Publications

Applying graph neural networks to predict fungal disease occurrences in precision agriculture
Stéphane Samson
Étienne Lord
Odile Carisse
Vladimir Makarenkov
Abstract Purpose Fungal diseases remain among the leading causes of global crop losses, with management still heavily reliant on fungicide a… (voir plus)pplications. While traditional decision support systems and machine learning models offer valuable predictive insights, they often overlook the spatial and relational dynamics underlying pathogen spread. This study evaluates the feasibility and advantages of Graph Neural Networks (GNNs) for predicting fungal disease occurrence in three key crops—onion ( Botrytis squamosa ), lettuce ( Botrytis lactucae ), and carrot ( Cercospora carotae )—to enhance precision agriculture decision-making. Methods Field observations from farms in southern Quebec were used to build plant-level graphs, with nodes representing plants enriched by biological and weather features, and edges defined by spatial proximity. Graph convolutional networks were trained for binary fungal disease occurrence classification and benchmarked against machine learning and deep learning baselines. Graph augmentation techniques and robustness tests under missing and noisy features were applied to assess GNN’s stability. Results Across the three pathosystems, GNNs achieved the strongest overall predictive performance. For onions ( B. squamosa ), Random Forest slightly outperformed the GNN on the complete feature set (accuracy = 76.4% and F1-score = 0.77); here, the GNN provided lower but comparable metric scores (accuracy = 74.8% and F1-score = 0.73). For lettuce ( B. lactucae ), the GNN achieved the highest metric scores with the accuracy of 90.4% and F1-score of 0.90, surpassing all other baselines. For carrot ( C. carotae ), GNNs reached the accuracy of 75.8% and F1-score of 0.77, clearly outperforming Decision Tree, Random Forest, k-NN, and Feed-Forward Neural Networks (FFNs). Graph augmentation further improved the GNN results: random walk sampling increased the model’s accuracy on onion data to 79.3% and F1-score to 0.79, and on lettuce data to 93.9% and to 0.94, respectively, while node/edge perturbation improved the model’s accuracy on carrot data to 78.6% and F1-score to 0.80. Furthermore, the results of the robustness experiments suggest that GNNs can still track overall field-level infection trends with up to 75% of features masked or 50% replaced by noise. Conclusion GNNs offer clear advantages for fungal disease occurrence prediction by incorporating spatial and relational plant patterns, thus improving both the accuracy and robustness of predicted outcomes.
2026-05-15
Precision Agriculture (publié)
doi.org
High-dimensional Limit of SGD for Diagonal Linear Networks
Begoña García Malaxechebarría
Courtney Paquette
Maryam Fazel
Dmitriy Drusvyatskiy
Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diago… (voir plus)nal linear networks as a simplified yet expressive setting for analyzing the optimization and generalization properties of neural models. In this work, we show that in the high-dimensional regime, stochastic gradient descent on diagonal linear networks is well-approximated by continuous dynamics governed by a stochastic differential equation (SDE), which explicitly decouples the drift from the gradient noise. We further derive a deterministic partial differential equation whose solution propagates the relevant state of the iterates and characterizes the time evolution of a broad class of observable statistics, including the risk, curvature, and other metrics for optimality. Finally, we show that, under a suitable parametrization, the stochastic dynamics are globally well posed and converge exponentially fast to zero risk with high probability, yielding a fully explicit non-asymptotic description of their long-time behavior. Numerical simulations corroborate our theoretical findings.
2026-05-15
arXiv (prépublication)
doi.org
arxiv.org
A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling
Mohamed Elhefnawy
Ahmed Ragab
Nicolas Pelletier
Jean-Martin Lussier
Mouloud Amazouz
The widespread adoption of digitalization across various industries has resulted in the accumulation of vast amounts of data from diverse so… (voir plus)urces, offering opportunities to analyze complex phenomena. However, such analysis remains challenging due to the heterogeneity in data types, structures, formats, sampling frequencies and other factors. While numerous artificial intelligence (AI) techniques exist to analyze these heterogeneous datasets, no single AI technique can effectively handle all types of heterogeneous data acquired from different sources, while accurately predicting multiple outputs. To address this challenge, this paper proposes an ensemble learning approach based on diversified regression techniques to predict multiple continuous outputs. This approach captures the data distribution of the targeted phenomena from different perspectives. A novel stratified modeling technique is introduced, in which the data are first classified by predicting stratification labels before undergoing the regression modeling. This stratification is achieved using an innovative fusion approach based on a polygon generation representation technique, effectively breaking down the primary complex discriminative problem into smaller and more manageable subproblems. The effectiveness of the proposed method is validated using a dataset collected from harvester machinery in the forest industry. The method predicts key outputs such as merchantable wood volume, the log count, and the proportion of hardwood and softwood of different sizes for predetermined forest plots. The results demonstrate that the performance of this stratified approach outperforms comparable methods from the literature, demonstrating its superior performance.
