In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection. To obtain a more efficient model architecture, we explore an architecture that has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. We further introduce soft labels when calculating matching costs in the dynamic label assignment to improve accuracy. Together with better training techniques, the resulting object detector, named RTMDet, achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, outperforming the current mainstream industrial detectors. RTMDet achieves the best parameter-accuracy trade-off with tiny/small/medium/large/extra-large model sizes for various application scenarios, and obtains new state-of-the-art performance on real-time instance segmentation and rotated object detection. We hope the experimental results can provide new insights into designing versatile real-time object detectors for many object recognition tasks. Code and models are released at https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet.

位置识别技术赋予了一种大满贯算法，具有消除累积错误并自身重新定位的能力。基于点云的位置识别的现有方法通常利用以激光雷达为中心的全局描述符的匹配。这些方法具有以下两个主要缺陷：当两个点云之间的距离很远时，不能执行位置识别，并且只能计算旋转角度，而无需在x和y方向上偏移。为了解决这两个问题，我们提出了一个新颖的全球描述符，该描述符围绕主要对象构建，以这种方式，描述符不再依赖于观察位置。我们分析了该方法可以完美地解决上述两个问题的理论，并在Kitti和一些极端情况下进行了许多实验，这表明我们的方法比传统方法具有明显的优势。

我们根据熵风险措施研究风险敏感的强化学习（RL）。虽然现有的作品已经建立了这个问题的非渐近遗憾担保，但它们会在上限和下限之间开放指数差距。我们确定现有算法中的缺陷及其分析，从而导致如此差距。为了解决这些缺陷，我们调查了风险敏感的Bellman方程的简单转变，我们称之为指数钟声方程。指数贝尔曼方程激励我们在风险敏感RL算法中开发对Bellman备份程序的新型分析，并进一步激励了一种新颖勘探机制的设计。我们表明，这些分析和算法创新共同导致现有的遗憾的上限。

现有的胃癌诊断深层学习方法，常用卷积神经网络。最近，视觉变压器由于其性能和效率而引起了极大的关注，但其应用主要在计算机视野领域。本文提出了一种用于Gashis变压器的多尺度视觉变压器模型，用于胃组织病理学图像分类（GHIC），其使微观胃图像自动分类为异常和正常情况。 GASHIS-COMPURANCER模型由两个关键模块组成：全球信息模块和局部信息模块有效提取组织病理特征。在我们的实验中，具有280个异常和正常图像的公共血毒素和曙红（H＆E）染色的胃组织病理学数据集分为训练，验证和测试组，比率为1：1：2胃组织病理学数据集测试组精度，召回，F1分数和准确性分别为98.0％，100.0％，96.0％和98.0％。此外，进行了关键的研究以评估Gashis变压器的稳健性，其中添加了10个不同的噪声，包括四种对抗性攻击和六种传统图像噪声。此外，执行临床上有意义的研究以测试Gashis变压器的胃肠癌鉴定性能，具有620个异常图像，精度达到96.8％。最后，进行比较研究以测试在淋巴瘤图像数据集和乳腺癌数据集上的H＆E和免疫组织化学染色图像的概括性，产生可比的F1分数（85.6％和82.8％）和精度（83.9％和89.4％） ， 分别。总之，Gashistransformer演示了高分类性能，并在GHIC任务中显示出其显着潜力。

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.

Noninvasive X-ray imaging of nanoscale three-dimensional objects, e.g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of complex electromagnetic field through ICs; and tomographic scanning, which collects complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach trained to provide accurate reconstructions of ICs despite incomplete measurements, using a dramatically reduced amount of angular scanning. Training process includes regularizing priors based on typical IC patterns and the physics of X-ray propagation. We demonstrate that APT with 12-time reduced angles achieves fidelity comparable to the gold standard with the original set of angles. With the same set of reduced angles, APT also outperforms baseline reconstruction methods. In our experiments, APT achieves 108-time aggregate reduction in data acquisition and computation without compromising quality. We expect our physics-assisted machine learning framework could also be applied to other branches of nanoscale imaging.

The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot learning has made it difficult to solve using pure machine learning. A more promising approach has been to perform program synthesis within an appropriately designed Domain Specific Language (DSL). However, these too have seen limited success. We propose Abstract Reasoning with Graph Abstractions (ARGA), a new object-centric framework that first represents images using graphs and then performs a search for a correct program in a DSL that is based on the abstracted graph space. The complexity of this combinatorial search is tamed through the use of constraint acquisition, state hashing, and Tabu search. An extensive set of experiments demonstrates the promise of ARGA in tackling some of the complicated object-centric tasks of the ARC rather efficiently, producing programs that are correct and easy to understand.

相干显微镜技术提供了跨科学和技术领域的材料的无与伦比的多尺度视图，从结构材料到量子设备，从综合电路到生物细胞。在构造更明亮的来源和高速探测器的驱动下，连贯的X射线显微镜方法（如Ptychography）有望彻底改变纳米级材料的特征。但是，相关的数据和计算需求显着增加意味着，常规方法不再足以从高速相干成像实验实时恢复样品图像。在这里，我们演示了一个工作流程，该工作流利用边缘的人工智能和高性能计算，以实现直接从检测器直接从检测器流出的X射线ptychography数据实时反演。拟议的AI支持的工作流程消除了传统的Ptychography施加的采样约束，从而使用比传统方法所需的数据较少的数据级允许低剂量成像。

科学文献是高质量的语料库，支持大量自然语言处理（NLP）研究。但是，现有数据集围绕英语，这限制了中国科学NLP的发展。在这项工作中，我们提出了CSL，这是一个大规模的中国科学文献数据集，其中包含396K论文的标题，摘要，关键字和学术领域。据我们所知，CSL是中文中的第一个科学文档数据集。 CSL可以用作中国语料库。同样，该半结构化数据是一种自然注释，可以构成许多监督的NLP任务。基于CSL，我们提出了一个基准，以评估跨科学领域任务的模型的性能，即摘要，关键字生成和文本分类。我们分析了现有文本到文本模型在评估任务上的行为，并揭示了中国科学NLP任务的挑战，该任务为未来的研究提供了宝贵的参考。数据和代码可在https://github.com/ydli-ai/csl上找到

联合学习（FL）是一个新兴的隐私机器学习范式（ML）。 FL的一种重要类型是Cross-Silo FL，它使少数组织能够通过在本地保密数据并在中央参数服务器上汇总权重来合作训练共享模型。但是，在实践中，中央服务器可能容易受到恶意攻击或软件故障的影响。为了解决这个问题，在本文中，我们提出了DEFL，这是一个新颖的分散体重聚集框架，用于交叉silo fl。 DEFL通过在每个参与节点上汇总权重来消除中央服务器，并且仅在所有节点之间维护并同步当前的训练回合的权重。我们使用Multi-Krum来启用诚实节点的正确权重，并使用HotStuff来确保训练循环数和权重的一致性。此外，我们从理论上分析了DEFL的拜占庭式容错，收敛性和复杂性。我们对两个广泛的公共数据集进行了广泛的实验，即CIFAR-10和Sentiment140，以评估DEFL的性能。结果表明，与最先进的分散FL方法相比，DEFL可以防御通用的威胁模型，并以最小的精度损失损失降低了100倍的存储空间和最多减少网络开销的12倍。