Substructure Aware Graph Neural Networks

1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2023 • Zeng, D., Liu, Chen, W., Zhou, L., Zhang, M., & Qu, H

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.

Graph Learning Graph Regression

Cosmic Background Removal with Deep Neural Networks in SBND

1 code implementation • 2 Dec 2020 • SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, G. Brandt, T. Brooks, F. Carneiro, Y. Chen, H. Chen, G. Chisnall, I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I., L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. Evans, C. Ezeribe, S. Fitzpatrick, T. Fleming, W. Foreman, D. Franco, I. Furic, P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R., S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M., J. Kim, A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B., R. Littlejohn, W., C. Louis, A., A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C., A. Moura, J. Mousseau, A. Navrer-Agasson, F., J. Nicolas-Arnaldos, A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V., L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D., W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N., J., C. Spooner, M. Stancari, V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G., A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.

Semantic Segmentation Data Analysis, Statistics and Probability

CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes

1 code implementation • Human Brain Mapping 2021 • Svanera, M., Benini, S., Bontempi, D., Muckli, L

An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures.

Brain Segmentation Segmentation

Improving existing segmentators performance with zero-shot segmentators

1 code implementation • journal 2023 • Nanni, L., Fusaro, D., Fantozzi, C., Pretto, A.

We release with this paper the open-source implementation of our method.

Camouflaged Object Segmentation Segmentation +1

LTE Cell Traffic Grow and Congestion Forecasting

1 code implementation • 2019 Open Conference of Electrical, Electronic and Information Sciences 2019 • Chmieliauskas, D., Gursnys, D

Recent popularity grow of predictive analysis is growing in many fields.

Pose Encoding for Robust Skeleton-Based Action Recognition

no code implementations • In Proceedings of the IEEE conference on computer vision and pattern recognition workshops 2018 • Demisse, G. G., Papadopoulos, K., Aouada, D., & Ottersten, B.

Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations.

Action Recognition Skeleton Based Action Recognition

Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera

no code implementations • 14 May 2014 • McDuff, D., Gontarek, S., and Picard, R

Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing.

Heart Rate Variability Photoplethysmography (PPG)

Angular Radon Spectrum for Rotation Estimation

no code implementations • Pattern Recognition 2018 • Lodi Rizzini, D.

This paper presents a robust method for rotation estimation of planar point sets using the Angular Radon Spectrum (ARS).