2026-05-15
Ecological Informatics (publié)
doi.org
A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling
Mohamed Elhefnawy
Ahmed Ragab
Nicolas Pelletier
Jean-Martin Lussier
Mouloud Amazouz
The widespread adoption of digitalization across various industries has resulted in the accumulation of vast amounts of data from diverse so… (voir plus)urces, offering opportunities to analyze complex phenomena. However, such analysis remains challenging due to the heterogeneity in data types, structures, formats, sampling frequencies and other factors. While numerous artificial intelligence (AI) techniques exist to analyze these heterogeneous datasets, no single AI technique can effectively handle all types of heterogeneous data acquired from different sources, while accurately predicting multiple outputs. To address this challenge, this paper proposes an ensemble learning approach based on diversified regression techniques to predict multiple continuous outputs. This approach captures the data distribution of the targeted phenomena from different perspectives. A novel stratified modeling technique is introduced, in which the data are first classified by predicting stratification labels before undergoing the regression modeling. This stratification is achieved using an innovative fusion approach based on a polygon generation representation technique, effectively breaking down the primary complex discriminative problem into smaller and more manageable subproblems. The effectiveness of the proposed method is validated using a dataset collected from harvester machinery in the forest industry. The method predicts key outputs such as merchantable wood volume, the log count, and the proportion of hardwood and softwood of different sizes for predetermined forest plots. The results demonstrate that the performance of this stratified approach outperforms comparable methods from the literature, demonstrating its superior performance.
2026-05-15
Ecological Informatics (publié)
doi.org
TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
Cormac Cureton
Narges Armanfard
Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are des… (voir plus)igned for single-task inference, meaning that predicting several target values within a context requires repeated forward calls and precludes inter-task information sharing. We propose TabPFN-MT, which is trained on an expanded multi-target synthetic prior to capture inter-task dependencies in context. This model uses an expanded
2026-05-15
arXiv (prépublication)
doi.org
arxiv.org
Navigating Potholes with Geometry-Aware Sharpness Minimization
Simon Dufort-Labbé
Mehrab Hamidi
Razvan Pascanu
Ioannis Mitliagkas
Damien Scieur
Aristide Baratin
Sharpness-aware minimization (SAM) encourages flat minima by perturbing parameters along directions of high loss curvature, but treats all p… (voir plus)arameter directions uniformly, ignoring the underlying loss geometry. We introduce LLQR+SAM, which combines SAM with a learned preconditioner obtained from the recently proposed LLQR framework, a second-order method that recasts steepest descent as a layerwise linear-quadratic regulator problem. The preconditioner is updated sparsely and maintained as a slow exponential moving average, so it captures a smoothed, low-resolution picture of the loss landscape geometry. The SAM perturbation then operates on top of this learned geometry, probing curvature at a faster timescale. We show that this two-timescale structure is not merely a computational convenience: theoretically, the preconditioner amplifies the SAM escape signal in directions that are flat under the average geometry but locally sharp (potholes). Wide, flat basins, by contrast, remain stable. Empirically, LLQR+SAM gives consistent gains over both SAM and LLQR alone across standard vision and sequence modeling benchmarks, supporting the view that slow learned geometry and fast sharpness correction are genuinely complementary.
