This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space fo...
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Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning...
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Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated ...
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Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi
We present in this paper a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator ...
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In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain. Existing instance segmentation methods typically rely on a tedious process of scene collection, mas...
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The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feat...
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In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is be...
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Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despit...
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.
Multiobjective optimization research has mostly focused on continuous-variable problems. However, real-world optimization problems often involve multiple types of variables (real, integer, and discrete) and multiple c...
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作者：
Wang, ZhongZhang, LinWang, HeshengShanghai Jiao Tong University
State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University
School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China
Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods l...
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