我们展示了如何relight一种场景，描绘在单个图像中，使得（a）整体着色已经改变，并且（b）得到的图像看起来像该场景的自然图像。此类过程的应用包括生成培训数据和构建创作环境。这样做的天真方法失败了。一个原因是阴影和反照学相当强烈相关;例如，阴影中的尖锐边界倾向于出现在深度不连续性，通常在Albedo中显而易见。相同的场景可以以不同的方式点亮，并且建立的理论表明了不同的灯具形成锥形（照明锥）。新颖的理论表明，人们可以使用类似的场景来估计适用于给定场景的不同照明，其中有界预期的误差。我们的方法利用该理论来估计照明锥的抵抗发生器形式的可用照明场的表示。我们的程序不需要昂贵的“逆图形”数据集，并且没有任何类型的地面真理数据。定性评估表明该方法可以擦除和恢复柔软的室内阴影，并可以在场景周围“转向”光。我们提供了对FID的新应用方法的总结定量评估。 FID的扩展允许每个生成的图像评估。此外，我们提供了与用户学习的定性评估，并显示我们的方法产生可以成功用于数据增强的图像。

潜水员在NERF的关键思想和其变体 - 密度模型和体积渲染的关键思想中建立 - 学习可以从少量图像实际渲染的3D对象模型。与所有先前的NERF方法相比，潜水员使用确定性而不是体积渲染积分的随机估计。潜水员的表示是基于体素的功能领域。为了计算卷渲染积分，将光线分为间隔，每个体素;使用MLP的每个间隔的特征估计体渲染积分的组件，并且组件聚合。结果，潜水员可以呈现其他集成商错过的薄半透明结构。此外，潜水员的表示与其他这样的方法相比相对暴露的语义 - 在体素空间中的运动特征向量导致自然编辑。对当前最先进的方法的广泛定性和定量比较表明，潜水员产生（1）在最先进的质量或高于最先进的质量，（2）的情况下非常小而不会被烘烤，（3）在不被烘烤的情况下渲染非常快，并且（4）可以以自然方式编辑。

我们展示了如何将一个对象从一个图像插入到另一个图像，并在硬情况下获得现实的结果，其中插入的对象的阴影与场景的阴影冲突。使用场景的照明模型渲染对象不起作用，因为这样做需要对象的几何和材料模型，这很难从单个图像中恢复。在本文中，我们介绍了一种方法，该方法可以纠正插入对象的阴影不一致，而无需几何和物理模型或环境图。我们的方法使用了深层图像先验（DIP），训练有素，可以通过一致的图像分解推理损耗来产生插入对象的重新添加效果。来自DIP的最终图像的目的是具有（a）类似于切割和贴合的反照率的反照率，（b）与目标场景相似的阴影场，以及（c）与切割 - 剪裁一致的阴影和帕斯特表面正常。结果是一个简单的过程，可以产生插入对象的令人信服的阴影。我们在定性和定量上对具有复杂表面特性的几个对象以及球形灯罩数据集进行定量评估的疗效。我们的方法明显优于所有这些对象的图像协调（IH）基线。在用100多名用户的用户研究中，他们还胜过剪切和IH基线。

Classically, the development of humanoid robots has been sequential and iterative. Such bottom-up design procedures rely heavily on intuition and are often biased by the designer's experience. Exploiting the non-linear coupled design space of robots is non-trivial and requires a systematic procedure for exploration. We adopt the top-down design strategy, the V-model, used in automotive and aerospace industries. Our co-design approach identifies non-intuitive designs from within the design space and obtains the maximum permissible range of the design variables as a solution space, to physically realise the obtained design. We show that by constructing the solution space, one can (1) decompose higher-level requirements onto sub-system-level requirements with tolerance, alleviating the "chicken-or-egg" problem during the design process, (2) decouple the robot's morphology from its controller, enabling greater design flexibility, (3) obtain independent sub-system level requirements, reducing the development time by parallelising the development process.

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data, scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). This paper presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant $t$ such that the overall release is user-level $\varepsilon$-DP and has the following error guarantee: Denoting by $M_t$ the maximum number of samples contributed by a user, as long as $\tilde{\Omega}(1/\varepsilon)$ users have $M_t/2$ samples each, the error at time $t$ is $\tilde{O}(1/\sqrt{t}+\sqrt{M}_t/t\varepsilon)$. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In this paper, we show this factorization can be combined with regression on a continuous response variable. In practice, the method performs better than regression done after topics are identified and retrains interpretability.

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.

One of the major errors affecting GNSS signals in urban canyons is GNSS multipath error. In this work, we develop a Gazebo plugin which utilizes a ray tracing technique to account for multipath effects in a virtual urban canyon environment using virtual satellites. This software plugin balances accuracy and computational complexity to run the simulation in real-time for both software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also construct a 3D virtual environment of Hong Kong and compare the results from our plugin with the GNSS data in the publicly available Urban-Nav dataset, to validate the efficacy of the proposed Gazebo Plugin. The plugin is openly available to all the researchers in the robotics community. https://github.com/kpant14/multipath_sim

In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images. The semi-supervised model uses less labeled data, and the performance is competitive with the supervised model with full labeled data. This framework could reduce the cost of labeling MRI images. We also introduced robust loss to reduce the noise effects of inaccurate labels generated in semi-supervised learning.