML-based Fast Simulation of FARICH Responses
May 17, 2026Foma Shipilov, Alexander Barnyakov, Artem Ivanov +1Generative Adversarial Network Monte Carlo
HSE University, Moscow, Russia · Budker Institute of Nuclear Physics SB RAS, Novosibirsk, Russia · Novosibirsk State Technical University, Novosibirsk, Russia +1
A semantic mutation metric for metamorphic relation adequacy in scientific computing programs
May 17, 2026Meng Li, Xiaohua Yang, Jie Liu +1Quality-Driven Selective Mutation Stress Testing
School of Computing, University of South China, Hengyang, China · Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China · CNNC Key Laboratory on High Trusted Computing, Hengyang, China
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
May 4, 2026Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1Xai Explainability
Applied Nuclear Physics Laboratory · State University of Londrina · Dept. of Engineering and Architecture +2
High-dimensional inference for the $γ$ -ray sky with differentiable programming
Apr 9, 2026Siddharth Mishra-Sharma, Tracy R. Slatyer, Yitian Sun +1Cosmic-Ray Syntactic Pool Astronomical Images
Faculty of Computing and Data Sciences, Boston University, Boston, MA 02215, USA · 2The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +2
Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks
Feb 26, 2026Arsène Ferrière, Aurélien Benoit-Lévy, Olivier Martineau-Huynh +1Cosmic-Ray Syntactic Pool Graph Neural Networks
Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sorbonne Université, CNRS, Laboratoire de Physique Nucléaire et des Hautes Énergies (LPNHE), 4 Pl. Jussieu, 75005 Paris, France · National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China +2
Domain-Specific Agents for Cherenkov Telescope Array Control Software and Gamma-Ray Data Analysis
Oct 1, 2025Dmitriy Kostunin, Elisa Jones, Vladimir Sotnikov +3Data Science Agents Canada-France-Hawaii Telescope Data
Deutsches Elektronen-Synchrotron DESY, 15738 Zeuthen, Germany · JetBrains Limited, 8046 Paphos, Cyprus · JetBrains GmbH, 80639 München, Germany +1

Sep 14, 2026, cs.CVJ/K move · Enter open · S save

G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity

Shuo Zhang, Xin Su, Wei Wang, Jun Liu+7

Wuhan University, Wuhan, China · Rongyun Robot (Guizhou) Co., Ltd. · Institute of Automation, Chinese Academy of Sciences, Beijing, China

Visual Geometry Grounded Transformer Positional Encoding 3D Reconstruction Self-Supervised Vision Transformers Cosmic-Ray Syntactic Pool

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We study relative position encoding for multi-view vision Transformers under camera heterogeneity, including varying fields of view (FoVs) or projection models. Existing rotary relative position encodings commonly use image-plane positional coordinates, producing projection-dependent relative phases and inconsistent geometric cues for cross-projection attention. We introduce G-ray, a ray-level relative position encoding whose rotary phases are parameterized by camera-local ray angles. The same camera-local ray pair induces the same relative phase across projections, providing projection-invariant positional consistency. G-ray can be used directly or integrated with existing encodings, retaining complementary geometric cues without additional learned parameters. We validate G-ray in three host encodings, RoPE, GTA, and RayRoPE, across 3D reconstruction and novel-view synthesis (NVS). Across three heterogeneous 3D reconstruction benchmarks at 50 views, G-ray leads all six averaged metrics and reduces mean pointmap relative error by 45.8% over MapAnything, with calibration supplied to both. Trained exclusively on homogeneous pinhole images, the 3D reconstruction model handles mixed pinhole and non-pinhole inputs without retraining and remains competitive on homogeneous pinhole 3D reconstruction protocols. For NVS, GTA and RayRoPE improve with G-ray under joint viewpoint and FoV variation. The project's webpage is available at https://g-ray-project.github.io/.

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