Neural Radiance Field (NeRF) shows a high prospect in the task of novel view synthesis. However, performance degrades drastically under limited input views since NeRF heavily relies on a large number of images to fit the geometry in scenes. Recent efforts focus on introducing extra constraints to improve rendering results, but problems still exist due to the lack of enough geometric information about scenes. In this work, we present a novel combination of learning-based Multi-View Stereo (MVS) and Monocular Depth Estimation (MDE) depths for high-quality geometric priors. We retain view-consistent values in MVS depths by consistency filtration. Then we introduce an MVS-guided patch-wise transformation strategy for monocular depths to resolve ambiguities and match the scale of MVS depths. Finally, we complete filtered MVS depths with transformed monocular depths to generate view-consistent, complete, and smooth geometric priors. Experiments show that our approach achieves state-of-the-art results.

Learned video compression has developed rapidly and shown promising rate-distortion performance recently. Existing works have made great progress on removing temporal redundancy between inter-frames, while neglecting spatial redundancy within a frame. In this paper, we propose to explore spatial redundancy to further improve compression efficiency and reduce computation complexity by extending inter-frame prediction from temporal prediction to spatial-temporal prediction. Specifically, the current frame is first down-sampled to reduce spatial redundancy, which is then temporally predicted to compensate motions and spatially predicted to restore resolutions. The spatial prediction module is designed in a reference-based manner, which improves prediction performance without additional bit rate cost by mining high-frequency details from a neighboring high-resolution decoded frame. Experimental results demonstrate that our proposed module can improve compression efficiency and reduce encoding/decoding time. Our framework achieves competitive compression efficiency compared with recent learned methods and traditional codecs.

In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user's occupational class.We show that the content information of a user's tweets, the profile descriptions of a user's follower/following community, and the user's social network provide useful information for classifying a user's occupational group.In our study, we extend an existing dataset for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work.In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this dataset with just a small fraction of the training data.

Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, in practice KwikCluster requires a large number of clustering rounds, a potential bottleneck for large graphs.We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds, and provably achieve nearly linear speedups. C4 uses concurrency control to enforce serializability of a parallel clustering process, and guarantees a 3-approximation ratio. ClusterWild! is a coordination free algorithm that abandons consistency for the benefit of better scaling; this leads to a provably small loss in the 3 approximation ratio.We provide extensive experimental results for both algorithms, where we outperform the state of the art, both in terms of clustering accuracy and running time. We show that our algorithms can cluster billion-edge graphs in under 5 seconds on 32 cores, while achieving a 15x speedup.

MLI is an Application Programming Interface designed to address the challenges of building Machine Learning algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.

Research on distributed machine learning algorithms has focused primarily on one of two extremes---algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this optimistic concurrency control'' paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment. "

Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning.

We present Kahuna, an approach that aims to diagnose performance problems in MapReduce systems. Central to Kahuna's approach is our insight on peer-similarity, that nodes behave alike in the absence of performance problems, and that a node that behaves differently is the likely culprit of a performance problem. We present applications of Kahuna's insight in techniques and their algorithms to statistically compare black-box (OS-level performance metrics) and white-box (Hadoop-log statistics) data across the different nodes of a MapReduce cluster, in order to identify the faulty node(s). We also present empirical evidence of our peer-similarity observations from the 4000-processor Yahoo! M45 Hadoop cluster. In addition, we demonstrate Kahuna's effectiveness through experimental evaluation of two algorithms for a number of reported performance problems, on four different workloads in a 100-node Hadoop cluster running on Amazon's EC2 infrastructure.