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摘要： Background Staff shortages are a major concern in nursing homes. The residents are experiencing reduced care. Care is suffering and actions are needed to address the shortage of staff. Method Humanoid robots can be employed to address the shortages in staff. These robots can be performing tasks that are repetitive and can release the staff to perform other important tasks Result Humanoid robots can improve the care provision to the elderly and people affected with dementia. Conclusion Consider employing robots in nursing homes to improve quality of care in nursing homes.

关键词： Scientific community   Science   technology and society   Computer science   Computational science   Information technology  

摘要： The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a DANN and its robustness to noise and adversarial attacks is less explored, especially in computer vision applications. Here we investigate the relationship between the robustness of DANNs in a vision task and their underlying graph architectures or structures. First we explored the design space of architectures of DANNs using graph-theoretic robustness measures and transformed the graphs to DANN architectures using various image classification tasks. Then we explored the relationship between the robustness of trained DANNs against noise and adversarial attacks and their underlying architectures. We show that robustness performance of DANNs can be quantified before training using graph structural properties such as topological entropy and Olivier-Ricci curvature, with the greatest reliability for complex tasks and large DANNs. Our results can also be applied for tasks other than computer vision such as natural language processing and recommender systems.

摘要： International law has ruled out the application of the death penalty on drug offenses. Despite failure to meet the threshold to be categorised as one of the 'most serious crimes', the practice of capital punishment for drug offences in many parts of the world continues to dismay human rights activists and practitioners. This article aims to exhibit a dynamic view on drug offences under Vietnamese law. It contends that the nation's understanding of the severity of drug offences has witnessed incremental changes corresponding with international standards, although not completely compatible. Drawing on comparative scholarship, the article then moves on to ask whether there is any room left for the total abolition of capital punishment for drug offences. It suggests that Vietnam's incrementalism towards abolition has reached its bounds. Renovation in the understanding on the severity of drug crimes is very much needed to re-fuel the abolitionist movement in the country. Copyright © 2022 The Author(s).

关键词： Aerospace engineering   Computer science  

摘要： Control of aerodynamic forces in gusty, turbulent conditions is critical for the safety and performance of technologies such as unmanned aerial vehicles and wind turbines. The presence and severity of extreme flow conditions are difficult to predict, and explicit modeling of fluid dynamics for control is not feasible in real time. Model-free reinforcement learning methods present an end-to-end control solution for nonlinear systems as they require no prior knowledge, can easily integrate different types of measurements, and can adapt to varying conditions through interaction. Here, we show that reinforcement learning methods can achieve effective aerodynamic control in a highly turbulent environment. Algorithms are trained with different neural network structures, and we find that reinforcement learning agents with recurrent neural networks can effectively learn the nonlinear dynamics involved in turbulent flows and strongly outperform conventional linear control techniques. We also find that augmenting state observations with measurements from a set of bioinspired flow sensors can improve learning stability and control performance in aerodynamic systems. These results can serve to inform future gust mitigation systems for unmanned aerial vehicles and wind turbines, enabling operation in previously prohibitively dangerous conditions.

关键词： Computer science   Consolidation   Forgetting   Replay  

摘要： Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.

关键词： Careers   Computer science   Epidemiology  

摘要： An estimate of the deaths associated with COVID-19, and the lack of ethnic diversity in UK academia.

关键词： Computational science   Computer science  

摘要： This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum-a well studied baseline for nonlinear model identification and control with continuous states and control inputs-measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties.

关键词： Computer science   Information technology  

摘要： Knowledge extraction aims to identify entities and extract relations between them from unstructured text, which are in the form of triplets. Analysis of the fruit nutrition domain corpus revealed many overlapping triplets, that is, multiple correspondences between a subject and multiple objects or the same subject and object. The current relevant methods mainly target the extraction of ordinary triplets, which cannot accurately identify overlapping triplets. To solve this problem, a deep learning based model for overlapping triplet extraction is proposed in this study. The relation is modeled as a function that maps a subject to an object. The hybrid information of the subject is entered into the relation-object extraction model to detect the object and relation. The experimental results show this model outperforms existing extraction models and achieves state-of-the-art performance on the manually labeled fruit nutrition domain dataset. In terms of application value, the proposed work can obtain a high-quality and structured fruit nutrition knowledge base, which provides application fundamentals for downstream applications of nutrition matching.

关键词： Scientific data   Computer science  

摘要： The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

摘要： In recent years, many adult returning students go back to school for college degree and retooling. Many of them are struggling with computer literacy skills. In this study, we try to understand the computer related challenges that adult returning students are facing. © 2022, The Authors. All rights reserved.