Swedish distance higher education development

Discussions regarding a Swedish Open University lead to the following solutions for different educational levels. The responsibility for carrying out distance education rested with the individual university departments, which at the same time organized traditional forms of university education. The National Broadcasting Company got a special assignment to arrange distance education courses for popular education. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2943

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure

Learning to Learn from Weak Supervision by Full Supervision

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.Comment: Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA, US

Cross-comparison of cardiac output trending accuracy of LiDCO, PiCCO, FloTrac and pulmonary artery catheters

Introduction: Although less invasive than pulmonary artery catheters (PACs), arterial pulse pressure analysis techniques for estimating cardiac output (CO) have not been simultaneously compared to PAC bolus thermodilution CO (COtd) or continuous CO (CCO) devices.Methods: We compared the accuracy, bias and trending ability of LiDCO™, PiCCO™ and FloTrac™ with PACs (COtd, CCO) to simultaneously track CO in a prospective observational study in 17 postoperative cardiac surgery patients for the first 4 hours following intensive care unit admission. Fifty-five paired simultaneous quadruple CO measurements were made before and after therapeutic interventions (volume, vasopressor/dilator, and inotrope).Results: Mean CO values for PAC, LiDCO, PiCCO and FloTrac were similar (5.6 ± 1.5, 5.4 ± 1.6, 5.4 ± 1.5 and 6.1 ± 1.9 L/min, respectively). The mean CO bias by each paired method was -0.18 (PAC-LiDCO), 0.24 (PAC-PiCCO), -0.43 (PAC-FloTrac), 0.06 (LiDCO-PiCCO), -0.63 (LiDCO-FloTrac) and -0.67 L/min (PiCCO-FloTrac), with limits of agreement (1.96 standard deviation, 95% confidence interval) of ± 1.56, ± 2.22, ± 3.37, ± 2.03, ± 2.97 and ± 3.44 L/min, respectively. The instantaneous directional changes between any paired CO measurements displayed 74% (PAC-LiDCO), 72% (PAC-PiCCO), 59% (PAC-FloTrac), 70% (LiDCO-PiCCO), 71% (LiDCO-FloTrac) and 63% (PiCCO-FloTrac) concordance, but poor correlation (r2 = 0.36, 0.11, 0.08, 0.20, 0.23 and 0.11, respectively). For mean CO < 5 L/min measured by each paired devices, the bias decreased slightly.Conclusions: Although PAC (COTD/CCO), FloTrac, LiDCO and PiCCO display similar mean CO values, they often trend differently in response to therapy and show different interdevice agreement. In the clinically relevant low CO range (< 5 L/min), agreement improved slightly. Thus, utility and validation studies using only one CO device may potentially not be extrapolated to equivalency of using another similar device. © 2010 Hadian et al.; licensee BioMed Central Ltd