Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

When a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order to minimize the number of calculations. The reduction in complexity is at least a constant and in the best case quadratic. We demonstrate that this method does indeed save significant computation time in a practical implementation, especially when the networks receives a large number of time-frames

Fractionally Predictive Spiking Neurons

Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional derivative, at least when signal variation induces neural adaptation. Here, we show that the actual neural spike-train itself can be considered as the fractional derivative, provided that the neural signal is approximated by a sum of power-law kernels. A simple standard thresholding spiking neuron suffices to carry out such an approximation, given a suitable refractory response. Empirically, we find that the online approximation of signals with a sum of power-law kernels is beneficial for encoding signals with slowly varying components, like long-memory self-similar signals. For such signals, the online power-law kernel approximation typically required less than half the number of spikes for similar SNR as compared to sums of similar but exponentially decaying kernels. As power-law kernels can be accurately approximated using sums or cascades of weighted exponentials, we demonstrate that the corresponding decoding of spike-trains by a receiving neuron allows for natural and transparent temporal signal filtering by tuning the weights of the decoding kernel.Comment: 13 pages, 5 figures, in Advances in Neural Information Processing 201

Seasonality in tourism: introducing golf as a touristic segment in order to prolong a destination’s touristic season. project of istria county in Croatia

The present study investigates the effect seasonality has on touristic destinations and whether introducing golf tourism in the touristic offer is the right solution for dealing with this effect. This study is an in- depth single case study, supported by qualitative research. The investigation was composed of literature review which encompassed the following theoretical concepts: seasonality effect, destination life cycle, image and branding, residential and niche tourism and MICE. The qualitative research was conducted by the researcher, through the use of in- depth electronic interviews in order to show why Istria is chosen to be the best golf destination in Croatia and what benefits golf would bring to Istrian tourism. The research showed that golf, accompanied by MICE, is one of the best solutions for minimizing the seasonality effect in tourism in Istria. By introducing new segments into touristic offer, off season stay would increase, and therefore, the difference between high and off season would be decreased. Furthermore, the brand of Istria as a golf destination would become recognizable and the image of Istrian tourism would be strengthened. In addition to above mentioned conclusions, the goal of this study was to show that by changing the destination's image and focusing on niche tourism, a destination could alter its path from Decline to Rejuvenation.O presente estudo tem como objetivo descrever o impacto da sazonalidade nos destinos turísticos e perceber se a introdução de turismo de golfe na oferta turística poderá ser parte da solução para lidar com esse mesmo efeito. Este estudo é suportado por uma pesquisa qualitativa e tem como objetivo analisar profundamente um exemplo específico. A investigação foi composta por uma revisão bibliográfica que abrangeu os seguintes conceitos teóricos: impacto da sazonalidade no turismo, ciclo de vida do destino turístico, imagem e branding, turismo de nicho, residencial e MICE. Para o efeito, foi realizada uma pesquisa qualitativa baseada em entrevistas eletrónicas com a finalidade de demonstrar o porquê da Ístria poder ser considerada como o melhor destino de golfe na Croácia e quais os benefícios que golfe traria para o turismo desta mesma zona. A pesquisa demonstrou que o golfe, juntamente com o MICE, é uma das melhores soluções para minimizar o efeito da sazonalidade no turismo na Ístria. Com a introdução de novos segmentos na oferta turística, as estadias durante a “época-baixa” aumentariam, fazendo com que a diferença entre as épocas baixa e alta não fosse tão relevante. Adicionalmente, a marca “Ístria” como destino preferencial de golfe seria reconhecida, levando, por sua vez, ao reforço generalizado do turismo nessa mesma zona. Além das conclusões acima mencionadas, outro objetivo deste estudo foi demonstrar que, mudando a imagem e colocando o foco no turismo de nicho, um destino poderia rejuvenescer, alterando, deste modo, a sua trajetória de declínio

Residential Self-Selection and Travel:

Most Western national governments aim to influence individual travel patterns – at least to some degree – through the spatial planning of residential areas. Nevertheless, the extent to which the characteristics of the built environment influence travel behaviour remains the subject of debate among travel behaviour researchers. This work addresses the role of residential-self-selection, an important issue within this debate. Households may not only adjust their travel behaviour to the built environment where they live, but they may also choose a residential location that corresponds to their travel-related attitudes. The empirical analysis in this work is based on data collected through an internet survey and a GPS-based survey, both of which were conducted among homeowners in three centrally located municipalities in the Netherlands. The study showed that residential self-selection has some limited effect on the relationship between distances to activity locations and travel mode use and daily kilometres travelled. The results also indicate that the inclusion of attitudes can help to detecting residential self-selection, provided that studies comply with several preconditions, such as the inclusion of the ‘reversed’ influence of behaviour on attitudes

Pricing options and computing implied volatilities using neural networks

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly

Efficient Computation in Adaptive Artificial Spiking Neural Networks

Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of communication. This contrasts sharply with biological neurons that communicate sparingly and efficiently using binary spikes. While artificial Spiking Neural Networks (SNNs) can be constructed by replacing the units of an ANN with spiking neurons, the current performance is far from that of deep ANNs on hard benchmarks and these SNNs use much higher firing rates compared to their biological counterparts, limiting their efficiency. Here we show how spiking neurons that employ an efficient form of neural coding can be used to construct SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on important benchmarks, while requiring much lower average firing rates. For this, we use spike-time coding based on the firing rate limiting adaptation phenomenon observed in biological spiking neurons. This phenomenon can be captured in adapting spiking neuron models, for which we derive the effective transfer function. Neural units in ANNs trained with this transfer function can be substituted directly with adaptive spiking neurons, and the resulting Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up to an order of magnitude fewer spikes compared to previous SNNs. Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention. AdSNNs thus hold promise as a novel and efficient model for neural computation that naturally fits to temporally continuous and asynchronous applications

Market-based Recommendation: Agents that Compete for Consumer Attention

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains