Task-driven Prompt Evolution for Foundation Models

Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segmentation. However, many evaluation studies have found that their performance on medical imaging modalities to be underwhelming compared to conventional deep learning methods. In the world of large pre-trained language and vision-language models, learning prompt from downstream tasks has achieved considerable success in improving performance. In this work, we propose a plug-and-play Prompt Optimization Technique for foundation models like SAM (SAMPOT) that utilizes the downstream segmentation task to optimize the human-provided prompt to obtain improved performance. We demonstrate the utility of SAMPOT on lung segmentation in chest X-ray images and obtain an improvement on a significant number of cases ($\sim75\%$) over human-provided initial prompts. We hope this work will lead to further investigations in the nascent field of automatic visual prompt-tuning

ZZ and the Art of Practical BFT Execution

The high replication cost of Byzantine fault-tolerance (BFT) methods has been a major barrier to their widespread adoption in commercial distributed applications. We present ZZ, a new approach that reduces the replication cost of BFT services from 2f + 1 to practically f + 1. The key insight in ZZ is to use f + 1 execution replicas in the normal case and to activate additional replicas only upon failures. In data centers where multiple applications share a physical server, ZZ reduces the aggregate number of execution replicas running in the data center, improving throughput and response times. ZZ relies on virtualization—a technology already employed in modern data centers—for fast replica activation upon failures, and enables newly activated replicas to immediately begin processing requests by fetching state on-demand. A prototype implementation of ZZ using the BASE library and Xen shows that, when compared to a system with 2f + 1 replicas, our approach yields lower response times and up to 33 % higher throughput in a prototype data center with four BFT web applications. We also show that ZZ can handle simultaneous failures and achieve sub-second recovery

Semantic code search via equational reasoning

© 2020 Owner/Author. We present a new approach to semantic code search based on equational reasoning, and the Yogo tool implementing this approach. Our approach works by considering not only the dataflow graph of a function, but also the dataflow graphs of all equivalent functions reachable via a set of rewrite rules. In doing so, it can recognize an operation even if it uses alternate APIs, is in a different but mathematically-equivalent form, is split apart with temporary variables, or is interleaved with other code. Furthermore, it can recognize when code is an instance of some higher-level concept such as iterating through a file. Because of this, from a single query, Yogo can find equivalent code in multiple languages. Our evaluation further shows the utility of Yogo beyond code search: encoding a buggy pattern as a Yogo query, we found a bug in Oracle's Graal compiler which had been missed by a hand-written static analyzer designed for that exact kind of bug. Yogo is built on the Cubix multi-language infrastructure, and currently supports Java and Python