Automatic correction of part-of-speech corpora

In this study a simple method for automatic correction of part-ofspeech corpora is presented, which works as follows: Initially two or more already available part-of-speech taggers are applied on the data. Then a sample of differing outputs is taken to train a classifier to predict for each difference which of the taggers (if any) delivered the correct output. As classifiers we employed instance-based learning, a C4.5 decision tree and a Bayesian classifier. Their performances ranged from 59.1 % to 67.3 %. Training on the automatically corrected data finally lead to significant improvements in tagger performance

Compositional Morphology for Word Representations and Language Modelling

This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the context of log-bilinear language models, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning (ICML

O-MOORE-NICE!: new methodologies and algorithms for design and simulation of analog integrated circuits

No abstract

Photoreceptor Outer Segment-like Structures in Long-Term 3D Retinas from Human Pluripotent Stem Cells.

The retinal degenerative diseases, which together constitute a leading cause of hereditary blindness worldwide, are largely untreatable. Development of reliable methods to culture complex retinal tissues from human pluripotent stem cells (hPSCs) could offer a means to study human retinal development, provide a platform to investigate the mechanisms of retinal degeneration and screen for neuroprotective compounds, and provide the basis for cell-based therapeutic strategies. In this study, we describe an in vitro method by which hPSCs can be differentiated into 3D retinas with at least some important features reminiscent of a mature retina, including exuberant outgrowth of outer segment-like structures and synaptic ribbons, photoreceptor neurotransmitter expression, and membrane conductances and synaptic vesicle release properties consistent with possible photoreceptor synaptic function. The advanced outer segment-like structures reported here support the notion that 3D retina cups could serve as a model for studying mature photoreceptor development and allow for more robust modeling of retinal degenerative disease in vitro

MODEL ORDER REDUCTION OF NONLINEAR DYNAMIC SYSTEMS USING MULTIPLE PROJECTION BASES AND OPTIMIZED STATE-SPACE SAMPLING

Model order reduction (MOR) is a very powerful technique that is used to deal with the increasing complexity of dynamic systems. It is a mature and well understood field of study that has been applied to large linear dynamic systems with great success. However, the continued scaling of integrated micro-systems, the use of new technologies, and aggressive mixed-signal design has forced designers to consider nonlinear effects for more accurate model representations. This has created the need for a methodology to generate compact models from nonlinear systems of high dimensionality, since only such a solution will give an accurate description for current and future complex systems.The goal of this research is to develop a methodology for the model order reduction of large multidimensional nonlinear systems. To address a broad range of nonlinear systems, which makes the task of generalizing a reduction technique difficult, we use the concept of transforming the nonlinear representation into a composite structure of well defined basic functions from multiple projection bases.We build upon the concept of a training phase from the trajectory piecewise-linear (TPWL) methodology as a practical strategy to reduce the state exploration required for a large nonlinear system. We improve upon this methodology in two important ways: First, with a new strategy for the use of multiple projection bases in the reduction process and their coalescence into a unified base that better captures the behavior of the overall system; and second, with a novel strategy for the optimization of the state locations chosen during training. This optimization technique is based on using the Hessian of the system as an error bound metric.Finally, in order to treat the overall linear/nonlinear reduction task, we introduce a hierarchical approach using a block projection base. These three strategies together offer us a new perspective to the problem of model order reduction of nonlinear systems and the tracking or preservation of physical parameters in the final compact model

A gas-rich nuclear bar fuelling a powerful central starburst in NGC 2782

We present evidence that the peculiar interacting starburst galaxy NGC 2782 (Arp 215) harbors a gas-rich nuclear stellar bar feeding an M82-class powerful central starburst, from a study based on OVRO CO (J=1->0) data, WIYN BVR & Halpha observations, along with available NIR images, a 5 GHz RC map and HST images. NGC 2782 harbors a clumpy, bar-like CO feature of radius ~ 7.5'' (1.3 kpc) which leads a nuclear stellar bar of similar size. The nuclear CO bar is massive: it contains ~2.5x10**9 M_sun of molecular gas, which makes up ~ 8 % of the dynamical'mass present within a 1.3 kpc radius. Within the CO bar, emission peaks in two extended clumpy lobes which lie on opposite sides of the nucleus, separated by ~ 6'' (1 kpc). Between the CO lobes, in the inner 200 pc radius, resides a powerful central starburst which is forming stars at a rate of 3 to 6 M_sun yr-1. While circular motions dominate the CO velocity field, the CO lobes show weak bar-like streaming motions on the leading side of the nuclear stellar bar, suggestive of gas inflow. We estimate semi-analytically the gravitational torque from the nuclear stellar bar on the gas, and suggest large gas inflow rates from the CO lobes into the central starburst. These observations, which are amongst the first ones showing a nuclear stellar bar fuelling molecular gas into an intense central starburst, are consistent with simulations and theory which suggest that nuclear bars provide an efficient way of transporting gas closer to the galactic center to fuel central activity. Furthermore, several massive clumps are present at low radii, and dynamical friction might produce further gas inflow. We suggest that the nuclear molecular gas bas and central activity will be very short-lived, likely disappearing within 5x10**8 years.Comment: Accepted by the Astrophysical Journal, 10 pages, Latex with emulateapj.sty, apjfonts.sty, 10 postscript & 2 gif figure

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system