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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

CAAI Transactions on intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference

ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Han, Yang Wang, Yiming Wang, Rui Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering SJTU China MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761681

Text summarization tasks commonly employ Pre-trained Language Models (PLMs) to fit diverse standard *** these PLMs excel in automatic evaluations, they frequently underperform in human evaluations, indicating a deviation between their generated summaries and human summarization *** discrepancy is likely due to the low quality of fine-tuning datasets and the limited availability of high-quality human-annotated data that reflect true human *** address this challenge, we introduce a novel human summarization preference alignment framework *** framework consists of three parts: Firstly, we construct a Data Pymarid with extractive, abstractive, and human-annotated summary ***, we conduct the Gaussian Resampling to remove summaries with extreme ***, we implement the two-stage hierarchical fine-tuning with Data Pymarid after Gaussian *** apply ALIGNSUM to PLMs on the human-annotated CNN/DailyMail and BBC XSum *** show that with ALIGNSUM, PLMs like BART-Large surpass 175B GPT-3 in both automatic and human *** demonstrates that ALIGNSUM significantly enhances the alignment of language models with human summarization preferences. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Software Engineering Research in a World with Generative artificial intelligence

Software Engineering Research in a World with Generative Art...

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International Conference on Software Engineering (ICSE)

作者： Martin Rinard Department of Electrical Engineering and Computer Science Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA

Generative artificial intelligence systems such as large language models (LLMs) exhibit powerful capabilities that many see as the kind of flexible and adaptive intelligence that previously only humans could exhibit. ... 详细信息

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

关键词： Adaptation models Adaptive systems Generative AI Software Software engineering

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A Novel Subspace-Based GMM Clustering Ensemble Algorithm for High-Dimensional Data

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Chinese Journal of Electronics 2025年第2期34卷 612-629页

作者： He Yulin He Yingting Zhan Zhaowu Philippe Fournier-Viger Huang Joshua Zhexue Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen 518107 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China China GridCom Co. Ltd. Shenzhen 518109 China

The Gaussian mixture model (GMM) is a classical probabilistic representation model widely used in unsupervised learning. GMM performs poorly on high-dimensional data (HDD) due to the requirement of estimating a large number of parameters with relatively few observations. To address this, the paper proposes a novel subspace-based GMM clustering ensemble (SubGMM-CE) algorithm tailored for HDD. The proposed SubGMM-CE algorithm comprises three key components. A series of low-dimensional subspaces are dynamically determined, considering the optimal number of GMM components. The GMM-based clustering algorithm is applied to each subspace to obtain a series of heterogeneous GMM models. These GMM base clustering results are merged using the newly-designed relabeling strategy based on the average shared affiliation probability, generating the final clustering result for high-dimensional unlabeled data. An exhaustive experimental evaluation validates the feasibility, rationality, effectiveness, and robustness to noise of the SubGMM-CE algorithm. Results show that SubGMM-CE achieves higher stability and more accurate clustering results, outperforming nine state-of-the-art clustering algorithms in normalized mutual information, clustering accuracy, and adjusted rand index scores. This demonstrates the viability of the SubGMM-CE algorithm in addressing HDD clustering challenges.

关键词： Gaussian mixture model (GMM) High-dimensional clustering Subspace learning Clustering ensemble Uncertainty-based relabeling

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Automated Detection and Geolocation of Olive Tree Crowns in UAV Imagery Using Deep Learning 1

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Hnida, Youness Mahraz, Mohamed Adnane Yahyaouy, Ali Achebour, Ali Riffi, Jamal Tairi, Hamid L3IA - Laboratory of Computer Science Innovation and Artificial Intelligence Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco Research and Development Department Drone Globe Rabat Morocco

ISBN: (数字)9783031876479

ISBN: (纸本)9783031876462

In precision agriculture, adopting innovative methods is essential to optimize resource management practices, such as irrigation, fertilization, and disease control, particularly in areas requiring targeted interventions. Accurate identification and mapping of individual trees are critical for efficient farm management, enabling us to monitor tree growth in real-time and implement precision agriculture techniques effectively. In this study, we introduce a novel method for the automated detection and geolocation of olive tree crowns in large-scale plantations using deep learning and aerial imagery. We employed drones, specifically UAVs (Unmanned Aerial Vehicles), to capture high-resolution images, providing comprehensive coverage of agricultural zones in Meknes, Morocco. These images were processed using four YOLOv8 variants (n, s, l, and m), each selected for its balance between speed, computational efficiency, and accuracy. YOLOv8-l achieved the highest precision of 0.990 and recall of 0.973 with an F1 score of 0.981 and a mAP@50 of 0.994. By integrating image metadata with photogrammetry principles, we refined GPS (Global Positioning System) coordinates for each tree, allowing precise mapping of their locations. This method improves crop management, supports modern agriculture, and enables accurate mapping for better decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning

arXiv

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arXiv 2025年

作者： Feng, Meng Parimi, Viraj Williams, Brian Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA01239 United States

Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods are effective for solving long-horizon tasks but depend on the availability of a graph representation with predefined distance metrics. In contrast, safe Reinforcement Learning (RL) is capable of learning complex behaviors without relying on manual heuristics but fails to solve long-horizon tasks, particularly in goal-conditioned and multi-agent scenarios. In this paper, we introduce a novel method that integrates the strengths of both planning and safe RL. Our method leverages goal-conditioned RL (GCRL) and safe RL to learn a goal-conditioned policy for navigation while concurrently estimating cumulative distance and safety levels using learned value functions via an automated self-training algorithm. By constructing a graph with states from the replay buffer, our method prunes unsafe edges and generates a waypoint-based plan that the agent then executes by following those waypoints sequentially until their goal locations are reached. This graph pruning and planning approach via the learned value functions allows our approach to flexibly balance the trade-off between faster and safer routes especially over extended horizons. Utilizing this unified high-level graph and a shared low-level safe GCRL policy, we extend this approach to address the multi-agent safe navigation problem. In particular, we leverage Conflict-Based Search (CBS) to create waypoint-based plans for multiple agents allowing for their safer navigation over extended horizons. This integration enhances the scalability of goal-conditioned safe RL in multi-agent scenarios, enabling efficient coordination among agents. Extensive benchmarking against state-of-the-art baselines demonstrates the effectiveness of our method in achieving distance goals safely for multiple agents in complex and hazardous environments. Our code and further details about or work is available at https://safe-visu

关键词： Reinforcement learning

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Embedded air channels transform soft lattices into sensorized grippers

Embedded air channels transform soft lattices into sensorize...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Annan Zhang Lillian Chin Daniel L. Tong Daniela Rus MIT Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Sensing plays a pivotal role in robotic manipulation, dictating the accuracy and versatility with which objects are handled. Vision-based sensing methods often suffer from fabrication complexity and low durability, while approaches that rely on direct measurements on the gripper often have limited resolution and are difficult to scale. Here, we present a soft robotic gripper made out of two cubic lattices that are sensorized by embedding air channels within the structure. The lattices are 3D printed from a single build material, simplifying the fabrication process. The flexibility of this approach offers significant control over sensor and lattice design, while the pressure-based internal sensing provides measurements with minimal disruption to the grasping surface. With only 12 sensors, 6 per lattice, this gripper can estimate an object’s weight and location and offer new insights into grasp parameters like friction coefficients and grasp force.

关键词： Fabrication Three-dimensional displays Lattices Transforms Grasping Soft robotics Robot sensing systems

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Directly 3D Printed, Pneumatically Actuated Multi-Material Robotic Hand

Directly 3D Printed, Pneumatically Actuated Multi-Material R...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hanna Matusik Chao Liu Daniela Rus Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedom requires a complex multi-step manual process, which significantly increases their cost. We present a design of a multi-material 15 DoF robotic hand with five fingers including an opposable thumb. Our design has 15 pneumatic actuators based on a series of hollow chambers that are driven by an external pressure system. The thumb utilizes rigid joints and the palm features internal rigid structure and soft skin. The design can be directly 3D printed using a multi-material additive manufacturing process without any assembly process and therefore our hand can be manufactured for less than 300 dollars. We test the hand in conjunction with a low-cost vision-based teleoperation system on different tasks.

关键词： Three-dimensional displays Costs Force Thumb Pneumatic systems Soft robotics Manipulators

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Exploiting Associations among Multi-Aspect Node Properties in Heterogeneous Graphs for Link Prediction 24

Exploiting Associations among Multi-Aspect Node Properties i...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Du, Chenguang Geng, Hao Wang, Deqing Zhuang, Fuzhen Zhang, Zhiqiang Zhang, Lanshan SKLSDE School of Computer Science Beihang University Beijing China Institute of Artificial Intelligence and SKLSDE School of Computer Science Beihang University Beijing China Ant Group Hangzhou China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400701726

Recent years have witnessed the abundant emergence of heterogeneous graph neural networks (HGNNs) for link prediction. In heterogeneous graphs, different meta-paths connected to nodes reflect different aspects of the nodes’ properties. Existing work fuses the multi-aspect properties of each node into a single vector representation, which makes them fail to capture fine-grained associations between multiple node properties. To this end, we propose a heterogeneous graph neural network with Multi-Aspect Node Association awareness, namely MANA. MANA leverages key associations among multi-aspect node properties to achieve link prediction. Specifically, to avoid the loss of effective association information for link prediction, we design a transformer-based Multi-Aspect Association Mining module to capture multi-aspect associations between nodes. Then, we introduce the Multi-Aspect Link Prediction module, empowering MANA to focus on the key associations among all, thus avoiding the negative impact of ineffective associations on the model’s performance. We conduct extensive experiments on three widely used datasets from Heterogeneous Graph Benchmark (HGB). Experimental results show that our proposed method outperforms state-of-the-art baselines. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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