Spark plasma sintering of TiNi nano-powders for biological application

Nano-sized TiNi powder with an average size of 50nm was consolidated using spark plasma sintering (SPS) at 800 °C for 5min. A layer of anatase TiO 2 coating was formed on the sintered TiNi by chemical reaction with a hydrogen peroxide (H2O2) solution at 60 °C followed by heat treatment at 400 °C to enhance the bioactivity of the metal surface. Cell culture using osteoblast cells and a biomimetic test in simulated body fluid proved the biocompatibility of the chemically treated SPS TiNi. © IOP Publishing Ltd.

SOME MEASUREMENTS OF THE PERMEABILITY OF KAOLIN.

This paper reports an experimental investigation of the vertical and horizontal permeabilities of speswhite kaolin clay. The permeabilities were measured using falling head permeability tests. A modification to a conventional oedometer was devised so that either vertical or horizontal permeabilities could be determined. It was found that the vertical and horizontal permeabilities of the clay slurry were similar, but that as the clay was consolidated one dimensionally the anisotropy of the clay fabric resulted in a greater horizontal permeability than the vertical permeability at any void ratio. Both permeabilities were uniquely related to the void ratio.

Some measurements of the permeability of kaolin

The writers wish to present some additional data obtained independently and with different techniques that confirm the results published in the paper. For these tests, the speswhite kaolin clay was prepared as a slurry with a water content of 133 percent and was then consolidated one-dimensionally under an axial stress of 100 kPa in a 203 mm dia. tube. The results presented here show that the anisotropy of permeability is completely preserved (even after the sample is compressed isotropical) as long as the initial part of the stress path corresponds to one-dimensional compression. The data supports the speculation by the authors regarding permeability anisotropy for stress paths other than one-dimensional compression.

The influence of water-miscible solvents on the permeability of clay

Centrifuge tests were carried out to determine the effect of 5 different water-soluble chemicals on a thin consolidated disc of clay. The evolution of changes in the clay permeability with time was investigated and other structural changes due to chemical attack were monitored. The findings presented here demonstrate that the permeability of the clay appear to be generally related to the polarity of the chemicals and the dielectric constant, with the exception of Butanol. In the case of Butanol at low flow rate and low stress level, the action of the chemical caused the clay to crack, with a consequent large increase in flow.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.

Effects of soil fabric on behaviors of granular soils: Microscopic modeling

The effects of initial soil fabric on behaviors of granular soils are investigated by using Distinct Element Method (DEM) numerical simulation. Soil specimens are represented by an assembly of non-uniform sized spheres with different initial contact normal distributions. Isotropically consolidated triaxial compression loading and extension unloading in both undrained and drained conditions are simulated for vertically- and horizontally-sheared specimens. The numerical simulation results are compared qualitatively with the published experimental data and the effects of initial soil fabric on resulting soil behaviors are discussed, including the effects of specimen reconstitution methods, effects of large preshearing, and anisotropic characteristics in undrained and drained conditions. The effects of initial soil fabric and mode of shearing on the quasi-steady state line are also investigated. The numerical simulation results can systematically explain that the observed experimental behaviors of granular soils are due principally to their conditions of the initial soil fabric. This outcome provides insights into the observed phenomena in microscopic view. © 2011 Elsevier Ltd.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

Effects of gelatin on mechanical properties of hydroxyapatite-gelatin nano-composltes

A biomimetic reactor has been developed to synthesize hydroxyapatite- gelatin (HAP-GEL) nanocomposites that mimic ultra-structures of natural bone. We hypothesize that in the reactor, gelatin concentration controls morphology and packing structures of HAP crystals. To test the hypothesis, three types of mechanical tests were conducted, including nanoindentation, compression, and fracture tests. Nanoindentation tests in conjunction with computer modeling were used to assess effects on gelatin-induced microstructures of HAP. The results showed that increasing gelatin content increased both the plane strain modulus and the fracture toughness. The gelatin appeared to shorten the HAP crystal distance, which consolidated the internal structure of the composite and made the material more rigid. The fracture toughness KIC increased partially due to the effect of fiber bridging between gelatin molecules. The highest fracture toughness (1.12 MPa·1/2) was equivalent to that of pure hydroxyapatite. The compressive strength of the HAP-GEL (107.7±26.8 MPa) was, however, less sensitive to microstructural changes and was within the range of natural cortical bone (human 170 MPa, pig: 100 MPa). The compression strength was dominated by void inclusions while the nanoindentation response reflected ultra-structural arrangement of the crystals. The gelatin concentration is likely to modify crystal arrangement as demonstrated in TEM experiments but not void distribution at macroscopic levels. © 2006 Materials Research Society.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Gaussian processes for fast policy optimisation of POMDP-based dialogue managers

Modelling dialogue as a Partially Observable Markov Decision Process (POMDP) enables a dialogue policy robust to speech understanding errors to be learnt. However, a major challenge in POMDP policy learning is to maintain tractability, so the use of approximation is inevitable. We propose applying Gaussian Processes in Reinforcement learning of optimal POMDP dialogue policies, in order (1) to make the learning process faster and (2) to obtain an estimate of the uncertainty of the approximation. We first demonstrate the idea on a simple voice mail dialogue task and then apply this method to a real-world tourist information dialogue task. © 2010 Association for Computational Linguistics.