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摘要： First-job readiness versus long-term career preparation.

关键词： Engineering   Computer science  

摘要： Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.

关键词： Artificial intelligence   AI-generated output   Computer science   Medical documentation  

摘要： Purpose Chat generative pre-trained transformer (GPT) is a novel large pre-trained natural language processing software that can enable scientific writing amongst a litany of other features. Given this, there is a growing interest in exploring the use of ChatGPT models as a modality to facilitate/assist in the provision of clinical care. Methods We investigated the time taken for the composition of neurosurgical discharge summaries and operative reports at a major University hospital. In so doing, we compared currently employed speech recognition software (i.e., SpeaKING) vs novel ChatGPT for three distinct neurosurgical diseases: chronic subdural hematoma, spinal decompression, and craniotomy. Furthermore, factual correctness was analyzed for the abovementioned diseases. Results The composition of neurosurgical discharge summaries and operative reports with the assistance of ChatGPT leads to a statistically significant time reduction across all three diseases/report types: p < 0.001 for chronic subdural hematoma, p < 0.001 for decompression of spinal stenosis, and p < 0.001 for craniotomy and tumor resection. However, despite a high degree of factual correctness, the preparation of a surgical report for craniotomy proved to be significantly lower (p = 0.002). Conclusion ChatGPT assisted in the writing of discharge summaries and operative reports as evidenced by an impressive reduction in time spent as compared to standard speech recognition software. While promising, the optimal use cases and ethics of AI-generated medical writing remain to be fully elucidated and must be further explored in future studies.

关键词： Computer science   Information technology   Machine learning   Mathematics and computing  

摘要： A tragic scandal at the UK Post Office highlights the need for legal change, especially as organizations embrace artificial intelligence to enhance decision-making.

关键词： Machine learning   Computer science   Medical research   Society  

摘要： Researchers say their artificial-intelligence system could help to democratize medicine.

关键词： Computer science   Machine learning   Technology  

摘要： ‘Sleeper agents’ seem benign during testing but behave differently once deployed. And methods to stop them aren’t working.

关键词： Students  

摘要： This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students. Although a number of research studies in the computing education community have explored the possibility of using LLMs for a variety of tasks, there is a lack of comprehensive research comparing different LLMs and evaluating which LLMs are most effective for different tasks. Our research systematically assesses some of the publicly available LLMs such as Google Bard, ChatGPT(3.5), GitHub Copilot Chat, and Microsoft Copilot across diverse tasks commonly encountered by undergraduate computer science students in India. These tasks include code explanation and documentation, solving class assignments, technical interview preparation, learning new concepts and frameworks, and email writing. Evaluation for these tasks was carried out by pre-final year and final year undergraduate computer science students and provides insights into the models’ strengths and limitations. This study aims to guide students as well as instructors in selecting suitable LLMs for any specific task and offers valuable insights on how LLMs can be used constructively by students and instructors. Copyright © 2024, The Authors. All rights reserved.

关键词： Applied sciences   Computer science   Machine learning  

摘要： Low computational efficiency is one of the crucial challenges in electromagnetic forming process simulation. However, the huge gap in switching from the electromagnetic field to mechanics hinders the development of high-efficiency parallel coupling. A graphics processing unit (GPU)-based explicit-implicit coupling method is proposed to address the high-computational cost in simulations of the electromagnetic forming process, in which the large deformation (explicit) and electromagnetic field (implicit) are intimately coupled. The electromagnetic-mechanical coupling is achieved by constructing five parallel algorithms, which include the parallel operations of vector-vector, matrix-matrix, matrix-vector, vector assembly, and matrix-free assembly parallel preconditioned conjugate gradient algorithm. The high -efficiency GPU parallel computing is realized through the special data storage structure and parallel strategies. The explicit-implicit GPU method is validated against the experimental results, and several examples are presented for simulations of the electromagnetic forming process to illustrate both the accuracy and high-efficiency parallel acceleration performance of the present program.

关键词： Neuroscience   Computer science  

摘要： Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal av-alanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Further-more, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

关键词： Computer science   Mathematics and computing  

摘要： A branch of computer science known as genetic programming has been given a boost with the application of large language models that are trained on the combined intuition of the world's programmers.