Abstract Modern distributed data management systems face a new challenge: how can autonomous, mutually distrusting parties cooperate safely and effectively? Addressing this challenge brings up familiar questions from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to synchronize concurrent access to data. Nevertheless, each of these issues requires rethinking when participants are autonomous and potentially adversarial. We propose the notion of a cross-chain deal, a new way to structure complex distributed computations that manage assets in an adversarial setting. Deals are inspired by classical atomic transactions, but are necessarily different, in important ways, to accommodate the decentralized and untrusting nature of the exchange. We describe novel safety and liveness properties, along with two alternative protocols for implementing cross-chain deals in a system of independent blockchain ledgers. One protocol, based on synchronous communication, is fully decentralized, while the other, based on semi-synchronous communication, requires a globally shared ledger. We also prove that some degree of centralization is required in the semi-synchronous communication model.

Box 1-An LLM suggests 10 simple rules governing its own usage in scienceHere, we ask ChatGPT to generate a list of 10 simple rules for using LLMs in science in order to illustrate its ability to provide detailed, human-like responses to short prompts.Q1: Can you provide me with a list of ten simple rules for using large language models in science?R1 (ChatGPT 3.5): Certainly!Here are ten simple rules for using large language models in science:1. Understand the limitations: Recognize that large language models like GPT-3 are powerful but not infallible.They may produce accurate-sounding information, but they can also generate false or biased content.Exercise caution and critical thinking.2. Validate and verify: Always cross-reference information generated by language models with reliable sources.Verify the accuracy of claims, check for

Once undertaken primarily by museum professionals, the activity of curatorship has been popularized via the Web. Social media tools, such as YouTube playlists and Pinterest Web bulletin boards, enable users to curate a diverse range of materials for personal use and for broader publication. But what makes one set of "curated" items more interesting than another? In this paper, we show how findings from an initial humanistic inquiry led to a lab-based user experiment, and how combined insights from these studies have illuminated new research streams in both humanistic and design research modes.