2019年底，最新的新型冠状病毒SARS-COV-2成为了一项显着的急性呼吸系统疾病，已成为全球大流行病。由于国家和市政当局的高社会经济差异，巴西等国家难以处理病毒。因此，本研究提出了一种使用不同机器学习的新方法，以及应用于巴西Covid-19数据的深度学习算法。首先，使用聚类算法来识别具有相似社会阶乘行为的县，而Benford的定律用于检查数据操作。基于这些结果，我们能够根据群集正确地模拟Sarima模型以预测新的每日情况。无监督的机器学习技术优化了定义Sarima模型参数的过程。此框架也很有用来在所谓的第二波期间提出限制方案。我们已经使用了来自巴西最有人口的S \〜Ao Paulo State的645个县。然而，这种方法可以用于其他州或国家。本文展示了机器学习，深度学习，数据挖掘和统计数据的不同技术可以一起使用，以在处理大流行数据时产生重要结果。虽然结果不能专门用于评估和影响政策决策，但它们提供了已使用的无效措施的替代方案。
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Aiming at highly accurate object detection for connected and automated vehicles (CAVs), this paper presents a Deep Neural Network based 3D object detection model that leverages a three-stage feature extractor by developing a novel LIDAR-Camera fusion scheme. The proposed feature extractor extracts high-level features from two input sensory modalities and recovers the important features discarded during the convolutional process. The novel fusion scheme effectively fuses features across sensory modalities and convolutional layers to find the best representative global features. The fused features are shared by a two-stage network: the region proposal network (RPN) and the detection head (DH). The RPN generates high-recall proposals, and the DH produces final detection results. The experimental results show the proposed model outperforms more recent research on the KITTI 2D and 3D detection benchmark, particularly for distant and highly occluded instances.
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Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult or impossible to maintain a large-scale model trained on growing annotation sets. Continual learning directly approaches this problem, with the ultimate goal of devising methods where a neural network effectively learns relevant patterns for new (unseen) classes without significantly altering its performance on previously learned ones. In this paper, we address the problem of continual learning for video data. We introduce PIVOT, a novel method that leverages the extensive knowledge in pre-trained models from the image domain, thereby reducing the number of trainable parameters and the associated forgetting. Unlike previous methods, ours is the first approach that effectively uses prompting mechanisms for continual learning without any in-domain pre-training. Our experiments show that PIVOT improves state-of-the-art methods by a significant 27% on the 20-task ActivityNet setup.
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Prior work has extensively studied the latent space structure of GANs for unconditional image synthesis, enabling global editing of generated images by the unsupervised discovery of interpretable latent directions. However, the discovery of latent directions for conditional GANs for semantic image synthesis (SIS) has remained unexplored. In this work, we specifically focus on addressing this gap. We propose a novel optimization method for finding spatially disentangled class-specific directions in the latent space of pretrained SIS models. We show that the latent directions found by our method can effectively control the local appearance of semantic classes, e.g., changing their internal structure, texture or color independently from each other. Visual inspection and quantitative evaluation of the discovered GAN controls on various datasets demonstrate that our method discovers a diverse set of unique and semantically meaningful latent directions for class-specific edits.
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Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern heterogeneity. To improve reproducibility, deep learning methods have been proposed in the last years using convolutional neural networks. However, these methods have been hindered by the process of data labelling, which usually solely consist of the mitosis centroids. Therefore, current literature proposes complex algorithms with multiple stages to refine the labels at pixel level, and to reduce the number of false positives. In this work, we propose to avoid complex scenarios, and we perform the localization task in a weakly supervised manner, using only image-level labels on patches. The results obtained on the publicly available TUPAC16 dataset are competitive with state-of-the-art methods, using only one training phase. Our method achieves an F1-score of 0.729 and challenges the efficiency of previous methods, which required multiple stages and strong mitosis location information.
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Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular-level tissue characterisation is then represented as a graph and used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87%, outperforming existing approaches by 10%.
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limiting the scalability of 3D recognition tasks. Thus, we study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D Recognition (SL3D) framework. SL3D simultaneously solves two coupled objectives, i.e., clustering and learning feature representation to generate pseudo-labeled data for unsupervised 3D recognition. SL3D is a generic framework and can be applied to solve different 3D recognition tasks, including classification, object detection, and semantic segmentation. Extensive experiments demonstrate its effectiveness. Code is available at https://github.com/fcendra/sl3d.
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我们研究保形预测的鲁棒性，这是标记噪声的不确定性定量的强大工具。我们的分析解决了回归和分类问题，表征了何时以及如何构建正确覆盖未观察到的无噪音地面真相标签的不确定性集。通过风格化的理论示例和实际实验，我们认为天真的保形预测涵盖了无噪声的地面真相标签，除非噪声分布是对手设计的。这使我们相信，除了病理数据分布或噪声源外，对标签噪声的纠正是不必要的。在这种情况下，我们还可以在保形预测算法中校正有界大小的噪声，以确保在没有得分或数据规律性的情况下正确覆盖地面真相标签。
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该技术报告描述了在Robocup SPL（Mario）中计算视觉统计的模块化且可扩展的体系结构，该结构在Robocup 2022的SPL Open Research Challenge期间提出，该挑战在曼谷（泰国）举行。马里奥（Mario）是一个开源的，可用的软件应用程序，其最终目标是为Robocup SPL社区的发展做出贡献。Mario带有一个GUI，该GUI集成了多个机器学习和基于计算机视觉的功能，包括自动摄像机校准，背景减法，同型计算，玩家 +球跟踪和本地化，NAO机器人姿势估计和跌落检测。马里奥（Mario）被排名第一。1在开放研究挑战中。
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