高斯工艺（GPS）是贝叶斯非参数模型，由于其准确性和天然不确定性定量（UQ），因此在各种应用中流行。调整GP超参数对于确保预测准确性和不确定性的有效性至关重要。独特地估计多个超参数，例如Matern内核也可能是一个重大挑战。此外，大规模数据集中的培训GPS是一个高度活跃的研究领域：传统的最大似然超参数训练需要二次记忆以形成协方差矩阵并具有立方训练的复杂性。为了解决可扩展的超参数调整问题，我们提出了一种新型算法，该算法估算了Matern内核中的平滑度和长度尺度参数，以提高所得预测不确定性的鲁棒性。使用与超参数估计算法MUYGPS提供的计算框架中的合并预测算法相似的新型损失函数，我们在数值实验中证明了高度可伸缩性，同时保持了高度可伸缩性。

明显大小的时间变化（称为光曲线）是望远镜在长时间内捕获的感兴趣的观察统计。光曲线提供了空间域意识（SDA）目标（例如对象识别或姿势估计）作为潜在变量推理问题等目标的探索。与较高的精确仪器相比，来自货架上商业架子（COTS）摄像机的地面观测仍然很便宜，但是，有限的传感器可用性与嘈杂的观察结果相结合，可能会产生可能难以建模的gappy时间序列数据。这些外部因素混淆了对光曲线的自动开发，这使光曲线预测和外推成为应用的关键问题。传统上，使用基于扩散或基于示例的方法解决了图像或时间序列的完成问题。最近，由于学习复杂的非线性嵌入方面的经验成功，深度神经网络（DNNS）已成为首选工具。但是，DNN通常需要大量的培训数据，而这些数据不一定在查看单个卫星的光曲线的独特功能时可用。在本文中，我们提出了一种新的方法，可以使用高斯工艺（GPS）预测光曲线的缺失和未来数据点。 GPS是非线性概率模型，可推断后验分布在功能上并自然量化不确定性。但是，GP推理和培训的立方缩放是其在应用中采用的主要障碍。特别是，单个光曲线可以具有数十万个观测值，这远远超出了单个机器上常规GP的实际实现极限。因此，我们采用MUYGP，这是一种可扩展的框架，用于使用最近的邻居稀疏和局部交叉验证的GP模型的超参数估计。 muygps ...

在联合学习（FL）中，通过跨设备的模型更新进行合作学习全球模型的目的倾向于通过本地信息反对个性化的目标。在这项工作中，我们通过基于多准则优化的框架以定量的方式校准了这一权衡，我们将其作为一个受约束的程序进行了：设备的目标是其本地目标，它试图最大程度地减少在满足非线性约束的同时，以使其满足非线性约束，这些目标是其本地目标。量化本地模型和全局模型之间的接近度。通过考虑该问题的拉格朗日放松，我们开发了一种算法，该算法允许每个节点通过查询到一阶梯度Oracle将其Lagrangian的本地组件最小化。然后，服务器执行Lagrange乘法器上升步骤，然后进行Lagrange乘法器加权步骤。我们称这种实例化的原始偶对方法是联合学习超出共识（$ \ texttt {fedBc} $）的实例。从理论上讲，我们确定$ \ texttt {fedBc} $以与最算好状态相匹配的速率收敛到一阶固定点，直到额外的错误项，取决于由于接近性约束而产生的公差参数。总体而言，该分析是针对非凸鞍点问题的原始偶对偶的方法的新颖表征。最后，我们证明了$ \ texttt {fedBc} $平衡了整个数据集（合成，MNIST，CIFAR-10，莎士比亚）的全球和本地模型测试精度指标，从而与艺术现状达到了竞争性能。

我们介绍了第一个分布式优化算法，该算法具有懒惰的通信，以进行协作几何估计，现代协作同时本地化和映射（SLAM）和结构 - 莫特 - 莫蒂（SFM）应用程序的骨干。我们的方法允许代理通过融合单个观察结果在中央服务器上合作重建共享的几何模型，但无需传输有关代理本身（例如其位置）的潜在敏感信息。此外，为了减轻迭代优化期间的通信负担，我们设计了一组通信触发条件，使代理能够选择性地上传针对性的本地信息的目标子集，该信息对全球优化有用。因此，我们的方法可实现大量的沟通减少，对优化性能的影响最小。作为我们的主要理论贡献，我们证明我们的方法以全球sublinear收敛速率收敛到一阶关键点。关于合作SLAM和SFM数据集的捆绑调整问题的数值评估表明，我们的方法在现有的分布式技术方面具有竞争力，同时达到了多达78％的总沟通减少。

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.

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.

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/ .

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.