in this paper, modeling and optimization of fischer-tropsch synthesis is considered in a fixed-bed catalytic reactor using an industrial fe-cu-k catalyst. a one dimensional pseudo-homogenous plug flow model without axial dispersion is developed for converting syngas to heavy hydrocarbons. the effects of temperature, pressure, h2 to co ratio in feed stream, and co molar flow on the mass flow rat...

We present a fast and scalable online method for tuning statistical machine translation models with large feature sets. The standard tuning algorithm—MERT—only scales to tens of features. Recent discriminative algorithms that accommodate sparse features have produced smaller than expected translation quality gains in large systems. Our method, which is based on stochastic gradient descent with ...

Algorithms that can efficiently recover principal components in very high-dimensional, streaming, and/or distributed data settings have become an important topic in the literature. In this paper, we propose an approach to principal component estimation that utilizes projections onto very sparse random vectors with Bernoulli-generated nonzero entries. Indeed, our approach is simultaneously effic...

Generation of simulated data is essential for analysis in particle physics, but current Monte Carlo methods are very computationally expensive. Deep-learning-based generative models have successfully generated at lower cost, struggle when the sparse. We introduce a novel deep sparse autoregressive model (SARM) that explicitly learns sparseness with tractable likelihood, making it more stable an...

Putative activity of hydroalcoholic and aqueous infusion extracts of Echium amoenum L. was investigated in mice using the rotarod model of motor coordination and the elevated plus maze model of anxiety. The extracts were administered intraperitonealy (i.p.) once, one hour before performing the tests. Preliminary phytochemical study of the plant, with standard procedures, showed that it contains...

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a...

Sparse coding is a proven principle for learning compact representations of images. However, sparse coding by itself often leads to very redundant dictionaries. With images, this often takes the form of similar edge detectors which are replicated many times at various positions, scales and orientations. An immediate consequence of this observation is that the estimation of the dictionary compon...