Trends in Outcomes for Neonates Born Very Preterm and Very Low Birth Weight in 11 High-Income Countries

Objective To evaluate outcome trends of neonates born very preterm in 11 high-income countries participating in the International Network for Evaluating Outcomes of neonates. Study design In a retrospective cohort study, we included 154 233 neonates admitted to 529 neonatal units between January 1, 2007, and December 31, 2015, at 24(0/7) to 31(6/7) weeks of gestational age and birth weight Results For composite outcome including BPD, the trend decreased in Canada and Israel but increased in Australia and New Zealand, Japan, Spain, Sweden, and the United Kingdom. For composite outcome excluding BPD, the trend decreased in all countries except Spain, Sweden, Tuscany, and the United Kingdom. The risk of composite outcome was lower in epoch 2 than epoch 1 in Canada (adjusted relative risks 0.78; 95% CI 0.74-0.82) only. The risk of composite outcome excluding BPD was significantly lower in epoch 2 compared with epoch 1 in Australia and New Zealand, Canada, Finland, Japan, and Switzerland. Mortality rates reduced in most countries in epoch 2. BPD rates increased significantly in all countries except Canada, Israel, Finland, and Tuscany. Conclusions In most countries, mortality decreased whereas BPD increased for neonates born very preterm.Peer reviewe

Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios

Multiangle social network recommendation algorithms and similarity network evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels

UK Science Festival Network Pilot Evaluation 2017

This report describes the UK Science Festival Network Pilot Evaluation 2017, exploring the feasibility of ongoing evaluations for science festivals. The pilot evaluation included three festivals from across the UK; Nottingham Festival of Science and Curiosity, Northern Ireland Science Festival and Bath Taps into Science

Asymptotics of Relativistic Spin Networks

The stationary phase technique is used to calculate asymptotic formulae for SO(4) Relativistic Spin Networks. For the tetrahedral spin network this gives the square of the Ponzano-Regge asymptotic formula for the SU(2) 6j symbol. For the 4-simplex (10j-symbol) the asymptotic formula is compared with numerical calculations of the Spin Network evaluation. Finally we discuss the asymptotics of the SO(3,1) 10j-symbol.Comment: 31 pages, latex. v3: minor clarification

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020