储层计算机（RCS）是所有神经网络训练最快的计算机之一，尤其是当它们与其他经常性神经网络进行比较时。 RC具有此优势，同时仍能很好地处理顺序数据。但是，由于该模型对其超参数（HPS）的敏感性，RC的采用率滞后于其他神经网络模型。文献中缺少一个自动调谐这些参数的现代统一软件包。手动调整这些数字非常困难，传统网格搜索方法的成本呈指数增长，随着所考虑的HP数量，劝阻RC的使用并限制了可以设计的RC模型的复杂性。我们通过引入RCTORCH来解决这些问题，Rctorch是一种基于Pytorch的RC神经网络软件包，具有自动HP调整。在本文中，我们通过使用它来预测不同力的驱动摆的复杂动力学来证明rctorch的实用性。这项工作包括编码示例。示例Python Jupyter笔记本可以在我们的GitHub存储库https://github.com/blindedjoy/rctorch上找到，可以在https://rctorch.readthedocs.io/上找到文档。

从经典动力学系统到量子力学的许多领域，在许多领域的进步核心，有效，准确地求解微分方程。人们对使用物理知识的神经网络（PINN）来解决此类问题，这引起了人们的兴趣，因为它们比传统的数值方法提供了许多好处。尽管它们在求解微分方程方面的潜在好处，但仍在探索转移学习。在这项研究中，我们提出了转移学习PINN的一般框架，该框架对普通和部分微分方程的线性系统进行了单次推断。这意味着，可以在不重新培训整个网络的情况下即时获得许多未知微分方程的方法。我们通过解决了几个现实世界中的问题，例如一阶线性普通方程，泊松方程以及时间依赖时间依赖的schrodinger复合物配合物部分差分方程来证明拟议的深度学习方法的功效。

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/ .

There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentation in recent years. As a result, a rapid increase in the usage of such systems can be observed in the healthcare industry, for instance in the form of medical image classification systems, where these models have achieved diagnostic parity with human physicians. One such application where this can be observed is in computer vision tasks such as the classification of skin lesions in dermatoscopic images. However, as stakeholders in the healthcare industry, such as insurance companies, continue to invest extensively in machine learning infrastructure, it becomes increasingly important to understand the vulnerabilities in such systems. Due to the highly critical nature of the tasks being carried out by these machine learning models, it is necessary to analyze techniques that could be used to take advantage of these vulnerabilities and methods to defend against them. This paper explores common adversarial attack techniques. The Fast Sign Gradient Method and Projected Descent Gradient are used against a Convolutional Neural Network trained to classify dermatoscopic images of skin lesions. Following that, it also discusses one of the most popular adversarial defense techniques, adversarial training. The performance of the model that has been trained on adversarial examples is then tested against the previously mentioned attacks, and recommendations to improve neural networks robustness are thus provided based on the results of the experiment.

Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behavior of complex interacting systems. They often take the form of a high-dimensional system of differential equations parameterized by an interaction kernel that models the underlying attractive or repulsive forces between agents. We consider the problem of constructing a data-based approximation of the interacting forces directly from noisy observations of the paths of the agents in time. The learned interaction kernels are then used to predict the agents behavior over a longer time interval. The approximation developed in this work uses a randomized feature algorithm and a sparse randomized feature approach. Sparsity-promoting regression provides a mechanism for pruning the randomly generated features which was observed to be beneficial when one has limited data, in particular, leading to less overfitting than other approaches. In addition, imposing sparsity reduces the kernel evaluation cost which significantly lowers the simulation cost for forecasting the multi-agent systems. Our method is applied to various examples, including first-order systems with homogeneous and heterogeneous interactions, second order homogeneous systems, and a new sheep swarming system.

Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks, of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large specialized databases for animal tracking systems and confirm the utility of our new ape database.

Euclidean geometry is among the earliest forms of mathematical thinking. While the geometric primitives underlying its constructions, such as perfect lines and circles, do not often occur in the natural world, humans rarely struggle to perceive and reason with them. Will computer vision models trained on natural images show the same sensitivity to Euclidean geometry? Here we explore these questions by studying few-shot generalization in the universe of Euclidean geometry constructions. We introduce Geoclidean, a domain-specific language for Euclidean geometry, and use it to generate two datasets of geometric concept learning tasks for benchmarking generalization judgements of humans and machines. We find that humans are indeed sensitive to Euclidean geometry and generalize strongly from a few visual examples of a geometric concept. In contrast, low-level and high-level visual features from standard computer vision models pretrained on natural images do not support correct generalization. Thus Geoclidean represents a novel few-shot generalization benchmark for geometric concept learning, where the performance of humans and of AI models diverge. The Geoclidean framework and dataset are publicly available for download.

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difficult to predict a patient's pathologic response to neoadjuvant chemotherapy. In this paper, we investigate the efficacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDI$^s$) for the purpose of pCR prediction. More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response. As the first study to explore the utility of CDI$^s$ within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, this approach to leverage volumetric deep radiomic features (which we name Cancer-Net BCa) can be further extended to other applications of CDI$^s$ in the cancer domain to further improve prediction performance.

数据集的质量在成功培训和部署深度学习模型中起着至关重要的作用。特别是在系统性能可能影响患者健康状况的医疗领域，干净的数据集是可靠预测的安全要求。因此，在构建自主临床决策系统时，离群值检测是一个必不可少的过程。在这项工作中，我们评估了自组织图对外离检测的适用性，专门针对包含白细胞定量相图像的医学数据集。我们根据量化误差和距离图检测和评估异常值。我们的发现证实了自组织地图对于手头数据集的无监督分布检测的适​​用性。根据专家领域知识，自组织地图与手动指定的过滤器相同。此外，它们在探索和清洁医疗数据集的工具方面显示了希望。作为未来研究的方向，我们建议将自组织地图和基于深度学习的特征提取的结合。

尽管在利用深度学习来自动化胸部X光片解释和疾病诊断任务方面取得了进展，但顺序胸部X射线（CXR）之间的变化受到了有限的关注。监测通过胸部成像可视化的病理的进展在解剖运动估计和图像注册中构成了几个挑战，即在空间上对齐这两个图像并在变化检测中对时间动力学进行建模。在这项工作中，我们提出了Chexrelnet，这是一种可以跟踪两个CXR之间纵向病理关系的神经模型。Chexrelnet结合了局部和全球视觉特征，利用图像间和图像内的解剖信息，并学习解剖区域属性之间的依赖性，以准确预测一对CXR的疾病变化。与基准相比，胸部成像组数据集的实验结果显示下游性能提高。代码可从https://github.com/plan-lab/chexrelnet获得