Userspace library file systems (LibFSes) promise to unleash the performance potential of non-volatile memory (NVM) by directly accessing it and enabling unprivileged applications to customize their LibFSes to their workloads. Unfortunately, such benefits pose a significant challenge to ensuring metadata integrity. Existing works either underutilize NVM's performance or forgo critical file system security guarantees.

With the increasing dominance of SSDs for local storage, today's network mounted virtual disks can no longer offer competitive performance. We propose a Log-Structured Virtual Disk (LSVD) that couples log-structured approaches at both the cache and storage layer to provide a virtual disk on top of S3-like storage. Both cache and backend store are order-preserving, enabling LSVD to provide strong consistency guarantees in case of failure. Our prototype demonstrates that the approach preserves all the advantages of virtual disks, while offering dramatic performance improvements over not only commonly used virtual disks, but the same disks combined with inconsistent (i.e. unsafe) local caching.

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.