We describe F, a tool for automatic evaluation of machine translation output based on ngram precision and recall. The tool calculates the F-score averaged on all n-grams of an arbitrary set of distinct units such as words, morphemes,  tags, etc. The arithmetic mean is used for n-gram averaging. As input, the tool requires reference translation(s) and hypothesis, both containing the same c...

Combined monostatic clutter (MSC) and terrain scattered interference (TSI) pose a diicult challenge for adaptive radar processing. Mitigation techniques exist for each interference alone but are insuucient for their combined effects. Current approaches separate the problem into two stages where TSI is suppressed rst and then MSC. The problem with this cascade approach is that during the initial...

In this paper, a two stages method to determine suitable benchmark and return scale of the decision making units set is presented. At first, all of the efficient reference set in no radial data envelopment analysis (DEA) based on linear programming is found. first, RAM model is introduced and units is investigated using this model, then, to run the given algorithm below steps is performed. At t...

In this paper, a feedforward neural network with sigmoid hidden units is used to design a neural network based iterative learning controller for nonlinear systems with state dependent input gains. No prior offline training phase is necessary, and only a single neural network is employed. All the weights of the neurons are tuned during the iteration process in order to achieve the desired learni...

We consider deployment of the particle filter on modern massively parallel hardware architectures, such as Graphics Processing Units (GPUs), with a focus on the resampling stage. While standard multinomial and stratified resamplers require a sum of importance weights computed collectively between threads, a Metropolis resampler favourably requires only pair-wise ratios between weights, computed...

A detailed compartmental model of a cortical pyramidal cell is used to determine the effect of the spatial distribution of synapses across the dendritic tree on the pattern recognition capability of the neu-ron. By setting synaptic strengths according to the clipped Hebbian learning rule used in the associative net neural network model, the cell is able to recognise input patterns, but with a o...

The full ranking or complete ranking of decision making units is one of the main ‎issues in data envelopment analysis. A full ranking is a ranking that considers all ‎efficient and inefficient units simultaneously and finds a ranking for them. Almost all ‎of the developed ranking methods consider only the efficient units. On the other hand, ‎ranking inefficient units by traditional data envelop...

Let us remember the mechanics of Multiplicative-Weights-Updates: At every time t, the learner maintains a weight vector wt ≥ 0 over the experts. Given the weight vector, the probability distribution over the experts is computed as pt = wt wt·1 . The weights are initialized at w1 = 1 n · 1. (Multiplicative-weights-update step.) Given the loss vector at time t the weights are updated as follows w...

low frequency noise (lfn) is ubiquitous in both occupational and general environments. mental performance of subjects exposed to lfn is expected to be impaired. individual characters seem to play a significant role in this process. this study aimed to evaluate the effects of moderate levels of lfn (20–200 hz) on mental performance. fifty-three subjects (in two groups) were exposed to low and fl...

a characteristic of data envelopment analysis (dea) is to allow individual decision making units (dmus) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. this flexibility in selecting the weights, on the other hand, deters the comparison among dmus on a common base. for dealing with this difficulty and assessing all the dmus on the sam...