Regularising the parameter matrices of neural networks is ubiquitous in training deep models. Typical regularisation approaches suggest initialising weights using small random values, and to penalise weights to promote sparsity. However, these widely used techniques may be less effective in certain scenarios. Here, we study the Koopman autoencoder model which includes an encoder, a Koopman operator layer, and a decoder. These models have been designed and dedicated to tackle physics-related problems with interpretable dynamics and an ability to incorporate physics-related constraints. However, the majority of existing work employs standard regularisation practices. In our work, we take a step toward augmenting Koopman autoencoders with initialisation and penalty schemes tailored for physics-related settings. Specifically, we propose the "eigeninit" initialisation scheme that samples initial Koopman operators from specific eigenvalue distributions. In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training. We demonstrate the utility of these schemes on two synthetic data sets: a driven pendulum and flow past a cylinder; and two real-world problems: ocean surface temperatures and cyclone wind fields. We find on these datasets that eigenloss and eigeninit improves the convergence rate by up to a factor of 5, and that they reduce the cumulative long-term prediction error by up to a factor of 3. Such a finding points to the utility of incorporating similar schemes as an inductive bias in other physics-related deep learning approaches.

因果贝叶斯网络提供了重要的工具，用于在不确定性下进行推理，并可能应用于许多复杂的因果系统。结构学习算法可以告诉我们一些有关这些系统的因果结构的信息，越来越重要。在文献中，这些算法的有效性通常经过对不同样本量，超参数以及偶尔客观函数的敏感性进行测试。在本文中，我们表明，从数据中读取变量的顺序可能比这些因素对算法的准确性产生更大的影响。由于变量排序是任意的，因此它对学习图的准确性的任何重大影响都与之有关，这引发了有关算法对敏感但未对不同可变订单敏感但尚未评估的算法产生的结果的有效性的问题。

发现和参数化的潜在混杂因素分别代表了因果结构学习和密度估计中的重要和具有挑战性的问题。在本文中，我们专注于发现和学习潜在混杂因素的分布。此任务需要来自不同领域和机器学习领域的解决方案。我们结合了各种贝叶斯方法的要素，期望最大化，爬山搜索以及在因果关系不足的假设下学习的元素。我们提出了两种学习策略。一种可以最大化模型选择准确性，另一种可以提高计算效率，以换取精确度的较小降低。前一种策略适用于小型网络，后者适用于中等大小的网络。两种学习策略相对于现有解决方案都表现良好。

在贝叶斯网络（BNS）中，边缘方向对于因果推理和推理至关重要。然而，马尔可夫等价类考虑因素意味着它并不总是可以建立边缘方向，这就是许多BN结构学习算法不能从纯粹观察数据定向所有边缘的原因。此外，潜在的混乱会导致假阳性边缘。已经提出了相对较少的方法来解决这些问题。在这项工作中，我们介绍了从涉及观察数据集的离散数据和一个或多个介入数据集的离散数据的结构学习的混合MFGS-BS（Meance规则和快速贪婪等价搜索）算法。该算法假设存在潜在变量的因果不足，并产生部分祖先图形（PAG）。结构学习依赖于混合方法和新的贝叶斯评分范式，用于计算添加到学习图表的每个定向边缘的后验概率。基于众所周知的网络的实验结果高达109个变量和10K样本大小表明，MFGS-BS相对于最先进的结构提高了结构学习准确性，并且它是计算效率的。

学习具有基于刻痕的解决方案的贝叶斯网络（BN）的结构涉及探索可能的图表的搜索空间并朝向最大化给定目标函数的图形移动。一些算法提供了确切的解决方案，可以保证以最高目标分数返回图形，而其他算法则提供近似解决方案以换取降低的计算复杂性。本文介绍了一种近似的BN结构学习算法，我们呼叫平均爬山（MAHC）的模型，相结合了两种与爬山搜索的新策略。该算法通过修剪图的搜索空间来开始，其中修剪策略可以被视为通常应用于组合优化结构学习问题的修剪策略的激进版本。然后，它在爬山搜索过程中执行模型平均值，并移动到相邻图，该曲线图平均为该相邻图和在所有有效的相邻图中最大化目标函数。与跨越不同学习类别的其他算法的比较表明，模型平均的攻击性修剪的组合既有效又有效，特别是在存在数据噪声。

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/ .

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.