Block implementation of a synchronized learning algorithm in adaptive lattice filters

In order to achieve fast convergence and less computation for adaptive filters, a joint method combining a whitening process and the NLMS algorithm is a hopeful approach. However, updating the filter coefficients is not synchronized with the reflection coefficient updating resulting in unstable behavior. We analyzed effects of this, and proposed the Synchronized Learning Algorithm to solve this problem. Asynchronous error between them is removed, and fast convergence and small residual error were obtained. This algorithm, however, requires O(ML) computations, where M is an adaptive filter length, and L is a lattice predictor length. It is still large compared with the NLMS algorithm. In order to achieve less computation while the fast convergence is maintained, a block implementation method is proposed. The reflection coefficients are updated at some period, and are fixed during this interval. The proposed block implementation can be effectively applied to parallel form adaptive filters, such as sub-band adaptive filters. Simulation using speech signal shows that a learning curve of the proposed block implementation a little slower than the our original algorithm, but can save the computational complexity

A prospective study of stomach cancer death in relation to green tea consumption in Japan

To evaluate whether green tea consumption provides protection against stomach cancer death, relative risks were calculated using Cox proportional hazards regression analysis in the Japan Collaborative Study for Evaluation of Cancer Risk, sponsored by the Ministry of Health and Welfare (JACC Study). The study was based on 30 370 men and 42 481 women aged 40–79. After adjustment for age, smoking status, history of peptic ulcer, family history of stomach cancer along with certain dietary items, the risks associated with drinking one or two, three or four, five to nine, and 10 or more cups of green tea per day, relative to those of drinking less than one cup per day, were 1.6 (95% CI: 0.9–2.9), 1.1 (95% CI: 0.6–1.9), 1.0 (95% CI: 0.5–2.0), and 1.0 (95% CI: 0.5–2.0), respectively, in men (P for trend=0.669), and 1.1 (95% CI: 0.5–2.5), 1.0 (95% CI: 0.5–2.5), 0.8 (95% CI: 0.4–1.6), and 0.8 (95% CI: 0.3–2.1), respectively, in women (P for trend=0.488). We found no inverse association between green tea consumption and the risk of stomach cancer death

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments

Music Composition with Interactive Evolutionary Computation

Interactive Evolutionary Computation (IEC), i.e., Evolutionary Computation whose fitness function is provided by a user his/herself, has been applied to esthetic areas, such as art, design and music. We cannot necessarily define fitness functions explicitly in these areas. With IEC, however, we can embed the user's implicit preference into the optimization system. This paper describes a new approach to music composition, more precisely the composition of rhythms, by means of IEC. The main feature of our method is to combine Genetic Algorithms (GA) and Genetic Programming (GP). In our system, GA individuals represent short pieces of rhythmic patterns, while GP individuals express how these patterns are arranged in terms of their functions. Both populations are evolved interactively through the user's evaluation. The integration of interactive GA and GP makes it possible to search for musical structures effectively in the vast search space. In this paper, we show how successfully our proposed method can generate attractive musical rhythms. The effectiveness of our system is demonstrated by the evolved rhythm phrases, which are available from our web site as sound files.