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Filippo Maria Bianchi
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2020 – today
- 2024
- [j34]Luigi Tommaso Luppino
, Mads A. Hansen, Michael Kampffmeyer
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. IEEE Trans. Neural Networks Learn. Syst. 35(1): 60-72 (2024) - [j33]Daniele Grattarola
Understanding Pooling in Graph Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2708-2718 (2024) - [j32]Vilde Jensen, Filippo Maria Bianchi
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting. IEEE Trans. Neural Networks Learn. Syst. 35(7): 9014-9025 (2024) - [c29]Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi:
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling. ICML 2024 - [i51]Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi:
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling. CoRR abs/2402.10634 (2024) - [i50]Carlo Abate, Filippo Maria Bianchi:
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks. CoRR abs/2409.05100 (2024) - [i49]Michele Guerra, Simone Scardapane, Filippo Maria Bianchi:
Interpreting Temporal Graph Neural Networks with Koopman Theory. CoRR abs/2410.13469 (2024) - 2023
- [j31]Michele Guerra
Probabilistic Load Forecasting With Reservoir Computing. IEEE Access 11: 145989-146002 (2023) - [c28]Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi:
Scalable Spatiotemporal Graph Neural Networks. AAAI 2023: 7218-7226 - [c27]Indro Spinelli, Michele Guerra, Filippo Maria Bianchi, Simone Scardapane:
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability. ESANN 2023 - [c26]Jonas Berg Hansen, Filippo Maria Bianchi:
Total Variation Graph Neural Networks. ICML 2023: 12445-12468 - [c25]Filippo Maria Bianchi, Veronica Lachi:
The expressive power of pooling in Graph Neural Networks. NeurIPS 2023 - [c24]Filippo Maria Bianchi:
Simplifying Clustering with Graph Neural Networks. NLDL 2023 - [c23]Michele Guerra, Indro Spinelli, Simone Scardapane, Filippo Maria Bianchi:
Explainability in subgraphs-enhanced Graph Neural Networks. NLDL 2023 - [i48]Filippo Maria Bianchi, Veronica Lachi
The expressive power of pooling in Graph Neural Networks. CoRR abs/2304.01575 (2023) - [i47]Indro Spinelli, Michele Guerra, Filippo Maria Bianchi, Simone Scardapane:
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability. CoRR abs/2304.07152 (2023) - [i46]Michele Guerra, Simone Scardapane, Filippo Maria Bianchi:
Probabilistic load forecasting with Reservoir Computing. CoRR abs/2308.12844 (2023) - 2022
- [j30]Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli
Pyramidal Reservoir Graph Neural Network. Neurocomputing 470: 389-404 (2022) - [j29]Filippo Maria Bianchi
Graph Neural Networks With Convolutional ARMA Filters. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3496-3507 (2022) - [j28]Jakob Grahn
Recognition of Polar Lows in Sentinel-1 SAR Images With Deep Learning. IEEE Trans. Geosci. Remote. Sens. 60: 1-12 (2022) - [j27]Luigi Tommaso Luppino
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. IEEE Trans. Geosci. Remote. Sens. 60: 1-22 (2022) - [j26]Filippo Maria Bianchi
Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling. IEEE Trans. Neural Networks Learn. Syst. 33(5): 2195-2207 (2022) - [i45]Vilde Jensen, Filippo Maria Bianchi, Stian Normann Anfinsen:
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting. CoRR abs/2202.08756 (2022) - [i44]Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen, Matteo Chiesa, Filippo Maria Bianchi:
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case. CoRR abs/2203.07080 (2022) - [i43]Jakob Grahn, Filippo Maria Bianchi:
Recognition of polar lows in Sentinel-1 SAR images with deep learning. CoRR abs/2203.16401 (2022) - [i42]Filippo Maria Bianchi:
Simplifying Clustering with Graph Neural Networks. CoRR abs/2207.08779 (2022) - [i41]Andrea Cini
Scalable Spatiotemporal Graph Neural Networks. CoRR abs/2209.06520 (2022) - [i40]Michele Guerra, Indro Spinelli, Simone Scardapane, Filippo Maria Bianchi:
Explainability in subgraphs-enhanced Graph Neural Networks. CoRR abs/2209.07926 (2022) - [i39]Jonas Berg Hansen, Filippo Maria Bianchi:
Clustering with Total Variation Graph Neural Networks. CoRR abs/2211.06218 (2022) - 2021
- [j25]Odin Foldvik Eikeland
