Combining Hebbian and reinforcement learning in a minibrain model

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of `path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate $\eta$) to the reinforcement term (proportional to $\rho$) in the learning rule. It is shown that the number of learning steps is reduced considerably if $1/4 < \eta/\rho < 1/2$, i.e., if the Hebbian term is neither too small nor too large compared to the reinforcement term

Deliverable 2 (SustainAQ)

The European Project SustainAQ (Framework 6) aims to identify the limiting factors for the sustainable production of aquatic origin food in Eastern Europe. It focuses on the possible use of Recirculation Aquaculture Systems (RAS) as sustainable method for the production of aquatic animals as mentioned in the communication of the European Commission on Aquaculture in 2009. RASs already exist mainly in western countries and proved economically feasible. RASs allow controlling the production process including effluents, biosecurity and escapes. Eastern European countries are facing challenges related to their excessive water use waste emission, and others. Therefore, these countries are potential beneficiaries of improved sustainability through RAS use. This project intends to assess the benefits of introducing and applying RAS for Eastern European aquaculture. This project involves three Western European countries (Norway, the Netherlands and France) and six East European countries (Croatia, Turkey, Romania, Hungary, Czech Republic and Poland). Ten research institutions collaborate in different tasks (coordination, data collection, data analysis, etc.), and nine small-medium enterprises (SME) participate in data mining (Table 1). The present data is therefore based on the situation in those countries during 2006 till 2008 before the report got finally compiled in 2008/2009

THE SCHOOL OF ATHENS: MOMENTS IN THE HISTORY OF AN IDEA

This article reflects on how ancient Athens - in its historical as well as metonymic sense — has been employed as an education for the world and for all time to come. In a broad sweep through history, it has little pretention to be either a disinterested or an in-depth historical enquiry. Rather, it presents yet another attempt to come to terms with the current position of the Classics in academia, taking its cue from the saying of Confucius that ‘one who understands the present by reviewing antiquity is worthy to be a teacher'.Simultaneously, it aims to remind us, albeit obliquely, of aspects of a humanities education which are currently neglected or perhaps even forgotten. It will be shown that Thucydides already connected the idea of Athens as a school to democratic ideology, a link still present in later associations between the liberal arts and a classical education

A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.Comment: Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin, German

A spectral theory approach for extreme value analysis in a tandem of fluid queues

We consider a model to evaluate performance of streaming media over an unreliable network. Our model consists of a tandem of two fluid queues. The first fluid queue is a Markov modulated fluid queue that models the network congestion, and the second queue represents the play-out buffer. For this model the distribution of the total amount of fluid in the congestion and play-out buffer corresponds to the distribution of the maximum attained level of the first buffer. We show that, under proper scaling and when we let time go to infinity, the distribution of the total amount of fluid converges to a Gumbel extreme value distribution. From this result, we derive a simple closed-form expression for the initial play-out buffer level that provides a probabilistic guarantee for undisturbed play-out

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

The significance of indirect costs—application to clinical laboratory test economics using computer facilities

The significance of indirect costs in the cost price calculation of clinical chemistry laboratory tests by way of the production centres method has been investigated. A cost structure model based on the ‘production centres’ method, the Academisch Ziekenhuis Groningen (AZG) 1-2-3 model, is used for the calculation of cost and cost prices as an add-in tool to the spreadsheet program Lotus 1-2-3. The system specifications of the AZG 1-2-3 cost structure model have been extended with facilities to impute all relevant indirect costs to cost centres by aid of allocation rules, which can be chosen freely. The inference is made that as indirect costs play a more important part in decision-making processes concerning planning and control, the specification of the relation to the cost centres should be determined in a more detailed way. The AZG 1-2-3 cost structure model has therefore been extended in order to increase the significance as a management tool for laboratory management

Let Me Vote! An experimental study of vote rotation in committees

We conduct an experiment to investigate (i) whether rotation in voting increases a committee’s efficiency, and (ii) the extent to which rotation is likely to critically influence collective and individual welfare. The experiment is based on the idea that voters have to trade-off individual versus common interests. Our findings indicate that the choice of a rotation scheme has important consequences: it ‘pays’ to be allowed to vote, as voting committee members earn significantly more than non-voting members. Hence, rotation is not neutral. We also find that smaller committees decide faster and block fewer decisions. This reduces frustration among committee members