Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting (3DGS) excels in real-time rendering and static scene reconstructions but struggles with modeling driving scenarios due to complex backgrounds, dynamic objects, and sparse camera views. We propose AutoSplat, a framework employing Gaussian splatting to realistically reconstruct autonomous driving scenes. By imposing geometric constraints on Gaussians representing the road and sky regions, our method enables multi-view consistent simulation of challenging scenarios, including lane changes. Leveraging 3D templates, we introduce a reflected Gaussian consistency constraint to supervise both the visible and unseen side of foreground objects. Moreover, to model the dynamic appearance of foreground objects, we estimate temporally-dependent residual spherical harmonics for each foreground Gaussian. Extensive experiments on Pandaset [1] and KITTI [2] demonstrate that AutoSplat outperforms state-of-the-art methods in scene reconstruction and novel view synthesis across diverse driving scenarios. Our project page can be found here: https://autosplat.github.io/

Users of social media platforms are negatively affected by the proliferation of hate or abusive content. There has been a rise in homophobic and transphobic content in recent years targeting LGBT+ individuals. The increasing levels of homophobia and transphobia online can make online platforms harmful and threatening for LGBT+ persons, potentially inhibiting equality, diversity, and inclusion. We are introducing a new dataset for three languages, namely Telugu, Kannada, and Gujarati. Additionally, we have created an expert-labeled dataset to automatically identify homophobic and transphobic content within comments collected from YouTube. We provided comprehensive annotation rules to educate annotators in this process. We collected approximately 10,000 comments from YouTube for all three languages. Marking the first dataset of these languages for this task, we also developed a baseline model with pre-trained transformers.

The robustness of visual navigation policies trained through imitation often hinges on the augmentation of the training image-action pairs. Traditionally, this has been done by collecting data from multiple cameras, by using standard data augmentations from computer vision, such as adding random noise to each image, or by synthesizing training images. In this paper we show that there is another practical alternative for data augmentation for visual navigation based on extrapolating viewpoint embeddings and actions nearby the ones observed in the training data. Our method makes use of the geometry of the visual navigation problem in 2D and 3D and relies on policies that are functions of equivariant embeddings, as opposed to images. Given an image-action pair from a training navigation dataset, our neural network model predicts the latent representations of images at nearby viewpoints, using the equivariance property, and augments the dataset. We then train a policy on the augmented dataset. Our simulation results indicate that policies trained in this way exhibit reduced cross-track error, and require fewer interventions compared to policies trained using standard augmentation methods. We also show similar results in autonomous visual navigation by a real ground robot along a path of over 500m.

With majority of the world's data and computation handled by cloud-based systems, cloud management stacks such as Apache's CloudStack, VMware's vSphere and OpenStack have become an increasingly important component in cloud software. However, like every other complex distributed system, these cloud stacks are susceptible to faults, whose root cause is often hard to diagnose. We present HANSEL, a system that leverages non-intrusive network monitoring to expedite root cause analysis of such faults manifesting in OpenStack operations. HANSEL is fast and accurate, and precise even under conditions of stress.

The existing methods for finding influencers use the process of information diffusion to discover the nodes with maximum information spread. These models capture only the process of information diffusion and not the actual social value of collaborations in the network. We have proposed a method for finding influencers using the idea that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the notion of individual social capital. We hypothesize and show that players with high social capital are often key influencers in the network. We propose a value-allocation model to compute the social capital and allocate the fair share of this capital to each individual involved in the collaboration. We show that our allocation satisfies several axioms of fairness and falls in the same class as the Myerson's allocation function. We implement our allocation rule using an efficient algorithm SoCap and show that our algorithm outperforms the baselines in several real-life data sets. Specifically, in DBLP network, our algorithm outperforms PageRank, PMIA and Weighted Degree baselines up to 8% in terms of precision, recall and F1-measure.