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning. IEEE Access 9: 150686-150699 (2021) - [j24]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. Pattern Recognit. 115: 107896 (2021) - [j23]Filippo Maria Bianchi
Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 75-82 (2021) - [j22]Filippo Maria Bianchi
Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series. IEEE Trans. Neural Networks Learn. Syst. 32(5): 2169-2179 (2021) - [c22]Davide Bacciu, Filippo Maria Bianchi, Benjamin Paassen, Cesare Alippi:
Deep learning for graphs. ESANN 2021 - [c21]Huamin Ren, Filippo Maria Bianchi, Jingyue Li, Rasmus L. Olsen, Robert Jenssen, Stian Normann Anfinsen
Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms. ICCE 2021: 1-5 - [c20]Odin Foldvik Eikeland
Detecting the Linear and Non-linear Causal Links for Disturbances in the Power Grid. INTAP 2021: 325-336 - [i38]Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli:
Pyramidal Reservoir Graph Neural Network. CoRR abs/2104.04710 (2021) - [i37]Odin Foldvik Eikeland, Inga Setså Holmstrand, Sigurd Bakkejord, Matteo Chiesa, Filippo Maria Bianchi:
Detecting and interpreting faults in vulnerable power grids with machine learning. CoRR abs/2108.07060 (2021) - [i36]Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, Cesare Alippi:
Understanding Pooling in Graph Neural Networks. CoRR abs/2110.05292 (2021) - [i35]Jonas Berg Hansen, Stian Normann Anfinsen
Power Flow Balancing with Decentralized Graph Neural Networks. CoRR abs/2111.02169 (2021) - 2020
- [j21]Filippo Maria Bianchi, Ponnuthurai Nagaratnam Suganthan:
Non-iterative Learning Approaches and Their Applications. Cogn. Comput. 12(2): 327-329 (2020) - [j20]Alessandro Cinti, Filippo Maria Bianchi
A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation. Cogn. Comput. 12(2): 369-387 (2020) - [j19]Filippo Maria Bianchi
Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning. Remote. Sens. 12(14): 2260 (2020) - [c19]Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli:
Pyramidal Graph Echo State Networks. ESANN 2020: 573-578 - [c18]Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi:
Spectral Clustering with Graph Neural Networks for Graph Pooling. ICML 2020: 874-883 - [i34]Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Sebastiano Bruno Serpico, Robert Jenssen, Stian Normann Anfinsen:
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. CoRR abs/2001.04271 (2020) - [i33]Luigi Tommaso Luppino, Mads A. Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen, Stian Normann Anfinsen
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. CoRR abs/2004.07011 (2020) - [i32]Filippo Maria Bianchi, Martine Mostervik Espeseth, Njål Borch:
Large-scale detection and categorization of oil spills from SAR images with deep learning. CoRR abs/2006.13575 (2020)
2010 – 2019
- 2019
- [j18]Michael Kampffmeyer
Deep divergence-based approach to clustering. Neural Networks 113: 91-101 (2019) - [j17]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz
Noisy multi-label semi-supervised dimensionality reduction. Pattern Recognit. 90: 257-270 (2019) - [j16]Filippo Maria Bianchi
Learning representations of multivariate time series with missing data. Pattern Recognit. 96 (2019) - [j15]Luigi Tommaso Luppino
Unsupervised Image Regression for Heterogeneous Change Detection. IEEE Trans. Geosci. Remote. Sens. 57(12): 9960-9975 (2019) - [i31]Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi:
Graph Neural Networks with convolutional ARMA filters. CoRR abs/1901.01343 (2019) - [i30]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen:
Deep Divergence-Based Approach to Clustering. CoRR abs/1902.04981 (2019) - [i29]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Robert Jenssen:
Noisy multi-label semi-supervised dimensionality reduction. CoRR abs/1902.07517 (2019) - [i28]Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi:
Mincut pooling in Graph Neural Networks. CoRR abs/1907.00481 (2019) - [i27]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. CoRR abs/1907.05251 (2019) - [i26]Luigi Tommaso Luppino, Filippo Maria Bianchi, Gabriele Moser, Stian Normann Anfinsen:
Unsupervised Image Regression for Heterogeneous Change Detection. CoRR abs/1909.05948 (2019) - [i25]Filippo Maria Bianchi, Jakob Grahn, Markus Eckerstorfer, Eirik Malnes, Hannah Vickers:
Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks. CoRR abs/1910.05411 (2019) - [i24]Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi:
