A tractable DDN-POMDP Approach to Affective Dialogue Modeling for General Probabilistic Frame-based Dialogue Systems

We propose a new approach to developing a tractable affective dialogue model for general probabilistic frame-based dialogue systems. The dialogue model, based on the Partially Observable Markov Decision Process (POMDP) and the Dynamic Decision Network (DDN) techniques, is composed of two main parts, the slot level dialogue manager and the global dialogue manager. Our implemented dialogue manager prototype can handle hundreds of slots; each slot might have many values. A first evaluation of the slot level dialogue manager (1-slot case) showed that with a 95% confidence level the DDN-POMDP dialogue strategy outperforms three simple handcrafted dialogue strategies when the user's action error is induced by stress

A POMDP approach to Affective Dialogue Modeling

We propose a novel approach to developing a dialogue model that is able to take into account some aspects of the user's affective state and to act appropriately. Our dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user's affective state and action. A simple example of route navigation is explained to clarify our approach. The preliminary results showed that: (1) the expected return of the optimal dialogue strategy depends on the correlation between the user's affective state & the user's action and (2) the POMDP dialogue strategy outperforms five other dialogue strategies (the random, three handcrafted and greedy action selection strategies)

A Reinforcement Learning Agent for Minutiae Extraction from Fingerprints

In this paper we show that reinforcement learning can be used for minutiae detection in fingerprint matching. Minutiae are characteristic features of fingerprints that determine their uniqueness. Classical approaches use a series of image processing steps for this task, but lack robustness because they are highly sensitive to noise and image quality. We propose a more robust approach, in which an autonomous agent walks around in the fingerprint and learns how to follow ridges in the fingerprint and how to recognize minutiae. The agent is situated in the environment, the fingerprint, and uses reinforcement learning to obtain an optimal policy. Multi-layer perceptrons are used for overcoming the difficulties of the large state space. By choosing the right reward structure and learning environment, the agent is able to learn the task. One of the main difficulties is that the goal states are not easily specified, for they are part of the learning task as well. That is, the recognition of minutiae has to be learned in addition to learning how to walk over the ridges in the fingerprint. Results of successful first experiments are presented

Within-ring movement of free water in dehydrating Norway spruce sapwood visualized by neutron radiography

This study is a first approach to visualize moisture distribution and movement between annual rings during sapwood drying by neutron imaging (NI). While Norway spruce [Picea abies (L.) Karst.] sapwood beams were allowed to dehydrate on a balance at ambient conditions, NI was performed in 1-10 min time steps. From NI raw files, radial dimensional changes were calculated during dehydration and transmission profiles were drawn for different relative moisture content (MC) steps from full saturation until equilibrium moisture content. The NI technique proved to be a useful tool to visualize the movement of free water within, and between, annual rings. Removal of free water in the middle part of the wood beam did not proceed continuously from the surface to the central part, but was strongly influenced by wood anatomy. Water is removed from earlywood during early stages of dehydration and later, at higher moisture loss (<50% MC), from the main latewood parts. It is therefore concluded that the radial dimensional changes measured at moderate moisture loss are not only caused by cell wall shrinkage of the outer wood parts located beneath the wood surface, but a result of elastic deformation of earlywood tracheids under the influence of negative hydrostatic pressure

Fast Data Sharing within a distributed multithreaded control framework for robot teams

In this paper a data sharing framework for multithreaded, distributed control programs is described that is realized in C++ by means of only a few, powerful classes and templates. Fast data exchange of entire data structures is supported using sockets as communication medium. Access methods are provided that preserve data consistency and synchronize the data exchange. The framework has been successfully used to build a distributed robot soccer control system running on as many computers as needed

Bayesian graphical models for regression on multiple data sets with different variables

Routinely collected administrative data sets, such as national registers, aim to collect information on a limited number of variables for the whole population. In contrast, survey and cohort studies contain more detailed data from a sample of the population. This paper describes Bayesian graphical models for fitting a common regression model to a combination of data sets with different sets of covariates. The methods are applied to a study of low birth weight and air pollution in England and Wales using a combination of register, survey, and small-area aggregate data. We discuss issues such as multiple imputation of confounding variables missing in one data set, survey selection bias, and appropriate propagation of information between model components. From the register data, there appears to be an association between low birth weight and environmental exposure to NO2, but after adjusting for confounding by ethnicity and maternal smoking by combining the register and survey data under our models, we find there is no significant association. However, NO2 was associated with a small but significant reduction in birth weight, modeled as a continuous variable

Distribution of moisture in reconstructed oil paintings on canvas during absorption and drying: a neutron radiography and NMR study

Moisture is a driving factor in the long-term mechanical deterioration of canvas paintings, as well as for a number of physico–chemical degradation processes. Since the 1990s a number of publications have addressed the equilibrium hygroscopic uptake and the hygro-mechanical deformation of linen canvas, oil paint, animal glue, and ground paint. In order to visualise and quantify the dynamic behaviour of these materials combined in a painting mock-up or reconstruction, we have performed custom-designed experiments with neutron radiography and nuclear magnetic resonance (NMR) imaging. This paper reports how both techniques were used to obtain spatially and temporally resolved information on moisture content, during alternate exposure to high and low relative humidity, or in contact with liquids of varying water activities. We observed how the canvas, which is the dominant component in terms of volumetric moisture uptake, absorbs and dries rapidly, and, due to its low vapour resistance, allows for vapour transfer towards the ground layer. Moisture desorption was generally found to be faster than absorption. The presence of sizing glue leads to a local increase of moisture content. It was observed that lining a painting with an extra canvas results in a damping effect: i.e. absorption and drying are significantly slowed down. The results obtained by NMR are complementary to neutron radiography in that they allow accurate monitoring of water ingress in contact with a liquid reservoir. Quantitative results are in good agreement with adsorption isotherms. The findings can be used for risk analysis of paintings exposed to changing micro-climates or subjected to conservation treatments using water. Future studies addressing moisture-driven deformation of paintings can make use of the proposed experimental techniques

Gas Evolution in Operating Lithium-Ion Batteries Studied in Situ by Neutron Imaging

Gas generation as a result of electrolyte decomposition is one of the major issues of highperformance rechargeable batteries. Here, we report the direct observation of gassing in operating lithium-ion batteries using neutron imaging. This technique can be used to obtain qualitative as well as quantitative information by applying a new analysis approach. Special emphasis is placed on high voltage LiNi0.5Mn1.5O4/graphite pouch cells. Continuous gassing due to oxidation and reduction of electrolyte solvents is observed. To separate gas evolution reactions occurring on the anode from those associated with the cathode interface and to gain more insight into the gassing behavior of LiNi0.5Mn1.5O4/graphite cells, neutron experiments were also conducted systematically on other cathode/anode combinations, including LiFePO4/graphite, LiNi0.5Mn1.5O4/Li4Ti5O12 and LiFePO4/Li4Ti5O12. In addition, the data were supported by gas pressure measurements. The results suggest that metal dissolution in the electrolyte and decomposition products resulting from the high potentials adversely affect the gas generation, particularly in the first charge cycle (i.e., during graphite solid-electrolyte interface layer formation)