SNP analysis reveals an evolutionary acceleration of the human-specific microRNAs

MicroRNAs are one class of important gene regulators at the post-transcriptional level by binding to the 3’UTRs of target mRNAs. It has been reported that human microRNAs are evolutionary conserved and show lower single nucleotide polymorphisms (SNPs) than their flanking regions. However, in this study, we report that the human-specific microRNAs show a higher SNP density than both the conserved microRNAs and other control regions, suggesting rapid evolution and positive selection has occurred in these regions. Furthermore, we observe that the human-specific microRNAs show greater SNPs minor allele frequency and the SNPs in the human-specific microRNAs show fewer effects on the stability of the microRNA secondary structure, indicating that the SNPs in the human-specific microRNAs tend to be less deleterious. Finally, two microRNAs hsa-mir-423 (SNP: rs6505162), hsa-mir-608 (SNP: rs4919510) and 288 target genes that have apparently been under recent positive selection are identified. These findings will improve our understanding of the functions, evolution, and population disease susceptibility of human microRNAs

Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects

Many sets of ethics principles for responsible AI have been proposed to allay concerns about misuse and abuse of AI/ML systems. The underlying aspects of such sets of principles include privacy, accuracy, fairness, robustness, explainability, and transparency. However, there are potential tensions between these aspects that pose difficulties for AI/ML developers seeking to follow these principles. For example, increasing the accuracy of an AI/ML system may reduce its explainability. As part of the ongoing effort to operationalise the principles into practice, in this work we compile and discuss a catalogue of 10 notable tensions, trade-offs and other interactions between the underlying aspects. We primarily focus on two-sided interactions, drawing on support spread across a diverse literature. This catalogue can be helpful in raising awareness of the possible interactions between aspects of ethics principles, as well as facilitating well-supported judgements by the designers and developers of AI/ML systems

New approach to improve the performance of fringe pattern profilometry using multiple triangular patterns for the measurement of objects in motion

Fringe pattern profilometry using triangular patterns and intensity ratios is a robust and computationally efficient method in three-dimensional shape measurement technique. However, similar to other multiple-shot techniques, the object must be kept static during the process of measurement, which is a challenging requirement for the case of fast-moving objects. Errors will be introduced if the traditional multiple-shot techniques are used directly in the measurement of a moving object. A new method is proposed to address this issue. First, the movement of the object is measured in real time and described by the rotation matrix and translation vector. Then, the expressions are derived for the fringe patterns under the influence of the two-dimensional movement of the object, based on which the normalized fringe patterns from the object without movement are estimated. Finally, the object is reconstructed using the existing intensity ratio algorithm incorporating the fringe patterns estimated, leading to improved measurement accuracy. The performance of the proposed method is verified by experiments

Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design

Decentralised Governance for Foundation Model based AI Systems: Exploring the Role of Blockchain in Responsible AI

Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks. Nevertheless, people are concerned about whether foundation model based AI systems are properly governed to ensure trustworthiness of foundation model based AI systems and to prevent misuse that could harm humans, society and the environment. In this paper, we identify eight governance challenges of foundation model based AI systems regarding the three fundamental dimensions of governance: decision rights, incentives, and accountability. Furthermore, we explore the potential of blockchain as a solution to address the challenges by providing a distributed ledger to facilitate decentralised governance. We present an architecture that demonstrates how blockchain can be leveraged to realise governance in foundation model based AI systems