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.

Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function that obeys gauge symmetry. In this paper, we have developed gauge equivariant neural network wave function techniques for simulating continuous-variable quantum lattice gauge theories in the Hamiltonian formulation. We have applied the gauge equivariant neural network approach to find the ground state of 2+1-dimensional lattice gauge theory with U(1) gauge group using variational Monte Carlo. We have benchmarked our approach against the state-of-the-art complex Gaussian wave functions, demonstrating improved performance in the strong coupling regime and comparable results in the weak coupling regime.

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains. However, deploying visual RL techniques in the real world remains challenging due to their low sample efficiency and large generalization gaps. To tackle these obstacles, data augmentation (DA) has become a widely used technique in visual RL for acquiring sample-efficient and generalizable policies by diversifying the training data. This survey aims to provide a timely and essential review of DA techniques in visual RL in recognition of the thriving development in this field. In particular, we propose a unified framework for analyzing visual RL and understanding the role of DA in it. We then present a principled taxonomy of the existing augmentation techniques used in visual RL and conduct an in-depth discussion on how to better leverage augmented data in different scenarios. Moreover, we report a systematic empirical evaluation of DA-based techniques in visual RL and conclude by highlighting the directions for future research. As the first comprehensive survey of DA in visual RL, this work is expected to offer valuable guidance to this emerging field.

在本文中，我们研究了代理人（个人）具有战略性或自我利益的情况，并且在报告数据时关注其隐私。与经典环境相比，我们的目标是设计机制，这些机制既可以激励大多数代理来真实地报告他们的数据并保留个人报告的隐私，而它们的输出也应接近基础参数。在本文的第一部分中，我们考虑了协变量是次高斯的情况，并且在他们只有有限的第四瞬间的情况下进行了重尾。首先，我们是受可能性功能最大化器的固定条件的动机，我们得出了一种新颖的私人和封闭式估计量。基于估算器，我们提出了一种机制，该机制通过对几种规范模型的计算和付款方案进行一些适当的设计具有以下属性，例如线性回归，逻辑回归和泊松回归：（1）机制为$ O（1） $ - 接点差异私有（概率至少$ 1-O（1）$）; （2）这是一个$ o（\ frac {1} {n}）$ - 近似于$（1-o（1））$的代理的近似贝叶斯nash平衡，以真实地报告其数据，其中$ n $是代理人的数量； （3）输出可能会达到基础参数的$ O（1）$； （4）对于机制中的$（1-o（1））$的代理分数是个人合理的； （5）分析师运行该机制所需的付款预算为$ O（1）$。在第二部分中，我们考虑了在更通用的环境下的线性回归模型，在该设置中，协变量和响应都是重尾，只有有限的第四次矩。通过使用$ \ ell_4 $ -norm收缩运算符，我们提出了一种私人估算器和付款方案，该方案具有与次高斯案例相似的属性。

机器学习（ML）具有改善医疗保健的巨大希望，但至关重要的是要确保其使用不会传播或扩大健康差异。一个重要的步骤是表征ML模型的（联合国）公平性 - 它们在人群的亚组中的表现趋势不同，并了解其潜在机制。当ML模拟培训数据中不正确相关性的基本预测时，就会出现算法不公平，快捷学习的潜在驱动力。但是，诊断这种现象很困难，尤其是当敏感属性与疾病有因果关系时。使用多任务学习，我们提出了第一种评估和减轻快捷方式学习的方法，作为临床ML系统公平评估的一部分，并证明了其在放射学和皮肤病学中的临床任务中的应用。最后，我们的方法揭示了捷径对不公平不公平负责的情况，强调了对医疗AI中的公平缓解的必要性。

