复杂的系统在现实世界中无处不在，并且往往具有复杂且理解不足的动态。对于他们的控制问题，挑战是保证在这种肿的和陷入困境的环境中的准确性，鲁棒性和概括。幸运的是，复杂的系统可以分为人类认知似乎可以利用的多个模块化结构。受到一种新型控制方法的启发，提出了一种新颖的控制方法，是一种因果关系机制（CCMS），它提出了探索组合分裂和竞争的合作。我们的方法采用了层次强化学习理论（HRL），其中1）具有竞争意识的高级政策将整个复杂系统划分为多种功能机制，以及2）低级政策完成了每种机制的控制任务。特别是用于合作的级联控制模块有助于CCM的串联操作，并使用向前耦合的推理模块来恢复分区过程中丢失的耦合信息。在合成系统和现实世界的生物调节系统上，CCM方法即使有不可预测的随机噪声，CCM方法也可以达到稳健和最新的控制结果。此外，概括结果表明，重复使用准备的专业CCM有助于在具有不同混杂因素和动态的环境中表现良好。

基于图形神经网络（GNN）的子图表学习在科学进步中表现出广泛的应用，例如对分子结构 - 特质关系和集体细胞功能的预测。特别是，图表增强技术在改善基于图和基于节点的分类任务方面显示出令人鼓舞的结果。尽管如此，在现有的基于GNN的子图表示学习研究中很少探索它们。在这项研究中，我们开发了一种新型的多视图增强机制，以改善子图表示学习模型，从而改善下游预测任务的准确性。我们的增强技术创建了多种子图的变体，并将这些变体嵌入原始图中，以实现高度改善的训练效率，可伸缩性和准确性。几个现实世界和生理数据集的基准实验证明了我们提出的多视图增强技术在子图表学习中的优越性。

图像的深度估计是自动驾驶3D感知的基本步骤，并且是LIDAR等昂贵深度传感器的经济替代方案。时间光度限制可实现无标签的自制深度估计，从而进一步促进其应用。但是，大多数现有方法仅根据每个单眼图像来预测深度​​，并忽略多个周围相机之间的相关性，这些相机通常可用于现代自动驾驶车辆。在本文中，我们提出了一种环绕方法，以合并来自多个周围视图的信息，以预测跨相机的深度图。具体来说，我们采用联合网络来处理所有周围的观点，并提出跨视图变压器，从多个视图中有效融合信息。我们应用跨视图自我注意力，有效地实现多相机特征图之间的全局相互作用。与自我监督的单眼深度估计不同，我们能够预测给定多相机外部矩阵的现实世界量表。为了实现这一目标，我们采用了两框结构，从而提取尺度感知的伪深度以预处理模型。此外，我们没有预测每个摄像机的自我运动，而是估计车辆的普遍自我运动并将其传输到每种视图中以实现多视图的自我运动一致性。在实验中，我们的方法在具有挑战性的多相机深度估计数据集DDAD和NUSCENES上实现了最新的性能。

视网膜血管疾病影响人体的福祉，有时会提供其他缺陷的身体损伤的生命体征。最近，已经成功地应用了深度学习技术以检测糖尿病视网膜病变（DR）。应用深层学习技术的主要障碍检测大多数其他视网膜血管疾病是可用的有限数量的数据。在本文中，我们提出了一种转移学习技术，其旨在利用用于检测视网膜血管疾病的特征相似性。我们选择良好的DR检测作为源任务，并确定作为目标任务的早产儿（ROP）视网膜病变的早期检测。我们的实验结果表明，我们的DR预训方法在所有指标中占据了传统的想象预训过的转移学习方法，目前在医学图像分析中采用。此外，我们的方法对培训过程的随机性以及减少训练样本方面更加强大。本研究表明，我们建议的转移学习方法具有广泛的视网膜血管疾病或病态的潜力，其中数据有限。

尽管取得了巨大的成功，但深入的学习严重遭受鲁棒性;也就是说，深度神经网络非常容易受到对抗的攻击，即使是最简单的攻击。灵感来自脑科学最近的进步，我们提出了一种新的内部模型（DIM），这是一种基于新的生成自动化器的模型来解决这一挑战。模拟人类大脑中的管道进行视觉信号处理，暗淡采用两级方法。在第一阶段，DIM使用丹组器来减少输入的噪声和尺寸，反映了塔马拉姆的信息预处理。从主视觉皮质中的内存相关迹线的稀疏编码启发，第二阶段产生一组内部模型，一个用于每个类别。我们评估了42次对抗攻击的衰弱，表明Dim有效地防御所有攻击，并且优于整体鲁棒性的SOTA。

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-global-graph representations of electroencephalography (EEG) for Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multi-scale 1D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely, the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant (p<0.05) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG

We consider the problem of unsupervised domain adaptation in semantic segmentation. A key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. One of the common strategies is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the category-level joint distribution. A possible consequence of such global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped, thus leading to worse segmentation results in target domain. To address this problem, we introduce a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level joint distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 → Cityscapes and SYN-THIA → Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.

Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying background distribution in the entire scene or the transmittance over the ray dimension, limiting their performance on static and occlusion areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance fields offers high-quality view synthesis and a 3D solution to $\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene, which is called $\text{D}^4$NeRF. Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively. Each ray sample is given an additional occlusion weight to indicate the transmittance lying in the static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic scenes and our urban driving scenes acquired from an autonomous-driving dataset. Extensive experiments demonstrate that our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background. Our code will be released at https://github.com/Luciferbobo/D4NeRF.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.

Machine learning-based segmentation in medical imaging is widely used in clinical applications from diagnostics to radiotherapy treatment planning. Segmented medical images with ground truth are useful for investigating the properties of different segmentation performance metrics to inform metric selection. Regular geometrical shapes are often used to synthesize segmentation errors and illustrate properties of performance metrics, but they lack the complexity of anatomical variations in real images. In this study, we present a tool to emulate segmentations by adjusting the reference (truth) masks of anatomical objects extracted from real medical images. Our tool is designed to modify the defined truth contours and emulate different types of segmentation errors with a set of user-configurable parameters. We defined the ground truth objects from 230 patient images in the Glioma Image Segmentation for Radiotherapy (GLIS-RT) database. For each object, we used our segmentation synthesis tool to synthesize 10 versions of segmentation (i.e., 10 simulated segmentors or algorithms), where each version has a pre-defined combination of segmentation errors. We then applied 20 performance metrics to evaluate all synthetic segmentations. We demonstrated the properties of these metrics, including their ability to capture specific types of segmentation errors. By analyzing the intrinsic properties of these metrics and categorizing the segmentation errors, we are working toward the goal of developing a decision-tree tool for assisting in the selection of segmentation performance metrics.