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.

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.

对深度学习的有效部署的强烈需求（DL）应用促使丰富的DL生态系统的快速发展。为了跟上其快速进步，对于DL框架来说至关重要，以有效地将各种优化的库和运行时作为其后端集成，并通过正确使用它们来生成最快的可执行文件。但是，当前的DL框架需要重大的手动努力来整合多样化的后果，并且通常无法提供高性能。在本文中，我们提出了一个用于集成DL后端的自动框架的拼贴画。拼贴提供后端注册界面，允许用户精确指定各个后端的功能。通过利用可用后端的规范，拼贴搜索给定工作负载和执行环境的优化后端放置。我们的评估表明，拼贴画在没有手动干预的情况下将多个后端集成在一起，并且分别在两个不同的NVIDIA GPU和英特尔CPU上以1.21倍，1.39倍，1.40倍的现有框架。

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。

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed Spi-derCNN, to efficiently extract geometric features from point clouds. Spi-derCNN is comprised of units called SpiderConv, which extend convolutional operations from regular grids to irregular point sets that can be embedded in R n , by parametrizing a family of convolutional filters. We design the filter as a product of a simple step function that captures local geodesic information and a Taylor polynomial that ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical architecture from classical CNNs, which allows it to extract semantic deep features. Experiments on ModelNet40[4] demonstrate that SpiderCNN achieves state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.

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.

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.

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。

越来越需要在各种新的硬件平台上为不同任务部署机器学习。这样的部署场景需要应对多个挑战，包括确定可以实现合适的预测准确性（体系结构搜索）的模型体系结构，并找到有效的模型实施，以满足基础硬件特定的系统约束，例如延迟（系统优化搜索）。现有作品将架构搜索和系统优化搜索视为单独的问题，并将其顺序解决。在本文中，我们建议共同解决这些问题，并引入一种简单但有效的基线方法，称为Sonar，该方法交织了这两个搜索问题。 Sonar的目标是通过将早期停止应用于两个搜索过程来有效地优化预测准确性和推理潜伏期。我们对多个不同硬件后端的实验表明，Sonar识别出几乎最佳体系结构的速度比蛮力方法快30倍。