793 research outputs found
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
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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
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