There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

自然行为由不可预测的动力学组成，可以突然切换并在许多不同的时间尺度上展开。尽管在受约束或简化的基于任务的条件下构建行为的表示方面已经找到了一些成功，但由于它们假设单一的时间动力学规模，因此无法将其中许多模型应用于自由和自然主义的设置。在这项工作中，我们跨多个尺度（BAMS）引入引导程序，这是一种多尺度表示模型：我们结合了一个汇总模块，该模块汇总了与具有不同时间接收场的编码器上提取的特征，并设计了一组潜在目标，以进行引导程序各个空间中的表示，以鼓励不同时间尺度的分离。我们首先将我们的方法应用于在不同地形类型中导航的四倍的数据集上，并表明我们的模型捕获了行为的时间复杂性。然后，我们将我们的方法应用于MABE 2022多代理行为挑战，我们的模型在两个子任务中排名第三，第一个排名第1，并在分析行为时显示了合并多时间尺度的重要性。

通常通过从单个组件的动力学上抽象来构建人口级动力学的模型来研究复杂的时变系统。但是，当构建人群级别的描述时，很容易忽略每个人，以及每个人如何贡献更大的情况。在本文中，我们提出了一种新颖的变压器体系结构，用于从时变数据中学习，该数据构建了个人和集体人口动态的描述。我们没有在一开始就将所有数据结合到我们的模型中，而是开发可分离的体系结构，该体系结构先在单个时间序列上运行，然后再将它们传递给它们。这会导致置换式属性属性，可用于跨不同大小和顺序的系统传输。在证明我们的模型可以应用于在多体系统中成功恢复复杂的相互作用和动力学之后，我们将方法应用于神经系统中的神经元种群。在神经活动数据集上，我们表明我们的多尺度变压器不仅会产生强大的解码性能，而且在转移方面提供了令人印象深刻的性能。我们的结果表明，可以从一种动物的大脑中的神经元学习并传递不同动物大脑中神经元的模型，并在集合和动物之间具有可解释的神经元对应。这一发现为解码并表示大量神经元的新途径开辟了一条新的途径。

神经活动的意义和简化表示可以产生深入了解如何以及什么信息被神经回路内处理。然而，如果没有标签，也揭示了大脑和行为之间的联系的发现表示可以挑战。在这里，我们介绍了所谓的交换，VAE学习神经活动的解开表示一种新型的无监督的办法。我们的方法结合了特定实例的排列损失，试图最大限度地输入（大脑状态）的转变观点之间的代表性相似性的生成模型框架。这些转化（或增强）视图是通过掉出神经元和抖动样品中的时间，这直观地应导致网络维护既时间一致性和不变性用于表示神经状态的特定的神经元的表示创建的。通过对从数百个不同的灵长类动物大脑的神经元的模拟数据和神经录音的评价，我们表明，它是不可能建立的表示沿有关潜在维度解开神经的数据集与行为相联系。

通过最大化示例的不同转换“视图”之间的相似性来构建自我监督学习（SSL）构建表示的最先进的方法。然而，在用于创建视图的转换中没有足够的多样性，难以克服数据中的滋扰变量并构建丰富的表示。这激励了数据集本身来查找类似但不同的样本，以彼此的视图。在本文中，我们介绍了我自己的观点（MISOW），一种新的自我监督学习方法，在数据集中定义预测的不同目标。我们的方法背后的想法是主动挖掘观点，发现在网络的表示空间中的邻居中的样本，然后从一个样本的潜在表示，附近样本的表示。在展示计算机愿景中使用的基准测试中，我们突出了在神经科学的新应用中突出了这个想法的力量，其中SSL尚未应用。在测试多单元神经记录时，我们发现Myow在所有示例中表现出其他自我监督的方法（在某些情况下超过10％），并且经常超越监督的基线。通过MOSO，我们表明可以利用数据的多样性来构建丰富的观点，并在增强的新域中利用自我监督，其中包括有限或未知。

自我监督的学习提供了一个有希望的途径，消除了在图形上的代表学习中的昂贵标签信息的需求。然而，为了实现最先进的性能，方法通常需要大量的负例，并依赖于复杂的增强。这可能是昂贵的，特别是对于大图。为了解决这些挑战，我们介绍了引导的图形潜伏（BGRL） - 通过预测输入的替代增强来学习图表表示学习方法。 BGRL仅使用简单的增强，并减轻了对否定例子对比的需求，因此通过设计可扩展。 BGRL胜过或匹配现有的几种建立的基准，同时降低了内存成本的2-10倍。此外，我们表明，BGR1可以缩放到半监督方案中的数亿个节点的极大的图表 - 实现最先进的性能并改善监督基线，其中表示仅通过标签信息而塑造。特别是，我们的解决方案以BGRL为中心，将kdd杯2021的开放图基准的大规模挑战组成了一个获奖条目，在比所有先前可用的基准更大的级别的图形订单上，从而展示了我们方法的可扩展性和有效性。

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.

Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream task has been a well-known paradigm in Natural Language Processing. However, with the ever-growing size of PLMs, training the entire model on several downstream tasks becomes very expensive and resource-hungry. Recently, different Parameter Efficient Tuning (PET) techniques are proposed to improve the efficiency of fine-tuning PLMs. One popular category of PET methods is the low-rank adaptation methods which insert learnable truncated SVD modules into the original model either sequentially or in parallel. However, low-rank decomposition suffers from limited representation power. In this work, we address this problem using the Kronecker product instead of the low-rank representation. We introduce KronA, a Kronecker product-based adapter module for efficient fine-tuning of Transformer-based PLMs. We apply the proposed methods for fine-tuning T5 on the GLUE benchmark to show that incorporating the Kronecker-based modules can outperform state-of-the-art PET methods.

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.

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.