Boosting Named Entity Recognition with Neural Character Embeddings

Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking. In this work we propose a language-independent NER system that uses automatically learned features only. Our approach is based on the CharWNN deep neural network, which uses word-level and character-level representations (embeddings) to perform sequential classification. We perform an extensive number of experiments using two annotated corpora in two different languages: HAREM I corpus, which contains texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in Spanish. Our experimental results shade light on the contribution of neural character embeddings for NER. Moreover, we demonstrate that the same neural network which has been successfully applied to POS tagging can also achieve state-of-the-art results for language-independet NER, using the same hyperparameters, and without any handcrafted features. For the HAREM I corpus, CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score for the total scenario (ten NE classes), and by 7.2 points in the F1 for the selective scenario (five NE classes).Comment: 9 page

On Gravity localization under Lorentz Violation in warped scenario

Recently Rizzo studied the Lorentz Invariance Violation (LIV) in a brane scenario with one extra dimension where he found a non-zero mass for the four-dimensional graviton. This leads to the conclusion that five-dimensional models with LIV are not phenomenologically viable. In this work we re-examine the issue of Lorentz Invariance Violation in the context of higher dimensional theories. We show that a six-dimensional geometry describing a string-like defect with a bulk-dependent cosmological constant can yield a massless 4D graviton, if we allow the cosmological constant variation along the bulk, and thus can provides a phenomenologically viable solution for the gauge hierarchy problem.Comment: 13 pages, 2 figures. To appear in Physics Letters

Constrained Conditional Moment Restriction Models

This paper examines a general class of inferential problems in semiparametric and nonparametric models defined by conditional moment restrictions. We construct tests for the hypothesis that at least one element of the identified set satisfies a conjectured (Banach space) "equality" and/or (a Banach lattice) "inequality" constraint. Our procedure is applicable to identified and partially identified models, and is shown to control the level, and under some conditions the size, asymptotically uniformly in an appropriate class of distributions. The critical values are obtained by building a strong approximation to the statistic and then bootstrapping a (conservatively) relaxed form of the statistic. Sufficient conditions are provided, including strong approximations using Koltchinskii's coupling. Leading important special cases encompassed by the framework we study include: (i) Tests of shape restrictions for infinite dimensional parameters; (ii) Confidence regions for functionals that impose shape restrictions on the underlying parameter; (iii) Inference for functionals in semiparametric and nonparametric models defined by conditional moment (in)equalities; and (iv) Uniform inference in possibly nonlinear and severely ill-posed problems

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Wearing a single DNA molecule with an AFM tip

While the fundamental limit on the resolution achieved in an atomic force microscope (AFM) is clearly related to the tip radius, the fact that the tip can creep and/or wear during an experiment is often ignored. This is mainly due to the difficulty in characterizing the tip, and in particular a lack of reliable methods that can achieve this in situ. Here, we provide an in situ method to characterize the tip radius and monitor tip creep and/or wear and biomolecular sample wear in ambient dynamic AFM. This is achieved by monitoring the dynamics of the cantilever and the critical free amplitude to observe a switch from the attractive to the repulsive regime. The method is exemplified on the mechanically heterogeneous sample of single DNA molecules bound to mica mineral surfaces. Simultaneous monitoring of apparent height and width of single DNA molecules while detecting variations in the tip radius R as small as one nanometer are demonstrated. The yield stress can be readily exceeded for sharp tips (R10nm). The ability to know the AFM tip radius in situ and in real-time opens up the future for quantitative nanoscale materials properties determination at the highest possible spatial resolution.Comment: 26 pages, 6 figure

Home Country Bias: Does Domestic Experience Help Investors Enter Foreign Markets?

This paper investigates whether investors' domestic experience helps them enter foreign markets. We show that investors first invest in domestic securities and only some time later they invest abroad in foreign securities. We also show that investors who trade more often in the domestic market start to invest abroad earlier. Our findings suggest that the experience investors acquire while they trade in the domestic market is a key reason why active investors enter the foreign market earlier. A reason is that highly educated investors as well as investors with more financial knowledge, arguably those for whom learning by trading is the least important, do not need to trade as much in the domestic market before they start investing in foreign securities. Another reason is that investors who start investing in foreign securities are able to improve on their performance afterwards. This improvement in performance constitutes further evidence that the home country bias is costly, thereby confirming that there are gains for investors from investing abroad.Learning, home country bias, duration analysis.