我们的目标是讨论其在其理论和实践术语中讨论了强化的计划，指出了在讨论计算模拟的优势的同时实施这些时间表的实际限制。在本文中，我们展示了一个名为喙的R脚本，建立了模拟与加固时间表交互的行为速率。使用喙，我们已经模拟了允许评估不同强化反馈功能（RFF）的数据。这是通过无与伦比的精确度制作的，因为模拟提供了巨大的数据样本，更重要的是，它产生的加强不会改变模拟行为。因此，我们可以系统地改变它。我们将不同的RFF与RI​​时间表进行了比较，用作标准：意义，精确，分析和一般性。我们的结果表明，RI计划的最佳反馈函数由BAUM（1981）公布。我们还建议Killeen（1975）使用的模型是RDRL计划的可行反馈函数。我们认为喙铺平了更多了解加强时间表，解决了关于时间表的定量特征的开放问题。此外，他们可以指导将来使用时间表作为理论和方法工具的实验。

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.

Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.

Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences about 3D shape, physical properties and object interactions. While the problem of tracking arbitrary physical points on surfaces over longer video clips has received some attention, no dataset or benchmark for evaluation existed, until now. In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark, TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. Central to the construction of our benchmark is a novel semi-automatic crowdsourced pipeline which uses optical flow estimates to compensate for easier, short-term motion like camera shake, allowing annotators to focus on harder sections of video. We validate our pipeline on synthetic data and propose a simple end-to-end point tracking model TAP-Net, showing that it outperforms all prior methods on our benchmark when trained on synthetic data.

神经网络是基于学习的软件系统的重要组成部分。但是，它们的高计算，内存和功率要求使在低资源域中使用它们具有挑战性。因此，在部署前通常对神经网络进行量化。现有的量化技术倾向于降低网络准确性。我们提出了反示例引导的神经网络量化改进（CEG4N）。该技术结合了基于搜索的量化和等效性验证：前者最小化了计算要求，而后者保证网络的输出在量化后不会改变。我们根据包括大型和小型网络在内的各种基准测试对CEG4N〜进行评估。我们的技术成功地量化了我们评估中的网络，同时生产的模型比最先进的技术高达72％。

这项工作探讨了物理驱动的机器学习技术运算符推理（IMIPF），以预测混乱的动力系统状态。 OPINF提供了一种非侵入性方法来推断缩小空间中多项式操作员的近似值，而无需访问离散模型中出现的完整订单操作员。物理系统的数据集是使用常规数值求解器生成的，然后通过主成分分析（PCA）投影到低维空间。在潜在空间中，设置了一个最小二乘问题以适合二次多项式操作员，该操作员随后在时间整合方案中使用，以便在同一空间中产生外推。解决后，将对逆PCA操作进行重建原始空间中的外推。通过标准化的根平方误差（NRMSE）度量评估了OPINF预测的质量，从中计算有效的预测时间（VPT）。考虑混乱系统Lorenz 96和Kuramoto-Sivashinsky方程的数值实验显示，具有VPT范围的OPINF降低订单模型的有希望的预测能力，这些模型均超过了最先进的机器学习方法，例如返回和储层计算循环新的Neural网络[1 ]，以及马尔可夫神经操作员[2]。

通用形态（UNIMORPH）项目是一项合作的努力，可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力：一种无独立的特征架构，用于丰富的形态注释，并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进（自McCarthy等人（2020年）以来）。众多语言学家的合作努力增加了67种新语言，其中包括30种濒危语言。我们已经对提取管道进行了一些改进，以解决一些问题，例如缺少性别和马克龙信息。我们还修改了模式，使用了形态学现象所需的层次结构，例如多肢体协议和案例堆叠，同时添加了一些缺失的形态特征，以使模式更具包容性。鉴于上一个UniMorph版本，我们还通过16种语言的词素分割增强了数据库。最后，这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。

通常，基于生物谱系的控制系统可能不依赖于各个预期行为或合作适当运行。相反，这种系统应该了解未经授权的访问尝试的恶意程序。文献中提供的一些作品建议通过步态识别方法来解决问题。这些方法旨在通过内在的可察觉功能来识别人类，尽管穿着衣服或配件。虽然该问题表示相对长时间的挑战，但是为处理问题的大多数技术存在与特征提取和低分类率相关的几个缺点，以及其他问题。然而，最近的深度学习方法是一种强大的一组工具，可以处理几乎任何图像和计算机视觉相关问题，为步态识别提供最重要的结果。因此，这项工作提供了通过步态认可的关于生物识别检测的最近作品的调查汇编，重点是深入学习方法，强调他们的益处，暴露出弱点。此外，它还呈现用于解决相关约束的数据集，方法和体系结构的分类和表征描述。

深度学习（DL）是各种计算机视觉任务中使用的主要方法，因为它在许多任务上取得了相关结果。但是，在具有部分或没有标记数据的实际情况下，DL方法也容易出现众所周知的域移位问题。多源无监督的域适应性（MSDA）旨在通过从一袋源模型中分配弱知识来学习未标记域的预测指标。但是，大多数作品进行域适应性仅利用提取的特征并从损失函数设计的角度降低其域的转移。在本文中，我们认为仅基于域级特征处理域移动不足，但是在功能空间上对此类信息进行对齐也是必不可少的。与以前的工作不同，我们专注于网络设计，并建议将多源版本的域对齐层（MS-DIAL）嵌入预测变量的不同级别。这些层旨在匹配不同域之间的特征分布，并且可以轻松地应用于各种MSDA方法。为了显示我们方法的鲁棒性，我们考虑了两个具有挑战性的情况：数字识别和对象分类，进行了广泛的实验评估。实验结果表明，我们的方法可以改善最新的MSDA方法，从而在其分类精度上获得 +30.64％的相对增长。

In the era of noisy intermediate scale quantum devices, variational quantum circuits (VQCs) are currently one of the main strategies for building quantum machine learning models. These models are made up of a quantum part and a classical part. The quantum part is given by a parametrization $U$, which, in general, is obtained from the product of different quantum gates. By its turn, the classical part corresponds to an optimizer that updates the parameters of $U$ in order to minimize a cost function $C$. However, despite the many applications of VQCs, there are still questions to be answered, such as for example: What is the best sequence of gates to be used? How to optimize their parameters? Which cost function to use? How the architecture of the quantum chips influences the final results? In this article, we focus on answering the last question. We will show that, in general, the cost function will tend to a typical average value the closer the parameterization used is from a $2$-design. Therefore, the closer this parameterization is to a $2$-design, the less the result of the quantum neural network model will depend on its parametrization. As a consequence, we can use the own architecture of the quantum chips to defined the VQC parametrization, avoiding the use of additional swap gates and thus diminishing the VQC depth and the associated errors.