2026-05-14
arXiv (prépublication)
doi.org
arxiv.org
Tensor Cookbook: Mastering Tensors through Diagrams
Beheshteh T. Rakhshan
Guillaume Rabusseau
High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational… (voir plus) physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal structure, their direct manipulation quickly becomes challenging as the order grows: the number of parameters increases exponentially, and algebraic expressions involving many indices become difficult to interpret and implement. Tensor networks (TNs) provide an effective framework for addressing these challenges. Originally introduced by Penrose and developed extensively in quantum physics, the graphical language of tensor networks encodes contractions as edges in a graph, reducing notational overhead and revealing structural properties obscured by index notation. Despite the central role of high-dimensional tensors in modern machine learning and numerical analysis, tensor network diagrams remain underutilized outside quantum computing, partly due to the lack of a self-contained mathematical reference accessible to a broad technical audience. This manuscript provides a self-contained guide to tensor networks and their use in tensor algebra. We present the main operations on tensors, contractions, products, and reshaping through, graphical notation, and show how classical tensor decompositions and related computations are naturally expressed in this framework. We also illustrate how tensor networks simplify the derivation of gradients and the manipulation of high-dimensional probability distributions. Throughout, we show that the diagrammatic approach yields genuinely shorter and more transparent proofs of classical identities, rank bounds, and gradient formulas that would otherwise require laborious index manipulation.
2026-05-14
arXiv (prépublication)
doi.org
arxiv.org
AMP2026: A Multi-Platform Marine Robotics Dataset for Tracking and Mapping
Edwin Meriaux
Shuo Wen
David Widhalm
Zhizun Wang
Junming Shi
Mariana Sosa Guzmán
Kalvik Jakkala
Bennett Carley
Elias Sokolova
Yogesh Girdhar
Monika Roznere
Jason O’Kane
Junaed Sattar
Gregory Dudek
Marine environments present significant challenges for perception and autonomy due to dynamic surfaces, limited visibility, and complex inte… (voir plus)ractions between aerial, surface, and submerged sensing modalities. This paper introduces the Aerial Marine Perception Dataset (AMP2026), a multi-platform marine robotics dataset collected across multiple field deployments designed to support research in two primary areas: multi-view tracking and marine environment mapping. The dataset includes synchronized data from aerial drones, boat-mounted cameras, and submerged robotic platforms, along with associated localization and telemetry information. The goal of this work is to provide a publicly available dataset enabling research in marine perception and multi-robot observation scenarios. This paper describes the data collection methodology, sensor configurations, dataset organization, and intended research tasks supported by the dataset.
2026-05-13
Conference on Robots and Vision (publié)
doi.org
arxiv.org
Beyond Sensory Summation: How Expectations and Sensory Evidence Shape Multisensory Perception
Elizaveta Sycheva
Léa St-Gelais
Jérémy Brunel
Karim Jerbi
Shahab Bakhtiari
Franco Lepore
Vanessa Hadid
Perceptual decisions arise through the interplay of incoming sensory evidence and prior expectations. However, it remains unclear how this i… (voir plus)nteraction shapes multisensory integration during the accumulation of decision evidence over time. Using dynamic audiovisual (AV) scenes in a semantic decision task, we examined how sensory reliability and semantic expectations influence decision-making. AV signals that were both coherent and congruent accelerated responses relative to unimodal conditions. This facilitation was strongest when visual input was degraded, consistent with increased reliance on joint AV contributions, as indicated by race-model violations demonstrating multisensory coactivation. Diffusion modeling revealed increased drift rates alongside longer non-decision times, indicating stronger evidence accumulation despite additional sensory processing, and resulting in faster overall responses. Together, these findings reveal that multisensory integration is not a fixed sensory-gain mechanism but a context-dependent coactivation process that selectively enhances evidence accumulation when signals converge on a shared semantic interpretation.
