In this paper, we focus on an often-overlooked component of serverless application cold starts: monetary costs and Function Initialization.Traditionally considered the user's responsibility, Function Initialization is billable and accounts for more than 50% of the monetary cost associated with cold starts in real-world machine-learning applications.We introduce 𝜆-trim, a system that optimizes Python serverless applications by eliminating redundant code while maintaining correctness.To maximize cost savings, 𝜆-trim leverages the typical serverless pricing model to prioritize modules that significantly impact latency and memory usage.𝜆-trim features an automated pipeline comprising a static analyzer, a profiler specialized for the serverless pricing model, and a debloater.The optimized application can be directly deployed on serverless platforms, leading to substantial reductions in both latency and cost for cold starts.

Cloud operators utilize collective communication optimizers to enhance the efficiency of the single-tenant, centrally managed training clusters they manage. However, current optimizers struggle to scale for such use cases and often compromise solution quality for scalability. Our solution, TE-CCL, adopts a traffic-engineering-based approach to collective communication. Compared to a state-of-the-art optimizer, TACCL, TE-CCL produced schedules with 2× better performance on topologies TACCL supports (and its solver took a similar amount of time as TACCL's heuristic-based approach). TECCL additionally scales to larger topologies than TACCL. On our GPU testbed, TE-CCL outperformed TACCL by 2.14× and RCCL by 3.18× in terms of algorithm bandwidth.

A recent survey claims that there are \em no general linguistic cues for deception. Since Internet societies are plagued with deceptive attacks such as phishing and fake news, this claim means that we must build individual datasets and detectors for each kind of attack. It also implies that when a new scam (e.g., Covid) arrives, we must start the whole process of data collection, annotation, and model building from scratch. In this paper, we put this claim to the test by building a quality domain-independent deception dataset and investigating whether a model can perform well on more than one form of deception.