对于许多应用，分析机器学习模型的不确定性是必不可少的。尽管不确定性量化（UQ）技术的研究对于计算机视觉应用非常先进，但对时空数据的UQ方法的研究较少。在本文中，我们专注于在线手写识别的模型，这是一种特定类型的时空数据。数据是从传感器增强的笔中观察到的，其目标是对书面字符进行分类。我们基于两种突出的贝叶斯推理，平均高斯（赃物）和深层合奏的突出技术对核心（数据）和认知（模型）UQ进行了广泛的评估。在对模型的更好理解后，UQ技术可以在组合右手和左撇子作家（一个代表性不足的组）时检测分布数据和域的变化。

允许合成现实细胞形状的方法可以帮助生成训练数据集，以改善生物医学图像中的细胞跟踪和分割。细胞形状合成的深层生成模型需要对细胞形状进行轻巧和柔性表示。但是，通常使用体素的表示不适合高分辨率形状合成，而多边形网格在建模拓扑变化（例如细胞生长或有丝分裂）时具有局限性。在这项工作中，我们建议使用符号距离功能（SDF）的级别集来表示细胞形状。我们将神经网络优化为3D+时域中任何点的SDF值的隐式神经表示。该模型以潜在代码为条件，从而允许合成新的和看不见的形状序列。我们在生长和分裂的秀丽隐杆线虫细胞上进行定量和质量验证方法，并具有生长的复杂丝虫突起的肺癌细胞。我们的结果表明，合成细胞的形状描述符类似于真实细胞的形状，并且我们的模型能够在3D+时间内生成复杂细胞形状的拓扑合理序列。

小规模过程的建模是气候模型中的主要误差来源，阻碍了低成本模型的准确性，必须通过参数化近似此类过程。红噪声对于许多操作参数化方案至关重要，有助于建模时间相关性。我们通过将随机性的已知好处与机器学习相结合，展示了如何基于红噪声的成功。这是在概率框架内使用物理信息的复发性神经网络完成的。当应用于Lorenz 96大气模拟时，我们的模型具有竞争力，通常优于定制基线和现有的概率机器学习方法（GAN）。这是由于其与标准一阶自回旋方案相比，它具有较高的时间模式的能力。这也是看不见的场景。我们评估了文献中的许多指标，还讨论了使用持有可能性的概率度量的好处。

表提取是一个重要但仍未解决的问题。在本文中，我们介绍了一种柔性和模块化的台式提取系统。我们开发了两个基于规则的算法，执行完整的表识别过程，包括表检测和分割，并支持最常见的表格格式。此外，为了纳入语义信息的提取，我们开发了一种基于图形的表解释方法。我们对挑战表识别基准ICDAR 2013和ICDAR 2019进行了广泛的实验，实现了与最先进的方法竞争的结果。我们完整的信息提取系统展出了0.7380的高F1得分。为了支持未来的信息提取研究，我们将来自我们的表解释实验，使资源（地面诠释，评估脚本，算法参数）公开可用。

大多数现实世界的应用需要处理传感器噪声或预测性不确定性等旋能性，其中正式规格的所需行为是固有的概率。尽管正式核查确保神经网络的可靠性，但概率规格方向的进展受到限制。在这个方向上，我们首先介绍神经网络的概率规范的一般性，它捕获了概率网络（例如，贝叶斯神经网络，MC-Dropout Networks）和不确定输入（通过传感器噪声或其他扰动而产生的输入）。然后，我们提出了一种通过概括拉格朗日二元性的概念来验证这些规范的一般技术，替换具有“功能乘法器”的标准拉格朗日乘法器，其可以是给定层上激活的任意功能。我们表明，功能乘法器的最佳选择导致精确的验证（即，声音和完全验证），以及特定形式的乘法器，我们开发了易诊的实际验证算法。我们通过将它们应用于贝叶斯神经网络（BNNS）和MC差动网络，以及认证属性，以及诸如对分发超出（OOD）数据的抗逆性鲁棒性和鲁棒检测的认证性能来验证我们的算法。在这些任务中，与现有工作相比，我们能够提供明显更强烈的保证 - 例如，对于在CiFar-10上培训的VGG-64 MC-Tropout CNN，我们改进了认证的AUC（真实AUC的验证的下限）对于鲁棒的OOD检测（在CIFAR-100上）起价$ 0 \％\ lightarrow 29 \％$。同样，对于在MNIST培训的BNN，我们从60.2美元\％\ lightarrow 74.6 \％$提高了强大的准确性。此外，在一种新颖的规范 - 分布稳健的检测 - 我们从5 \％\ lightarrow 23 \％$的5 \％$。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a direct mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. The system was successfully evaluated during the ANA Avatar XPRIZE competition semifinals. In addition, we performed in lab experiments and carried out a small user study with mostly untrained operators.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.