The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Earthquakes have a deep impact on wide areas, and emergency rescue operations may benefit from social media information about the scope and extent of the disaster. Therefore, this work presents a text miningbased approach to collect and analyze social media data for early earthquake impact analysis. First, disasterrelated microblogs are collected from the Sina microblog based on crawler technology. Then, after data cleaning a series of analyses are conducted including (1) the hot words analysis, (2) the trend of the number of microblogs, (3) the trend of public opinion sentiment, and (4) a keyword and rule-based text classification for earthquake impact analysis. Finally, two recent earthquakes with the same magnitude and focal depth in China are analyzed to compare their impacts. The results show that the public opinion trend analysis and the trend of public opinion sentiment can estimate the earthquake's social impact at an early stage, which will be helpful to decision-making and rescue management.

Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.

Recent success of vision transformers has inspired a series of vision backbones with novel feature transformation paradigms, which report steady performance gain. Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, which makes it hard to identify the real gain from the key feature transformation operators. In this paper, we aim to identify real gain of popular convolution and attention operators and make an in-depth study of them. We observe that the main difference among these feature transformation modules, e.g., attention or convolution, lies in the way of spatial feature aggregation, or the so-called "spatial token mixer" (STM). Hence, we first elaborate a unified architecture to eliminate the unfair impact of different engineering techniques, and then fit STMs into this architecture for comparison. Based on various experiments on upstream/downstream tasks and the analysis of inductive bias, we find that the engineering techniques boost the performance significantly, but the performance gap still exists among different STMs. The detailed analysis also reveals some interesting findings of different STMs, such as effective receptive fields and invariance tests. The code and trained models will be publicly available at https://github.com/OpenGVLab/STM-Evaluation

自2016年成立以来，Alexa奖计划使数百名大学生能够通过Socialbot Grand Challenge探索和竞争以发展对话代理商。挑战的目的是建立能够与人类在流行主题上连贯而诱人的代理人20分钟，同时达到至少4.0/5.0的平均评分。但是，由于对话代理商试图帮助用户完成日益复杂的任务，因此需要新的对话AI技术和评估平台。成立于2021年的Alexa奖Taskbot Challenge建立在Socialbot Challenge的成功基础上，通过引入交互式协助人类进行现实世界烹饪和做自己动手做的任务的要求，同时同时使用语音和视觉方式。这项挑战要求TaskBots识别和理解用户的需求，识别和集成任务和域知识，并开发新的方式，不分散用户的注意力，而不必分散他们的任务，以及其他挑战。本文概述了Taskbot挑战赛，描述了使用Cobot Toolkit提供给团队提供的基础架构支持，并总结了参与团队以克服研究挑战所采取的方法。最后，它分析了比赛第一年的竞争任务机器人的性能。

在鸟眼中学习强大的表现（BEV），以进行感知任务，这是趋势和吸引行业和学术界的广泛关注。大多数自动驾驶算法的常规方法在正面或透视视图中执行检测，细分，跟踪等。随着传感器配置变得越来越复杂，从不同的传感器中集成了多源信息，并在统一视图中代表功能至关重要。 BEV感知继承了几个优势，因为代表BEV中的周围场景是直观和融合友好的。对于BEV中的代表对象，对于随后的模块，如计划和/或控制是最可取的。 BEV感知的核心问题在于（a）如何通过从透视视图到BEV来通过视图转换来重建丢失的3D信息； （b）如何在BEV网格中获取地面真理注释； （c）如何制定管道以合并来自不同来源和视图的特征； （d）如何适应和概括算法作为传感器配置在不同情况下各不相同。在这项调查中，我们回顾了有关BEV感知的最新工作，并对不同解决方案进行了深入的分析。此外，还描述了该行业的BEV方法的几种系统设计。此外，我们推出了一套完整的实用指南，以提高BEV感知任务的性能，包括相机，激光雷达和融合输入。最后，我们指出了该领域的未来研究指示。我们希望该报告能阐明社区，并鼓励对BEV感知的更多研究。我们保留一个活跃的存储库来收集最新的工作，并在https://github.com/openperceptionx/bevperception-survey-recipe上提供一包技巧的工具箱。

