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.

Point cloud analysis is receiving increasing attention, however, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown objects. This paper mainly discusses point cloud analysis under open-set settings, where we train the model without data from unknown classes and identify them in the inference stage. Basically, we propose to solve open-set point cloud analysis using a novel Point Cut-and-Mix mechanism consisting of Unknown-Point Simulator and Unknown-Point Estimator modules. Specifically, we use the Unknown-Point Simulator to simulate unknown data in the training stage by manipulating the geometric context of partial known data. Based on this, the Unknown-Point Estimator module learns to exploit the point cloud's feature context for discriminating the known and unknown data. Extensive experiments show the plausibility of open-set point cloud analysis and the effectiveness of our proposed solutions. Our code is available at \url{https://github.com/ShiQiu0419/pointcam}.

骨架序列是紧凑而轻巧的。已经提出了许多基于骨架的动作识别者来对人类行为进行分类。在这项工作中，我们旨在结合与现有模型兼容的组件，并进一步提高其准确性。为此，我们设计了两个时间配件：离散余弦编码（DCE）和按时间顺序损失（CRL）。DCE促进模型以分析频域的运动模式，同时减轻信号噪声的影响。CRL指导网络明确捕获序列的时间顺序。这两个组件一致地赋予许多最近提供的动作识别器具有准确性的提升，从而在两个大数据集上实现了新的最先进（SOTA）精度。

鉴于3D扫描仪的快速发展，Point云在AI驱动的机器中变得流行。但是，点云数据本质上是稀疏和不规则的，导致机器感知的主要困难。在这项工作中，我们专注于云上采样任务，该任务旨在从稀疏输入数据生成密集的高保真点云。具体而言，为了激活变压器在代表功能方面的强大功能，我们开发了多头自我关注结构的新变体，以增强特征图的点明智和渠道关系。此外，我们利用位置融合块来全面地捕获点云数据的本地背景，提供有关分散点的更多位置相关信息。由于第一变压器模型引入点云上采样，我们通过与定量和定性的不同基准的基于基准的方法相比，通过比较了我们的方法的出色性能。

借助深度学习范式，许多点云网络已经发明了用于视觉分析。然而，由于点云数据的给定信息尚未完全利用，因此对这些网络的发展存在很大的潜力。为了提高现有网络在分析点云数据中的有效性，我们提出了一个即插即用模块，PNP-3D，旨在通过涉及更多来自显式3D空间的本地背景和全球双线性响应来改进基本点云特征表示隐含的功能空间。为了彻底评估我们的方法，我们对三个标准点云分析任务进行实验，包括分类，语义分割和对象检测，在那里我们从每个任务中选择三个最先进的网络进行评估。作为即插即用模块，PNP-3D可以显着提高已建立的网络的性能。除了在四个广泛使用的点云基准测试中实现最先进的结果，我们还提供了全面的消融研究和可视化，以展示我们的方法的优势。代码将在https://github.com/shiqiu0419/pnp-3d上获得。

骨架序列轻巧且紧凑，因此是在边缘设备上进行动作识别的理想候选者。最新的基于骨架的动作识别方法从3D关节坐标作为时空提示提取特征，在图神经网络中使用这些表示形式来提高识别性能。一阶和二阶特征（即关节和骨骼表示）的使用导致了很高的精度。但是，许多模型仍然被具有相似运动轨迹的动作所困惑。为了解决这些问题，我们建议以角度编码为现代体系结构的形式融合高阶特征，以稳健地捕获关节和身体部位之间的关系。这种与流行的时空图神经网络的简单融合可在包括NTU60和NTU120在内的两个大型基准中实现新的最新精度，同时使用较少的参数和减少的运行时间。我们的源代码可公开可用：https：//github.com/zhenyueqin/angular-skeleton-soding。

为了充分利用潜在的分钟和微妙的差异，细粒度分类器收集有关阶级变异的信息。由于同一类实体中的颜色，观点和结构之间的差异，任务是非常具有挑战性的。由于与自己的其他类别和差异的观点之间的差异之间的相似性，分类变得更加困难。在这项工作中，我们调查了地标通用CNN分类器的性能，它在细粒度数据集上呈现了大规模分类数据集的顶部缺口结果，并将其与最先进的细粒度分类器进行比较。在本文中，我们提出了两个特定问题：（i）一般的CNN分类器是否可以实现与细粒度的分类器相当的结果？ （ii）将军CNN分类器是否需要任何特定信息来改善细粒度的信息？在整个工作中，我们培训一般的CNN分类器而不引入特定于细粒度数据集的任何方面。我们对六个数据集进行了广泛的评估，以确定细粒度分类器是否能够在实验中提升基线。

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks.