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.

Implementing fully automatic unmanned surface vehicles (USVs) monitoring water quality is challenging since effectively collecting environmental data while keeping the platform stable and environmental-friendly is hard to approach. To address this problem, we construct a USV that can automatically navigate an efficient path to sample water quality parameters in order to monitor the aquatic environment. The detection device needs to be stable enough to resist a hostile environment or climates while enormous volumes will disturb the aquaculture environment. Meanwhile, planning an efficient path for information collecting needs to deal with the contradiction between the restriction of energy and the amount of information in the coverage region. To tackle with mentioned challenges, we provide a USV platform that can perfectly balance mobility, stability, and portability attributed to its special round-shape structure and redundancy motion design. For informative planning, we combined the TSP and CPP algorithms to construct an optimistic plan for collecting more data within a certain range and limiting energy restrictions.We designed a fish existence prediction scenario to verify the novel system in both simulation experiments and field experiments. The novel aquaculture environment monitoring system significantly reduces the burden of manual operation in the fishery inspection field. Additionally, the simplicity of the sensor setup and the minimal cost of the platform enables its other possible applications in aquatic exploration and commercial utilization.

视觉预读（VLP）模型最近成功地促进了许多跨模式下游任务。大多数现有作品通过比较微调的下游任务性能来评估其系统。但是，只有平均下游任务准确性才能提供有关每种VLP方法的优缺点的几乎没有信息，更不用说有关社区如何改善系统的见解。受清单进行自然语言处理的启发，我们引入了VL-CheckList，这是一个新颖的框架，以了解VLP模型的功能。所提出的方法将VLP模型的图像定位能力分为三类：对象，属性和关系，并使用新颖的分类法进一步分解这三个方面。我们进行了全面的研究，通过提出的框架分析了七个最近流行的VLP模型。结果通过揭示了仅在下游任务评估中看不见的模型之间的细粒度差异来证实所提出的方法的有效性。进一步的结果表明，在构建更好的VLP模型方面有希望的研究方向。数据和代码：https：//github.com/om--ai-lab/vl-checklist

心电图（ECG）是一种简单的非侵入性措施，用于识别与心律失常相关的问题，例如称为心律失常的不规则心跳。尽管人工智能和机器学习被用于广泛的与医疗保健相关的应用程序和数据集中，但近年来已经提出了许多使用深度学习方法的心律失常分类器。但是，可以从中构建和评估机器学习模型的可用数据集的尺寸通常很小，并且缺乏通知的公共ECG数据集很明显。在本文中，我们提出了一个深入的转移学习框架，旨在在小型培训数据集上执行分类。提出的方法是根据AAMI EC57标准，用MIT-BIH心律失常数据集微调通用图像分类器RESNET-18。本文进一步研究了许多现有的深度学习模型，这些模型未能避免根据AAMI建议泄漏数据。我们比较不同的数据拆分方法如何影响模型性能。这项比较研究表明，在使用包括MIT-BIH心律失常数据集在内时，心律不齐分类的未来工作应遵循AAMI EC57标准。

先进的体积成像方法和遗传编码的活性指标已允许在\ textit {caenorhabditis elegans}中对全脑活性进行全面表征。然而，线虫神经系统的恒定运动和变形对行为动物中的密集填充神经元的一致构成了巨大的挑战。在这里，我们提出了一种级联解决方案，用于在自由移动的\ textit {c中长期和快速识别头发神经节神经元。秀丽隐杆线}。首先，通过深度学习算法检测到来自荧光图像的潜在神经元区。第二，二维神经元区域被融合到三维神经元实体中。第三，通过利用神经元和神经元之间的相对位置信息的神经元密度分布，多级人工神经网络将工程的神经元向量转化为数字神经元身份。有了少量的培训样品，我们的自下而上的方法能够处理每一卷 - $ 1024 \ times 1024 \ times 18 $ in Voxels-少于1秒钟，并获得了$ 91 \％\％$ $ $ 91 \％的神经元检测及以上的准确性$ 80 \％$ in Neuronal跟踪在长时间的视频录制中。我们的工作代表了迈向快速和完全自动化算法的一步，用于解码自然主义行为的全部大脑活动。

