Am Rsim
Am Rsim
28 февр. 2025 г.
rSIM is the world's first truly resilient and intelligent SIM card. It is device agnostic and constantly monitors connectivity. If the connection is lost, rSIM uses patented technology to actively switch profiles to maximise uptime and minimise downtime.
1 сент. 2025 г.
CSL's world's first patented rSIM is a game-changer for IoT connectivity. Designed for life-critical, business-critical, and mission-critical applications, it provides always-on, intelligent resilience — switching autonomously between network profiles to keep your devices connected, even when others go offline. rSIM is trusted across key sectors including Telecare, PoS, Lone Worker, CCTV ...
20 февр. 2024 г.
30 сент. 2024 г.
6 янв. 2025 г.
rSIM The World's First Intelligent SIM Brand new to the market - we are now offering a SIM that maintains data connectivity whenever there is a loss of connection. It actively switches profile to guarantee it is always on and never off.
26 нояб. 2024 г.
Discover how to purchase rSIM. Ideal for operators, OEMs, and resellers looking for resilient and flexible SIM options.
write description Home The AMRSim research team has developed a training intervention designed to change perception of the risk of bacterial contamination and, ultimately, related behaviours. It comprises a digital tool, educational content, and an interactive mode of presentation and delivery.
Ziyi Shou, Fangzhen Lin. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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The intervention provided short-term knowledge gain in IPC protocols and enhanced procedural skills via active learning and motivation to learn in large groups of students. Future improvements would be to include large animal clinical scenario discussions and evaluate longer-term knowledge gain.
Code for our paper "Evaluate AMR Graph Similarity via Self-supervised Learning" in ACL 2023 - AMRSim/README.md at main · zzshou/AMRSim
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Code for our paper "Evaluate AMR Graph Similarity via Self-supervised Learning" in ACL 2023 - zzshou/AMRSim
An Abstract Meaning Representation (AMR;Ba- narescu et al.,2013) is a rooted, directed graph where nodes represent concepts and edges corre- spond to relations of concepts. The goal of an AMR metric is to evaluate the similarities of pairs of AMR graphs so that it can be used to evaluate the outputs of AMR generators such as parsers. Therefore, a good AMR metric is crucial for the design and ...
PDF | Adaptive mesh refinement is complicated by both the algorithms and the dynamic nature of the computations. In parallel the complexity of getting... | Find, read and cite all the research you ...
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ABSTRACT Adaptive mesh refinement is complicated by both the al- gorithms and the dynamic nature of the computations. In parallel the complexity of getting good performance is de- pendent upon the architecture and the application. Most attempts to address the complexity of AMR have lead to the development of library solutions, most have developed object-oriented libraries or frameworks. All ...
This studentship aims to determine if the Antimicrobial Resistance Simulator tool (AMRSim | Welcome) can be used to change the behaviour of veterinary staff towards infection control that subsequently results in reduced infection rates and therefore improve animal health and welfare. Applications ...
The source code for ARMSim# is currently available on request under the terms of the Gnu Public License (GPL). We plan to publish the code via GitHub in the near future. Bug reports can be emailed to Nigel Horspool (nigelh AT uvic.ca). Please attach the source code file (s) which exhibit the problem and describe the problem. If appropriate, also list the steps needed to reproduce the problem ...
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write description Outputs Wales, A. (2018). Research adds a new dimension to general practice, providing stimulus and variety. Veterinary Record (Viewpoint article ...
Alberto Soto, Software Engineer at Siemens PLM Software, explains how Simcenter Amesim helps you analyze the start-up of an air cycle machine and optimize th...
Code for our paper "Evaluate AMR Graph Similarity via Self-supervised Learning" in ACL 2023 - AMRSim/requirements.txt at main · zzshou/AMRSim
Code for our paper "Evaluate AMR Graph Similarity via Self-supervised Learning" in ACL 2023 - zzshou/AMRSim
This work proposes AMRSim, an automatic AMR graph similarity evaluation metric that automatically generates silver AMR graphs and utilizes self-supervised learning methods to overcome the high cost of collecting human-annotated data. In work on AMR (Abstract Meaning Representation), similarity metrics are crucial as they are used to evaluate AMR systems such as AMR parsers. Current AMR metrics ...
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Antimicrobial-resistant bacteria are an established and growing issue in small animal veterinary practice in the developed world. The problem is, people can't see the bacteria on them, on animals, or on the surfaces and objects they touch. This makes it difficult to prevent and control infection in the most effective manner, as habits and standard practice are hard to change if you don't know ...
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AbstractBackground and Objective. Antimicrobial resistance (AMR) is a global health challenge driven by inappropriate prescribing. Antimicrobial stewardshi
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In work on AMR (Abstract Meaning Representation), similarity metrics are crucial as they are used to evaluate AMR systems such as AMR parsers. Current AMR metrics are all based on nodes or triples matching without considering the entire structures of AMR graphs. To address this problem, and inspired by learned similarity evaluation on plain text, we propose AMRSim, an automatic AMR graph ...
Abstract Purpose Simulation-based medical education has changed the teaching of clinical practice skills, with scenario-based simulations being particularly effective in supporting learning in veterinary medicine. In this study, we explore the efficacy of simulation education to teach infection prevention and control (IPC) as part of Antimicrobial Stewardship (AMS) teaching for early years ...
@inproceedings{shou-lin-2023-evaluate, title = "Evaluate {AMR} Graph Similarity via Self-supervised Learning", author = "Shou, Ziyi and Lin, Fangzhen", editor ...
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