Smart health braceletes

Smart health bracelets: a fashion accessory or an invention of the future? In this article you will learn useful information about the history of the appearance of smart health bracelets, the principle of their operation and additional functions. The article also includes a study of the popularity of these gadgets and a list of criteria for choosing a smart health bracelet

A HMM POS Tagger for Micro-blogging Type Texts

The high volume of communication via micro-blogging type messages has created an increased demand for text processing tools customised the unstructured text genre. The available text processing tools developed on structured texts has been shown to deteriorate significantly when used on unstructured, micro-blogging type texts. In this paper, we present the results of testing a HMM based POS (Part-Of-Speech) tagging model customized for unstructured texts. We also evaluated the tagger against published CRF based state-of-the-art POS tagging models customized for Tweet messages using three publicly available Tweet corpora. Finally, we did cross-validation tests with both the taggers by training them on one Tweet corpus and testing them on another one

End-to-end neural segmental models for speech recognition

Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time. Neural segmental models are segmental models that use neural network-based weight functions. Neural segmental models have achieved competitive results for speech recognition, and their end-to-end training has been explored in several studies. In this work, we review neural segmental models, which can be viewed as consisting of a neural network-based acoustic encoder and a finite-state transducer decoder. We study end-to-end segmental models with different weight functions, including ones based on frame-level neural classifiers and on segmental recurrent neural networks. We study how reducing the search space size impacts performance under different weight functions. We also compare several loss functions for end-to-end training. Finally, we explore training approaches, including multi-stage vs. end-to-end training and multitask training that combines segmental and frame-level losses

Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction

We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free grammars. Our model extends the correlated topic model framework to probabilistic grammars, exploiting the logistic normal distribution as a prior over the grammar parameters. We derive a variational EM algorithm for that model, and then experiment with the task of unsupervised grammar induction for natural language dependency parsing. We show that our model achieves superior results over previous models that use different priors.

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Natural variants of photosystem II subunit D1 tune photochemical fitness to solar intensity

Background: Cyanobacteria use multiple PSII-D1 isoforms to adapt to environmental conditions. Results: D1:2 achieves higher quantum efficiency of water oxidation and biomass accumulation rate at high light versus D1:1; the latter is more efficient at low light due to less charge recombination. Conclusion: A functional advantage for D1:1 is revealed for the first time. Significance: Improved photochemical efficiency at low light suggests an evolutionary advantage to retain D1:1. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl

From unlabelled tweets to Twitter-specific opinion words

In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification

Determination of the Carrier-Envelope Phase of Few-Cycle Laser Pulses with Terahertz-Emission Spectroscopy

The availability of few-cycle optical pulses opens a window to physical phenomena occurring on the attosecond time scale. In order to take full advantage of such pulses, it is crucial to measure and stabilise their carrier-envelope (CE) phase, i.e., the phase difference between the carrier wave and the envelope function. We introduce a novel approach to determine the CE phase by down-conversion of the laser light to the terahertz (THz) frequency range via plasma generation in ambient air, an isotropic medium where optical rectification (down-conversion) in the forward direction is only possible if the inversion symmetry is broken by electrical or optical means. We show that few-cycle pulses directly produce a spatial charge asymmetry in the plasma. The asymmetry, associated with THz emission, depends on the CE phase, which allows for a determination of the phase by measurement of the amplitude and polarity of the THz pulse