Uncertainty in diffusion of competing technologies and application to electric vehicles

The diffusion of innovations is an important process and its models have applications in many fields, with particular relevance in technological forecast. The logistic equation is one of most important models in this context. Extensions of this approach as the Lotka-Volterra model have been developed to include the effect of mutual influences between technologies such as competition. However, many of the parameters entering this description are uncertain, difficult to estimate or simply unknown, particularly at early stages of the diffusion. Here, a systematic way to study the effect of uncertain or unknown parameters on the future diffusion of interacting innovations is proposed. The input required is a general qualitative understanding of the system: is the mutual influence positive or negative and does it apply symmetrically to either technology? Since the parameters enter the problem via a set of coupled non-linear differential equations, the approach proposed here goes beyond simple Monte-Carlo-like methods where the result is an explicit function of the parameters. The methodology is developed in detail and applied the case of three types of upcoming electric vehicle propulsion technologies. The findings indicate that competition between electric vehicles and mild hybrid vehicles implies a slow decline of the latter. The approach can easily be generalised to include other initial conditions, more technologies or other technological areas to find stable results for future market evolution independent of specific parameters. --diffusion of innovations,logistic equation,competition,electric vehicles,Monte Carlo methods

Neural Nearest Neighbors Networks

Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r.t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of KNN selection that maintains differentiability w.r.t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification as well as for image restoration, including image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at https://github.com/visinf/n3net

Surfactant therapy and extracorporeal life support

I read with great interest the article by Hermon and coworkers [1], who reported that surfactant application in children with severe respiratory failure treated with extracorporeal membrane oxygenation was associated with improved lung volume and pulmonary mechanics. Although these findings must be confirmed in prospective studies, they are very promising. Based on a series of animal studies, more than 10 years ago we advocated use of a combination of surfactant therapy and extracorporeal life support in the treatment of severe respiratory failure. We found that one effective option is to combine intratracheal instillation of a large fluid volume with diluted surfactant and LFV-ECCO2R (low frequency ventilation and extracorporeal carbon dioxide removal). In animal studies using 141 Ce-labelled microspheres mixed with the surfactant [2,3], we observed that, followin

Gender gap developments in tertiary education : a cross-country time-series analysis on European level

During recent decades, a reversal of the gender gap in tertiary enrollment and a subsequent growing gap in favor of women could be observed in most industrialized countries. This dissertation shows developments of the female-male gender gap in tertiary educational enrollment and analyzes factors behind the widening female-male gap with time-series data on European level. The analysis is based on a model of educational investment, which suggests that gender differences in benefits and costs of tertiary education help to explain gender gaps in tertiary educational investment. Using a first difference model to ensure stationarity, we find that only gender differences in cognitive and non-cognitive skills, as measured by PISA scores in levels and standard deviations, significantly correlate with the gender gap in tertiary educational enrollment. We further find significant differences across time and country subgroup. Whether levels or the dispersion of cognitive and non-cognitive skills have explanatory power varies with country subregions and with the type of the PISA score used (Math or Reading).Recentemente, na maioria dos países industrializados, verifica-se uma inversão da diferença de genéro nas matrículas no ensino superior e, subsequentemente, um crescimento da diferença em favor das mulheres. Esta tese expõe tendências recentes das diferenças de género mulher-homem no acesso ao ensino superior. Analisa ainda os factores que levam ao aumento das diferenças de género com dados de séries temporais de países europeus. A análise é baseada no modelo básico de investimento educativo que sugere que diferenças de género em benefícios e custos do ensino superior podem ajudar a explicar a evolução no investimento feito no ensino superior. Para garantir estacionaridade, usamos um modelo em primeiras diferenças e concluímos que apenas as diferenças em competências cognitivas e não-cognitivas, medidas pelas classificações de leitura do PISA (níveis ou dispersão), se correlacionam significativamente com as diferenças de género no número de matrículas no ensino superior. Este resultado varia com o tempo e subgrupo de países. O poder explanatório dos níveis ou da dispersão de competências cognitivas e não-cognitivas varia consoante as sub-regiões dos países e a classificação das disciplinas do teste PISA

Antarctic marine mammals and ocean acoustics

Marine mammals rely on sound and hearing as their primary means of communication and sensing their world. Concerns that anthropogenic sound in the ocean could infer their sensing, cause stress or even damage their hearing physically rose a controversial discussion and triggered a worldwide boost in marine bioacoustic research. Innovative acoustic technologies and field methods are required to provide a basis for carefully designed and technically challenging research projects on free-ranging marine mammals, especially under the harsh environmental conditions of polar regions. The Ocean Acoustics group within the Marine Observing Systems section endeavors multidisciplinary research of environmental scientists, geophysicists, oceanographers, physicists, physiologists, and biologists to investigate the need and scope of mitigation measures for the effects of man-generated sound in the ocean, develop acoustic census techniques, explore marine mammal responses to various anthropogenic sounds, and study the vocal behaviour and hearing physiology of Antarctic marine mammals