正交频分复用（OFDM）已广泛应用于当前通信系统。人工智能（AI）addm接收器目前被带到最前沿替换和改进传统的OFDM接收器。在这项研究中，我们首先比较两个AI辅助OFDM接收器，即数据驱动的完全连接的深神经网络和模型驱动的COMNet，通过广泛的仿真和实时视频传输，使用5G快速原型制作系统进行跨越式-Air（OTA）测试。我们在离线训练和真实环境之间的频道模型之间的差异差异导致的模拟和OTA测试之间找到了性能差距。我们开发一种新颖的在线培训系统，称为SwitchNet接收器，以解决此问题。该接收器具有灵活且可扩展的架构，可以通过在线训练几个参数来适应真实频道。从OTA测试中，AI辅助OFDM接收器，尤其是SwitchNet接收器，对真实环境具有鲁棒，并且对未来的通信系统有前途。我们讨论了本文初步研究的潜在挑战和未来的研究。

We consider the problem of reconstructing the signal and the hidden variables from observations coming from a multi-layer network with rotationally invariant weight matrices. The multi-layer structure models inference from deep generative priors, and the rotational invariance imposed on the weights generalizes the i.i.d.\ Gaussian assumption by allowing for a complex correlation structure, which is typical in applications. In this work, we present a new class of approximate message passing (AMP) algorithms and give a state evolution recursion which precisely characterizes their performance in the large system limit. In contrast with the existing multi-layer VAMP (ML-VAMP) approach, our proposed AMP -- dubbed multi-layer rotationally invariant generalized AMP (ML-RI-GAMP) -- provides a natural generalization beyond Gaussian designs, in the sense that it recovers the existing Gaussian AMP as a special case. Furthermore, ML-RI-GAMP exhibits a significantly lower complexity than ML-VAMP, as the computationally intensive singular value decomposition is replaced by an estimation of the moments of the design matrices. Finally, our numerical results show that this complexity gain comes at little to no cost in the performance of the algorithm.

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods are based on stochastic gradient descent ascent algorithms whose trajectories are connected to the optimal policy only after a mixing output stage that depends on the algorithm's history. As a result, there is a mismatch between the behavioral policy and the optimal one. In this work, we propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories converge to the optimal policy almost surely.

变压器验证引起了机器学习研究和行业的越来越多的关注。它正式验证了变压器对对抗性攻击的鲁棒性，例如用同义词交换单词。但是，由于以中线为中心的计算，变压器验证的性能仍然不令人满意，这与标准神经网络有显着差异。在本文中，我们提出了信仰，这是用于GPU的变压器验证的有效框架。我们首先提出一个语义意识的计算图转换，以识别语义信息，例如变压器验证中的结合计算。我们利用此类语义信息，以在计算图级别启用有效的内核融合。其次，我们提出了一个验证专门的内核手工艺品，以有效地将变压器验证映射到现代GPU。该手工艺者利用了一组GPU硬件支持，以加速通常是内存密集型的验证专业操作。第三，我们提出了一个专家指导的自动调整，以纳入有关GPU后端的专家知识，以促进大型搜索空间探索。广泛的评估表明，Faith在最先进的框架上实现了$ 2.1 \ times $至$ 3.4 \ times $（$ 2.6 \ times $）的加速。

数据驱动的设计和创新是重复使用和提供宝贵和有用信息的过程。但是，现有的设计创新语义网络基于仅限于技术和科学信息的数据源。此外，现有研究仅在统计或语义关系上建立语义网络的边缘，这不太可能充分利用两种类型的关系中的好处，并发现设计创新的隐性知识。因此，我们构建了基于Wikipedia的语义网络Wikilink。 Wikilink引入了概念之间的统计重量和语义权重的合并重量，并开发了四种算法来启发新想法。进行评估实验，结果表明，该网络的特征是术语，关系和学科的高度覆盖范围，这证明了网络的有效性和实用性。然后，演示和案例研究结果表明，Wikilink可以作为概念设计创新的思想生成工具。 Wikilink的源代码和后端数据提供开源，供更多用户探索和构建。