Recommendation systems represent one of the largest machine learning applications on the planet -- industry-scale recommendation models are trained with petabytes of data and serve billions of users every day. To utilize the rich user signals in the long user history, these models have been scaled up to unprecedented complexity, up to trillions of floating-point operations (TFLOPs) per example. This scale, coupled with the huge amount of training data, necessitates new storage and training algorithms to efficiently improve the quality of these complex recommendation systems.

Abstract Null dereference is a common type of runtime failure in Java programs, and it is necessary to verify whether a dereference in the program is safe. However, previous works often have redundant path exploration and high false positive rate. In this paper, we propose a merged null dereference verification (MNDV) approach. MNDV employs a backward, path‐sensitive inter‐procedural analysis technique to verify a given dereference as safe or potentially unsafe. It uses a branch merging strategy to remove redundant paths, and a method call's relevance to the null references is checked to determine whether it is necessary to explore the internal codes of the method. We have evaluated the approach in some standard benchmark programs. Compared with some existing approaches, our approach reduces false alarm rate and effectively reduce time and memory consumption.

Humans have difficulty evaluating the effects of uncertainty on schedules. People often mitigate the effects of uncertainty by adding slack based on experience and non-stochastic analyses such as the critical path method (CPM). This is costly as it leads to longer than necessary schedules, and can be ineffective without a clear understanding of where slack is needed. COMPASS is an interactive real-time tool that analyzes schedule uncertainty for a stochastic task network. An important feature is that it concurrently calculates stochastic critical paths and critical tasks. COMPASS visualizes this information on top of a traditional Gantt view, giving users insight into how delays caused by uncertain durations propagate down the schedule. Evaluations with 10 users show that users can use COMPASS to answer a variety of questions about the possible evolutions of a schedule (e.g., what is the likelihood that all activities will complete before a given date?)