Ferroelectric and dielectric characterization studies on relaxor- and ferroelectric-like strontium-barium niobates

Ferroelectric domain structure evolution induced by an external electric field was investigated by means of nematic liquid crystal (NLC) method in two strontium-barium niobate single crystals of nominal composition: Sr_{0.70}Ba_{0.30}Nb_{2}O_{6} (SBN:70 - relaxor) and Sr_{0.26}Ba_{0.74}Nb_{2}O_{6} (SBN:26 - ferroelectric). Our results provide evidence that the broad phase transition and frequency dispersion that are exhibited in SBN:70 crystal have a strong link to the configuration of ferroelectric microdomains. The large leakage current revealed in SBN:26 may compensate internal charges acting as pinning centers for domain walls, which gives rise to a less restricted domain growth similar to that observed in classical ferroelectrics. Microscale studies of a switching process in conjunction with electrical measurements allowed us to establish a relationship between local properties of the domain dynamics and macroscopic response i.e., polarization hysteresis loop and dielectric properties.Comment: 10 pages, 7 figure

Fredkin Gates for Finite-valued Reversible and Conservative Logics

The basic principles and results of Conservative Logic introduced by Fredkin and Toffoli on the basis of a seminal paper of Landauer are extended to d-valued logics, with a special attention to three-valued logics. Different approaches to d-valued logics are examined in order to determine some possible universal sets of logic primitives. In particular, we consider the typical connectives of Lukasiewicz and Godel logics, as well as Chang's MV-algebras. As a result, some possible three-valued and d-valued universal gates are described which realize a functionally complete set of fundamental connectives.Comment: 57 pages, 10 figures, 16 tables, 2 diagram

“Stickier” learning through gameplay: an effective approach to climate change education

As the impacts of climate change grow, we need better ways to raise awareness and motivate action. Here we assess the effectiveness of an Arctic climate change card game in comparison with the more conventional approach of reading an illustrated article. In-person assessments with control/reading and treatment/game groups (N = 41), were followed four weeks later with a survey. The game was found to be as effective as the article in teaching content of the impacts of climate change over the short term, and was more effective than the article in long-term retention of new information. Game players also had higher levels of engagement and perceptions that they knew ways to help protect Arctic ecosystems. They were also more likely to recommend the game to friends or family than those in the control group were likely to recommend the article to friends or family. As we consider ways to broaden engagement with climate change, we should include games in our portfolio of approaches

Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

\ua9 2022 IEEE. Electronic health records (EHR) represent a holistic overview of patients\u27 trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures. This can result in missing crucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a hierarchical Transformer-based model that can significantly expand the receptive field of Transformers and extract associations from much longer sequences. Using a multimodal large-scale linked longitudinal EHR, the Hi-BEHRT exceeds the state-of-the-art deep learning models 1% to 5% for area under the receiver operating characteristic (AUROC) curve and 1% to 8% for area under the precision recall (AUPRC) curve on average, and 2% to 8% (AUROC) and 2% to 11% (AUPRC) for patients with long medical history for 5-year heart failure, diabetes, chronic kidney disease, and stroke risk prediction. Additionally, because pretraining for hierarchical Transformer is not well-established, we provide an effective end-to-end contrastive pre-training strategy for Hi-BEHRT using EHR, improving its transferability on predicting clinical events with relatively small training dataset

Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts

\ua9 2022 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. Aims: Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models. Methods and results: Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated three established statistical models for predicting 5-year risk of incident heart failure, stroke, and coronary heart disease. The results were compared with a widely accepted machine learning model (random forests), and a novel deep learning model (BEHRT). In addition to internal validation, we investigated how data shifts affect model discrimination and calibration. To this end, we tested the models on cohorts from (i) distinct geographical regions; (ii) different periods. Using internal validation, the deep learning models substantially outperformed the best statistical models by 6%, 8%, and 11% in heart failure, stroke, and coronary heart disease, respectively, in terms of the area under the receiver operating characteristic curve. Conclusion: The performance of all models declined as a result of data shifts; despite this, the deep learning models maintained the best performance in all risk prediction tasks. Updating the model with the latest information can improve discrimination but if the prior distribution changes, the model may remain miscalibrated