Implementation of Smart Contracts Using Hybrid Architectures with On- and Off-Blockchain Components

Recently, decentralised (on-blockchain) platforms have emerged to complement centralised (off-blockchain) platforms for the implementation of automated, digital (smart) contracts. However, neither alternative can individually satisfy the requirements of a large class of applications. On-blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Off-blockchain platforms are afflicted by drawbacks due to their dependence on single trusted third parties. We argue that in several application areas, hybrid platforms composed from the integration of on- and off-blockchain platforms are more able to support smart contracts that deliver the desired quality of service (QoS). Hybrid architectures are largely unexplored. To help cover the gap, in this paper we discuss the implementation of smart contracts on hybrid architectures. As a proof of concept, we show how a smart contract can be split and executed partially on an off-blockchain contract compliance checker and partially on the Rinkeby Ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool.Comment: 12 pages, 7 figure

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Management of coronary disease in patients with advanced kidney disease

BACKGROUND Clinical trials that have assessed the effect of revascularization in patients with stable coronary disease have routinely excluded those with advanced chronic kidney disease. METHODS We randomly assigned 777 patients with advanced kidney disease and moderate or severe ischemia on stress testing to be treated with an initial invasive strategy consisting of coronary angiography and revascularization (if appropriate) added to medical therapy or an initial conservative strategy consisting of medical therapy alone and angiography reserved for those in whom medical therapy had failed. The primary outcome was a composite of death or nonfatal myocardial infarction. A key secondary outcome was a composite of death, nonfatal myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. RESULTS At a median follow-up of 2.2 years, a primary outcome event had occurred in 123 patients in the invasive-strategy group and in 129 patients in the conservative-strategy group (estimated 3-year event rate, 36.4% vs. 36.7%; adjusted hazard ratio, 1.01; 95% confidence interval [CI], 0.79 to 1.29; P=0.95). Results for the key secondary outcome were similar (38.5% vs. 39.7%; hazard ratio, 1.01; 95% CI, 0.79 to 1.29). The invasive strategy was associated with a higher incidence of stroke than the conservative strategy (hazard ratio, 3.76; 95% CI, 1.52 to 9.32; P=0.004) and with a higher incidence of death or initiation of dialysis (hazard ratio, 1.48; 95% CI, 1.04 to 2.11; P=0.03). CONCLUSIONS Among patients with stable coronary disease, advanced chronic kidney disease, and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of death or nonfatal myocardial infarction

Health status after invasive or conservative care in coronary and advanced kidney disease

BACKGROUND In the ISCHEMIA-CKD trial, the primary analysis showed no significant difference in the risk of death or myocardial infarction with initial angiography and revascularization plus guideline-based medical therapy (invasive strategy) as compared with guideline-based medical therapy alone (conservative strategy) in participants with stable ischemic heart disease, moderate or severe ischemia, and advanced chronic kidney disease (an estimated glomerular filtration rate of <30 ml per minute per 1.73 m2 or receipt of dialysis). A secondary objective of the trial was to assess angina-related health status. METHODS We assessed health status with the Seattle Angina Questionnaire (SAQ) before randomization and at 1.5, 3, and 6 months and every 6 months thereafter. The primary outcome of this analysis was the SAQ Summary score (ranging from 0 to 100, with higher scores indicating less frequent angina and better function and quality of life). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate the treatment effect with the invasive strategy. RESULTS Health status was assessed in 705 of 777 participants. Nearly half the participants (49%) had had no angina during the month before randomization. At 3 months, the estimated mean difference between the invasive-strategy group and the conservative-strategy group in the SAQ Summary score was 2.1 points (95% credible interval, 120.4 to 4.6), a result that favored the invasive strategy. The mean difference in score at 3 months was largest among participants with daily or weekly angina at baseline (10.1 points; 95% credible interval, 0.0 to 19.9), smaller among those with monthly angina at baseline (2.2 points; 95% credible interval, 122.0 to 6.2), and nearly absent among those without angina at baseline (0.6 points; 95% credible interval, 121.9 to 3.3). By 6 months, the between-group difference in the overall trial population was attenuated (0.5 points; 95% credible interval, 122.2 to 3.4). CONCLUSIONS Participants with stable ischemic heart disease, moderate or severe ischemia, and advanced chronic kidney disease did not have substantial or sustained benefits with regard to angina-related health status with an initially invasive strategy as compared with a conservative strategy