Despite their strong performance, Dense Passage Retrieval (DPR) models suffer from a lack of interpretability.In this work, we propose a novel interpretability framework that leverages Sparse Autoencoders (SAEs) to decompose previously uninterpretable dense embeddings from DPR models into distinct, interpretable latent concepts.We generate natural language descriptions for each latent concept, enabling human interpretations of both the dense embeddings and the query-document similarity scores of DPR models.We further introduce Concept-Level Sparse Retrieval (CL-SR), a retrieval framework that directly utilizes the extracted latent concepts as indexing units.CL-SR effectively combines the semantic expressiveness of dense embeddings with the transparency and efficiency of sparse representations.We show that CL-SR achieves high index-space and computational efficiency while maintaining robust performance across vocabulary and semantic mismatches 1 .

The proliferation of system virtualization poses a new challenge for the coarse-grained time sharing techniques for consolidation, since operating systems are running on virtual CPUs. The current system stack was designed under the assumption that operating systems can seize CPU resources at any moment. However, for the guest operating system on a virtual machine (VM), such assumption cannot be guaranteed, since virtual CPUs of VMs share a limited number of physical cores. Due to the time-sharing of physical cores, the execution of a virtual CPU is not contiguous, with a gap between the virtual and real time spaces. Such a virtual time discontinuity problem leads to significant inefficiency for lock and interrupt handling, which rely on the immediate availability of CPUs whenever the operating system requires computation. To reduce scheduling latencies of virtual CPUs, shortening time slices can be a straightforward strategy, but it may lead to the increased overhead of context switching costs across virtual machines for some workloads. It is challenging to determine a single time slice to satisfy all the VMs. In this article, we propose to have dual time slice to resolve the time slice conflict problem occurred in different types of virtual machines.