随着预训练的语言模型（PLM）的继续增长，精细调整PLM的硬件和数据要求也会增长。因此，研究人员提出了一种称为\ textit {提示学习}的较轻方法。但是，在调查过程中，我们观察到及时的学习方法是脆弱的，很容易被一些非法构造的提示攻击，从而导致分类错误和PLM的严重安全问题。当前的大多数研究都忽略了基于及时方法的安全问题。因此，在本文中，我们提出了一种恶意提示模板构建方法（\ textbf {stressAttack}）来探测PLM的安全性能。研究了几种不友好的模板构建方法，以指导模型错误分类任务。在三个数据集和三个PLM上进行了广泛的实验证明了我们提出的方法提示的有效性。我们还进行实验，以验证我们的方法是否适用于几种镜头。

本文介绍了Z-Code ++，这是一种针对抽象文本摘要优化的新的预训练的语言模型。该模型使用三种技术扩展了艺术编码器模型的状态。首先，我们使用两阶段的预训练过程来改善模型在低资源摘要任务上的性能。该模型首先是使用文本语料库进行语言理解的预先培训的，然后在汇总语料库中不断预先培训，以进行基础文本生成。其次，我们用分离的注意力层代替编码器中的自我发项层，其中每个单词都使用两个向量分别代表其内容和位置。第三，我们使用融合编码器，这是一种以层次方式编码长序列的简单而有效的方法。 Z-Code ++在13个文本摘要任务中的9个跨5种语言中创建了新的艺术状态。我们的模型的参数有效，因为它的表现优于XSUM上600倍较大的Palm-540b，并且在Samsum上的易经的200倍GPT3-175B较大。在零射击和少量设置中，我们的模型大大优于竞争模型。

生成的对抗网络（GAN）已受过培训，成为能够创作出令人惊叹的艺术品（例如面部生成和图像样式转移）的专业艺术家。在本文中，我们专注于现实的业务方案：具有所需的移动应用程序和主题样式的可自定义图标的自动生成。我们首先引入一个主题应用图标数据集，称为Appicon，每个图标都有两个正交主题和应用标签。通过研究强大的基线样式，我们观察到由正交标签的纠缠引起的模式崩溃。为了解决这一挑战，我们提出了由有条件的发电机和双重歧视器组成的ICONGAN，具有正交扩大，并且进一步设计了对比的特征分离策略，以使两个歧视器的特征空间正常。与其他方法相比，ICONGAN在Appicon基准测试中表明了优势。进一步的分析还证明了解开应用程序和主题表示的有效性。我们的项目将在以下网址发布：https：//github.com/architect-road/icongan。

逐渐射击的语义分割（IFSS）目标以逐步扩展模型的能力逐渐扩大了仅由几个样本监督的新图像。但是，在旧课程中学到的特征可能会大大漂移，从而导致灾难性遗忘。此外，很少有针对新课程的像素级细分样本会导致每个学习课程中臭名昭著的过度拟合问题。在本文中，我们明确表示基于类别的语义分割的知识作为类别嵌入和超级类嵌入，前者描述了独家的语义属性，而后者则表示超级类知识作为类共享语义属性。为了解决IFSS问题，我们提出了EHNET，即从两个方面嵌入自适应更高和超级级表示网络。首先，我们提出了一种嵌入自适应的策略，以避免特征漂移，该策略通过超级班级表示保持旧知识，并使用类似课程的方案自适应地更新类别嵌入类别，以涉及在各个会话中学习的新课程。其次，为了抵制很少有培训样本引起的过度拟合问题，通过将所有类别嵌入以进行初始化并与新班级的类别保持一致以进行增强，从而学习了超级班级的嵌入，从而使学会知识有助于学习新知识，从而减轻了绩效绩效的绩效，依赖培训数据量表。值得注意的是，这两种设计为具有足够语义和有限偏见的类提供了表示能力，从而可以执行需要高语义依赖性的分割任务。 Pascal-5i和可可数据集的实验表明，EHNET具有显着优势的新最先进的性能。