2026-05-13
Cognitive Computational Neuroscience (poster)
openreview.net
openreview.net
Partitioning Signal and Noise (PSN): A modality-general denoising technique for neural responses
Jacob S. Prince
Heiko H. Schütt
Dora Hermes
Greta Tuckute
Ian Charest
Peter Brotherwood
David G. C. Hildebrand
Michael J. Tarr
George A. Alvarez
Talia Konkle
Kendrick Kay
Large-scale neural datasets typically contain only a few repeated trials per stimulus, resulting in substantial residual noise even after tr… (voir plus)ial averaging. This noise limits our ability to characterize neural tuning, assess representational geometry, and evaluate computational models. We introduce Partitioning Signal and Noise (PSN), a low-rank denoising method applicable to any repeated-trial dataset. Unlike standard PCA, which retains dimensions with high total variance and therefore conflates signal with noise, PSN uses a generative model to explicitly estimate the signal and noise covariance structure of the data. This enables low-rank reconstruction that targets signal-rich dimensions while suppressing noise-dominated ones. Applied to human fMRI data from the Natural Scenes Dataset, PSN substantially improves voxel reliability, stability of ROI tuning profiles and representational geometry, and encoding model performance relative to both trial averaging and PCA denoising. Because PSN can operate on any repeated-trial data matrix, it extends naturally to intracranial EEG, scalp EEG, electrophysiology, and calcium imaging, suggesting its utility as a general-purpose tool for improving signal fidelity across neural recording modalities.
2026-05-13
Cognitive Computational Neuroscience (poster)
openreview.net
openreview.net
Vision-Based Semantic SLAM for Autonomous Navigation in Mill Yard
Junrui Huang
Inna Sharf
Elie Ayoub
Nicolas Lemieux
Heshan Fernando
Log-loading machines are essential in mill-yard operations for unloading logs from incoming transport trucks onto mill infeed deck, as well … (voir plus)as managing log inventory in stockpiles. This paper focuses on the log-loading operation in the vicinity of the infeed deck, with the goal of enabling higher levels of autonomy in this task. Near the infeed deck, the machine must localize reliably relative to the infeed deck and adjacent buffer piles, while also detecting and localizing arriving trucks and trailers; this is a highly dynamic outdoor environment. We present a vision-based semantic SLAM system that uses a stereo camera mounted on the log-loading machine as the sole perception sensor. The proposed pipeline is based on stereo ORB-SLAM2 for real-time pose estimation and mapping. It integrates a parallel semantic thread that converts stereo depth into pseudo-LiDAR point clouds and predicts oriented 3D bounding boxes for key objects, including the infeed deck, log piles, and log trucks. The estimated 3D bounding boxes are used to remove features on potentially dynamic objects during SLAM tracking for improving robustness, and to construct a persistent object-level semantic map by transforming 3D bounding boxes into the global SLAM frame. We evaluated the system in a virtual NVIDIA Isaac Sim infeed-deck environment using synthetic stereo image sequences. The evaluation reports camera trajectory accuracy, semantic object localization accuracy, and runtime performance, and includes ablations to isolate the impact of dynamic-feature removal and object-level semantic mapping. The results indicate that incorporating object-level 3D detections improves the robustness and accuracy of stereo SLAM in dynamic infeed-deck scenes while producing a globally consistent semantic map in practical runtime.
2026-05-13
Conference on Robots and Vision (publié)
doi.org
World models, artificial general intelligence and the hard problems of life–mind continuity: toward a unified understanding of natural and artificial intelligence
Adam Safron
Michael Levin
Victoria Klimaj
Zahra Sheikhbahaee
Dalton Sakthivadivel
Adeel Razi
David Ha
Nick Hay
Kevin Schmidt
Irina Rish
David Krakauer
Melanie Mitchell
Samuel J. Gershman
Joshua B. Tenenbaum
Abstract This special issue examines how natural and artificial intelligences (AIs) model the world, and what this modelling reveals about c… (voir plus)ognition and relationships between life and mind. Rather than adopting a single definition, the collection considers how world models function and emerge in biological and artificial systems, exploring a diverse range of world modelling including causal, self-referential, individual goal-directed, collective and narrative forms. A recurring theme is the extent to which current AI systems trained on vast quantities of data learn the context-sensitive, temporally embedded, value-laden dimensions of world modelling that characterize diverse biological intelligences, or whether their impressive capabilities arise primarily from statistical surface regularities. The contributions also raise broader issues concerning embodiment, complexity, learning architectures and the social and scientific contexts in which world models operate. With this collection, we hope to clarify the conceptual landscape, identify key points of similarity and divergence between natural and artificial minds, and outline questions that may guide future research on the forms of world modelling that support grounded understanding, robust agency and potentially human-like general intelligence. This article is part of the theme issue ‘World models in natural and artificial intelligence’.
2026-05-13
Philosophical Transactions of Royal Society A: Mathematical, Physical and Engineering Sciences (publié)
doi.org