在文档级事件提取（DEE）任务中，事件参数始终散布在句子（串行问题）中，并且多个事件可能存在于一个文档（多事件问题）中。在本文中，我们认为事件参数的关系信息对于解决上述两个问题具有重要意义，并提出了一个新的DEE框架，该框架可以对关系依赖关系进行建模，称为关系授权的文档级事件提取（REDEE）。更具体地说，该框架具有一种新颖的量身定制的变压器，称为关系增强的注意变形金刚（RAAT）。 RAAT可扩展以捕获多尺度和多启动参数关系。为了进一步利用关系信息，我们介绍了一个单独的事件关系预测任务，并采用多任务学习方法来显式增强事件提取性能。广泛的实验证明了该方法的有效性，该方法可以在两个公共数据集上实现最新性能。我们的代码可在https：// github上找到。 com/tencentyouturesearch/raat。

3D面重建结果的评估通常取决于估计的3D模型和地面真相扫描之间的刚性形状比对。我们观察到，将两个形状与不同的参考点进行排列可以在很大程度上影响评估结果。这给精确诊断和改进3D面部重建方法带来了困难。在本文中，我们提出了一种新的评估方法，并采用了新的基准测试，包括100张全球对齐的面部扫描，具有准确的面部关键点，高质量的区域口罩和拓扑符合的网格。我们的方法执行区域形状比对，并导致计算形状误差期间更准确，双向对应关系。细粒度，区域评估结果为我们提供了有关最先进的3D面部重建方法表现的详细理解。例如，我们对基于单图像的重建方法的实验表明，DECA在鼻子区域表现最好，而Ganfit在脸颊区域的表现更好。此外，使用与我们构造的相同过程以对齐和重新构造几个3D面部数据集的新型和高质量的3DMM基础HIFI3D ++。我们将在https://realy3dface.com上发布真正的HIFI3D ++以及我们的新评估管道。

初步任务设计需要高效且准确地近似于低推力的聚合轨迹，这可能通常是三维的并且涉及多次转。本文开发了一种用于分析近似的使用立方样条函数的新成形方法，其显示了最优性和计算效率的优点。在假设预先指定立方样条函数的边界条件和段数，全部满足边界状态和转移时间的约束约束。然后根据是否具有自由优化参数，配制两种特定形状。没有自由参数的形状提供了有效且稳健的估计，而另一个则允许随后的优化来满足诸如推力幅度上的约束的额外约束。所提出的方法与粒子群优化算法结合的应用通过两个典型的行星际的间行序列任务讨论，即，从地球到小行星狄俄尼索斯的倾斜的多转轨迹和样本返回的多串轨迹。仿真示例表明，在为全球搜索的良好估计和为随后的轨迹优化产生合适的初始猜测方面，所提出的方法优于现有方法。

近年来，卷积神经网络（CNNS）已成功应用于单个目标跟踪任务。通常，训练深层CNN模型需要众多标记的训练样本，并且这些样品的数量和质量直接影响训练模型的代表性能力。然而，这种方法在实践中是限制性的，因为手动标记了这么大的训练样本是耗时的并且非常昂贵。在本文中，我们提出了一种用于深度视觉跟踪的主动学习方法，其选择和注释未标记的样本以培训深度CNNS模型。在主动学习的指导下，基于受过训练的深CNN模型的跟踪器可以实现竞争性跟踪性能，同时降低标签成本。更具体地，为了确保所选样本的多样性，我们提出了一种基于多帧协作的主动学习方法，以选择应该是并且需要注释的那些训练样本。同时，考虑到这些所选样本的代表性，我们采用基于平均最近邻距离的最近邻差异距离筛选隔离样本和低质量样品。因此，基于我们的方法选择的训练样本子集仅需要一个给定的预算来维持整个样本集的多样性和代表性。此外，我们采用TVERSKY亏损来改进跟踪器的边界框估计，这可以确保跟踪器实现更准确的目标状态。广泛的实验结果证实，我们的积极学习的跟踪器（ALT）与七个最具挑战性评估基准的最先进的跟踪器相比，与最先进的跟踪器相比，实现了竞争性的跟踪精度和速度。

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.