Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss...

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods...

and apply it to an entirely different one. For example, Wall Street firms often hire physicists to solve finance problems. Even though these two domains have superficially nothing in common, training as a physicist provides knowledge and skills that are highly applicable in finance (for example, solving differential equations and performing Monte Carlo simulations). Yet standard machine-learnin...

criteria exist for a given disorder, then surely it is unethical to categorically exclude children and adolescent patients from receiving the only treatment available that could dramatically increase their quality of life. Experts that oppose the use of DBS for pediatric TS (Mink et al., 2006; Porta et al., 2009) defend their position based upon the nature of childhood TS. According to a recent...

In this paper we present a novel transliteration technique which is based on deep belief networks. Common approaches use finite state machines or other methods similar to conventional machine translation. Instead of using conventional NLP techniques, the approach presented here builds on deep belief networks, a technique which was shown to work well for other machine learning problems. We show ...

The primary objective of this paper is to predict onset of using deep learning and also as to predict the risk factor and severity of diabetics. The methods are implemented on in a conditional data set of diabetes. The model involves deep learning, in the form of a deep neural network through which we are able to apply predictive analytics on said diabetes data set and obtain optimal results. A...

This paper presents a Hybrid Approach to Deep Machine Translation in the language direction from English to Bulgarian. The set-up uses preand post-processing modules as well as two-level transfer. The language resources that have been incorporated are: WordNets for both languages; a valency lexicon for Bulgarian; aligned parallel corpora. The architecture comprises a predominantly statistical c...

Wearable devices have the potential to enhance sports performance, yet they are not fulfilling this promise. Our previous studies with 6 professional tennis coaches and 20 players indicate that this could be due the lack of psychological or mental state feedback, which the coaches claim to provide. Towards this end, we propose to detect the flow state, mental state of optimal performance, using...

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...

terms of indications. The authors seem to have dealt with various conditions of infection, including abscesses, fistulas and fistulous conditions without an external opening. In my opinion, the technical similarity, the differences in indications, and the small number of patients may prevent readers of this article from taking the results as a convincing outcome. There may be few surgeons who w...