In recent years, we have seen increased interest in applying machine learning to system problems. For example, there has been work on improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, and sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: is used model and/or workload order...

We identify a notion of reducibility between predicates, called instance reducibility, which commonly appears in reverse constructive mathematics. The can be generally used to compare and classify various principles studied mathematics (formal Church's thesis, Brouwer's Continuity principle Fan theorem, Excluded middle, Limited principle, Function choice, Markov's etc.). show that the degrees f...

Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As result, large foreground dominate minor instances and s...

Due to the few annotated labels of 3D point clouds, how learn discriminative features clouds segment object instances is a challenging problem. In this paper, we propose simple yet effective instance segmentation framework that can achieve good performance by annotating only one for each instance. Specifically, tackle extremely segmentation, first oversegment cloud into superpoints in an unsupe...

Image instance segmentation is a fundamental research topic in autonomous driving, which crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations training. In this paper, we present more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), leverages off-the-shelf 3D data, i.e., Point Cloud, togeth...