变压器已成为自然兰格格处理和视觉中许多任务的首选模型。在更有效地进行培训和部署变压器的最新努力已经确定了许多策略，以近似自我发挥作用矩阵，这是变压器体系结构中的关键模块。有效的想法包括各种预先指定的稀疏模式，低级基础扩展及其组合。在本文中，我们重新访问了小波等经典多分辨率分析（MRA）概念，在这种情况下，在这种情况下的潜在价值迄今仍未被逐渐解散。我们表明，基于现代硬件和实施挑战所告知的经验反馈和设计选择的简单近似值，最终在大多数感兴趣的标准中产生了基于MRA的自我注意力方法，具有出色的性能。我们进行了一系列广泛的实验，并证明该多分辨率方案的表现优于最有效的自我注意力建议，并且对短序列和长序列都有利。代码可在\ url {https://github.com/mlpen/mra-witchention}中获得。

Reinforcement Learning is a powerful tool to model decision-making processes. However, it relies on an exploration-exploitation trade-off that remains an open challenge for many tasks. In this work, we study neighboring state-based, model-free exploration led by the intuition that, for an early-stage agent, considering actions derived from a bounded region of nearby states may lead to better actions when exploring. We propose two algorithms that choose exploratory actions based on a survey of nearby states, and find that one of our methods, ${\rho}$-explore, consistently outperforms the Double DQN baseline in an discrete environment by 49\% in terms of Eval Reward Return.

Micro-CT images of the renal arteries of intact rat kidneys, which had their vasculature injected with the contrast agent polymer Microfil, were characterized. Measurement of inter-branch segment properties and the hierarchical structure of the vessel trees were computed by an automated algorithmic approach. The perfusion territories of the different kidneys, as well as the local diameters of the segmented vasculature were mapped onto the representative structures and visually explored. Various parameters were compared in order to outline key geometrical properties, properties which were shown to not have a wide range of inter-specimen variation. It is shown that the fractal scaling in non-symmetric branching reveals itself differently, than in symmetric branching (e.g., in the lung the mean bronchial diameters at each generation are closely related). Also, perfused tissue is shown to have very little inter-specimen variation and therefore could be used in future studies related to characterizing various disease states of tissues and organs based on vascular branching geometry.

In a wide variety of fields, analysis of images involves defining a region and measuring its inherent properties. Such measurements include a region's surface area, curvature, volume, average gray and/or color scale, and so on. Furthermore, the subsequent subdivision of these regions is sometimes performed. These subdivisions are then used to measure local information, at even finer scales. However, simple griding or manual editing methods are typically used to subdivide a region into smaller units. The resulting subdivisions can therefore either not relate well to the actual shape or property of the region being studied (i.e., gridding methods), or be time consuming and based on user subjectivity (i.e., manual methods). The method discussed in this work extracts subdivisional units based on a region's general shape information. We present the results of applying our method to the medical image analysis of nested regions-of-interest of myocardial wall, where the subdivisions are used to study temporal and/or spatial heterogeneity of myocardial perfusion. This method is of particular interest for creating subdivision regions-of-interest (SROIs) when no variable intensity or other criteria within a region need be used to separate a particular region into subunits.

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.

The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libraries such as PyRadiomics. But differences in image acquisition, systematic differences between observers' annotations, and preprocessing steps still pose challenges. These can change the distribution of voxels altering extracted features and can be exacerbated with dynamic binning.

从诸如蛋白质折叠或配体 - 受体结合如蛋白质 - 折叠或配体 - 受体结合等生物分子过程的长时间轨迹的低尺寸表示是基本的重要性和动力学模型，例如Markov建模，这些模型已经证明是有用的，用于描述这些系统的动力学。最近，引入了一种被称为vampnet的无监督机器学习技术，以以端到端的方式学习低维度表示和线性动态模型。 Vampnet基于Markov进程（VAMP）的变分方法，并依赖于神经网络来学习粗粒度的动态。在此贡献中，我们将Vampnet和图形神经网络组合生成端到端的框架，以从长时间的分子动力学轨迹有效地学习高级动态和亚稳态。该方法承载图形表示学习的优点，并使用图形消息传递操作来生成用于VAMPNET中使用的每个数据点以生成粗粒化表示的嵌入。这种类型的分子表示结果导致更高的分辨率和更可接定的Markov模型，而不是标准Vampnet，使得对生物分子过程更详细的动力学研究。我们的GraphVampNet方法也具有注意机制，以找到分类为不同亚稳态的重要残留物。

