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… (see more)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 (published)
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… (see more)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 (preprint)
doi.org
arxiv.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… (see more)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 (preprint)
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… (see more)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 (preprint)
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… (see more) 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 (preprint)
doi.org
arxiv.org
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 … (see more)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 (published)
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… (see more)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 (published)
doi.org
Bidirectional modulation of pain by neurofeedback: Preliminary findings with fMRI at 7T
Konstantin A. Demin
Jun Seo Hwang
Wonyi Che
Dongho Kim
Wani Woo
Hakwan Lau
Vincent Taschereau‐Dumouchel
Abstract Previous brain decoding studies indicate that an individual’s pain experience can be robustly predicted from distributed patterns… (see more) of brain activity. Two brain decoders have notably been associated respectively with the nociceptive and cognitive aspects of pain experience, the Neurologic Pain Signature (NPS) and the Stimulus-Intensity Independent Pain Signature (SIIPS). Yet, we still do not know if these brain patterns are also causally related to pain experience. To evaluate this possibility, we used high-field (7-Tesla) fMRI to test whether humans can alter their pain experience by bidirectionally modulating their pain-related brain activity in decoded neurofeedback paradigm. In a double-blind design, participants were trained to up- and down-regulate the NPS or the SIIPS. Our results indicate that participants can achieve bidirectional control of both signatures. NPS expression reliably increased during pain stimulation and covaried with both stimulus intensity and subjective ratings. In contrast, SIIPS expression did not show consistent stimulus-locked effects in the primary analyses. Importantly, reduction in pain rating was specific for SIIPS-training, whereas NPS has failed to show any consistent behavioral effect. Based on these preliminary findings, we hereby preregister a follow-up study, with specified rationale, hypotheses, experimental design, and analysis protocols.
2026-05-12
bioRxiv (preprint)
doi.org
CA2: Code-Aware Agent for Automated Game Testing
Valliappan Chidambaram Adaikkappan
Vincent Martineau
Joshua Romoff
David Meger
Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing oft… (see more)en misses edge cases, and current automated methods struggle to provide full code coverage. Prior work has explored reinforcement learning (RL) for game testing, but without leveraging internal code signals such as the call stack. We present Code Aware Agent (CA2), which uses call stack information to learn effective testing strategies. The agent receives the current function call trace along with the game state and learns to reach specific target functions. We instrument two types of environments, 1) State-based and 2) Image-based, with support for efficient call stack extraction. Through experimental evaluation, we find that CA2 achieves consistent improvement over the non-code aware baselines, which does not leverage call stack information. Our results show that incorporating code signals like the call stack enables more effective and targeted game testing.
2026-05-12
arXiv (preprint)
doi.org
arxiv.org
Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Tellez
Amirhossein Zamani
Philippe Martin
Shuang Ni
Guy Wolf
Eugene Belilovsky
Sina Sanjari
Yanlei Zhang
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formu… (see more)lation is not designed to capture multi-parameter variations where the resulting transport should be path-independent. Path independence is crucial because it ensures that transformations depend only on the initial and target distributions, not on the specific path. In this work, we introduce Path-independent Flow Matching (PiFM), a method for learning vector fields whose induced flows yield path-independent transport between distributions. We show that PiFM generalizes Flow Matching to higher-dimensional parameter domains while enforcing structural conditions that ensure consistency of composed transformations. In addition, we show that, under suitable assumptions, PiFM approximates the Wasserstein barycenter, linking the framework to a notion of distributional interpolation. To enable practical training, we propose a tractable, simulation-free objective that regresses onto multi-parameter conditional probability paths. We showcase empirically that PiFM outperforms other approaches on both synthetic and real world data in interpolating path-independent trajectories and generating desired out of distribution samples.
2026-05-12
arXiv (preprint)
doi.org
openreview.net
Reliability-Gated Source Anchoring for Continual Test-Time Adaptation
Vikash Singh
Debargha Ganguly
Weicong Chen
Sabyasachi Sahoo
Sreehari Sankar
Biyao Zhang
Mohsen Hariri
Shouren Wang
Osama Zafar
Christian Gagné
Vipin Chaudhary
Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a froze… (see more)n source checkpoint. This anchor is useful only when the source remains reliable. On CCC-Hard, however, a ResNet-50 source falls to approximately
2026-05-12
arXiv (preprint)
doi.org
arxiv.org
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
Paul Hoareau
Kuan Yi Wang
Brandon Bujak
Roy Sun
Govind Nair
Irene Cortese
Charidimos Tsagkas
Daniel Reich
Julien Cohen-Adad
INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, … (see more)forcing reliance on sparse 2D slices. Weakly supervised Sparse-to-Dense frameworks bridge this gap, but guidelines remain ambiguous regarding human-centric visual enhancements and transferring optimization strategies across dimensions. We analyze divergent regularization needs for multi-class segmentation of high-resolution ex vivo spinal cord MRI. METHODS | We used 9.4T MRI of multiple sclerosis spinal cords (>104,000 slices) with sparse annotations (428 slices). A 2D Teacher trained on sparse slices generated dense pseudo-labels to train a 3D Student. We systematically evaluated the impact of human-centric preprocessing, spatial augmentation, and soft-label regularization on both architectures. RESULTS | We identified a critical divergence in training dynamics. The 2D Teacher required strong spatial augmentation and soft-labeling to overcome data scarcity, improving White Matter Lesion Dice scores by>11 points. However, propagating these techniques to the 3D Student degraded its performance. Furthermore, human-centric preprocessing (e.g., CLAHE) disrupted global statistical cues, dropping Gray Matter Lesion Dice scores by ~25 points. DISCUSSION | Our study highlights a perception divergence (human-centric contrast enhancement harms machine models) and a regularization conflict across dimensions. 3D architectures trained on dense pseudo-labels exhibit fundamentally different optimization landscapes than 2D counterparts and require distinct, conservative regularization. Code and models: https://github.com/ivadomed/model_seg_sc-gm-lesion_human_ms_exvivo_t2star.
2026-05-11
arXiv (preprint)
doi.org
arxiv.org