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling. CoRR abs/1910.11436 (2019) - 2018
- [j14]Michael Kampffmeyer
The deep kernelized autoencoder. Appl. Soft Comput. 71: 816-825 (2018) - [j13]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz
Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recognit. 76: 569-581 (2018) - [j12]Filippo Maria Bianchi, Lorenzo Livi
Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis. IEEE Trans. Neural Networks Learn. Syst. 29(2): 427-439 (2018) - [j11]Lorenzo Livi
Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization. IEEE Trans. Neural Networks Learn. Syst. 29(3): 706-717 (2018) - [c17]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. BHI 2018: 307-310 - [c16]Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi:
On the Interpretation and Characterization of Echo State Networks Dynamics: A Complex Systems Perspective. Advances in Data Analysis with Computational Intelligence Methods 2018: 143-167 - [c15]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. ESANN 2018 - [c14]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Bidirectional deep-readout echo state networks. ESANN 2018 - [c13]Filippo Maria Bianchi, Lorenzo Livi, Alberto Ferrante
Time Series Kernel Similarities for Predicting Paroxysmal Atrial Fibrillation from ECGs. IJCNN 2018: 1-8 - [c12]Luigi Tommaso Luppino, Filippo Maria Bianchi, Gabriele Moser
Remote Sensing Image Regression for Heterogeneous Change Detection. MLSP 2018: 1-6 - [i23]Filippo Maria Bianchi, Lorenzo Livi, Alberto Ferrante, Jelena Milosevic, Miroslaw Malek:
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs. CoRR abs/1801.06845 (2018) - [i22]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Reservoir computing approaches for representation and classification of multivariate time series. CoRR abs/1803.07870 (2018) - [i21]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples. CoRR abs/1803.07879 (2018) - [i20]Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders. CoRR abs/1805.03473 (2018) - [i19]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
The Deep Kernelized Autoencoder. CoRR abs/1807.07868 (2018) - [i18]Luigi Tommaso Luppino, Filippo Maria Bianchi, Gabriele Moser, Stian Normann Anfinsen:
Remote sensing image regression for heterogeneous change detection. CoRR abs/1807.11766 (2018) - 2017
- [b1]Filippo Maria Bianchi, Enrico Maiorino
Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis. Springer Briefs in Computer Science, Springer 2017, ISBN 978-3-319-70337-4, pp. 1-72 - [j10]Simone Scardapane, John B. Butcher
Advances in Biologically Inspired Reservoir Computing. Cogn. Comput. 9(3): 295-296 (2017) - [j9]Sigurd Løkse
Training Echo State Networks with Regularization Through Dimensionality Reduction. Cogn. Comput. 9(3): 364-378 (2017) - [j8]Enrico Maiorino
Data-driven detrending of nonstationary fractal time series with echo state networks. Inf. Sci. 382-383: 359-373 (2017) - [j7]Filippo Maria Bianchi
An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery. Soft Comput. 21(5): 1347-1369 (2017) - [c11]Filippo Maria Bianchi, Lorenzo Livi, Robert Jenssen, Cesare Alippi:
Critical echo state network dynamics by means of Fisher information maximization. IJCNN 2017: 852-858 - [c10]The-Hien Dang-Ha, Filippo Maria Bianchi, Roland Olsson:
Local short term electricity load forecasting: Automatic approaches. IJCNN 2017: 4267-4274 - [c9]Filippo Maria Bianchi, Michael Kampffmeyer
Temporal overdrive recurrent neural network. IJCNN 2017: 4275-4282 - [c8]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen:
Deep divergence-based clustering. MLSP 2017: 1-6 - [c7]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz
The time series cluster kernel. MLSP 2017: 1-6 - [c6]Luigi Tommaso Luppino, Stian Normann Anfinsen
A Clustering Approach to Heterogeneous Change Detection. SCIA (2) 2017: 181-192 - [c5]Michael Kampffmeyer
Deep Kernelized Autoencoders. SCIA (1) 2017: 419-430 - [c4]Sigurd Løkse
Spectral Clustering Using PCKID - A Probabilistic Cluster Kernel for Incomplete Data. SCIA (1) 2017: 431-442 - [e2]Puneet Sharma, Filippo Maria Bianchi:
Image Analysis - 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12-14, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10269, Springer 2017, ISBN 978-3-319-59125-4 [contents] - [e1]Puneet Sharma, Filippo Maria Bianchi:
Image Analysis - 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12-14, 2017, Proceedings, Part II. Lecture Notes in Computer Science 10270, Springer 2017, ISBN 978-3-319-59128-5 [contents] - [i17]Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen:
Temporal Overdrive Recurrent Neural Network. CoRR abs/1701.05159 (2017) - [i16]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
Deep Kernelized Autoencoders. CoRR abs/1702.02526 (2017) - [i15]Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano B. Serpico, Grégoire Mercier:
A clustering approach to heterogeneous change detection. CoRR abs/1702.03176 (2017) - [i14]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, Robert Jenssen:
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data. CoRR abs/1704.00794 (2017) - [i13]Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, Robert Jenssen:
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting. CoRR abs/1705.04378 (2017) - [i12]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. CoRR abs/1710.07547 (2017) - [i11]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Bidirectional deep echo state networks. CoRR abs/1711.06509 (2017) - [i10]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz, Robert Jenssen:
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. CoRR abs/1711.06516 (2017) - [i9]Filippo Maria Bianchi, Antonello Rizzi, Alireza Sadeghian, Corrado Moiso:
Identifying user habits through data mining on call data records. CoRR abs/1711.08398 (2017) - [i8]Alessandro Cinti, Filippo Maria Bianchi, Antonello Rizzi:
A novel algorithm for online inexact string matching and its FPGA implementation. CoRR abs/1712.03560 (2017) - 2016
- [j6]Filippo Maria Bianchi
Granular Computing Techniques for Classification and Semantic Characterization of Structured Data. Cogn. Comput. 8(3): 442-461 (2016) - [j5]Filippo Maria Bianchi, Antonello Rizzi
Identifying user habits through data mining on call data records. Eng. Appl. Artif. Intell. 54: 49-61 (2016) - [j4]Filippo Maria Bianchi, Lorenzo Livi
Two density-based k-means initialization algorithms for non-metric data clustering. Pattern Anal. Appl. 19(3): 745-763 (2016) - [i7]Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi:
Investigating echo state networks dynamics by means of recurrence analysis. CoRR abs/1601.07381 (2016) - [i6]Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi:
Determination of the edge of criticality in echo state networks through Fisher information maximization. CoRR abs/1603.03685 (2016) - [i5]Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen:
Training Echo State Networks with Regularization through Dimensionality Reduction. CoRR abs/1608.04622 (2016) - [i4]Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi, Robert Jenssen:
Multiplex visibility graphs to investigate recurrent neural networks dynamics. CoRR abs/1609.03068 (2016) - 2015
- [j3]Filippo Maria Bianchi, Enrico De Santis
Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition. IEEE Access 3: 1931-1943 (2015) - [j2]Filippo Maria Bianchi
Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks 71: 204-213 (2015) - [i3]Enrico Maiorino, Filippo Maria Bianchi, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian:
Data-driven detrending of nonstationary fractal time series with echo state networks. CoRR abs/1510.07146 (2015) - [i2]Filippo Maria Bianchi, Enrico De Santis, Hedieh Montazeri, Parisa Naraei, Alireza Sadeghian:
Position paper: a general framework for applying machine learning techniques in operating room. CoRR abs/1511.09099 (2015) - 2014
- [j1]Filippo Maria Bianchi, Lorenzo Livi
A Granular Computing approach to the design of optimized graph classification systems. Soft Comput. 18(2): 393-412 (2014) - [c3]Filippo Maria Bianchi, Simone Scardapane
An interpretable graph-based image classifier. IJCNN 2014: 2339-2346 - [i1]Filippo Maria Bianchi, Enrico Maiorino, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian:
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery. CoRR abs/1409.4988 (2014) - 2013
- [c2]Filippo Maria Bianchi, Lorenzo Livi
Matching of time-varying labeled graphs. IJCNN 2013: 1-8 - [c1]Lorenzo Livi
Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure. IJCNN 2013: 1-8
Coauthor Index
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