自闭症谱系障碍（ASD）是一种神经发育障碍，导致发生改变的行为，社会发展和通信模式。在过去几年中，自闭症患病率增加了两倍，现在有1分中有1个现在受到影响。鉴于传统诊断是一种冗长，劳动密集型的过程，已经对自动筛选自闭症的发展系统来说，已经提出了重大关注。韵律异常是自闭症的最明显的迹象，受影响的儿童展示言语特质，包括梯度，单调的语调，非典型音高和不规则语言压力模式。在这项工作中，我们展示了一套机器学习方法，以检测自闭症和神经典型（NT）儿童在家庭环境中捕获的自闭症语音音频中的自闭症。我们考虑了三种方法来检测儿童的自闭症语言：首先，在提取的音频特征（包括熔融频率跳跃系数）上培训的随机森林;二，卷积神经网络（CNNS）培训谱图;第三，微调Wav2Vec 2.0 - 基于最先进的基于变压器的ASR模型。我们在从斯坦福的猜测中培训我们的小说Todaset的小说数据集的分类器？移动游戏，一个应用程序，旨在在自然家庭环境中占有自闭症和神经典型的儿童的视频。随机森林分类器实现了70％的精度，微调Wav2Vec 2.0型号达到了77％的精度，CNN在将儿童的音频视为ASD或NT时，CNN可实现79％的准确性。我们的模型能够在具有不一致的录制质量选择的家庭音频剪辑上培训时预测自闭症状态，这可能更广泛地对现实世界的条件。这些结果表明，机器学习方法提供了在没有专门设备的语音中自动检测自闭症的承诺。

元钢筋学习（Meta-RL）算法使得能够快速适应动态环境中的少量样本的任务。通过代理策略网络中的动态表示（通过推理关于任务上下文，模型参数更新或两者）获得的动态表示来实现这样的壮举。然而，由于在策略网络上满足不同的政策，因此获得了超越简单基准问题的快速适应的丰富动态表示是具有挑战性的。本文通过将神经调节引入模块化组件来解决挑战，以增加调节神经元活动的标准策略网络，以便为任务适应提供有效的动态表示。策略网络的建议扩展是在越来越复杂的多个离散和连续控制环境中进行评估。为了证明在Meta-R1中的延伸的一般性和益处，将神经调序的网络应用于两个最先进的META-RL算法（胱瓦和珍珠）。结果表明，与基线相比，通过神经调节增强的Meta-R1产生明显更好的结果和更丰富的动态表示。

什么是学习？ 20美元^ {st} Centure的学习理论形式化 - 这是人工智能中沉淀的革命 - 主要是在$ \ mathit {in-diversion} $学习，即在假设训练数据被取样的假设下学习与评估分布相同的分配。这种假设使这些理论不足以表征21美元^ $ {st} MENTURE的现实世界数据问题，其通常是通过与培训数据分布（称为公共学习）不同的评估分布来表征。因此，我们通过放松这种假设来对现有可读性的正式定义进行小小的变化。然后，我们介绍$ \ MATHBF {学习\效率} $（LE）来量化学习者能够利用给定问题的数据的金额，无论它是一个或分发的问题如何。然后，我们定义并证明了可读性的广义概念之间的关系，并展示了该框架是如何足够一般的，以表征传输，多任务，元，持续和终身学习。我们希望本统一有助于弥合现实世界问题的实证实践与理论指导之间的差距。最后，因为生物学学习继续胜过机器学习算法的某些挑战，我们讨论了这一框架VI的局限性 - \'A-is-is-is-is-is-is-is-vis，它的形式化生物学学习能力，旨在为未来研究的多个途径。