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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yunan Li, Weiwei Liu, Dun Li, Huaxiong Jia, Xiuyi School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Economics and Management Southwest Jiaotong University Chengdu China Department of Control Science and Intelligence Engineering Nanjing University Nanjing China

ISBN: (纸本)157735897X

Label polysemy, where an instance can be associated with multiple labels, is common in real-world tasks. LDL (label distribution learning) is an effective learning paradigm for handling label polysemy, where each instance is associated with a label distribution. Although numerous LDL algorithms have been proposed and achieved satisfactory performance on most existing datasets, they are typically trained directly on the collected label distributions which often lack quality guarantees in real-world tasks due to annotator subjectivity and algorithm assumptions. Consequently, direct learning from such uncertain label distributions can lead to unpredictable generalization performance. To address this problem, we propose an adaptive-grained label distribution learning framework whose main idea is to extract relatively reliable supervision information from unreliable label distributions, and thus the label distribution learning task can be decomposed into three subtasks: coarsening label distributions, learning coarse-grained labels and refining coarse-grained labels. In this framework, we design an adaptive label coarsening algorithm to extract an optimal coarsen-grained labels and a label refining function to enhance the coarse-grained label into the final label distributions. Finally, we conduct extensive experiments on real-world datasets to demonstrate the advantages of our proposal. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Multi-dimensional networks as a tool to model, analyze, and interpret multi-subject brain connectivity in hyperscanning settings*

Multi-dimensional networks as a tool to model, analyze, and ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Maria Grazia Puxeddu Greta Rinaldini Laura Astolfi Department of Computer Control and Management Engineering University of Rome La Sapienza Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions.

关键词： Neuroscience Mental disorders Biological system modeling Neural circuits Brain modeling Weaving Graph theory Fabrics Integrated circuit modeling Bioinformatics

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EVALUATING THE EFFICACY OF CUT-AND-PASTE DATA AUGMENTATION IN SEMANTIC SEGMENTATION FOR SATELLITE IMAGERY

arXiv

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arXiv 2024年

作者： Motoi, Ionut M. Saraceni, Leonardo Nardi, Daniele Ciarfuglia, Thomas A. Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Two Performance Indicators Assisted Infill Strategy for Expensive Many-Objective Optimization

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Journal of Harbin Institute of Technology(New series) 2025年

作者： Yi Zhao Jianchao Zeng Ying Tan School of Economics and Management Taiyuan University of Science and Technology Department of Computer Science and Control Engineering North University of China Department of Computer Science and Technology Taiyuan University of Science and Technology

In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time-intensive. However,the individuals in the population become indistinguishable as the curse of dimensionality increase in the objective space and the accumulation of surrogate approximated errors. Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi-objective evolutionary algorithm of strengthened dominance relation. The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators. Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set. The algorithm is contrasted against several advanced surrogate-assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many-objective optimization problems.

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ARIES: An Intelligent System for Landslide and Wildfire Risk management

ARIES: An Intelligent System for Landslide and Wildfire Risk...

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Mediterranean Conference on control and Automation (MED)

作者： Alessandro Giuseppi Antonio Di Paola Alessandro Santopaolo Syed Saad Saif Federico Fiorini Antonio Pietrabissa Department of Computer Control and Management Engineering of the University of Rome La Sapienza Rome Italy

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

With the rise in frequency of catastrophic events, enviromental protection and risk management have become critical challenges for assuring both the safety of human pop-ulations and the sustainability of ecosystems. In this direction, the ARIES project has deveoped a suite of functionalities and software solution to support emergency operators in all phases of emergency management, from risk estimation to enviromental monitoring, early detection and response. The present paper provides an overview of the main functionalities that consistute the buidling blocks of the enviromental monitoring system developed by the project through a combination of dynamical systems, digital twins and convolutional neural networks, also reporting on its early validation activities.

关键词： Wildfires Estimation Emergency services Terrain factors Software Risk management Sustainable development

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Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery

Self-Supervised Data Generation for Precision Agriculture: B...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Leonardo Saraceni Ionut M. Motoi Daniele Nardi Thomas A. Ciarfuglia Department of Computer Control and Management Engineering (DIAG) Sapienza University of Rome Rome Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In precision agriculture, the scarcity of labeled data and significant covariate shifts pose unique challenges for training machine learning models. This scarcity is particularly problematic due to the dynamic nature of the environment and the evolving appearance of agricultural subjects as living things. We propose a novel system for generating realistic synthetic data to address these challenges. Utilizing a vineyard simulator based on the Unity engine, our system employs a cut-and-paste technique with geometrical consistency considerations to produce accurate photo-realistic images and labels from synthetic environments to train detection algorithms. This approach generates diverse data samples across various viewpoints and lighting conditions. We demonstrate considerable performance improvements in training a state-of-the-art detector by applying our method to table grapes cultivation. The combination of techniques can be easily automated, an increasingly important consideration for adoption in agricultural practice.

关键词： Training YOLO Precision agriculture Pipelines Lighting Detectors Machine learning Robustness Synthetic data Photorealistic images

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VBR: A Vision Benchmark in Rome

VBR: A Vision Benchmark in Rome

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Leonardo Brizi Emanuele Giacomini Luca Di Giammarino Simone Ferrari Omar Salem Lorenzo De Rebotti Giorgio Grisetti Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment (BA). All sequences divided in training and testing are accessible at ***/datasets/slam.

关键词： Training Point cloud compression Bundle adjustment Simultaneous localization and mapping Laser radar Estimation Benchmark testing

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MAD-ICP: It Is All About Matching Data – Robust and Informed LiDAR Odometry

arXiv

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arXiv 2024年

作者： Ferrari, Simone Di Giammarino, Luca Brizi, Leonardo Grisetti, Giorgio The Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy

LiDAR odometry is the task of estimating the ego-motion of the sensor from sequential laser scans. This problem has been addressed by the community for more than two decades, and many effective solutions are available nowadays. Most of these systems implicitly rely on assumptions about the operating environment, the sensor used, and motion pattern. When these assumptions are violated, several well-known systems tend to perform poorly. This paper presents a LiDAR odometry system that can overcome these limitations and operate well under different operating conditions while achieving performance comparable with domain-specific methods. Our algorithm follows the well-known ICP paradigm that leverages a PCA-based kd-tree implementation that is used to extract structural information about the clouds being registered and to compute the minimization metric for the alignment. The drift is bound by managing the local map based on the estimated uncertainty of the tracked pose. To benefit the community, we release an open-source C++ anytime real-time implementation at https://***/ rvp-group/mad-icp. © 2024, CC BY-NC-ND.

关键词： Uncertainty analysis

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Understanding Parental Characteristics of Child Adoption Candidates using MMPI-2 and Evolutionary Clustering 9

Understanding Parental Characteristics of Child Adoption Can...

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9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Iacobelli, Emanuele Randieri, Cristian Roma, Paolo Russo, Samuele Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Università degli Studi eCampus CO Novedrate Italy Department of Human Neuroscience Sapienza University of Rome Italy Department of Psychology Sapienza University of Rome Italy

In the context of adoption, evaluating prospective adoptive parents using psychometric assessments such as the Minnesota Multiphasic Personality Inventory (MMPI) questionnaire is essential for understanding their psychological profiles. However, interpreting such complex data can be both challenging and time-consuming. In this study, we propose a meta-analysis tool to assist psychologists in their initial interpretation and analysis of MMPI-2 results by providing a clear data-driven visualization of key psychometric scales. Our system employs unsupervised learning techniques to uncover meaningful patterns and relationships in the data with minimal prior input. Specifically, a genetic algorithm is used to optimize clustering quality by selecting the most relevant psychological scales, enhancing cluster separation, and improving data interpretability. We also explored and compared the effectiveness of several clustering algorithms, including K-Means, Gaussian Mixture Model, and Spectral Clustering, to maximize the capabilities of our tool. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Adversarial machine learning

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Dynamic Mode Decomposition for Individualized Model Predictive control with Application to Type 1 Diabetes

Dynamic Mode Decomposition for Individualized Model Predicti...

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Mediterranean Conference on control and Automation (MED)

作者： Valentina Becchetti Mohab M.H. Atanasious Danilo Menegatti Federico Baldisseri Alessandro Giuseppi Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Italy

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Type 1 diabetes is one of the main issues in current medical research. If not properly managed, it can lead to serious long-term complications such as blindness, stroke, coma and, ultimately, death. In recent years, research endeavors have focused on the development of a fully automated system for glucose control through automated insulin injections, known as Artificial Pancreas, which, however, is not yet robust enough for practical use. Currently, the UVA/Padova simulator provides the most accurate model for effectively representing the insulin-glucose dynamics of individual diabetic patients. As it is highly complex, consisting of 18 strongly nonlinear and time-varying equations, the objective of this work is to linearize it and reduce its dimensionality exploiting Dynamic Mode Decomposition with control. This allows for the synthesis of a controller based on Linear Model Predictive control to be applied to the original model. This is justified by the need to integrate the control algorithm into portable devices with limited computational capabilities. The proposed dimensionality reduction method is validated through in-vitro simulations showing that an accurate representation of the system dynamics is effectively preserved, while conveniently reducing required computational power.

关键词： Dimensionality reduction Accuracy Computational modeling Predictive models Mathematical models Nonlinear dynamical systems Diabetes

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