Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Numerical investigation of the active role of the actin cytoskeleton in the compression resistance of cells.

Numerous in-vitro studies have established that cells react to their physical environment and to applied mechanical loading. However, the mechanisms underlying such phenomena are poorly understood. Previous modelling of cell compression considered the cell as a passive homogenous material, requiring an artificial increase in the stiffness of spread cells to replicate experimentally measured forces. In this study, we implement a fully 3D active constitutive formulation that predicts the distribution, remodelling, and contractile behaviour of the cytoskeleton. Simulations reveal that polarised and axisymmetric spread cells contain stress fibres which form dominant bundles that are stretched during compression. These dominant fibres exert tension; causing an increase in computed compression forces compared to round cells. In contrast, fewer stress fibres are computed for round cells and a lower resistance to compression is predicted. The effect of different levels of cellular contractility associated with different cell phenotypes is also investigated. Highly contractile cells form more dominant circumferential stress fibres and hence provide greater resistance to compression. Computed predictions correlate strongly with published experimentally observed trends of compression resistance as a function of cellular contractility and offer an insight into the link between cell geometry, stress fibre distribution and contractility, and cell deformability. Importantly, it is possible to capture the behaviour of both round and spread cells using a given, unchanged set of material parameters for each cell type. Finally, it is demonstrated that stress distributions in the cell cytoplasm and nucleus computed using the active formulation differ significantly from those computed using passive material models.

Coexistence of high-bit-rate quantum key distribution and data on optical fiber

Quantum key distribution (QKD) uniquely allows distribution of cryptographic keys with security verified by quantum mechanical limits. Both protocol execution and subsequent applications require the assistance of classical data communication channels. While using separate fibers is one option, it is economically more viable if data and quantum signals are simultaneously transmitted through a single fiber. However, noise-photon contamination arising from the intense data signal has severely restricted both the QKD distances and secure key rates. Here, we exploit a novel temporal-filtering effect for noise-photon rejection. This allows high-bit-rate QKD over fibers up to 90 km in length and populated with error-free bidirectional Gb/s data communications. With high-bit rate and range sufficient for important information infrastructures, such as smart cities and 10 Gbit Ethernet, QKD is a significant step closer towards wide-scale deployment in fiber networks.

Enhanced light emission in active silicon-on-insulator photonic crystal slabs and slot waveguides

We present experimental measurements on Silicon-on-insulator (SOI) photonic crystal slabs with an active layer containing Er3+ ions-doped Silicon nanoclusters (Si-nc), showing strong enhancement of 1.54 μm emission at room temperature. We provide a systematic theoretical analysis to interpret such results. In order to get further insight, we discuss experimental data on the guided luminescence of unpatterned SOI planar slot waveguides, which show enhanced light emission in transverse-magnetic (TM) modes over transverse-electric (TE) ones. ©2007 IEEE.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

Active learning of model evidence using Bayesian quadrature

Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a modelbased method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model's hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

High resolution direct measurement of temperature distribution in silicon nanophotonics devices.

Following the miniaturization of photonic devices and the increase in data rates, the issues of self heating and heat removal in active nanophotonic devices should be considered and studied in more details. In this paper we use the approach of Scanning Thermal Microscopy (SThM) to obtain an image of the temperature field of a silicon micro ring resonator with sub-micron spatial resolution. The temperature rise in the device is a result of self heating which is caused by free carrier absorption in the doped silicon. The temperature is measured locally and directly using a temperature sensitive AFM probe. We show that this local temperature measurement is feasible in the photonic device despite the perturbation that is introduced by the probe. Using the above method we observed a significant self heating of about 10 degrees within the device.

Characterization and deformation response of orthotropic fibre networks with auxetic out-of-plane behaviour

In the present paper, highly porous fibre networks made of 316L fibres, with different fibre volume fractions, are characterized in terms of network architecture, elastic constants and fracture energies. Elastic constants are measured using quasi-static mechanical and modal vibration testing, yielding local and globally averaged properties, respectively. Differences between quasi-static and dynamic elastic constants are attributed to through-thickness shear effects. Regardless of the method employed, networks show signs of material inhomogeneity at high fibre densities, in agreement with X-ray nanotomography results. Strong auxetic (or negative Poisson's ratio) behaviour is observed in the through-thickness direction, which is attributed to fibre kinking induced during processing. Measured fracture energies are compared with model predictions incorporating information about in-plane fibre orientation distribution, fibre volume fraction and single fibre work of fracture. Experimental values are broadly consistent with model predictions, based on the assumption that this energy is primarily associated with plastic deformation of individual fibres within a process zone of the same order as the inter-joint spacing. © 2013 Published by Elsevier Ltd. on behalf of Acta Materialia Inc. All rights reserved.

Human mesenchymal stem cell response to 444 ferritic stainless steel networks

The aim of this work is to improve bone-implant bonding. This can, potentially, be achieved through the use of an implant coating composed of fibre networks. It is hypothesised that such an implant can achieve strong peri-prosthetic bone anchorage, when seeded with human mesenchymal stem cells (hMSCs). The materials employed were 444 and 316L stainless steel fibre networks of the same fibre volume fraction. The present work confirms that hMSCs are able to proliferate and differentiate towards the osteogenic lineage when seeded onto the fibre networks. Cellular viability, proliferation and metabolic activity were assessed and the results suggest higher proliferation rates when hMSC are seeded onto the 444 networks as compared to 316L. Cell distribution was found uniform across the seeded surfaces with 444 showing a somewhat higher infiltration depth. Copyright © Materials Research Society 2013.

Fabrication and characterization of active and sequential circuits utilizing Schottky-wrap-gate-controlled GaAs hexagonal nanowire network structures

For realization of hexagonal BDD-based digital systems, active and sequential circuits including inverters, flip flops and ring oscillators are designed and fabricated on GaAs-based hexagonal nanowire networks controlled by Schottky wrap gates (WPGs), and their operations are characterized. Fabricated inverters show comparatively high transfer gain of more than 10. Clear and correct operation of hexagonal set-reset flip flops (SR-FFs) is obtained at room temperature. Fabricated hexagonal D-type flip flop (D-FF) circuits integrating twelve WPG field effect transistors (FETs) show capturing input signal by triggering although the output swing is small. Oscillatory output is successfully obtained in a fabricated 7-stage hexagonal ring oscillator. Obtained results confirm that a good possibility to realize practical digital systems can be implemented by the present circuit approach.

The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

An algorithm of analysis tools on points distribution in high dimension space: The distance of a point and an infinite sub-space

This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.

Temperature Distribution in Ridge Structure InGaN Laser Diodes and Its Influence on Device Characteristics

Time-dependent thermal simulation of ridge-geometry InGaN laser diodes is carried out with a two-dimensional model. A high temperature in the waveguide layer and a large temperature step between the regions under and outside the ridge are generated due to the poor thermal conductivity of the sapphire substrate and the large threshold current and voltage. The temperature step is thought to have a strong influence on the characteristics of the laser diodes. Time-resolved measurements of light-current curves,spectra, and the far-field pattern of the InGaN laser diodes under pulsed operation are performed. The results show that the thermal lensing effect improves the confinement of the higher order modes and leads to a lower threshold current and a higher slope efficiency of the device while the high temperature in the active layer results in a drastic decrease in the slope efficiency.

Preparation of highly active Pt/C cathode electrocatalysts for DMFCs by an improved aqueous impregnation method

An improved aqueous impregnation method was used to prepare 40 wt% Pt/C electrocatalysts. TEM analysis of the samples showed that the Pt particles impregnated for a short time have a very narrow size distribution in the range of 1-4 nm with an average size of 2.6 nm. UV-vis spectroscopy measurements verified that the redox reaction between PtCl62- and formaldehyde took place with a slow rate at ambient temperature via a two-step reaction path, where PtCl42- serves as an intermediate. The use of the short-time-impregnated 40 wt% Pt/C as cathode electrocatalysts in direct methanol fuel cells yields better performance than that of commercial 40 wt% Pt/C electrocatalyst. Experimental evidence provides clues for the fundamental understanding of elementary steps of the redox reactions, which helps in guiding the design and preparation of highly dispersed Pt catalyst for fuel cells.